{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T20:42:43Z","timestamp":1790109763004,"version":"4.0.1"},"reference-count":57,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"20","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2022,10,18]]},"abstract":"<jats:sec sec-type=\"background\" xml:lang=\"en\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p xml:lang=\"en\">Peripheral artery disease (PAD) affects &gt;10\u00a0million 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\u2010hospital mortality in patients hospitalized for PAD based on a national database.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Methods and Results<\/jats:title>\n                    <jats:p xml:lang=\"en\">\n                      Inpatient hospitalization data were obtained from the 2016 to 2019 National Inpatient Sample. A total of 150\u2009921 inpatients were identified with a primary diagnosis of PAD and PAD\u2010related procedures using codes of the\n                      <jats:italic>International Classification of Diseases, Tenth Revision, Clinical Modification<\/jats:italic>\n                      (\n                      <jats:italic>ICD\u201010\u2010CM<\/jats:italic>\n                      ) and\n                      <jats:italic>International Classification of Diseases, Tenth Revision, Procedure Coding System (ICD\u201010\u2010PCS)<\/jats:italic>\n                      . Four ML models, including logistic regression, random forest, light gradient boosting, and extreme gradient boosting models, were trained to predict the risk of in\u2010hospital death based on a selection of variables, including patient characteristics, comorbidities, procedures, and hospital\u2010related factors. In\u2010hospital 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\u2010making. 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\u2010hospital mortality.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p xml:lang=\"en\">This study demonstrates the feasibility of ML in predicting in\u2010hospital mortality in patients with a primary PAD diagnosis. Findings highlight the potential of ML models in identifying high\u2010risk patients for poor outcomes and guiding personalized intervention.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/jaha.122.026987","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T20:54:47Z","timestamp":1665435287000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":28,"title":["Machine Learning Approach to Predict In\u2010Hospital Mortality in Patients Admitted for Peripheral Artery Disease in the United States"],"prefix":"10.1161","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5225-4721","authenticated-orcid":false,"given":"Donglan","family":"Zhang","sequence":"first","affiliation":[{"name":"Division of Health Services Research, Department of Foundations of Medicine New York University Long Island School of Medicine Mineola 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