SAN ANTONIO, October 24, 2022 — An artificial intelligence (AI) model developed by researchers can predict the likelihood that a patient may have an unplanned hospitalization during their radiation treatments for cancer. The machine-learning model uses daily step counts as a proxy to monitor patients’ health as they go through cancer therapy, offering clinicians a real-time method to provide personalized care. Findings will be presented today at the American Society for Radiation Oncology (ASTRO) Annual Meeting.
An estimated 10-20% of patients who receive outpatient radiation or chemoradiation therapy will need acute care in the form of an emergency department (ED) visit or hospital admission during their cancer treatment. These unplanned hospitalizations can be a major challenge for people undergoing cancer treatment, causing treatment interruptions and stress that may impact clinical outcomes. Early identification and intervention for patients at higher risk of complications can prevent these events.
“If you can anticipate a patient’s risk of unplanned hospitalization, you can change how you support them through their cancer treatments and reduce the likelihood that they will end up in the ED or hospital,” said Julian Hong, MD, senior author of the study and an assistant professor of radiation oncology and computational health sciences at the University of California, San Francisco (UCSF), where he also serves as Medical Director of Radiation Oncology Informatics.
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Dr. Hong’s team previously demonstrated that a machine learning algorithm using health data such as cancer history and treatment plan could identify patients at higher risk of ED visits during cancer treatment, and that additional surveillance from their providers reduced acute care rates for these patients.
For the current study, he and Isabel Friesner, lead author and a clinical data scientist at UCSF, collaborated with Nitin Ohri, MD, and colleagues at Montefiore Medical Center in New York to apply machine learning approaches to data from wearable consumer devices. Dr. Ohri and his team previously collected data from 214 patients in three prospective clinical trials (NCT02649569, NCT03102229, NCT03115398). In each of these trials, participants wore fitness trackers that monitored their activity over several weeks while they received chemoradiation therapy. Trial participants had different types of primary cancers, most commonly head and neck (30%) or lung (29%) cancer.
