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Xu L, Cao F, Wang L, Liu W, Gao M, Zhang L, Hong F, Lin M. Machine learning model and nomogram to predict the risk of heart failure hospitalization in peritoneal dialysis patients. Ren Fail 2024; 46:2324071. [PMID: 38494197 PMCID: PMC10946267 DOI: 10.1080/0886022x.2024.2324071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2024] Open
Abstract
INTRODUCTION The study presented here aimed to establish a predictive model for heart failure (HF) and all-cause mortality in peritoneal dialysis (PD) patients with machine learning (ML) algorithm. METHODS We retrospectively included 1006 patients who initiated PD from 2010 to 2016. XGBoost, random forest (RF), and AdaBoost were used to train models for assessing risk for 1-year and 5-year HF hospitalization and mortality. The performance was validated using fivefold cross-validation. The optimal ML algorithm was used to construct the models to predictive the risk of the HF and all-cause mortality. The prediction performance of ML methods and Cox regression was compared. RESULTS Over a median follow-up of 49 months. Two hundred and ninety-eight patients developed HF required hospitalization; 199 patients died during the follow-up. The RF model (AUC = 0.853) was the best performing model for predicting HF, and the XGBoost model (AUC = 0.871) was the best model for predicting mortality. Baseline moderate or severe renal disease, systolic blood pressure (SBP), body mass index (BMI), age, Charlson Comorbidity Index (CCI) score were strongly associated with HF hospitalization, whereas age, CCI score, creatinine, age, high-density lipoprotein cholesterol (HDL-C), total cholesterol, baseline estimated glomerular filtration rate (eGFR) were the most significant predictors of mortality. For all the above endpoints, the ML models demonstrated better discrimination than Cox regression. CONCLUSIONS We developed and validated a novel method to predict the risk factors of HF and all-cause mortality that integrates readily available clinical, laboratory, and electrocardiographic variables to predict the risk of HF among PD patients.
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Affiliation(s)
- Liping Xu
- Department of Nephrology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, China
| | - Fang Cao
- Department of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
- Department of Nursing, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Lian Wang
- Department of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Weihua Liu
- Department of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Meizhu Gao
- Department of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Li Zhang
- Department of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Fuyuan Hong
- Department of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Miao Lin
- Department of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
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Tedeschi A, Palazzini M, Trimarchi G, Conti N, Di Spigno F, Gentile P, D’Angelo L, Garascia A, Ammirati E, Morici N, Aschieri D. Heart Failure Management through Telehealth: Expanding Care and Connecting Hearts. J Clin Med 2024; 13:2592. [PMID: 38731120 PMCID: PMC11084728 DOI: 10.3390/jcm13092592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 04/21/2024] [Accepted: 04/25/2024] [Indexed: 05/13/2024] Open
Abstract
Heart failure (HF) is a leading cause of morbidity worldwide, imposing a significant burden on deaths, hospitalizations, and health costs. Anticipating patients' deterioration is a cornerstone of HF treatment: preventing congestion and end organ damage while titrating HF therapies is the aim of the majority of clinical trials. Anyway, real-life medicine struggles with resource optimization, often reducing the chances of providing a patient-tailored follow-up. Telehealth holds the potential to drive substantial qualitative improvement in clinical practice through the development of patient-centered care, facilitating resource optimization, leading to decreased outpatient visits, hospitalizations, and lengths of hospital stays. Different technologies are rising to offer the best possible care to many subsets of patients, facing any stage of HF, and challenging extreme scenarios such as heart transplantation and ventricular assist devices. This article aims to thoroughly examine the potential advantages and obstacles presented by both existing and emerging telehealth technologies, including artificial intelligence.
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Affiliation(s)
- Andrea Tedeschi
- Cardiology Unit of Emergency Department, Guglielmo da Saliceto Hospital, 29121 Piacenza, Italy; (F.D.S.); (D.A.)
| | - Matteo Palazzini
- “De Gasperis” Cardio Center, Niguarda Hospital, ASST Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy; (M.P.); (N.C.); (P.G.); (L.D.); (A.G.); (E.A.)
| | - Giancarlo Trimarchi
- Department of Clinical and Experimental Medicine, University of Messina, 98100 Messina, Italy;
| | - Nicolina Conti
- “De Gasperis” Cardio Center, Niguarda Hospital, ASST Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy; (M.P.); (N.C.); (P.G.); (L.D.); (A.G.); (E.A.)
| | - Francesco Di Spigno
- Cardiology Unit of Emergency Department, Guglielmo da Saliceto Hospital, 29121 Piacenza, Italy; (F.D.S.); (D.A.)
| | - Piero Gentile
- “De Gasperis” Cardio Center, Niguarda Hospital, ASST Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy; (M.P.); (N.C.); (P.G.); (L.D.); (A.G.); (E.A.)
| | - Luciana D’Angelo
- “De Gasperis” Cardio Center, Niguarda Hospital, ASST Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy; (M.P.); (N.C.); (P.G.); (L.D.); (A.G.); (E.A.)
| | - Andrea Garascia
- “De Gasperis” Cardio Center, Niguarda Hospital, ASST Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy; (M.P.); (N.C.); (P.G.); (L.D.); (A.G.); (E.A.)
| | - Enrico Ammirati
- “De Gasperis” Cardio Center, Niguarda Hospital, ASST Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy; (M.P.); (N.C.); (P.G.); (L.D.); (A.G.); (E.A.)
| | - Nuccia Morici
- IRCCS Fondazione Don Carlo Gnocchi, 20148 Milan, Italy;
| | - Daniela Aschieri
- Cardiology Unit of Emergency Department, Guglielmo da Saliceto Hospital, 29121 Piacenza, Italy; (F.D.S.); (D.A.)
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Xu Z, Hu Y, Shao X, Shi T, Yang J, Wan Q, Liu Y. The Efficacy of Machine Learning Models for Predicting the Prognosis of Heart Failure: A Systematic Review and Meta-Analysis. Cardiology 2024:1-19. [PMID: 38648752 DOI: 10.1159/000538639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 03/28/2024] [Indexed: 04/25/2024]
Abstract
INTRODUCTION Heart failure (HF) is a major global public health concern. The application of machine learning (ML) to identify individuals at high risk and enable early intervention is a promising approach for improving HF prognosis. We aim to systematically evaluate the performance and value of ML models for predicting HF prognosis. METHODS PubMed, Web of Science, Scopus, and Embase online databases were searched up to April 30, 2023, to identify studies on the use of ML models to predict HF prognosis. HF prognosis primarily encompasses readmission and mortality. The meta-analysis was conducted by MedCalc software. Subgroup analyses include grouping based on types of ML models, time intervals, sample sizes, the number of predictive variables, validation methods, whether to conduct hyperparameter optimization and calibration, data set partitioning methods. RESULTS A total of 31 studies were included. The most common ML models were random forest, boosting, support vector machine, neural network. The area under the receiver operating characteristic curve (AUC) for predicting HF readmission was 0.675 (95% CI: 0.651-0.699, p < 0.001), and the AUC for predicting HF mortality was 0.790 (95% CI: 0.765-0.816, p < 0.001). Subgroup analyses revealed that models with the prediction time interval of 1 year, sample sizes ≥10,000, the number of predictive variables ≥100, external validation, hyperparameter tuning, calibration adjustment, and data set partitioning using 10-fold cross-validation exhibited favorable performance within their respective subgroups. CONCLUSION The performance of ML models in predicting HF readmission is relatively poor, while its performance in predicting HF mortality is moderate. The quality of the relevant studies is generally low, it is essential to enhance the predictive capabilities of ML models through targeted improvements in practical applications.
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Affiliation(s)
- Zhaohui Xu
- Department of Cardiovascular Disease, ShuGuang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China,
| | - Yinqin Hu
- Department of Cardiovascular Disease, ShuGuang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xinyi Shao
- The Grier School, Tyrone, Pennsylvania, USA
| | - Tianyun Shi
- Department of Cardiovascular Disease, ShuGuang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jiahui Yang
- Department of Cardiovascular Disease, ShuGuang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Qiqi Wan
- Department of Cardiovascular Disease, ShuGuang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yongming Liu
- Department of Cardiovascular Disease, ShuGuang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Cardiovascular Disease, Anhui Provincial Hospital of Integrated Medicine, Hefei Anhui, China
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Samuel O, Zewotir T, North D. Application of machine learning methods for predicting under-five mortality: analysis of Nigerian demographic health survey 2018 dataset. BMC Med Inform Decis Mak 2024; 24:86. [PMID: 38528495 DOI: 10.1186/s12911-024-02476-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 03/06/2024] [Indexed: 03/27/2024] Open
Abstract
BACKGROUND Under-five mortality remains a significant public health issue in developing countries. This study aimed to assess the effectiveness of various machine learning algorithms in predicting under-five mortality in Nigeria and identify the most relevant predictors. METHODS The study used nationally representative data from the 2018 Nigeria Demographic and Health Survey. The study evaluated the performance of the machine learning models such as the artificial neural network, k-nearest neighbourhood, Support Vector Machine, Naïve Bayes, Random Forest, and Logistic Regression using the true positive rate, false positive rate, accuracy, precision, F-measure, Matthew's correlation coefficient, and the Area Under the Receiver Operating Characteristics. RESULTS The study found that machine learning models can accurately predict under-five mortality, with the Random Forest and Artificial Neural Network algorithms emerging as the best models, both achieving an accuracy of 89.47% and an AUROC of 96%. The results show that under-five mortality rates vary significantly across different characteristics, with wealth index, maternal education, antenatal visits, place of delivery, employment status of the woman, number of children ever born, and region found to be the top determinants of under-five mortality in Nigeria. CONCLUSIONS The findings suggest that machine learning models can be useful in predicting U5M in Nigeria with high accuracy. The study emphasizes the importance of addressing social, economic, and demographic disparities among the population in Nigeria. The study's findings can inform policymakers and health workers about developing targeted interventions to reduce under-five mortality in Nigeria.
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Affiliation(s)
- Oduse Samuel
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, 4001, Durban, South Africa.
| | - Temesgen Zewotir
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, 4001, Durban, South Africa
| | - Delia North
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, 4001, Durban, South Africa
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Yoon M, Park JJ, Hur T, Hua CH, Hussain M, Lee S, Choi DJ. Application and Potential of Artificial Intelligence in Heart Failure: Past, Present, and Future. INTERNATIONAL JOURNAL OF HEART FAILURE 2024; 6:11-19. [PMID: 38303917 PMCID: PMC10827704 DOI: 10.36628/ijhf.2023.0050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 11/24/2023] [Accepted: 11/26/2023] [Indexed: 02/03/2024]
Abstract
The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.
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Affiliation(s)
- Minjae Yoon
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Jin Joo Park
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Taeho Hur
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
- Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea
| | - Cam-Hao Hua
- Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea
| | - Musarrat Hussain
- Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea
| | - Sungyoung Lee
- Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea
| | - Dong-Ju Choi
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
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Schenk A, Kowark A, Berger M, Rossaint R, Schmid M, Coburn M. Pre-Interventional Risk Assessment in The Elderly (PIRATE): Development of a scoring system to predict 30-day mortality using data of the Peri-Interventional Outcome Study in the Elderly. PLoS One 2023; 18:e0294431. [PMID: 38127877 PMCID: PMC10734910 DOI: 10.1371/journal.pone.0294431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 11/01/2023] [Indexed: 12/23/2023] Open
Abstract
Risk assessment before interventions in elderly patients becomes more and more vital due to an increasing number of elderly patients requiring surgery. Existing risk scores are often not tailored to marginalized groups such as patients aged 80 years or older. We aimed to develop an easy-to-use and readily applicable risk assessment tool that implements pre-interventional predictors of 30-day mortality in elderly patients (≥80 years) undergoing interventions under anesthesia. Using Cox regression analysis, we compared different sets of predictors by taking into account their ease of availability and by evaluating predictive accuracy. Coefficient estimates were utilized to set up a scoring system that was internally validated. Model building and evaluation were based on data from the Peri-Interventional Outcome Study in the Elderly (POSE), which was conducted as a European multicenter, observational prospective cohort study. Our risk assessment tool, named PIRATE, contains three predictors assessable at admission (urgency, severity and living conditions). Discriminatory power, as measured by the concordance index, was 0.75. The estimated prediction error, as measured by the Brier score, was 0.036 (covariate-free reference model: 0.043). PIRATE is an easy-to-use risk assessment tool that helps stratifying elderly patients undergoing interventions with anesthesia at increased risk of mortality. PIRATE is readily available and applies to a wide variety of settings. In particular, it covers patients needing elective or emergency surgery and undergoing in-hospital or day-case surgery. Also, it applies to all types of interventions, from minor to major. It may serve as a basis for multidisciplinary and informed shared decision-making.
