Incidence of CT Contrast Agent–Induced Nephropathy: Toward a More Accurate Estimation
Bibliographic record
Abstract
OBJECTIVE: Acute kidney injury is common among hospitalized patients and likely leads to inflated reports of the incidence of CT contrast agent-induced nephropathy as a cause of acute kidney injury. For a more accurate estimation, we compared the incidence of acute kidney injury immediately after contrast agent administration and a few days afterward in the same population. We also controlled for a creatinine level increase starting before and continuing after CT, which may be incorrectly associated with the scan itself. MATERIALS AND METHODS: After excluding patients undergoing dialysis, we included all adults who underwent CT from January 2006 through May 2013 in our health region. The incidence of acute kidney injury (Acute Kidney Injury Network stages) and dialysis after acute kidney injury were assessed in the immediate period (24-48 hours) and in a delayed period (72-96 hours) after the scan. New acute kidney injury in either period occurred if the creatinine level had increased at a greater rate than that in a preceding 24-hour interval. The incidence of acute kidney injury and dialysis after acute kidney injury attributable to CT were calculated by subtracting the delayed incidence from the immediate incidence. RESULTS: Incidences of acute kidney injury and dialysis after acute kidney injury attributable to contrast-enhanced CT were statistically insignificant across glomerular filtration rate (GFR) subgroups. Acute kidney injury incidences (Acute Kidney Injury Network stage I or worse) were 0.5% (95% CI, -0.4% to 1.4%) for GFR greater than 60 mL/min/1.73 m(2), 2.4% (95% CI, -0.7% to 5.6%) for GFR 30-59 mL/min/1.73 m(2), -4.3% (95% CI, -19.8% to 11.3%) for GFR 15-29 mL/min/1.73 m(2), and 0% (95% CI, -24.5% to 24.5%) for GFR less than 15 mL/min/1.73 m(2). CONCLUSION: There appears to be a minimal risk of CT contrast agent-induced nephropathy at mild to moderate levels of renal dysfunction.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".