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Machine Learning Approach to Predict In‐Hospital Mortality in Patients Admitted for Peripheral Artery Disease in the United States

BACKGROUND: Peripheral artery disease (PAD) affects >10 million people in the United States. PAD is associated with poor outcomes, including premature death. Machine learning (ML) has been increasingly used on big data to predict clinical outcomes. This study aims to develop ML models to predict...

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Autores principales: Zhang, Donglan, Li, Yike, Kalbaugh, Corey Andrew, Shi, Lu, Divers, Jasmin, Islam, Shahidul, Annex, Brian H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9673668/
https://www.ncbi.nlm.nih.gov/pubmed/36216437
http://dx.doi.org/10.1161/JAHA.122.026987
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author Zhang, Donglan
Li, Yike
Kalbaugh, Corey Andrew
Shi, Lu
Divers, Jasmin
Islam, Shahidul
Annex, Brian H.
author_facet Zhang, Donglan
Li, Yike
Kalbaugh, Corey Andrew
Shi, Lu
Divers, Jasmin
Islam, Shahidul
Annex, Brian H.
author_sort Zhang, Donglan
collection PubMed
description BACKGROUND: Peripheral artery disease (PAD) affects >10 million people in the United States. PAD is associated with poor outcomes, including premature death. Machine learning (ML) has been increasingly used on big data to predict clinical outcomes. This study aims to develop ML models to predict in‐hospital mortality in patients hospitalized for PAD based on a national database. METHODS AND RESULTS: Inpatient hospitalization data were obtained from the 2016 to 2019 National Inpatient Sample. A total of 150 921 inpatients were identified with a primary diagnosis of PAD and PAD‐related procedures using codes of the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD‐10‐CM) and International Classification of Diseases, Tenth Revision, Procedure Coding System (ICD‐10‐PCS). Four ML models, including logistic regression, random forest, light gradient boosting, and extreme gradient boosting models, were trained to predict the risk of in‐hospital death based on a selection of variables, including patient characteristics, comorbidities, procedures, and hospital‐related factors. In‐hospital mortality occurred in 1.8% of patients. The performance of the 4 models was comparable, with the area under the receiver operating characteristic curve ranging from 0.83 to 0.85, sensitivity of 77% to 82%, and specificity of 72% to 75%. These results suggest adequate predictability for clinical decision‐making. In all 4 models, the total number of diagnoses and procedures, age, endovascular revascularization procedure, congestive heart failure, diabetes, and diabetes with complications were critical predictors of in‐hospital mortality. CONCLUSIONS: This study demonstrates the feasibility of ML in predicting in‐hospital mortality in patients with a primary PAD diagnosis. Findings highlight the potential of ML models in identifying high‐risk patients for poor outcomes and guiding personalized intervention.
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spelling pubmed-96736682022-11-21 Machine Learning Approach to Predict In‐Hospital Mortality in Patients Admitted for Peripheral Artery Disease in the United States Zhang, Donglan Li, Yike Kalbaugh, Corey Andrew Shi, Lu Divers, Jasmin Islam, Shahidul Annex, Brian H. J Am Heart Assoc Original Research BACKGROUND: Peripheral artery disease (PAD) affects >10 million people in the United States. PAD is associated with poor outcomes, including premature death. Machine learning (ML) has been increasingly used on big data to predict clinical outcomes. This study aims to develop ML models to predict in‐hospital mortality in patients hospitalized for PAD based on a national database. METHODS AND RESULTS: Inpatient hospitalization data were obtained from the 2016 to 2019 National Inpatient Sample. A total of 150 921 inpatients were identified with a primary diagnosis of PAD and PAD‐related procedures using codes of the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD‐10‐CM) and International Classification of Diseases, Tenth Revision, Procedure Coding System (ICD‐10‐PCS). Four ML models, including logistic regression, random forest, light gradient boosting, and extreme gradient boosting models, were trained to predict the risk of in‐hospital death based on a selection of variables, including patient characteristics, comorbidities, procedures, and hospital‐related factors. In‐hospital mortality occurred in 1.8% of patients. The performance of the 4 models was comparable, with the area under the receiver operating characteristic curve ranging from 0.83 to 0.85, sensitivity of 77% to 82%, and specificity of 72% to 75%. These results suggest adequate predictability for clinical decision‐making. In all 4 models, the total number of diagnoses and procedures, age, endovascular revascularization procedure, congestive heart failure, diabetes, and diabetes with complications were critical predictors of in‐hospital mortality. CONCLUSIONS: This study demonstrates the feasibility of ML in predicting in‐hospital mortality in patients with a primary PAD diagnosis. Findings highlight the potential of ML models in identifying high‐risk patients for poor outcomes and guiding personalized intervention. John Wiley and Sons Inc. 2022-10-10 /pmc/articles/PMC9673668/ /pubmed/36216437 http://dx.doi.org/10.1161/JAHA.122.026987 Text en © 2022 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Original Research
Zhang, Donglan
Li, Yike
Kalbaugh, Corey Andrew
Shi, Lu
Divers, Jasmin
Islam, Shahidul
Annex, Brian H.
Machine Learning Approach to Predict In‐Hospital Mortality in Patients Admitted for Peripheral Artery Disease in the United States
title Machine Learning Approach to Predict In‐Hospital Mortality in Patients Admitted for Peripheral Artery Disease in the United States
title_full Machine Learning Approach to Predict In‐Hospital Mortality in Patients Admitted for Peripheral Artery Disease in the United States
title_fullStr Machine Learning Approach to Predict In‐Hospital Mortality in Patients Admitted for Peripheral Artery Disease in the United States
title_full_unstemmed Machine Learning Approach to Predict In‐Hospital Mortality in Patients Admitted for Peripheral Artery Disease in the United States
title_short Machine Learning Approach to Predict In‐Hospital Mortality in Patients Admitted for Peripheral Artery Disease in the United States
title_sort machine learning approach to predict in‐hospital mortality in patients admitted for peripheral artery disease in the united states
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9673668/
https://www.ncbi.nlm.nih.gov/pubmed/36216437
http://dx.doi.org/10.1161/JAHA.122.026987
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