The Full model predicted 30-day outcomes in cardiogenic shock post-AMI with an AUC of 0.80, outperforming existing risk scores and highlighting the value of early laboratory trends.
Do machine learning models incorporating early clinical and laboratory data improve 30-day prognosis prediction in patients with cardiogenic shock complicating acute myocardial infarction compared to existing risk scores?
A machine learning model incorporating 24-hour laboratory trends provides superior 30-day prognostic prediction in cardiogenic shock compared to models using only admission data or existing risk scores.
Absolute Event Rate: 0% vs 0%
Cardiogenic shock (CS) is a severe and frequent complication of acute myocardial infarction (AMI), necessitating rapid and accurate prognosis as-sessment to guide treatment and intensive care unit (ICU) resource allocation. We developed two machine learning models to predict 30-day outcomes following CS in AMI: an Admission model (using only data available at admission, like demography, comorbidities) and a Full model (incorporating additional laboratory values obtained within 24 hours). The models were trained on the CULPRIT-SHOCK dataset and externally validated using the eICU database. The Admission model achieved an out-of-sample AUC of 0.71 (95% CI: 0.6–0.83) in the development cohort and 0.68 in the validation cohort, while the Full model attained significantly higher performance, with AUCs of 0.80 (95% CI: 0.69–0.9) and 0.78, respectively. The Full model’s superior performance underscores the prognostic value of early laboratory trends, suggesting that dynamic data integration improves risk stratification. Both models outperformed existing risk scores across multiple metrics, provided well-calibrated probabilistic predictions, and demonstrated robustness to missing data. Additionally, they offered patient-level explainability, enhancing clinical interpretability. While promising, the models’ generalizability may be influenced by differences between the CULPRIT-SHOCK and eICU cohorts (e.g., demographics, CS severity thresholds); further validation in larger, prospective cohorts is warranted.
Nieto et al. (Wed,) reported a other. The Full model predicted 30-day outcomes in cardiogenic shock post-AMI with an AUC of 0.80, outperforming existing risk scores and highlighting the value of early laboratory trends.