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31st October 2022
Using a daily steps AI model, US researchers were able to predict an unplanned hospitalisation for cancer patients undergoing chemo-radiation according to the findings of a study presented at the recent American Society for Radiation Oncology (ASTRO) annual meeting.
Globally, cancer is a leading cause of death and the World Health Organisation has estimated that in 2020, there were nearly 10 million deaths in 2020. While oncologists manage patients with cancer, such individuals may also develop health issues due to treatment-related side-effects that prompts an emergency department (ED) visit. In fact, such unplanned visits are not uncommon and in one study of 402 study participants, 20% experienced an ED visit, and 18% experienced a hospital admission while receiving cancer treatment. The potential consequences of these visits might include interruption of chemotherapy, and this may impact on cancer therapy outcomes. As a result, there is a need for interventions to identify patients at a higher risk of complications and therefore prevent unplanned hospital visits.
The current researchers previously developed a machine learning model which could predict emergency visits and hospitalisation during cancer therapy. Moreover, in a further study, they also showed that a machine learning model, accurately triaged patients undergoing radiotherapy and chemoradiation and was able to direct clinical management, reducing acute care rates in comparison to standard care. With increased use of wearable devices which collect large amounts of health data, the researchers wondered if it would be possible to utilise this data, such as daily step count, to predict unplanned ED visits. The team developed a daily steps AI model and set out to validate the model before and during chemoradiation (CRT). They turned to data collected in three prospective trials in which patients were asked to wear commercial fitness trackers continuously before and during curative-intent CRT for multiple cancer types. The team collated a wealth of data including age, ECOG performance status, sex, diagnosis, radiotherapy plan metrics and daily step count. The model was trained both with and without step count-derived features and used to predict a first hospitalisation event within one week based on data from the preceding two weeks. The models were then evaluated in terms of the area under the receiver operating characteristic curve (AUC).
Daily steps AI model and prediction of hospitalisation
In total, 214 patients with a median age 61 were included and the most common diagnoses were head and neck cancer (30%) and lung cancer (29%). The model was trained using 70% of patients and validated in the remaining 30%.
When step count was included in the model, it had strong a predictive performance for hospitalisation the following week (AUC = 0.81, 95% CI 0.62 – 0.91). In fact, inclusion of step count significantly improved the predictability of the model compared to when this data was excluded (AUC = 0.57, 95% CI 0.40 – 0.74, p = 0.004). The top five contributing variables were step counts from each of the past two days, the absolute difference in minimum step counts over the past two weeks, relative decrease in the maximum step count over the past two weeks, and relative decrease in the step count range over the past two weeks.
In an associated press release, lead author Dr Hong said that ‘The step counts immediately preceding the prediction window ended up being generally more predictive than clinical variables. The dynamic nature of the step counts, the fact that they’re changing every day, seems to make them a particularly good indicator of a patient’s health status.’
The authors concluded that based on these findings, they plan to clinically validate the model in a further study which will randomise patients undergoing CRT for lung cancer to treatment with or without daily step count monitoring.
Friesner I et al. Machine Learning-Based Prediction of Hospitalization Using Daily Step Counts for Patients Undergoing Chemoradiation. No 132. ASTRO annual meeting, 2022