Evidence map›Paper›PMID 39865251›Full record

ArticleBMC anesthesiology2025

Development and validation of an EHR-based risk prediction model for geriatric patients undergoing urgent and emergency surgery.

Edward N Yap, Jie Huang, Joshua Chiu, Robert W Chang, Bradley Cohn, Judith C F Hwang, Mary Reed

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC anesthesiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Edward N YapDepartment of Anesthesia, The Permanente Medical Group, Kaiser Permanente South San Francisco, 1200 El Camino Real, South San Francisco, CA, 94080, USA. Edward.N.Yap@kp.org.
Jie HuangKaiser Permanente Division of Research, 2000 Broadway, Oakland, CA, 94612, USA.
Joshua ChiuDepartment of Anesthesia and Perioperative Care, University of California San Francisco, 505 Parnassus Ave, San Francisco, CA, 94143, USA.
Robert W ChangKaiser Permanente Division of Research, 2000 Broadway, Oakland, CA, 94612, USA.
Bradley CohnDepartment of Anesthesia, The Permanente Medical Group, 3600 Broadway, Oakland, CA, 94611, USA.
Judith C F HwangDepartment of Anesthesia, The Permanente Medical Group, 975 Sereno Dr, Vallejo, CA, 94589, USA.
Mary ReedKaiser Permanente Division of Research, 2000 Broadway, Oakland, CA, 94612, USA.

Funding

Translational Research Core - Health Engagement & Action Translational (HEAT)P30DK092924 · NIDDK · KAISER FOUNDATION RESEARCH INSTITUTE · PI Alyce Sophia Adams, HILARY Kessler SELIGMAN · 2011 to 2026
$9.2M
NIDDK NIH HHS P30 DK092924
6 · The paper itself

Abstract

backgroundClinical determination of patients at high risk of poor surgical outcomes is complex and may be supported by clinical tools to summarize the patient's own personalized electronic health record (EHR) history and vitals data through predictive risk models. Since prior models were not readily available for EHR-integration, our objective was to develop and validate a risk stratification tool, named the Assessment of Geriatric Emergency Surgery (AGES) score, predicting risk of 30-day major postoperative complications in geriatric patients under consideration for urgent and emergency surgery using pre-surgical existing electronic health record (EHR) data.

methodsPatients 65-years and older undergoing urgent or emergency non-cardiac surgery within 21 hospitals 2017-2021 were used to develop the model (randomly split: 80% training, 20% test). The primary outcome was a 30-day composite outcome including several postoperative major complications and mortality; secondary outcomes included common individual complications included in the primary composite outcome (sepsis, progressive renal insufficiency or renal failure, and mortality). Patients' EHR-based clinical history, vital signs, labs, and demographics were included in logistic regression, LASSO, decision tree, Random Forest, and XGBoost models. Area under the receiver operating characteristics curve (AUCROC) was used to compare model performance.

resultsOverall, 66,262 patients (median [IQR] age 78 [(70.9-84.0], female 53.9%, White race 68.5%) received urgent or emergency non-cardiac surgery (25.7% orthopedic cases, 21.9% general surgery cases). AUCROC ranged from 0.655 (Decision Tree) - 0.804 (XGBoost) for the primary composite outcome. XGBoost AUCROC was 0.823, 0.781, and 0.839 in predicting outcomes of sepsis, progressive renal insufficiency or renal failure, and mortality, respectively.

conclusionsWe developed a model to accurately predict major postoperative complications in geriatric patients undergoing urgent or emergency surgery using the patient's own existing EHR data. EHR implementation of this model could efficiently support clinicians' surgical risk assessment and perioperative decision-making discussions in this vulnerable patient population.

Indexed as

Electronic Health RecordsGeriatric AssessmentPostoperative ComplicationsSurgical Procedures, OperativeAgedAged, 80 and overEmergenciesFemaleHumansMaleRisk AssessmentEmergency surgeryGeriatric surgeryRisk prediction model

Identifiers

PMID39865251
PMCPMC11771050

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Registered trials

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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.