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Affiliation(s)
- Alina Schenk
- Department of Medical Biometry, Informatics and Epidemiology, Faculty of Medicine, University of Bonn, Bonn, Germany
| | - Ana Kowark
- Department of Anaesthesiology and Intensive Care Medicine, University Hospital Bonn, Bonn, Germany
- Department of Anaesthesiology, Medical Faculty University Hospital RWTH Aachen, Aachen, Germany
| | - Moritz Berger
- Department of Medical Biometry, Informatics and Epidemiology, Faculty of Medicine, University of Bonn, Bonn, Germany
| | - Rolf Rossaint
- Department of Anaesthesiology, Medical Faculty University Hospital RWTH Aachen, Aachen, Germany
| | - Matthias Schmid
- Department of Medical Biometry, Informatics and Epidemiology, Faculty of Medicine, University of Bonn, Bonn, Germany
| | - Mark Coburn
- Department of Anaesthesiology and Intensive Care Medicine, University Hospital Bonn, Bonn, Germany
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Sabouri M, Rajabi AB, Hajianfar G, Gharibi O, Mohebi M, Avval AH, Naderi N, Shiri I. Machine learning based readmission and mortality prediction in heart failure patients. Sci Rep 2023; 13:18671. [PMID: 37907666 PMCID: PMC10618467 DOI: 10.1038/s41598-023-45925-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 10/25/2023] [Indexed: 11/02/2023] Open
Abstract
This study intends to predict in-hospital and 6-month mortality, as well as 30-day and 90-day hospital readmission, using Machine Learning (ML) approach via conventional features. A total of 737 patients remained after applying the exclusion criteria to 1101 heart failure patients. Thirty-four conventional features were collected for each patient. First, the data were divided into train and test cohorts with a 70-30% ratio. Then train data were normalized using the Z-score method, and its mean and standard deviation were applied to the test data. Subsequently, Boruta, RFE, and MRMR feature selection methods were utilized to select more important features in the training set. In the next step, eight ML approaches were used for modeling. Next, hyperparameters were optimized using tenfold cross-validation and grid search in the train dataset. All model development steps (normalization, feature selection, and hyperparameter optimization) were performed on a train set without touching the hold-out test set. Then, bootstrapping was done 1000 times on the hold-out test data. Finally, the obtained results were evaluated using four metrics: area under the ROC curve (AUC), accuracy (ACC), specificity (SPE), and sensitivity (SEN). The RFE-LR (AUC: 0.91, ACC: 0.84, SPE: 0.84, SEN: 0.83) and Boruta-LR (AUC: 0.90, ACC: 0.85, SPE: 0.85, SEN: 0.83) models generated the best results in terms of in-hospital mortality. In terms of 30-day rehospitalization, Boruta-SVM (AUC: 0.73, ACC: 0.81, SPE: 0.85, SEN: 0.50) and MRMR-LR (AUC: 0.71, ACC: 0.68, SPE: 0.69, SEN: 0.63) models performed the best. The best model for 3-month rehospitalization was MRMR-KNN (AUC: 0.60, ACC: 0.63, SPE: 0.66, SEN: 0.53) and regarding 6-month mortality, the MRMR-LR (AUC: 0.61, ACC: 0.63, SPE: 0.44, SEN: 0.66) and MRMR-NB (AUC: 0.59, ACC: 0.61, SPE: 0.48, SEN: 0.63) models outperformed the others. Reliable models were developed in 30-day rehospitalization and in-hospital mortality using conventional features and ML techniques. Such models can effectively personalize treatment, decision-making, and wiser budget allocation. Obtained results in 3-month rehospitalization and 6-month mortality endpoints were not astonishing and further experiments with additional information are needed to fetch promising results in these endpoints.
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Affiliation(s)
- Maziar Sabouri
- Department of Medical Physics, School of Medicine, Iran University of Medical Science, Tehran, Iran
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Ahmad Bitarafan Rajabi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
- Echocardiography Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
- Cardiovascular Interventional Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Ghasem Hajianfar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Omid Gharibi
- Department of Medical Physics, School of Medicine, Iran University of Medical Science, Tehran, Iran
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Mobin Mohebi
- Department of Biomedical Engineering, Tarbiat Modares University, Tehran, Iran
| | | | - Nasim Naderi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran.
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland.
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
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Khan MS, Arshad MS, Greene SJ, Van Spall HGC, Pandey A, Vemulapalli S, Perakslis E, Butler J. Artificial intelligence and heart failure: A state-of-the-art review. Eur J Heart Fail 2023; 25:1507-1525. [PMID: 37560778 DOI: 10.1002/ejhf.2994] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 08/06/2023] [Accepted: 08/08/2023] [Indexed: 08/11/2023] Open
Abstract
Heart failure (HF) is a heterogeneous syndrome affecting more than 60 million individuals globally. Despite recent advancements in understanding of the pathophysiology of HF, many issues remain including residual risk despite therapy, understanding the pathophysiology and phenotypes of patients with HF and preserved ejection fraction, and the challenges related to integrating a large amount of disparate information available for risk stratification and management of these patients. Risk prediction algorithms based on artificial intelligence (AI) may have superior predictive ability compared to traditional methods in certain instances. AI algorithms can play a pivotal role in the evolution of HF care by facilitating clinical decision making to overcome various challenges such as allocation of treatment to patients who are at highest risk or are more likely to benefit from therapies, prediction of adverse outcomes, and early identification of patients with subclinical disease or worsening HF. With the ability to integrate and synthesize large amounts of data with multidimensional interactions, AI algorithms can supply information with which physicians can improve their ability to make timely and better decisions. In this review, we provide an overview of the AI algorithms that have been developed for establishing early diagnosis of HF, phenotyping HF with preserved ejection fraction, and stratifying HF disease severity. This review also discusses the challenges in clinical deployment of AI algorithms in HF, and the potential path forward for developing future novel learning-based algorithms to improve HF care.
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Affiliation(s)
| | | | - Stephen J Greene
- Division of Cardiology, Duke University School of Medicine, Durham, NC, USA
- Duke Clinical Research Institute, Durham, NC, USA
| | - Harriette G C Van Spall
- Department of Medicine and Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Ambarish Pandey
- Canada Population Health Research Institute, Hamilton, ON, Canada
- Division of Cardiology, Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX, USA
| | - Sreekanth Vemulapalli
- Division of Cardiology, Duke University School of Medicine, Durham, NC, USA
- Duke Clinical Research Institute, Durham, NC, USA
| | | | - Javed Butler
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
- Baylor Scott and White Research Institute, Dallas, TX, USA
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Nakayama M, Goto S, Sakano T, Goto S. Detection of the Relationship between the Multi-Dimensional Data Sets of Serially Measured Blood Pressure and the Future Risk of Death in Healthy Elderly Japanese Population. J Atheroscler Thromb 2023; 30:1002-1009. [PMID: 36273901 PMCID: PMC10406660 DOI: 10.5551/jat.63798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023] Open
Abstract
AIMS Whether the multi-dimensional data of serially measured blood pressure contains information for predicting the future risk of death in elderly individuals in nursing homes is unclear. METHODS Of the elderly individuals staying in a nursing home, 19,740 and 40,055 individuals with serially measured blood pressure from day 1 to 365 (for AI-long) and 1 to 90 (for AI-short) along with the death information at day 366 to 730 and 91-365 were included. The neural network-based artificial intelligence (AI) was applied to find the relationship between BP time-series and the future risks of death in both populations. RESULTS AI-long found a significant relationship between the serially measured BP from day 1 to day 365 days and the risk of death occurring 366-730 days with c-statistics of 0.57 (95% CI: 0.51-0.63). AI-short also found a significant relationship between the serially measured BP from day 1 to day 90 and the rate of death occurring 91-365 days with c-statistics of 0.58 (95%CI: 0.52-0.63). CONCLUSION Our results suggest that neural network-based AI could find the hidden subtle relationship between multi-dimensional data of serially measured BP and the future risk of death in apparently healthy elderly Japanese individuals under nursing care.
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Affiliation(s)
- Masamitsu Nakayama
- Department of Medicine (Cardiology), Tokai University School of Medicine, Kanagawa, Japan
| | - Shinichi Goto
- Department of Medicine (Cardiology), Tokai University School of Medicine, Kanagawa, Japan
| | | | - Shinya Goto
- Department of Medicine (Cardiology), Tokai University School of Medicine, Kanagawa, Japan
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10
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Mohammad MA. Advancing heart failure research using machine learning. Lancet Digit Health 2023; 5:e331-e332. [PMID: 37236694 DOI: 10.1016/s2589-7500(23)00085-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 04/17/2023] [Indexed: 05/28/2023]
Affiliation(s)
- Moman A Mohammad
- Department of Cardiology, Clinical Sciences, Lund University, Skåne University Hospital, SE-221 85 Lund, Sweden.
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11
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Ciccarelli M, Giallauria F, Carrizzo A, Visco V, Silverio A, Cesaro A, Calabrò P, De Luca N, Mancusi C, Masarone D, Pacileo G, Tourkmani N, Vigorito C, Vecchione C. Artificial intelligence in cardiovascular prevention: new ways will open new doors. J Cardiovasc Med (Hagerstown) 2023; 24:e106-e115. [PMID: 37186561 DOI: 10.2459/jcm.0000000000001431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Prevention and effective treatment of cardiovascular disease are progressive issues that grow in tandem with the average age of the world population. Over recent decades, the potential role of artificial intelligence in cardiovascular medicine has been increasingly recognized because of the incredible amount of real-world data (RWD) regarding patient health status and healthcare delivery that can be collated from a variety of sources wherein patient information is routinely collected, including patient registries, clinical case reports, reimbursement claims and billing reports, medical devices, and electronic health records. Like any other (health) data, RWD can be analysed in accordance with high-quality research methods, and its analysis can deliver valuable patient-centric insights complementing the information obtained from conventional clinical trials. Artificial intelligence application on RWD has the potential to detect a patient's health trajectory leading to personalized medicine and tailored treatment. This article reviews the benefits of artificial intelligence in cardiovascular prevention and management, focusing on diagnostic and therapeutic improvements without neglecting the limitations of this new scientific approach.
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Affiliation(s)
- Michele Ciccarelli
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
| | - Francesco Giallauria
- Department of Translational Medical Sciences, Federico II University, Naples, Italy
| | - Albino Carrizzo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
- Vascular Physiopathology Unit, IRCCS Neuromed, Pozzilli
| | - Valeria Visco
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
| | - Angelo Silverio
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
| | - Arturo Cesaro
- Department of Translational Medical Sciences, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Paolo Calabrò
- Department of Translational Medical Sciences, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Nicola De Luca
- Department of Advanced Biomedical Sciences, Federico II University, Naples, Italy
| | - Costantino Mancusi
- Department of Advanced Biomedical Sciences, Federico II University, Naples, Italy
| | - Daniele Masarone
- Heart Failure Unit, Department of Cardiology, AORN dei Colli-Monaldi Hospital Naples, Naples, Italy
| | - Giuseppe Pacileo
- Heart Failure Unit, Department of Cardiology, AORN dei Colli-Monaldi Hospital Naples, Naples, Italy
| | - Nidal Tourkmani
- Cardiology and Cardiac Rehabilitation Unit, 'Mons. Giosuè Calaciura Clinic', Catania, Italy
- ABL, Guangzhou, China
| | - Carlo Vigorito
- Department of Translational Medical Sciences, Federico II University, Naples, Italy
| | - Carmine Vecchione
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
- Vascular Physiopathology Unit, IRCCS Neuromed, Pozzilli
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12
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Hyde B, Paoli CJ, Panjabi S, Bettencourt KC, Bell Lynum KS, Selej M. A claims-based, machine-learning algorithm to identify patients with pulmonary arterial hypertension. Pulm Circ 2023; 13:e12237. [PMID: 37287599 PMCID: PMC10243208 DOI: 10.1002/pul2.12237] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 04/14/2023] [Accepted: 05/01/2023] [Indexed: 06/09/2023] Open
Abstract
Many patients with pulmonary arterial hypertension (PAH) experience substantial delays in diagnosis, which is associated with worse outcomes and higher costs. Tools for diagnosing PAH sooner may lead to earlier treatment, which may delay disease progression and adverse outcomes including hospitalization and death. We developed a machine-learning (ML) algorithm to identify patients at risk for PAH earlier in their symptom journey and distinguish them from patients with similar early symptoms not at risk for developing PAH. Our supervised ML model analyzed retrospective, de-identified data from the US-based Optum® Clinformatics® Data Mart claims database (January 2015 to December 2019). Propensity score matched PAH and non-PAH (control) cohorts were established based on observed differences. Random forest models were used to classify patients as PAH or non-PAH at diagnosis and at 6 months prediagnosis. The PAH and non-PAH cohorts included 1339 and 4222 patients, respectively. At 6 months prediagnosis, the model performed well in distinguishing PAH and non-PAH patients, with area under the curve of the receiver operating characteristic of 0.84, recall (sensitivity) of 0.73, and precision of 0.50. Key features distinguishing PAH from non-PAH cohorts were a longer time between first symptom and the prediagnosis model date (i.e., 6 months before diagnosis); more diagnostic and prescription claims, circulatory claims, and imaging procedures, leading to higher overall healthcare resource utilization; and more hospitalizations. Our model distinguishes between patients with and without PAH at 6 months before diagnosis and illustrates the feasibility of using routine claims data to identify patients at a population level who might benefit from PAH-specific screening and/or earlier specialist referral.
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Affiliation(s)
- Bethany Hyde
- Janssen Business Technology Commercial Data Insights & Data ScienceTitusvilleNew JerseyUSA
| | | | | | | | | | - Mona Selej
- Janssen R&D Data ScienceSouth San FranciscoCaliforniaUSA
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13
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Clinical application of artificial intelligence algorithm for prediction of one-year mortality in heart failure patients. Heart Vessels 2023; 38:785-792. [PMID: 36802023 DOI: 10.1007/s00380-023-02237-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 01/18/2023] [Indexed: 02/23/2023]
Abstract
Risk prediction for heart failure (HF) using machine learning methods (MLM) has not yet been established at practical application levels in clinical settings. This study aimed to create a new risk prediction model for HF with a minimum number of predictor variables using MLM. We used two datasets of hospitalized HF patients: retrospective data for creating the model and prospectively registered data for model validation. Critical clinical events (CCEs) were defined as death or LV assist device implantation within 1 year from the discharge date. We randomly divided the retrospective data into training and testing datasets and created a risk prediction model based on the training dataset (MLM-risk model). The prediction model was validated using both the testing dataset and the prospectively registered data. Finally, we compared predictive power with published conventional risk models. In the patients with HF (n = 987), CCEs occurred in 142 patients. In the testing dataset, the substantial predictive power of the MLM-risk model was obtained (AUC = 0.87). We generated the model using 15 variables. Our MLM-risk model showed superior predictive power in the prospective study compared to conventional risk models such as the Seattle Heart Failure Model (c-statistics: 0.86 vs. 0.68, p < 0.05). Notably, the model with an input variable number (n = 5) has comparable predictive power for CCE with the model (variable number = 15). This study developed and validated a model with minimized variables to predict mortality more accurately in patients with HF, using a MLM, than the existing risk scores.
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14
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Kamio T, Ikegami M, Machida Y, Uemura T, Chino N, Iwagami M. Machine learning-based prognostic modeling of patients with acute heart failure receiving furosemide in intensive care units. Digit Health 2023; 9:20552076231194933. [PMID: 37576718 PMCID: PMC10422900 DOI: 10.1177/20552076231194933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/28/2023] [Indexed: 08/15/2023] Open
Abstract
Purpose This study developed machine learning models to predict in-hospital mortality, initiation of acute renal replacement therapy, and mechanical ventilation in patients with acute heart failure receiving furosemide in intensive care units. Method An extensive database comprising static and dynamic features obtained from a Japanese hospital chain was used to construct and train the machine learning models. Results The results revealed that the proposed machine learning models predict in-hospital mortality, initiation of acute renal replacement therapy, and mechanical ventilation with good accuracy. However, the optimal models vary depending on the predicted outcomes. The linear support vector machine classification models exhibited the highest in-hospital mortality and mechanical ventilation prediction accuracy, with the area under the receiver operating characteristic curve of 0.73 and 0.73, respectively, whereas the multi-layer neural network exhibited the highest accuracy for acute renal replacement therapy initiation prediction with an area under the receiver operating characteristic curve of 0.70. Conclusions In conclusion, this study demonstrated that machine learning models could help predict the clinical outcomes of patients with acute heart failure receiving furosemide. However, the optimal models may differ depending on the outcome of interest.
