Time to Revisit the Problem of CIN? The Low Incidence of Acute Kidney Injury with and without Contrast in Hospitalized Patients: An Observational Cohort Study
Bibliographic record
Abstract
BACKGROUND: Acute kidney injury (AKI) following imaging procedures with contrast medium in hospitalized patients is commonly attributed to contrast-induced nephropathy (CIN). This study sought to establish a benchmark of the incidence of AKI in hospitalized patients who underwent computed tomography (CT) scans, with and without intravenous contrast administration. METHODS: This was a multi-center observational cohort study. Hospitalized patients in four hospitals with CT scans during two time periods in 2012 and 2013 were included. AKI post-scan was defined as a change in serum creatinine (sCr) in absolute terms of ≥26.5 μmol/L (≥0.3 mg/dl), occurring within 7 days of the CT scan. AKI incidence was examined by study phases and CT-scan types using logistic regression models. Multinomial logistic regression was used to examine the proportions of sCr availability between two study phases. RESULTS: Three hundred and twenty-five patients in Period 1 and 518 patients in Period 2 were included in the study. The incidence of AKI in Period 1 was similar in those who received contrast and in those who did not (11.6 % [95 % C.I.: 6.5, 18.7] vs. 10.1 % [95 % C.I.: 5.1, 17.3]; p = 0.38). The incidence of AKI remained not significantly different between the two periods in those who received contrast (11.6 % [95 % C.I.: 6.5, 18.7] vs. 10.7 % [95 % C.I.: 6.8, 15.8]; p = 0.89) and those who did not (10.1 % [95 % C.I.: 5.1, 17.3] vs. 9.1 % [95 % C.I.: 5.2, 14.6]; p = 0.54). Among those who received contrast, there was a significant increase in the availability of both pre- and post- CT scan sCr in Period 2 compared to Period 1 (73.6 % [95 % C.I.: 67.7, 80.6] vs. 79.8 % [95 % C.I.: 75.2, 84.7]; p = 0.006). LIMITATIONS: Our study was not targeted to specifically assess the impact of a prevention protocol on the incidence of AKI and was limited to settings within one health authority in the province. CONCLUSION: In hospitalized patients, the incidence of AKI is low, not different between those who did and did not receive contrast, and was not impacted by improvement in the monitoring of sCr in at risk patients. A better understanding of the determinants of AKI post-contrast scan is required to improve strategies to reduce the incidence of AKI.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".