Patients with NSCLC frequently experience toxicities that require urgent care visits or other forms of unplanned health care use. In some cases, these toxicities necessitate treatment changes.
The reacher team aimed to assess if patient-generated health data — including patient-reported outcomes and passively collected data from wearable sensors — could predict risk for urgent care visits among patients with NSCLC within 60 days of systemic therapy initiation.
Researchers used Bayesian networks to develop four models to predict risk of urgent care visits, measuring predictive performance by area under the curve (AUC). DeLong tests that compared the two pre-systemic therapy models and during-systemic therapy models showed statistically significant outperformance with the addition of patient-reported outcomes and wearable sensor data.
A low-burden system that alerts clinicians that a specific patient may be at risk for an unplanned medical visit and prompts them to check in with the patient would be highly sought after.
【MORE】