Outcomes Associated With Renal Replacement Therapy Use and Modality in Cardiogenic Shock Patients
Bibliographic record
Abstract
Background Renal replacement therapy (RRT) is frequently employed to treat severe acute kidney injury (AKI), which can occur in the setting of cardiogenic shock (CS). The outcomes associated with the utilization and modality of RRT—namely, intermittent hemodialysis (IHD) and continuous renal replacement therapy (CRRT)—in patients with CS remain poorly understood. Methods We included adult cardiac intensive care unit (CICU) patients with CS from 2007 to 2018 and compared outcomes based on the utilization and modality of RRT (IHD or CRRT). The primary outcome of in-hospital mortality was analyzed using logistic regression. The secondary outcome of 1-year mortality was analyzed using Cox regression. Regression models were adjusted for known mortality predictors. Results Out of 1498 patients with a CS diagnosis, 204 (13.6%) patients received RRT, of which 147 (77.0%) received CRRT. RRT patients were sicker, especially those who received CRRT, who had the highest severity of illness. A total of 499 (33.3%) patients died in hospital. RRT patients had higher in-hospital mortality (48.0% vs. 31.0%, adjusted odds ratio [OR] 2.04, 95% confidence interval [CI] 1.43–2.92, P < .001) and higher 1-year mortality, particularly among hospital survivors (adjusted hazard ratio [HR] 1.80, 95% CI 1.19–2.72, P = .005). In-hospital mortality (55.8% vs. 28.2%, adjusted OR 2.70, 95% CI 1.79–4.07, P < .001) and 1-year mortality were higher for patients who received CRRT compared with IHD. Overall 1-year survival was 33% for patients who received RRT. Conclusion The need for RRT is associated with higher short- and intermediate-term mortality in patients with CS. Patients requiring CRRT have an incrementally higher risk compared with those who receive IHD.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".