COVID-19 Evidence Alerts
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Current best evidence for clinical care (more info)

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Clinical Prediction Guide Yang Y, Zhu XF, Huang J, et al. Nomogram for prediction of fatal outcome in patients with severe COVID-19: a multicenter study. Mil Med Res. 2021 Mar 17;8(1):21. doi: 10.1186/s40779-021-00315-6.

BACKGROUND: To develop an effective model of predicting fatal outcomes in the severe coronavirus disease 2019 (COVID-19) patients.

METHODS: Between February 20, 2020 and April 4, 2020, consecutive confirmed 2541 COVID-19 patients from three designated hospitals were enrolled in this study. All patients received chest computed tomography (CT) and serological examinations at admission. Laboratory tests included routine blood tests, liver function, renal function, coagulation profile, C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), and arterial blood gas. The SaO2 was measured using pulse oxygen saturation in room air at resting status. Independent high-risk factors associated with death were analyzed using Cox proportional hazard model. A prognostic nomogram was constructed to predict the survival of severe COVID-19 patients.

RESULTS: There were 124 severe patients in the training cohort, and there were 71 and 76 severe patients in the two independent validation cohorts, respectively. Multivariate Cox analysis indicated that age = 70 years (HR = 1.184, 95% CI 1.061-1.321), panting (breathing rate = 30/min) (HR = 3.300, 95% CI 2.509-6.286), lymphocyte count < 1.0 × 109/L (HR = 2.283, 95% CI 1.779-3.267), and interleukin-6 (IL-6) >  10 pg/ml (HR = 3.029, 95% CI 1.567-7.116) were independent high-risk factors associated with fatal outcome. We developed the nomogram for identifying survival of severe COVID-19 patients in the training cohort (AUC = 0.900, 95% CI 0.841-0.960, sensitivity 95.5%, specificity 77.5%); in validation cohort 1 (AUC = 0.811, 95% CI 0.763-0.961, sensitivity 77.3%, specificity 73.5%); in validation cohort 2 (AUC = 0.862, 95% CI 0.698-0.924, sensitivity 92.9%, specificity 64.5%). The calibration curve for probability of death indicated a good consistence between prediction by the nomogram and the actual observation. The prognosis of severe COVID-19 patients with high levels of IL-6 receiving tocilizumab were better than that of those patients without tocilizumab both in the training and validation cohorts, but without difference (P = 0.105 for training cohort, P = 0.133 for validation cohort 1, and P = 0.210 for validation cohort 2).

CONCLUSIONS: This nomogram could help clinicians to identify severe patients who have high risk of death, and to develop more appropriate treatment strategies to reduce the mortality of severe patients. Tocilizumab may improve the prognosis of severe COVID-19 patients with high levels of IL-6.

Discipline / Specialty Area Score
Hospital Doctor/Hospitalists
Internal Medicine
Infectious Disease
Intensivist/Critical Care
Comments from MORE raters

Hospital Doctor/Hospitalists rater

Multiple rick models that predict the risk of adverse outcomes from COVID-19 are already available.

Infectious Disease rater

There are at least one hundred prediction models for Covid-19, all without appropriate external and independent validation. This one is just an additional model, with the additional problem of a variable (IL-6) that is not available commonly in emergency services.