HeLP-BAG score: a novel data-driven scoring system for predicting post-operative 30-day mortality using 24-hour preoperative data

BACKGROUND: Accurate preoperative risk stratification remains challenging, as existing scoring systems are often complex, invasive, or limited to specific patient populations. We aimed to develop a simple, interpretable, and broadly applicable risk score to screen for 30-day postoperative mortality using routinely available variables. METHODS: We developed the HeLP-BAG score using three large surgical cohorts. To reduce demographic bias, cohorts were matched by age and sex, yielding 24,617 male and female patients per dataset. Clinical data collected within 24 h before surgery were used as inputs, and 30-day postoperative mortality was defined as the outcome. Candidate variables were selected based on clinical relevance, statistical separation, monotonicity, noninvasiveness, and general applicability. Change points were identified using a change-point detection algorithm and refined through clinical consensus. Model performance was evaluated in a combined validation cohort and compared with ASA-PS, seven established scoring systems, and five machine learning models using the area under the receiver operating characteristic curve (AUROC). ICU admission was additionally assessed using a post-hoc weighting strategy. RESULTS: Ten variables were included in the final score: ICU admission, age, mean arterial pressure, heart rate, hemoglobin, lactate, arterial pH, blood urea nitrogen, albumin, and glucose. The combined validation cohort consisted of 98,468 patients, with a 30-day mortality rate of 1.47%. The HeLP-BAG score achieved an AUROC of 0.873, showing comparable performance to ASA-PS (0.865; p = 0.120) and higher performance than SAPS3 (0.831). Performance was particularly robust in high-risk subgroups, including emergency surgery (AUROC 0.843; p < 0.001 vs. ASA-PS) and older patients aged ≥ 65 years (0.866; p = 0.004 vs. ASA-PS). Mortality increased monotonically with higher scores, and web-based calculator was developed to facilitate bedside implementation. CONCLUSIONS: The HeLP-BAG score is a simple and interpretable tool for screening 30-day postoperative mortality, demonstrating consistent performance across diverse surgical populations.

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Publication Details

Journal
BMC Anesthesiology
Published
2026-06-08
DOI
https://doi.org/10.1186/s12871-026-03907-8
Primary Topic
Cardiac, Anesthesia and Surgical Outcomes
Type
article
Field-Weighted Citation Impact
0.00

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article

HeLP-BAG score: a novel data-driven scoring system for predicting post-operative 30-day mortality using 24-hour preoperative data

Joo Heung Yoon, Choon Hee Chung, Young Moo Lee, Jinhyong Goh et al.
BMC Anesthesiology
Cardiac, Anesthesia and Surgical Outcomes
article

HeLP-BAG score: a novel data-driven scoring system for predicting post-operative 30-day mortality using 24-hour preoperative data

Joo Heung Yoon, Choon Hee Chung, Young Moo Lee, Jinhyong Goh, Hyun Sik Kim, Seung Ho Jun, Hyo Seop Shin
article en

Abstract

BACKGROUND: Accurate preoperative risk stratification remains challenging, as existing scoring systems are often complex, invasive, or limited to specific patient populations. We aimed to develop a simple, interpretable, and broadly applicable risk score to screen for 30-day postoperative mortality using routinely available variables. METHODS: We developed the HeLP-BAG score using three large surgical cohorts. To reduce demographic bias, cohorts were matched by age and sex, yielding 24,617 male and female patients per dataset. Clinical data collected within 24 h before surgery were used as inputs, and 30-day postoperative mortality was defined as the outcome. Candidate variables were selected based on clinical relevance, statistical separation, monotonicity, noninvasiveness, and general applicability. Change points were identified using a change-point detection algorithm and refined through clinical consensus. Model performance was evaluated in a combined validation cohort and compared with ASA-PS, seven established scoring systems, and five machine learning models using the area under the receiver operating characteristic curve (AUROC). ICU admission was additionally assessed using a post-hoc weighting strategy. RESULTS: Ten variables were included in the final score: ICU admission, age, mean arterial pressure, heart rate, hemoglobin, lactate, arterial pH, blood urea nitrogen, albumin, and glucose. The combined validation cohort consisted of 98,468 patients, with a 30-day mortality rate of 1.47%. The HeLP-BAG score achieved an AUROC of 0.873, showing comparable performance to ASA-PS (0.865; p = 0.120) and higher performance than SAPS3 (0.831). Performance was particularly robust in high-risk subgroups, including emergency surgery (AUROC 0.843; p < 0.001 vs. ASA-PS) and older patients aged ≥ 65 years (0.866; p = 0.004 vs. ASA-PS). Mortality increased monotonically with higher scores, and web-based calculator was developed to facilitate bedside implementation. CONCLUSIONS: The HeLP-BAG score is a simple and interpretable tool for screening 30-day postoperative mortality, demonstrating consistent performance across diverse surgical populations.

BMC Anesthesiology
University of Pittsburgh (US), Yonsei University (KR), Seoul National University Hospital (KR), Kyung Hee University (KR), University of Pittsburgh Medical Center (US), Chungbuk National University Hospital (KR), Massachusetts Institute of Technology (US)
National Institutes of Health
Good health and well-being
Openalex Percentile: Top 7%
Cardiac, Anesthesia and Surgical Outcomes
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