A Systematic Review Of Machine Learning Models For Sepsis Prediction: An Appraise-Ai Approach

Sepsis, a leading cause of ICU mortality and high healthcare costs, results from a systemic inflammatory response to infection. Early detection is crucial but challenging. Machine learning models present a promising solution by analyzing diverse data for real-time predictions.

To evaluate the impact of ML models on sepsis prediction, assess the methodological and reporting quality of existing research, and report on model accuracy, sensitivity, specificity, and clinical applicability.

The studies included were RCTs, cohort studies, or nested case-control studies utilizing ML for sepsis prediction. Independent reviewers used the APPRAISE-AI tool to extract and assess data across six domains: clinical relevance, data quality, methodological conduct, result robustness, reporting quality, and reproducibility.

The review included 53 papers, mainly retrospective cohort studies from 2020-2024. Evaluated with the APPRAISE-AI tool, most studies were of moderate quality. Model AUC values ranged from 0.64 to 0.98, with Light GBM and MLP models performing best at 0.98. AI models generally outperformed non-AI methods in predicting sepsis.

Machine learning models hold promise for accurately predicting sepsis, but study quality varies, with notable flaws in methodology, robustness, and reproducibility. Future research should enhance data quality, advance algorithms, and perform multi-center prospective studies for validation.