Abstract 1570P
Background
We hypothesized chemotherapy infusion related reaction might be predictable according to continuous monitoring of heart rate recorded by a wearable device. In this study we propose a deep learning model that predicts patient level symptoms per minute during chemotherapy infusion.
Methods
We prospectively enrolled patients with cancer who will receive chemotherapy and at high risk for infusion related reaction. During chemotherapy infusion, 1 minute interval heart rate was recorded from a smart band and self-recorded symptom log was obtained. Patient's status at every minute was classified into 4 categories; normal, mild symptom without intervention, serious symptom which needed intervention, and patient's voluntary movement. The prediction model used the time windows of 120 minutes to predict infusion related symptoms over the current time. The model was trained 1500 epochs and we used the Adam optimizer with initial learning rate of 1e-3. We trained a multi-percentron layer based deep learning model. The losses were calculated by the cross-entropy. The model was validated using the holdout method with 20% of the datasts. The performance of the model was assessed with the receiver operating characteristic curves (AUROC) of 4 different events.
Results
We collected 1-minute interval heart rate record and patient's symptom logs during 4,716 minutes of 35 cycles of chemotherapy administration, from December 2021 to February 2022. The median age of enrolled patients was 60(range 35-85), and breast cancer(68.6%) was the most common diagnosis. Among 4,716 minutes, 277 (5.87%) minutes were labeled as non-normal status. Our model showed AUROC of 0.92 for normal status, 1.00 for mild symptoms, 0.82 for serious symptoms, and 0.97 for patient's voluntary movement. The proposed prediction model shows the 97.38% accuracy for 20% validation set.
Conclusions
This study showed that a deep learning model could recognize infusion related symptoms associated heart rate variability obtained by wearable device in real world patients receiving chemotherapy. Further studies for validation of predictive capability in a real time setting are warranted.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
Has not received any funding.
Disclosure
All authors have declared no conflicts of interest.