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Correlation Between Remote Symptom Reporting by Caregivers and Adverse Clinical Outcomes: Mixed Methods Study.
Ingrid Oakley-Girvan1, Reem Yunis1, Stephanie J Fonda2
1Medable Inc, Palo Alto, CA, United States.
Caregiver-reported outcomes via smartphone apps predicted patient clinical events, including hospitalizations and adverse events. This approach may reduce patient burden and offer early insights into symptom severity for cancer patients.
Area of Science:
- Oncology
- Digital Health
- Patient-Reported Outcomes
Background:
- Timely collection of patient-reported outcomes (PROs) is crucial for improving cancer patient survival and reducing healthcare utilization.
- Limited data exists on the predictive value of informal caregivers reporting outcomes using clinical assessment tools.
Purpose of the Study:
- To evaluate caregiver and patient adherence to a scheduled electronic PRO data collection regimen.
- To determine if PROs collected electronically are associated with future clinical events in cancer patients.
Main Methods:
- Developed two iPhone applications for PRO data collection: one for patients and one for caregivers.
- Enrolled 52 patient-caregiver dyads in a 4-week nonrandomized study, collecting PROs via apps.
- Utilized electronic health records for clinical event data and logistic/quasi-Poisson regression for analysis.
Main Results:
- High adherence rates observed: 97% for Patient-Reported Outcomes Common Terminology Criteria for Adverse Events (PRO-CTCAE) and 98% for Patient-Reported Outcomes Measurement Information System (PROMIS) surveys.
- Caregiver-reported PRO-CTCAE data significantly associated with patient hospitalizations, emergency visits, severe adverse events, dose reductions, and hospice referrals.
- Caregiver-reported PROMIS data linked to hospice referrals; patient-reported PROMIS data associated with mortality and other clinical events.
Conclusions:
- Smartphone app-based PRO collection by both patients and caregivers is feasible and adhered to.
- Caregiver-reported PRO-CTCAE data shows promise in predicting patient clinical events, potentially reducing patient burden in data collection.
- This method may offer early indicators of worsening symptom severity in cancer patients.
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