Bibliographic Details
| Title: |
Machine Learning Algorithm Prospectively Predicts Survival for High-Risk Patients Undergoing Radiotherapy: A Survival Analysis of SHIELD-RT. |
| Authors: |
Natesan, D.1 (AUTHOR), Thomas, S.M.2 (AUTHOR), Eclov, N.1 (AUTHOR), Dalal, N.H.3 (AUTHOR), Stephens, S.J.1 (AUTHOR), Malicki, M.4 (AUTHOR), Shields, S.1 (AUTHOR), Cobb, A.1 (AUTHOR), Mowery, Y.M.1 (AUTHOR), Niedzwiecki, D.2 (AUTHOR), Tenenbaum, J.D.5 (AUTHOR), Palta, M.1 (AUTHOR), Hong, J.C.6 (AUTHOR) |
| Source: |
International Journal of Radiation Oncology, Biology, Physics. 2021 Supplement, Vol. 111 Issue 3, pS113-S113. 1p. |
| Subjects: |
Clinical prediction rules, Machine learning, Survival analysis (Biometry), Individualized medicine, Proportional hazards models, Head & neck cancer, Oncologists |
| Abstract: |
Purpose/objective(s): Mortality prediction is critical to appropriate cancer care planning. This has become a topic of interest, with machine learning (ML) tools demonstrating accurate binary predictions for mortality at specific time points. There are limited prospective data validating the clinical utility of health care ML tools. We previously developed a ML algorithm which predicts risk of acute care (ER visits or hospitalizations). SHIELD-RT was a randomized controlled study which demonstrated that a ML algorithm could direct supplemental clinic visits to reduce the rate of acute care during RT from 22% to 12%. While the algorithm was trained for acute care visits, we sought to determine whether ML risk would facilitate multiple clinical uses through survival prediction in this prospective study.Materials/methods: Patients who initiated RT from 1/7/2019-6/30/19 at a single institution were evaluated during the first week of treatment by the electronic health record-based ML algorithm. High risk patients (> 10% risk of acute visit) were randomized to standard weekly evaluations (S) or twice weekly evaluations (TW). Patient, disease, and treatment factors were collected prospectively. Survival was recorded retrospectively. Unadjusted OS was estimated with the Kaplan Meier method and compared between arms with the log rank test. Cox proportional hazards models were used to estimate the association of covariates with OS after adjustment for study arm. Hazard ratios (HRs) and 95% confidence intervals (CIs) are reported.Results: Patients undergoing 311 distinct courses were randomized to S (n = 157) or TW (n = 154) evaluations. The 1-year OS was similar for those who underwent S (58%, 95% CI 50%-65%) or TW (66%, 95% CI 58%-73%) evaluations, P = 0.35. ML risk on a continuous basis was significantly associated with survival (HR 4.27, 95% CI 0.98-18.6, P = 0.05). Other factors associated with OS included age (HR 1.01, 95% CI 1.00-1.03, P = 0.04), palliative treatment vs curative treatment (HR 6.59 (95% CI 4.62-9.41, < 0.001), widely metastatic disease vs no metastatic disease (HR 6.86, 95% CI 4.74-9.92, P < 0.001). Treatment of certain disease sites were also associated with OS: brain metastases (HR 5.00, 95% CI 3.16-7.93, P < 0.001), bone metastases (HR 4.69, 95% CI 3.09-7.12, P < 0.001), thoracic (HR 2.44, 95% CI 1.61-3.7, P < 0.001), head and neck cancer (HR 0.44, 95% CI 0.22-0.91, P = 0.03), genitourinary cancer (HR 0.32, 95% CI 0.12-0.85, P = 0.02). Admission during RT + 15 days was associated with decreased OS (HR 2.32, 95% CI 1.58-3.40, P < 0.001).Conclusion: Our ML algorithm, developed to predict acute care, also predicted OS. This supports the correlation between the two event types and demonstrates the utility of ML to provide multiple predictive functions. Implementation of predictive ML algorithms into oncology clinical workflows may aid in prognostication to guide personalized patient care and influence treatment decisions. (NCT03775265). [ABSTRACT FROM AUTHOR] |
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| Database: |
Engineering Source |
