{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T16:08:18Z","timestamp":1777997298956,"version":"3.51.4"},"reference-count":33,"publisher":"American Medical Association (AMA)","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Oncol"],"published-print":{"date-parts":[[2025,10,1]]},"abstract":"<jats:sec id=\"ab-coi250044-4\"><jats:title>Importance<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec id=\"ab-coi250044-5\"><jats:title>Objective<\/jats:title><jats:p>To characterize the incidence of CKD after cisplatin treatment and evaluate prediction models.<\/jats:p><\/jats:sec><jats:sec id=\"ab-coi250044-6\"><jats:title>Design, Setting, and Participants<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec id=\"ab-coi250044-7\"><jats:title>Exposures<\/jats:title><jats:p>Predictive features included demographics, cancer diagnosis, cisplatin dose and schedule, comorbidities, laboratory testing, and patient-reported symptoms.<\/jats:p><\/jats:sec><jats:sec id=\"ab-coi250044-8\"><jats:title>Main Outcomes and Measures<\/jats:title><jats:p>The outcomes were CKD (estimated glomerular filtration rate [eGFR] &amp;amp;lt;60 mL\/min\/1.73 m<jats:sup>2<\/jats:sup>) and the eGFR after cisplatin treatment. Measures included the area under the receiver operating characteristic curve and the mean absolute error (MAE).<\/jats:p><\/jats:sec><jats:sec id=\"ab-coi250044-9\"><jats:title>Results<\/jats:title><jats:p>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 m<jats:sup>2<\/jats:sup> (95% CI, 7.8-8.4 mL\/min\/1.73 m<jats:sup>2<\/jats:sup>). 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 m<jats:sup>2<\/jats:sup> [95% CI, 12.3-13.0 mL\/min\/1.73 m<jats:sup>2<\/jats:sup>]; external-test MAE, 14.3 mL\/min\/1.73 m<jats:sup>2<\/jats:sup> [95% CI, 13.2-15.5 mL\/min\/1.73 m<jats:sup>2<\/jats:sup>]). Complex machine learning systems incorporating all features failed to improve predictions over the univariable models.<\/jats:p><\/jats:sec><jats:sec id=\"ab-coi250044-10\"><jats:title>Conclusions and Relevance<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.1001\/jamaoncol.2025.2590","type":"journal-article","created":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T15:00:31Z","timestamp":1755788431000},"page":"1179","source":"Crossref","is-referenced-by-count":2,"title":["Predicting Chronic Kidney Disease After Cisplatin Treatment Using Population-Level Data"],"prefix":"10.1001","volume":"11","author":[{"given":"Robert C.","family":"Grant","sequence":"first","affiliation":[{"name":"Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada"},{"name":"Ontario Institute for Cancer Research, Toronto, Ontario, Canada"},{"name":"ICES, Toronto, Ontario, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiang Chen","family":"He","sequence":"additional","affiliation":[{"name":"Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, 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