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Deva A, Juthani R, Kugan E, Balamurugan N, Ayyan M. Utility of ED triage tools in predicting the need for intensive respiratory or vasopressor support in adult patients with COVID-19. Am J Emerg Med 2024; 78:151-156. [PMID: 38281375 DOI: 10.1016/j.ajem.2024.01.034] [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: 06/28/2023] [Revised: 01/16/2024] [Accepted: 01/20/2024] [Indexed: 01/30/2024] Open
Abstract
BACKGROUND Serum and radiological parameters used to predict prognosis in COVID patients are not feasible in the Emergency Department. Due to its damaging effect on multiple organs and lungs, scores used to assess multiorgan damage and pneumonia such as Pandemic Medical Early Warning Score (PMEWS), National Early Warning Score 2 (NEWS2), WHO score, quick Sequential Organ Failure Assessment (qSOFA), and DS-CRB 65 can be used to triage patients in the Emergency Department. They can be used to predict patients with the highest risk of seven-day mortality and need for intensive respiratory or vasopressor support (IRVS). PURPOSE The primary purpose was to find the score with the highest AUC in predicting IRVS and mortality at seven days. Additional objective was to find out any independent factors associated with IRVS and mortality. METHODS The data of adult patients who presented to the Emergency Department (ED) between April 1, 2021 and June 30, 2021 were collected. The WHO score, CRB-65, DS-CRB 65, PMEWS, NEWS2, and qSOFA score were calculated for all patients. Statistical analysis was done and an ROC curve was calculated for all the tools for mortality and need for IRVS at seven days. FINDINGS 677 patients presented to the Emergency Department with COVID-19 during the period above. Presence of Diabetes Mellitus (p = 0.001), Hypertension (p = 0.001), and chronic kidney disease(CKD) (p = 0.04) was significantly associated with need for IRVS. Age, duration of symptoms, pulse rate, respiratory rate, room air saturation, mental status at admission, and time to IRVS need were identified as independent predictors of in-hospital mortality. The longer the time to IRVS need from ED arrival, the higher the likelihood of mortality. PMEWS (0.830) had the highest AUC, followed by NEWS2 (0.805). A PMEWS cut-off of 6.5 was 74.2% sensitive and 78.3% specific in predicting the need for IRVS. ROC analysis to predict 7-day mortality showed that PMEWS had an AUC of 0.802 (0.766-0.839). QSOFA performed poorly in predicting IRVS (AUC 0.645) and 7-day mortality (AUC 0.677). CONCLUSION PMEWS may be used for triaging patients presenting to the Emergency Department with COVID-19 and accurately predicts the need for IRVS and seven day mortality.
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Affiliation(s)
- Anandhi Deva
- Department of Emergency Medicine & Trauma, JIPMER, Puducherry, India
| | - Ronit Juthani
- Department of Medicine, Saint Vincent Hospital, Worcester, MA, United States.
| | - Ezhil Kugan
- Department of Emergency Medicine & Trauma, JIPMER, Puducherry, India
| | - N Balamurugan
- Department of Emergency Medicine & Trauma, JIPMER, Puducherry, India
| | - Manu Ayyan
- Department of Emergency Medicine & Trauma, JIPMER, Puducherry, India
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Samadi ME, Guzman-Maldonado J, Nikulina K, Mirzaieazar H, Sharafutdinov K, Fritsch SJ, Schuppert A. A hybrid modeling framework for generalizable and interpretable predictions of ICU mortality across multiple hospitals. Sci Rep 2024; 14:5725. [PMID: 38459085 PMCID: PMC10923850 DOI: 10.1038/s41598-024-55577-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 02/26/2024] [Indexed: 03/10/2024] Open
Abstract
The development of reliable mortality risk stratification models is an active research area in computational healthcare. Mortality risk stratification provides a standard to assist physicians in evaluating a patient's condition or prognosis objectively. Particular interest lies in methods that are transparent to clinical interpretation and that retain predictive power once validated across diverse datasets they were not trained on. This study addresses the challenge of consolidating numerous ICD codes for predictive modeling of ICU mortality, employing a hybrid modeling approach that integrates mechanistic, clinical knowledge with mathematical and machine learning models . A tree-structured network connecting independent modules that carry clinical meaning is implemented for interpretability. Our training strategy utilizes graph-theoretic methods for data analysis, aiming to identify the functions of individual black-box modules within the tree-structured network by harnessing solutions from specific max-cut problems. The trained model is then validated on external datasets from different hospitals, demonstrating successful generalization capabilities, particularly in binary-feature datasets where label assessment involves extrapolation.
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Affiliation(s)
- Moein E Samadi
- Institute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany.
| | | | - Kateryna Nikulina
- Institute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany
| | - Hedieh Mirzaieazar
- Institute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany
| | | | - Sebastian Johannes Fritsch
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
- Center for Advanced Simulation and Analytics (CASA), Forschungszentrum Jülich, Jülich, Germany
| | - Andreas Schuppert
- Institute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany
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Wang B, Li Y, Tian Y, Ju C, Xu X, Pei S. Novel pneumonia score based on a machine learning model for predicting mortality in pneumonia patients on admission to the intensive care unit. Respir Med 2023; 217:107363. [PMID: 37451647 DOI: 10.1016/j.rmed.2023.107363] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 07/10/2023] [Accepted: 07/11/2023] [Indexed: 07/18/2023]
Abstract
BACKGROUND Scores for predicting the long-term mortality of severe pneumonia are lacking. The purpose of this study is to use machine learning methods to develop new pneumonia scores to predict the 1-year mortality and hospital mortality of pneumonia patients on admission to the intensive care unit (ICU). METHODS The study population was screened from the MIMIC-IV and eICU databases. The main outcomes evaluated were 1-year mortality and hospital mortality in the MIMIC-IV database and hospital mortality in the eICU database. From the full data set, we separated patients diagnosed with community-acquired pneumonia (CAP) and ventilator-associated pneumonia (VAP) for subgroup analysis. We used common shallow machine learning algorithms, including logistic regression, decision tree, random forest, multilayer perceptron and XGBoost. RESULTS The full data set of the MIMIC-IV database contained 4697 patients, while that of the eICU database contained 13760 patients. We defined a new pneumonia score, the "Integrated CCI-APS", using a multivariate logistic regression model including six variables: metastatic solid tumor, Charlson Comorbidity Index, readmission, congestive heart failure, age, and Acute Physiology Score III. The area under the curve (AUC) and accuracy of the integrated CCI-APS were assessed in three data sets (full, CAP, and VAP) using both the test set derived from the MIMIC-IV database and the external validation set derived from the eICU database. The AUC value ranges in predicting 1-year and hospital mortality were 0.784-0.797 and 0.691-0.780, respectively, and the corresponding accuracy ranges were 0.723-0.725 and 0.641-0.718, respectively. CONCLUSIONS The main contribution of this study was a benchmark for using machine learning models to build pneumonia scores. Based on the idea of integrated learning, we propose a new integrated CCI-APS score for severe pneumonia. In the prediction of 1-year mortality and hospital mortality, our new pneumonia score outperformed the existing score.
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Affiliation(s)
- Bin Wang
- Department of Infectious Diseases, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Yuanxiao Li
- Department of Pediatric Gastroenterology, Lanzhou University Second Hospital, Lanzhou, China.
| | - Ying Tian
- Department of Clinical Medicine, Lanzhou University Second Hospital, Lanzhou, China.
| | - Changxi Ju
- Department of Clinical Medicine, Lanzhou University Second Hospital, Lanzhou, China.
| | - Xiaonan Xu
- Department of Pediatric Gastroenterology, Lanzhou University Second Hospital, Lanzhou, China.
| | - Shufen Pei
- Department of Clinical Medicine, North Sichuan Medical College, Nanchong, China.
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4
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Mohammedain SA, Badran S, Elzouki AY, Salim H, Chalaby A, Siddiqui MYA, Hussein YY, Rahim HA, Thalib L, Alam MF, Al-Badriyeh D, Al-Maadeed S, Doi SAR. Validation of a risk prediction model for COVID-19: the PERIL prospective cohort study. Future Virol 2023:10.2217/fvl-2023-0036. [PMID: 37970094 PMCID: PMC10630949 DOI: 10.2217/fvl-2023-0036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 10/03/2023] [Indexed: 11/17/2023]
Abstract
Aim: This study aims to perform an external validation of a recently developed prognostic model for early prediction of the risk of progression to severe COVID-19. Patients & methods/materials: Patients were recruited at their initial diagnosis at two facilities within Hamad Medical Corporation in Qatar. 356 adults were included for analysis. Predictors for progression of COVID-19 were all measured at disease onset and first contact with the health system. Results: The C statistic was 83% (95% CI: 78%-87%) and the calibration plot showed that the model was well-calibrated. Conclusion: The published prognostic model for the progression of COVID-19 infection showed satisfactory discrimination and calibration and the model is easy to apply in clinical practice.d.
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Affiliation(s)
- Shahd A Mohammedain
- Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar
| | - Saif Badran
- Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar
- Department of Plastic Surgery, Hamad Medical Corporation, Doha, Qatar
| | - AbdelNaser Y Elzouki
- Department of Internal Medicine Hamad General Hospital Hamad Medical Corporation, Doha, Qatar
| | - Halla Salim
- Department of Internal Medicine Hamad General Hospital Hamad Medical Corporation, Doha, Qatar
| | - Ayesha Chalaby
- Department of Internal Medicine Hamad General Hospital Hamad Medical Corporation, Doha, Qatar
| | - MYA Siddiqui
- Department of Internal Medicine Hamad General Hospital Hamad Medical Corporation, Doha, Qatar
| | - Yehia Y Hussein
- Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar
| | - Hanan Abdul Rahim
- Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | - Lukman Thalib
- Department of Biostatistics, Faculty of Medicine, Istanbul Aydin University, Istanbul, Turkey
| | - Mohammed Fasihul Alam
- Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | | | - Sumaya Al-Maadeed
- Department of Computer Science, College of Engineering, Qatar University, Doha, Qatar
| | - Suhail AR Doi
- Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar
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5
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Bartenschlager CC, Grieger M, Erber J, Neidel T, Borgmann S, Vehreschild JJ, Steinbrecher M, Rieg S, Stecher M, Dhillon C, Ruethrich MM, Jakob CEM, Hower M, Heller AR, Vehreschild M, Wyen C, Messmann H, Piepel C, Brunner JO, Hanses F, Römmele C. Covid-19 triage in the emergency department 2.0: how analytics and AI transform a human-made algorithm for the prediction of clinical pathways. Health Care Manag Sci 2023; 26:412-429. [PMID: 37428304 PMCID: PMC10485125 DOI: 10.1007/s10729-023-09647-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 06/01/2023] [Indexed: 07/11/2023]
Abstract
The Covid-19 pandemic has pushed many hospitals to their capacity limits. Therefore, a triage of patients has been discussed controversially primarily through an ethical perspective. The term triage contains many aspects such as urgency of treatment, severity of the disease and pre-existing conditions, access to critical care, or the classification of patients regarding subsequent clinical pathways starting from the emergency department. The determination of the pathways is important not only for patient care, but also for capacity planning in hospitals. We examine the performance of a human-made triage algorithm for clinical pathways which is considered a guideline for emergency departments in Germany based on a large multicenter dataset with over 4,000 European Covid-19 patients from the LEOSS registry. We find an accuracy of 28 percent and approximately 15 percent sensitivity for the ward class. The results serve as a benchmark for our extensions including an additional category of palliative care as a new label, analytics, AI, XAI, and interactive techniques. We find significant potential of analytics and AI in Covid-19 triage regarding accuracy, sensitivity, and other performance metrics whilst our interactive human-AI algorithm shows superior performance with approximately 73 percent accuracy and up to 76 percent sensitivity. The results are independent of the data preparation process regarding the imputation of missing values or grouping of comorbidities. In addition, we find that the consideration of an additional label palliative care does not improve the results.
