Initial Emergency Department Triage Narratives for Predicting Hospital Admission and Early Critical Outcomes: A Retrospective Chronological Split Study

Background/Objectives: We assessed whether initial triage narratives add predictive information beyond incompletely recorded structured data for recorded admission and early hospital outcomes. Methods: This single-center retrospective chronological split study included adults with Emergency Severity Index (ESI) levels 3–5 and available narratives. Penalized logistic models were developed on 51,735 visits (4852 admissions) from April to December 2025 and evaluated on 40,433 visits from January to June 2026. These data had previously been examined. Initial assessment selection was confirmed by the exporting investigator, but predictor timing could not be independently verified at record level. The primary outcome was recorded admission, including accepted external transfers; the secondary outcome was general intensive care unit (ICU) transfer or index-hospital death within seven days. Results: Evaluation included 3517 admissions (8.70%). Text increased admission area under the receiver operating characteristic curve (AUROC) from 0.763 to 0.835 (95% CI 0.828–0.841) and average precision from 0.246 to 0.340; the combined-model calibration slope was 1.083. The secondary outcome had 167 development events; evaluation included 40,190 visits and 123 events (0.306%); combined AUROC was 0.913 and average precision 0.055. At the development-selected 90% sensitivity target, secondary-model positive predictive value was 1.33%, with 204.5 flags per 1000 visits and 14 missed events. Conclusions: Narratives added information for local disposition beyond sparse structured documentation. These results do not establish prediction of physiological deterioration or an actionable alert policy. Independent temporal verification and external validation remain necessary. No prospective registration was obtained.

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Journal
Journal of Clinical Medicine
Published
2026-10-30
DOI
https://doi.org/10.3390/jcm15218477
Primary Topic
Machine Learning in Healthcare
Type
article
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article

Initial Emergency Department Triage Narratives for Predicting Hospital Admission and Early Critical Outcomes: A Retrospective Chronological Split Study

Julia Żukowska-Karolak, Tomasz Kłosiewicz, Patryk Konieczka
3 links
Journal of Clinical Medicine
Machine Learning in Healthcare
article

Initial Emergency Department Triage Narratives for Predicting Hospital Admission and Early Critical Outcomes: A Retrospective Chronological Split Study

Julia Żukowska-Karolak, Tomasz Kłosiewicz, Patryk Konieczka
article en

Abstract

Background/Objectives: We assessed whether initial triage narratives add predictive information beyond incompletely recorded structured data for recorded admission and early hospital outcomes. Methods: This single-center retrospective chronological split study included adults with Emergency Severity Index (ESI) levels 3–5 and available narratives. Penalized logistic models were developed on 51,735 visits (4852 admissions) from April to December 2025 and evaluated on 40,433 visits from January to June 2026. These data had previously been examined. Initial assessment selection was confirmed by the exporting investigator, but predictor timing could not be independently verified at record level. The primary outcome was recorded admission, including accepted external transfers; the secondary outcome was general intensive care unit (ICU) transfer or index-hospital death within seven days. Results: Evaluation included 3517 admissions (8.70%). Text increased admission area under the receiver operating characteristic curve (AUROC) from 0.763 to 0.835 (95% CI 0.828–0.841) and average precision from 0.246 to 0.340; the combined-model calibration slope was 1.083. The secondary outcome had 167 development events; evaluation included 40,190 visits and 123 events (0.306%); combined AUROC was 0.913 and average precision 0.055. At the development-selected 90% sensitivity target, secondary-model positive predictive value was 1.33%, with 204.5 flags per 1000 visits and 14 missed events. Conclusions: Narratives added information for local disposition beyond sparse structured documentation. These results do not establish prediction of physiological deterioration or an actionable alert policy. Independent temporal verification and external validation remain necessary. No prospective registration was obtained.

Journal of Clinical MedicineVol. 15(21)
Poznan University of Medical Sciences (PL)
Good health and well-being
Openalex Percentile: Top 14%
Machine Learning in Healthcare
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