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

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.

A retrospective cross-sectional study: comparison of the clinicopathological features of schistosomal and non-schistosomal colorectal cancer in Central China.

AIM: To analyze the clinicopathological features of schistosomal and non-schistosomal colorectal cancer in Central China and compare them with other areas of the Yangtze River Basin. METHOD: The 501 cases of colorectal cancer (CRC) were retrospectively analyzed from 2020 to 2022. They were divided into two groups: 406 cases of colorectal cancer without schistosomiasis (CRC-NS) and 95 cases of colorectal cancer with schistosomiasis (CRC-S).The clinicopathological characteristics included the distribution of schistosomiasis eggs, patient age, sex, tumor differentiation, lymph node metastasis, and clinical stage. By retrieving the database, this study compared the clinicopathological differences of colorectal cancer with schistosomiasis in other areas of the Yangtze River basin. RESULTS: The cases of colorectal cancer with schistosomiasis accounted for 18.9%(95/501) in the study. The patients of CRC-S were older than the patients of CRC-NS (P = 0.002, P < 0.05). There was a statistical difference in the location of occurrence (P = 0.000, P < 0.05) between the two groups. There were no significant differences between CRC-S and CRC-NS in other clinicopathological features, such as sex (P = 0.054), Type(P = 0.242), histological type(P = 0.654), infiltrative depth(P = 0.811), differentiation(P = 0.837), lymph node metastasis(P = 0.574), intravascular tumor thrombus(P = 0.698), T stage(P = 0.354). In other areas of the Yangtze River Basin, there were statistical differences in the age of occurrence and T stage (P < 0.05) between colorectal cancer with schistosomiasis and non-schistosomal colorectal cancer. CONCLUSION: In Central China, colorectal cancer with chronic schistosomiasis infection occurs more in the rectum and sigmoid colon. It is more common in individuals over 60 years old, consistent with the findings in the Yangtze River Basin. Additionally, schistosomal colorectal cancer had a higher T stage in the Yangtze River Basin. This may be related to the malignant biological behavior of colorectal cancer and could result in a relatively poor prognosis. Therefore, the elderly population in schistosomiasis endemic areas should pay more attention to early screening and tumor prevention.