Abstract CN7
Background
Breakthrough cancer pain (BTcP), a transitory flare of pain that occurs on a background of relatively well-controlled baseline pain, is a challenging clinical problem in managing cancer pain. We hypothesized that the breakthrough pain could be predictable according to the patients’ previous observed patterns. In this study, we propose a deep learning model that predicts a patient’s level of BTcP per hour.
Methods
We defined the BTcP as the pain with numerical rating scale (NRS) score 4 or above and developed models predicting the onset time of BTcP with the temporal resolution of 1 hour. The dataset contains only 20 or more measured records obtained from patients admitted to the hematological oncology ward of Samsung Medical Center from July 2016 to February 2020. The model used the time windows of 3 days to predict NRS scores over the next 24 hours. To capture irregular pain patterns, we created the sequence of average pain patterns over 24 hours from the previous 3 days and used it for normalization. We trained a Bi-directional long-short term memory based deep learning model. The model was validated using the holdout method with 20% of the datasets. Its performance was assessed with the receiver operating characteristic curve (AUROC) and the precision-recall curve (AURPC).
Results
We included pain log data containing 4,660 admissions from 3,258 patients with cancer in the analysis. The median age was 57[interquartile range (IQR), 47-64], the most frequent type of cancer was lung cancer (18.0%), and most patients had stage 4 (60.7%). Among the 103,948 hours from patients in whole datasets, 1,091 (4.7%) hours were labeled as the period of BTcP. The patients have the records of NRS score with a median of 3(IQR, 2.0-4.5) and BTcP with a median of 1.1 (IQR, 0.5-2.0) per day. We allocated approximately 20% of patients (653 patients with 932 admissions) to the holdout test dataset. Our model showed AUROC of 0.719 and AUPRC 0.680 for predicting the BTcP in the test dataset.
Conclusions
Our study showed that cancer pain could be predictive by using a deep learning model. Though our exploratory study has limitation of generalization, future warranted subgroup analysis and verification research could make our model more applicable in a real-world setting.
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.