Sex-specific prediction of cardiogenic shock complicating acute coronary syndromes: the SEX-SHOCK score

Abstract Background Mortality of cardiogenic shock (CS) complicating acute coronary syndromes (ACS) remained nearly unchanged over the last two decades. Female CS patients are less likely to receive guideline recommended care, while having worse in-hospital outcomes as compared to males.1 Presently available tools to assess the risk of developing CS in the setting of ACS, such as the ORBI risk score, were developed in predominantly male patient populations, while not considering inflammatory mediators or proxies of cardiorenal function. Purpose The present study aimed (i) to test the sex-specific performance of the ORBI model to predict in-hospital CS in patients presenting with ACS, and (ii) to develop and externally validate a better performing risk prediction model for both, women and men. Methods Among 44’220 ACS patients recruited in the Swiss AMIS-Plus study, the sex-specific performance of the ORBI risk score to estimate the probability for the development of CS was tested. By harnessing regression- and machine-learning based modelling approaches (ie, random forest [RF], multiple layer perceptron [MLR], and logistic regression [LR]), independent predictors of in-hospital CS were identified in sex-disaggregated data. Best performing models and variables were then used to develop SEX-SHOCK, a novel risk prediction model that accounts for sex-specific disease characteristics. External validation was done in 4’787 ACS patients recruited in the SPUM-ACS study. Results The ORBI risk prediction model demonstrated lower discriminatory performance in female patients relative to males (AUC [95%CI]: 0.76 (0.74-0.78) vs. 0.81(0.80-0.83); p<0.001). RF, MLR, and LR identified C-reactive protein, creatinine, left-ventricular function, and ST-segment elevation as most potent predictors of in-hospital CS beyond the parameters included in ORBI, with the performance of LR-based models superseding those of RF and MLR. By combining most important predictors with best performing models, the SEX-SHOCK score was developed, showing superior performance as compared to ORBI in both females (AUC 0.80, 95%CI 0.78-0.82; p<0.01) and males ACS patients (AUC 0.83, 0.82-0.85; p<0.001), which prevailed upon 10-fold cross validation. Following external validation in SPUM-ACS, SEX-SHOCK showed improved discriminatory performance over ORBI in both females (0.84, 95% CI 0.78-0.89; p<0.05) and males (0.81, 95% CI 0.78-0.85; p<0.01), with the most pronounced improvement observed in female patients (△C-statistics%=6.23 vs. 2.79%; p <0.001). Conclusion Our study highlights important sex differences in the performance of available tools for risk prediction of developing in-hospital CS among patients presenting with ACS. By harnessing regression- and machine-learning-based approaches, the SEX-SHOCK risk score was developed, outperforming existing risk prediction models in internal and external validation cohorts.