Risk factors for disease severity among children with Covid-19: a clinical prediction model

David Chun‑Ern Ng and Liew, Chuin‑Hen and Tan, Kah Kee and Ling Chin and Grace Sieng Sing Ting and Nur Fadzreena Fadzilah and Lim, Hui Yi and Nur Emylia Zailanalhuddin and Shir Fong Tan and Muhamad Akmal Afan and Fatin Farihah Wan Ahmad Nasir and Thayasheri Subramaniam and Marlindawati Mohd Ali and Mohammad Faid Abd Rashid and Ong, Song Quan and Ch’ng, Chin Chin (2023) Risk factors for disease severity among children with Covid-19: a clinical prediction model. BMC Infectious Diseases, 23 (398). pp. 1-12. ISSN 1471-2334

	Text ABSTRACT.pdf Download (46kB)
	Text FULL TEXT.pdf Restricted to Registered users only Download (1MB) | Request a copy

Abstract

Background: Children account for a significant proportion of COVID-19 hospitalizations, but data on the predictors of disease severity in children are limited. We aimed to identify risk factors associated with moderate/severe COVID-19 and develop a nomogram for predicting children with moderate/severe COVID-19. Methods: We identified children ≤ 12 years old hospitalized for COVID-19 across five hospitals in Negeri Sembilan, Malaysia, from 1 January 2021 to 31 December 2021 from the state’s pediatric COVID-19 case registration system. The primary outcome was the development of moderate/severe COVID-19 during hospitalization. Multivariate logistic regression was performed to identify independent risk factors for moderate/severe COVID-19. A nomogram was constructed to predict moderate/severe disease. The model performance was evaluated using the area under the curve (AUC), sensitivity, specificity, and accuracy. Results: A total of 1,717 patients were included. After excluding the asymptomatic cases, 1,234 patients (1,023 mild cases and 211 moderate/severe cases) were used to develop the prediction model. Nine independent risk factors were identified, including the presence of at least one comorbidity, shortness of breath, vomiting, diarrhea, rash, seizures, temperature on arrival, chest recessions, and abnormal breath sounds. The nomogram’s sensitivity, specificity, accuracy, and AUC for predicting moderate/severe COVID-19 were 58·1%, 80·5%, 76·8%, and 0·86 (95% CI, 0·79 – 0·92) respectively. Conclusion: Our nomogram, which incorporated readily available clinical parameters, would be useful to facilitate individualized clinical decisions.

Item Type:	Article
Keyword:	COVID-19, Pediatric, Nomogram, Predictor severity
Subjects:	R Medicine > RA Public aspects of medicine > RA1-1270 Public aspects of medicine > RA421-790.95 Public health. Hygiene. Preventive medicine > RA643-645 Disease (Communicable and noninfectious) and public health R Medicine > RJ Pediatrics > RJ1-570 Pediatrics
Department:	INSTITUTE > Institute for Tropical Biology and Conservation
Depositing User:	SITI AZIZAH BINTI IDRIS -
Date Deposited:	01 Sep 2023 08:41
Last Modified:	01 Sep 2023 08:41
URI:	https://eprints.ums.edu.my/id/eprint/36605

Actions (login required)

View Item