Literature DB >> 17451087

A clinical prediction model for the presence of peripheral arterial disease--the benefit of screening individuals before initiation of measurement of the ankle-brachial index: an observational study.

Bianca L W Bendermacher1, Joep A W Teijink, Edith M Willigendael, Marie-Louise Bartelink, Ron J G Peters, Rob A de Bie, Harry R Büller, Jelis Boiten, Machteld Langenberg, Martin H Prins.   

Abstract

Measurement of the ankle-brachial index (ABI) can provide important information about the presence of subclinical atherosclerosis. Performing the ABI in the overall population is not feasible, but it can be used in a selected population. A simple prediction rule could be of much use to estimate the risk of an abnormal ABI. This was designed as an observational study in the setting of 955 general practices in The Netherlands. A total of 7454 patients aged > or = 55 years presenting with at least one vascular risk factor (smoking, hypertension, diabetes, and hypercholesterolemia) and no complaints of intermittent claudication were included. Patients were selected by the general practitioner during visiting hours and from medical records. Main outcome measures included the prevalence of PAD, defined as an ABI below 0.9, which was related to vascular risk factors using regression analyses on which the PREVALENT clinical prediction model was developed. The overall prevalence of PAD was 18.4%. Since the treatment of individuals with a history of coronary heart disease and cerebrovascular disease will not be influenced by the finding of asymptomatic PAD, these individuals were not taken into account for the development of the clinical prediction model. Analyses showed a significantly increased risk for PAD with increasing age, smoking, and hypertension. The clinical prediction model giving risk factor points per factor (age: 1 point per 5 years starting at 55 years; ever smoked: 2 points; currently smoking: 7 points; and hypertension: 3 points), showed a proportional increase of the PAD prevalence with each increasing risk profile (range: 7.0-40.6%). In conclusion, based on the PREVALENT clinical prediction model, the general practitioner is able to identify a high-risk population in which measurement of ABI is useful.

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Year:  2007        PMID: 17451087     DOI: 10.1177/1358863X07076827

Source DB:  PubMed          Journal:  Vasc Med        ISSN: 1358-863X            Impact factor:   3.239


  10 in total

1.  A risk score assessment tool for peripheral arterial disease in women: From the National Health and Nutrition Examination Survey.

Authors:  Hend Mansoor; Islam Y Elgendy; Renessa S Williams; Verlin W Joseph; Young-Rock Hong; Arch G Mainous
Journal:  Clin Cardiol       Date:  2018-08-17       Impact factor: 2.882

2.  ACCF/AHA/ACP 2009 competence and training statement: a curriculum on prevention of cardiovascular disease: a report of the American College of Cardiology Foundation/American Heart Association/American College of Physicians Task Force on Competence and Training (Writing Committee to Develop a Competence and Training Statement on Prevention of Cardiovascular Disease): developed in collaboration with the American Academy of Neurology; American Association of Cardiovascular and Pulmonary Rehabilitation; American College of Preventive Medicine; American College of Sports Medicine; American Diabetes Association; American Society of Hypertension; Association of Black Cardiologists; Centers for Disease Control and Prevention; National Heart, Lung, and Blood Institute; National Lipid Association; and Preventive Cardiovascular Nurses Association.

Authors:  C Noel Bairey Merz; Mark J Alberts; Gary J Balady; Christie M Ballantyne; Kathy Berra; Henry R Black; Roger S Blumenthal; Michael H Davidson; Sara B Fazio; Keith C Ferdinand; Lawrence J Fine; Vivian Fonseca; Barry A Franklin; Patrick E McBride; George A Mensah; Geno J Merli; Patrick T O'Gara; Paul D Thompson; James A Underberg
Journal:  J Am Coll Cardiol       Date:  2009-09-29       Impact factor: 24.094

3.  Ankle brachial index measurement in primary care: are we doing it right?

Authors:  Saskia P A Nicolaï; Lotte M Kruidenier; Ellen V Rouwet; Marie-Louise E L Bartelink; Martin H Prins; Joep A W Teijink
Journal:  Br J Gen Pract       Date:  2009-06       Impact factor: 5.386

4.  High prevalence of peripheral arterial disease in Korean patients with coronary or cerebrovascular disease.

Authors:  Sanghyun Ahn; Yang Jin Park; Sang-Il Min; Seong Yup Kim; Jongwon Ha; Sang Joon Kim; Hyo-Soo Kim; Byung-Woo Yoon; Seung-Kee Min
Journal:  J Korean Med Sci       Date:  2012-05-26       Impact factor: 2.153

5.  A Prediction Model for the Peripheral Arterial Disease Using NHANES Data.

Authors:  Yang Zhang; Jinxing Huang; Ping Wang
Journal:  Medicine (Baltimore)       Date:  2016-04       Impact factor: 1.889

6.  Pulse Oximetry as a Screening Test for Hemodynamically Significant Lower Extremity Peripheral Artery Disease in Adults with Type 2 Diabetes Mellitus.

Authors:  Ria Mari Siao; Marc Josef So; Maria Honolina Gomez
Journal:  J ASEAN Fed Endocr Soc       Date:  2018-10-31

7.  Predictive value of 10-year atherosclerotic cardiovascular disease risk equations from the China-PAR for new-onset lower extremity peripheral artery disease.

Authors:  Pengkang He; Fangfang Fan; Chuyun Chen; Bo Liu; Jia Jia; Pengfei Sun; Jianping Li; Jing Zhou; Yan Zhang
Journal:  Front Cardiovasc Med       Date:  2022-10-04

8.  Comment on: Hanssen et al. Associations between the ankle-brachial index and cardiovascular and all-cause mortality are similar in individuals without and with type 2 diabetes: nineteen-year follow-up of a population-based cohort study. Diabetes Care 2012;35:1731-1735.

Authors:  Ilker Tasci
Journal:  Diabetes Care       Date:  2013-02       Impact factor: 19.112

9.  Applicability of the ankle-brachial-index measurement as screening device for high cardiovascular risk: an observational study.

Authors:  Bianca L W Bendermacher; Joep A W Teijink; Edith M Willigendael; Marie-Louise Bartelink; Ron J G Peters; Machteld Langenberg; Harry R Büller; Martin H Prins
Journal:  BMC Cardiovasc Disord       Date:  2012-07-30       Impact factor: 2.298

10.  Screen or not to screen for peripheral arterial disease: guidance from a decision model.

Authors:  Anil Vaidya; Manuela A Joore; Arina J Ten Cate-Hoek; Hugo Ten Cate; Johan L Severens
Journal:  BMC Public Health       Date:  2014-01-29       Impact factor: 3.295

  10 in total

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