title: Predicting the prevalence of peripheral arterial diseases: modelling and validation in different cohorts.
abstract: BACKGROUND
  To develop models for prevalence estimation of peripheral arterial disease (PAD) and to validate them in an external cohort.
  
  
  METHODS
  Model training cohort was a population based cross-sectional survey. Age, sex, smoking status, body mass index, total cholesterol (TC), high density lipoprotein (HDL), TC/HDL ratio, low density lipoprotein, fasting glucose, diabetes, hypertension, pulse pressure, and stroke history were considered candidate predicting variables. Ankle brachial index ≤ 0.9 was defined as the presence of peripheral arterial disease. Logistic regression method was used to build the prediction models. The likelihood ratio test was applied to select predicting variables. The bootstrap method was used for model internal validation. Model performance was validated in an external cohort.
  
  
  RESULTS
  The final models included age, sex, pulse pressure, TC/HDL ratio, smoking status, diabetes, and stroke history. Area under receiver operating characteristics (AUC) with 95% confidence interval (CI) of the final model from the training cohort was 0.74 (0.70, 0.77). Model validation in another cohort revealed AUC (95% CI) of 0.72 (0.70, 0.73). P value of Hosmer-Lemeshow's model goodness of fit test was 0.75 indicating good model calibration.
  
  
  CONCLUSIONS
  The developed model yielded a moderate usefulness for predicting the prevalence of PAD in general population.
authors.affiliation: 1 School of Public Health, Fudan University, Shanghai, P. R. China.
authors.forename: Yiqiang
authors.initials: Y
authors.lastname: Zhan
authors.affiliation: 2 Leibniz Institute for Plasma Science and Technology, Greifswald, Germany.
authors.forename: Jie
authors.initials: J
authors.lastname: Zhuang
authors.affiliation: 3 Department of Preventive Medicine, School of Basic Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, P. R. China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Dong
authors.affiliation: 4 Divisions of Renal Medicine and Baxter Novum, Department of Clinical Science, Intervention, and Technology, and Center for Molecular Medicine, Karolinska Institute, Stockholm, Sweden.
authors.forename: Hong
authors.initials: H
authors.lastname: Xu
authors.affiliation: 5 Heart Center, Peking University People's Hospital, Peking, P. R. China.
authors.forename: Dayi
authors.initials: D
authors.lastname: Hu
authors.affiliation: 1 School of Public Health, Fudan University, Shanghai, P. R. China.
authors.forename: Jinming
authors.initials: J
authors.lastname: Yu
mesh_terms: D000367:Age Factors; D000368:Aged; D055109:Ankle Brachial Index; D019540:Area Under Curve; D015415:Biomarkers / Q000097:blood; D002681:China / Q000453:epidemiology; D015897:Comorbidity; D003430:Cross-Sectional Studies; D005260:Female; D006306:Health Surveys; D006801:Humans; D008019:Life Style; D016013:Likelihood Functions; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D015999:Multivariate Analysis; D016017:Odds Ratio; D058729:Peripheral Arterial Disease / Q000097:blood / Q000175:diagnosis / Q000453:epidemiology*; D011237:Predictive Value of Tests; D015995:Prevalence; D012372:ROC Curve; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D012737:Sex Factors; D011795:Surveys and Questionnaires
chemical_list: D015415:Biomarkers
keywords: Ankle brachial index; model predicting; peripheral arterial diseases; validation