Impact of Heart Failure and Chronic Kidney Disease on All‐Cause Mortality in Patients Undergoing Coronary Angiography: Results From the Finnish Kardio Multicentre Registry
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) and chronic kidney disease (CKD) are common comorbidities among patients undergoing coronary angiography. Both conditions are associated with increased risk of adverse cardiovascular outcomes and mortality. However, the joint prognostic impact of HF and CKD in this patient population remains unclear. AIMS: We aimed to evaluate the separate and combined associations of HF and CKD with all-cause mortality in patients undergoing coronary angiography. METHODS: We analyzed data from the KARDIO registry, an ongoing real-life clinical database of patients undergoing coronary angiography. Multivariable Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for all-cause mortality. RESULTS: Over a median follow-up of 5.5 years, 11,896 all-cause deaths were recorded. In multivariable-adjusted analyses, a history of HF was associated with a higher risk of mortality compared to no HF (HR: 2.19; 95% CI: 2.04-2.34), as was a history of CKD compared to no CKD (HR: 2.06; 95% CI: 1.94-2.19). The associations persisted on mutual adjustment for each exposure. When assessed jointly, and using patients with neither condition (No HF-No CKD) as the reference group, the adjusted HRs (95% CI) for mortality were 2.18 (2.01-2.36) for HF only, 2.04 (1.90-2.20) for CKD only, and 3.09 (2.77-3.46) for patients with both HF and CKD. Interaction analyses revealed that the associations of HF and CKD with mortality were significantly modified by each other, with the highest risk observed among individuals with both conditions. Subgroup analyses showed consistent directions of association across most categories, although the prognostic impact of HF and CKD varied by age, sex, and cardiovascular disease history. CONCLUSIONS: In patients undergoing coronary angiography, HF and CKD were each independently associated with a twofold increased risk of all-cause mortality. Interaction effects suggest that the co-occurrence of HF and CKD synergistically amplifies this risk. These findings highlight the importance of identifying and managing cardiorenal comorbidity to improve outcomes in this high-risk population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".