Background and aims: Cardiogenic shock (CS) remains the primary cause of in-hospital death after acute coronary syndromes (ACS), with its plateauing mortality rates approaching 50%. To test novel interventions, personalized risk prediction is essential. The ORBI (Observatoire Régional Breton sur l'Infarctus) score represents the first-of-its-kind risk score to predict in-hospital CS in ACS patients undergoing percutaneous coronary intervention (PCI). However, its sex-specific performance remains unknown, and refined risk prediction strategies are warranted.
Methods: This multinational study included a total of 53 537 ACS patients without CS on admission undergoing PCI. Following sex-specific evaluation of ORBI, regression and machine-learning models were used for variable selection and risk prediction. By combining best-performing models with highest-ranked predictors, SEX-SHOCK was developed, and internally and externally validated.
Results: The ORBI score showed lower discriminative performance for the prediction of CS in females than males in Swiss (area under the receiver operating characteristic curve [95% confidence interval]: 0.78 [0.76-0.81] vs. 0.81 [0.79-0.83]; P =.048) and French ACS patients (0.77 [0.74-0.81] vs. 0.84 [0.81-0.86]; P = .002). The newly developed SEX-SHOCK score, now incorporating ST-segment elevation, creatinine, C-reactive protein, and left ventricular ejection fraction, outperformed ORBI in both sexes (females: 0.81 [0.78-0.83]; males: 0.83 [0.82-0.85]; P < .001), which prevailed following internal and external validation in RICO (females: 0.82 [0.79-0.85]; males: 0.88 [0.86-0.89]; P < .001) and SPUM-ACS (females: 0.83 [0.77-0.90], P = .004; males: 0.83 [0.80-0.87], P = .001).
Conclusions: The ORBI score showed modest sex-specific performance. The novel SEX-SHOCK score provides superior performance in females and males across the entire spectrum of ACS, thus providing a basis for future interventional trials and contemporary ACS management.
Keywords: Acute coronary syndromes; Atherosclerosis; C-reactive protein; Cardiogenic shock; Gender medicine; Inflammation; LVEF; Machine learning; Multilayer perceptron; Percutaneous coronary intervention; Personalized risk prediction; Precision medicine; Random forest.
© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.