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Prediction of 1 and 2 week nonelective hospitalization and sepsis hospitalization risk in adults.


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.

SUBMITTER: Liu VX 

PROVIDER: S-EPMC11976996 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature

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Prediction of 1 and 2 week nonelective hospitalization and sepsis hospitalization risk in adults.

Liu Vincent X VX   Escobar Gabriel J GJ   O'Suilleabhain Liam L   Thai Khanh K KK   Schlessinger David D   Myers Laura C LC   Greene John D JD   Barreda Fernando F   Gerstley Lawrence D LD   Kipnis Patricia P  

NPJ digital medicine 20250407 1


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 f  ...[more]

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