Detection of lung cancer relapse using self-reported symptoms transmitted via an Internet Web-application: pilot study of the sentinel follow-up

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer(2014)

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摘要
Purpose We aimed to investigate whether patient self-evaluated symptoms transmitted via Internet can be used between planned visits to provide an early indication of disease relapse in lung cancer. Methods Between 2/2013 and 8/2013, 42 patients with lung cancer having access to Internet were prospectively recruited to weekly fill a form of 11 self-assessed symptoms called “sentinel follow-up”. Data were sent to the oncologist in real-time between planned visits. An alert email was sent to oncologist when self-scored symptoms matched some predefined criteria. Follow-up visit and imaging were then organized after a phone call for confirming suspect symptoms. Weekly and monthly compliances, easiness with which patients used the web-application and the accuracy of the sentinel follow-up for relapse detection were assessed and compared to a routine visit and imaging follow-up. Results Median follow-up duration was 18 weeks (8–32). Weekly and monthly average compliances were 79 and 94 %, respectively. Sixty percents of patients declared to be less anxious during the few days before planned visit and imaging with the sentinel follow-up than without. Sensitivity, specificity, positive, and negative predictive values provided by the sentinel (planned imaging) follow-up were 100 %(84 %), 89 %(96 %), 81 %(91 %), and 100 %(93 %), respectively and well correlated with relapse ( pχ 2 < 0.001). On average, relapses were detectable 5 weeks earlier with sentinel than planned visit. Conclusion An individualized cancer follow-up that schedule visit and imaging according to the patient status based on weekly self-reported symptoms transmitted via Internet is feasible with high compliance. It may even provide earlier detection of lung cancer relapse and care.
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关键词
Lung cancer,Follow-up,Supportive care,Personalized medicine,Early relapse detection,M-health
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