JMIR Publications

ABSTRACT

Background:

Current risk stratification in acute myocardial infarction complicated by cardiogenic shock (AMI-CS) relies on static admission-time scores that cannot capture a patient's dynamic response to initial resuscitation. We developed and externally validated a parsimonious, interpretable machine learning model—V20-lite—that predicts hemodynamic deterioration during the critical 24- to 48-hour window using only the physiological trajectory from the first 24 hours of intensive care.

Methods:

We conducted a retrospective cohort study using the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.1) database for model development and internal validation, with the eICU Collaborative Research Database (eICU-CRD, version 2.0) serving as an independent, geographically diverse external validation cohort. Adult patients with AMI-CS were identified using ICD-9/10 codes. The primary outcome was a composite of hemodynamic deterioration occurring between 24 and 48 hours after ICU admission, defined by sustained hypotension, worsening hyperlactatemia, progressive oliguria, acute kidney injury, vasopressor escalation, or death. We engineered 70 candidate features from the 0–24-hour window and trained seven machine learning algorithms. SHapley Additive exPlanations (SHAP) were used to distill an 8-feature composite model (V20-lite) from the best-performing XGBoost algorithm. We assessed discrimination, calibration (Integrated Calibration Index [ICI], Brier score), and clinical utility (decision curve analysis). Logistic recalibration was pre-specified to address calibration drift in external validation. The model was deployed as an open-access Shiny application.

Results:

The derivation cohort included 1,633 patients (mean age 70.5 years, 61.8% male) from MIMIC-IV; 653 patients (mean age 68.1 years, 61.9% male) from eICU-CRD comprised the external validation cohort. The primary outcome occurred in 482 (29.5%) and 246 (37.7%) patients, respectively. In the internal test set (n=327, event rate 30.6%), V20-lite achieved an area under the receiver operating characteristic curve (AUC) of 0.815 (95% CI, 0.763–0.867), significantly outperforming the admission Sequential Organ Failure Assessment (SOFA) score (AUC 0.639; DeLong P<0.001). Sensitivity analysis confirmed that removing trajectory data reduced AUC to 0.588. In external validation, the original model maintained robust discrimination (AUC 0.756; 95% CI, 0.716–0.795). Observed calibration drift (intercept −0.324; ICI 0.144) was fully corrected by logistic recalibration (intercept 0.000; ICI 0.032; Brier score 0.186). SHAP analysis identified the 24-hour creatinine ratio, 24-hour lactate, and lactate-to-MAP ratio as the dominant prognostic drivers. Subgroup analysis revealed reduced performance in patients with initially normal lactate (AUC 0.671; P for interaction <0.001).

Conclusions:

V20-lite provides accurate, interpretable, and dynamically informed risk stratification for early hemodynamic deterioration in AMI-CS using eight routine bedside parameters. The model's calibration is transportable across heterogeneous healthcare systems through simple logistic recalibration, and its open-access deployment facilitates immediate clinical application.