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Affiliation(s)
- Christina C Bartenschlager
- Health Care Operations/Health Information Management, Faculty of Business and Economics, Faculty of Medicine, University of Augsburg, Universitätsstraße 16, 86159, Augsburg, Germany
- Professor of Applied Data Science in Health Care, Nürnberg School of Health, Ohm University of Applied Sciences Nuremberg, Nuremberg, Germany
- Anaesthesiology and Operative Intensive Care Medicine, Faculty of Medicine, University of Augsburg, Stenglinstrasse 2, 86156, Augsburg, Germany
| | - Milena Grieger
- Health Care Operations/Health Information Management, Faculty of Business and Economics, Faculty of Medicine, University of Augsburg, Universitätsstraße 16, 86159, Augsburg, Germany
| | - Johanna Erber
- Department of Internal Medicine II, Technical University of Munich, School of Medicine, University Hospital Rechts Der Isar, Munich, Germany
| | - Tobias Neidel
- Anaesthesiology and Operative Intensive Care Medicine, Faculty of Medicine, University of Augsburg, Stenglinstrasse 2, 86156, Augsburg, Germany
| | - Stefan Borgmann
- Hygiene and Infectiology, Klinikum Ingolstadt, Ingolstadt, Germany
| | - Jörg J Vehreschild
- Department of Internal Medicine, Hematology and Oncology, Goethe University Frankfurt, Frankfurt Am Main, Germany
- Department I of Internal Medicine, University of Cologne, University Hospital of Cologne, Cologne, Germany
- German Center for Infection Research, Partner Site Bonn-Cologne, Cologne, Germany
| | - Markus Steinbrecher
- Clinic for Internal Medicine III - Gastroenterology and Infectious Diseases, University Hospital Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany
| | - Siegbert Rieg
- Clinic for Internal Medicine II - Infectiology, University Hospital Freiburg, Freiburg, Germany
| | - Melanie Stecher
- Department I of Internal Medicine, University of Cologne, University Hospital of Cologne, Cologne, Germany
- German Center for Infection Research, Partner Site Bonn-Cologne, Cologne, Germany
| | - Christine Dhillon
- COVID-19 Task Force, University Hospital Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany
| | - Maria M Ruethrich
- Hematology and Internal Oncology, University Hospital Jena, Jena, Germany
| | - Carolin E M Jakob
- Department I of Internal Medicine, University of Cologne, University Hospital of Cologne, Cologne, Germany
- German Center for Infection Research, Partner Site Bonn-Cologne, Cologne, Germany
| | - Martin Hower
- Pneumology, Infectiology and Internal Intensive Care Medicine, Klinikum Dortmund, Germany
| | - Axel R Heller
- Anaesthesiology and Operative Intensive Care Medicine, Faculty of Medicine, University of Augsburg, Stenglinstrasse 2, 86156, Augsburg, Germany
| | - Maria Vehreschild
- Department of Internal Medicine, Infectious Diseases, University Hospital Frankfurt, Goethe University Frankfurt, Frankfurt Am Main, Germany
| | - Christoph Wyen
- Praxis am Ebertplatz, Cologne, Germany
- Department of Medicine I, University Hospital of Cologne, Cologne, Germany
| | - Helmut Messmann
- Clinic for Internal Medicine III - Gastroenterology and Infectious Diseases, University Hospital Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany
| | - Christiane Piepel
- Department of Hemato-Oncology and Infectious Diseases, Klinikum Bremen-Mitte, Bremen, Germany
| | - Jens O Brunner
- Health Care Operations/Health Information Management, Faculty of Business and Economics, Faculty of Medicine, University of Augsburg, Universitätsstraße 16, 86159, Augsburg, Germany.
- Department of Technology, Management, and Economics, Technical University of Denmark, Hovedstaden, Denmark.
- Data and Development Support, Region Zealand, Denmark.
| | - Frank Hanses
- Internal Medicine and Infectious Diseases, University Hospital Regensburg, Regensburg, Germany
| | - Christoph Römmele
- Clinic for Internal Medicine III - Gastroenterology and Infectious Diseases, University Hospital Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany
- COVID-19 Task Force, University Hospital Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany
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Xin Y, Li H, Zhou Y, Yang Q, Mu W, Xiao H, Zhuo Z, Liu H, Wang H, Qu X, Wang C, Liu H, Yu K. The accuracy of artificial intelligence in predicting COVID-19 patient mortality: a systematic review and meta-analysis. BMC Med Inform Decis Mak 2023; 23:155. [PMID: 37559062 PMCID: PMC10410953 DOI: 10.1186/s12911-023-02256-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 08/02/2023] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND The purpose of this paper was to systematically evaluate the application value of artificial intelligence in predicting mortality among COVID-19 patients. METHODS The PubMed, Embase, Web of Science, CNKI, Wanfang, China Biomedical Literature, and VIP databases were systematically searched from inception to October 2022 to identify studies that evaluated the predictive effects of artificial intelligence on mortality among COVID-19 patients. The retrieved literature was screened according to the inclusion and exclusion criteria. The quality of the included studies was assessed using the QUADAS-2 tools. Statistical analysis of the included studies was performed using Review Manager 5.3, Stata 16.0, and Meta-DiSc 1.4 statistical software. This meta-analysis was registered in PROSPERO (CRD42022315158). FINDINGS Of 2193 studies, 23 studies involving a total of 25 AI models met the inclusion criteria. Among them, 18 studies explicitly mentioned training and test sets, and 5 studies did not explicitly mention grouping. In the training set, the pooled sensitivity was 0.93 [0.87, 0.96], the pooled specificity was 0.94 [0.87, 0.97], and the area under the ROC curve was 0.98 [0.96, 0.99]. In the validation set, the pooled sensitivity was 0.84 [0.78, 0.88], the pooled specificity was 0.89 [0.85, 0.92], and the area under the ROC curve was 0.93 [1.00, 0.00]. In the subgroup analysis, the areas under the summary receiver operating characteristic (SROC) curves of the artificial intelligence models KNN, SVM, ANN, RF and XGBoost were 0.98, 0.98, 0.94, 0.92, and 0.91, respectively. The Deeks funnel plot indicated that there was no significant publication bias in this study (P > 0.05). INTERPRETATION Artificial intelligence models have high accuracy in predicting mortality among COVID-19 patients and have high prognostic value. Among them, the KNN, SVM, ANN, RF, XGBoost, and other models have the highest levels of accuracy.
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Affiliation(s)
- Yu Xin
- Department of Critical Care Medicine, the First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang Province, China
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Hongxu Li
- Department of Critical Care Medicine, the First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang Province, China
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Yuxin Zhou
- Department of Critical Care Medicine, the First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang Province, China
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Qing Yang
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Wenjing Mu
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Han Xiao
- Departments of Pharmacy and Cardiology, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Zipeng Zhuo
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Hongyu Liu
- Department of Anesthesiology, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Hongying Wang
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Xutong Qu
- Department of Anesthesiology, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China
| | - Changsong Wang
- Department of Critical Care Medicine, the First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang Province, China.
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China.
| | - Haitao Liu
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China.
| | - Kaijiang Yu
- Department of Critical Care Medicine, the First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang Province, China.
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Zakariaee SS, Naderi N, Ebrahimi M, Kazemi-Arpanahi H. Comparing machine learning algorithms to predict COVID‑19 mortality using a dataset including chest computed tomography severity score data. Sci Rep 2023; 13:11343. [PMID: 37443373 PMCID: PMC10345104 DOI: 10.1038/s41598-023-38133-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 07/04/2023] [Indexed: 07/15/2023] Open
Abstract
Since the beginning of the COVID-19 pandemic, new and non-invasive digital technologies such as artificial intelligence (AI) had been introduced for mortality prediction of COVID-19 patients. The prognostic performances of the machine learning (ML)-based models for predicting clinical outcomes of COVID-19 patients had been mainly evaluated using demographics, risk factors, clinical manifestations, and laboratory results. There is a lack of information about the prognostic role of imaging manifestations in combination with demographics, clinical manifestations, and laboratory predictors. The purpose of the present study is to develop an efficient ML prognostic model based on a more comprehensive dataset including chest CT severity score (CT-SS). Fifty-five primary features in six main classes were retrospectively reviewed for 6854 suspected cases. The independence test of Chi-square was used to determine the most important features in the mortality prediction of COVID-19 patients. The most relevant predictors were used to train and test ML algorithms. The predictive models were developed using eight ML algorithms including the J48 decision tree (J48), support vector machine (SVM), multi-layer perceptron (MLP), k-nearest neighbourhood (k-NN), Naïve Bayes (NB), logistic regression (LR), random forest (RF), and eXtreme gradient boosting (XGBoost). The performances of the predictive models were evaluated using accuracy, precision, sensitivity, specificity, and area under the ROC curve (AUC) metrics. After applying the exclusion criteria, a total of 815 positive RT-PCR patients were the final sample size, where 54.85% of the patients were male and the mean age of the study population was 57.22 ± 16.76 years. The RF algorithm with an accuracy of 97.2%, the sensitivity of 100%, a precision of 94.8%, specificity of 94.5%, F1-score of 97.3%, and AUC of 99.9% had the best performance. Other ML algorithms with AUC ranging from 81.2 to 93.9% had also good prediction performances in predicting COVID-19 mortality. Results showed that timely and accurate risk stratification of COVID-19 patients could be performed using ML-based predictive models fed by routine data. The proposed algorithm with the more comprehensive dataset including CT-SS could efficiently predict the mortality of COVID-19 patients. This could lead to promptly targeting high-risk patients on admission, the optimal use of hospital resources, and an increased probability of survival of patients.
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Affiliation(s)
| | - Negar Naderi
- Department of Midwifery, Ilam University of Medical Sciences, Ilam, Iran
| | - Mahdi Ebrahimi
- Department of Emergency Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran.
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Bardakci O, Das M, Akdur G, Akman C, Siddikoglu D, Akdur O, Beyazit Y. Haemogram indices are as reliable as CURB-65 to assess 30-day mortality in Covid-19 pneumonia. THE NATIONAL MEDICAL JOURNAL OF INDIA 2023; 35:221-228. [PMID: 36715048 DOI: 10.25259/nmji_474_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Background Mortality due to Covid-19 and severe community-acquired pneumonia (CAP) remains high, despite progress in critical care management. We compared the precision of CURB-65 score with monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) in prediction of mortality among patients with Covid-19 and CAP presenting to the emergency department. Methods We retrospectively analysed two cohorts of patients admitted to the emergency department of Canakkale University Hospital, namely (i) Covid-19 patients with severe acute respiratory symptoms presenting between 23 March 2020 and 31 October 2020, and (ii) all patients with CAP either from bacterial or viral infection within the 36 months preceding the Covid-19 pandemic. Mortality was defined as in-hospital death or death occurring within 30 days after discharge. Results The first study group consisted of 324 Covid-19 patients and the second group of 257 CAP patients. The non-survivor Covid-19 group had significantly higher MLR, NLR and PLR values. In univariate analysis, in Covid-19 patients, a 1-unit increase in NLR and PLR was associated with increased mortality, and in multivariate analysis for Covid-19 patients, age and NLR remained significant in the final step of the model. According to this model, we found that in the Covid-19 group an increase in 1-unit in NLR would result in an increase by 5% and 7% in the probability of mortality, respectively. According to pairwise analysis, NLR and PLR are as reliable as CURB-65 in predicting mortality in Covid-19. Conclusions Our study indicates that NLR and PLR may serve as reliable predictive factors as CURB-65 in Covid-19 pneumonia, which could easily be used to triage and manage severe patients in the emergency department.
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Affiliation(s)
- Okan Bardakci
- Department of Emergency Medicine, Canakkale Onsekiz Mart University, 17020, Canakkale, Turkey
| | - Murat Das
- Department of Emergency Medicine, Canakkale Onsekiz Mart University, 17020, Canakkale, Turkey
| | - Gökhan Akdur
- Department of Emergency Medicine, Canakkale Onsekiz Mart University, 17020, Canakkale, Turkey
| | - Canan Akman
- Department of Emergency Medicine, Canakkale Onsekiz Mart University, 17020, Canakkale, Turkey
| | - Duygu Siddikoglu
- Department of Biostatistic, Canakkale Onsekiz Mart University, 17020, Canakkale, Turkey
| | - Okhan Akdur
- Department of Emergency Medicine, Canakkale Onsekiz Mart University, 17020, Canakkale, Turkey
| | - Yavuz Beyazit
- Departments of Gastroenterology, Canakkale Onsekiz Mart University, 17020, Canakkale, Turkey
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9
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Kwok SWH, Wang G, Sohel F, Kashani KB, Zhu Y, Wang Z, Antpack E, Khandelwal K, Pagali SR, Nanda S, Abdalrhim AD, Sharma UM, Bhagra S, Dugani S, Takahashi PY, Murad MH, Yousufuddin M. An artificial intelligence approach for predicting death or organ failure after hospitalization for COVID-19: development of a novel risk prediction tool and comparisons with ISARIC-4C, CURB-65, qSOFA, and MEWS scoring systems. Respir Res 2023; 24:79. [PMID: 36915107 PMCID: PMC10010216 DOI: 10.1186/s12931-023-02386-6] [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: 12/20/2022] [Accepted: 03/07/2023] [Indexed: 03/14/2023] Open
Abstract
BACKGROUND We applied machine learning (ML) algorithms to generate a risk prediction tool [Collaboration for Risk Evaluation in COVID-19 (CORE-COVID-19)] for predicting the composite of 30-day endotracheal intubation, intravenous administration of vasopressors, or death after COVID-19 hospitalization and compared it with the existing risk scores. METHODS This is a retrospective study of adults hospitalized with COVID-19 from March 2020 to February 2021. Patients, each with 92 variables, and one composite outcome underwent feature selection process to identify the most predictive variables. Selected variables were modeled to build four ML algorithms (artificial neural network, support vector machine, gradient boosting machine, and Logistic regression) and an ensemble model to generate a CORE-COVID-19 model to predict the composite outcome and compared with existing risk prediction scores. The net benefit for clinical use of each model was assessed by decision curve analysis. RESULTS Of 1796 patients, 278 (15%) patients reached primary outcome. Six most predictive features were identified. Four ML algorithms achieved comparable discrimination (P > 0.827) with c-statistics ranged 0.849-0.856, calibration slopes 0.911-1.173, and Hosmer-Lemeshow P > 0.141 in validation dataset. These 6-variable fitted CORE-COVID-19 model revealed a c-statistic of 0.880, which was significantly (P < 0.04) higher than ISARIC-4C (0.751), CURB-65 (0.735), qSOFA (0.676), and MEWS (0.674) for outcome prediction. The net benefit of the CORE-COVID-19 model was greater than that of the existing risk scores. CONCLUSION The CORE-COVID-19 model accurately assigned 88% of patients who potentially progressed to 30-day composite events and revealed improved performance over existing risk scores, indicating its potential utility in clinical practice.
