Author: Jiao Gong; Jingyi Ou; Xueping Qiu; Yusheng Jie; Yaqiong Chen; Lianxiong Yuan; Jing Cao; Mingkai Tan; Wenxiong Xu; Fang Zheng; Yaling Shi; Bo Hu
Title: A Tool to Early Predict Severe 2019-Novel Coronavirus Pneumonia (COVID-19) : A Multicenter Study using the Risk Nomogram in Wuhan and Guangdong, China Document date: 2020_3_20
ID: 51b7hss1_32
Snippet: In the train cohort, the nomogram had a significantly high AUC 0.912 (95% CI 0.846-0.978) to discriminate individuals with severe COVID-19 from non-severe COVID-19, with a sensitivity of 85.71 % and specificity of 87.58% ( Figure 3C , Table 2 ). Cutpoint R package was used to calculate optimal cutpoints by bootstraping the variability of the optimal cutpoints, which was 188.6358 for our nomogram (corresponding to a threshold probability of 0.190).....
Document: In the train cohort, the nomogram had a significantly high AUC 0.912 (95% CI 0.846-0.978) to discriminate individuals with severe COVID-19 from non-severe COVID-19, with a sensitivity of 85.71 % and specificity of 87.58% ( Figure 3C , Table 2 ). Cutpoint R package was used to calculate optimal cutpoints by bootstraping the variability of the optimal cutpoints, which was 188.6358 for our nomogram (corresponding to a threshold probability of 0.190). Then patients in the validation cohorts were divided into the low group (score ≤ 188.6358) and the high group (score>188.6358) for further analysis. In consistent with the train cohort, in validation cohort 1, AUC was 0.853 for patients with severe COVID-19 versus non-severe COVID-19 with a sensitivity of 77.5 % and specificity of 78.4% ( Figure 3D , Table 3 ). In validation cohort 2, the sensitivity and the specificity of the nomogram were observed to be 75% and 100%, respectively.
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