Use of Electronic Health Data to Estimate Heart Failure Events in a Population-Based Cohort with CKD

Abstract

Background and objectives: Studies that use electronic health data typically identify heart failure (HF) events from hospitalizations with a principal diagnosis of HF. This approach may underestimate the total burden of HF among persons with CKD. We assessed the accuracy of algorithms for identifying validated HF events from hospitalizations and outpatient encounters, and we used this validation information to estimate the rate of HF events in a large CKD population.

Design, setting, participants, & measurements: We identified a cohort of 15,141 adults age 18-89 years with an eGFR<60 ml/min per 1.73 m² from 2008 to 2011. Potential HF events during follow-up were randomly sampled for validation with medical record review. Positive predictive values from the validation study were used to estimate the rate of validated HF events in the full cohort.

Results: A total of 1864 participants had at least one health care encounter that qualified as a potential HF event during 2.7 years of mean follow-up. Among 313 potential events that were randomly sampled for validation, positive predictive values were 92% for hospitalizations with a principal diagnosis of HF, 32% for hospitalizations with a secondary diagnosis of HF, and 70% for qualifying outpatient HF encounters. Through use of this validation information in the full cohort, the rate of validated HF events estimated from the most comprehensive algorithm that included principal and secondary diagnosis hospitalizations and outpatient encounters was 35.2 events/1000 person-years (95% confidence interval, 33.1 to 37.4), compared with 9.5 events/1000 person-years (95% confidence interval, 8.7 to 10.5) from the algorithm that included only principal diagnosis hospitalizations. Outpatient encounters accounted for 20% of the total number of validated HF events.

Conclusions: In studies that rely on electronic health data, algorithms that include hospitalizations with a secondary diagnosis of HF and outpatient HF encounters more fully capture the burden of HF, although validation of HF events may be necessary with this approach.

Keywords: EGFR protein, human; adult; algorithms; chronic kidney disease; electronic health records; epidemiology and outcomes; follow-up studies; heart failure; hospitalization; humans; medical records; outpatients; receptor, epidermal growth factor.

Figure 1.

Eligibility criteria and study design. GHC, Group Health Cooperative; PPV, positive predictive value.

Figure 2.

Estimated incidence rates for heart failure events in the full cohort by age. Heart failure events were estimated from the algorithm that included all heart failure hospitalizations and outpatient heart failure encounters using the validation results in Table 2.

Figure 3.

Estimated incidence rates for heart failure events in the full cohort by eGFR. Heart failure events were estimated from the algorithm that included all heart failure hospitalizations and outpatient heart failure encounters using the validation results in Table 2.