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Use of Electronic Health Data to Estimate Heart Failure Events in a Population-Based Cohort with CKD
James S Floyd1,2,3, Robert Wellman4, Sharon Fuller4
1Cardiovascular Health Research Unit.
Insights
Electronic health data algorithms that include secondary heart failure (HF) diagnoses and outpatient encounters more accurately capture HF burden in chronic kidney disease (CKD) patients. Validation is key for precise HF event identification.
Area of Science:
- Nephrology
- Cardiology
- Health Informatics
Background:
- Electronic health data (EHR) studies often identify heart failure (HF) using only principal hospitalization diagnoses.
- This method may underestimate HF prevalence in patients with chronic kidney disease (CKD).
Purpose of the Study:
- To assess the accuracy of algorithms for identifying validated HF events from hospitalizations and outpatient encounters.
- To estimate the rate of HF events in a large CKD population using validated data.
Main Methods:
- A cohort of 15,141 adults with eGFR<60 ml/min/1.73 m² was identified (2008-2011).
- Potential HF events were randomly sampled for validation via medical record review.
- Positive predictive values (PPVs) were calculated for different encounter types.
Main Results:
- PPVs were 92% for principal diagnosis hospitalizations, 32% for secondary diagnosis hospitalizations, and 70% for outpatient encounters.
- The most comprehensive algorithm (including secondary diagnoses and outpatient encounters) estimated 35.2 HF events/1000 person-years.
- Algorithms using only principal diagnosis hospitalizations estimated 9.5 HF events/1000 person-years.
Conclusions:
- Algorithms incorporating secondary HF diagnoses and outpatient encounters provide a more complete picture of HF burden in EHR studies.
- Validation of HF events is crucial when using these broader data sources.
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.
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