ArticleFrontiers in medicine2025
Development and validation of an interpretable machine learning model for predicting the risk of non-cardiac surgery postoperative heart failure: a multicenter study.
Article in Frontiers in 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.
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
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: This study developed a machine learning model to predict postoperative heart failure (HF) risk in non-cardiac surgery patients. Methods: Using data from 489 patients (109 HF cases, 380 controls), the dataset was split 8:2 into training and testing sets, with under-sampling for class imbalance. Eight algorithms were evaluated, with random forest (RF) performing best. Results: The RF model achieved AUROCs of 0.919 (training) and 0.923 (testing), validated externally (AUC = 0.878). SHAP analysis identified key predictors: age, neutrophil-to-lymphocyte ratio, blood glucose, INR, pulse and serum creatinine (positively associated); serum albumin, MCHC, eGFR and diastolic blood pressure (negatively associated). A web-based tool was developed for clinical use. Conclusion: The model integrates 10 clinical variables reflecting age, inflammation, renal dysfunction, and hemodynamic instability, enabling preoperative risk stratification and guiding targeted interventions to improve perioperative outcomes.
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.