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Zeng J, Zhang D, Lin S, Su X, Wang P, Zhao Y, Zheng Z. Comparative analysis of machine learning vs. traditional modeling approaches for predicting in-hospital mortality after cardiac surgery: temporal and spatial external validation based on a nationwide cardiac surgery registry. EUROPEAN HEART JOURNAL. QUALITY OF CARE & CLINICAL OUTCOMES 2024; 10:121-131. [PMID: 37218710 DOI: 10.1093/ehjqcco/qcad028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 05/12/2023] [Accepted: 05/21/2023] [Indexed: 05/24/2023]
Abstract
AIMS Preoperative risk assessment is crucial for cardiac surgery. Although previous studies suggested machine learning (ML) may improve in-hospital mortality predictions after cardiac surgery compared to traditional modeling approaches, the validity is doubted due to lacking external validation, limited sample sizes, and inadequate modeling considerations. We aimed to assess predictive performance between ML and traditional modelling approaches, while addressing these major limitations. METHODS AND RESULTS Adult cardiac surgery cases (n = 168 565) between 2013 and 2018 in the Chinese Cardiac Surgery Registry were used to develop, validate, and compare various ML vs. logistic regression (LR) models. The dataset was split for temporal (2013-2017 for training, 2018 for testing) and spatial (geographically-stratified random selection of 83 centers for training, 22 for testing) experiments, respectively. Model performances were evaluated in testing sets for discrimination and calibration. The overall in-hospital mortality was 1.9%. In the temporal testing set (n = 32 184), the best-performing ML model demonstrated a similar area under the receiver operating characteristic curve (AUC) of 0.797 (95% CI 0.779-0.815) to the LR model (AUC 0.791 [95% CI 0.775-0.808]; P = 0.12). In the spatial experiment (n = 28 323), the best ML model showed a statistically better but modest performance improvement (AUC 0.732 [95% CI 0.710-0.754]) than LR (AUC 0.713 [95% CI 0.691-0.737]; P = 0.002). Varying feature selection methods had relatively smaller effects on ML models. Most ML and LR models were significantly miscalibrated. CONCLUSION ML provided only marginal improvements over traditional modelling approaches in predicting cardiac surgery mortality with routine preoperative variables, which calls for more judicious use of ML in practice.
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Affiliation(s)
- Juntong Zeng
- National Clinical Research Center of Cardiovascular Diseases, Fuwai Hospital, National Center for Cardiovascular Diseases, 167 Beilishi Road, Xicheng, Beijing, 100037, People's Republic of China
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, 167 Beilishi Road, Xicheng, Beijing, 100037, People's Republic of China
- Chinese Academy of Medical Sciences and Peking Union Medical College, 9 Dongdansantiao, Dongcheng, Beijing, 100730, People's Republic of China
| | - Danwei Zhang
- National Clinical Research Center of Cardiovascular Diseases, Fuwai Hospital, National Center for Cardiovascular Diseases, 167 Beilishi Road, Xicheng, Beijing, 100037, People's Republic of China
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, 167 Beilishi Road, Xicheng, Beijing, 100037, People's Republic of China
- Chinese Academy of Medical Sciences and Peking Union Medical College, 9 Dongdansantiao, Dongcheng, Beijing, 100730, People's Republic of China
- Department of Cardiac Surgery, Fujian Children's Hospital (Fujian Branch of Shanghai Children's Medical Center), College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, 966 Hengyu Road, Jinan, Fuzhou, 350014, People's Republic of China
| | - Shen Lin
- National Clinical Research Center of Cardiovascular Diseases, Fuwai Hospital, National Center for Cardiovascular Diseases, 167 Beilishi Road, Xicheng, Beijing, 100037, People's Republic of China
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, 167 Beilishi Road, Xicheng, Beijing, 100037, People's Republic of China
- Chinese Academy of Medical Sciences and Peking Union Medical College, 9 Dongdansantiao, Dongcheng, Beijing, 100730, People's Republic of China
- Department of Cardiovascular Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases, 167 Beilishi Road, Xicheng, Beijing, 100037, People's Republic of China
| | - Xiaoting Su
- National Clinical Research Center of Cardiovascular Diseases, Fuwai Hospital, National Center for Cardiovascular Diseases, 167 Beilishi Road, Xicheng, Beijing, 100037, People's Republic of China
