PURPOSE: This prospective study aimed to determine the accuracy of radiation oncologists in predicting the survival of patients with metastatic disease receiving radiation therapy and to understand factors associated with their accuracy. METHODS AND MATERIALS: This single-institution study surveyed 22 attending radiation oncologists to estimate patient survival. Survival predictions were defined as accurate if the observed survival (OS) was within the correct survival prediction category (0-6 months, >6-12 months, >12-24 months, and >24 months). The physicians made survival estimates for each course of radiation, yielding 877 analyzable predictions for 689 unique patients. Data analysis included Stuart's Tau C, logistic regression models, ordinal logistic regression models, and stepwise selection to examine variable interactions. RESULTS: Of the 877 radiation oncologists' predictions, 39.7% were accurate, 26.5% were underestimations, and 33.9% were overestimations. Stuart's Tau C showed low correlation between OS and survival estimates (0.3499), consistent with the inaccuracy reported in the literature. However, results showed less systematic overprediction than reported in the literature. Karnofsky performance status was the most significant predictor of accuracy, with greater accuracy for patients with shorter OS. Estimates were also more accurate for patients with lower Karnofsky performance status. Accuracy by patient age varied by primary site and race. Physician years of experience did not correlate with accuracy. CONCLUSIONS: The sampled radiation oncologists have a 40% accuracy in predicting patient survival. Future investigation should explore how survival estimates influence treatment decisions and how to improve survival prediction accuracy.
PURPOSE: This prospective study aimed to determine the accuracy of radiation oncologists in predicting the survival of patients with metastatic disease receiving radiation therapy and to understand factors associated with their accuracy. METHODS AND MATERIALS: This single-institution study surveyed 22 attending radiation oncologists to estimate patient survival. Survival predictions were defined as accurate if the observed survival (OS) was within the correct survival prediction category (0-6 months, >6-12 months, >12-24 months, and >24 months). The physicians made survival estimates for each course of radiation, yielding 877 analyzable predictions for 689 unique patients. Data analysis included Stuart's Tau C, logistic regression models, ordinal logistic regression models, and stepwise selection to examine variable interactions. RESULTS: Of the 877 radiation oncologists' predictions, 39.7% were accurate, 26.5% were underestimations, and 33.9% were overestimations. Stuart's Tau C showed low correlation between OS and survival estimates (0.3499), consistent with the inaccuracy reported in the literature. However, results showed less systematic overprediction than reported in the literature. Karnofsky performance status was the most significant predictor of accuracy, with greater accuracy for patients with shorter OS. Estimates were also more accurate for patients with lower Karnofsky performance status. Accuracy by patient age varied by primary site and race. Physician years of experience did not correlate with accuracy. CONCLUSIONS: The sampled radiation oncologists have a 40% accuracy in predicting patient survival. Future investigation should explore how survival estimates influence treatment decisions and how to improve survival prediction accuracy.
Authors: Michael F Gensheimer; Sonya Aggarwal; Kathryn R K Benson; Justin N Carter; A Solomon Henry; Douglas J Wood; Scott G Soltys; Steven Hancock; Erqi Pollom; Nigam H Shah; Daniel T Chang Journal: J Am Med Inform Assoc Date: 2021-06-12 Impact factor: 4.497
Authors: Adrianna E Mojica-Márquez; Joshua L Rodríguez-López; Ankur K Patel; Diane C Ling; Malolan S Rajagopalan; Sushil Beriwal Journal: Cancer Med Date: 2020-06-26 Impact factor: 4.452
Authors: Johnny Kao; Mark K Farrugia; Samantha Frontario; Amanda Zucker; Emily Copel; John Loscalzo; Ashish Sangal; Boramir Darakchiev; Anurag Singh; Symeon Missios Journal: Cancer Med Date: 2021-09-30 Impact factor: 4.452