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Affiliation(s)
| | - Guanjin Wang
- Department of Information Technology, Murdoch University, Murdoch, Australia
| | - Ferdous Sohel
- Department of Information Technology, Murdoch University, Murdoch, Australia
| | - Kianoush B Kashani
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
| | - Ye Zhu
- Robert D. and Patricia E. Kern Centre for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN, USA
| | - Zhen Wang
- Robert D. and Patricia E. Kern Centre for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN, USA
| | - Eduardo Antpack
- Division of Hospital Internal Medicine, Mayo Clinic Health System, Austin, MN, USA
| | | | - Sandeep R Pagali
- Division of Hospital Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Sanjeev Nanda
- Division of General Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Ahmed D Abdalrhim
- Division of General Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Umesh M Sharma
- Division of Hospital Internal Medicine, Mayo Clinic, Phoenix, AZ, USA
| | - Sumit Bhagra
- Department of Endocrine and Metabolism, Mayo Clinic Health System, Austin, MN, USA
| | - Sagar Dugani
- Division of Hospital Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Paul Y Takahashi
- Division of Community Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Mohammad H Murad
- Robert D. and Patricia E. Kern Centre for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN, USA.,Division of Preventive Medicine, Mayo Clinic, Rochester, MN, USA
| | - Mohammed Yousufuddin
- Division of Surgery, Mayo Clinic, Rochester, MN, USA. .,Hospital Internal Medicine, Mayo Clinic Health System, Mayo Clinic, 1000 1st Drive NW, Austin, MN, USA.
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Nopour R, Shanbezadeh M, Kazemi-Arpanahi H. Predicting intubation risk among COVID-19 hospitalized patients using artificial neural networks. JOURNAL OF EDUCATION AND HEALTH PROMOTION 2023; 12:16. [PMID: 37034879 PMCID: PMC10079178 DOI: 10.4103/jehp.jehp_20_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 04/26/2022] [Indexed: 06/19/2023]
Abstract
BACKGROUND Accurately predicting the intubation risk in COVID-19 patients at the admission time is critical to optimal use of limited hospital resources, providing customized and evidence-based treatments, and improving the quality of delivered medical care services. This study aimed to design a statistical algorithm to select the best features influencing intubation prediction in coronavirus disease 2019 (COVID-19) hospitalized patients. Then, using selected features, multiple artificial neural network (ANN) configurations were developed to predict intubation risk. MATERIAL AND METHODS In this retrospective single-center study, a dataset containing 482 COVID-19 patients who were hospitalized between February 9, 2020 and July 20, 2021 was used. First, the Phi correlation coefficient method was performed for selecting the most important features affecting COVID-19 patients' intubation. Then, the different configurations of ANN were developed. Finally, the performance of ANN configurations was assessed using several evaluation metrics, and the best structure was determined for predicting intubation requirements among hospitalized COVID-19 patients. RESULTS The ANN models were developed based on 18 validated features. The results indicated that the best performance belongs to the 18-20-1 ANN configuration with positive predictive value (PPV) = 0.907, negative predictive value (NPV) = 0.941, sensitivity = 0.898, specificity = 0.951, and area under curve (AUC) = 0.906. CONCLUSIONS The results demonstrate the effectiveness of the ANN models for timely and reliable prediction of intubation risk in COVID-19 hospitalized patients. Our models can inform clinicians and those involved in policymaking and decision making for prioritizing restricted mechanical ventilation and other related resources for critically COVID-19 patients.
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Affiliation(s)
- Raoof Nopour
- Department of Health Information Management, Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran
| | - Mostafa Shanbezadeh
- Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran
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11
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Zakariaee SS, Abdi AI, Naderi N, Babashahi M. Prognostic significance of chest CT severity score in mortality prediction of COVID-19 patients, a machine learning study. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2023; 54:73. [PMCID: PMC10116092 DOI: 10.1186/s43055-023-01022-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 04/13/2023] [Indexed: 04/05/2024] Open
Abstract
Background The high mortality rate of COVID-19 makes it necessary to seek early identification of high-risk patients with poor prognoses. Although the association between CT-SS and mortality of COVID-19 patients was reported, its prognosis significance in combination with other prognostic parameters was not evaluated yet. Methods This retrospective single-center study reviewed a total of 6854 suspected patients referred to Imam Khomeini hospital, Ilam city, west of Iran, from February 9, 2020 to December 20, 2020. The prognostic performances of k-Nearest Neighbors (kNN), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and J48 decision tree algorithms were evaluated based on the most important and relevant predictors. The metrics derived from the confusion matrix were used to determine the performance of the ML models. Results After applying exclusion criteria, 815 hospitalized cases were entered into the study. Of these, 447(54.85%) were male and the mean (± SD) age of participants was 57.22(± 16.76) years. The results showed that the performances of the ML algorithms were improved when they are fed by the dataset with CT-SS data. The kNN model with an accuracy of 94.1%, sensitivity of 100. 0%, precision of 89.5%, specificity of 88.3%, and AUC around 97.2% had the best performance among the other three ML techniques. Conclusions The integration of CT-SS data with demographics, risk factors, clinical manifestations, and laboratory parameters improved the prognostic performances of the ML algorithms. An ML model with a comprehensive collection of predictors could identify high-risk patients more efficiently and lead to the optimal use of hospital resources.
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Affiliation(s)
- Seyed Salman Zakariaee
- Department of Medical Physics, Faculty of Paramedical Sciences, Ilam University of Medical Sciences, Ilam, Iran
| | - Aza Ismail Abdi
- Department of Radiology, Erbil Medical Technical Institute, Erbil Polytechnic University, Erbil, Iraq
| | - Negar Naderi
- Department of Midwifery, Faculty of Nursing and Midwifery, Ilam University of Medical Sciences, Ilam, Iran
| | - Mashallah Babashahi
- Department of Pathology, Faculty of Paramedical Sciences, Ilam University of Medical Sciences, Ilam, Iran
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Saadatmand S, Salimifard K, Mohammadi R, Kuiper A, Marzban M, Farhadi A. Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients. ANNALS OF OPERATIONS RESEARCH 2022; 328:1-29. [PMID: 36196268 PMCID: PMC9521862 DOI: 10.1007/s10479-022-04984-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/06/2022] [Indexed: 05/19/2023]
Abstract
The recent COVID-19 pandemic has affected health systems across the world. Especially, Intensive Care Units (ICUs) have played a pivotal role in the treatment of critically-ill patients. At the same time however, the increasing number of admissions due to the vast prevalence of the virus have caused several problems for ICU wards such as overburdening of staff and shortages of medical resources. These issues might have affected the quality of healthcare services provided directly impacting a patient's survival. The objective of this research is to leverage Machine Learning (ML) on hospital data in order to support hospital managers and practitioners with the treatment of COVID-19 patients. This is accomplished by providing more detailed inference about a patient's likelihood of ICU admission, mortality and in case of hospitalization the length of stay (LOS). In this pursuit, the outcome variables are in three separate models predicted by five different ML algorithms: eXtreme Gradient Boosting (XGB), K-Nearest Neighbor (KNN), Random Forest (RF), bagged-CART (b-CART), and LogitBoost (LB). With the exception of KNN, the studied models show good predictive capabilities when evaluating relevant accuracy scores, such as area under the curve. By implementing an ensemble stacking approach (either a Neural Net or a General Linear Model) on top of the aforementioned ML algorithms the performance is further boosted. Ultimately, for the prediction of admission to the ICU, the ensemble stacking via a Neural Net achieved the best result with an accuracy of over 95%. For mortality at the ICU, the vanilla XGB performed slightly better (1% difference with the meta-model). To predict large length of stays both ensemble stacking approaches yield comparable results. Besides it direct implications for managing COVID-19 patients, the approach presented serves as an example how data can be employed in future pandemics or crises.
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Affiliation(s)
- Sara Saadatmand
- Computational Intelligence and Intelligent Optimization Research Group, Persian Gulf University, Bushehr, 75169 Iran
| | - Khodakaram Salimifard
- Computational Intelligence and Intelligent Optimization Research Group, Persian Gulf University, Bushehr, 75169 Iran
| | - Reza Mohammadi
- Section Business Analytics, Amsterdam Business School, University of Amsterdam, Amsterdam, The Netherlands
| | - Alex Kuiper
- Section Business Analytics, Amsterdam Business School, University of Amsterdam, Amsterdam, The Netherlands
| | - Maryam Marzban
- Department of Public Health, School of Public Health, Bushehr University of Medical Science, Bushehr, Iran
| | - Akram Farhadi
- The Persian Gulf Tropical Medicine Research Center, The Persian Gulf Biomedical Science Research Institute, Bushehr University of Medical Science, Bushehr, Iran
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Nopour R, Shanbehzadeh M, Kazemi-Arpanahi H. Predicting the Need for Intubation among COVID-19 Patients Using Machine Learning Algorithms: A Single-Center Study. Med J Islam Repub Iran 2022; 36:30. [PMID: 35999913 PMCID: PMC9386770 DOI: 10.47176/mjiri.36.30] [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] [Received: 09/07/2021] [Accepted: 04/04/2022] [Indexed: 12/15/2022] Open
Abstract
Background: Owing to the shortage of ventilators, there is a crucial demand for an objective and accurate prognosis for 2019 coronavirus disease (COVID-19) critical patients, which may necessitate a mechanical ventilator (MV). This study aimed to construct a predictive model using machine learning (ML) algorithms for frontline clinicians to better triage endangered patients and priorities who would need MV.
Methods: In this retrospective single-center study, the data of 482 COVID-19 patients from February 9, 2020, to December 20, 2020, were analyzed by several ML algorithms including, multi-layer perception (MLP), logistic regression (LR), J-48 decision tree, and Naïve Bayes (NB). First, the most important clinical variables were identified using the Chi-square test at P < 0.01. Then, by comparing the ML algorithms' performance using some evaluation criteria, including TP-Rate, FP-Rate, precision, recall, F-Score, MCC, and Kappa, the best performing one was identified. Results: Predictive models were trained using 15 validated features, including cough, contusion, oxygen therapy, dyspnea, loss of taste, rhinorrhea, blood pressure, absolute lymphocyte count, pleural fluid, activated partial thromboplastin time, blood glucose, white cell count, cardiac diseases, length of hospitalization, and other underline diseases. The results indicated the J-48 with F-score = 0.868 and AUC = 0.892 yielded the best performance for predicting intubation requirement.
Conclusion: ML algorithms are potentials to improve traditional clinical criteria to forecast the necessity for intubation in COVID-19 in-hospital patients. Such ML-based prediction models may help physicians with optimizing the timing of intubation, better sharing of MV resources and personnel, and increase patient clinical status.
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Affiliation(s)
- Raoof Nopour
- Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran
| | - Mostafa Shanbehzadeh
- Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran.,Department of Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran
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Shen J, Casie Chetty S, Shokouhi S, Maharjan J, Chuba Y, Calvert J, Mao Q. Massive external validation of a machine learning algorithm to predict pulmonary embolism in hospitalized patients. Thromb Res 2022; 216:14-21. [DOI: 10.1016/j.thromres.2022.05.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 05/26/2022] [Accepted: 05/30/2022] [Indexed: 10/18/2022]
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Machine Learning Models to Predict In-Hospital Mortality among Inpatients with COVID-19: Underestimation and Overestimation Bias Analysis in Subgroup Populations. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:1644910. [PMID: 35756093 PMCID: PMC9226971 DOI: 10.1155/2022/1644910] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 04/17/2022] [Accepted: 05/22/2022] [Indexed: 12/13/2022]
Abstract
Prediction of the death among COVID-19 patients can help healthcare providers manage the patients better. We aimed to develop machine learning models to predict in-hospital death among these patients. We developed different models using different feature sets and datasets developed using the data balancing method. We used demographic and clinical data from a multicenter COVID-19 registry. We extracted 10,657 records for confirmed patients with PCR or CT scans, who were hospitalized at least for 24 hours at the end of March 2021. The death rate was 16.06%. Generally, models with 60 and 40 features performed better. Among the 240 models, the C5 models with 60 and 40 features performed well. The C5 model with 60 features outperformed the rest based on all evaluation metrics; however, in external validation, C5 with 32 features performed better. This model had high accuracy (91.18%), F-score (0.916), Area under the Curve (0.96), sensitivity (94.2%), and specificity (88%). The model suggested in this study uses simple and available data and can be applied to predict death among COVID-19 patients. Furthermore, we concluded that machine learning models may perform differently in different subpopulations in terms of gender and age groups.
