Machine learning versus multivariate logistic regression for predicting severe COVID‐19 in hospitalized children with Omicron variant infection

Abstract With the emergence of the Omicron variant, the number of pediatric Coronavirus Disease 2019 (COVID‐19) cases requiring hospitalization and developing severe or critical illness has significantly increased. Machine learning and multivariate logistic regression analysis were used to predict risk factors and develop prognostic models for severe COVID‐19 in hospitalized children with the Omicron variant in this study. Of the 544 hospitalized children including 243 and 301 in the mild and severe groups, respectively. Fever (92.3%) was the most common symptom, followed by cough (79.4%), convulsions (36.8%), and vomiting (23.2%). The multivariate logistic regression analysis showed that age (1–3 years old, odds ratio (OR): 3.193, 95% confidence interval (CI): 1.778–5.733], comorbidity (OR: 1.993, 95% CI:1.154–3.443), cough (OR: 0.409, 95% CI:0.236–0.709), and baseline neutrophil‐to‐lymphocyte ratio (OR: 1.108, 95% CI: 1.023–1.200), lactate dehydrogenase (OR: 1.993, 95% CI: 1.154–3.443), blood urea nitrogen (OR: 1.002, 95% CI: 1.000–1.003) and total bilirubin (OR: 1.178, 95% CI: 1.005–3.381) were independent risk factors for severe COVID‐19. The area under the curve (AUC) of the prediction models constructed by multivariate logistic regression analysis and machine learning (RandomForest + TomekLinks) were 0.7770 and 0.8590, respectively. The top 10 most important variables of random forest variables were selected to build a prediction model, with an AUC of 0.8210. Compared with multivariate logistic regression, machine learning models could more accurately predict severe COVID‐19 in children with Omicron variant infection.