Validity of Heart Failure Diagnoses in Administrative Databases: A Systematic Review and Meta-Analysis

Abstract

ObjectiveHeart failure (HF) is an important covariate and outcome in studies of elderly populations and cardiovascular disease cohorts, among others. Administrative data is increasingly being used for long-term clinical research in these populations. We aimed to conduct the first systematic review and meta-analysis of studies reporting on the validity of diagnostic codes for identifying HF in administrative data.MethodsMEDLINE and EMBASE were searched (inception to November 2010) for studies: (a) Using administrative data to identify HF; or (b) Evaluating the validity of HF codes in administrative data; and (c) Reporting validation statistics (sensitivity, specificity, positive predictive value [PPV], negative predictive value, or Kappa scores) for HF, or data sufficient for their calculation. Additional articles were located by hand search (up to February 2011) of original papers. Data were extracted by two independent reviewers; article quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. Using a random-effects model, pooled sensitivity and specificity values were produced, along with estimates of the positive (LR+) and negative (LR−) likelihood ratios, and diagnostic odds ratios (DOR = LR+/LR−) of HF codes.ResultsNineteen studies published from1999–2009 were included in the qualitative review. Specificity was ≥95% in all studies and PPV was ≥87% in the majority, but sensitivity was lower (≥69% in ≥50% of studies). In a meta-analysis of the 11 studies reporting sensitivity and specificity values, the pooled sensitivity was 75.3% (95% CI: 74.7–75.9) and specificity was 96.8% (95% CI: 96.8–96.9). The pooled LR+ was 51.9 (20.5–131.6), the LR− was 0.27 (0.20–0.37), and the DOR was 186.5 (96.8–359.2).ConclusionsWhile most HF diagnoses in administrative databases do correspond to true HF cases, about one-quarter of HF cases are not captured. The use of broader search parameters, along with laboratory and prescription medication data, may help identify more cases.

Limited Accuracy of Administrative Data for the Identification and Classification of Adult Congenital Heart Disease

“…A recent meta‐analysis of the validity of heart failure diagnoses in administrative data sets suggests that the specificity and positive predictive value of these codes are high but with low sensitivity 7. Other studies have shown that the positive predictive value of ICD codes is highly variable8, 9, 10, 11 but also that the performance of these codes may be improved by the application of algorithms or the addition of multiple associated billing codes or further clinical information, such as we have tried to include 8, 12.…”

ICU Use and Quality of Care for Patients With Myocardial Infarction and Heart Failure

“…First, although previously validated definitions were used, our approach may have improperly identified patients with AMI or HF. 15,16 Second, risk adjustment was performed by using available administrative claims, which, if inadequate, may have introduced bias. 34 For instance, administrative claims may miss important clinical variables or imperfectly capture severity of illness; however, we used risk adjustment models similar to those used by the Centers for Medicare & Medicaid Services.…”

“…13,14 These methods of identifying patients with AMI or HF have been validated with high sensitivity and specificity. 15,16 We excluded patients admitted to hospitals without ICU capabilities, with < 25 total AMI or HF admissions, transfers from another hospital, or those discharged against medical advice.…”

Corpus domain effects on distributional semantic modeling of medical terms

“…The use of ICD-9 code 428.x as a primary or secondary diagnosis has been shown in multiple studies to have high precision (reported positive predictive value $87%) but relatively low recall (reported sensitivity $60%) for identification of patients with heart failure (see McCormick et al, (2014) for review). Thus, we focused on measuring the value added by the semantically expanded queries in terms of improvements in recall -additional patients that this query found as compared to baseline.…”