Development and validation of a nomogram model for predicting the risk of MAFLD in the young population.

This study aimed to develop and validate a nomogram model that includes clinical and laboratory indicators to predict the risk of metabolic-associated fatty liver disease (MAFLD) in young Chinese individuals. This study retrospectively analyzed a cohort of young population who underwent health examination from November 2018 to December 2021 at The Affiliated Hospital of Southwest Medical University in Luzhou City, Sichuan Province, China. We extracted the clinical and laboratory data of 43,040 subjects and randomized participants into the training and validation groups (7:3). Univariate logistic regression analysis, the least absolute shrinkage and selection operator regression, and multivariate logistic regression models identified significant variables independently associated with MAFLD. The predictive accuracy of the model was analyzed in the training and validation sets using area under the receiver operating characteristic (AUROC), calibration curves, and decision curve analysis. In this study, we identified nine predictors from 31 variables, including age, gender, body mass index, waist-to-hip ratio, alanine aminotransferase, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, uric acid, and smoking. The AUROC for the subjects in the training and validation groups was 0.874 and 0.875, respectively. The calibration curves show excellent accuracy of the nomogram. This nomogram which was based on demographic characteristics, lifestyle habits, anthropometrics, and laboratory data can visually and individually predict the risk of developing MAFLD. This nomogram is a quick and effective screening tool for assessing the risk of MAFLD in younger populations and identifying individuals at high risk of MAFLD, thereby contributing to the improvement of MAFLD management.

Prognostic nomogram for colorectal cancer patients with multi-organ metastases: a Surveillance, Epidemiology, and End Results program database analysis.

BACKGROUND: A nomogram that integrates risk models and clinical characteristics can accurately predict the prognosis of individual patients. We aimed to identify the prognostic factors and establish nomograms for predicting overall survival (OS) and cause-specific survival (CSS) in patients with multi-organ metastatic colorectal cancer (CRC). METHODS: Demographic and clinical information on multi-organ metastases from 2010 to 2019 were extracted from the Surveillance, Epidemiology, and End Results (SEER) Program. Univariate and multivariate Cox analyses were used to identify independent prognostic factors that were used to develop nomograms to predict CSS and OS, and to assess the concordance index (C-index), area under the curve (AUC), and calibration curve. RESULTS: The patients were randomly assigned to the training and validation groups at a 7:3 ratio. A Cox proportional hazards model was conducted for CRC patients to identify independent prognostic factors, including age, sex, tumor size, metastases, degree of differentiation, stage T, stage N, primary and metastasis surgery. The competing risk models employed by Fine and Gray were used to identify the risk factors for CRC. Death from other causes was treated as a competing event, and Cox models were used to identify the factors for death to identify the independent factors of CSS. By incorporating the corresponding independent prognostic factors, we established prognostic nomograms for OS and CSS. Finally, we used the C-index, ROC curve, and calibration plots to assess the utility of the nomogram. CONCLUSIONS: Using the SEER database, we constructed a predictive model for CRC patients with multi-organ metastases. Nomograms provide clinicians with 1-, 3-, and 5-year OS and CSS predictions for CRC, allowing them to formulate appropriate treatment plans.