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.

Mortality among hospitalized COVID-19 patients during surges of SARS-CoV-2 alpha (B.1.1.7) and delta (B.1.617.2) variants.

The aim of this study was to evaluate the death proportion and death risk of COVID-19 hospitalized patients over time and in different surges of COVID-19. This multi-center observational study was conducted from March 21, 2021 to October 3, 2021 which included the alpha and delta SARS-CoV-2 surges occurred in April and August in Tehran, respectively. The risk of COVID-19 death was compared in different months of admission. A total of 270,624 patients with COVID-19, of whom 6.9% died, were admitted to hospitals in Tehran province. Compared to patients admitted in March, a higher risk of COVID-19 death was observed among patients admitted to the hospital in July (HR 1.28; 95% CI 1.17, 1.40), August (HR 1.40; 95% CI 1.28, 1.52), September (HR 1.37; 95% CI 1.25, 1.50) and October (HR 4.63; 95% CI 2.77, 7.74). The ICU death proportion was 36.8% (95% CI: 35.5, 38.1) in alpha surge and increased significantly to 39.8 (95% CI 38.6, 41.1) in delta surge. The risk of COVID-19 death was significantly higher in delta surge compared to alpha surge (HR 1.22; 95% CI 1.17, 1.27). Delta surge was associated with a higher risk of death compared to alpha surge. High number of hospitalizations, a shortage of hospital beds, ICU spaces and medical supplies, poor nutritional status of hospitalized patients, and lack of the intensivist physicians or specialized nurses in the ICU were factors that contributed to the high mortality rate in the delta surge in Iran.