System for High-Intensity Evaluation During Radiation Therapy (SHIELD-RT): A Prospective Randomized Study of Machine Learning–Directed Clinical Evaluations During Radiation and Chemoradiation

Mené sur 963 patients atteints d'un cancer traité en ambulatoire par radiothérapie ou chimioradiothérapie, cet essai évalue l'intérêt d'un algorithme d'apprentissage automatique pour identifier les patients nécessitant une évaluation clinique soutenue (une ou deux fois par semaine) et réduire ainsi le taux de visites pour soins actifs durant le traitement

Résumé en anglais

PURPOSE : Patients undergoing outpatient radiotherapy (RT) or chemoradiation (CRT) frequently require acute care (emergency department evaluation or hospitalization). Machine learning (ML) may guide interventions to reduce this risk. There are limited prospective studies investigating the clinical impact of ML in health care. The objective of this study was to determine whether ML can identify high-risk patients and direct mandatory twice-weekly clinical evaluation to reduce acute care visits during treatment.

PATIENTS AND METHODS : During this single-institution randomized quality improvement study (ClinicalTrials.gov identifier: NCT04277650), 963 outpatient adult courses of RT and CRT started from January 7 to June 30, 2019, were evaluated by an ML algorithm. Among these, 311 courses identified by ML as high risk (> 10% risk of acute care during treatment) were randomized to standard once-weekly clinical evaluation (n = 157) or mandatory twice-weekly evaluation (n = 154). Both arms allowed additional evaluations on the basis of clinician discretion. The primary end point was the rate of acute care visits during RT. Model performance was evaluated using receiver operating characteristic area under the curve (AUC) and decile calibration plots.

RESULTS : Twice-weekly evaluation reduced rates of acute care during treatment from 22.3% to 12.3% (difference,