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Affiliation(s)
- Tadashi Kamio
- Division of Critical Care, Shonan Kamakura General Hospital, Kanagawa, Japan
| | - Masaru Ikegami
- Terumo Corporation R and D Center, Shonan Center, Ashigarakami-gun, Kanagawa, Japan
| | - Yoshihito Machida
- Terumo Corporation R and D Center, Shonan Center, Ashigarakami-gun, Kanagawa, Japan
| | - Tomoko Uemura
- Terumo Corporation R and D Center, Shonan Center, Ashigarakami-gun, Kanagawa, Japan
| | - Naotaka Chino
- Terumo Corporation R and D Center, Shonan Center, Ashigarakami-gun, Kanagawa, Japan
| | - Masao Iwagami
- Department of Health Services Research, University of Tsukuba, Ibaraki, Japan
- Health Services Research and Development Center, University of Tsukuba, Ibaraki, Japan
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, UK
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15
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Peng S, Huang J, Liu X, Deng J, Sun C, Tang J, Chen H, Cao W, Wang W, Duan X, Luo X, Peng S. Interpretable machine learning for 28-day all-cause in-hospital mortality prediction in critically ill patients with heart failure combined with hypertension: A retrospective cohort study based on medical information mart for intensive care database-IV and eICU databases. Front Cardiovasc Med 2022; 9:994359. [PMID: 36312291 PMCID: PMC9597462 DOI: 10.3389/fcvm.2022.994359] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 09/15/2022] [Indexed: 11/13/2022] Open
Abstract
Background Heart failure (HF) combined with hypertension is an extremely important cause of in-hospital mortality, especially for the intensive care unit (ICU) patients. However, under intense working pressure, the medical staff are easily overwhelmed by the large number of clinical signals generated in the ICU, which may lead to treatment delay, sub-optimal care, or even wrong clinical decisions. Individual risk stratification is an essential strategy for managing ICU patients with HF combined with hypertension. Artificial intelligence, especially machine learning (ML), can develop superior models to predict the prognosis of these patients. This study aimed to develop a machine learning method to predict the 28-day mortality for ICU patients with HF combined with hypertension. Methods We enrolled all critically ill patients with HF combined with hypertension in the Medical Information Mart for IntensiveCare Database-IV (MIMIC-IV, v.1.4) and the eICU Collaborative Research Database (eICU-CRD) from 2008 to 2019. Subsequently, MIMIC-IV was divided into training cohort and testing cohort in an 8:2 ratio, and eICU-CRD was designated as the external validation cohort. The least absolute shrinkage and selection operator (LASSO) Cox regression with internal tenfold cross-validation was used for data dimension reduction and identifying the most valuable predictive features for 28-day mortality. Based on its accuracy and area under the curve (AUC), the best model in the validation cohort was selected. In addition, we utilized the Shapley Additive Explanations (SHAP) method to highlight the importance of model features, analyze the impact of individual features on model output, and visualize an individual’s Shapley values. Results A total of 3,458 and 6582 patients with HF combined with hypertension in MIMIC-IV and eICU-CRD were included. The patients, including 1,756 males, had a median (Q1, Q3) age of 75 (65, 84) years. After selection, 22 out of a total of 58 clinical parameters were extracted to develop the machine-learning models. Among four constructed models, the Neural Networks (NN) model performed the best predictive performance with an AUC of 0.764 and 0.674 in the test cohort and external validation cohort, respectively. In addition, a simplified model including seven variables was built based on NN, which also had good predictive performance (AUC: 0.741). Feature importance analysis showed that age, mechanical ventilation (MECHVENT), chloride, bun, anion gap, paraplegia, rdw (RDW), hyperlipidemia, peripheral capillary oxygen saturation (SpO2), respiratory rate, cerebrovascular disease, heart rate, white blood cell (WBC), international normalized ratio (INR), mean corpuscular hemoglobin concentration (MCHC), glucose, AIDS, mean corpuscular volume (MCV), N-terminal pro-brain natriuretic peptide (Npro. BNP), calcium, renal replacement therapy (RRT), and partial thromboplastin time (PTT) were the top 22 features of the NN model with the greatest impact. Finally, after hyperparameter optimization, SHAP plots were employed to make the NN-based model interpretable with an analytical description of how the constructed model visualizes the prediction of death. Conclusion We developed a predictive model to predict the 28-day mortality for ICU patients with HF combined with hypertension, which proved superior to the traditional logistic regression analysis. The SHAP method enables machine learning models to be more interpretable, thereby helping clinicians to better understand the reasoning behind the outcome and assess in-hospital outcomes for critically ill patients.
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Affiliation(s)
- Shengxian Peng
- Scientific Research Department, First People’s Hospital of Zigong City, Zigong, China
| | - Jian Huang
- Graduate School, Guangxi University of Chinese Medicine, Nanning, China
| | - Xiaozhu Liu
- Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jiewen Deng
- Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Chenyu Sun
- AMITA Health Saint Joseph Hospital Chicago, Chicago, IL, United States
| | - Juan Tang
- Scientific Research Department, First People’s Hospital of Zigong City, Zigong, China
| | - Huaqiao Chen
- Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wenzhai Cao
- Department of Cardiology, First People’s Hospital of Zigong City, Zigong, China
| | - Wei Wang
- Department of Cardiology, First People’s Hospital of Zigong City, Zigong, China,Information Department, First People’s Hospital of Zigong City, Zigong, China
| | - Xiangjie Duan
- Department of Infectious Diseases, The First People’s Hospital of Changde City, Changde, China
| | - Xianglin Luo
- Information Department, First People’s Hospital of Zigong City, Zigong, China
| | - Shuang Peng
- General Affairs Section, The People’s Hospital of Tongnan District, Chongqing, China,*Correspondence: Shuang Peng,
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16
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Emergency department risk assessment and disposition of acute heart failure patients: existing evidence and ongoing challenges. Heart Fail Rev 2022:10.1007/s10741-022-10272-4. [PMID: 36123519 PMCID: PMC9485013 DOI: 10.1007/s10741-022-10272-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/07/2022] [Indexed: 12/02/2022]
Abstract
Heart failure (HF) is a global public health burden, characterized by frequent emergency department (ED) visits and hospitalizations. Identifying successful strategies to avoid admissions is crucial for the management of acutely decompensated HF, let alone resource utilization. The primary challenge for ED management of patients with acute heart failure (AHF) lies in the identification of those who can be safely discharged home instead of being admitted. This is an elaborate decision, based on limited objective evidence. Thus far, current biomarkers and risk stratification tools have had little impact on ED disposition decision-making. A reliable definition of a low-risk patient profile is warranted in order to accurately identify patients who could be appropriate for early discharge. A brief period of observation can facilitate risk stratification and allow for close monitoring, aggressive treatment, continuous assessment of response to initial therapy and patient education. Lung ultrasound may represent a valid bedside tool to monitor cardiogenic pulmonary oedema and determine the extent of achieved cardiac unloading after treatment in the observation unit setting. Safe discharge mandates multidisciplinary collaboration and thoughtful assessment of socioeconomic and behavioural factors, along with a clear post-discharge plan put forward and a close follow-up in an outpatient setting. Ongoing research to improve ED risk stratification and disposition of AHF patients may mitigate the tremendous public health challenge imposed by the HF epidemic.
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17
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Błaziak M, Urban S, Wietrzyk W, Jura M, Iwanek G, Stańczykiewicz B, Kuliczkowski W, Zymliński R, Pondel M, Berka P, Danel D, Biegus J, Siennicka A. An Artificial Intelligence Approach to Guiding the Management of Heart Failure Patients Using Predictive Models: A Systematic Review. Biomedicines 2022; 10:biomedicines10092188. [PMID: 36140289 PMCID: PMC9496386 DOI: 10.3390/biomedicines10092188] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 08/19/2022] [Accepted: 08/27/2022] [Indexed: 11/23/2022] Open
Abstract
Heart failure (HF) is one of the leading causes of mortality and hospitalization worldwide. The accurate prediction of mortality and readmission risk provides crucial information for guiding decision making. Unfortunately, traditional predictive models reached modest accuracy in HF populations. We therefore aimed to present predictive models based on machine learning (ML) techniques in HF patients that were externally validated. We searched four databases and the reference lists of the included papers to identify studies in which HF patient data were used to create a predictive model. Literature screening was conducted in Academic Search Ultimate, ERIC, Health Source Nursing/Academic Edition and MEDLINE. The protocol of the current systematic review was registered in the PROSPERO database with the registration number CRD42022344855. We considered all types of outcomes: mortality, rehospitalization, response to treatment and medication adherence. The area under the receiver operating characteristic curve (AUC) was used as the comparator parameter. The literature search yielded 1649 studies, of which 9 were included in the final analysis. The AUCs for the machine learning models ranged from 0.6494 to 0.913 in independent datasets, whereas the AUCs for statistical predictive scores ranged from 0.622 to 0.806. Our study showed an increasing number of ML predictive models concerning HF populations, although external validation remains infrequent. However, our findings revealed that ML approaches can outperform conventional risk scores and may play important role in HF management.
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Affiliation(s)
- Mikołaj Błaziak
- Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland
- Correspondence: (M.B.); (W.K.); Tel.: +48-71-733-11-12 (M.B.)
| | - Szymon Urban
- Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland
| | - Weronika Wietrzyk
- Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland
| | - Maksym Jura
- Department of Physiology and Pathophysiology, Wroclaw Medical University, 50-368 Wroclaw, Poland
| | - Gracjan Iwanek
- Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland
| | - Bartłomiej Stańczykiewicz
- Department of Psychiatry, Division of Consultation Psychiatry and Neuroscience, Wroclaw Medical University, 50-367 Wroclaw, Poland
| | - Wiktor Kuliczkowski
- Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland
- Correspondence: (M.B.); (W.K.); Tel.: +48-71-733-11-12 (M.B.)
| | - Robert Zymliński
- Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland
| | - Maciej Pondel
- Institute of Information Systems in Economics, Wroclaw University of Economics and Business, 53-345 Wroclaw, Poland
| | - Petr Berka
- Department of Information and Knowledge Engineering, Prague University of Economics and Business, W. Churchill Sq. 1938/4, 130 67 Prague, Czech Republic
| | - Dariusz Danel
- Department of Anthropology, Ludwik Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, 53-114 Wroclaw, Poland
| | - Jan Biegus
- Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland
| | - Agnieszka Siennicka
- Department of Physiology and Pathophysiology, Wroclaw Medical University, 50-368 Wroclaw, Poland
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18
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McGilvray MMO, Heaton J, Guo A, Masood MF, Cupps BP, Damiano M, Pasque MK, Foraker R. Electronic Health Record-Based Deep Learning Prediction of Death or Severe Decompensation in Heart Failure Patients. JACC. HEART FAILURE 2022; 10:637-647. [PMID: 36049815 DOI: 10.1016/j.jchf.2022.05.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/12/2022] [Accepted: 05/18/2022] [Indexed: 02/08/2023]
Abstract
BACKGROUND Surgical mechanical ventricular assistance and cardiac replacement therapies, although life-saving in many heart failure (HF) patients, remain high-risk. Despite this, the difficulty in timely identification of medical therapy nonresponders and the dire consequences of nonresponse have fueled early, less selective surgical referral. Patients who would have ultimately responded to medical therapy are therefore subjected to the risk and life disruption of surgical therapy. OBJECTIVES The purpose of this study was to develop deep learning models based upon commonly-available electronic health record (EHR) variables to assist clinicians in the timely and accurate identification of HF medical therapy nonresponders. METHODS The study cohort consisted of all patients (age 18 to 90 years) admitted to a single tertiary care institution from January 2009 through December 2018, with International Classification of Disease HF diagnostic coding. Ensemble deep learning models employing time-series and densely-connected networks were developed from standard EHR data. The positive class included all observations resulting in severe progression (death from any cause or referral for HF surgical intervention) within 1 year. RESULTS A total of 79,850 distinct admissions from 52,265 HF patients met observation criteria and contributed >350 million EHR datapoints for model training, validation, and testing. A total of 20% of model observations fit positive class criteria. The model C-statistic was 0.91. CONCLUSIONS The demonstrated accuracy of EHR-based deep learning model prediction of 1-year all-cause death or referral for HF surgical therapy supports clinical relevance. EHR-based deep learning models have considerable potential to assist HF clinicians in improving the application of advanced HF surgical therapy in medical therapy nonresponders.
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Affiliation(s)
- Martha M O McGilvray
- Division of Cardiothoracic Surgery, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Jeffrey Heaton
- Sever Institute, McKelvey School of Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Aixia Guo
- Veterans Affairs St. Louis Health Care System-St. Louis, St. Louis, Missouri, USA
| | - M Faraz Masood
- Division of Cardiothoracic Surgery, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Brian P Cupps
- Division of Cardiothoracic Surgery, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Marci Damiano
- Division of Cardiothoracic Surgery, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Michael K Pasque
- Division of Cardiothoracic Surgery, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA.
| | - Randi Foraker
- Institute for Informatics, Division of General Medical Sciences, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
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19
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Mumtaz H, Saqib M, Ansar F, Zargar D, Hameed M, Hasan M, Muskan P. The future of Cardiothoracic surgery in Artificial intelligence. Ann Med Surg (Lond) 2022; 80:104251. [PMID: 36045824 PMCID: PMC9422274 DOI: 10.1016/j.amsu.2022.104251] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/19/2022] [Accepted: 07/20/2022] [Indexed: 12/23/2022] Open
Abstract
Humans' great and quick technological breakthroughs in the previous decade have undoubtedly influenced how surgical procedures are executed in the operating room. AI is becoming incredibly influential for surgical decision-making to help surgeons make better projections about the implications of surgical operations by considering different sources of data such as patient health conditions, disease natural history, patient values, and finance. Although the application of artificial intelligence in healthcare settings is rapidly increasing, its mainstream application in clinical practice remains limited. The use of machine learning algorithms in thoracic surgery is extensive, including different clinical stages. By leveraging techniques such as machine learning, computer vision, and robotics, AI may play a key role in diagnostic augmentation, operative management, pre-and post-surgical patient management, and upholding safety standards. AI, particularly in complex surgical procedures such as cardiothoracic surgery, may be a significant help to surgeons in executing more intricate surgeries with greater success, fewer complications, and ensuring patient safety, while also providing resources for robust research and better dissemination of knowledge. In this paper, we present an overview of AI applications in thoracic surgery and its related components, including contemporary projects and technology that use AI in cardiothoracic surgery and general care. We also discussed the future of AI and how high-tech operating rooms will use human-machine collaboration to improve performance and patient safety, as well as its future directions and limitations. It is vital for the surgeons to keep themselves acquainted with the latest technological advancement in AI order to grasp this technology and easily integrate it into clinical practice when it becomes accessible. This review is a great addition to literature, keeping practicing and aspiring surgeons up to date on the most recent advances in AI and cardiothoracic surgery. This literature review tells about the role of Artificial Intelligence in Cardiothoracic Surgery. Discussed the future of AI and how high-tech operating rooms will use human-machine collaboration to improve performance and patient safety, as well as its future directions and limitations. Vital for the surgeons to keep themselves acquainted with the latest technological advancement in AI order to grasp this technology and easily integrate it into clinical practice when it becomes accessible.
