Background Prognosis models based on stepwise regression methods show modest performance in patients with cardiogenic shock (CS). Automated variable selection allows data-driven risk evaluation by recognizing distinct patterns in data. We sought to evaluate an automated variable selection method (least absolute shrinkage and selection operator, LASSO) for predicting 30-day mortality in patients with acute myocardial infarction and CS (AMICS) receiving acute percutaneous coronary intervention (PCI) compared to two established scores. Methods and results Consecutive patients with AMICS receiving acute PCI at one of two tertiary heart centres in Denmark 2010–2017. Patients were divided according to treatment with mechanical circulatory support (MCS); PCI–MCS cohort (n = 220) versus PCI cohort (n = 1180). The latter was divided into a development (2010–2014) and a temporal validation cohort (2015–2017). Cohort-specific LASSO models were based on data obtained before PCI. LASSO models outperformed IABP-SHOCK II and CardShock risk scores in discriminative ability for 30-day mortality in the PCI validation [receiver operating characteristics area under the curve (ROC AUC) 0.80 (95% CI 0.76–0.84) vs 0.73 (95% CI 0.69–0.77) and 0.70 (95% CI 0.65–0.75), respectively, P < 0.01 for both] and PCI–MCS development cohort [ROC AUC 0.77 (95% CI 0.70–0.83) vs 0.64 (95% CI 0.57–0.71) and 0.64 (95% CI 0.57–0.71), respectively, P < 0.01 for both]. Variable influence differed depending on MCS, with age being the most influential factor in the LASSO–PCI model, whereas haematocrit and estimated glomerular filtration rate were the highest-ranking factors in the LASSO–PCI–MCS model. Conclusion Data-driven prognosis models outperformed established risk scores in patients with AMICS receiving acute PCI and exhibited good discriminative abilities. Observations indicate a potential use of machinelearning to facilitate individualized patient care and targeted interventions in the future.

中文翻译：

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数据驱动的急性心肌梗死和心源性休克患者的即时风险模型

背景 基于逐步回归方法的预后模型在心源性休克 (CS) 患者中表现出适度的表现。自动变量选择允许通过识别数据中的不同模式来进行数据驱动的风险评估。我们试图评估一种自动变量选择方法（最小绝对收缩和选择算子，LASSO），与两个既定评分相比，用于预测接受急性经皮冠状动脉介入治疗（PCI）的急性心肌梗死和 CS（AMIS）患者的 30 天死亡率。方法和结果 2010-2017 年在丹麦两个三级心脏中心之一接受急性 PCI 的连续 AMICS 患者。患者根据机械循环支持（MCS）治疗进行分组；PCI-MCS 队列 (n = 220) 与 PCI 队列 (n = 1180)。后者分为发展（2010-2014）和时间验证队列（2015-2017）。特定队列的 LASSO 模型基于 PCI 前获得的数据。LASSO 模型在 PCI 验证中对 30 天死亡率的判别能力优于 IABP-SHOCK II 和 CardShock 风险评分[接受者操作特征曲线下面积 (ROC AUC) 0.80 (95% CI 0.76–0.84) vs 0.73 (95% CI 0.69–0.77) 和 0.70 (95% CI 0.65–0.75)，P < 0.01 ] 和 PCI-MCS 开发队列 [ROC AUC 0.77 (95% CI 0.70–0.83) vs 0.64 (95% CI 0.57–0.71) 和 0.64 (95% CI 0.57–0.71)，P <; 两者均为 0.01]。变量影响因 MCS 而异，年龄是 LASSO-PCI 模型中影响最大的因素，而血细胞比容和估计的肾小球滤过率是 LASSO-PCI-MCS 模型中排名最高的因素。结论 数据驱动的预后模型在接受急性 PCI 的 AMICS 患者中优于既定的风险评分，并表现出良好的判别能力。观察结果表明，机器学习有可能在未来促进个性化的患者护理和有针对性的干预措施。