Establishment and evaluation of a risk-prediction model for hypertension in elderly patients with NAFLD from a health management perspective

Abstract

Elderly patients with nonalcoholic fatty liver disease are at a higher risk of developing. This study established an effective, individualised, early Hypertension risk-prediction model and proposed health management advice for patients over 60 years of age with NAFLD. Questionnaire surveys, physical examinations, and biochemical tests were conducted in 11,136 participants. The prevalence of NAFLD among 11,136 participants was 52.1%. Risk factors were screened using the least absolute shrinkage and selection operator model and random forest model. A risk-prediction model was established using logistic regression analysis and a dynamic nomogram was drawn. The model was evaluated for discrimination, calibration, and clinical applicability using receiver operating characteristic curves, calibration curves, decision curve analysis, net reclassification index (NRI), and external validation. The results suggested that the model showed moderate predictive ability. The area under curve (AUC) of internal validation was 0.707 (95% CI: 0.688–0.727) and the AUC of external validation was 0.688 (95% CI: 0.672–0.705). The calibration plots showed good calibration, the risk threshold of the decision curve was 30–56%, and the NRI value was 0.109. This Hypertension risk factor model may be used in clinical practice to predict the Hypertension risk in NAFLD patients.

Establishment and health management application of a prediction model for high-risk complication combination of type 2 diabetes mellitus based on data mining

“…The rms (version 6.2–0), DynNom (version 5.0.1), and rsconnect (version 0.8.24) packages in R software were used to draw the nomogram and the online web application of the prediction model. The length of the line corresponding to each variable in the column line graph was proportional to the degree of influence of that variable on the predicted outcome [ 32 , 33 ]. A nomogram can predict the possibility of disease occurrence simply and effectively and can serve as a clinical decision support tool and reduce healthcare costs [ 34 ].…”