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20
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Segar MW, Hall JL, Jhund PS, Powell-Wiley TM, Morris AA, Kao D, Fonarow GC, Hernandez R, Ibrahim NE, Rutan C, Navar AM, Stevens LM, Pandey A. Machine Learning-Based Models Incorporating Social Determinants of Health vs Traditional Models for Predicting In-Hospital Mortality in Patients With Heart Failure. JAMA Cardiol 2022; 7:844-854. [PMID: 35793094 PMCID: PMC9260645 DOI: 10.1001/jamacardio.2022.1900] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Importance Traditional models for predicting in-hospital mortality for patients with heart failure (HF) have used logistic regression and do not account for social determinants of health (SDOH). Objective To develop and validate novel machine learning (ML) models for HF mortality that incorporate SDOH. Design, Setting, and Participants This retrospective study used the data from the Get With The Guidelines-Heart Failure (GWTG-HF) registry to identify HF hospitalizations between January 1, 2010, and December 31, 2020. The study included patients with acute decompensated HF who were hospitalized at the GWTG-HF participating centers during the study period. Data analysis was performed January 6, 2021, to April 26, 2022. External validation was performed in the hospitalization cohort from the Atherosclerosis Risk in Communities (ARIC) study between 2005 and 2014. Main Outcomes and Measures Random forest-based ML approaches were used to develop race-specific and race-agnostic models for predicting in-hospital mortality. Performance was assessed using C index (discrimination), regression slopes for observed vs predicted mortality rates (calibration), and decision curves for prognostic utility. Results The training data set included 123 634 hospitalized patients with HF who were enrolled in the GWTG-HF registry (mean [SD] age, 71 [13] years; 58 356 [47.2%] female individuals; 65 278 [52.8%] male individuals. Patients were analyzed in 2 categories: Black (23 453 [19.0%]) and non-Black (2121 [2.1%] Asian; 91 154 [91.0%] White, and 6906 [6.9%] other race and ethnicity). The ML models demonstrated excellent performance in the internal testing subset (n = 82 420) (C statistic, 0.81 for Black patients and 0.82 for non-Black patients) and in the real-world-like cohort with less than 50% missingness on covariates (n = 553 506; C statistic, 0.74 for Black patients and 0.75 for non-Black patients). In the external validation cohort (ARIC registry; n = 1205 Black patients and 2264 non-Black patients), ML models demonstrated high discrimination and adequate calibration (C statistic, 0.79 and 0.80, respectively). Furthermore, the performance of the ML models was superior to the traditional GWTG-HF risk score model (C index, 0.69 for both race groups) and other rederived logistic regression models using race as a covariate. The performance of the ML models was identical using the race-specific and race-agnostic approaches in the GWTG-HF and external validation cohorts. In the GWTG-HF cohort, the addition of zip code-level SDOH parameters to the ML model with clinical covariates only was associated with better discrimination, prognostic utility (assessed using decision curves), and model reclassification metrics in Black patients (net reclassification improvement, 0.22 [95% CI, 0.14-0.30]; P < .001) but not in non-Black patients. Conclusions and Relevance ML models for HF mortality demonstrated superior performance to the traditional and rederived logistic regressions models using race as a covariate. The addition of SDOH parameters improved the prognostic utility of prediction models in Black patients but not non-Black patients in the GWTG-HF registry.
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Affiliation(s)
| | | | - Pardeep S. Jhund
- British Heart Foundation Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, Scotland
| | - Tiffany M. Powell-Wiley
- Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland,Intramural Research Program, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland
| | - Alanna A. Morris
- Division of Cardiology, Emory University School of Medicine, Atlanta, Georgia
| | - David Kao
- Divisions of Cardiology and Bioinformatics + Personalized Medicine, University of Colorado School of Medicine, Aurora
| | - Gregg C. Fonarow
- Ahmanson-UCLA Cardiomyopathy Center, Ronald Reagan UCLA Medical Center, Los Angeles, California,Associate Editor for Health Care Quality and Guidelines, JAMA Cardiology
| | - Rosalba Hernandez
- School of Social Work, University of Illinois at Urbana-Champaign, Urbana
| | - Nasrien E. Ibrahim
- Heart Failure Clinical Research, Inova Heart and Vascular Institute, Washington, DC
| | - Christine Rutan
- Quality, Outcomes Research and Analytics, American Heart Association, Dallas, Texas
| | - Ann Marie Navar
- Department of Internal Medicine, Division of Cardiology, University of Texas Southwestern Medical Center, Dallas,Deputy Editor, Diversity, Equity and Inclusion, JAMA Cardiology
| | - Laura M. Stevens
- Data Science, American Heart Association, Dallas, Texas,Divisions of Cardiology and Bioinformatics + Personalized Medicine, University of Colorado School of Medicine, Aurora
| | - Ambarish Pandey
- Department of Internal Medicine, Division of Cardiology, University of Texas Southwestern Medical Center, Dallas
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Park J, Hwang IC, Yoon YE, Park JB, Park JH, Cho GY. Predicting Long-Term Mortality in Patients With Acute Heart Failure by Using Machine Learning. J Card Fail 2022; 28:1078-1087. [PMID: 35301108 DOI: 10.1016/j.cardfail.2022.02.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 01/19/2022] [Accepted: 02/19/2022] [Indexed: 01/15/2023]
Abstract
BACKGROUND High mortality rates in patients with acute heart failure (AHF) necessitate proper risk stratification. However, risk-assessment tools for long-term mortality are largely lacking. We aimed to develop a machine-learning (ML)-based risk-prediction model for long-term all-cause mortality in patients admitted for AHF. METHODS AND RESULTS The ML model, based on boosted a Cox regression algorithm (CoxBoost), was trained with 2704 consecutive patients hospitalized for AHF (median age 73 years, 55% male, and median left ventricular ejection fraction 38%). We selected 27 input variables, including 19 clinical features and 8 echocardiographic parameters, for model development. The best-performing model, along with pre-existing risk scores (BIOSTAT-CHF and AHEAD scores), was validated in an independent test cohort of 1608 patients. During the median 32 months (interquartile range 12-54 months) of the follow-up period, 1050 (38.8%) and 690 (42.9%) deaths occurred in the training and test cohorts, respectively. The area under the receiver operating characteristic curve (AUROC) of the ML model for all-cause mortality at 3 years was 0.761 (95% CI: 0.754-0.767) in the training cohort and 0.760 (95% CI: 0.752-0.768) in the test cohort. The discrimination performance of the ML model significantly outperformed those of the pre-existing risk scores (AUROC 0.714, 95% CI 0.706-0.722 by BIOSTAT-CHF; and 0.681, 95% CI 0.672-0.689 by AHEAD). Risk stratification based on the ML model identified patients at high mortality risk regardless of heart failure phenotypes. CONCLUSIONS The ML-based mortality-prediction model can predict long-term mortality accurately, leading to optimal risk stratification of patients with AHF.
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Affiliation(s)
- Jiesuck Park
- Department of Cardiology, Cardiovascular Center, Seoul National University Bundang Hospital, Seongnam, Gyeonggi-do, 13620, Republic of Korea; Department of Internal Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongro-gu, Seoul, 03080, South Korea
| | - In-Chang Hwang
- Department of Cardiology, Cardiovascular Center, Seoul National University Bundang Hospital, Seongnam, Gyeonggi-do, 13620, Republic of Korea; Department of Internal Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongro-gu, Seoul, 03080, South Korea
| | - Yeonyee E Yoon
- Department of Cardiology, Cardiovascular Center, Seoul National University Bundang Hospital, Seongnam, Gyeonggi-do, 13620, Republic of Korea; Department of Internal Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongro-gu, Seoul, 03080, South Korea
| | - Jun-Bean Park
- Department of Internal Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongro-gu, Seoul, 03080, South Korea; Cardiovascular Center and Department of Internal Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongro-gu, Seoul, 03080, South Korea
| | - Jae-Hyeong Park
- Department of Cardiology, Internal Medicine, Chungnam National University Hospital, 282 Munhwa-ro, Jung-gu Daejeon, 35015, South Korea
| | - Goo-Yeong Cho
- Department of Cardiology, Cardiovascular Center, Seoul National University Bundang Hospital, Seongnam, Gyeonggi-do, 13620, Republic of Korea; Department of Internal Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongro-gu, Seoul, 03080, South Korea.
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22
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Leclercq C, Witt H, Hindricks G, Katra RP, Albert D, Belliger A, Cowie MR, Deneke T, Friedman P, Haschemi M, Lobban T, Lordereau I, McConnell MV, Rapallini L, Samset E, Turakhia MP, Singh JP, Svennberg E, Wadhwa M, Weidinger F. Wearables, telemedicine, and artificial intelligence in arrhythmias and heart failure: Proceedings of the European Society of Cardiology: Cardiovascular Round Table. Europace 2022; 24:1372-1383. [PMID: 35640917 DOI: 10.1093/europace/euac052] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 04/05/2022] [Indexed: 12/31/2022] Open
Abstract
Digital technology is now an integral part of medicine. Tools for detecting, screening, diagnosis, and monitoring health-related parameters have improved patient care and enabled individuals to identify issues leading to better management of their own health. Wearable technologies have integrated sensors and can measure physical activity, heart rate and rhythm, and glucose and electrolytes. For individuals at risk, wearables or other devices may be useful for early detection of atrial fibrillation or sub-clinical states of cardiovascular disease, disease management of cardiovascular diseases such as hypertension and heart failure, and lifestyle modification. Health data are available from a multitude of sources, namely clinical, laboratory and imaging data, genetic profiles, wearables, implantable devices, patient-generated measurements, and social and environmental data. Artificial intelligence is needed to efficiently extract value from this constantly increasing volume and variety of data and to help in its interpretation. Indeed, it is not the acquisition of digital information, but rather the smart handling and analysis that is challenging. There are multiple stakeholder groups involved in the development and effective implementation of digital tools. While the needs of these groups may vary, they also have many commonalities, including the following: a desire for data privacy and security; the need for understandable, trustworthy, and transparent systems; standardized processes for regulatory and reimbursement assessments; and better ways of rapidly assessing value.
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Affiliation(s)
- Christophe Leclercq
- Department of Cardiology, CHU Rennes and Inserm, LTSI, University of Rennes, Centre Cardio-Pneumologique, CHU Pontchaillou, Service de Cardiologie et Maladies Vasculaires, 2 Rue Henri le Guilloux, 35000, Rennes, France
| | - Henning Witt
- Department of Internal Medicine, Pfizer, Berlin, Germany
| | - Gerhard Hindricks
- Department of Electrophysiology, Heart Center, Leipzig Heart Institute, Leipzig, Germany
| | - Rodolphe P Katra
- Cardiac Rhythm Management, Research & Technology, Medtronic, Minneapolis, MN, USA
| | | | - Andrea Belliger
- Institute for Communication and Leadership, and Lucerne University of Education, Lucerne, Switzerland
| | - Martin R Cowie
- Royal Brompton Hospital & School of Cardiovascular Medicine & Sciences, Faculty of Life Sciences & Medicine, King's College London, London, UK
| | - Thomas Deneke
- Clinic for Interventional Electrophysiology and Arrhythmology Heart Center, Bad Neustadt, Germany
| | - Paul Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Mehdiyar Haschemi
- Siemens Healthineers, Segment Advanced Therapies, Clinical Segment Cardiovascular Care, Forchheim, Bavaria, Germany
| | - Trudie Lobban
- Atrial Fibrillation Association (AF Association), Arrhythmia Alliance (A-A), and STARS (Syncope Trust And Reflex anoxic Seizures), UK & International
| | | | - Michael V McConnell
- Fitbit/Google; Division of Cardiovascular Medicine, Stanford School of Medicine, Stanford, CA, USA
| | - Leonardo Rapallini
- Research and Development, Cardiac Diagnostics and Services Business, Medtronic, Minneapolis, MN, USA
| | - Eigil Samset
- GE Healthcare Cardiology Solutions, Chicago, IL, USA
| | - Mintu P Turakhia
- Center for Digital Health, Stanford University School of Medicine, Stanford, CA, USA.,VA Palo Alto Health Care System, Palo Alto, CA, USA
| | - Jagmeet P Singh
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Emma Svennberg
- Department Electrophysiology, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden
| | | | - Franz Weidinger
- 2nd Medical Department with Cardiology and Intensive Care Medicine, Klinik Landstrasse, Vienna, Austria
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23
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Averbuch T, Sullivan K, Sauer A, Mamas MA, Voors AA, Gale CP, Metra M, Ravindra N, Van Spall HGC. Applications of artificial intelligence and machine learning in heart failure. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2022; 3:311-322. [PMID: 36713018 PMCID: PMC9707916 DOI: 10.1093/ehjdh/ztac025] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 04/15/2022] [Indexed: 02/01/2023]
Abstract
Machine learning (ML) is a sub-field of artificial intelligence that uses computer algorithms to extract patterns from raw data, acquire knowledge without human input, and apply this knowledge for various tasks. Traditional statistical methods that classify or regress data have limited capacity to handle large datasets that have a low signal-to-noise ratio. In contrast to traditional models, ML relies on fewer assumptions, can handle larger and more complex datasets, and does not require predictors or interactions to be pre-specified, allowing for novel relationships to be detected. In this review, we discuss the rationale for the use and applications of ML in heart failure, including disease classification, early diagnosis, early detection of decompensation, risk stratification, optimal titration of medical therapy, effective patient selection for devices, and clinical trial recruitment. We discuss how ML can be used to expedite implementation and close healthcare gaps in learning healthcare systems. We review the limitations of ML, including opaque logic and unreliable model performance in the setting of data errors or data shift. Whilst ML has great potential to improve clinical care and research in HF, the applications must be externally validated in prospective studies for broad uptake to occur.
