title: Predictors of health care use among patients with or at high risk of atherothrombotic disease: two-year follow-up data.
abstract: BACKGROUND
  Atherothrombotic diseases are the leading health problems in the world, both in terms of morbidity and mortality. This study aimed to identify and quantify the predictors of medication, hospital and outpatient service use among patients with or at high risk of atherothrombotic disease.
  
  
  METHODS
  Two-year follow-up data were analyzed for 2873 Australian participants of the Reduction of Atherothrombosis for Continued Health (REACH) registry. The analysis was performed using generalized linear models with Poisson and Gamma distributions and log link function.
  
  
  RESULTS
  Participants with hypercholesterolemia, diabetes, hypertension, atrial fibrillation (AF), and history of coronary artery disease (CAD) used more medications (p<0.0001). The presence of diabetes predicted higher number of outpatient visits (RR=1.09, 95% CI: 1.07-1.11), as did AF (RR=1.10, 95% CI: 1.08-1.12). The presence of peripheral artery disease (PAD) regardless of ankle brachial index (ABI) status (abnormal or normal) increased the use of outpatient visits (RR=1.24, 95% CI: 1.20-1.29 and RR=1.12, 95% CI: 1.08-1.15), compared to those without PAD. Similarly, the presence of PAD regardless of ABI status increased the risk of vascular interventions, including coronary angioplasty, carotid surgery, amputation affecting lower-limb and peripheral bypass graft (RR=3.64, 95% CI: 2.01-6.60) (RR=2.8, 95% CI: 1.6-4.92) compared to patients without PAD.
  
  
  CONCLUSIONS
  The presence of PAD regardless of ABI status predicts a higher number of outpatient visits, non-fatal cardiovascular endpoints and vascular-interventions, while diabetes predicts higher pharmaceutical use and outpatient visits. AF predicts the higher number of outpatient visits and non-fatal cardiovascular events.
authors.affiliation: Melbourne EpiCentre, Department of Medicine (The Royal Melbourne Hospital), The University of Melbourne, Australia; Department of Epidemiology & Preventive Medicine, Monash University, Australia. Electronic address: zademi@unimelb.edu.au.
authors.forename: Zanfina
authors.initials: Z
authors.lastname: Ademi
authors.affiliation: Melbourne EpiCentre, Department of Medicine (The Royal Melbourne Hospital), The University of Melbourne, Australia.
authors.forename: Danny
authors.initials: D
authors.lastname: Liew
authors.affiliation: Melbourne EpiCentre, Department of Medicine (The Royal Melbourne Hospital), The University of Melbourne, Australia.
authors.forename: Alexandra
authors.initials: A
authors.lastname: Gorelik
authors.affiliation: Melbourne EpiCentre, Department of Medicine (The Royal Melbourne Hospital), The University of Melbourne, Australia.
authors.forename: Megan
authors.initials: M
authors.lastname: Bohensky
authors.affiliation: Department of Epidemiology & Preventive Medicine, Monash University, Australia.
authors.forename: Ella
authors.initials: E
authors.lastname: Zomer
authors.affiliation: Division of Health Research, Lancaster University, UK.
authors.forename: Bruce
authors.initials: B
authors.lastname: Hollingsworth
authors.affiliation: INSERM U-698, University Paris-Diderot, Sorbonne Paris-Cité, France.
authors.forename: Gabriel
authors.initials: G
authors.lastname: Steg
authors.affiliation: VA Boston Healthcare System, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
authors.forename: Deepak L
authors.initials: DL
authors.lastname: Bhatt
authors.affiliation: Department of Epidemiology & Preventive Medicine, Monash University, Australia.
authors.forename: Christopher M
authors.initials: CM
authors.lastname: Reid
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D001315:Australia / Q000453:epidemiology; D015331:Cohort Studies; D003324:Coronary Artery Disease / Q000175:diagnosis / Q000453:epidemiology* / Q000628:therapy*; D003328:Coronary Thrombosis / Q000175:diagnosis / Q000453:epidemiology* / Q000628:therapy*; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D008875:Middle Aged; D010342:Patient Acceptance of Health Care; D011237:Predictive Value of Tests; D012042:Registries; D012307:Risk Factors
keywords: Comorbidities; Predictors; Resource use