Wearable-derived resting heart rate deviations from a patient's baseline, scored via machine learning, were significantly associated with a higher risk of chemotherapy-related adverse events.
Does machine-learning analysis of wearable-derived resting heart rate predict adverse events in patients receiving systemic therapy for solid organ cancers?
Machine learning analysis of wearable-derived resting heart rate can detect early physiologic warning signals that predict chemotherapy-related adverse events in patients with solid organ cancers.
Absolute Event Rate: 0% vs 0%
Abstract Objective: (1) To use wearable-derived biometrics to examine the relationship between physiologic patterns and adverse events during chemotherapy for solid organ cancers. (2) To use machine-learning analysis of these physiologic patterns to predict adverse events. Methods: Patients with solid organ cancers receiving systemic therapy were enrolled in an ongoing trial evaluating the effects of chemotherapy/ immunotherapy on physiologic patterns. Patients continuously wore a Fitbit for 5 days before and during treatment. We analyzed intra- and inter-personal variation in normalized daily average resting heart rate (RHR) across cycles. We developed a novel, personalized, baseline-referenced statistical framework based on the Change-of-Heart machine learning algorithm, “HeartSense,” which standardizes each participant’s RHR against their own baseline during sleep and non-sleep periods, generating a score that quantifies daily physiologic stability. Higher scores reflect RHR patterns consistent with pre-treatment baseline and lower scores indicate greater deviation from baseline. We compared lowest HeartSense scores by patients who experienced adverse events vs those who did not in cycle 1. We also examined whether scores from cycle 1 were associated with adverse events in cycle 2. Results: A total of 54 cancer patients who underwent 1 cycle of systemic therapy were included in analysis. Distinct physiologic patterns emerged during therapy. One patient showed cyclic decreases in heart rate after each infusion with recovery to baseline (Figure A), while another exhibited progressive physiologic disruption preceding treatment termination (Figure B). Intrapersonal variation in sleep and non-sleep RHR was significantly smaller than interpersonal variation (Figure C). Lower median HeartSense scores within a cycle were significantly more likely to experience adverse events (Figure D). Lower scores were also associated with adverse events in cycle 2 (Figure E). Conclusions: Patients exhibit distinct physiologic changes in resting heart rate during systemic therapy and deviation from normal patterns may indicate treatment-related adverse events. Our novel machine learning algorithm identified that lower scores—reflecting abnormal physiologic regulation—were linked to a higher risk of adverse events both during and after the current treatment cycle, suggesting that chemotherapy-related toxicity can be preceded by early physiologic warning signals. Citation Format: Alexander Zhu, Lizi Zhang, Yuhang Zhang, Alexandra Potter, Bryan Rettner, Alisha Keshwani, Alina Keshwani, Nicole Hu, Anton Melki, Zhengyu Fang, Meghan McCarthy, Jacob Baird, Aubrey Pope, Samuel Schwartz, Quiana Guo, Michael Lanuti, Xiao Li, Chi-Fu Jeffrey Yang. Artificial intelligence-derived analysis of wearables-derived biometrics to characterize physiologic response to chemotherapy in solid organ cancers abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4224.
Zhu et al. (Fri,) reported a other. Wearable-derived resting heart rate deviations from a patient's baseline, scored via machine learning, were significantly associated with a higher risk of chemotherapy-related adverse events.