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, 167 Beilishi Road, Xicheng, Beijing, 100037, People's Republic of China
- Chinese Academy of Medical Sciences and Peking Union Medical College, 9 Dongdansantiao, Dongcheng, Beijing, 100730, People's Republic of China
| | - Peng Wang
- National Clinical Research Center of Cardiovascular Diseases, Fuwai Hospital, National Center for Cardiovascular Diseases, 167 Beilishi Road, Xicheng, Beijing, 100037, People's Republic of China
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, 167 Beilishi Road, Xicheng, Beijing, 100037, People's Republic of China
- Chinese Academy of Medical Sciences and Peking Union Medical College, 9 Dongdansantiao, Dongcheng, Beijing, 100730, People's Republic of China
| | - Yan Zhao
- National Clinical Research Center of Cardiovascular Diseases, Fuwai Hospital, National Center for Cardiovascular Diseases, 167 Beilishi Road, Xicheng, Beijing, 100037, People's Republic of China
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, 167 Beilishi Road, Xicheng, Beijing, 100037, People's Republic of China
| | - Zhe Zheng
- National Clinical Research Center of Cardiovascular Diseases, Fuwai Hospital, National Center for Cardiovascular Diseases, 167 Beilishi Road, Xicheng, Beijing, 100037, People's Republic of China
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, 167 Beilishi Road, Xicheng, Beijing, 100037, People's Republic of China
- Chinese Academy of Medical Sciences and Peking Union Medical College, 9 Dongdansantiao, Dongcheng, Beijing, 100730, People's Republic of China
- Department of Cardiovascular Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases, 167 Beilishi Road, Xicheng, Beijing, 100037, People's Republic of China
- Key Laboratory of Coronary Heart Disease Risk Prediction and Precision Therapy, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Road, Xicheng, Beijing, 100037, People's Republic of China
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Yu Y, Peng C, Zhang Z, Shen K, Zhang Y, Xiao J, Xi W, Wang P, Rao J, Jin Z, Wang Z. Machine Learning Methods for Predicting Long-Term Mortality in Patients After Cardiac Surgery. Front Cardiovasc Med 2022; 9:831390. [PMID: 35592400 PMCID: PMC9110683 DOI: 10.3389/fcvm.2022.831390] [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: 12/08/2021] [Accepted: 03/21/2022] [Indexed: 11/21/2022] Open
Abstract
Objective: This study aims to construct and validate several machine learning (ML) algorithms to predict long-term mortality and identify risk factors in unselected patients post-cardiac surgery. Methods The Medical Information Mart for Intensive Care (MIMIC-III) database was used to perform a retrospective administrative database study. Candidate predictors consisted of the demographics, comorbidity, vital signs, laboratory test results, scoring systems, and treatment information on the first day of ICU admission. Four-year mortality was set as the study outcome. We used the ML methods of logistic regression (LR), artificial neural network (NNET), naïve bayes (NB), gradient boosting machine (GBM), adapting boosting (Ada), random forest (RF), bagged trees (BT), and eXtreme Gradient Boosting (XGB). The prognostic capacity and clinical utility of these ML models were compared using the area under the receiver operating characteristic curves (AUC), calibration curves, and decision curve analysis (DCA). Results Of 7,368 patients in MIMIC-III included in the final cohort, a total of 1,337 (18.15%) patients died during a 4-year follow-up. Among 65 variables extracted from the database, a total of 25 predictors were selected using recursive feature elimination and included in the subsequent analysis. The Ada model performed best among eight models in both discriminatory ability with the highest AUC of 0.801 and goodness of fit (visualized by calibration curve). Moreover, the DCA shows that the net benefit of the RF, Ada, and BT models surpassed that of other ML models for almost all threshold probability values. Additionally, through the Ada technique, we determined that red blood cell distribution width (RDW), blood urea nitrogen (BUN), SAPS II, anion gap (AG), age, urine output, chloride, creatinine, congestive heart failure, and SOFA were the Top 10 predictors in the feature importance rankings. Conclusions The Ada model performs best in predicting 4-year mortality after cardiac surgery among the eight ML models, which might have significant application in the development of early warning systems for patients following operations.
