Tubular Injury Biomarkers to Predict CKD and Hypertension at 3 Months Post-Cisplatin in Children
Bibliographic record
Abstract
Key Points Tubular injury biomarkers are not individually strong predictors of 3-month post-cisplatin CKD. When combined with clinical measures, tubular injury biomarkers can predict post-therapy hypertension and identify high-risk patients. Background Urine kidney injury biomarkers measured during cisplatin therapy may identify patients at risk of adverse subsequent kidney outcomes. We examined relationships between tubular injury biomarkers collected early (early visit [EV]: first or s econd cisplatin cycle) and late (late visit: last or second-last cisplatin cycle) during cisplatin therapy, with 3-month post-cisplatin CKD and hypertension (HTN). Methods We analyzed data from the Applying Biomarkers to Minimize Long-Term Effects of Childhood/Adolescent Cancer Treatment Nephrotoxicity study, a 12-center prospective cohort study of 159 children receiving cisplatin. We measured urine neutrophil gelatinase-associated lipocalin (NGAL)/creatinine, kidney injury molecule-1/creatinine, tissue inhibitor of metalloproteinase-2 (TIMP-2), and insulin-like growth factor-binding protein 7 (IGFBP-7) (TIMP-2 and IGFBP-7 expressed as their product, ng/ml 2 /1000) at an EV and late visit during cisplatin therapy with preinfusion, postinfusion, and hospital discharge sampling. Area under the curve (AUC) was calculated for biomarkers to detect 3-month post-cisplatin CKD (Kidney Disease Improving Global Outcomes guidelines: low eGFR or elevated urine albumin-to-creatinine ratio for age) and HTN (three BPs; per American Academy of Pediatrics guidelines). Results At median follow-up of 90 days, 52 of 118 patients (44%) and 17 of 125 patients (14%) developed CKD and HTN, respectively. Biomarker prediction for 3-month CKD was low to modest; NGAL combined with kidney injury molecule-1 at EV discharge yielded the highest AUC (0.67; 95% confidence interval, 0.57 to 0.77). Biomarker prediction of 3-month HTN was stronger, but modest; the highest AUC was from combining EV preinfusion NGAL and TIMP-2×IGFBP-7 (0.71; 95% confidence interval, 0.62 to 0.80). When EV preinfusion NGAL and TIMP-2×IGFBP-7 were added to the 3-month HTN clinical predictive model, AUCs increased from 0.81 (0.72 to 0.91) to 0.89 (0.83 to 0.95) ( P < 0.05). Conclusions Tubular injury biomarkers we studied were individually not strong predictors of 3-month post-cisplatin kidney outcomes. Adding biomarkers to existing clinical prediction models may help predict post-therapy HTN and 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".