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Med Care. 2014 May;52(5):e30-8. doi: 10.1097/MLR.0b013e31825a8c22.

Performance of claims-based algorithms for identifying heart failure and cardiomyopathy among patients diagnosed with breast cancer.

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*Section of Advanced Heart Failure and Transplantation, Division of Cardiology, University of Colorado Anschutz Medical Center, Aurora, CO †Department of Population Sciences, Henry Ford Hospital and Health System, Detroit, MI ‡Department of Epidemiology, Boston University School of Public Health, Boston, MA §Group Health Research Institute, Group Health Cooperative, Seattle, WA ∥Division of Research, Kaiser Permanente Northern California, Oakland, CA ¶Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School #Department of Medicine, Harvard Vanguard Medical Associates, Boston, MA **Center for Health Research-Southeast, Kaiser Permanente Georgia, Atlanta, GA ††Marshfield Clinic Research Foundation, Marshfield Clinic, Marshfield ‡‡Department of Hematology/Oncology, Weston, WI §§Institute for Health Research, Kaiser Permanente Colorado, Denver, CO.



Cardiotoxicity is a known complication of certain breast cancer therapies, but rates come from clinical trials with design features that limit external validity. The ability to accurately identify cardiotoxicity from administrative data would enhance safety information.


To characterize the performance of clinical coding algorithms for identification of cardiac dysfunction in a cancer population.


We sampled 400 charts among 6460 women diagnosed with incident breast cancer, tumor size ≥ 2 cm or node positivity, treated within 8 US health care systems between 1999 and 2007. We abstracted medical records for clinical diagnoses of heart failure (HF) and cardiomyopathy (CM) or evidence of reduced left ventricular ejection fraction. We then assessed the performance of 3 different International Classification of Diseases, 9th Edition (ICD-9)-based algorithms.


The HF/CM coding algorithm designed a priori to balance performance characteristics provided a sensitivity of 62% (95% confidence interval, 40%-80%), specificity of 99% (range, 97% to 99%), positive predictive value (PPV) of 69% (range, 45% to 85%), and negative predictive value (NPV) of 98% (range, 96% to 99%). When applied only to incident HF/CM (ICD-9 codes and gold standard diagnosis both occurring after breast cancer diagnosis) in patients exposed to anthracycline and/or trastuzumab therapy, the PPV was 42% (range, 14% to 76%).


Claims-based algorithms have moderate sensitivity and high specificity for identifying HF/CM among patients with invasive breast cancer. As the prevalence of HF/CM among the breast cancer population is low, ICD-9 codes have high NPV but only moderate PPV. These findings suggest a significant degree of misclassification due to HF/CM overcoding versus incomplete clinical documentation of HF/CM in the medical record.

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