Risk stratification with explainable machine learning for 30-day procedure-related mortality and 30-day unplanned readmission in patients with peripheral arterial disease
Predicting 30-day procedure-related mortality risk and 30-day unplanned readmission in patients undergoing lower extremity endovascular interventions for peripheral artery disease (PAD) may assist in improving patient outcomes. Risk prediction of 30-day mortality can help clinicians identify treatme...
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| Veröffentlicht in: | PloS one 2022-11, Vol.17 (11), p.e0277507 |
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| creator | Cox, Meredith Panagides, J C Tabari, Azadeh Kalva, Sanjeeva Kalpathy-Cramer, Jayashree Daye, Dania |
| description | Predicting 30-day procedure-related mortality risk and 30-day unplanned readmission in patients undergoing lower extremity endovascular interventions for peripheral artery disease (PAD) may assist in improving patient outcomes. Risk prediction of 30-day mortality can help clinicians identify treatment plans to reduce the risk of death, and prediction of 30-day unplanned readmission may improve outcomes by identifying patients who may benefit from readmission prevention strategies. The goal of this study is to develop machine learning models to stratify risk of 30-day procedure-related mortality and 30-day unplanned readmission in patients undergoing lower extremity infra-inguinal endovascular interventions. We used a cohort of 14,444 cases from the American College of Surgeons National Surgical Quality Improvement Program database. For each outcome, we developed and evaluated multiple machine learning models, including Support Vector Machines, Multilayer Perceptrons, and Gradient Boosting Machines, and selected a random forest as the best-performing model for both outcomes. Our 30-day procedure-related mortality model achieved an AUC of 0.75 (95% CI: 0.71-0.79) and our 30-day unplanned readmission model achieved an AUC of 0.68 (95% CI: 0.67-0.71). Stratification of the test set by race (white and non-white), sex (male and female), and age (≥65 years and |
| doi_str_mv | 10.1371/journal.pone.0277507 |
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Risk prediction of 30-day mortality can help clinicians identify treatment plans to reduce the risk of death, and prediction of 30-day unplanned readmission may improve outcomes by identifying patients who may benefit from readmission prevention strategies. The goal of this study is to develop machine learning models to stratify risk of 30-day procedure-related mortality and 30-day unplanned readmission in patients undergoing lower extremity infra-inguinal endovascular interventions. We used a cohort of 14,444 cases from the American College of Surgeons National Surgical Quality Improvement Program database. For each outcome, we developed and evaluated multiple machine learning models, including Support Vector Machines, Multilayer Perceptrons, and Gradient Boosting Machines, and selected a random forest as the best-performing model for both outcomes. Our 30-day procedure-related mortality model achieved an AUC of 0.75 (95% CI: 0.71-0.79) and our 30-day unplanned readmission model achieved an AUC of 0.68 (95% CI: 0.67-0.71). Stratification of the test set by race (white and non-white), sex (male and female), and age (≥65 years and <65 years) and subsequent evaluation of demographic parity by AUC shows that both models perform equally well across race, sex, and age groups. We interpret the model globally and locally using Gini impurity and SHapley Additive exPlanations (SHAP). Using the top five predictors for death and mortality, we demonstrate differences in survival for subgroups stratified by these predictors, which underscores the utility of our model.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0277507</identifier><identifier>PMID: 36409699</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Aged ; Biology and Life Sciences ; Cardiovascular system ; Care and treatment ; Computer and Information Sciences ; Death ; Decision-making ; Diabetes ; Diagnosis ; Disease prevention ; Female ; Health aspects ; Health risks ; Hospital care ; Humans ; Ischemia ; Laboratories ; Learning algorithms ; Machine Learning ; Male ; Medicine and Health Sciences ; Methods ; Modelling ; Mortality ; Mortality risk ; Multilayer perceptrons ; Patient Readmission ; Patients ; Peripheral Arterial Disease - surgery ; Peripheral vascular diseases ; Prevention ; Quality control ; Quality improvement ; Race ; Risk ; Risk Assessment ; Risk Factors ; Sex ; Statistical methods ; Subgroups ; Support vector machines ; Variables ; Vascular diseases</subject><ispartof>PloS one, 2022-11, Vol.17 (11), p.e0277507</ispartof><rights>Copyright: © 2022 Cox et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Cox et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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Risk prediction of 30-day mortality can help clinicians identify treatment plans to reduce the risk of death, and prediction of 30-day unplanned readmission may improve outcomes by identifying patients who may benefit from readmission prevention strategies. The goal of this study is to develop machine learning models to stratify risk of 30-day procedure-related mortality and 30-day unplanned readmission in patients undergoing lower extremity infra-inguinal endovascular interventions. We used a cohort of 14,444 cases from the American College of Surgeons National Surgical Quality Improvement Program database. For each outcome, we developed and evaluated multiple machine learning models, including Support Vector Machines, Multilayer Perceptrons, and Gradient Boosting Machines, and selected a random forest as the best-performing model for both outcomes. Our 30-day procedure-related mortality model achieved an AUC of 0.75 (95% CI: 0.71-0.79) and our 30-day unplanned readmission model achieved an AUC of 0.68 (95% CI: 0.67-0.71). Stratification of the test set by race (white and non-white), sex (male and female), and age (≥65 years and <65 years) and subsequent evaluation of demographic parity by AUC shows that both models perform equally well across race, sex, and age groups. We interpret the model globally and locally using Gini impurity and SHapley Additive exPlanations (SHAP). Using the top five predictors for death and mortality, we demonstrate differences in survival for subgroups stratified by these predictors, which underscores the utility of our model.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>36409699</pmid><doi>10.1371/journal.pone.0277507</doi><tpages>e0277507</tpages><orcidid>https://orcid.org/0000-0001-9642-653X</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Aged Biology and Life Sciences Cardiovascular system Care and treatment Computer and Information Sciences Death Decision-making Diabetes Diagnosis Disease prevention Female Health aspects Health risks Hospital care Humans Ischemia Laboratories Learning algorithms Machine Learning Male Medicine and Health Sciences Methods Modelling Mortality Mortality risk Multilayer perceptrons Patient Readmission Patients Peripheral Arterial Disease - surgery Peripheral vascular diseases Prevention Quality control Quality improvement Race Risk Risk Assessment Risk Factors Sex Statistical methods Subgroups Support vector machines Variables Vascular diseases |
| title | Risk stratification with explainable machine learning for 30-day procedure-related mortality and 30-day unplanned readmission in patients with peripheral arterial disease |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-28T14%3A49%3A12IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Risk%20stratification%20with%20explainable%20machine%20learning%20for%2030-day%20procedure-related%20mortality%20and%2030-day%20unplanned%20readmission%20in%20patients%20with%20peripheral%20arterial%20disease&rft.jtitle=PloS%20one&rft.au=Cox,%20Meredith&rft.date=2022-11-21&rft.volume=17&rft.issue=11&rft.spage=e0277507&rft.pages=e0277507-&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0277507&rft_dat=%3Cgale_plos_%3EA727461713%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2738407838&rft_id=info:pmid/36409699&rft_galeid=A727461713&rft_doaj_id=oai_doaj_org_article_6ba68cc4121d4a31a1a87dcbb30fc0f3&rfr_iscdi=true |