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. 2022 May 17:18:579-591.
doi: 10.2147/TCRM.S361936. eCollection 2022.

Development and Validation of a Predictive Nomogram with Age and Laboratory Findings for Severe COVID-19 in Hunan Province, China

Junyi Jiang #  1   2   3   4   5 , WeiJun Zhong #  1   2   3   4 , WeiHua Huang  1   2   3   4 , Yongchao Gao  1   2   3   4 , Yijing He  1   2   3   4 , Xi Li  1   2   3   4 , Zhaoqian Liu  1   2   3   4 , Honghao Zhou  1   2   3   4 , Yacheng Fu  1   6 , Rong Liu  1   2   3   4 , Wei Zhang  1   2   3   4
Affiliations

Development and Validation of a Predictive Nomogram with Age and Laboratory Findings for Severe COVID-19 in Hunan Province, China

Junyi Jiang et al. Ther Clin Risk Manag. .

Abstract

Purpose: To identify more objectively predictive factors of severe outcome among patients hospitalized for coronavirus disease 2019 (COVID-19).

Patients and methods: A retrospective cohort of 479 hospitalized patients diagnosed with COVID-19 in Hunan Province was selected. The prognostic effects of factors such as age and laboratory indicators were analyzed using the Kaplan-Meier method and Cox proportional hazards model. A prognostic nomogram model was established to predict the progression of patients with COVID-19.

Results: A total of 524 patients in Hunan province with COVID-19 from December 2019 to October 2020 were retrospectively recruited. Among them, 479 eligible patients were randomly assigned into the training cohort (n = 383) and validation cohort (n = 96), at a ratio of 8:2. Sixty-eight (17.8%) and 15 (15.6%) patients developed severe COVID-19 after admission in the training cohort and validation cohort, respectively. The differences in baseline characteristics were not statistically significant between the two cohorts with regard to age, sex, and comorbidities (P > 0.05). Multivariable analyses included age, C-reactive protein, fibrinogen, lactic dehydrogenase, neutrophil-to-lymphocyte ratio, urea, albumin-to-globulin ratio, and eosinophil count as predictive factors for patients with progression to severe COVID-19. A nomogram was constructed with sufficient discriminatory power (C index = 0.81), and proper consistency between the prediction and observation, with an area under the ROC curve of 0.81 and 0.86 in the training and validation cohort, respectively.

Conclusion: We proposed a simple nomogram for early detection of patients with non-severe COVID-19 but at high risk of progression to severe COVID-19, which could help optimize clinical care and personalized decision-making therapies.

Keywords: C-reactive protein; COVID-19; SARS-CoV-2; nomogram; predictive model.

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Conflict of interest statement

The authors declare no conflicts of Interest.

Figures

Figure 1
Figure 1
Study flowchart detailing which samples of patients with COVID-19 were utilized at each phase of statistical analysis.
Figure 2
Figure 2
Prognostic associations of laboratory findings in the training dataset.
Figure 3
Figure 3
Kaplan-Meier survival plots for prognostic factors.
Figure 4
Figure 4
Prognostic nomogram for predicting the severe illness-free survival probability of patients with COVID-19.
Figure 5
Figure 5
Calibration curves of the nomogram predicting severe illness-free survival in patients with COVID-19.
Figure 6
Figure 6
Receiver operating characteristic curves and severe illness-free survival curves for high and low severity risk groups for the risk prediction model.
See this image and copyright information in PMC

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