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A Data-Driven Analysis of Ward Capacity Strain Metrics That Predict Clinical Outcomes Among Survivors of Acute Respiratory Failure

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Abstract

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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Funding

Dr. Kohn was supported by NIH/NHLBI K23 HL146894. Dr. Harhay was supported by NIH/NHLBI R00 HL141678.

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Authors and Affiliations

  1. Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Rachel Kohn, Gary E. Weissman, George L. Anesi, S. Ryan Greysen, Scott D. Halpern & Meeta Prasad Kerlin

  2. Palliative and Advanced Illness Research (PAIR) Center, University of Pennsylvania, Philadelphia, PA, USA

    Rachel Kohn, Michael O. Harhay, Gary E. Weissman, Wei Wang, George L. Anesi, Stefania Scott, Brian Bayes, Scott D. Halpern & Meeta Prasad Kerlin

  3. Leonard Davis Institute of Health Economics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Rachel Kohn, Gary E. Weissman, George L. Anesi, S. Ryan Greysen, Scott D. Halpern & Meeta Prasad Kerlin

  4. Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Michael O. Harhay, Scott D. Halpern & Meeta Prasad Kerlin

  5. Cedars-Sinai, West Hollywood, CA, USA

    Ryan Urbanowicz

  6. Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Scott D. Halpern

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Contributions

RK, MOH, SDH, and MPK contributed to the conception and design of the study. RK, MOH, GEW, RU, WW, SS, and BB contributed to the acquisition, analysis, or interpretation of data. RK, MOH, GEW, RU, WW, GLA, SS, BB, SRG, SDH, and MPK contributed to the drafting or revising of the manuscript.

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Correspondence to Rachel Kohn.

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The University of Pennsylvania Institutional Review Board deemed this protocol exempt (protocol #833060).

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Dr. Kohn received an honorarium from the University of Nebraska for presenting her research portfolio at Pulmonary and Critical Care Medicine Grand Rounds. No authors have additional financial disclosures or conflicts of interest related to this work.

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Kohn, R., Harhay, M.O., Weissman, G.E. et al. A Data-Driven Analysis of Ward Capacity Strain Metrics That Predict Clinical Outcomes Among Survivors of Acute Respiratory Failure. J Med Syst 47, 83 (2023). https://doi.org/10.1007/s10916-023-01978-5

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