Machine Learning-Based Prediction of Clinical Outcomes in Patients With Cancer Receiving Systemic Treatment Using Step Count Data Measured With Smartphones.

Purpose: This study aimed to investigate whether changes in step count, measured using patients' own smartphones, could predict a clinical adverse event in the upcoming week in patients undergoing systemic anticancer treatments using machine learning models.

Methods: This prospective observational cohort study included patients with various cancer types receiving systemic anticancer treatment. Physical activity was monitored continuously using patients' own smartphones, measuring daily step count for 90 days during treatment. Clinical adverse events (ie, unplanned hospitalizations and treatment modifications) were extracted from medical records. Models predicting adverse events in the upcoming 7 days were created using physical activity data from the preceding 2 weeks. Machine learning models (elastic net [EN], random forest [RF], and neural network [NN]) were trained and validated on a 70:30 split cohort. Model performance was evaluated using the AUC.

Results: Among the 76 patients analyzed (median age 61 [IQR, 53-69] years, 39 [51%] female), 11 (14%) were hospitalized during the study period. The median step count during the first week of systemic treatment was 4,303 [IQR, 1926-7,056]. Unplanned hospitalizations in the upcoming 7 days could be predicted with high accuracy using RF (AUC = 0.88), NN (AUC = 0.84), and EN (AUC = 0.83). The models could not predict treatment modifications (AUC = 0.28-0.51) or the occurrence of any clinically relevant adverse event (AUC = 0.32-0.50).

Conclusion: A decline in daily step counts can serve as an early predictor for hospitalizations in the upcoming 7 days, facilitating proactive and preventive toxicity management strategies.