Unveiling gaps in heart failure management in Germany: a retrospective analysis of national data and healthcare utilisation patterns
Bibliographic record
Abstract
Abstract Background Heart failure (HF) is a severe condition marked by high morbidity and mortality rates. However, there is a paucity of recent national data, crucial for policymakers and healthcare professionals to make informed decisions regarding disease management and resource allocation. Here, we present a secondary analysis of insurance claims data, aiming to explore the epidemiology, comorbidities, and healthcare utilisation patterns of HF patients in Germany. Methods A retrospective analysis was conducted using German claims data, involving ca. 4.5 million individuals from Statutory Health Insurance (SHI) providers from January 2018 to December 2022. This dataset was representative of age, sex, and morbidity, allowing for extrapolation to the entire German SHI population. We examined disease prevalence, all-cause mortality, comorbidities, and various healthcare resource metrics. HF patients were identified using ICD-10 codes (I50 [terminal], I50.01, I50.1, I50.9, I11.0, I13.0, I13.2), including patients with reduced (HFrEF), mildly reduced (HFmrEF), and preserved ejection fraction (HFpEF), though phenotype differentiation was not possible. Medications were classified by Anatomical Therapeutic Chemical codes. Results In 2018, HF prevalence was 5.0%, with an average hospitalisation rate of 9.1% for HF. By 2022, prevalence slightly decreased to 4.8%, while hospitalization rates increased to 9.4%. The all-cause mortality among HF patients rose from 9.7% in 2018 to 10.7% in 2022. Average annual expenses increased from 12,477€ per patient in 2018 to 14,530€ in 2022. Notably, in 2022, only 23.0% of HF patients received a mineralocorticoid receptor antagonist (MRA), and only 17.2% were treated with a sodium-glucose cotransporter 2 inhibitor (SGLT2i), with a substantial decrease of SGLT2i prescriptions among older patients (9.9% for those over 85 years). Despite an estimated half of the cohort potentially having HFrEF1, only 7.0% received comprehensive "four pillar" therapy with SGLT2i, beta-blockers (BB), MRA, and renin-angiotensin system inhibitors (RAASi). [Note: The observation period ended in 2022, prior to the guideline recommendations for SGLT2i usage in HFmrEF and HFpEF established in 2023.] Common comorbidities of HF patients in 2022 included arterial hypertension (93.2%), dyslipidaemia (65.2%), coronary heart disease (52.9%), type 2 diabetes (41.6%), atrial fibrillation and flutter (39.5%), and chronic kidney disease (38.9%). Conclusion This analysis provides a comprehensive overview of HF epidemiology and management in Germany. The consistently high HF prevalence and HF-related hospitalisation rates, coupled with low adherence to guideline-directed medical therapy, highlight the need for targeted interventions to alleviate this growing burden. These might comprise the support of effective HF management programmes, which are yet to be established for most HF patients in Germany, despite the strong recommendation of international guidelines.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".