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Affiliation(s)
- Tauben Averbuch
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Kristen Sullivan
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Andrew Sauer
- Department of Cardiology, University of Kansas Health System, Kansas City, KS, USA
| | - Mamas A Mamas
- Keele Cardiovascular research group, Keele University, Stoke on Trent, Staffordshire
| | | | - Chris P Gale
- Department of Cardiology, University of Leeds, Leeds, West Yorkshire
| | - Marco Metra
- Azienda Socio Sanitaria Territoriale Spedali Civili and University of Brescia, Brescia, Italy
| | - Neal Ravindra
- Department of Computer Science, Yale University, New Haven, CT, USA
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24
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Dutra GP, Gomes BFDO, do Carmo PR, Petriz JLF, Nascimento EM, Pereira BDB, de Oliveira GMM. Mortality from Heart Failure with Mid-Range Ejection Fraction. Arq Bras Cardiol 2022; 118:694-700. [PMID: 35508046 PMCID: PMC9007002 DOI: 10.36660/abc.20210050] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 04/04/2021] [Accepted: 05/12/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND The prognostic importance of the classification 'heart failure (HF) with mid-range ejection fraction (EF)' remains uncertain. OBJECTIVE To analyze the clinical characteristics, comorbidities, complications, and in-hospital and late mortality of patients classified as having HF with mid-range EF (HFmrEF - EF: 40%-49%), and to compare them to those of patients with HF with preserved EF (HFpEF - EF > 50%) and with HF with reduced EF (HFrEF - EF < 40%) on admission for decompensated HF. METHODS Ambispective cohort of patients admitted to the cardiac intensive care unit due to decompensated HF. Clinical characteristics, comorbidities, complications, and in-hospital and late mortality were assessed. The software R was used, with a 5% significance, for the tests chi-square, analysis of variance, Cox multivariate, and Kaplan-Meier survival curve, in addition to machine-learning techniques (Elastic Net and survival tree). RESULTS 519 individuals were included between September 2011 and June 2019 (mean age, 74.87 ± 13.56 years; 57.6% were men). The frequencies of HFpEF, HFmrEF and HFrEF were 25.4%, 27% and 47.6%, respectively. Previous infarction was more frequent in HFmrEF. The mean follow-up time was 2.94 ± 2.55 years, with no statistical difference in mortality between the groups (53.8%, 52.1%, 57.9%). In the survival curve, there was difference between neither the HFpEF and HFmrEF groups, nor the HFpEF and HFrEF groups, but between the HFmrEF and HFrEF groups. Age over 77 years, previous HF, history of readmission, dementia and need for vasopressors were associated with higher late mortality in the survival tree. CONCLUSION The EF was not selected as a variable associated with mortality in patients with decompensated HF.
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Affiliation(s)
- Giovanni Possamai Dutra
- Universidade Federal do Rio de JaneiroRio de JaneiroRJBrasilUniversidade Federal do Rio de Janeiro, Rio de Janeiro, RJ – Brasil
- Hospital Barra D’orRio de JaneiroRJBrasilHospital Barra D’or – Cardiologia, Rio de Janeiro, RJ – Brasil
| | - Bruno Ferraz de Oliveira Gomes
- Universidade Federal do Rio de JaneiroRio de JaneiroRJBrasilUniversidade Federal do Rio de Janeiro, Rio de Janeiro, RJ – Brasil
- Hospital Barra D’orRio de JaneiroRJBrasilHospital Barra D’or – Cardiologia, Rio de Janeiro, RJ – Brasil
| | - Plínio Resende do Carmo
- Universidade Federal do Rio de JaneiroRio de JaneiroRJBrasilUniversidade Federal do Rio de Janeiro, Rio de Janeiro, RJ – Brasil
- Hospital Barra D’orRio de JaneiroRJBrasilHospital Barra D’or – Cardiologia, Rio de Janeiro, RJ – Brasil
| | | | - Emilia Matos Nascimento
- UEZORio de JaneiroRJBrasilCentro Universitário Estadual da Zona Oeste – UEZO, Rio de Janeiro, RJ – Brasil
| | - Basilio de Bragança Pereira
- Universidade Federal do Rio de JaneiroRio de JaneiroRJBrasilUniversidade Federal do Rio de Janeiro, Rio de Janeiro, RJ – Brasil
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25
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Soffer S, Zimlichman E, Levin MA, Zebrowski AM, Glicksberg BS, Freeman R, Reich DL, Klang E. Machine learning to predict in‐hospital mortality among patients with severe obesity: Proof of concept study. Obes Sci Pract 2022; 8:474-482. [PMID: 35949284 PMCID: PMC9358726 DOI: 10.1002/osp4.571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 09/25/2021] [Accepted: 10/01/2021] [Indexed: 11/22/2022] Open
Abstract
Objectives Hospitalized patients with severe obesity require adapted hospital management. The aim of this study was to evaluate a machine learning model to predict in‐hospital mortality among this population. Methods Data of unselected consecutive emergency department admissions of hospitalized patients with severe obesity (BMI ≥ 40 kg/m2) was analyzed. Data was retrieved from five hospitals from the Mount Sinai health system, New York. The study time frame was between January 2011 and December 2019. Data was used to train a gradient‐boosting machine learning model to identify in‐hospital mortality. The model was trained and evaluated based on the data from four hospitals and externally validated on held‐out data from the fifth hospital. Results A total of 14,078 hospital admissions of inpatients with severe obesity were included. The in‐hospital mortality rate was 297/14,078 (2.1%). In univariate analysis, albumin (area under the curve [AUC] = 0.77), blood urea nitrogen (AUC = 0.76), acuity level (AUC = 0.73), lactate (AUC = 0.72), and chief complaint (AUC = 0.72) were the best single predictors. For Youden’s index, the model had a sensitivity of 0.77 (95% CI: 0.67–0.86) with a false positive rate of 1:9. Conclusion A machine learning model trained on clinical measures provides proof of concept performance in predicting mortality in patients with severe obesity. This implies that such models may help to adopt specific decision support tools for this population.
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Affiliation(s)
- Shelly Soffer
- Internal Medicine B Assuta Medical Center Ashdod Israel
- Ben‐Gurion University of the Negev Be’er Sheva Israel
| | - Eyal Zimlichman
- Hospital Management Sheba Medical Center Tel Hashomer Israel
- Sackler Medical School Tel Aviv University Tel Aviv Israel
- Sheba Talpiot Medical Leadership Program Tel Hashomer Israel
| | - Matthew A. Levin
- Department of Population Health Science and Policy Institute for Healthcare Delivery Science Icahn School of Medicine at Mount Sinai New York New York USA
- Department of Anesthesiology, Perioperative and Pain Medicine Icahn School of Medicine at Mount Sinai New York New York USA
| | - Alexis M. Zebrowski
- Department of Emergency Medicine Icahn School of Medicine at Mount Sinai New York New York USA
- Department of Population Health Science and Policy Institute for Translational Epidemiology Icahn School of Medicine at Mount Sinai New York New York USA
| | - Benjamin S. Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai Icahn School of Medicine at Mount Sinai New York New York USA
- Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York New York USA
| | - Robert Freeman
- Department of Population Health Science and Policy Institute for Healthcare Delivery Science Icahn School of Medicine at Mount Sinai New York New York USA
| | - David L. Reich
- Department of Anesthesiology, Perioperative and Pain Medicine Icahn School of Medicine at Mount Sinai New York New York USA
| | - Eyal Klang
- Sackler Medical School Tel Aviv University Tel Aviv Israel
- Sheba Talpiot Medical Leadership Program Tel Hashomer Israel
- Department of Diagnostic Imaging Sheba Medical Center Tel Hashomer Israel
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26
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Li S, Hickey GW, Lander MM, Kanwar MK. Artificial Intelligence and Mechanical Circulatory Support. Heart Fail Clin 2022; 18:301-309. [DOI: 10.1016/j.hfc.2021.11.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Kagiyama N, Tokodi M, Sengupta PP. Machine Learning in Cardiovascular Imaging. Heart Fail Clin 2022; 18:245-258. [DOI: 10.1016/j.hfc.2021.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Effectiveness of Artificial Intelligence Models for Cardiovascular Disease Prediction: Network Meta-Analysis. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5849995. [PMID: 35251153 PMCID: PMC8894073 DOI: 10.1155/2022/5849995] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Accepted: 01/18/2022] [Indexed: 11/23/2022]
Abstract
Heart failure is the most common cause of death in both males and females around the world. Cardiovascular diseases (CVDs), in particular, are the main cause of death worldwide, accounting for 30% of all fatalities in the United States and 45% in Europe. Artificial intelligence (AI) approaches such as machine learning (ML) and deep learning (DL) models are playing an important role in the advancement of heart failure therapy. The main objective of this study was to perform a network meta-analysis of patients with heart failure, stroke, hypertension, and diabetes by comparing the ML and DL models. A comprehensive search of five electronic databases was performed using ScienceDirect, EMBASE, PubMed, Web of Science, and IEEE Xplore. The search strategy was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. The methodological quality of studies was assessed by following the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) guidelines. The random-effects network meta-analysis forest plot with categorical data was used, as were subgroups testing for all four types of treatments and calculating odds ratio (OR) with a 95% confidence interval (CI). Pooled network forest, funnel plots, and the league table, which show the best algorithms for each outcome, were analyzed. Seventeen studies, with a total of 285,213 patients with CVDs, were included in the network meta-analysis. The statistical evidence indicated that the DL algorithms performed well in the prediction of heart failure with AUC of 0.843 and CI [0.840–0.845], while in the ML algorithm, the gradient boosting machine (GBM) achieved an average accuracy of 91.10% in predicting heart failure. An artificial neural network (ANN) performed well in the prediction of diabetes with an OR and CI of 0.0905 [0.0489; 0.1673]. Support vector machine (SVM) performed better for the prediction of stroke with OR and CI of 25.0801 [11.4824; 54.7803]. Random forest (RF) results performed well in the prediction of hypertension with OR and CI of 10.8527 [4.7434; 24.8305]. The findings of this work suggest that the DL models can effectively advance the prediction of and knowledge about heart failure, but there is a lack of literature regarding DL methods in the field of CVDs. As a result, more DL models should be applied in this field. To confirm our findings, more meta-analysis (e.g., Bayesian network) and thorough research with a larger number of patients are encouraged.
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Xiong Y, Ma Y, Ruan L, Li D, Lu C, Huang L. Comparing different machine learning techniques for predicting COVID-19 severity. Infect Dis Poverty 2022; 11:19. [PMID: 35177120 PMCID: PMC8851750 DOI: 10.1186/s40249-022-00946-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 02/09/2022] [Indexed: 12/28/2022] Open
Abstract
Background Coronavirus disease 2019 (COVID-19) is still ongoing spreading globally, machine learning techniques were used in disease diagnosis and to predict treatment outcomes, which showed favorable performance. The present study aims to predict COVID-19 severity at admission by different machine learning techniques including random forest (RF), support vector machine (SVM), and logistic regression (LR). Feature importance to COVID-19 severity were further identified. Methods A retrospective design was adopted in the JinYinTan Hospital from January 26 to March 28, 2020, eighty-six demographic, clinical, and laboratory features were selected with LassoCV method, Spearman’s rank correlation, experts’ opinions, and literature evaluation. RF, SVM, and LR were performed to predict severe COVID-19, the performance of the models was compared by the area under curve (AUC). Additionally, feature importance to COVID-19 severity were analyzed by the best performance model. Results A total of 287 patients were enrolled with 36.6% severe cases and 63.4% non-severe cases. The median age was 60.0 years (interquartile range: 49.0–68.0 years). Three models were established using 23 features including 1 clinical, 1 chest computed tomography (CT) and 21 laboratory features. Among three models, RF yielded better overall performance with the highest AUC of 0.970 than SVM of 0.948 and LR of 0.928, RF also achieved a favorable sensitivity of 96.7%, specificity of 69.5%, and accuracy of 84.5%. SVM had sensitivity of 93.9%, specificity of 79.0%, and accuracy of 88.5%. LR also achieved a favorable sensitivity of 92.3%, specificity of 72.3%, and accuracy of 85.2%. Additionally, chest-CT had highest importance to illness severity, and the following features were neutrophil to lymphocyte ratio, lactate dehydrogenase, and D-dimer, respectively. Conclusions Our results indicated that RF could be a useful predictive tool to identify patients with severe COVID-19, which may facilitate effective care and further optimize resources. Graphical Abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1186/s40249-022-00946-4.
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Affiliation(s)
- Yibai Xiong
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16, Nanxiao Street, Dongzhimen, Dongcheng District, Beijing, 100700, Beijing, China
| | - Yan Ma
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16, Nanxiao Street, Dongzhimen, Dongcheng District, Beijing, 100700, Beijing, China
| | - Lianguo Ruan
- Department of Infectious Diseases, JinYinTan Hospital, Wuhan, 430040, China
| | - Dan Li
- Information Center, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Cheng Lu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16, Nanxiao Street, Dongzhimen, Dongcheng District, Beijing, 100700, Beijing, China.
| | - Luqi Huang
- National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, No. 16, Nanxiao Street, Dongzhimen, Dongcheng District, Beijing, 100700, Beijing, China.
