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Development of a Model for Predicting Early Discontinuation of Adjuvant Chemotherapy in Stage III Colon Cancer

Development of a Model for Predicting Early Discontinuation of Adjuvant Chemotherapy in Stage III... PURPOSE: To develop a tool that can be used to predict early discontinuation of adjuvant chemotherapy among patients with stage III colon cancer. PATIENTS AND METHODS: Through record linkage of Alberta administrative and tumor registry databases, we identified a cohort of individuals age >= 18 years who were diagnosed with stage III colon cancer and who received adjuvant chemotherapy in Alberta between 2004 and 2015. Early discontinuation was defined as receipt of < 5 months of a planned 6-month course of chemotherapy. By a systematic review of the literature and a survey of medical oncologists, the following candidate variables were identified: age (years), number of comorbidities (0, 1, >= 2), cancer stage (IIIC v IIIA-B), type of chemotherapy (fluorouracil, leucovorin, and oxaliplatin; capecitabine and oxaliplatin; or monotherapy), time from surgery to chemotherapy initiation (weeks), type of treatment facility (academic or community), and distance from home to treatment center (kilometers). Models developed using penalized logistic regression and the random forest algorithm were compared. Model performance was assessed using the C-statistic, Brier score, and a calibration plot. Internal validation was performed using the bootstrap method. RESULTS: From an initial 3,115 patients identified, 1,378 were deemed eligible for inclusion. Of these patients, 474 patients (34.4%) failed to complete at least 5 months of chemotherapy. Although well calibrated, the penalized logistic regression model had poor discrimination (optimism-adjusted C-statistic, 0.63; 95% CI, 0.60 to 0.67). In contrast, the random forest model achieved adequate discrimination (optimism-adjusted C-statistic, 0.80; 95% CI, 0.79 to 0.82). Although the degree of calibration of the random forest was acceptable, it was slightly worse than that of the penalized logistic regression model. CONCLUSION: Internal validation of our random forest model suggests that it may have clinical utility. Additional research regarding its external validation and clinical impact is needed. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JCO Clinical Cancer Informatics Wolters Kluwer Health

Development of a Model for Predicting Early Discontinuation of Adjuvant Chemotherapy in Stage III Colon Cancer

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Publisher
Wolters Kluwer Health
Copyright
(C) 2020 American Society of Clinical Oncology
ISSN
2473-4276
DOI
10.1200/CCI.20.00065
Publisher site
See Article on Publisher Site

Abstract

PURPOSE: To develop a tool that can be used to predict early discontinuation of adjuvant chemotherapy among patients with stage III colon cancer. PATIENTS AND METHODS: Through record linkage of Alberta administrative and tumor registry databases, we identified a cohort of individuals age >= 18 years who were diagnosed with stage III colon cancer and who received adjuvant chemotherapy in Alberta between 2004 and 2015. Early discontinuation was defined as receipt of < 5 months of a planned 6-month course of chemotherapy. By a systematic review of the literature and a survey of medical oncologists, the following candidate variables were identified: age (years), number of comorbidities (0, 1, >= 2), cancer stage (IIIC v IIIA-B), type of chemotherapy (fluorouracil, leucovorin, and oxaliplatin; capecitabine and oxaliplatin; or monotherapy), time from surgery to chemotherapy initiation (weeks), type of treatment facility (academic or community), and distance from home to treatment center (kilometers). Models developed using penalized logistic regression and the random forest algorithm were compared. Model performance was assessed using the C-statistic, Brier score, and a calibration plot. Internal validation was performed using the bootstrap method. RESULTS: From an initial 3,115 patients identified, 1,378 were deemed eligible for inclusion. Of these patients, 474 patients (34.4%) failed to complete at least 5 months of chemotherapy. Although well calibrated, the penalized logistic regression model had poor discrimination (optimism-adjusted C-statistic, 0.63; 95% CI, 0.60 to 0.67). In contrast, the random forest model achieved adequate discrimination (optimism-adjusted C-statistic, 0.80; 95% CI, 0.79 to 0.82). Although the degree of calibration of the random forest was acceptable, it was slightly worse than that of the penalized logistic regression model. CONCLUSION: Internal validation of our random forest model suggests that it may have clinical utility. Additional research regarding its external validation and clinical impact is needed.

Journal

JCO Clinical Cancer InformaticsWolters Kluwer Health

Published: Oct 30, 2020

References