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邓 宇, 姜 勇, 王 子, 刘 爽, 汪 雨, 刘 宝. [Long short-term memory and Logistic regression for mortality risk prediction of intensive care unit patients with stroke]. BEIJING DA XUE XUE BAO. YI XUE BAN = JOURNAL OF PEKING UNIVERSITY. HEALTH SCIENCES 2022; 54:458-467. [PMID: 35701122 PMCID: PMC9197695 DOI: 10.19723/j.issn.1671-167x.2022.03.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVE To select variables related to mortality risk of stroke patients in intensive care unit (ICU) through long short-term memory (LSTM) with attention mechanisms and Logistic regression with L1 norm, and to construct mortality risk prediction model based on conventional Logistic regression with important variables selected from the two models and to evaluate the model performance. METHODS Medical Information Mart for Intensive Care (MIMIC)-Ⅳ database was retrospectively analyzed and the patients who were primarily diagnosed with stroke were selected as study population. The outcome was defined as whether the patient died in hospital after admission. Candidate predictors included demogra-phic information, complications, laboratory tests and vital signs in the initial 48 h after ICU admission. The data were randomly divided into a training set and a test set for ten times at a ratio of 8 ∶2. In training sets, LSTM with attention mechanisms and Logistic regression with L1 norm were constructed to select important variables. In the test sets, the mean importance of variables of ten times was used as a reference to pick out the top 10 variables in each of the two models, and then these variables were included in conventional Logistic regression to build the final prediction model. Model evaluation was based on the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. And the model performance was compared with the forward Logistic regression model which hadn't conducted variable selection previously. RESULTS A total of 2 755 patients with 2 979 ICU admission records were included in the analysis, of which 526 recorded deaths. The AUC of Logistic regression model with L1 norm was statistically better than that of LSTM with attention mechanisms (0.819±0.031 vs. 0.760±0.018, P < 0.001). Age, blood glucose, and blood urea nitrogen were at the top ten important variables in both of the two models. AUC, sensitivity, specificity, and accuracy of Logistic regression models were 0.85, 85.98%, 71.74% and 74.26%, respectively. And the final prediction model was superior to forward Logistic regression model. CONCLUSION The variables selected by Logistic regression with L1 norm and LSTM with attention mechanisms had good prediction performance, which showed important implications on the mortality prediction of stroke patients in ICU.
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Affiliation(s)
- 宇含 邓
- 北京大学公共卫生学院社会医学与健康教育学系,北京 100191Department of Social Medicine and Health Education, Peking University School of Public Health, Beijing 100191, China
| | - 勇 姜
- 国家神经系统疾病临床医学研究中心,首都医科大学附属北京天坛医院神经病学中心,北京 100050China National Clinical Research Center for Neurological Diseases, Department of Neurology, Beijing Tian Tan Hospital, Capital Medical University, Beijing 100050, China
- 北京大数据精准医疗高精尖创新中心(北京航空航天大学&首都医科大学),北京 100070Beijing Advanced Innovation Center for Big Data-Based Precision Medicine (Beihang University & Capital Medical University), Beijing 100070, China
| | - 子尧 王
- 北京大学公共卫生学院社会医学与健康教育学系,北京 100191Department of Social Medicine and Health Education, Peking University School of Public Health, Beijing 100191, China
| | - 爽 刘
- 北京大学公共卫生学院社会医学与健康教育学系,北京 100191Department of Social Medicine and Health Education, Peking University School of Public Health, Beijing 100191, China
| | - 雨欣 汪
- 北京大学公共卫生学院社会医学与健康教育学系,北京 100191Department of Social Medicine and Health Education, Peking University School of Public Health, Beijing 100191, China
| | - 宝花 刘
- 北京大学公共卫生学院社会医学与健康教育学系,北京 100191Department of Social Medicine and Health Education, Peking University School of Public Health, Beijing 100191, China
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Mortality prediction of COVID-19 patients using soft voting classifier. INTERNATIONAL JOURNAL OF COGNITIVE COMPUTING IN ENGINEERING 2022; 3:172-179. [PMCID: PMC9472476 DOI: 10.1016/j.ijcce.2022.09.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 09/07/2022] [Accepted: 09/11/2022] [Indexed: 05/29/2023]
Abstract
COVID-19 is a novel coronavirus that spread around the globe with the initial reports coming from Wuhan, China, turned into a pandemic and caused enormous casualties. Various countries have faced multiple COVID spikes which put the medical infrastructure of these countries under immense pressure with third-world countries being hit the hardest. It can be thus concluded that determining the likeliness of death of a patient helps in avoiding fatalities which inspired the authors to research the topic. There are various ways to approach the problem such as past medical records, chest X-rays, CT scans, and blood biomarkers. Since blood biomarkers are most easily available in emergency scenarios, blood biomarkers were used as the features for the model. The data was first imputed and the training data was oversampled to avoid class imbalance in the model training. The model is composed of a voting classifier that takes in outputs from multiple classifiers. The model was then compared to base models such as Random Forest, XGBoost, and Extra Trees Classifier on multiple evaluation criteria. The F1 score was the concerned evaluation criterion as it maximizes the use of the medical infrastructure with the minimum possible casualties by maximizing true positives and minimizing false negatives.
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Shanbehzadeh M, Nopour R, Kazemi-Arpanahi H. Design of an artificial neural network to predict mortality among COVID-19 patients. INFORMATICS IN MEDICINE UNLOCKED 2022; 31:100983. [PMID: 35664686 PMCID: PMC9148440 DOI: 10.1016/j.imu.2022.100983] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 05/26/2022] [Accepted: 05/26/2022] [Indexed: 12/23/2022] Open
Abstract
Introduction The fast pandemic of coronavirus disease 2019 (COVID-19) has challenged clinicians with many uncertainties and ambiguities regarding disease outcomes and complications. To deal with these uncertainties, our study aimed to develop and evaluate several artificial neural networks (ANNs) to predict the mortality risk in hospitalized COVID-19 patients. Material and methods The data of 1710 hospitalized COVID-19 patients were used in this retrospective and developmental study. First, a Chi-square test (P < 0.05), Eta coefficient (η > 0.4), and binary logistics regression (BLR) analysis were performed to determine the factors affecting COVID-19 mortality. Then, using the selected variables, two types of feed-forward (FF) models, including the back-propagation (BP) and distributed time delay (DTD) were trained. The models' performance was assessed using mean squared error (MSE), error histogram (EH), and area under the ROC curve (AUC-ROC) metrics. Results After applying the univariate and multivariate analysis, 13 variables were selected as important features in predicting COVID-19 mortality at P < 0.05. A comparison of the two ANN architectures using the MSE showed that the BP-ANN (validation error: 0.067, most of the classified samples having 0.049 and 0.05 error rates, and AUC-ROC: 0.888) was the best model. Conclusions Our findings show the acceptable performance of ANN for predicting the risk of mortality in hospitalized COVID-19 patients. Application of the developed ANN-based CDSS in a real clinical environment will improve patient safety and reduce disease severity and mortality.
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Munera N, Garcia-Gallo E, Gonzalez Á, Zea J, Fuentes YV, Serrano C, Ruiz-Cuartas A, Rodriguez A, Reyes LF. A novel model to predict severe COVID-19 and mortality using an artificial intelligence algorithm to interpret chest X-Rays and clinical variables. ERJ Open Res 2022; 8:00010-2022. [PMID: 35765299 PMCID: PMC9059131 DOI: 10.1183/23120541.00010-2022] [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: 01/07/2022] [Accepted: 04/19/2022] [Indexed: 11/25/2022] Open
Abstract
Background Patients with coronavirus disease 2019 (COVID-19) could develop severe disease requiring admission to the intensive care unit (ICU). This article presents a novel method that predicts whether a patient will need admission to the ICU and assesses the risk of in-hospital mortality by training a deep-learning model that combines a set of clinical variables and features in chest radiographs. Methods This was a prospective diagnostic test study. Patients with confirmed severe acute respiratory syndrome coronavirus 2 infection between March 2020 and January 2021 were included. This study was designed to build predictive models obtained by training convolutional neural networks for chest radiograph images using an artificial intelligence (AI) tool and a random forest analysis to identify critical clinical variables. Then, both architectures were connected and fine-tuned to provide combined models. Results 2552 patients were included in the clinical cohort. The variables independently associated with ICU admission were age, fraction of inspired oxygen (FiO2) on admission, dyspnoea on admission and obesity. Moreover, the variables associated with hospital mortality were age, FiO2 on admission and dyspnoea. When implementing the AI model to interpret the chest radiographs and the clinical variables identified by random forest, we developed a model that accurately predicts ICU admission (area under the curve (AUC) 0.92±0.04) and hospital mortality (AUC 0.81±0.06) in patients with confirmed COVID-19. Conclusions This automated chest radiograph interpretation algorithm, along with clinical variables, is a reliable alternative to identify patients at risk of developing severe COVID-19 who might require admission to the ICU. In patients with #COVID19, an automated chest radiograph interpretation algorithm, along with clinical variables, is a reliable alternative to identify patients at risk of developing severe COVID-19, who might require admission to the intensive care unithttps://bit.ly/3Kf61TK
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Rahmani K, Garikipati A, Barnes G, Hoffman J, Calvert J, Mao Q, Das R. Early prediction of central line associated bloodstream infection using machine learning. Am J Infect Control 2022; 50:440-445. [PMID: 34428529 DOI: 10.1016/j.ajic.2021.08.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 08/16/2021] [Accepted: 08/17/2021] [Indexed: 11/01/2022]
Abstract
BACKGROUND Central line-associated bloodstream infections (CLABSIs) are associated with significant morbidity, mortality, and increased healthcare costs. Despite the high prevalence of CLABSIs in the U.S., there are currently no tools to stratify a patient's risk of developing an infection as the result of central line placement. To this end, we have developed and validated a machine learning algorithm (MLA) that can predict a patient's likelihood of developing CLABSI using only electronic health record data in order to provide clinical decision support. METHODS We created three machine learning models to retrospectively analyze electronic health record data from 27,619 patient encounters. The models were trained and validated using an 80:20 split for the train and test data. Patients designated as having a central line procedure based on International Statistical Classification of Diseases and Related Health Problems 10 codes were included. RESULTS XGBoost was the highest performing MLA out of the three models, obtaining an AUROC of 0.762 for CLABSI risk prediction at 48 hours after the recorded time for central line placement. CONCLUSIONS Our results demonstrate that MLAs may be effective clinical decision support tools for assessment of CLABSI risk and should be explored further for this purpose.
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Bartoszko J, Dranitsaris G, Wilcox ME, Del Sorbo L, Mehta S, Peer M, Parotto M, Bogoch I, Riazi S. Development of a repeated-measures predictive model and clinical risk score for mortality in ventilated COVID-19 patients. Can J Anaesth 2022; 69:343-352. [PMID: 34931293 PMCID: PMC8687635 DOI: 10.1007/s12630-021-02163-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 09/13/2021] [Accepted: 10/04/2021] [Indexed: 11/09/2022] Open
Abstract
PURPOSE The COVID-19 pandemic has caused intensive care units (ICUs) to reach capacities requiring triage. A tool to predict mortality risk in ventilated patients with COVID-19 could inform decision-making and resource allocation, and allow population-level comparisons across institutions. METHODS This retrospective cohort study included all mechanically ventilated adults with COVID-19 admitted to three tertiary care ICUs in Toronto, Ontario, between 1 March 2020 and 15 December 2020. Generalized estimating equations were used to identify variables predictive of mortality. The primary outcome was the probability of death at three-day intervals from the time of ICU admission (day 0), with risk re-calculation every three days to day 15; the final risk calculation estimated the probability of death at day 15 and beyond. A numerical algorithm was developed from the final model coefficients. RESULTS One hundred twenty-seven patients were eligible for inclusion. Median ICU length of stay was 26.9 (interquartile range, 15.4-52.0) days. Overall mortality was 42%. From day 0 to 15, the variables age, temperature, lactate level, ventilation tidal volume, and vasopressor use significantly predicted mortality. Our final clinical risk score had an area under the receiver-operating characteristics curve of 0.9 (95% confidence interval [CI], 0.8 to 0.9). For every ten-point increase in risk score, the relative increase in the odds of death was approximately 4, with an odds ratio of 4.1 (95% CI, 2.9 to 5.9). CONCLUSION Our dynamic prediction tool for mortality in ventilated patients with COVID-19 has excellent diagnostic properties. Notwithstanding, external validation is required before widespread implementation.
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Affiliation(s)
- Justyna Bartoszko
- Department of Anesthesia and Pain Management, University Health Network, 323-200 Elizabeth St, Toronto, ON, M5G 2C4, Canada
- Department of Anesthesiology and Pain Medicine, University of Toronto, Toronto, ON, Canada
| | - George Dranitsaris
- Department of Public Health, Falk College, Syracuse University, Syracuse, NY, USA
| | - M Elizabeth Wilcox
- Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada
- Department of Medicine (Critical Care Medicine), University Health Network, Toronto, ON, Canada
| | - Lorenzo Del Sorbo
- Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada
- Department of Medicine (Critical Care Medicine), University Health Network, Toronto, ON, Canada
| | - Sangeeta Mehta
- Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada
- Department of Medicine, Sinai Health System, Toronto, ON, Canada
| | - Miki Peer
- Department of Anesthesia and Pain Management, University Health Network, 323-200 Elizabeth St, Toronto, ON, M5G 2C4, Canada
| | - Matteo Parotto
- Department of Anesthesia and Pain Management, University Health Network, 323-200 Elizabeth St, Toronto, ON, M5G 2C4, Canada
- Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada
| | - Isaac Bogoch
- Division of General Internal Medicine and Infectious Diseases, University Health Network, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Sheila Riazi
- Department of Anesthesia and Pain Management, University Health Network, 323-200 Elizabeth St, Toronto, ON, M5G 2C4, Canada.
