Impact of Comorbid Critical Limb Ischemia And Diabetes on Healthcare Resource Use And Costs

OBJECTIVES: Prevalence of diabetes in peripheral artery disease patients is high and these patients are at increased risk for major cardiovascular events. This study's aim was to use a retrospective cohort to assess healthcare resource use (HRU) and costs among critical limb ischemia (CLI) pati...

Full description

Saved in:
Bibliographic Details
Published in:Value in health 2017-10, Vol.20 (9), p.A481
Main Authors: Ting, W, Haskell, L, Lurie, F, Berger, JS, Eapen, Z, Valko, M, Rich, K, Crivera, C, Schein, JR, Alas, V
Format: Article
Language:English
Subjects:
After care
Bayesian analysis
Beta blockers
Cardiovascular disease
Cardiovascular diseases
Cellulitis
Chronic pain
Comorbidity
Computerized medical records
Cost control
Diabetes
Diabetes mellitus
Diabetics
Diagnosis
Electronic medical records
Gangrene
Health care expenditures
Health records
Hospitalization
Ischemia
Kidney diseases
Learning algorithms
Mathematical models
Medical diagnosis
Pain
Peripheral arterial disease
Pharmacy
Prediction models
Savings
Simulation
Ulcers
Vascular diseases
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:OBJECTIVES: Prevalence of diabetes in peripheral artery disease patients is high and these patients are at increased risk for major cardiovascular events. This study's aim was to use a retrospective cohort to assess healthcare resource use (HRU) and costs among critical limb ischemia (CLI) patients with diabetes. METHODS: Using a major US database comprised of integrated administrative claims and electronic health records (2007-15), we estimated annual all-cause HRU and total healthcare costs for a sample of 3,189 CLI adults >50 years. CLI was characterized by rest pain, ulceration or gangrene. HRU and costs were calculated from medical and pharmacy claims for 1 year following first diagnosis of CLI (index date). Patients who died in the post-index period were included. We stratified patients into 2 cohorts: with and without pre-index diabetes diagnosis. Reverse Engineering and Forward Simulation (REFSTM), a hypothesis free machine learning platform that uses Bayesian network inference, was used to build an ensemble of prediction models for hospitalization and annual total healthcare costs of CLI patients. RESULTS: Nearly 50% of CLI patients had comorbid diabetes. Diabetics had more hospitalizations (mean [SD]: 1.5 [2.0] vs 1.1 [1.6], p
ISSN:1098-3015
1524-4733
DOI:10.1016/j.jval.2017.08.468