Comparison of risk factors for contrast‐induced acute kidney injury between patients with and without diabetes
Bibliographic record
Abstract
Although it is well known that diabetics are at a higher risk of contrast-induced acute kidney injury (CI-AKI) than nondiabetic patients, the reason for this discrepancy is not well known. Thus, in this study, we compared the predisposing factors for CI-AKI between patients with and without diabetes. We prospectively studied 290 consecutive in-hospital patients including 88 diabetics undergoing coronary angiography or a percutaneous coronary intervention in Kowsar hospital, and we compared risk factors for CI-AKI between diabetic and nondiabetic patients. CI-AKI was defined as RIFLE criteria within 48 hours after contrast exposure. The incidence of CR-AKI was significantly higher in diabetic patients compared with nondiabetics (P<0.05). The incidence of CI-AKI was significantly higher in patients with diabetes and left-ventricular ejection fraction ≤40%, hypercholesterolemia, serum creatinine ≥1.1 mg/dL, estimated glomerular filtration rate (eGFR) <90 mL/min, Contrast volume ≥80 (mL), maximum safe contrast volume factor of 1.5, and dehydration, while in nondiabetics, a significantly higher incidence of CR-AKI was observed in those with serum creatinine ≥1.1 mg/dL (P=0.02) and/or eGFR<60 mL/min (P=0.01). Multiple logistic regression analysis showed hyperchlosteremia to be the strongest predictor of AKI (P=0.01, B:14.5) in diabetics, followed by eGFR<90 (P=0.05, B:12.4) but, in nondiabetics, only eGFR<60 predicted the occurrence of CI-AKI (P=0.04, B:2.3). It seems that the predisposing factors to CI-AKI differ in diabetics and nondiabetics. In patients with diabetes, hypercholesterolemia is the strongest predictor of CI-AKI, followed by eGFR and diabetics are at risk for CI-AKI in the early stage of chronic kidney disease (stage 2), accounting for the higher incidence of CI-AKI in them.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".