Novel biomarkers to predict chronic kidney disease and hypertension at 3 and 12 months post-cisplatin.
Bibliographic record
Abstract
e22003 Background: Urine kidney injury biomarkers measured during cisplatin therapy may help identify patients at risk for adverse long-term kidney outcomes. In children treated for cancer, we examined associations of urine tubular injury biomarkers collected during two cisplatin cycles, with chronic kidney disease (CKD) and hypertension at 3 and 12 months post-cisplatin. Methods: We analyzed data from the Applying Biomarkers to Minimize Long-Term Effects of Childhood/Adolescent Cancer Treatment (ABLE) Nephrotoxicity Study: a twelve-center prospective cohort of 159 children receiving cisplatin. Urine tubular injury biomarkers (by ELISA: neutrophil gelatinase-associated lipocalin [NGAL]; kidney injury molecule-1 [KIM-1]; tissue inhibitor of metalloproteinase-2 [TIMP-2]; insulin-like growth factor-binding protein-7 [IGFBP-7]) were measured at three timepoints (pre-infusion; 12-24 hours post-infusion; discharge after cisplatin cycle) during an early cisplatin cycle (EarlyCisP: first or second cycle of therapy) and a later cycle (LateCisP: last or second-to-last cycle). Area under the curve (AUC) for biomarkers to predict CKD (estimated glomerular filtration rate below normal or urine albumin/creatinine above normal for age, per international definition) and hypertension (3 blood pressures; per American Academy of Pediatrics guidelines) at 3 and 12 months post-cisplatin was calculated. AUC change from adding biomarkers to a published clinical prediction model for 3-month outcomes was evaluated (DeLong method). Results: 156 patients were available for analysis (mainly solid tumors). 3-month (median 90 days) outcomes were 52/118 (44%) CKD; 17/125 (14%) hypertension. 12-month (median 335 days) outcomes were 47/118 (40%) CKD; 15/125 (12%) hypertension. AUCs for all biomarkers to predict 3 and 12-month CKD and hypertension were poor to modest (best AUC = 0.70, 95% CI 0.60-0.80 for pre-infusion EarlyCisP TIMP-2*IGFBP-7 to predict 3-month hypertension). Biomarkers did not improve the clinical prediction model for 3-month CKD (AUC change not statistically significant with vs. without biomarkers). Adding pre-infusion EarlyCisP NGAL and TIMP-2*IGFBP-7 to the clinical prediction model for 3-month hypertension led to AUC change from 0.81 (0.72-0.91) to 0.89 (0.83-0.95) (p < 0.05). Conclusions: Tubular injury biomarkers we studied were individually not strong predictors of 3 or 12-month post-cisplatin kidney outcomes. Adding biomarkers to existing clinical prediction models may help predict post-therapy hypertension development or identify higher kidney-risk patients.
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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 | 0.009 |
| 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".