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Affiliation(s)
- Yue Yu
- Department of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Chi Peng
- Department of Health Statistics, Naval Medical University, Shanghai, China
| | - Zhiyuan Zhang
- Department of Cardiothoracic Surgery, No. 988 Hospital of Joint Logistic Support Force of PLA, Zhengzhou, China
| | - Kejia Shen
- Department of Personnel Administration, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Yufeng Zhang
- Department of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Jian Xiao
- Department of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Wang Xi
- Department of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Pei Wang
- Department of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Jin Rao
- Department of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Zhichao Jin
- Department of Health Statistics, Naval Medical University, Shanghai, China
- *Correspondence: Zhichao Jin
| | - Zhinong Wang
- Department of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
- Zhinong Wang
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Rellum SR, Schuurmans J, van der Ven WH, Eberl S, Driessen AHG, Vlaar APJ, Veelo DP. Machine learning methods for perioperative anesthetic management in cardiac surgery patients: a scoping review. J Thorac Dis 2022; 13:6976-6993. [PMID: 35070381 PMCID: PMC8743411 DOI: 10.21037/jtd-21-765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 08/27/2021] [Indexed: 12/27/2022]
Abstract
Background Machine learning (ML) is developing fast with promising prospects within medicine and already has several applications in perioperative care. We conducted a scoping review to examine the extent and potential limitations of ML implementation in perioperative anesthetic care, specifically in cardiac surgery patients. Methods We mapped the current literature by searching three databases: MEDLINE (Ovid), EMBASE (Ovid), and Cochrane Library. Articles were eligible if they reported on perioperative ML use in the field of cardiac surgery with relevance to anesthetic practices. Data on the applicability of ML and comparability to conventional statistical methods were extracted. Results Forty-six articles on ML relevant to the work of the anesthesiologist in cardiac surgery were identified. Three main categories emerged: (I) event and risk prediction, (II) hemodynamic monitoring, and (III) automation of echocardiography. Prediction models based on ML tend to behave similarly to conventional statistical methods. Using dynamic hemodynamic or ultrasound data in ML models, however, shifts the potential to promising results. Conclusions ML in cardiac surgery is increasingly used in perioperative anesthetic management. The majority is used for prediction purposes similar to conventional clinical scores. Remarkable ML model performances are achieved when using real-time dynamic parameters. However, beneficial clinical outcomes of ML integration have yet to be determined. Nonetheless, the first steps introducing ML in perioperative anesthetic care for cardiac surgery have been taken.
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Affiliation(s)
- Santino R Rellum
- Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands.,Department of Intensive Care, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
| | - Jaap Schuurmans
- Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands.,Department of Intensive Care, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
| | - Ward H van der Ven
- Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands.,Department of Intensive Care, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
| | - Susanne Eberl
- Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
| | - Antoine H G Driessen
- Department of Cardiothoracic Surgery, Heart Center, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
| | - Alexander P J Vlaar
- Department of Intensive Care, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
| | - Denise P Veelo
- Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands
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Amin A, Chatterjee S. Commentary: Earlier warning for pulmonary complications: It's never too soon to take action. J Thorac Cardiovasc Surg 2021; 165:2148-2150. [PMID: 34635316 DOI: 10.1016/j.jtcvs.2021.09.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 09/24/2021] [Accepted: 09/24/2021] [Indexed: 12/01/2022]
Affiliation(s)
- Arsalan Amin
- Division of Cardiothoracic Surgery, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex
| | - Subhasis Chatterjee
- Division of Cardiothoracic Surgery, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex; Division of General Surgery, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex; Department of Cardiovascular Surgery, Texas Heart Institute, Houston, Tex.
