Accepted for/Published in: JMIR Formative Research
Date Submitted: Jan 20, 2020
Date Accepted: Sep 22, 2020
Follow-up of Cancer Patients Receiving Anti-PD-(L)1 Therapy by Electronic Patient Reported Outcomes-tool (KISS): a pilot feasibility study
ABSTRACT
Background:
Immune checkpoint inhibitors (ICIs) have become standard of care in various tumor types. Their unique spectrum of side-effects demands continuous and long-lasting assessment of symptoms. Electronic patient reported outcome (ePRO) follow-up has been shown to improve survival and QoL of cancer patients treated with chemotherapy.
Objective:
This study aimed to investigate whether ePRO follow-up of cancer patients treated with ICIs is feasible. The study analyzed (1) the variety of patient reported symptoms, (2) etiology of alerts, (3) symptom correlations, and (4) patient compliance.
Methods:
Prospective one-arm multi-institutional study recruited adult cancer patients whose advanced cancer was treated with PD-(L)1 agents in outpatient settings. ePRO tool consisted of a weekly questionnaire evaluating the presence of typical side-effects with an algorithm assessing the severity of the symptom according to NCI-CTCAE, and an urgency algorithm sending alerts to care team. Patient experience survey was conducted monthly.
Results:
37 patients (lung cancer, n=15; melanoma, n=9; genito-urinary cancer, n=9; head and neck cancer, n=4) filled altogether 889 symptom questionnaires. Patients showed good adherence to ePRO follow-up. The most common gr1-2 symptoms were fatigue (39%) and cough (21%) whereas the most common gr3-4 were cough (6%) and loss-of-appetite (4%). The most common reasons for alerts were loss-of-appetite and shortness-of-breath. In the treatment benefit analysis, positive correlations were seen between clinical benefit and itching, and progressive disease and chest pain.
Conclusions:
According to results, ePRO follow-up of cancer patients receiving ICIs is feasible. ePROs capture a wide range of symptoms. Some symptoms correlate to treatment benefit suggesting that individual prediction models could be generated. Clinical Trial: Clinical Trials Register, (NCT3928938)
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