The Effect of Urine pH and Urinary Uric Acid Levels on the Development of Contrast Nephropathy
Bibliographic record
Abstract
BACKGROUND: Hyperuricemia may cause acute kidney injury by activating inflammatory, pro-oxidative and vasoconstrictive pathways. In addition, radiocontrast causes an acute uricosuria, potentially leading to crystal formation. We therefore aimed to investigate the effect of urine acidity and urine uric acid level on the development of contrast-induced nephropathy (CIN) in patients undergoing elective coronary angiography. METHODS: We enrolled 175 patients who underwent elective coronary angiography. CIN was defined as a >25% increase in the serum creatinine levels relative to basal values 48-72 h after contrast use. Prior to coronary angiography and 48-72 h later, serum uric acid, urea, creatinine, bicarbonate levels, and spot uric acid to creatinine ratio (UACR) were measured. RESULTS: Of the 175 subjects included, 29 (16.6%) developed CIN. Those who developed CIN had a higher prevalence of diabetes, higher UACR (0.60 vs. 0.44, p = 0.014), higher contrast volume, and lower serum sodium level. With univariate analysis of a logistic regression model, the risk of CIN was found to be associated with diabetes (p = 0.0016, OR = 3.8 [95% CI: 1.7-8.7]), urine UACR (p = 0.0027, OR = 9.6 [95% CI: 2.2-42.2]), serum sodium (p = 0.0079, OR = 0.8 [95% CI: 0.77-0.96]), and contrast volume (p = 0.0385, OR = 1.8 [95% CI: 1.03-3.09]). In a multiple logistic regression model with stepwise method of selection, diabetes (p = 0.0120, OR = 3.2 [95% CI: 1.3-8.1]) and UACR (p = 0.0163, OR = 6.9 [95% CI: 1.4-33.4]) were the 2 risk factors finally identified. CONCLUSIONS: We have demonstrated that higher urine UACR is associated with the development of CIN in patients undergoing elective coronary angiography.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".