Development a Nomogram to Predict Prognosis in Severe and Critically Ill Patients with COVID-19

Research Square(2020)

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摘要
Abstract Background: The number of deaths caused by COVID-19 are on the rising worldwide. This study focused on severe and critically ill COVID-19, aim to explore independent risk factors associated with disease severity and to build a nomogram to predict patients’ prognosis.Methods: Patients with laboratory-confirmed COVID-19 admitted to the Union Hospital, Tongji Medical College and Hankou Hospital of Wuhan, China, from February 8th to April 6th, 2020. LASSO Regression and Multivariate Analysis were applied to screen independent factors. COX Nomogram was built to predict the 7-day, 14-day and 1-month survival probability.Results: A total of 115 severe [73 (63.5%)] and critically ill [42 (36.5%)] patients were included in this study, containing 93 (80.9%) survivors and 22 (19.1%) non-survivors. For disease severity, D-dimer [OR 6.33 (95%CI, 1.27-45.57], eosinophil percentage [OR 8.02 (95%CI, 1.82-45.04)], total bilirubin [OR 12.38 (95%CI, 1.24-223.65)] and lung involvement score [OR 1.22 (95%CI, 1.08-1.40)] were the independent factors associated with critical illness. Troponin [HR 9.02 (95%CI, 3.02, 26.97)] and total bilirubin [HR 3.16 (95%CI, 1.13, 8.85)] were the independent predictors for patients’ prognosis. Troponin≥26.2 ng/L and total bilirubin>20 μmol/L were associated with poor prognosis. The nomogram based on the independent risk factors had a C-index of 0.92 (95%CI, 0.87, 0.98) for predicting survival probability. The survival nomogram validated in the critically ill patients had a C-index of 0.83 (95%CI: 0.75, 0.94).Conclusions: In conclusion, in severe and critically ill patients with COVID-19, D-dimer, eosinophil percentage, total bilirubin and lung involvement score were the independent risk factors associated with disease severity. The proposed survival nomogram accurately predicted prognosis. The survival analysis may suggest that early incidence of multiple organ dysfunction may be an important predictor of poor prognosis.
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