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Benedetto U, Sinha S, Lyon M, Dimagli A, Gaunt TR, Angelini G, Sterne J. Can machine learning improve mortality prediction following cardiac surgery? Eur J Cardiothorac Surg 2021; 58:1130-1136. [PMID: 32810233 DOI: 10.1093/ejcts/ezaa229] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 05/20/2020] [Accepted: 05/26/2020] [Indexed: 01/07/2023] Open
Abstract
OBJECTIVES Interest in the clinical usefulness of machine learning for risk prediction has bloomed recently. Cardiac surgery patients are at high risk of complications and therefore presurgical risk assessment is of crucial relevance. We aimed to compare the performance of machine learning algorithms over traditional logistic regression (LR) model to predict in-hospital mortality following cardiac surgery. METHODS A single-centre data set of prospectively collected information from patients undergoing adult cardiac surgery from 1996 to 2017 was split into 70% training set and 30% testing set. Prediction models were developed using neural network, random forest, naive Bayes and retrained LR based on features included in the EuroSCORE. Discrimination was assessed using area under the receiver operating characteristic curve, and calibration analysis was undertaken using the calibration belt method. Model calibration drift was assessed by comparing Goodness of fit χ2 statistics observed in 2 equal bins from the testing sample ordered by procedure date. RESULTS A total of 28 761 cardiac procedures were performed during the study period. The in-hospital mortality rate was 2.7%. Retrained LR [area under the receiver operating characteristic curve 0.80; 95% confidence interval (CI) 0.77-0.83] and random forest model (0.80; 95% CI 0.76-0.83) showed the best discrimination. All models showed significant miscalibration. Retrained LR proved to have the weakest calibration drift. CONCLUSIONS Our findings do not support the hypothesis that machine learning methods provide advantage over LR model in predicting operative mortality after cardiac surgery.
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Affiliation(s)
- Umberto Benedetto
- Translational Health Sciences, Bristol Heart Institute, University of Bristol, Bristol, UK.,NIHR Bristol Biomedical Research Centre, University of Bristol, University Hospitals Bristol NHS Foundation Trust, Bristol, UK
| | - Shubhra Sinha
- Translational Health Sciences, Bristol Heart Institute, University of Bristol, Bristol, UK
| | - Matt Lyon
- NIHR Bristol Biomedical Research Centre, University of Bristol, University Hospitals Bristol NHS Foundation Trust, Bristol, UK.,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.,MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Arnaldo Dimagli
- Translational Health Sciences, Bristol Heart Institute, University of Bristol, Bristol, UK
| | - Tom R Gaunt
- NIHR Bristol Biomedical Research Centre, University of Bristol, University Hospitals Bristol NHS Foundation Trust, Bristol, UK.,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.,MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Gianni Angelini
- Translational Health Sciences, Bristol Heart Institute, University of Bristol, Bristol, UK.,NIHR Bristol Biomedical Research Centre, University of Bristol, University Hospitals Bristol NHS Foundation Trust, Bristol, UK
| | - Jonathan Sterne
- NIHR Bristol Biomedical Research Centre, University of Bristol, University Hospitals Bristol NHS Foundation Trust, Bristol, UK.,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.,MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
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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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Shahian DM, Lippmann RP. Commentary: Machine learning and cardiac surgery risk prediction. J Thorac Cardiovasc Surg 2020; 163:2090-2092. [PMID: 32951875 DOI: 10.1016/j.jtcvs.2020.08.058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 08/18/2020] [Accepted: 08/18/2020] [Indexed: 10/23/2022]
Affiliation(s)
- David M Shahian
- Division of Cardiac Surgery, Department of Surgery, and Center for Quality and Safety, Massachusetts General Hospital, Boston, Mass.
