Predicting Chronic Kidney Disease After Cisplatin Treatment Using Population-Level Data
Bibliographic record
Abstract
Importance: Cisplatin is a widely used treatment for cancer that can permanently damage the kidneys. Treatment modifications and other strategies may prevent chronic kidney disease (CKD) in patients at risk; however, the incidence and predictability of CKD following cisplatin treatment remain poorly understood. Objective: To characterize the incidence of CKD after cisplatin treatment and evaluate prediction models. Design, Setting, and Participants: In this population-based prognostic study, prediction models were developed based on a retrospective cohort study of patients who received cisplatin chemotherapy for nonhematologic cancer in an outpatient setting between July 1, 2014, and June 30, 2017. Models were tested on a temporal-test cohort of patients from Ontario, Canada, who started treatment between July 1, 2017, and June 30, 2020, and an external-test cohort of patients from a single center in the United States. Data were analyzed from May 1, 2021 to May 7, 2025. Exposures: Predictive features included demographics, cancer diagnosis, cisplatin dose and schedule, comorbidities, laboratory testing, and patient-reported symptoms. Main Outcomes and Measures: The outcomes were CKD (estimated glomerular filtration rate [eGFR] <60 mL/min/1.73 m2) and the eGFR after cisplatin treatment. Measures included the area under the receiver operating characteristic curve and the mean absolute error (MAE). Results: The population-level cohort included 9521 patients (median age, 63 years [IQR, 56-70 years]; 4841 men [50.8%]). Among the 9010 patients without pretreatment CKD, 1228 (13.6%) developed CKD, 81 (0.9%) developed grade 4 or worse CKD, and 16 (0.18%) required dialysis. The eGFR decreased by a mean of 8.1 mL/min/1.73 m2 (95% CI, 7.8-8.4 mL/min/1.73 m2). A simple spline-based regression model based solely on the pretreatment eGFR predicted posttreatment CKD in the temporal-test cohort (area under the curve, 0.80 [95% CI, 0.78-0.82]) and the external-test cohort (area under the curve, 0.73 [95% CI, 0.66-0.78]). Similarly, the posttreatment eGFR was predicted by a spline regression based solely on the pretreatment eGFR (temporal-test MAE, 12.6 mL/min/1.73 m2 [95% CI, 12.3-13.0 mL/min/1.73 m2]; external-test MAE, 14.3 mL/min/1.73 m2 [95% CI, 13.2-15.5 mL/min/1.73 m2]). Complex machine learning systems incorporating all features failed to improve predictions over the univariable models. Conclusions and Relevance: This study found that cisplatin treatment was followed by a predictable decrease in the eGFR, placing patients with a lower baseline eGFR at the highest risk of CKD. A simple model based on the pretreatment eGFR predicts CKD risk and could guide clinical decision-making.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".