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Nomogram for prediction of fatal outcome in patients with severe COVID-19: a multicenter study
Yun Yang1,2, Xiao-Fei Zhu2,3, Jian Huang1
1The Third Affiliated Hospital of Second Military Medical University, 225 Changhai Road, Shanghai, 200438, China.
Insights
This study developed a nomogram to predict fatal outcomes in severe COVID-19 patients. Key risk factors include age, rapid breathing, low lymphocyte count, and high interleukin-6 levels, aiding clinical decision-making.
Area of Science:
- Medical research
- Infectious diseases
- Critical care medicine
Background:
- Severe coronavirus disease 2019 (COVID-19) poses a significant threat, necessitating accurate prediction of patient outcomes.
- Effective models are crucial for identifying high-risk individuals and optimizing treatment strategies.
Purpose of the Study:
- To develop and validate a predictive model for fatal outcomes in severe COVID-19 patients.
- To identify independent high-risk factors associated with mortality in severe COVID-19.
Main Methods:
- A cohort of 2541 COVID-19 patients was analyzed, with a focus on severe cases.
- Multivariate Cox proportional hazard models were used to identify risk factors.
- A prognostic nomogram was constructed and validated using independent cohorts.
Main Results:
- Age ≥ 70 years, panting (respiratory rate ≥ 30/min), lymphocyte count < 1.0 × 109/L, and interleukin-6 (IL-6) > 10 pg/ml were identified as independent predictors of death.
- The nomogram demonstrated high predictive accuracy (AUCs ranging from 0.811 to 0.900) across training and validation cohorts.
- Tocilizumab showed a potential trend towards improved prognosis in severe COVID-19 patients with high IL-6 levels, though not statistically significant.
Conclusions:
- The developed nomogram serves as a valuable tool for clinicians to identify severe COVID-19 patients at high risk of mortality.
- The findings support the potential benefit of tocilizumab in managing severe COVID-19 cases with elevated IL-6.
- Early identification and targeted interventions can potentially reduce mortality rates in severe COVID-19.
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.
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