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外文期刊>Korean journal of radiology :
>Development and Validation of a Prognostic Nomogram Based on Clinical and CT Features for Adverse Outcome Prediction in Patients with COVID-19
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Development and Validation of a Prognostic Nomogram Based on Clinical and CT Features for Adverse Outcome Prediction in Patients with COVID-19
OBJECTIVE:The purpose of our study was to investigate the predictive abilities of clinical and computed tomography (CT) features for outcome prediction in patients with coronavirus disease (COVID-19).MATERIALS AND METHODS:The clinical and CT data of 238 patients with laboratory-confirmed COVID-19 in our two hospitals were retrospectively analyzed. One hundred sixty-six patients (103 males; age 43.8 ± 12.3 years) were allocated in the training cohort and 72 patients (38 males; age 45.1 ± 15.8 years) from another independent hospital were assigned in the validation cohort. The primary composite endpoint was admission to an intensive care unit, use of mechanical ventilation, or death. Univariate and multivariate Cox proportional hazard analyses were performed to identify independent predictors. A nomogram was constructed based on the combination of clinical and CT features, and its prognostic performance was externally tested in the validation group. The predictive value of the combined model was compared with models built on the clinical and radiological attributes alone.RESULTS:Overall, 35 infected patients (21.1%) in the training cohort and 10 patients (13.9%) in the validation cohort experienced adverse outcomes. Underlying comorbidity (hazard ratio [HR], 3.35; 95% confidence interval [CI], 1.67-6.71; p 0.001), lymphocyte count (HR, 0.12; 95% CI, 0.04-0.38; p 0.001) and crazy-paving sign (HR, 2.15; 95% CI, 1.03-4.48; p = 0.042) were the independent factors. The nomogram displayed a concordance index (C-index) of 0.82 (95% CI, 0.76-0.88), and its prognostic value was confirmed in the validation cohort with a C-index of 0.89 (95% CI, 0.82-0.96). The combined model provided the best performance over the clinical or radiological model (p 0.050).CONCLUSION:Underlying comorbidity, lymphocyte count and crazy-paving sign were independent predictors of adverse outcomes. The prognostic nomogram based on the combination of clinical and CT features could be a useful tool for predicting adverse outcomes of patients with COVID-19.Copyright ? 2020 The Korean Society of Radiology.
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机译：目的：我们研究的目的是探讨冠心病病毒（Covid-19）的临床和计算断层扫描（CT）特征的预测能力（Covid-19）。材料和方法：238例实验室患者的临床和CT数据 - 回顾性分析了两家医院的Covid-19。培训队列和72名患者（38名患者）分配了一百六十六名患者（103名男性;年龄43.8±12.3岁），在验证队列中分配了另一医院的72名患者（38名男性;年龄45.1±15.8岁）。主要复合终点入院，用于密集护理单元，使用机械通气或死亡。进行单变量和多元COX比例危害分析以识别独立预测因子。基于临床和CT特征的组合构建了一种载体图，其预后性能在验证组中进行了外部测试。将组合模型的预测值与单独的临床和放射性属性建立的模型进行了比较。结果：总体而言，35名受感染的患者（21.1％）在培训队列和10名患者（13.9％）中验证队列经历过不利结果。潜在的合并症（危险比[HR]，3.35; 95％置信区间[CI]，1.67-6.71; P <0.001），淋巴细胞计数（HR，0.12; 95％CI，0.04-0.38; P <0.001）和疯狂 - 铺路标志（HR，2.15; 95％CI，1.03-4.48; P = 0.042）是独立因素。 NOM图显示了0.82（95％CI，0.76-0.88）的一致性指数（C折射率），并且在验证队列中确认了验证队列的预后值，C折射率为0.89（95％CI，0.82-0.96）。组合模型提供了临床或放射学模型的最佳性能（P <0.050）。结论：潜在的合并症，淋巴细胞计数和疯狂铺路标志是不利结果的独立预测因子。基于临床和CT特征的组合的预后NOM图可能是预测Covid-19.Copyright患者的不良结果的有用工具？ 2020韩国放射学会。
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