Clinical risk scores for the early prediction of severe outcomes in patients hospitalized for COVID-19.
Clinical risk scores for the early prediction of severe outcomes in patients hospitalized for COVID-19.
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DOI：
10.1007/s11739-020-02617-4
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发表时间：
2021-06
影响因子：
4.6
通讯作者：
List of contributors
中科院分区：
文献类型：
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作者：
Ageno W;Cogliati C;Perego M;Girelli D;Crisafulli E;Pizzolo F;Olivieri O;Cattaneo M;Benetti A;Corradini E;Bertù L;Pietrangelo A;List of contributors
2019冠状病毒病（COVID-19）与严重急性呼吸衰竭有关。早期识别高风险COVID-19患者至关重要。我们的目的是推导和验证一个简单的评分来预测严重的结果。意大利内科学会对因COVID-19住院的患者进行了一项回顾性队列研究。流行病学，临床，实验室和治疗变量收集在五家医院入院。三种算法选择模型用于构建预测风险评分：向后选择、最小绝对收缩和选择算子（LASSO）和随机森林。重度结局定义为需要无创通气、需要经口气管插管或死亡的复合终点。共有610例患者被纳入分析，313例有重度结局。推导分析的子集包括335例患者，验证分析的子集包括275例患者。LASSO选择确定了6个变量（年龄、冠心病史、CRP、AST、D-二聚体和中性粒细胞/淋巴细胞比率），并得出了最佳表现评分，推导队列的曲线下面积为0.79，验证队列为0.80。使用13分中的7分作为临界值，灵敏度为0.93，特异性为0.34，阳性预测值为0.59，阴性预测值为0.82。拟议的评分可以识别严重结局风险较低的患者，这些患者在因COVID-19入院后可以在低强度环境中安全管理。
Coronavirus disease of 2019 (COVID-19) is associated with severe acute respiratory failure. Early identification of high-risk COVID-19 patients is crucial. We aimed to derive and validate a simple score for the prediction of severe outcomes. A retrospective cohort study of patients hospitalized for COVID-19 was carried out by the Italian Society of Internal Medicine. Epidemiological, clinical, laboratory, and treatment variables were collected at hospital admission at five hospitals. Three algorithm selection models were used to construct a predictive risk score: backward Selection, Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest. Severe outcome was defined as the composite of need for non-invasive ventilation, need for orotracheal intubation, or death. A total of 610 patients were included in the analysis, 313 had a severe outcome. The subset for the derivation analysis included 335 patients, the subset for the validation analysis 275 patients. The LASSO selection identified 6 variables (age, history of coronary heart disease, CRP, AST, D-dimer, and neutrophil/lymphocyte ratio) and resulted in the best performing score with an area under the curve of 0.79 in the derivation cohort and 0.80 in the validation cohort. Using a cut-off of 7 out of 13 points, sensitivity was 0.93, specificity 0.34, positive predictive value 0.59, and negative predictive value 0.82. The proposed score can identify patients at low risk for severe outcome who can be safely managed in a low-intensity setting after hospital admission for COVID-19.