Combined Risk Stratification With Patient Characteristics and Biomarkers in Patients Treated With the Impella for Cardiogenic Shock

结合患者特征和生物标志物对接受 Impella 治疗的心源性休克患者进行风险分层

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Abstract

BACKGROUND: In patients with cardiogenic shock (CS), a percutaneous microaxial ventricular assist device (Impella, Abiomed, Danvers, MA) is a choice for temporary mechanical circulatory support. Given the high morbidity and mortality in this patient population, early risk stratification is relevant when making treatment decisions. METHODS: Using nationwide registry data between February 2020 and December 2022 in Japan, we included a total of 4122 patients with cardiogenic shock treated with the Impella devices. Using logistic regression analysis, we incorporated patient characteristics and biomarkers to develop a risk-stratifying model for in-hospital mortality. The model was also tested if applicable to composite outcomes of in-hospital death and major complications. RESULTS: Of the 4122 patients with cardiogenic shock, the Impella was indicated for acute myocardial infarction in 2575 (62.5%). Multivariable analysis identified 4 patient characteristics (age, body mass index, out-of-hospital cardiac arrest, and blood pressure) and 6 biomarkers (lactate, lactate dehydrogenase, creatinine, total bilirubin, albumin, and creatinine kinase) with cutoff values as factors significantly associated with in-hospital mortality. We developed a risk-stratifying model using the 10 variables, which was predictive of in-hospital death (area under the curve, 0.711; P<0.001). Adding biomarkers to patient characteristics significantly improved the diagnostic accuracy (area under the curve, from 0.649 to 0.711; P<0.001). This risk score was also predictive of death and major complications (area under the curve, 0.680; P<0.001). CONCLUSIONS: In patients with cardiogenic shock treated with the Impella devices, our risk-stratifying system, consisting of 4 patient characteristics and 6 biomarkers, strongly correlated with in-hospital mortality, potentially facilitating clinical decision-making.

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