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A Data-Driven Analysis of Ward Capacity Strain Metrics That Predict Clinical Outcomes Among Survivors of Acute Respiratory Failure.
Kohn, Rachel; Harhay, Michael O; Weissman, Gary E; Urbanowicz, Ryan; Wang, Wei; Anesi, George L; Scott, Stefania; Bayes, Brian; Greysen, S Ryan; Halpern, Scott D; Kerlin, Meeta Prasad.
Afiliación
  • Kohn R; Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. rachel.kohn2@pennmedicine.upenn.edu.
  • Harhay MO; Palliative and Advanced Illness Research (PAIR) Center, University of Pennsylvania, Philadelphia, PA, USA. rachel.kohn2@pennmedicine.upenn.edu.
  • Weissman GE; Leonard Davis Institute of Health Economics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. rachel.kohn2@pennmedicine.upenn.edu.
  • Urbanowicz R; Palliative and Advanced Illness Research (PAIR) Center, University of Pennsylvania, Philadelphia, PA, USA.
  • Wang W; Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Anesi GL; Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Scott S; Palliative and Advanced Illness Research (PAIR) Center, University of Pennsylvania, Philadelphia, PA, USA.
  • Bayes B; Leonard Davis Institute of Health Economics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Greysen SR; Cedars-Sinai, West Hollywood, CA, USA.
  • Halpern SD; Palliative and Advanced Illness Research (PAIR) Center, University of Pennsylvania, Philadelphia, PA, USA.
  • Kerlin MP; Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
J Med Syst ; 47(1): 83, 2023 Aug 05.
Article en En | MEDLINE | ID: mdl-37542590
Supply-demand mismatch of ward resources ("ward capacity strain") alters care and outcomes. Narrow strain definitions and heterogeneous populations limit strain literature. Evaluate the predictive utility of a large set of candidate strain variables for in-hospital mortality and discharge destination among acute respiratory failure (ARF) survivors. In a retrospective cohort of ARF survivors transferred from intensive care units (ICUs) to wards in five hospitals from 4/2017-12/2019, we applied 11 machine learning (ML) models to identify ward strain measures during the first 24 hours after transfer most predictive of outcomes. Measures spanned patient volume (census, admissions, discharges), staff workload (medications administered, off-ward transports, transfusions, isolation precautions, patients per respiratory therapist and nurse), and average patient acuity (Laboratory Acute Physiology Score version 2, ICU transfers) domains. The cohort included 5,052 visits in 43 wards. Median age was 65 years (IQR 56-73); 2,865 (57%) were male; and 2,865 (57%) were white. 770 (15%) patients died in the hospital or had hospice discharges, and 2,628 (61%) were discharged home and 964 (23%) to skilled nursing facilities (SNFs). Ward admissions, isolation precautions, and hospital admissions most consistently predicted in-hospital mortality across ML models. Patients per nurse most consistently predicted discharge to home and SNF, and medications administered predicted SNF discharge. In this hypothesis-generating analysis of candidate ward strain variables' prediction of outcomes among ARF survivors, several variables emerged as consistently predictive of key outcomes across ML models. These findings suggest targets for future inferential studies to elucidate mechanisms of ward strain's adverse effects.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Insuficiencia Respiratoria / Benchmarking Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Aged / Female / Humans / Male Idioma: En Revista: J Med Syst Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Insuficiencia Respiratoria / Benchmarking Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Aged / Female / Humans / Male Idioma: En Revista: J Med Syst Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos