Analysis of risk factors of nosocomial death in patients with cardiogenic shock / 中华急诊医学杂志

ABSTRACT

Objective:To investigate the risk factors of death in patients with cardiogenic shock (CS) in the Intensive Care Unit (ICU).Methods:This retrospective cohort study was conducted to collect the clinical data on critically ill patients from a number of hospitals in the United States released by the eICU Collaborative Research Database v2.0 (eICU-CRD v2.0) as of May 2018. The patients diagnosed with CS were selected and categorized into the survival and death groups according to the death in the hospital. The age, sex, and body mass index (BMI) of the enrolled patients were recorded, along with the acute physiology and chronic health evaluation Ⅳ (APACHE Ⅳ) score, simplified acute physiology score Ⅱ (SAPS Ⅱ), ethnicity, ICU type, clinical complications, diagnosis at admission, hemodynamic parameters, important treatments, and clinical outcomes. A propensity score was used to match age, BMI, and APACHE Ⅳ score, and SAPS Ⅱ. Multivariate Logistic regression analysis was performed to analyze the risk factors influencing ICU and hospital mortality, and the receiver operator characteristic (ROC) curve was used to evaluate its clinical utility.Results:In total, 33 998 in-hospital patients were included, among whom 27 596 patients survived and 6 402 died (18.83%), and 6 301 pairs were matched in preference. After matching, there were statistically significant differences between the two groups in the incidence of acute renal failure (29.33% vs. 31.82%), duration of mechanical ventilation [(6.05 ± 5.77) d vs (4.97 ± 5.11) d], length of ICU stay [(101.35 ± 154.59) h vs (110.15 ± 175.58) h] and length of hospital stay[ (12.73 ± 10.53) d vs (9.53 ± 10.35) d, P<0.01]. Multivariable logistic regression analysis revealed that age, BMI, APACHE Ⅳ score, SAPS Ⅱ, partial complications (except pacemaker implantation), diagnosis at admission (cardiac arrest, acute myocardial infarction, heart failure, respiratory system diseases, and digestive tract bleeding), and some treatments (noninvasive mechanical ventilation, blood purification, coronary artery bypass graft surgery, and vascular active drug application) were risk factors for hospital mortality in patients with CS ( P<0.05). Implantation of a ventricular assist device (VAD) was a protective measure against in-hospital death in patients with CS [hazard ratio ( HR)=0.49; 95% confidence interval (95% CI) 0.24-0.98; P=0.045). Multivariate ROC curve analysis revealed that the model could better predict ICU mortality [the area under the curve (AUC) =0.80 (95% CI 0.784-0.816)] and hospital mortality [AUC=0.779 (95% CI, 0.765-0.793)] ( P <0.01). Conclusions:For patients with CS in ICU, age, BMI, APACHE Ⅳ score, SAPS Ⅱ, partial complications, diagnosis at admission (cardiac arrest, acute myocardial infarction, heart failure, respiratory system diseases and digestive tract bleeding), and some treatments (noninvasive mechanical ventilation, blood purification, CABG surgery, vascular active drug application) are independent risk factors for death. Implantation of a VAD can reduce the hospital mortality rate of patients with CS. The ROC curve of the related factors revealed that the model can better predict the clinical outcomes.