Association of Preoperative Diuretic Use With Early Acute Kidney Injury in Infants With Biventricular Hearts Following Cardiac Surgery
Bibliographic record
Abstract
Background Diuretics are used to manage congestive heart failure in infants with congenital heart disease. Adult data indicate that preoperative diuretic use increases the risk of cardiac surgery associated acute kidney injury (CS‐AKI). We have sought to understand if preoperative diuretics in infants increases the risk of CS‐AKI. Methods and Results This is a single‐center retrospective study of infants (1–12 months) who had CS requiring cardiopulmonary bypass between 2013 and 2018. The diagnosis and severity of CS‐AKI was defined according to the Kidney Disease Improving Global Outcomes guidelines. Three hundred patients were included (mean 6 months, SD 2.4, range 1.2–12.9 months). A total of 149 (49.7%) patients were diagnosed with CS‐AKI (stage 1: 80 [54%], stage 2: 57 [38%], stage 3: 12 [8%]). Logistic regression analysis showed preoperative diuretics were not associated with CS‐AKI (odds ratio [OR], 0.79; 95% CI, 0.43–1.44; P =0.45). A diagnosis of tetralogy of Fallot was an independent risk factor for CS‐AKI (OR, 3.49; 95% CI, 1.33–9.1, P =0.01). A diagnosis of tetralogy of Fallot (OR, 3.6; 95% CI, 1.28–10.22; P =0.02) and longer cardiopulmonary bypass (OR, 1.01; 95% CI, 1.0–1.02; P =0.04) time are risk factors for moderate to severe CS‐AKI. Conclusions Preoperative diuretic use does not contribute to the risk of CS‐AKI in infants early after surgery. A diagnosis of tetralogy of Fallot was the only risk factor for CS‐AKI identified using multivariate analysis in our cohort. Furthermore, a diagnosis of tetralogy of Fallot and longer cardiopulmonary bypass time are risk factors for moderate to severe CS‐AKI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.000 |
| 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".