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Penso M, Solbiati S, Moccia S, Caiani EG. Decision Support Systems in HF based on Deep Learning Technologies. Curr Heart Fail Rep 2022; 19:38-51. [PMID: 35142985 PMCID: PMC9023383 DOI: 10.1007/s11897-022-00540-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/20/2022] [Indexed: 11/26/2022]
Abstract
Purpose of Review Application of deep learning (DL) is growing in the last years, especially in the healthcare domain. This review presents the current state of DL techniques applied to electronic health record structured data, physiological signals, and imaging modalities for the management of heart failure (HF), focusing in particular on diagnosis, prognosis, and re-hospitalization risk, to explore the level of maturity of DL in this field. Recent Findings DL allows a better integration of different data sources to distillate more accurate outcomes in HF patients, thus resulting in better performance when compared to conventional evaluation methods. While applications in image and signal processing for HF diagnosis have reached very high performance, the application of DL to electronic health records and its multisource data for prediction could still be improved, despite the already promising results. Summary Embracing the current big data era, DL can improve performance compared to conventional techniques and machine learning approaches. DL algorithms have potential to provide more efficient care and improve outcomes of HF patients, although further investigations are needed to overcome current limitations, including results generalizability and transparency and explicability of the evidences supporting the process.
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Affiliation(s)
- Marco Penso
- Department of Electronics, Information and Biomedical Engineering, Politecnico Di Milano, P.zza L. da Vinci 32, 20133, Milan, Italy
- Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Sarah Solbiati
- Department of Electronics, Information and Biomedical Engineering, Politecnico Di Milano, P.zza L. da Vinci 32, 20133, Milan, Italy
- Institute of Electronics, Information Engineering and Telecommunications (IEIIT), Italian National Research Council (CNR), Milan, Italy
| | - Sara Moccia
- The BioRobotics Institute, Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Enrico G Caiani
- Department of Electronics, Information and Biomedical Engineering, Politecnico Di Milano, P.zza L. da Vinci 32, 20133, Milan, Italy.
- Institute of Electronics, Information Engineering and Telecommunications (IEIIT), Italian National Research Council (CNR), Milan, Italy.
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Jasinska-Piadlo A, Bond R, Biglarbeigi P, Brisk R, Campbell P, McEneaneny D. What can machines learn about heart failure? A systematic literature review. INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2021. [DOI: 10.1007/s41060-021-00300-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
AbstractThis paper presents a systematic literature review with respect to application of data science and machine learning (ML) to heart failure (HF) datasets with the intention of generating both a synthesis of relevant findings and a critical evaluation of approaches, applicability and accuracy in order to inform future work within this field. This paper has a particular intention to consider ways in which the low uptake of ML techniques within clinical practice could be resolved. Literature searches were performed on Scopus (2014-2021), ProQuest and Ovid MEDLINE databases (2014-2021). Search terms included ‘heart failure’ or ‘cardiomyopathy’ and ‘machine learning’, ‘data analytics’, ‘data mining’ or ‘data science’. 81 out of 1688 articles were included in the review. The majority of studies were retrospective cohort studies. The median size of the patient cohort across all studies was 1944 (min 46, max 93260). The largest patient samples were used in readmission prediction models with the median sample size of 5676 (min. 380, max. 93260). Machine learning methods focused on common HF problems: detection of HF from available dataset, prediction of hospital readmission following index hospitalization, mortality prediction, classification and clustering of HF cohorts into subgroups with distinctive features and response to HF treatment. The most common ML methods used were logistic regression, decision trees, random forest and support vector machines. Information on validation of models was scarce. Based on the authors’ affiliations, there was a median 3:1 ratio between IT specialists and clinicians. Over half of studies were co-authored by a collaboration of medical and IT specialists. Approximately 25% of papers were authored solely by IT specialists who did not seek clinical input in data interpretation. The application of ML to datasets, in particular clustering methods, enabled the development of classification models assisting in testing the outcomes of patients with HF. There is, however, a tendency to over-claim the potential usefulness of ML models for clinical practice. The next body of work that is required for this research discipline is the design of randomised controlled trials (RCTs) with the use of ML in an intervention arm in order to prospectively validate these algorithms for real-world clinical utility.
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Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine. J Clin Med 2021; 10:jcm10235710. [PMID: 34884412 PMCID: PMC8658222 DOI: 10.3390/jcm10235710] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 12/02/2021] [Indexed: 12/21/2022] Open
Abstract
The future of healthcare is an organic blend of technology, innovation, and human connection. As artificial intelligence (AI) is gradually becoming a go-to technology in healthcare to improve efficiency and outcomes, we must understand our limitations. We should realize that our goal is not only to provide faster and more efficient care, but also to deliver an integrated solution to ensure that the care is fair and not biased to a group of sub-population. In this context, the field of cardio-cerebrovascular diseases, which encompasses a wide range of conditions-from heart failure to stroke-has made some advances to provide assistive tools to care providers. This article aimed to provide an overall thematic review of recent development focusing on various AI applications in cardio-cerebrovascular diseases to identify gaps and potential areas of improvement. If well designed, technological engines have the potential to improve healthcare access and equitability while reducing overall costs, diagnostic errors, and disparity in a system that affects patients and providers and strives for efficiency.
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Guo A, Foraker RE, MacGregor RM, Masood FM, Cupps BP, Pasque MK. The Use of Synthetic Electronic Health Record Data and Deep Learning to Improve Timing of High-Risk Heart Failure Surgical Intervention by Predicting Proximity to Catastrophic Decompensation. Front Digit Health 2021; 2:576945. [PMID: 34713050 PMCID: PMC8521851 DOI: 10.3389/fdgth.2020.576945] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Accepted: 11/13/2020] [Indexed: 12/24/2022] Open
Abstract
Objective: Although many clinical metrics are associated with proximity to decompensation in heart failure (HF), none are individually accurate enough to risk-stratify HF patients on a patient-by-patient basis. The dire consequences of this inaccuracy in risk stratification have profoundly lowered the clinical threshold for application of high-risk surgical intervention, such as ventricular assist device placement. Machine learning can detect non-intuitive classifier patterns that allow for innovative combination of patient feature predictive capability. A machine learning-based clinical tool to identify proximity to catastrophic HF deterioration on a patient-specific basis would enable more efficient direction of high-risk surgical intervention to those patients who have the most to gain from it, while sparing others. Synthetic electronic health record (EHR) data are statistically indistinguishable from the original protected health information, and can be analyzed as if they were original data but without any privacy concerns. We demonstrate that synthetic EHR data can be easily accessed and analyzed and are amenable to machine learning analyses. Methods: We developed synthetic data from EHR data of 26,575 HF patients admitted to a single institution during the decade ending on 12/31/2018. Twenty-seven clinically-relevant features were synthesized and utilized in supervised deep learning and machine learning algorithms (i.e., deep neural networks [DNN], random forest [RF], and logistic regression [LR]) to explore their ability to predict 1-year mortality by five-fold cross validation methods. We conducted analyses leveraging features from prior to/at and after/at the time of HF diagnosis. Results: The area under the receiver operating curve (AUC) was used to evaluate the performance of the three models: the mean AUC was 0.80 for DNN, 0.72 for RF, and 0.74 for LR. Age, creatinine, body mass index, and blood pressure levels were especially important features in predicting death within 1-year among HF patients. Conclusions: Machine learning models have considerable potential to improve accuracy in mortality prediction, such that high-risk surgical intervention can be applied only in those patients who stand to benefit from it. Access to EHR-based synthetic data derivatives eliminates risk of exposure of EHR data, speeds time-to-insight, and facilitates data sharing. As more clinical, imaging, and contractile features with proven predictive capability are added to these models, the development of a clinical tool to assist in timing of intervention in surgical candidates may be possible.
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Affiliation(s)
- Aixia Guo
- Institute for Informatics (I2), Washington University School of Medicine, St. Louis, MO, United States
| | - Randi E Foraker
- Institute for Informatics (I2), Washington University School of Medicine, St. Louis, MO, United States.,Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, United States
| | - Robert M MacGregor
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, United States
| | - Faraz M Masood
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, United States
| | - Brian P Cupps
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, United States
| | - Michael K Pasque
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, United States
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Maurya MR, Riyaz NUSS, Reddy MSB, Yalcin HC, Ouakad HM, Bahadur I, Al-Maadeed S, Sadasivuni KK. A review of smart sensors coupled with Internet of Things and Artificial Intelligence approach for heart failure monitoring. Med Biol Eng Comput 2021; 59:2185-2203. [PMID: 34611787 DOI: 10.1007/s11517-021-02447-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 09/01/2021] [Indexed: 02/07/2023]
Abstract
Over the last decade, there has been a huge demand for health care technologies such as sensors-based prediction using digital health. With the continuous rise in the human population, these technologies showed to be potentially effective solutions to life-threatening diseases such as heart failure (HF). Besides being a potential for early death, HF has a significantly reduced quality of life (QoL). Heart failure has no cure. However, treatment can help you live a longer and more active life with fewer symptoms. Thus, it is essential to develop technological aid solutions allowing early diagnosis and consequently, effective treatment with possibly delayed mortality. Commonly, forecasts of HF are based on the generation of vast volumes of data usually collected from an individual patient by different components of the family history, physical examination, basic laboratory results, and other medical records. Though, these data are not effectively useful for predicting this failure, nevertheless, with the aid of advanced medical technology such as interconnected multi-sensory-based devices, and based on several medical history characteristics, the broad data provided machine learning algorithms to predict risk factors for heart disease of an individual is beneficial. There will be many challenges for the next decade of advancements in HF care: exploiting an increasingly growing repertoire of interconnected internal and external sensors for the benefit of patients and processing large, multimodal datasets with new Artificial Intelligence (AI) software. Various methods for predicting heart failure and, primarily the significance of invasive and non-invasive sensors along with different strategies for machine learning to predict heart failure are presented and summarized in the present study.
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Affiliation(s)
- Muni Raj Maurya
- Center for Advanced Materials, Qatar University, P.O. Box 2713, Doha, Qatar
- Department of Mechanical and Industrial Engineering, Qatar University, P.O. Box 2713, Doha, Qatar
| | | | - M Sai Bhargava Reddy
- Center for Nanoscience and Technology, Institute of Science and Technology, Jawaharlal Nehru Technological University, Hyderabad, Telangana State, 500085, India
| | | | - Hassen M Ouakad
- Mechanical and Industrial Engineering Department, College of Engineering, Sultan Qaboos University, Al-Khoudh, 123, PO-BOX 33, Muscat, Oman.
| | - Issam Bahadur
- Mechanical and Industrial Engineering Department, College of Engineering, Sultan Qaboos University, Al-Khoudh, 123, PO-BOX 33, Muscat, Oman
| | - Somaya Al-Maadeed
- Department of Computer Engineering, Qatar University, P.O. Box 2713, Doha, Qatar
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Guo A, Mazumder NR, Ladner DP, Foraker RE. Predicting mortality among patients with liver cirrhosis in electronic health records with machine learning. PLoS One 2021; 16:e0256428. [PMID: 34464403 PMCID: PMC8407576 DOI: 10.1371/journal.pone.0256428] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 08/08/2021] [Indexed: 02/06/2023] Open
Abstract
OBJECTIVE Liver cirrhosis is a leading cause of death and effects millions of people in the United States. Early mortality prediction among patients with cirrhosis might give healthcare providers more opportunity to effectively treat the condition. We hypothesized that laboratory test results and other related diagnoses would be associated with mortality in this population. Our another assumption was that a deep learning model could outperform the current Model for End Stage Liver disease (MELD) score in predicting mortality. MATERIALS AND METHODS We utilized electronic health record data from 34,575 patients with a diagnosis of cirrhosis from a large medical center to study associations with mortality. Three time-windows of mortality (365 days, 180 days and 90 days) and two cases with different number of variables (all 41 available variables and 4 variables in MELD-NA) were studied. Missing values were imputed using multiple imputation for continuous variables and mode for categorical variables. Deep learning and machine learning algorithms, i.e., deep neural networks (DNN), random forest (RF) and logistic regression (LR) were employed to study the associations between baseline features such as laboratory measurements and diagnoses for each time window by 5-fold cross validation method. Metrics such as area under the receiver operating curve (AUC), overall accuracy, sensitivity, and specificity were used to evaluate models. RESULTS Performance of models comprising all variables outperformed those with 4 MELD-NA variables for all prediction cases and the DNN model outperformed the LR and RF models. For example, the DNN model achieved an AUC of 0.88, 0.86, and 0.85 for 90, 180, and 365-day mortality respectively as compared to the MELD score, which resulted in corresponding AUCs of 0.81, 0.79, and 0.76 for the same instances. The DNN and LR models had a significantly better f1 score compared to MELD at all time points examined. CONCLUSION Other variables such as alkaline phosphatase, alanine aminotransferase, and hemoglobin were also top informative features besides the 4 MELD-Na variables. Machine learning and deep learning models outperformed the current standard of risk prediction among patients with cirrhosis. Advanced informatics techniques showed promise for risk prediction in patients with cirrhosis.
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Affiliation(s)
- Aixia Guo
- Institute for Informatics (I2), Washington University School of Medicine, St. Louis, MO, United States of America
| | - Nikhilesh R. Mazumder
- Division of Gastroenterology, Northwestern Memorial Hospital, Chicago, IL, United States of America
- Northwestern University Transplant Outcomes Research Collaborative (NUTORC), Comprehensive Transplant Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America
| | - Daniela P. Ladner
- Northwestern University Transplant Outcomes Research Collaborative (NUTORC), Comprehensive Transplant Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America
- Division of Transplant, Department of Surgery, Northwestern Medicine, Chicago, IL, United States of America
| | - Randi E. Foraker
- Institute for Informatics (I2), Washington University School of Medicine, St. Louis, MO, United States of America
- Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, United States of America
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Arfat Y, Mittone G, Esposito R, Cantalupo B, DE Ferrari GM, Aldinucci M. A review of machine learning for cardiology. Minerva Cardiol Angiol 2021; 70:75-91. [PMID: 34338485 DOI: 10.23736/s2724-5683.21.05709-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This paper reviews recent cardiology literature and reports how Artificial Intelligence Tools (specifically, Machine Learning techniques) are being used by physicians in the field. Each technique is introduced with enough details to allow the understanding of how it works and its intent, but without delving into details that do not add immediate benefits and require expertise in the field. We specifically focus on the principal Machine Learning based risk scores used in cardiovascular research. After introducing them and summarizing their assumptions and biases, we discuss their merits and shortcomings. We report on how frequently they are adopted in the field and suggest why this is the case based on our expertise in Machine Learning. We complete the analysis by reviewing how corresponding statistical approaches compare with them. Finally, we discuss the main open issues in applying Machine Learning tools to cardiology tasks, also drafting possible future directions. Despite the growing interest in these tools, we argue that there are many still underutilized techniques: while Neural Networks are slowly being incorporated in cardiovascular research, other important techniques such as Semi-Supervised Learning and Federated Learning are still underutilized. The former would allow practitioners to harness the information contained in large datasets that are only partially labeled, while the latter would foster collaboration between institutions allowing building larger and better models.
