October 30, 2020Open Access
View Full PaperEvaluation of claims‐based computable phenotypes to identify heart failure patients with preserved ejection fraction
Key Result
Computable phenotypes using one in-patient or two out-patient ICD-9/ICD-10 diagnosis codes identified HFpEF with 46% sensitivity, 88% specificity, and 84% positive predictive value.
Study Design
Type
Cohort (n=3,755)
Multicenter
No
Structured PICO
Do claims-based computable phenotypes accurately identify patients with HFpEF compared to gold standard clinical criteria?
P
Population
3,755 residents of Olmsted County, Minnesota aged ≥ 20 years diagnosed with HF between 2007 and 2015, including a Framingham-validated cohort of 2,035 patients (1,172 with HFpEF).
I
Intervention
Computable phenotypes using claims-type data elements (ICD-9/ICD-10 diagnostic codes and lab test codes)
C
Comparator
Gold standard definition of HFpEF (Framingham criteria for HF and LVEF ≥ 50%)
O
Outcome
Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV)
Specific claims-based computable phenotypes can identify HFpEF patients with high specificity and PPV, though sensitivity remains low, enabling better use of administrative databases for HFpEF research.
Limitations
- Some misclassification remains
Abstract
The purpose of this analysis was to develop and validate computable phenotypes for heart failure (HF) with preserved ejection fraction (HFpEF) using claims-type measures using the Rochester Epidemiology Project. This retrospective study utilized an existing cohort of Olmsted County, Minnesota residents aged ≥ 20 years diagnosed with HF between 2007 and 2015. The gold standard definition of HFpEF included meeting the validated Framingham criteria for HF and having an LVEF ≥ 50%. Computable phenotypes of claims-type data elements (including ICD-9/ICD-10 diagnostic codes and lab test codes) both individually and in combinations were assessed via sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with respect to the gold standard. In the Framingham-validated cohort, 2,035 patients had HF; 1,172 (58%) had HFpEF. One in-patient or two out-patient diagnosis codes of ICD-9 428.3X or ICD-10 I50.3X had 46% sensitivity, 88% specificity, 84% PPV, and 54% NPV. The addition of a BNP/NT-proBNP test code reduced sensitivity to 35% while increasing specificity to 91% (PPV = 84%, NPV = 51%). Broadening the diagnostic codes to ICD-9 428.0, 428.3X, and 428.9/ICD-10 I50.3X and I50.9 increased sensitivity at the expense of decreasing specificity (diagnostic code-only model: 87% sensitivity, 8% specificity, 56% PPV, 30% NPV; diagnostic code and BNP lab code model: 61% sensitivity, 43% specificity, 60% PPV, 45% NPV). In an analysis conducted to mimic real-world use of the computable phenotypes, any one in-patient or out-patient code of ICD-9 428/ICD-10 150 among the broader population (N = 3,755) resulted in lower PPV values compared with the Framingham cohort. However, one in-patient or two out-patient instances of ICD-9 428.0, 428.9, or 428.3X/ICD-10 150.3X or 150.9 brought the PPV values from the two cohorts closer together. While some misclassification remains, the computable phenotypes defined here may be used in claims databases to identify HFpEF patients and to gain a greater understanding of the characteristics of patients with HFpEF.