Evidence map›Paper›PMID 39762671›Full record

ArticleJournal of general internal medicine2025

Comparing Single-Hospital and National Models to Predict 30-Day Inpatient Mortality.

Steven Cogill, Kent Heberer, Amit Kaushal, Daniel Fang, Jennifer Lee

Abstract readComparative Study
In one paragraph

Article in Journal of general internal medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Steven CogillVA Palo Alto Cooperative Studies Program Coordinating Center, Palo Alto, CA, USA.
Kent HebererVA Palo Alto Cooperative Studies Program Coordinating Center, Palo Alto, CA, USA.
Amit KaushalVA Palo Alto Health Care System, Hospital Medicine, Palo Alto, CA, USA.
Daniel Fang *VA Palo Alto Health Care System, Hospital Medicine, Palo Alto, CA, USA. daniel.fang@va.gov.ORCID 0000-0001-5229-1338
Jennifer Lee *VA Palo Alto Cooperative Studies Program Coordinating Center, Palo Alto, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAdvances in artificial intelligence and machine learning have facilitated the creation of mortality prediction models which are increasingly used to assess quality of care and inform clinical practice. One open question is whether a hospital should utilize a mortality model trained from a diverse nationwide dataset or use a model developed primarily from their local hospital data.

objectiveTo compare performance of a single-hospital, 30-day all-cause mortality model against an established national benchmark on the task of mortality prediction. DESIGN/

participantsWe developed a single-hospital mortality prediction model using 9975 consecutive inpatient admissions at the Department of Veterans Affairs Palo Alto Healthcare System from July 26, 2018, to September 30, 2021, and compared performance against an established national model with similar features. MAIN MEASURES: Both the single-hospital model and the national model placed each patient in one of five prediction bins: < 2.5%, 2.5-5%, 5-10%, 10-30%, and ≥ 30% risks of 30-day mortality. Evaluation metrics included receiver operator characteristic area under the curve (ROC AUC), sensitivity, specificity, and balanced accuracy. Final comparisons were made between the single-hospital model trained on the full training set and the national model for both metrics and prediction overlap. KEY

resultsWith sufficiently large training sets of 2720 or greater inpatient admissions, there was no statistically significant difference between the performances of the national model (ROC AUC 0.89, 95%CI [0.858, 0.919]) and single-hospital model (ROC AUC 0.878, 95%CI [0.84, 0.912]). For the 89 mortality events in the test set, the single-hospital model agreed with the national model risk assessment or an adjacent risk assessment in 92.1% of the encounters.

conclusionsA single-hospital inpatient mortality prediction model can achieve performance comparable to a national model when evaluated on a single-hospital population, given sufficient sample size.

Indexed as

Hospital MortalityAgedAged, 80 and overFemaleHumansMachine LearningMaleMiddle AgedRisk AssessmentUnited Stateshospital careinpatient mortalityprecision medicinepredictive modelingsingle hospital interpretability

Identifiers

PMID39762671
PMCPMC11914419

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.