| | - Richard P Lippmann
- Massachusetts Institute of Technology Lincoln Laboratory, Lexington, Mass
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Benedetto U, Dimagli A, Sinha S, Cocomello L, Gibbison B, Caputo M, Gaunt T, Lyon M, Holmes C, Angelini GD. Machine learning improves mortality risk prediction after cardiac surgery: Systematic review and meta-analysis. J Thorac Cardiovasc Surg 2020; 163:2075-2087.e9. [PMID: 32900480 DOI: 10.1016/j.jtcvs.2020.07.105] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 07/16/2020] [Accepted: 07/30/2020] [Indexed: 02/01/2023]
Abstract
BACKGROUND Interest in the usefulness of machine learning (ML) methods for outcomes prediction has continued to increase in recent years. However, the advantage of advanced ML model over traditional logistic regression (LR) remains controversial. We performed a systematic review and meta-analysis of studies comparing the discrimination accuracy between ML models versus LR in predicting operative mortality following cardiac surgery. METHODS The present systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement. Discrimination ability was assessed using the C-statistic. Pooled C-statistics and its 95% credibility interval for ML models and LR were obtained were obtained using a Bayesian framework. Pooled estimates for ML models and LR were compared to inform on difference between the 2 approaches. RESULTS We identified 459 published citations of which 15 studies met inclusion criteria and were used for the quantitative and qualitative analysis. When the best ML model from individual study was used, meta-analytic estimates showed that ML were associated with a significantly higher C-statistic (ML, 0.88; 95% credibility interval, 0.83-0.93 vs LR, 0.81; 95% credibility interval, 0.77-0.85; P = .03). When individual ML algorithms were instead selected, we found a nonsignificant trend toward better prediction with each of ML algorithms. We found no evidence of publication bias (P = .70). CONCLUSIONS The present findings suggest that when compared with LR, ML models provide better discrimination in mortality prediction after cardiac surgery. However, the magnitude and clinical influence of such an improvement remains uncertain.
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Affiliation(s)
- Umberto Benedetto
- Department of Translational Health Sciences, Bristol Heart Institute, University of Bristol, London, United Kingdom.
| | - Arnaldo Dimagli
- Department of Translational Health Sciences, Bristol Heart Institute, University of Bristol, London, United Kingdom
| | - Shubhra Sinha
- Department of Translational Health Sciences, Bristol Heart Institute, University of Bristol, London, United Kingdom
| | - Lucia Cocomello
- Department of Translational Health Sciences, Bristol Heart Institute, University of Bristol, London, United Kingdom
| | - Ben Gibbison
- Department of Translational Health Sciences, Bristol Heart Institute, University of Bristol, London, United Kingdom
| | - Massimo Caputo
- Department of Translational Health Sciences, Bristol Heart Institute, University of Bristol, London, United Kingdom
| | - Tom Gaunt
- Population Health Sciences, University of Bristol, London, United Kingdom
| | - Matt Lyon
- Population Health Sciences, University of Bristol, London, United Kingdom
| | - Chris Holmes
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Gianni D Angelini
- Department of Translational Health Sciences, Bristol Heart Institute, University of Bristol, London, United Kingdom
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Rau CS, Wu SC, Chuang JF, Huang CY, Liu HT, Chien PC, Hsieh CH. Machine Learning Models of Survival Prediction in Trauma Patients. J Clin Med 2019; 8:jcm8060799. [PMID: 31195670 PMCID: PMC6616432 DOI: 10.3390/jcm8060799] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 06/03/2019] [Accepted: 06/03/2019] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND We aimed to build a model using machine learning for the prediction of survival in trauma patients and compared these model predictions to those predicted by the most commonly used algorithm, the Trauma and Injury Severity Score (TRISS). METHODS Enrolled hospitalized trauma patients from 2009 to 2016 were divided into a training dataset (70% of the original data set) for generation of a plausible model under supervised classification, and a test dataset (30% of the original data set) to test the performance of the model. The training and test datasets comprised 