{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T23:31:30Z","timestamp":1791502290123,"version":"4.3.4"},"reference-count":39,"publisher":"American Medical Association (AMA)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Netw Open"],"abstract":"\n Importance<\/jats:title>\n Hospital-onset bacteremia and fungemia (HOB) are common and potentially preventable complications of hospital care.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To assess whether nonindividual patient features, which summarize interactions with other patients and health care workers (HCWs), can contribute to predictive and causal machine learning models for HOB.<\/jats:p>\n <\/jats:sec>\n \n Design, Setting, and Participants<\/jats:title>\n This prognostic study included adult patients admitted to Barnes-Jewish Hospital, an academic hospital in St Louis, Missouri, in 2021. Analyses were developed between October 2023 and August 2024 and in April 2025.<\/jats:p>\n <\/jats:sec>\n \n Exposure<\/jats:title>\n Individual patient features were extracted from electronic health records and used to engineer nonpatient features, including interactions with HCWs and direct or indirect (consecutive room occupancy) patient contact.<\/jats:p>\n <\/jats:sec>\n \n Main Outcomes and Measures<\/jats:title>\n \n HOB was defined as a positive blood culture after the third day of hospitalization. Patients who were hospitalized for more than 3 days were considered at risk for the outcome. We developed 3 gradient boosting models: 2 predictive (with patient features only and with both patient and nonpatient features to predict the occurrence of HOB) and 1 causal to test the association of nonpatient features and HOB. Predictive performance is reported using area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC), and the results of the causal model are reported as difference in average effects. Sensitivity analyses separated intensive care unit\u2013onset and ward-onset HOB and included a methicillin-resistant\n Staphylococcus aureus<\/jats:italic>\n \u2013specific model to adjust for colonization pressure.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Among the 52\u202f442 patients, 34\u202f855 (66.5%) had admissions longer than 72 hours and were included for analysis; of these, 556 (1.6%) developed HOB. The median age for the included patients was 60 (IQR, 44-70) years, 50.5% were female, and obesity was the most frequent comorbidity (25.0%). Nonpatient features, such as a prior occupant of the same room receiving antipseudomonal beta-lactams and the mean number of HCWs per day for the 7 days preceding HOB, improved the model\u2019s performance (AUROC, 0.88 [95% CI, 0.88-0.89]; AUPRC, 0.20 [95% CI, 0.20-0.22]) compared with the patient-only model (AUROC, 0.85 [95% CI, 0.85-0.86]; AUPRC, 0.13 [95% CI, 0.12-0.14]) (\n P<\/jats:italic>\n \u2009&amp;lt;\u2009.001). These 2 features were also associated with a higher likelihood of HOB in the causal gradient boosting model.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions and Relevance<\/jats:title>\n These findings suggest that nonindividual patient features may contribute to a comprehensive analysis of HOB when integrated with individual patient features in a machine learning model.<\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jamanetworkopen.2025.18815","type":"journal-article","created":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T11:00:27Z","timestamp":1751454027000},"page":"e2518815","source":"Crossref","is-referenced-by-count":9,"title":["Integrating Nonindividual Patient Features in Machine Learning Models of Hospital-Onset Bacteremia"],"prefix":"10.1001","volume":"8","author":[{"given":"M. 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Missouri"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenyang","family":"Lu","sequence":"additional","affiliation":[{"name":"McKelvey School of Engineering, Department of Computer Science & Engineering, Washington University, St Louis, Missouri"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marin H.","family":"Kollef","sequence":"additional","affiliation":[{"name":"Division of Pulmonary and Critical Care Medicine, Department of Medicine, Washington University School of Medicine, St Louis, Missouri"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philip","family":"Payne","sequence":"additional","affiliation":[{"name":"Department of Medicine, Washington University School of Medicine, St Louis, Missouri"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David K.","family":"Warren","sequence":"additional","affiliation":[{"name":"Division of Infectious Diseases, Department of Medicine, Washington University School of 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