- Department of Anesthesiology and Pain Medicine, University of Toronto, Toronto, ON, Canada.
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22
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A COVID-19 mortality prediction model for Korean patients using nationwide Korean disease control and prevention agency database. Sci Rep 2022; 12:3311. [PMID: 35228578 PMCID: PMC8885855 DOI: 10.1038/s41598-022-07051-4] [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: 02/26/2021] [Accepted: 01/31/2022] [Indexed: 01/08/2023] Open
Abstract
AbstractThe experience of the early nationwide COVID-19 pandemic in South Korea led to an early shortage of medical resources. For efficient resource allocation, accurate prediction of the prognosis or mortality of confirmed patients is essential. Therefore, the aim of this study was to develop an accurate model for predicting COVID-19 mortality using epidemiolocal and clinical variables and for identifying a high-risk group of confirmed patients. Clinical and epidemiolocal variables of 4049 patients with confirmed COVID-19 between January 20, 2020 and April 30, 2020 collected by the Korean Disease Control and Prevention Agency were used. Among the 4049 total confirmed patients, 223 patients died, while 3826 patients were released from isolation. Patients who had the following risk factors showed significantly higher risk scores: age over 60 years, male sex, difficulty breathing, diabetes, cancer, dementia, change of consciousness, and hospitalization in the intensive care unit. High accuracy was shown for both the development set (n = 2467) and the validation set (n = 1582), with AUCs of 0.96 and 0.97, respectively. The prediction model developed in this study based on clinical features and epidemiological factors could be used for screening high-risk groups of patients and for evidence-based allocation of medical resources.
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23
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Miller JL, Tada M, Goto M, Chen H, Dang E, Mohr NM, Lee S. Prediction models for severe manifestations and mortality due to COVID-19: A systematic review. Acad Emerg Med 2022; 29:206-216. [PMID: 35064988 DOI: 10.1111/acem.14447] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 12/21/2021] [Accepted: 12/29/2021] [Indexed: 12/19/2022]
Abstract
BACKGROUND Throughout 2020, the coronavirus disease 2019 (COVID-19) has become a threat to public health on national and global level. There has been an immediate need for research to understand the clinical signs and symptoms of COVID-19 that can help predict deterioration including mechanical ventilation, organ support, and death. Studies thus far have addressed the epidemiology of the disease, common presentations, and susceptibility to acquisition and transmission of the virus; however, an accurate prognostic model for severe manifestations of COVID-19 is still needed because of the limited healthcare resources available. OBJECTIVE This systematic review aims to evaluate published reports of prediction models for severe illnesses caused COVID-19. METHODS Searches were developed by the primary author and a medical librarian using an iterative process of gathering and evaluating terms. Comprehensive strategies, including both index and keyword methods, were devised for PubMed and EMBASE. The data of confirmed COVID-19 patients from randomized control studies, cohort studies, and case-control studies published between January 2020 and May 2021 were retrieved. Studies were independently assessed for risk of bias and applicability using the Prediction Model Risk Of Bias Assessment Tool (PROBAST). We collected study type, setting, sample size, type of validation, and outcome including intubation, ventilation, any other type of organ support, or death. The combination of the prediction model, scoring system, performance of predictive models, and geographic locations were summarized. RESULTS A primary review found 445 articles relevant based on title and abstract. After further review, 366 were excluded based on the defined inclusion and exclusion criteria. Seventy-nine articles were included in the qualitative analysis. Inter observer agreement on inclusion 0.84 (95%CI 0.78-0.89). When the PROBAST tool was applied, 70 of the 79 articles were identified to have high or unclear risk of bias, or high or unclear concern for applicability. Nine studies reported prediction models that were rated as low risk of bias and low concerns for applicability. CONCLUSION Several prognostic models for COVID-19 were identified, with varying clinical score performance. Nine studies that had a low risk of bias and low concern for applicability, one from a general public population and hospital setting. The most promising and well-validated scores include Clift et al.,15 and Knight et al.,18 which seem to have accurate prediction models that clinicians can use in the public health and emergency department setting.
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Affiliation(s)
- Jamie L. Miller
- University of Iowa Carver College of Medicine Iowa City Iowa USA
| | - Masafumi Tada
- Department of Health Promotion and Human Behavior School of Public Health, Kyoto University Graduate School of Medicine Kyoto Japan
| | - Michihiko Goto
- Division of Infectious Diseases, Department of Internal Medicine University of Iowa Carver College of Medicine Iowa City Iowa USA
| | - Hao Chen
- University of Iowa Iowa City Iowa USA
| | | | - Nicholas M. Mohr
- Department of Emergency Medicine, Department of Anesthesia, Department of Epidemiology University of Iowa Carver College of Medicine Iowa City Iowa USA
| | - Sangil Lee
- Department of Emergency Medicine The University of Iowa Carver College of Medicine Iowa City Iowa USA
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24
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Maharjan J, Ektefaie Y, Ryan L, Mataraso S, Barnes G, Shokouhi S, Green-Saxena A, Calvert J, Mao Q, Das R. Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm. Front Neurol 2022; 12:784250. [PMID: 35145468 PMCID: PMC8823366 DOI: 10.3389/fneur.2021.784250] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 12/22/2021] [Indexed: 11/24/2022] Open
Abstract
Background Strokes represent a leading cause of mortality globally. The evolution of developing new therapies is subject to safety and efficacy testing in clinical trials, which operate in a limited timeframe. To maximize the impact of these trials, patient cohorts for whom ischemic stroke is likely during that designated timeframe should be identified. Machine learning may improve upon existing candidate identification methods in order to maximize the impact of clinical trials for stroke prevention and treatment and improve patient safety. Methods A retrospective study was performed using 41,970 qualifying patient encounters with ischemic stroke from inpatient visits recorded from over 700 inpatient and ambulatory care sites. Patient data were extracted from electronic health records and used to train and test a gradient boosted machine learning algorithm (MLA) to predict the patients' risk of experiencing ischemic stroke from the period of 1 day up to 1 year following the patient encounter. The primary outcome of interest was the occurrence of ischemic stroke. Results After training for optimization, XGBoost obtained a specificity of 0.793, a positive predictive value (PPV) of 0.194, and a negative predictive value (NPV) of 0.985. The MLA further obtained an area under the receiver operating characteristic (AUROC) of 0.88. The Logistic Regression and multilayer perceptron models both achieved AUROCs of 0.862. Among features that significantly impacted the prediction of ischemic stroke were previous stroke history, age, and mean systolic blood pressure. Conclusion MLAs have the potential to more accurately predict the near risk of ischemic stroke within a 1-year prediction window for individuals who have been hospitalized. This risk stratification tool can be used to design clinical trials to test stroke prevention treatments in high-risk populations by identifying subjects who would be more likely to benefit from treatment.
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Varzaneh ZA, Orooji A, Erfannia L, Shanbehzadeh M. A new COVID-19 intubation prediction strategy using an intelligent feature selection and K-NN method. INFORMATICS IN MEDICINE UNLOCKED 2022; 28:100825. [PMID: 34977330 PMCID: PMC8712462 DOI: 10.1016/j.imu.2021.100825] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 12/13/2021] [Accepted: 12/14/2021] [Indexed: 12/14/2022] Open
Abstract
Background Predicting severe respiratory failure due to COVID-19 can help triage patients to higher levels of care, resource allocation and decrease morbidity and mortality. The need for this research derives from the increasing demand for innovative technologies to overcome complex data analysis and decision-making tasks in critical care units. Hence the aim of our paper is to present a new algorithm for selecting the best features from the dataset and developing Machine Learning(ML) based models to predict the intubation risk of hospitalized COVID-19 patients. Methods In this retrospective single-center study, the data of 1225 COVID-19 patients from February 9, 2020, to July 20, 2021, were analyzed by several ML algorithms which included, Decision Tree(DT), Support Vector Machine (SVM), Multilayer perceptron (MLP), and K-Nearest Neighbors(K-NN). First, the most important predictors were identified using the Horse herd Optimization Algorithm (HOA). Then, by comparing the ML algorithms' performance using some evaluation criteria, the best performing one was identified. Results Predictive models were trained using 12 validated features. Also, it found that proposed DT-based predictive model enables a reasonable level of accuracy (=93%) in predicting the risk of intubation among hospitalized COVID-19 patients. Conclusions The experimental results demonstrate the effectiveness of the proposed meta-heuristic feature selection technique in combining with DT model in predicting intubation risk for hospitalized patients with COVID-19. The proposed model have the potential to inform frontline clinicians with quantitative and non-invasive tool to assess illness severity and to identify high risk patients.
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Affiliation(s)
- Zahra Asghari Varzaneh
- Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Azam Orooji
- Department of Advanced Technologies, School of Medicine, North Khorasan University of Medical Science (NKUMS), North Khorasan, Iran
| | - Leila Erfannia
- Department of Health Information Management, School of Management and Medical Informatics, Health Human Resources Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mostafa Shanbehzadeh
- Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
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26
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Moulaei K, Shanbehzadeh M, Mohammadi-Taghiabad Z, Kazemi-Arpanahi H. Comparing machine learning algorithms for predicting COVID-19 mortality. BMC Med Inform Decis Mak 2022; 22:2. [PMID: 34983496 PMCID: PMC8724649 DOI: 10.1186/s12911-021-01742-0] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 12/28/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND The coronavirus disease (COVID-19) hospitalized patients are always at risk of death. Machine learning (ML) algorithms can be used as a potential solution for predicting mortality in COVID-19 hospitalized patients. So, our study aimed to compare several ML algorithms to predict the COVID-19 mortality using the patient's data at the first time of admission and choose the best performing algorithm as a predictive tool for decision-making. METHODS In this study, after feature selection, based on the confirmed predictors, information about 1500 eligible patients (1386 survivors and 144 deaths) obtained from the registry of Ayatollah Taleghani Hospital, Abadan city, Iran, was extracted. Afterwards, several ML algorithms were trained to predict COVID-19 mortality. Finally, to assess the models' performance, the metrics derived from the confusion matrix were calculated. RESULTS The study participants were 1500 patients; the number of men was found to be higher than that of women (836 vs. 664) and the median age was 57.25 years old (interquartile 18-100). After performing the feature selection, out of 38 features, dyspnea, ICU admission, and oxygen therapy were found as the top three predictors. Smoking, alanine aminotransferase, and platelet count were found to be the three lowest predictors of COVID-19 mortality. Experimental results demonstrated that random forest (RF) had better performance than other ML algorithms with accuracy, sensitivity, precision, specificity, and receiver operating characteristic (ROC) of 95.03%, 90.70%, 94.23%, 95.10%, and 99.02%, respectively. CONCLUSION It was found that ML enables a reasonable level of accuracy in predicting the COVID-19 mortality. Therefore, ML-based predictive models, particularly the RF algorithm, potentially facilitate identifying the patients who are at high risk of mortality and inform proper interventions by the clinicians.
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Affiliation(s)
- Khadijeh Moulaei
- Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Mostafa Shanbehzadeh
- Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Zahra Mohammadi-Taghiabad
- Department of Health Information Management, School of Health Management and Information Sciences, Kerman University of Medical Sciences, Kerman, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran.
- Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran.
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Peng Y, Liu E, Peng S, Chen Q, Li D, Lian D. Using artificial intelligence technology to fight COVID-19: a review. Artif Intell Rev 2022; 55:4941-4977. [PMID: 35002010 PMCID: PMC8720541 DOI: 10.1007/s10462-021-10106-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/12/2021] [Indexed: 02/10/2023]
Abstract
In late December 2019, a new type of coronavirus was discovered, which was later named severe acute respiratory syndrome coronavirus 2(SARS-CoV-2). Since its discovery, the virus has spread globally, with 2,975,875 deaths as of 15 April 2021, and has had a huge impact on our health systems and economy. How to suppress the continued spread of new coronary pneumonia is the main task of many scientists and researchers. The introduction of artificial intelligence technology has provided a huge contribution to the suppression of the new coronavirus. This article discusses the main application of artificial intelligence technology in the suppression of coronavirus from three major aspects of identification, prediction, and development through a large amount of literature research, and puts forward the current main challenges and possible development directions. The results show that it is an effective measure to combine artificial intelligence technology with a variety of new technologies to predict and identify COVID-19 patients.
