OBJECTIVES: Coding of diagnoses in primary care lacks granularity, posing challenges to determining the clinical profile of patients. We investigated whether hospital data may improve the profiling of patients with heart failure (HF) diagnosed in primary care.

METHODS: The PHARMO Data Network includes anonymous, patient-level data from primary and secondary healthcare settings in the Netherlands. GP and hospital records were linked based on patient demographics and clinical characteristics. In GP data, patients with HF from 2015 to 2022 were identified using ICPC codes or a text-mining algorithm. In the linked hospital data, ICD-10 codes were used to further differentiate HF subtypes. Patients were followed from the first HF diagnosis in the study period until the end of the study period or death. Differences between patients with and without hospital-based HF diagnosis were tested using chi-squared tests, t-tests, standardised mean differences (SMD), and hazard ratios (HR).

RESULTS: The source population included 4,279,786 GP patients. After applying all inclusion and exclusion criteria, 66,525 patients with a recorded HF diagnosis in GP data were included, of which 48% (N=32,086) also had an HF diagnosis in the linked hospital data.

Among these, 80% had an unspecified HF diagnosis in primary care. Of these, 64% also had an unspecified diagnosis in hospital data, whereas 23% were diagnosed with left ventricular failure, 13% with congestive heart disease, and 1% had HF with hypertension.

Compared to patients identified in primary care only, those identified in both GP and hospital data had a higher number of comorbidities (3.5 vs 2.2, SMD=0.811), greater use of HF-related medications (4.4 vs 3.4, SMD=0.694), and greater risk of death (47% vs 40%, HR=1.13, CI: 1.10-1.16).

CONCLUSIONS: Linking GP and hospital data can provide more detailed clinical information and improve the identification of HF subtypes in real-world data, compared to GP data alone.