Renal complications and subsequent mortality in acute critically ill patients without pre-existing renal disease
Bibliographic record
Abstract
BACKGROUND: Most studies of long-term renal outcomes after acute critical illness have enrolled patients with pre-existing renal dysfunction. We assessed renal outcomes in patients who did not have pre-existing renal disease and who were admitted to hospital for acute critical illness. METHODS: We identified adults who did not have pre-existing renal disease and who were admitted to hospital for acute critical illness between 2000 and 2011, from the Taiwan National Health Insurance Research Database. Each patient was matched 1:2 with controls without acute critical illness, according to age, sex and index date. A subset was further matched 1:1 with controls using propensity scores. Outcomes included acute kidney injury, chronic kidney disease and end-stage renal disease. RESULTS: We evaluated 33 613 patients with acute critical illness matched to 63 148 controls, of whom 14 218 were propensity matched to 14 218 controls. Patients with acute critical illness had incidence rates per 10 000 person-years of 9.45 for acute kidney injury, 78.3 for chronic kidney disease and 21.0 for end-stage renal disease. In the propensity-matched cohort, patients with acute critical illness had significantly higher risks of acute kidney injury (adjusted hazard ratio [aHR] 2.92, 95% confidence interval [CI] 1.78-4.77), chronic kidney disease (aHR 1.81, 95% CI 1.57-2.08), and end-stage renal disease (aHR 3.60, 95% CI 2.50-5.18). Acute critical illness conferred higher mortality risk among patients who subsequently developed end-stage renal disease (aHR 3.37, 95% CI 2.07-5.49) or chronic kidney disease (aHR 2.16, 95% CI 1.67-2.80). INTERPRETATION: Patients with acute critical illness and without pre-existing renal disease have a higher risk of adverse renal outcomes and subsequent mortality. A resolved episode of critical illness has implications for future renal function surveillance, even in patients without pre-existing renal disease.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".