Development and Validation of a Diagnostic Nomogram to Predict SARS-CoV-2 Pneumonia
24 Pages Posted: 1 Apr 2020More...
Background: This study aimed to establish an effective diagnostic nomogram for suspected SARS-CoV-2 pneumonia patients.
Methods: We used the LASSO aggression and multivariable logistic regression methods to explore the predictive factors associated with SARS-Cov-2 pneumonia, and established the diagnostic nomogram for SARS-Cov-2 pneumonia using multivariable regression. This diagnostic nomogram was assessed by the internal and external validation data set. Further, we plotted decision curves and clinical impact curve to evaluate the clinical usefulness of this diagnostic nomogram.
Findings: The predictive factors including the epidemiological history, wedge-shaped or fan-shaped lesion parallel to or near the pleura, bilateral lower lobes, ground glass opacities, crazy paving pattern and white blood cell (WBC) count were contained in the nomogram. In the primary cohort, the C-statistic for predicting the probability of the SARS-CoV-2 pneumonia was 0.967, even higher than the C-statistic (0.961) in initial viral nucleic acid nomogram which was established using the univariable regression. The C-statistic was 0.848 in external validation cohort. Good calibration curves were observed for the prediction probability in the internal validation and external validation cohort. The nomogram both performed well in terms of discrimination and calibration. Moreover, decision curve and clinical impact curve were also beneficial for SARS-CoV-2 pneumonia patients.
Interpretation: Our nomogram can be used to predict SARS-CoV-2 pneumonia accurately and favourably.
Funding Statement: None.
Declaration of Interests: The author(s) indicated no potential conflict of interest.
Ethics Approval Statement: The study was approved by the Ethics Committee of the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University.
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