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Affiliation(s)
- Yong Peng
- Petroleum Engineering School, Southwest Petroleum University, Chengdu, 610500 China
| | - Enbin Liu
- Petroleum Engineering School, Southwest Petroleum University, Chengdu, 610500 China
| | - Shanbi Peng
- School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu, 610500 China
| | - Qikun Chen
- School of Engineering, Cardiff University, Cardiff, CF24 3AA UK
| | - Dangjian Li
- Petroleum Engineering School, Southwest Petroleum University, Chengdu, 610500 China
| | - Dianpeng Lian
- Petroleum Engineering School, Southwest Petroleum University, Chengdu, 610500 China
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28
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Hong W, Zhou X, Jin S, Lu Y, Pan J, Lin Q, Yang S, Xu T, Basharat Z, Zippi M, Fiorino S, Tsukanov V, Stock S, Grottesi A, Chen Q, Pan J. A Comparison of XGBoost, Random Forest, and Nomograph for the Prediction of Disease Severity in Patients With COVID-19 Pneumonia: Implications of Cytokine and Immune Cell Profile. Front Cell Infect Microbiol 2022; 12:819267. [PMID: 35493729 PMCID: PMC9039730 DOI: 10.3389/fcimb.2022.819267] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Accepted: 03/07/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND AND AIMS The aim of this study was to apply machine learning models and a nomogram to differentiate critically ill from non-critically ill COVID-19 pneumonia patients. METHODS Clinical symptoms and signs, laboratory parameters, cytokine profile, and immune cellular data of 63 COVID-19 pneumonia patients were retrospectively reviewed. Outcomes were followed up until Mar 12, 2020. A logistic regression function (LR model), Random Forest, and XGBoost models were developed. The performance of these models was measured by area under receiver operating characteristic curve (AUC) analysis. RESULTS Univariate analysis revealed that there was a difference between critically and non-critically ill patients with respect to levels of interleukin-6, interleukin-10, T cells, CD4+ T, and CD8+ T cells. Interleukin-10 with an AUC of 0.86 was most useful predictor of critically ill patients with COVID-19 pneumonia. Ten variables (respiratory rate, neutrophil counts, aspartate transaminase, albumin, serum procalcitonin, D-dimer and B-type natriuretic peptide, CD4+ T cells, interleukin-6 and interleukin-10) were used as candidate predictors for LR model, Random Forest (RF) and XGBoost model application. The coefficients from LR model were utilized to build a nomogram. RF and XGBoost methods suggested that Interleukin-10 and interleukin-6 were the most important variables for severity of illness prediction. The mean AUC for LR, RF, and XGBoost model were 0.91, 0.89, and 0.93 respectively (in two-fold cross-validation). Individualized prediction by XGBoost model was explained by local interpretable model-agnostic explanations (LIME) plot. CONCLUSIONS XGBoost exhibited the highest discriminatory performance for prediction of critically ill patients with COVID-19 pneumonia. It is inferred that the nomogram and visualized interpretation with LIME plot could be useful in the clinical setting. Additionally, interleukin-10 could serve as a useful predictor of critically ill patients with COVID-19 pneumonia.
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Affiliation(s)
- Wandong Hong
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- *Correspondence: Wandong Hong, ; Jingye Pan,
| | - Xiaoying Zhou
- School of the First Clinical Medical Sciences, Wenzhou Medical University, Wenzhou, China
| | - Shengchun Jin
- School of the First Clinical Medical Sciences, Wenzhou Medical University, Wenzhou, China
| | - Yajing Lu
- School of the First Clinical Medical Sciences, Wenzhou Medical University, Wenzhou, China
| | - Jingyi Pan
- School of the First Clinical Medical Sciences, Wenzhou Medical University, Wenzhou, China
| | - Qingyi Lin
- School of the First Clinical Medical Sciences, Wenzhou Medical University, Wenzhou, China
| | - Shaopeng Yang
- School of the First Clinical Medical Sciences, Wenzhou Medical University, Wenzhou, China
| | - Tingting Xu
- School of the First Clinical Medical Sciences, Wenzhou Medical University, Wenzhou, China
| | - Zarrin Basharat
- Jamil-ur-Rahman Center for Genome Research, Dr. Panjwani Centre for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan
| | - Maddalena Zippi
- Unit of Gastroenterology and Digestive Endoscopy, Sandro Pertini Hospital, Rome, Italy
| | - Sirio Fiorino
- Internal Medicine Unit, Budrio Hospital, Bologna, Italy
| | - Vladislav Tsukanov
- Department of Gastroenterology, Scientific Research Institute of Medical Problems of the North, Krasnoyarsk, Russia
| | - Simon Stock
- Department of Surgery, World Mate Emergency Hospital, Battambang, Cambodia
| | | | - Qin Chen
- Department of Intensive Care Unit, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jingye Pan
- Department of Intensive Care Unit, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- *Correspondence: Wandong Hong, ; Jingye Pan,
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29
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Linden T, Hanses F, Domingo-Fernández D, DeLong LN, Kodamullil AT, Schneider J, Vehreschild MJGT, Lanznaster J, Ruethrich MM, Borgmann S, Hower M, Wille K, Feldt T, Rieg S, Hertenstein B, Wyen C, Roemmele C, Vehreschild JJ, Jakob CEM, Stecher M, Kuzikov M, Zaliani A, Fröhlich H. Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases. ARTIFICIAL INTELLIGENCE IN THE LIFE SCIENCES 2021; 1:100020. [PMID: 34988543 PMCID: PMC8677630 DOI: 10.1016/j.ailsci.2021.100020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 11/22/2021] [Accepted: 11/22/2021] [Indexed: 02/08/2023]
Abstract
Despite available vaccinations COVID-19 case numbers around the world are still growing, and effective medications against severe cases are lacking. In this work, we developed a machine learning model which predicts mortality for COVID-19 patients using data from the multi-center 'Lean European Open Survey on SARS-CoV-2-infected patients' (LEOSS) observational study (>100 active sites in Europe, primarily in Germany), resulting into an AUC of almost 80%. We showed that molecular mechanisms related to dementia, one of the relevant predictors in our model, intersect with those associated to COVID-19. Most notably, among these molecules was tyrosine kinase 2 (TYK2), a protein that has been patented as drug target in Alzheimer's Disease but also genetically associated with severe COVID-19 outcomes. We experimentally verified that anti-cancer drugs Sorafenib and Regorafenib showed a clear anti-cytopathic effect in Caco2 and VERO-E6 cells and can thus be regarded as potential treatments against COVID-19. Altogether, our work demonstrates that interpretation of machine learning based risk models can point towards drug targets and new treatment options, which are strongly needed for COVID-19.
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Affiliation(s)
- Thomas Linden
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany
- University of Bonn, Bonn-Aachen International Center for IT, Friedrich Hirzebruch-Allee 6, 53115 Bonn, Germany
| | - Frank Hanses
- Emergency Department, University Hospital Regensburg, 93053 Regensburg, Germany
- Department for Infectious Diseases and Infection Control, University Hospital Regensburg, Germany
| | - Daniel Domingo-Fernández
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany
| | - Lauren Nicole DeLong
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany
- University of Bonn, Bonn-Aachen International Center for IT, Friedrich Hirzebruch-Allee 6, 53115 Bonn, Germany
| | - Alpha Tom Kodamullil
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany
| | - Jochen Schneider
- Technical University of Munich, School of Medicine, University Hospital rechts der Isar, Department of Internal Medicine II, 81675 Munich, Germany
| | - Maria J G T Vehreschild
- Department II of Internal Medicine, Infectious Diseases, University Hospital Frankfurt, Goethe University, 60590 Frankfurt, Germany
| | - Julia Lanznaster
- Department of Internal Medicine II, Hospital Passau, Innstraße 76, 94032 Passau, Germany
| | - Maria Madeleine Ruethrich
- Institute for Infection Medicine and Hospital Hygiene, University Hospital Jena, 07743 Jena, Germany
| | - Stefan Borgmann
- Department of Infectious Diseases and Infection Control, Hospital Ingolstadt, 85049 Ingolstadt, Germany
| | - Martin Hower
- Department of Pneumology, Infectious Diseases and Intensive Care, Klinikum Dortmund gGmbH, Hospital of University Witten / Herdecke, 44137 Dortmund, Germany
| | - Kai Wille
- University Clinic for Haematology, Oncology, Haemostaseology and Palliative Care, Johannes Wesling Medical Centre Minden, 32429 Minden, Germany
| | - Torsten Feldt
- Department of Gastroenterology, Hepatology and Infectious Diseases, University Hospital Düsseldorf, Medical Faculty of Heinrich Heine University Düsseldorf, Moorenstrasse 5, 40225 Düsseldorf, Germany
| | - Siegbert Rieg
- Department of Medicine II, University Hospital Freiburg, 79110 Freiburg, Germany
| | - Bernd Hertenstein
- Department of Medicine II, University Hospital Freiburg, 79110 Freiburg, Germany
| | - Christoph Wyen
- Christoph Wyen, Praxis am Ebertplatz Cologne, 50668 Cologne, Germany
| | - Christoph Roemmele
- Internal Medicine III - Gastroenterology and Infectious Diseases, University Hospital Augsburg, 86156 Augsburg, Germany
| | - Jörg Janne Vehreschild
- Department II of Internal Medicine, Infectious Diseases, University Hospital Frankfurt, Goethe University, 60590 Frankfurt, Germany
| | - Carolin E M Jakob
- Department I for Internal Medicine, University Hospital of Cologne, University of Cologne, 50931 Cologne, Germany
| | - Melanie Stecher
- Fraunhofer Institute for Translational Medicine and Pharmacologie (ITMP), VolksparkLabs, Schnackenburgallee 114, 22535 Hamburg, Germany
| | - Maria Kuzikov
- Department for Infectious Diseases and Infection Control, University Hospital Regensburg, Germany
| | - Andrea Zaliani
- Department for Infectious Diseases and Infection Control, University Hospital Regensburg, Germany
| | - Holger Fröhlich
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany
- University of Bonn, Bonn-Aachen International Center for IT, Friedrich Hirzebruch-Allee 6, 53115 Bonn, Germany
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30
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Barboi C, Tzavelis A, Muhammad LN. Comparison of Severity of Illness Scores and Artificial Intelligence Models Predictive of Intensive Care Unit Mortality: Meta-analysis and review of the literature (Preprint). JMIR Med Inform 2021; 10:e35293. [PMID: 35639445 PMCID: PMC9198821 DOI: 10.2196/35293] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 04/24/2022] [Accepted: 04/25/2022] [Indexed: 12/23/2022] Open
Affiliation(s)
- Cristina Barboi
- Indiana University Purdue University, Regenstrief Institue, Indianapolis, IN, United States
| | - Andreas Tzavelis
- Medical Scientist Training Program, Feinberg School of Medicine, Chicago, IL, United States
- Department of Biomedical Engineering, Northwestern University, Chicago, IL, United States
| | - Lutfiyya NaQiyba Muhammad
- Department of Preventive Medicine and Biostatistics, Northwestern University, Evanston, IL, United States
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Individual outcome prediction models for patients with COVID-19 based on their first day of admission to the intensive care unit. Clin Biochem 2021; 100:13-21. [PMID: 34767791 PMCID: PMC8577569 DOI: 10.1016/j.clinbiochem.2021.11.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 10/26/2021] [Accepted: 11/06/2021] [Indexed: 11/26/2022]
Abstract
Background Currently, good prognosis and management of critically ill patients with COVID-19 are crucial for developing disease management guidelines and providing a viable healthcare system. We aimed to propose individual outcome prediction models based on binary logistic regression (BLR) and artificial neural network (ANN) analyses of data collected in the first 24 hours of intensive care unit (ICU) admission for patients with COVID-19 infection. We also analysed different variables for ICU patients who survived and those who died. Methods Data from 326 critically ill patients with COVID-19 were collected. Data were captured on laboratory variables, demographics, comorbidities, symptoms and hospital stay related information. These data were compared with patient outcomes (survivor and non-survivor patients). BLR was assessed using the Wald Forward Stepwise method, and the ANN model was constructed using multilayer perceptron architecture. Results The area under the receiver operating characteristic curve of the ANN model was significantly larger than the BLR model (0.917 vs 0.810; p<0.001) for predicting individual outcomes. In addition, ANN model presented similar negative predictive value than the BLR model (95.9% vs 94.8%). Variables such as age, pH, potassium ion, partial pressure of oxygen, and chloride were present in both models and they were significant predictors of death in COVID-19 patients. Conclusions Our study could provide helpful information for other hospitals to develop their own individual outcome prediction models based, mainly, on laboratory variables. Furthermore, it offers valuable information on which variables could predict a fatal outcome for ICU patients with COVID-19.