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Affiliation(s)
- Yasir Arfat
- Computer Science Department, University of Turin, Turin, Italy -
| | | | | | | | - Gaetano M DE Ferrari
- Division of Cardiology, Cardiovascular and Thoracic Department, Città della Salute e della Scienza, Turin, Italy.,Cardiology, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Marco Aldinucci
- Computer Science Department, University of Turin, Turin, Italy
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Predicting mortality and hospitalization in heart failure using machine learning: A systematic literature review. IJC HEART & VASCULATURE 2021; 34:100773. [PMID: 33912652 PMCID: PMC8065274 DOI: 10.1016/j.ijcha.2021.100773] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 03/11/2021] [Accepted: 03/23/2021] [Indexed: 12/13/2022]
Abstract
Objective The partnership between humans and machines can enhance clinical decisions accuracy, leading to improved patient outcomes. Despite this, the application of machine learning techniques in the healthcare sector, particularly in guiding heart failure patient management, remains unpopular. This systematic review aims to identify factors restricting the integration of machine learning derived risk scores into clinical practice when treating adults with acute and chronic heart failure. Methods Four academic research databases and Google Scholar were searched to identify original research studies where heart failure patient data was used to build models predicting all-cause mortality, cardiac death, all-cause and heart failure-related hospitalization. Results Thirty studies met the inclusion criteria. The selected studies' sample size ranged between 71 and 716 790 patients, and the median age was 72.1 (interquartile range: 61.1–76.8) years. The minimum and maximum area under the receiver operating characteristic curve (AUC) for models predicting mortality were 0.48 and 0.92, respectively. Models predicting hospitalization had an AUC of 0.47 to 0.84. Nineteen studies (63%) used logistic regression, 53% random forests, and 37% of studies used decision trees to build predictive models. None of the models were built or externally validated using data originating from Africa or the Middle-East. Conclusions The variation in the aetiologies of heart failure, limited access to structured health data, distrust in machine learning techniques among clinicians and the modest accuracy of existing predictive models are some of the factors precluding the widespread use of machine learning derived risk calculators.
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Banerjee A, Chen S, Fatemifar G, Zeina M, Lumbers RT, Mielke J, Gill S, Kotecha D, Freitag DF, Denaxas S, Hemingway H. Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility. BMC Med 2021; 19:85. [PMID: 33820530 PMCID: PMC8022365 DOI: 10.1186/s12916-021-01940-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 02/12/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Machine learning (ML) is increasingly used in research for subtype definition and risk prediction, particularly in cardiovascular diseases. No existing ML models are routinely used for cardiovascular disease management, and their phase of clinical utility is unknown, partly due to a lack of clear criteria. We evaluated ML for subtype definition and risk prediction in heart failure (HF), acute coronary syndromes (ACS) and atrial fibrillation (AF). METHODS For ML studies of subtype definition and risk prediction, we conducted a systematic review in HF, ACS and AF, using PubMed, MEDLINE and Web of Science from January 2000 until December 2019. By adapting published criteria for diagnostic and prognostic studies, we developed a seven-domain, ML-specific checklist. RESULTS Of 5918 studies identified, 97 were included. Across studies for subtype definition (n = 40) and risk prediction (n = 57), there was variation in data source, population size (median 606 and median 6769), clinical setting (outpatient, inpatient, different departments), number of covariates (median 19 and median 48) and ML methods. All studies were single disease, most were North American (n = 61/97) and only 14 studies combined definition and risk prediction. Subtype definition and risk prediction studies respectively had limitations in development (e.g. 15.0% and 78.9% of studies related to patient benefit; 15.0% and 15.8% had low patient selection bias), validation (12.5% and 5.3% externally validated) and impact (32.5% and 91.2% improved outcome prediction; no effectiveness or cost-effectiveness evaluations). CONCLUSIONS Studies of ML in HF, ACS and AF are limited by number and type of included covariates, ML methods, population size, country, clinical setting and focus on single diseases, not overlap or multimorbidity. Clinical utility and implementation rely on improvements in development, validation and impact, facilitated by simple checklists. We provide clear steps prior to safe implementation of machine learning in clinical practice for cardiovascular diseases and other disease areas.
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Affiliation(s)
- Amitava Banerjee
- Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK.
- Health Data Research UK, University College London, London, UK.
- University College London Hospitals NHS Trust, 235 Euston Road, London, UK.
- Barts Health NHS Trust, The Royal London Hospital, Whitechapel Rd, London, UK.
| | - Suliang Chen
- Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK
- Health Data Research UK, University College London, London, UK
| | - Ghazaleh Fatemifar
- Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK
- Health Data Research UK, University College London, London, UK
| | | | - R Thomas Lumbers
- Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK
- Health Data Research UK, University College London, London, UK
- University College London Hospitals NHS Trust, 235 Euston Road, London, UK
| | - Johanna Mielke
- Bayer AG, Division Pharmaceuticals, Open Innovation & Digital Technologies, Wuppertal, Germany
| | - Simrat Gill
- University of Birmingham Institute of Cardiovascular Sciences and University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Dipak Kotecha
- University of Birmingham Institute of Cardiovascular Sciences and University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Department of Cardiology, University Medical Centre Utrecht, Utrecht, the Netherlands
| | - Daniel F Freitag
- Bayer AG, Division Pharmaceuticals, Open Innovation & Digital Technologies, Wuppertal, Germany
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK
- Health Data Research UK, University College London, London, UK
- The Alan Turing Institute, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK
- Health Data Research UK, University College London, London, UK
- University College London Hospitals Biomedical Research Centre (UCLH BRC), London, UK
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Tran L, Chi L, Bonti A, Abdelrazek M, Chen YPP. Mortality Prediction of Patients With Cardiovascular Disease Using Medical Claims Data Under Artificial Intelligence Architectures: Validation Study. JMIR Med Inform 2021; 9:e25000. [PMID: 33792549 PMCID: PMC8050753 DOI: 10.2196/25000] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 11/17/2020] [Accepted: 12/05/2020] [Indexed: 11/23/2022] Open
Abstract
Background Cardiovascular disease (CVD) is the greatest health problem in Australia, which kills more people than any other disease and incurs enormous costs for the health care system. In this study, we present a benchmark comparison of various artificial intelligence (AI) architectures for predicting the mortality rate of patients with CVD using structured medical claims data. Compared with other research in the clinical literature, our models are more efficient because we use a smaller number of features, and this study could help health professionals accurately choose AI models to predict mortality among patients with CVD using only claims data before a clinic visit. Objective This study aims to support health clinicians in accurately predicting mortality among patients with CVD using only claims data before a clinic visit. Methods The data set was obtained from the Medicare Benefits Scheme and Pharmaceutical Benefits Scheme service information in the period between 2004 and 2014, released by the Department of Health Australia in 2016. It included 346,201 records, corresponding to 346,201 patients. A total of five AI algorithms, including four classical machine learning algorithms (logistic regression [LR], random forest [RF], extra trees [ET], and gradient boosting trees [GBT]) and a deep learning algorithm, which is a densely connected neural network (DNN), were developed and compared in this study. In addition, because of the minority of deceased patients in the data set, a separate experiment using the Synthetic Minority Oversampling Technique (SMOTE) was conducted to enrich the data. Results Regarding model performance, in terms of discrimination, GBT and RF were the models with the highest area under the receiver operating characteristic curve (97.8% and 97.7%, respectively), followed by ET (96.8%) and LR (96.4%), whereas DNN was the least discriminative (95.3%). In terms of reliability, LR predictions were the least calibrated compared with the other four algorithms. In this study, despite increasing the training time, SMOTE was proven to further improve the model performance of LR, whereas other algorithms, especially GBT and DNN, worked well with class imbalanced data. Conclusions Compared with other research in the clinical literature involving AI models using claims data to predict patient health outcomes, our models are more efficient because we use a smaller number of features but still achieve high performance. This study could help health professionals accurately choose AI models to predict mortality among patients with CVD using only claims data before a clinic visit.
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Affiliation(s)
- Linh Tran
- School of Info Technology, Deakin University, Burwood, Australia
| | - Lianhua Chi
- Department of Computer Science and Information Technology, La Trobe University, Bundoora, Australia
| | - Alessio Bonti
- School of Info Technology, Deakin University, Burwood, Australia
| | | | - Yi-Ping Phoebe Chen
- Department of Computer Science and Information Technology, La Trobe University, Bundoora, Australia
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Nath T, Ahima RS, Santhanam P. Body fat predicts exercise capacity in persons with Type 2 Diabetes Mellitus: A machine learning approach. PLoS One 2021; 16:e0248039. [PMID: 33788855 PMCID: PMC8011752 DOI: 10.1371/journal.pone.0248039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 02/18/2021] [Indexed: 12/16/2022] Open
Abstract
Diabetes mellitus is associated with increased cardiovascular disease (CVD) related morbidity, mortality and death. Exercise capacity in persons with type 2 diabetes has been shown to be predictive of cardiovascular events. In this study, we used the data from the prospective randomized LOOK AHEAD study and used machine learning algorithms to help predict exercise capacity (measured in Mets) from the baseline data that included cardiovascular history, medications, blood pressure, demographic information, anthropometric and Dual-energy X-Ray Absorptiometry (DXA) measured body composition metrics. We excluded variables with high collinearity and included DXA obtained Subtotal (total minus head) fat percentage and Subtotal lean mass (gms). Thereafter, we used different machine learning methods to predict maximum exercise capacity. The different machine learning models showed a strong predictive performance for both females and males. Our study shows that using baseline data from a large prospective cohort, we can predict maximum exercise capacity in persons with diabetes mellitus. We show that subtotal fat percentage is the most important feature for predicting the exercise capacity for males and females after accounting for other important variables. Until now, BMI and waist circumference were commonly used surrogates for adiposity and there was a relative under-appreciation of body composition metrics for understanding the pathophysiology of CVD. The recognition of body fat percentage as an important marker in determining CVD risk has prognostic implications with respect to cardiovascular morbidity and mortality.
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Affiliation(s)
- Tanmay Nath
- Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Rexford S. Ahima
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Prasanna Santhanam
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
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Radhachandran A, Garikipati A, Zelin NS, Pellegrini E, Ghandian S, Calvert J, Hoffman J, Mao Q, Das R. Prediction of short-term mortality in acute heart failure patients using minimal electronic health record data. BioData Min 2021; 14:23. [PMID: 33789700 PMCID: PMC8010502 DOI: 10.1186/s13040-021-00255-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 03/21/2021] [Indexed: 12/15/2022] Open
Abstract
Background Acute heart failure (AHF) is associated with significant morbidity and mortality. Effective patient risk stratification is essential to guiding hospitalization decisions and the clinical management of AHF. Clinical decision support systems can be used to improve predictions of mortality made in emergency care settings for the purpose of AHF risk stratification. In this study, several models for the prediction of seven-day mortality among AHF patients were developed by applying machine learning techniques to retrospective patient data from 236,275 total emergency department (ED) encounters, 1881 of which were considered positive for AHF and were used for model training and testing. The models used varying subsets of age, sex, vital signs, and laboratory values. Model performance was compared to the Emergency Heart Failure Mortality Risk Grade (EHMRG) model, a commonly used system for prediction of seven-day mortality in the ED with similar (or, in some cases, more extensive) inputs. Model performance was assessed in terms of area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. Results When trained and tested on a large academic dataset, the best-performing model and EHMRG demonstrated test set AUROCs of 0.84 and 0.78, respectively, for prediction of seven-day mortality. Given only measurements of respiratory rate, temperature, mean arterial pressure, and FiO2, one model produced a test set AUROC of 0.83. Neither a logistic regression comparator nor a simple decision tree outperformed EHMRG. Conclusions A model using only the measurements of four clinical variables outperforms EHMRG in the prediction of seven-day mortality in AHF. With these inputs, the model could not be replaced by logistic regression or reduced to a simple decision tree without significant performance loss. In ED settings, this minimal-input risk stratification tool may assist clinicians in making critical decisions about patient disposition by providing early and accurate insights into individual patient’s risk profiles. Supplementary Information The online version contains supplementary material available at 10.1186/s13040-021-00255-w.
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Affiliation(s)
| | - Anurag Garikipati
- Dascena, Inc, 12333 Sowden Rd Ste B PMB 65148, Houston, TX, 77080-2059, USA
| | - Nicole S Zelin
- Dascena, Inc, 12333 Sowden Rd Ste B PMB 65148, Houston, TX, 77080-2059, USA
| | - Emily Pellegrini
- Dascena, Inc, 12333 Sowden Rd Ste B PMB 65148, Houston, TX, 77080-2059, USA.
| | - Sina Ghandian
- Dascena, Inc, 12333 Sowden Rd Ste B PMB 65148, Houston, TX, 77080-2059, USA
| | - Jacob Calvert
- Dascena, Inc, 12333 Sowden Rd Ste B PMB 65148, Houston, TX, 77080-2059, USA
| | - Jana Hoffman
- Dascena, Inc, 12333 Sowden Rd Ste B PMB 65148, Houston, TX, 77080-2059, USA
| | - Qingqing Mao
- Dascena, Inc, 12333 Sowden Rd Ste B PMB 65148, Houston, TX, 77080-2059, USA
| | - Ritankar Das
- Dascena, Inc, 12333 Sowden Rd Ste B PMB 65148, Houston, TX, 77080-2059, USA
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A Real-Time Artificial Intelligence-Assisted System to Predict Weaning from Ventilator Immediately after Lung Resection Surgery. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18052713. [PMID: 33800239 PMCID: PMC7967444 DOI: 10.3390/ijerph18052713] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 03/01/2021] [Accepted: 03/03/2021] [Indexed: 12/11/2022]
Abstract
Assessment of risk before lung resection surgery can provide anesthesiologists with information about whether a patient can be weaned from the ventilator immediately after surgery. However, it is difficult for anesthesiologists to perform a complete integrated risk assessment in a time-limited pre-anesthetic clinic. We retrospectively collected the electronic medical records of 709 patients who underwent lung resection between 1 January 2017 and 31 July 2019. We used the obtained data to construct an artificial intelligence (AI) prediction model with seven supervised machine learning algorithms to predict whether patients could be weaned immediately after lung resection surgery. The AI model with Naïve Bayes Classifier algorithm had the best testing result and was therefore used to develop an application to evaluate risk based on patients' previous medical data, to assist anesthesiologists, and to predict patient outcomes in pre-anesthetic clinics. The individualization and digitalization characteristics of this AI application could improve the effectiveness of risk explanations and physician-patient communication to achieve better patient comprehension.
