ArticleBMC anesthesiology2026
HeLP-BAG score: a novel data-driven scoring system for predicting post-operative 30-day mortality using 24-hour preoperative data.
Article in BMC anesthesiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundAccurate 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.
methodsWe 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.
resultsTen 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.
conclusionsThe HeLP-BAG score is a simple and interpretable tool for screening 30-day postoperative mortality, demonstrating consistent performance across diverse surgical populations.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.