A Prediction Model for the Peripheral Arterial Disease Using NHANES Data. Issue 16 (April 2016)

• Record Type: Journal Article
• Authors: Zhang, Yang; Huang, Jinxing; Wang, Ping
• Other Names: Hoefer. Imo section editor.
• Is Part Of: Medicine. Volume 95:Issue 16(2016)
• Publication Date: 2016-04
• Subjects: Medicine -- Periodicals; Medicine -- Periodicals; Médecine -- Périodiques; Geneeskunde; Medicine; Periodicals; Periodicals; 610.5
• DOI: 10.1097/MD.0000000000003454
• Languages: English
• ISSNs: 0025-7974

Abstract

Abstract : Abstract: We aim to build models for peripheral arterial disease (PAD) risk prediction and seek to validate these models in 2 different surveys in the US general population. Model building survey was based on the National Health and Nutrition Examination Surveys (NHANES, 1999–2002). Potential predicting variables included race, gender, age, smoking status, total cholesterol (TC), body mass index, high-density lipoprotein (HDL), ratio of TC to HDL, diabetes status, HbA1c, hypertension status, and pulse pressure. The PAD was diagnosed as ankle brachial index <0.9. We used multiple logistic regression method for the prediction model construction. The final predictive variables were chosen based on the likelihood ratio test. Model internal validation was done by the bootstrap method. The NHANES 2003–2004 survey was used for model external validation. Age, race, sex, pulse pressure, the ratio of TC to HDL, and smoking status were selected in the final prediction model. The odds ratio (OR) and 95% confidence interval (CI) for age with 10 years increase was 2.00 (1.72, 2.33), whereas that of pulse pressure for 10 mm Hg increase was 1.19 (1.10, 1.28). The OR of PAD was 1.11 (95% CI: 1.02, 1.21) for 1 unit increase in the TC to HDL ratio and was 1.61 (95% CI: 1.40, 1.85) for people who were currently smoking compared with those who were not. The respective area under receiver operating characteristics (AUC) of the final model from the training survey and validation survey were 0.82 (0.82, 0.83) and 0.76 (0.72, 0.79) indicating good model calibrations. Our model, to some extent, has a moderate usefulness for PAD risk prediction in the general US population.