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Esposito A, Casiraghi E, Chiaraviglio F, Scarabelli A, Stellato E, Plensich G, Lastella G, Di Meglio L, Fusco S, Avola E, Jachetti A, Giannitto C, Malchiodi D, Frasca M, Beheshti A, Robinson PN, Valentini G, Forzenigo L, Carrafiello G. Artificial Intelligence in Predicting Clinical Outcome in COVID-19 Patients from Clinical, Biochemical and a Qualitative Chest X-Ray Scoring System. REPORTS IN MEDICAL IMAGING 2021. [DOI: 10.2147/rmi.s292314] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
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Manco L, Maffei N, Strolin S, Vichi S, Bottazzi L, Strigari L. Basic of machine learning and deep learning in imaging for medical physicists. Phys Med 2021; 83:194-205. [DOI: 10.1016/j.ejmp.2021.03.026] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 03/07/2021] [Accepted: 03/16/2021] [Indexed: 02/08/2023] Open
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Applications of Artificial Intelligence (AI) for cardiology during COVID-19 pandemic. SUSTAINABLE OPERATIONS AND COMPUTERS 2021; 2. [PMCID: PMC8052508 DOI: 10.1016/j.susoc.2021.04.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Background and aims Artificial Intelligence (AI) shows extensive capabilities to impact different healthcare areas during the COVID-19 pandemic positively. This paper tries to assess the capabilities of AI in the field of cardiology during the COVID-19 pandemic. This technology is useful to provide advanced technology-based treatment in cardiology as it can help analyse and measure the functioning of the human heart. Methods We have studied a good number of research papers on Artificial Intelligence on cardiology during the COVID-19 pandemic to identify its significant benefits, applications, and future scope. AI uses artificial neuronal networks (ANN) to predict. In cardiology, it is used to predict the survival of a COVID-19 patient from heart failure. Results AI involves complex algorithms for predicting somewhat successful diagnosis and treatments. This technology uses different techniques, such as cognitive computing, deep learning, and machine learning. It is incorporated to make a decision and resolve complex challenges. It can focus on a large number of diseases, their causes, interactions, and prevention during the COVID-19 pandemic. This paper introduces AI-based care and studies its need in the field of cardiology. Finally, eleven major applications of AI in cardiology during the COVID-19 pandemic are identified and discussed. Conclusions Cardiovascular diseases are one of the major causes of death in human beings, and it is increasing for the last few years. Cardiology patients' treatment is expensive, so this technology is introduced to provide a new pathway and visualise cardiac anomalies. AI is used to identify novel drug therapies and improve the efficiency of a physician. It is precise to predict the outcome of the COVID-19 patient from cardiac-based algorithms. Artificial Intelligence is becoming a popular feature of various engineering and healthcare sectors, is thought for providing a sustainable treatment platform. During the COVID-19 pandemic, this technology digitally controls some processes of treatments.
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Mpanya D, Celik T, Klug E, Ntsinjana H. Machine learning and statistical methods for predicting mortality in heart failure. Heart Fail Rev 2020; 26:545-552. [PMID: 33169338 DOI: 10.1007/s10741-020-10052-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/27/2020] [Indexed: 11/27/2022]
Abstract
Heart failure is a debilitating clinical syndrome associated with increased morbidity, mortality, and frequent hospitalization, leading to increased healthcare budget utilization. Despite the exponential growth in the introduction of pharmacological agents and medical devices that improve survival, many heart failure patients, particularly those with a left ventricular ejection fraction less than 40%, still experience persistent clinical symptoms that lead to an overall decreased quality of life. Clinical risk prediction is one of the strategies that has been implemented for the selection of high-risk patients and for guiding therapy. However, most risk predictive models have not been well-integrated into the clinical setting. This is partly due to inherent limitations, such as creating risk predicting models using static clinical data that does not consider the dynamic nature of heart failure. Another limiting factor preventing clinicians from utilizing risk prediction models is the lack of insight into how predictive models are built. This review article focuses on describing how predictive models for risk-stratification of patients with heart failure are built.
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Affiliation(s)
- Dineo Mpanya
- Department of Internal Medicine, Division of Cardiology, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand and the Charlotte Maxeke Johannesburg Academic Hospital, 17 Jubilee Road, Parktown, Johannesburg, Gauteng, 2193, South Africa. .,Institute of Data Science , University of the Witwatersrand , Johannesburg, South Africa.
| | - Turgay Celik
- Faculty of Engineering and Built Environment, School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa.,Institute of Data Science , University of the Witwatersrand , Johannesburg, South Africa
| | - Eric Klug
- Netcare Sunninghill, Sunward Park Hospitals and Division of Cardiology, Department of Internal Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand and the Charlotte Maxeke Johannesburg Academic Hospital, Johannesburg, South Africa
| | - Hopewell Ntsinjana
- Department of Paediatrics and Child Health, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
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Heart Failure Diagnosis, Readmission, and Mortality Prediction Using Machine Learning and Artificial Intelligence Models. CURR EPIDEMIOL REP 2020. [DOI: 10.1007/s40471-020-00259-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Abstract
Purpose of Review
One in five people will develop heart failure (HF), and 50% of HF patients die in 5 years. The HF diagnosis, readmission, and mortality prediction are essential to develop personalized prevention and treatment plans. This review summarizes recent findings and approaches of machine learning models for HF diagnostic and outcome prediction using electronic health record (EHR) data.
Recent Findings
A set of machine learning models have been developed for HF diagnostic and outcome prediction using diverse variables derived from EHR data, including demographic, medical note, laboratory, and image data, and achieved expert-comparable prediction results.
Summary
Machine learning models can facilitate the identification of HF patients, as well as accurate patient-specific assessment of their risk for readmission and mortality. Additionally, novel machine learning techniques for integration of diverse data and improvement of model predictive accuracy in imbalanced data sets are critical for further development of these promising modeling methodologies.
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Casiraghi E, Malchiodi D, Trucco G, Frasca M, Cappelletti L, Fontana T, Esposito AA, Avola E, Jachetti A, Reese J, Rizzi A, Robinson PN, Valentini G. Explainable Machine Learning for Early Assessment of COVID-19 Risk Prediction in Emergency Departments. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:196299-196325. [PMID: 34812365 PMCID: PMC8545262 DOI: 10.1109/access.2020.3034032] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 10/19/2020] [Indexed: 05/06/2023]
Abstract
Between January and October of 2020, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus has infected more than 34 million persons in a worldwide pandemic leading to over one million deaths worldwide (data from the Johns Hopkins University). Since the virus begun to spread, emergency departments were busy with COVID-19 patients for whom a quick decision regarding in- or outpatient care was required. The virus can cause characteristic abnormalities in chest radiographs (CXR), but, due to the low sensitivity of CXR, additional variables and criteria are needed to accurately predict risk. Here, we describe a computerized system primarily aimed at extracting the most relevant radiological, clinical, and laboratory variables for improving patient risk prediction, and secondarily at presenting an explainable machine learning system, which may provide simple decision criteria to be used by clinicians as a support for assessing patient risk. To achieve robust and reliable variable selection, Boruta and Random Forest (RF) are combined in a 10-fold cross-validation scheme to produce a variable importance estimate not biased by the presence of surrogates. The most important variables are then selected to train a RF classifier, whose rules may be extracted, simplified, and pruned to finally build an associative tree, particularly appealing for its simplicity. Results show that the radiological score automatically computed through a neural network is highly correlated with the score computed by radiologists, and that laboratory variables, together with the number of comorbidities, aid risk prediction. The prediction performance of our approach was compared to that that of generalized linear models and shown to be effective and robust. The proposed machine learning-based computational system can be easily deployed and used in emergency departments for rapid and accurate risk prediction in COVID-19 patients.
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Affiliation(s)
- Elena Casiraghi
- Department of Computer Science “Giovanni degli Antoni,”Università degli Studi di Milano20133MilanItaly
- CINI National Laboratory of Artificial Intelligence and Intelligent Systems (AIIS)Università di Roma00185RomaItaly
| | - Dario Malchiodi
- Department of Computer Science “Giovanni degli Antoni,”Università degli Studi di Milano20133MilanItaly
- CINI National Laboratory of Artificial Intelligence and Intelligent Systems (AIIS)Università di Roma00185RomaItaly
- Data Science Research CenterUniversità degli Studi di Milano20133MilanItaly
| | - Gabriella Trucco
- Department of Computer Science “Giovanni degli Antoni,”Università degli Studi di Milano20133MilanItaly
| | - Marco Frasca
- Department of Computer Science “Giovanni degli Antoni,”Università degli Studi di Milano20133MilanItaly
| | - Luca Cappelletti
- Department of Computer Science “Giovanni degli Antoni,”Università degli Studi di Milano20133MilanItaly
| | - Tommaso Fontana
- Dipartimento di ElettronicaInformazione e BioingegneriaPolitecnico di Milano20133MilanItaly
| | | | - Emanuele Avola
- Postgraduate School in RadiodiagnosticsUniversità degli Studi di Milano20122MilanItaly
| | - Alessandro Jachetti
- Accident and Emergency DepartmentFondazione IRCCS Ca Granda Ospedale Maggiore Policlinico20122MilanItaly
| | - Justin Reese
- Division of Environmental Genomics and Systems BiologyLawrence Berkeley National LaboratoryBerkeleyCA94720USA
| | - Alessandro Rizzi
- Department of Computer Science “Giovanni degli Antoni,”Università degli Studi di Milano20133MilanItaly
| | | | - Giorgio Valentini
- Department of Computer Science “Giovanni degli Antoni,”Università degli Studi di Milano20133MilanItaly
- CINI National Laboratory of Artificial Intelligence and Intelligent Systems (AIIS)Università di Roma00185RomaItaly
- Data Science Research CenterUniversità degli Studi di Milano20133MilanItaly
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Jentzer JC, Kashou AH, Lopez-Jimenez F, Attia ZI, Kapa S, Friedman PA, Noseworthy PA. Mortality risk stratification using artificial intelligence-augmented electrocardiogram in cardiac intensive care unit patients. EUROPEAN HEART JOURNAL-ACUTE CARDIOVASCULAR CARE 2020; 10:532-541. [PMID: 33620440 DOI: 10.1093/ehjacc/zuaa021] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 09/01/2020] [Accepted: 09/04/2020] [Indexed: 01/07/2023]
Abstract
AIMS An artificial intelligence-augmented electrocardiogram (AI-ECG) algorithm can identify left ventricular systolic dysfunction (LVSD). We sought to determine whether this AI-ECG algorithm could stratify mortality risk in cardiac intensive care unit (CICU) patients, independent of the presence of LVSD by transthoracic echocardiography (TTE). METHODS AND RESULTS We included 11 266 unique Mayo Clinic CICU patients admitted from 2007 to 2018 who underwent AI-ECG after CICU admission. Left ventricular ejection fraction (LVEF) data were extracted for patients with a TTE during hospitalization. Hospital mortality was analysed using multivariable logistic regression. Mean age was 68 ± 15 years, including 37% females. Higher AI-ECG probability of LVSD remained associated with higher hospital mortality [adjusted odds ratio (OR) 1.05 per 0.1 higher, 95% confidence interval (CI) 1.02-1.08, P = 0.003] after adjustment for LVEF, which itself was inversely related with the risk of hospital mortality (adjusted OR 0.96 per 5% higher, 95% CI 0.93-0.99, P = 0.02). Patients with available LVEF data (n = 8242) were divided based on the presence of predicted (by AI-ECG) vs. observed (by TTE) LVSD (defined as LVEF ≤ 35%), using TTE as the gold standard. A stepwise increase in hospital mortality was observed for patients with a true negative, false positive, false negative, and true positive AI-ECG. CONCLUSION The AI-ECG prediction of LVSD is associated with hospital mortality in CICU patients, affording risk stratification in addition to that provided by echocardiographic LVEF. Our results emphasize the prognostic value of electrocardiographic patterns reflecting underlying myocardial disease that are recognized by the AI-ECG.
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Affiliation(s)
- Jacob C Jentzer
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.,Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Anthony H Kashou
- Department of Internal Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Francisco Lopez-Jimenez
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Suraj Kapa
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.,Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
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Bridge J, Harding S, Zheng Y. Development and validation of a novel prognostic model for predicting AMD progression using longitudinal fundus images. BMJ Open Ophthalmol 2020; 5:e000569. [PMID: 33083553 PMCID: PMC7566421 DOI: 10.1136/bmjophth-2020-000569] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 09/20/2020] [Accepted: 09/27/2020] [Indexed: 01/27/2023] Open
Abstract
Objective To develop a prognostic tool to predict the progression of age-related eye disease progression using longitudinal colour fundus imaging. Methods and analysis Previous prognostic models using deep learning with imaging data require annotation during training or only use a single time point. We propose a novel deep learning method to predict the progression of diseases using longitudinal imaging data with uneven time intervals, which requires no prior feature extraction. Given previous images from a patient, our method aims to predict whether the patient will progress onto the next stage of the disease. The proposed method uses InceptionV3 to produce feature vectors for each image. In order to account for uneven intervals, a novel interval scaling is proposed. Finally, a recurrent neural network is used to prognosticate the disease. We demonstrate our method on a longitudinal dataset of colour fundus images from 4903 eyes with age-related macular degeneration (AMD), taken from the Age-Related Eye Disease Study, to predict progression to late AMD. Results Our method attains a testing sensitivity of 0.878, a specificity of 0.887 and an area under the receiver operating characteristic of 0.950. We compare our method to previous methods, displaying superior performance in our model. Class activation maps display how the network reaches the final decision. Conclusion The proposed method can be used to predict progression to advanced AMD at some future visit. Using multiple images at different time points improves predictive performance.
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Affiliation(s)
- Joshua Bridge
- Department of Eye and Vision Science, University of Liverpool, Liverpool, UK
| | - Simon Harding
- Department of Eye and Vision Science, University of Liverpool, Liverpool, UK
| | - Yalin Zheng
- Department of Eye and Vision Science, University of Liverpool, Liverpool, UK
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