13,208 (12,871 survival and 337 mortality) and 5603 (5473 survival and 130 mortality) patients, respectively. With the provision of additional information such as pre-existing comorbidity status or laboratory data, logistic regression (LR), support vector machine (SVM), and neural network (NN) (with the Stuttgart Neural Network Simulator (RSNNS)) were used to build models of survival prediction and compared to the predictive performance of TRISS. Predictive performance was evaluated by accuracy, sensitivity, and specificity, as well as by area under the curve (AUC) measures of receiver operating characteristic curves. RESULTS In the validation dataset, NN and the TRISS presented the highest score (82.0%) for balanced accuracy, followed by SVM (75.2%) and LR (71.8%) models. In the test dataset, NN had the highest balanced accuracy (75.1%), followed by the TRISS (70.2%), SVM (70.6%), and LR (68.9%) models. All four models (LR, SVM, NN, and TRISS) exhibited a high accuracy of more than 97.5% and a sensitivity of more than 98.6%. However, NN exhibited the highest specificity (51.5%), followed by the TRISS (41.5%), SVM (40.8%), and LR (38.5%) models. CONCLUSIONS These four models (LR, SVM, NN, and TRISS) exhibited a similar high accuracy and sensitivity in predicting the survival of the trauma patients. In the test dataset, the NN model had the highest balanced accuracy and predictive specificity.
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Affiliation(s)
- Cheng-Shyuan Rau
- Department of Neurosurgery, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung 833, Taiwan.
| | - Shao-Chun Wu
- Department of Anesthesiology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung 833, Taiwan.
| | - Jung-Fang Chuang
- Department of Trauma Surgery, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung 833, Taiwan.
| | - Chun-Ying Huang
- Department of Trauma Surgery, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung 833, Taiwan.
| | - Hang-Tsung Liu
- Department of Trauma Surgery, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung 833, Taiwan.
| | - Peng-Chen Chien
- Department of Plastic Surgery, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung 833, Taiwan.
| | - Ching-Hua Hsieh
- Department of Plastic Surgery, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung 833, Taiwan.
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Sanson G, Sartori M, Dreas L, Ciraolo R, Fabiani A. Predictors of extubation failure after open-chest cardiac surgery based on routinely collected data. The importance of a shared interprofessional clinical assessment. Eur J Cardiovasc Nurs 2018; 17:751-759. [PMID: 29879852 DOI: 10.1177/1474515118782103] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Extubation failure (ExtF) is associated with prolonged hospital length of stay and mortality in adult cardiac surgery patients postoperatively. In this population, ExtF-related variables such as the arterial partial pressure of oxygen to fraction of inspired oxygen ratio (PaO2/FiO2), rapid shallow breathing index, cough strength, endotracheal secretions and neurological function have been sparsely researched. AIM To identify variables that are predictive of ExtF and related outcomes. METHOD Prospective observational longitudinal study. Consecutively presenting patients ( n=205) undergoing open-heart cardiac surgery and admitted to the Cardiosurgical Intensive Care Unit (CICU) were recruited. The clinical data were collected at CICU admission and immediately prior to extubation. ExtF was defined as the need to restart invasive or non-invasive mechanical ventilation while the patient was in the CICU. RESULTS The ExtF incidence was 13%. ExtF related significantly to hospital mortality, CICU length of stay and total hospital length of stay. The risk of ExtF decreased significantly, by 93% in patients with good neurological function and by 83% in those with a Rapid Shallow Breathing Index of ≥57 breaths/min per litre. Conversely, ExtF risk increased 27 times when the PaO2/FiO2 was <150 and 11 times when it was ≥450. Also, a reassuring PaO2/FiO2 value may hide critical pulmonary or extra-pulmonary conditions independent from alveolar function. CONCLUSION The decision to extubate patients should be taken after thoroughly discussing and combining the data derived from nursing and medical clinical assessments. Extubation should be delayed until the patient achieves safe respiratory, oxygenation and haemodynamic conditions, and good neurocognitive function.