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The Predictive Role of Modified Early Warning Score in 174 Hematological Patients at the Point of Transfer to the Intensive Care Unit. J Clin Med 2021; 10:jcm10204766. [PMID: 34682889 PMCID: PMC8539931 DOI: 10.3390/jcm10204766] [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: 08/16/2021] [Revised: 10/07/2021] [Accepted: 10/14/2021] [Indexed: 12/30/2022] Open
Abstract
Introduction: The examination of vital signs and their changes during illness can alert physicians to possible impending deterioration and organ dysfunction. The Modified Early Warning Score (MEWS) is used worldwide as a track and trigger system that can help to identify patients at risk of critical illness. Thus, the current study aimed to assess the ability of MEWS to predict the mortality of hematologic patients at the point of transfer from the ward to the intensive care unit (ICU). Materials and Methods: The present study was retrospective, longitudinal, and observational, conducted at an oncology hospital in the city of Cluj-Napoca, Romania. We included 174 patients with hematological disorders transferred from the ward to the ICU between the 1st of January 2018 and the 1st of May 2020. We assessed the MEWS at the moment of admission in these patients in the ICU. The accuracy of MEWS in predicting mortality was assessed via the area under the receiver operating characteristic curves (AUC), and sensitivity, specificity, and hazard ratio (HR) were calculated for different MEWS cutoffs. MEWS values considering the status at discharge and frequency of death by MEWS were also analyzed. Results: We calculated MEWS values considering the status at discharge (p < 0.0001), and we assessed the frequency of death by MEWS. We also calculated the hazard ratio (HR) of death depending on the selected MEWS cutoff. The best cutoff point was found to be ≥6, with an accuracy of 0.667, sensitivity of 0.675, specificity of 0.646, and AUC of 0.731. Patients with higher MEWS had a higher probability of mortality. Conclusion: The MEWS and cutoff points were determined on a sample of hematologic patients at the moment of admission to the ICU. The final aim is to encourage physicians to use these scores to improve awareness of organ failure to admit patients to the ICU sooner and limit overall morbidity and mortality. The presence of an ICU physician on ward rounds might help in reducing the timeframe of access to a high-dependency unit (HDU) or ICU. An extension of these scores outside hematologic patients or considering hematologic patients outside ICU must be further studied.
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Cardona M, Dobler CC, Koreshe E, Heyland DK, Nguyen RH, Sim JPY, Clark J, Psirides A. A catalogue of tools and variables from crisis and routine care to support decision-making about allocation of intensive care beds and ventilator treatment during pandemics: Scoping review. J Crit Care 2021; 66:33-43. [PMID: 34438132 DOI: 10.1016/j.jcrc.2021.08.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 07/15/2021] [Accepted: 08/06/2021] [Indexed: 01/16/2023]
Abstract
PURPOSE This scoping review sought to identify objective factors to assist clinicians and policy-makers in making consistent, objective and ethically sound decisions about resource allocation when healthcare rationing is inevitable. MATERIALS AND METHODS Review of guidelines and tools used in ICUs, hospital wards and emergency departments on how to best allocate intensive care beds and ventilators either during routine care or developed during previous epidemics, and association with patient outcomes during and after hospitalisation. RESULTS Eighty publications from 20 countries reporting accuracy or validity of prognostic tools/algorithms, or significant correlation between prognostic variables and clinical outcomes met our eligibility criteria: twelve pandemic guidelines/triage protocols/consensus statements, twenty-two pandemic algorithms, and 46 prognostic tools/variables from non-crisis situations. Prognostic indicators presented here can be combined to create locally-relevant triage algorithms for clinicians and policy makers deciding about allocation of ICU beds and ventilators during a pandemic. No consensus was found on the ethical issues to incorporate in the decision to admit or triage out of intensive care. CONCLUSIONS This review provides a unique reference intended as a discussion starter for clinicians and policy makers to consider formalising an objective a locally-relevant triage consensus document that enhances confidence in decision-making during healthcare rationing of critical care and ventilator resources.
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Affiliation(s)
- Magnolia Cardona
- Institute for Evidence-Based Healthcare, Bond University Gold Coast, Queensland, Australia; Gold Coast University Hospital Evidence-Based Practice Professorial Unit, Southport, Queensland, Australia.
| | - Claudia C Dobler
- Institute for Evidence-Based Healthcare, Bond University Gold Coast, Queensland, Australia; Evidence-Based Practice Center, Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, MN, USA; The University of New South Wales, South Western Sydney Clinical School, NSW, Australia
| | - Eyza Koreshe
- InsideOut Institute, Central Clinical School, The University of Sydney, NSW, Australia
| | - Daren K Heyland
- Department of Critical Care Medicine, Queens University, Kingston, Ontario, Canada
| | - Rebecca H Nguyen
- The University of New South Wales, South Western Sydney Clinical School, NSW, Australia
| | - Joan P Y Sim
- The University of New South Wales, South Western Sydney Clinical School, NSW, Australia
| | - Justin Clark
- Institute for Evidence-Based Healthcare, Bond University Gold Coast, Queensland, Australia
| | - Alex Psirides
- Intensive Care Unit, Wellington Regional Hospital, Wellington, New Zealand
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Aljouie AF, Almazroa A, Bokhari Y, Alawad M, Mahmoud E, Alawad E, Alsehawi A, Rashid M, Alomair L, Almozaai S, Albesher B, Alomaish H, Daghistani R, Alharbi NK, Alaamery M, Bosaeed M, Alshaalan H. Early Prediction of COVID-19 Ventilation Requirement and Mortality from Routinely Collected Baseline Chest Radiographs, Laboratory, and Clinical Data with Machine Learning. J Multidiscip Healthc 2021; 14:2017-2033. [PMID: 34354361 PMCID: PMC8331117 DOI: 10.2147/jmdh.s322431] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 07/15/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in Wuhan, China, in late 2019 and created a global pandemic that overwhelmed healthcare systems. COVID-19, as of July 3, 2021, yielded 182 million confirmed cases and 3.9 million deaths globally according to the World Health Organization. Several patients who were initially diagnosed with mild or moderate COVID-19 later deteriorated and were reclassified to severe disease type. OBJECTIVE The aim is to create a predictive model for COVID-19 ventilatory support and mortality early on from baseline (at the time of diagnosis) and routinely collected data of each patient (CXR, CBC, demographics, and patient history). METHODS Four common machine learning algorithms, three data balancing techniques, and feature selection are used to build and validate predictive models for COVID-19 mechanical requirement and mortality. Baseline CXR, CBC, demographic, and clinical data were retrospectively collected from April 2, 2020, till June 18, 2020, for 5739 patients with confirmed PCR COVID-19 at King Abdulaziz Medical City in Riyadh. However, of those patients, only 1508 and 1513 have met the inclusion criteria for ventilatory support and mortalilty endpoints, respectively. RESULTS In an independent test set, ventilation requirement predictive model with top 20 features selected with reliefF algorithm from baseline radiological, laboratory, and clinical data using support vector machines and random undersampling technique attained an AUC of 0.87 and a balanced accuracy of 0.81. For mortality endpoint, the top model yielded an AUC of 0.83 and a balanced accuracy of 0.80 using all features with balanced random forest. This indicates that with only routinely collected data our models can predict the outcome with good performance. The predictive ability of combined data consistently outperformed each data set individually for intubation and mortality. For the ventilator support, chest X-ray severity annotations alone performed better than comorbidity, complete blood count, age, or gender with an AUC of 0.85 and balanced accuracy of 0.79. For mortality, comorbidity alone achieved an AUC of 0.80 and a balanced accuracy of 0.72, which is higher than models that use either chest radiograph, laboratory, or demographic features only. CONCLUSION The experimental results demonstrate the practicality of the proposed COVID-19 predictive tool for hospital resource planning and patients' prioritization in the current COVID-19 pandemic crisis.
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Affiliation(s)
- Abdulrhman Fahad Aljouie
- Bioinformatics Section, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Ahmed Almazroa
- King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- Department of Imaging Research, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
| | - Yahya Bokhari
- Bioinformatics Section, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Mohammed Alawad
- Bioinformatics Section, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Ebrahim Mahmoud
- Department of Medicine, Ministry of National Guard - Health Affairs, Riyadh, Saudi Arabia
| | - Eman Alawad
- Department of Medicine, Ministry of National Guard - Health Affairs, Riyadh, Saudi Arabia
| | - Ali Alsehawi
- Department of Medical Imaging, Ministry of National Guard - Health Affairs, Riyadh, Saudi Arabia
| | - Mamoon Rashid
- Bioinformatics Section, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Lamya Alomair
- Bioinformatics Section, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Shahad Almozaai
- College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Bedoor Albesher
- College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Hassan Alomaish
- Department of Medical Imaging, Ministry of National Guard - Health Affairs, Riyadh, Saudi Arabia
| | - Rayyan Daghistani
- Department of Medical Imaging, Ministry of National Guard - Health Affairs, Riyadh, Saudi Arabia
| | - Naif Khalaf Alharbi
- King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- Department of Infectious Disease Research, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
| | - Manal Alaamery
- King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- Department of Developmental Medicine, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- KACST-BWH Center of Excellence for Biomedicine, Joint Centers of Excellence Program, King Abdulaziz City for Science and Technology (KACST), Riyadh, Saudi Arabia
- King Abdulaziz City for Science and Technology (KACST)-Saudi Human Genome Satellite Lab at Abdulaziz Medical City, Ministry of National Guard Health Affairs (MNGHA), Riyadh, Saudi Arabia
| | - Mohammad Bosaeed
- King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- Department of Imaging Research, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Department of Medicine, Ministry of National Guard - Health Affairs, Riyadh, Saudi Arabia
| | - Hesham Alshaalan
- Department of Medical Imaging, Ministry of National Guard - Health Affairs, Riyadh, Saudi Arabia
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Computational Intelligence-Based Model for Mortality Rate Prediction in COVID-19 Patients. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18126429. [PMID: 34198547 PMCID: PMC8296243 DOI: 10.3390/ijerph18126429] [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: 04/18/2021] [Revised: 06/07/2021] [Accepted: 06/08/2021] [Indexed: 12/12/2022]
Abstract
The COVID-19 outbreak is currently one of the biggest challenges facing countries around the world. Millions of people have lost their lives due to COVID-19. Therefore, the accurate early detection and identification of severe COVID-19 cases can reduce the mortality rate and the likelihood of further complications. Machine Learning (ML) and Deep Learning (DL) models have been shown to be effective in the detection and diagnosis of several diseases, including COVID-19. This study used ML algorithms, such as Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) and DL model (containing six layers with ReLU and output layer with sigmoid activation), to predict the mortality rate in COVID-19 cases. Models were trained using confirmed COVID-19 patients from 146 countries. Comparative analysis was performed among ML and DL models using a reduced feature set. The best results were achieved using the proposed DL model, with an accuracy of 0.97. Experimental results reveal the significance of the proposed model over the baseline study in the literature with the reduced feature set.
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Adamidi ES, Mitsis K, Nikita KS. Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review. Comput Struct Biotechnol J 2021; 19:2833-2850. [PMID: 34025952 PMCID: PMC8123783 DOI: 10.1016/j.csbj.2021.05.010] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 05/01/2021] [Accepted: 05/02/2021] [Indexed: 12/23/2022] Open
Abstract
The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of the disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared the COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques to optimize these steps on clinical settings in terms of quality, accuracy and most importantly time. The objective of this study is to conduct a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019. We included 101 studies, published from January 1st, 2020 to December 30th, 2020, that developed AI prediction models which can be applied in the clinical setting. We identified in total 14 models for screening, 38 diagnostic models for detecting COVID-19 and 50 prognostic models for predicting ICU need, ventilator need, mortality risk, severity assessment or hospital length stay. Moreover, 43 studies were based on medical imaging and 58 studies on the use of clinical parameters, laboratory results or demographic features. Several heterogeneous predictors derived from multimodal data were identified. Analysis of these multimodal data, captured from various sources, in terms of prominence for each category of the included studies, was performed. Finally, Risk of Bias (RoB) analysis was also conducted to examine the applicability of the included studies in the clinical setting and assist healthcare providers, guideline developers, and policymakers.
