Hybrid model based on fuzzy logic and classification trees for the prediction of mortality of critical trauma patients: Pre-hospital variables from the RETRAUCI registry.

内容摘要

In critically injured trauma patients, tools that stratify injury severity and estimate mortality are essential. Fuzzy logic (FL) enables the creation of accurate, interpretable models but requires decision rules, which can be generated using machine learning (ML) techniques like classification trees (CT). Our objective was to develop a hybrid model combining fuzzy logic and classification trees to estimate ICU mortality risk using only prehospital variables. We conducted a retrospective study using data from the Spanish Trauma ICU registry (RETRAUCI) from 2015 to 2022. Patients were randomly divided into derivation (DS) and validation sets (VS) (70:30). Candidate variables were those available in the prehospital phase. A hybrid model (HFL) was developed using a Fuzzy Inference System built with the 'FuzzyR' library in RStudio (v 2024.04.2) and the Mamdani method. CHAID (Chi-squared Automatic Interaction Detection) classification trees were used to derive the rules. The HFL's discrimination and calibration were compared with other scores: Revised Trauma Score (RTS); Glasgow Coma Scale, Age, and Arterial Pressure (GAP); Mechanism, Glasgow Coma Scale, Age, and Arterial Pressure (MGAP); Reverse shock index multiplied by Glasgow Coma Scale score (rSIG); and Trauma Rating Index in Age, Glasgow Coma Scale, Respiratory Rate, and Systolic Blood Pressure (TRIAGE). The study included 11,030 records, with 7,728 in the DS and 3,302 in the VS, and an overall mortality of 11.1%. Five variables were selected, ordered by importance: GCS, age, systolic blood pressure, respiratory rate, and heart rate. A total of 32 classification rules were generated. The HFL model achieved the highest accuracy in DS and VS, with AUROC of 0.87 (0.86-0.88) and 0.86 (0.83-0.88), and acceptable calibration with intercepts of -0.11 (-0.18 to -0.04) and - 0.19 (-0.32 to -0.06) and slopes of 0.99 (0.94-1.05) and 0.96 (0.83-0.88). Our hybrid model achieves accuracy comparable to commonly used models and provides clear clinical interpretation, with GCS and age as key variables.