{"id":23028,"date":"2025-08-21T15:51:34","date_gmt":"2025-08-21T19:51:34","guid":{"rendered":"https:\/\/www.ices.on.ca\/?post_type=journal_article&#038;p=23028"},"modified":"2025-12-16T15:55:32","modified_gmt":"2025-12-16T20:55:32","slug":"predicting-chronic-kidney-disease-after-cisplatin-treatment-using-population-level-data","status":"publish","type":"journal_article","link":"https:\/\/www.ices.on.ca\/fr\/publications\/journal-articles\/predicting-chronic-kidney-disease-after-cisplatin-treatment-using-population-level-data\/","title":{"rendered":"Predicting chronic kidney disease after cisplatin treatment using population-level data"},"content":{"rendered":"<p><strong>Importance<\/strong> \u2014 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.<\/p>\n<p><strong>Objective<\/strong> \u2014 To characterize the incidence of CKD after cisplatin treatment and evaluate prediction models.<\/p>\n<p><strong>Design, setting, and participants<\/strong> \u2014 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.<\/p>\n<p><strong>Exposures<\/strong> \u2014 Predictive features included demographics, cancer diagnosis, cisplatin dose and schedule, comorbidities, laboratory testing, and patient-reported symptoms.<\/p>\n<p><strong>Main outcomes and measures<\/strong> \u2014The outcomes were CKD (estimated glomerular filtration rate [eGFR] &lt;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).<\/p>\n<p><strong>Results<\/strong> \u2014 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.<\/p>\n<p><strong>Conclusions and relevance<\/strong> \u2014 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.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Importance \u2014 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 \u2014 To characterize the incidence of CKD after cisplatin treatment [&hellip;]<\/p>\n","protected":false},"template":"","migration-helper-automated":[],"migration-manual":[],"topic":[49,56],"migration-helper-qa-sample-set":[],"class_list":["post-23028","journal_article","type-journal_article","status-publish","hentry","topic-kidney-disease","topic-pharmacoepidemiology-and-drug-safety"],"acf":{"citation":"Grant RC, He JC, Liu N, Podolsky S, Notta F, Ghassemi M, Gallinger S, Knezevic A, Latcha S, Jaimes E, Kitchlu A, Chan K. <em>JAMA Oncol<\/em>. 2025; 11(10): 1179-1185.","source_url":"https:\/\/doi.org\/10.1001\/jamaoncol.2025.2590","ices_scientist":[17439,1185],"site":[6733],"research_program":[6741],"news_release":"","journal_article":"","atlas":"","research_report":"","infographic":"","video":"","downloads":null,"links":null,"sitecore_item_id":"","sitecore_item_name":"","sitecore_field_values":"","previous_url":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>ICES | Predicting chronic kidney disease after cisplatin treatment using population-level data<\/title>\n<meta name=\"description\" content=\"Importance \u2014 Cisplatin is a widely used treatment for cancer that can permanently damage the kidneys. 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