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Key Words
- ABG, Arterial Blood Gas
- ADA, Adenosine Deaminase
- AI, Artificial Intelligence
- ANN, Artificial Neural Networks
- APTT, Activated Partial Thromboplastin Time
- ARMED, Attribute Reduction with Multi-objective Decomposition Ensemble optimizer
- AUC, Area Under the Curve
- Acc, Accuracy
- Adaboost, Adaptive Boosting
- Apol AI, Apolipoprotein AI
- Apol B, Apolipoprotein B
- Artificial intelligence
- BNB, Bernoulli Naïve Bayes
- BUN, Blood Urea Nitrogen
- CI, Confidence Interval
- CK-MB, Creatine Kinase isoenzyme
- CNN, Convolutional Neural Networks
- COVID-19
- CPP, COVID-19 Positive Patients
- CRP, C-Reactive Protein
- CRT, Classification and Regression Decision Tree
- CoxPH, Cox Proportional Hazards
- DCNN, Deep Convolutional Neural Networks
- DL, Deep Learning
- DLC, Density Lipoprotein Cholesterol
- DNN, Deep Neural Networks
- DT, Decision Tree
- Diagnosis
- ED, Emergency Department
- ESR, Erythrocyte Sedimentation Rate
- ET, Extra Trees
- FCV, Fold Cross Validation
- FL, Federated Learning
- FiO2, Fraction of Inspiration O2
- GBDT, Gradient Boost Decision Tree
- GBM light, Gradient Boosting Machine light
- GDCNN, Genetic Deep Learning Convolutional Neural Network
- GFR, Glomerular Filtration Rate
- GFS, Gradient boosted feature selection
- GGT, Glutamyl Transpeptidase
- GNB, Gaussian Naïve Bayes
- HDLC, High Density Lipoprotein Cholesterol
- INR, International Normalized Ratio
- Inception Resnet, Inception Residual Neural Network
- L1LR, L1 Regularized Logistic Regression
- LASSO, Least Absolute Shrinkage and Selection Operator
- LDA, Linear Discriminant Analysis
- LDH, Lactate Dehydrogenase
- LDLC, Low Density Lipoprotein Cholesterol
- LR, Logistic Regression
- LSTM, Long-Short Term Memory
- MCHC, Mean Corpuscular Hemoglobin Concentration
- MCV, Mean corpuscular volume
- ML, Machine Learning
- MLP, MultiLayer Perceptron
- MPV, Mean Platelet Volume
- MRMR, Maximum Relevance Minimum Redundancy
- Multimodal data
- NB, Naïve Bayes
- NLP, Natural Language Processing
- NPV, Negative Predictive Values
- Nadam optimizer, Nesterov Accelerated Adaptive Moment optimizer
- OB, Occult Blood test
- PCT, Thrombocytocrit
- PPV, Positive Predictive Values
- PWD, Platelet Distribution Width
- PaO2, Arterial Oxygen Tension
- Paco2, Arterial Carbondioxide Tension
- Prognosis
- RBC, Red Blood Cell
- RBF, Radial Basis Function
- RBP, Retinol Binding Protein
- RDW, Red blood cell Distribution Width
- RF, Random Forest
- RFE, Recursive Feature Elimination
- RSV, Respiratory Syncytial Virus
- SEN, Sensitivity
- SG, Specific Gravity
- SMOTE, Synthetic Minority Oversampling Technique
- SPE, Specificity
- SRLSR, Sparse Rescaled Linear Square Regression
- SVM, Support Vector Machine
- SaO2, Arterial Oxygen saturation
- Screening
- TBA, Total Bile Acid
- TTS, Training Test Split
- WBC, White Blood Cell count
- XGB, eXtreme Gradient Boost
- k-NN, K-Nearest Neighbor
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Affiliation(s)
- Eleni S. Adamidi
- Biomedical Simulations and Imaging Lab, School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Konstantinos Mitsis
- Biomedical Simulations and Imaging Lab, School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Konstantina S. Nikita
- Biomedical Simulations and Imaging Lab, School of Electrical and Computer Engineering, National Technical University of Athens, Greece
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Islam MM, Poly TN, Alsinglawi B, Lin MC, Hsu MH, Li YC(J. A State-of-the-Art Survey on Artificial Intelligence to Fight COVID-19. J Clin Med 2021; 10:1961. [PMID: 34063302 PMCID: PMC8124542 DOI: 10.3390/jcm10091961] [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: 03/07/2021] [Revised: 04/27/2021] [Accepted: 04/28/2021] [Indexed: 01/08/2023] Open
Abstract
Artificial intelligence (AI) has shown immense potential to fight COVID-19 in many ways. This paper focuses primarily on AI's role in managing COVID-19 using digital images, clinical and laboratory data analysis, and a summary of the most recent articles published last year. We surveyed the use of AI for COVID-19 detection, screening, diagnosis, the progression of severity, mortality, drug repurposing, and other tasks. We started with the technical overview of all models used to fight the COVID-19 pandemic and ended with a brief statement of the current state-of-the-art, limitations, and challenges.
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Affiliation(s)
- Md. Mohaimenul Islam
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110301, Taiwan; (M.M.I.); (T.N.P.); (M.C.L.)
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110301, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 110301, Taiwan
| | - Tahmina Nasrin Poly
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110301, Taiwan; (M.M.I.); (T.N.P.); (M.C.L.)
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110301, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 110301, Taiwan
| | - Belal Alsinglawi
- School of Computer, Data and Mathematical Sciences, Parramatta South Campus Western, Sydney University, Sydney, NSW 2116, Australia;
| | - Ming Chin Lin
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110301, Taiwan; (M.M.I.); (T.N.P.); (M.C.L.)
- Department of Neurosurgery, Shuang Ho Hospital, Taipei Medical University, Taipei 110301, Taiwan
- Professional Master Program in Artificial Intelligence in Medicine, Taipei Medical University, Taipei 110301, Taiwan
| | - Min-Huei Hsu
- Graduate Institute of Data Science, Taipei Medical University, Taipei 110301, Taiwan;
| | - Yu-Chuan (Jack) Li
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110301, Taiwan; (M.M.I.); (T.N.P.); (M.C.L.)
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110301, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 110301, Taiwan
- Department of Dermatology, Wan Fang Hospital, Taipei 116081, Taiwan
- TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei 110301, Taiwan
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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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39
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Fiest KM, Krewulak KD, Plotnikoff KM, Kemp LG, Parhar KKS, Niven DJ, Kortbeek JB, Stelfox HT, Parsons Leigh J. Allocation of intensive care resources during an infectious disease outbreak: a rapid review to inform practice. BMC Med 2020; 18:404. [PMID: 33334347 PMCID: PMC7746486 DOI: 10.1186/s12916-020-01871-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 11/25/2020] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic has placed sustained demand on health systems globally, and the capacity to provide critical care has been overwhelmed in some jurisdictions. It is unknown which triage criteria for allocation of resources perform best to inform health system decision-making. We sought to summarize and describe existing triage tools and ethical frameworks to aid healthcare decision-making during infectious disease outbreaks. METHODS We conducted a rapid review of triage criteria and ethical frameworks for the allocation of critical care resources during epidemics and pandemics. We searched Medline, EMBASE, and SCOPUS from inception to November 3, 2020. Full-text screening and data abstraction were conducted independently and in duplicate by three reviewers. Articles were included if they were primary research, an adult critical care setting, and the framework described was related to an infectious disease outbreak. We summarized each triage tool and ethical guidelines or framework including their elements and operating characteristics using descriptive statistics. We assessed the quality of each article with applicable checklists tailored to each study design. RESULTS From 11,539 unique citations, 697 full-text articles were reviewed and 83 articles were included. Fifty-nine described critical care triage protocols and 25 described ethical frameworks. Of these, four articles described both a protocol and ethical framework. Sixty articles described 52 unique triage criteria (29 algorithm-based, 23 point-based). Few algorithmic- or point-based triage protocols were good predictors of mortality with AUCs ranging from 0.51 (PMEWS) to 0.85 (admitting SOFA > 11). Most published triage protocols included the substantive values of duty to provide care, equity, stewardship and trust, and the procedural value of reason. CONCLUSIONS This review summarizes available triage protocols and ethical guidelines to provide decision-makers with data to help select and tailor triage tools. Given the uncertainty about how the COVID-19 pandemic will progress and any future pandemics, jurisdictions should prepare by selecting and adapting a triage tool that works best for their circumstances.
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Affiliation(s)
- Kirsten M Fiest
- Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary & Alberta Health Services, 3134 Hospital Drive NW, Calgary, T2N4Z6, Canada
- Department of Community Health Sciences and O'Brien Institute for Public Health, Cumming School of Medicine, University of Calgary, 3134 Hospital Drive NW, Calgary, T2N4Z6, Canada
| | - Karla D Krewulak
- Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary & Alberta Health Services, 3134 Hospital Drive NW, Calgary, T2N4Z6, Canada
| | - Kara M Plotnikoff
- Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary & Alberta Health Services, 3134 Hospital Drive NW, Calgary, T2N4Z6, Canada
| | - Laryssa G Kemp
- Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary & Alberta Health Services, 3134 Hospital Drive NW, Calgary, T2N4Z6, Canada
| | - Ken Kuljit S Parhar
- Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary & Alberta Health Services, 3134 Hospital Drive NW, Calgary, T2N4Z6, Canada
| | - Daniel J Niven
- Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary & Alberta Health Services, 3134 Hospital Drive NW, Calgary, T2N4Z6, Canada
- Department of Community Health Sciences and O'Brien Institute for Public Health, Cumming School of Medicine, University of Calgary, 3134 Hospital Drive NW, Calgary, T2N4Z6, Canada
| | - John B Kortbeek
- Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary & Alberta Health Services, 3134 Hospital Drive NW, Calgary, T2N4Z6, Canada
- Department of Surgery, Cumming School of Medicine, University of Calgary & Alberta Health Services, 3134 Hospital Drive NW, Calgary, T2N4Z6, Canada
- Department of Anaesthesia, Cumming School of Medicine, University of Calgary & Alberta Health Services, 3134 Hospital Drive NW, Calgary, T2N4Z6, Canada
| | - Henry T Stelfox
- Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary & Alberta Health Services, 3134 Hospital Drive NW, Calgary, T2N4Z6, Canada
- Department of Community Health Sciences and O'Brien Institute for Public Health, Cumming School of Medicine, University of Calgary, 3134 Hospital Drive NW, Calgary, T2N4Z6, Canada
| | - Jeanna Parsons Leigh
- Faculty of Health, School of Health Administration, Dalhousie University, 5850 College Street, Halifax, Nova Scotia, B3H4R2, Canada.
- Department of Critical Care Medicine, Faculty of Medicine, Dalhousie University, 6299 South St, Halifax, Nova Scotia, B3H4R2, Canada.
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Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, Bonten MMJ, Dahly DL, Damen JAA, Debray TPA, de Jong VMT, De Vos M, Dhiman P, Haller MC, Harhay MO, Henckaerts L, Heus P, Kammer M, Kreuzberger N, Lohmann A, Luijken K, Ma J, Martin GP, McLernon DJ, Andaur Navarro CL, Reitsma JB, Sergeant JC, Shi C, Skoetz N, Smits LJM, Snell KIE, Sperrin M, Spijker R, Steyerberg EW, Takada T, Tzoulaki I, van Kuijk SMJ, van Bussel B, van der Horst ICC, van Royen FS, Verbakel JY, Wallisch C, Wilkinson J, Wolff R, Hooft L, Moons KGM, van Smeden M. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ 2020; 369:m1328. [PMID: 32265220 PMCID: PMC7222643 DOI: 10.1136/bmj.m1328] [Citation(s) in RCA: 1624] [Impact Index Per Article: 406.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/31/2020] [Indexed: 12/12/2022]
Abstract
OBJECTIVE To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. DESIGN Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. DATA SOURCES PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. STUDY SELECTION Studies that developed or validated a multivariable covid-19 related prediction model. DATA EXTRACTION At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). RESULTS 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. CONCLUSION Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. SYSTEMATIC REVIEW REGISTRATION Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. READERS' NOTE This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.
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Affiliation(s)
- Laure Wynants
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Musculoskeletal Sciences, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Richard D Riley
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
| | - Georg Heinze
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Ewoud Schuit
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Marc M J Bonten
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Department of Medical Microbiology, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Darren L Dahly
- HRB Clinical Research Facility, Cork, Ireland
- School of Public Health, University College Cork, Cork, Ireland
| | - Johanna A A Damen
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Valentijn M T de Jong
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten De Vos
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT Stadius, KU Leuven, Leuven, Belgium
| | - Paul Dhiman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Musculoskeletal Sciences, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Maria C Haller
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Ordensklinikum Linz, Hospital Elisabethinen, Department of Nephrology, Linz, Austria
| | - Michael O Harhay
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Palliative and Advanced Illness Research Center and Division of Pulmonary and Critical Care Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Liesbet Henckaerts
- Department of Microbiology, Immunology and Transplantation, KU Leuven-University of Leuven, Leuven, Belgium
- Department of General Internal Medicine, KU Leuven-University Hospitals Leuven, Leuven, Belgium
| | - Pauline Heus
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Michael Kammer
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Department of Nephrology, Medical University of Vienna, Vienna, Austria
| | - Nina Kreuzberger
- Evidence-Based Oncology, Department I of Internal Medicine and Centre for Integrated Oncology Aachen Bonn Cologne Dusseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Anna Lohmann
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, Netherlands
| | - Kim Luijken
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, Netherlands
| | - Jie Ma
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Glen P Martin
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | - David J McLernon
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
| | - Constanza L Andaur Navarro
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Johannes B Reitsma
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Jamie C Sergeant
- Centre for Biostatistics, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Chunhu Shi
- Division of Nursing, Midwifery and Social Work, School of Health Sciences, University of Manchester, Manchester, UK
| | - Nicole Skoetz
- Department of Nephrology, Medical University of Vienna, Vienna, Austria
| | - Luc J M Smits
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
| | - Kym I E Snell
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
| | - Matthew Sperrin
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - René Spijker
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Amsterdam UMC, University of Amsterdam, Amsterdam Public Health, Medical Library, Netherlands
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
| | - Toshihiko Takada
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, Imperial College London School of Public Health, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Sander M J van Kuijk
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre+, Maastricht, Netherlands
| | - Bas van Bussel
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
- Department of Intensive Care, Maastricht University Medical Centre+, Maastricht University, Maastricht, Netherlands
| | - Iwan C C van der Horst
- Department of Intensive Care, Maastricht University Medical Centre+, Maastricht University, Maastricht, Netherlands
| | - Florien S van Royen
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Jan Y Verbakel
- EPI-Centre, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Christine Wallisch
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Jack Wilkinson
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | | | - Lotty Hooft
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
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