Global, regional, and national burden of chronic kidney disease-related heart failure from 1990 to 2021: an analysis of data from the Global Burden of Disease Study 2021
Bibliographic record
Abstract
Abstract Background The global burden of chronic kidney diseases related heart failure (CKD-related HF) has yet to be reported. The study aims to analyze the global burden of CKD-related HF. Methods Utilizing the data from the Global Burden of Disease Study 2021, we delineated the prevalence and years lived with disability (YLDs) of CKD-related HF at global, regional, and national levels, alongside its age and sex distributions, as well as temporal trends. Furthermore, we conducted transnational inequality analysis and frontier analysis. Additionally, we analyzed the burden of CKD-related HF with diverse etiological factors. Results Over the past three decades, the global burden of CKD-related HF has escalated, reaching 1,936,886 cases in 2021, with an age-standardized prevalence of 24.21 per 100,000 and an age-standardized YLDs rate of 3.07 per 100,000. This burden is projected to continue rising over the next decade. The burden of CKD-related HF is notably concentrated among the elderly and children. Notably, there is health inequity in its distribution, disproportionately affecting low sociodemographic index (SDI) groups, although alleviation opportunities exist across all SDI levels. Globally, the primary contributors to CKD-related HF are type 2 Diabetes Mellitus (T2DM) and hypertension, apart from CKD of other and unspecified causes. Conclusions The global burden of CKD-related HF has witnessed increase and is projected to persist over the next decade. Certain populations, including children, the elderly, and regions with low SDI levels, experienced a heavier burden. Effective management of primary diseases contributes to mitigating the burden of CKD-related HF.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 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 | 0.001 |
| 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".