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Affiliation(s)
- Gianfranco Sanson
- 1 School of Nursing, University of Trieste, Italy
- 2 Azienda Sanitaria Universitaria Integrata, Trieste, Italy
| | | | - Lorella Dreas
- 3 Cardiac Surgery Intensive Care Unit, Azienda Sanitaria Universitaria Integrata, Trieste, Italy
| | | | - Adam Fabiani
- 3 Cardiac Surgery Intensive Care Unit, Azienda Sanitaria Universitaria Integrata, Trieste, Italy
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Li G, Zhou X, Liu J, Chen Y, Zhang H, Chen Y, Liu J, Jiang H, Yang J, Nie S. Comparison of three data mining models for prediction of advanced schistosomiasis prognosis in the Hubei province. PLoS Negl Trop Dis 2018; 12:e0006262. [PMID: 29447165 PMCID: PMC5831639 DOI: 10.1371/journal.pntd.0006262] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Revised: 02/28/2018] [Accepted: 01/23/2018] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND In order to better assist medical professionals, this study aimed to develop and compare the performance of three models-a multivariate logistic regression (LR) model, an artificial neural network (ANN) model, and a decision tree (DT) model-to predict the prognosis of patients with advanced schistosomiasis residing in the Hubei province. METHODOLOGY/PRINCIPAL FINDINGS Schistosomiasis surveillance data were collected from a previous study based on a Hubei population sample including 4136 advanced schistosomiasis cases. The predictive models use LR, ANN, and DT methods. From each of the three groups, 70% of the cases (2896 cases) were used as training data for the predictive models. The remaining 30% of the cases (1240 cases) were used as validation groups for performance comparisons between the three models. Prediction performance was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Univariate analysis indicated that 16 risk factors were significantly associated with a patient's outcome of prognosis. In the training group, the mean AUC was 0.8276 for LR, 0.9267 for ANN, and 0.8229 for DT. In the validation group, the mean AUC was 0.8349 for LR, 0.8318 for ANN, and 0.8148 for DT. The three models yielded similar results in terms of accuracy, sensitivity, and specificity. CONCLUSIONS/SIGNIFICANCE Predictive models for advanced schistosomiasis prognosis, respectively using LR, ANN and DT models were proved to be effective approaches based on our dataset. The ANN model outperformed the LR and DT models in terms of AUC.
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Affiliation(s)
- Guo Li
- Department of Epidemiology and Health Statistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Provincial Center for Disease Control and Prevention, Wuhan, Hubei, China
| | - Xiaorong Zhou
- Hubei Provincial Center for Disease Control and Prevention, Wuhan, Hubei, China
| | - Jianbing Liu
- Hubei Provincial Center for Disease Control and Prevention, Wuhan, Hubei, China
| | - Yuanqi Chen
- Department of Mathematics, Wuhan University, Wuhan, Hubei, China
| | - Hengtao Zhang
- Department of Mathematics, Wuhan University, Wuhan, Hubei, China
| | - Yanyan Chen
- Hubei Provincial Center for Disease Control and Prevention, Wuhan, Hubei, China
| | - Jianhua Liu
- Yichang Center for Disease Control and Prevention, Yichang, Hubei, China
| | - Hongbo Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Guangdong Pharmaceutical University, Guangzhou, China
| | - Junjing Yang
- Hubei Provincial Center for Disease Control and Prevention, Wuhan, Hubei, China
| | - Shaofa Nie
- Department of Epidemiology and Health Statistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
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