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Encephalopathy at admission predicts adverse outcomes in patients with SARS-CoV-2 infection

机译:入学患者的脑病预测SARS-COV-2感染患者的不良结果

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Aims To determine if neurologic symptoms at admission can predict adverse outcomes in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Methods Electronic medical records of 1053 consecutively hospitalized patients with laboratory-confirmed infection of SARS-CoV-2 from one large medical center in the USA were retrospectively analyzed. Univariable and multivariable Cox regression analyses were performed with the calculation of areas under the curve (AUC) and concordance index (C-index). Patients were stratified into subgroups based on the presence of encephalopathy and its severity using survival statistics. In sensitivity analyses, patients with mild/moderate and severe encephalopathy (defined as coma) were separately considered. Results Of 1053 patients (mean age 52.4?years, 48.0% men [ n =?505]), 35.1% ( n =?370) had neurologic manifestations at admission, including 10.3% ( n =?108) with encephalopathy. Encephalopathy was an independent predictor for death (hazard ratio [HR] 2.617, 95% confidence interval [CI] 1.481–4.625) in multivariable Cox regression. The addition of encephalopathy to multivariable models comprising other predictors for adverse outcomes increased AUCs (mortality: 0.84–0.86, ventilation/ intensive care unit [ICU]: 0.76–0.78) and C-index (mortality: 0.78 to 0.81, ventilation/ICU: 0.85–0.86). In sensitivity analyses, risk stratification survival curves for mortality and ventilation/ICU based on severe encephalopathy ( n =?15) versus mild/moderate encephalopathy ( n =?93) versus no encephalopathy ( n =?945) at admission were discriminative ( p ?0.001). Conclusions Encephalopathy at admission predicts later progression to death in SARS-CoV-2 infection, which may have important implications for risk stratification in clinical practice.
机译:旨在确定入院中的神经系统症状是否可以预测严重急性呼吸综合征冠状病毒2(SARS-COV-2)患者的不利结果。方法回顾性分析了1053次的电子医疗记录的1053名与美国大型医疗中心的SARS-COV-2感染患者的连续住院治疗患者。通过计算曲线(AUC)和一致性指数(C-INDEX)下的区域进行了不可变量和多变量的COX回归分析。利用存活统计,基于脑病的存在及其严重程度,患者分层分层分为亚组。在敏感性分析中,单独考虑患有轻度/中度和严重的脑病(定义为昏迷)的患者。结果1053名患者(平均52.4岁以下,48.0%男性[n =Δ505]),35.1%(n =Δ370)在入院时具有神经系统表现,包括10.3%(n =Δ108)患有脑病。脑病是多变量Cox回归中的死亡(危害比[HR] 2.617,95%] 1.481-4.625)的独立预测因素。向多变量模型添加脑病,包括其他预测因子,用于不良结果增加AUC(死亡率:0.84-0.86,通风/重症监护单元[ICU]:0.76-0.78)和C-Index(死亡率:0.78至0.81,通风/ ICU: 0.85-0.86)。在敏感性分析中,基于严重脑病的死亡率和通风/ ICU的风险分层存活曲线(n =β15)与轻度/中度脑病(n =β93)相比,入院时没有脑病(n =Δ945)是辨别性的(p & 0.001)。结论入学期间的脑病预测SARS-COV-2感染的后期对死亡的进展,这可能对临床实践中的风险分层具有重要意义。

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