Predicting costs of care in heart failure patients

David H Smith1, Eric S Johnson, David K Blough

  • 1The Center for Health Research, Kaiser Permanente Northwest, 3800 N, Interstate Avenue, Portland, OR 97227, USA. David.h.smith@kpchr.org

Insights

Identifying heart failure patients with higher costs or poor outcomes is key. Comorbidities like diabetes and depression significantly increase costs, while anemia and low ejection fraction predict higher mortality.

Area of Science:

  • Cardiology
  • Health Economics
  • Clinical Research

Background:

  • Identifying patients with heart failure at high risk for adverse outcomes is crucial for targeted interventions.
  • Understanding the economic burden associated with heart failure events is essential for resource allocation.

Purpose of the Study:

  • To comprehensively analyze heart failure events and their economic impact.
  • To identify patient characteristics predicting increased healthcare costs or poor outcomes in heart failure patients.

Main Methods:

  • Electronic medical data from 4,696 heart failure patients within a large HMO (1999-2004) were analyzed.
  • Patient data included demographics, clinical findings, laboratory results, comorbidities, and incident heart failure status.
  • One-year follow-up data were used, employing regression methods suitable for censored cost data and mortality analysis.

Main Results:

  • Several comorbidities significantly increased costs: chronic lung disease (29%), diabetes (38%), depression (36%), atrial fibrillation (15%), coronary artery disease (14%), and hyperlipidemia (21%).
  • Anemia and lower ejection fraction were paradoxically associated with lower costs but higher mortality rates.
  • Incident heart failure occurred in 8% of the patient cohort.

Conclusions:

  • Management of heart failure should focus on controlling factors independently linked to increased costs and adverse outcomes.
  • Economic analyses in high-mortality diseases like heart failure necessitate careful consideration of mortality's impact on overall cost assessment.
Abstract

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