Classification and Diagnostic Prediction of Colorectal Cancer Mortality Based on Machine Learning Algorithms: A Multicenter National Study.

INTRODUCTION: Colorectal cancer (CRC) ranks as the second leading cause of cancer-related deaths. This study aimed to predict survival outcomes of CRC patients using machine learning (ML) methods. MATERIAL AND METHODS: A retrospective analysis included 1853 CRC patients admitted to three prominent tertiary hospitals in Iran from October 2006 to July 2019. Six ML methods, namely logistic regression (LR), Naïve Bayes (NB), Support Vector Machine (SVM), Neural Network (NN), Decision Tree (DT), and Light Gradient Boosting Machine (LGBM), were developed with 10-fold cross-validation. Feature selection employed the Random Forest method based on mean decrease GINI criteria. Model performance was assessed using Area Under the Curve (AUC). RESULTS: Time from diagnosis, age, tumor size, metastatic status, lymph node involvement, and treatment type emerged as crucial predictors of survival based on mean decrease GINI. The NB (AUC = 0.70, 95% Confidence Interval [CI] 0.65-0.75) and LGBM (AUC = 0.70, 95% CI 0.65-0.75) models achieved the highest predictive AUC values for CRC patient survival. CONCLUSIONS: This study highlights the significance of variables including time from diagnosis, age, tumor size, metastatic status, lymph node involvement, and treatment type in predicting CRC survival. The NB model exhibited optimal efficacy in mortality prediction, maintaining a balanced sensitivity and specificity. Policy recommendations encompass early diagnosis and treatment initiation for CRC patients, improved data collection through digital health records and standardized protocols, support for predictive analytics integration in clinical decisions, and the inclusion of identified prognostic variables in treatment guidelines to enhance patient outcomes.

Prevalence and outcome of COVID-19 among Iranian celiac patients.

Aim: This study aimed to evaluate the prevalence and outcome of COVID-19 among Iranian celiac disease patients. Background: Patients with celiac disease (CD) might be at greater risk for opportunistic viral infections. Coronavirus disease-2019 (COVID-19) is a new coronavirus (SARS-CoV-2) cause of respiratory disorder which spread around the world at the end of 2019. The question is does COVID-19 infection increase the risk of severe outcome and/or a higher mortality in treated celiac disease?. Methods: Data regarding demographic details, clinical history, and COVID-19 infection symptoms among treated celiac disease patients was collected from July 2020 to January 2021 and analyzed using SPSS version 25. Results: A total of 455 celiac disease patients were included in this study. The prevalence of Covid-19 infection among celiac disease patients was 2.4%. Infection among women (72.7%) was higher than the men, and only one overweight man who smoked was hospitalized. Among COVID-19 infected celiac disease patients, the most common symptoms were myalgia 90.9% (10/11), fever, body trembling, headache, shortness of breath, loss of smell and taste, and anorexia (72.7%). Treatments for COVID-19, included antibiotics (90.9%), pain analgesics (54.5%), antihistamines (27.3%), antivirals (9.1%) and hydroxychloroquine (9.1%). Conclusion: This study shows that treated celiac disease is not a risk factor for severity or higher mortality in patients infected with COVID-19. Women, however, might need extra-protection to prevent COVID-19 infection.