Vincent X. Liu, Gabriel J. Escobar, Liam O’Suilleabhain, Khanh K. Thai, David Schlessinger, Laura C. Myers, John D. Greene, Fernando Barreda, Lawrence D. Gerstley, Patricia Kipnis
{"title":"Prediction of 1 and 2 week nonelective hospitalization and sepsis hospitalization risk in adults","authors":"Vincent X. Liu, Gabriel J. Escobar, Liam O’Suilleabhain, Khanh K. Thai, David Schlessinger, Laura C. Myers, John D. Greene, Fernando Barreda, Lawrence D. Gerstley, Patricia Kipnis","doi":"10.1038/s41746-025-01574-6","DOIUrl":null,"url":null,"abstract":"<p>We developed and validated models to predict 1- and 2-week risk of non-elective hospitalization (NEH) and sepsis hospitalization following outpatient clinic, emergency department treat and release (EDTR), or hospitalization encounters. We employed data from 4,488,579 adults with 1,481,430 hospital, 6,035,296 EDTR, and 86,013,893 clinic encounters. Predictors included administrative, clinical (laboratory tests, vital signs), utilization, and prescription pattern data. We employed 2012–2018 data for development and 2019 data for validation. In validation datasets, discrimination (area under the receiver operator characteristic curve) ranged from 0.687 for NEH within 1 week of hospital discharge to 0.904 for sepsis hospitalization within 2 weeks of clinic visits. At a sensitivity of 40%, numbers needed to evaluate (NNE) ranged from 4.3 for NEH within 2 weeks of hospitalization to 45 for sepsis hospitalization within 1 week of a clinic visit. Our models have potentially clinically actionable NNEs and could support clinical programs for the prevention of short-term hospitalizations and sepsis.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"87 1","pages":""},"PeriodicalIF":12.4000,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"NPJ Digital Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1038/s41746-025-01574-6","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0
Abstract
We developed and validated models to predict 1- and 2-week risk of non-elective hospitalization (NEH) and sepsis hospitalization following outpatient clinic, emergency department treat and release (EDTR), or hospitalization encounters. We employed data from 4,488,579 adults with 1,481,430 hospital, 6,035,296 EDTR, and 86,013,893 clinic encounters. Predictors included administrative, clinical (laboratory tests, vital signs), utilization, and prescription pattern data. We employed 2012–2018 data for development and 2019 data for validation. In validation datasets, discrimination (area under the receiver operator characteristic curve) ranged from 0.687 for NEH within 1 week of hospital discharge to 0.904 for sepsis hospitalization within 2 weeks of clinic visits. At a sensitivity of 40%, numbers needed to evaluate (NNE) ranged from 4.3 for NEH within 2 weeks of hospitalization to 45 for sepsis hospitalization within 1 week of a clinic visit. Our models have potentially clinically actionable NNEs and could support clinical programs for the prevention of short-term hospitalizations and sepsis.
期刊介绍:
npj Digital Medicine is an online open-access journal that focuses on publishing peer-reviewed research in the field of digital medicine. The journal covers various aspects of digital medicine, including the application and implementation of digital and mobile technologies in clinical settings, virtual healthcare, and the use of artificial intelligence and informatics.
The primary goal of the journal is to support innovation and the advancement of healthcare through the integration of new digital and mobile technologies. When determining if a manuscript is suitable for publication, the journal considers four important criteria: novelty, clinical relevance, scientific rigor, and digital innovation.