A nomogram predicting the severity of COVID-19 based on initial clinical and radiologic characteristics

Aim: This study aimed to build an easy-to-use nomogram to predict the severity of COVID-19. Patients & methods: From December 2019 to January 2020, patients confirmed with COVID-19 in our hospital were enrolled. The initial clinical and radiological characteristics were extracted. Univariate and...

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Detalles Bibliográficos
Autores principales: Zhang, Hanfei, Zhong, Feiyang, Wang, Binchen, Liao, Meiyan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Future Medicine Ltd 2022
Materias:
Short Communication
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8862443/
https://www.ncbi.nlm.nih.gov/pubmed/35371273
http://dx.doi.org/10.2217/fvl-2020-0193
Descripción
Sumario:Aim: This study aimed to build an easy-to-use nomogram to predict the severity of COVID-19. Patients & methods: From December 2019 to January 2020, patients confirmed with COVID-19 in our hospital were enrolled. The initial clinical and radiological characteristics were extracted. Univariate and multivariate logistic regression were used to identify variables for the nomogram. Results: In total, 104 patients were included. Based on statistical analysis, age, levels of neutrophil count, creatinine, procalcitonin and numbers of involved lung segments were identified for nomogram. The area under the curve was 0.939 (95% CI: 0.893–0.984). The calibration curve showed good agreement between prediction of nomogram and observation in the primary cohort. Conclusion: An easy-to-use nomogram with great discrimination was built to predict the severity of COVID-19.

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