J Natl Cancer Inst 2017 09;109(9)
CORIA UMR 6614-Normandie Université, CNRS Université et INSA de Rouen, Campus Universitaire du Madrillet, Saint-Etienne du Rouvray, France.
Background: The use of web-based monitoring for lung cancer patients is growing in interest because of promising recent results suggesting improvement in cancer and resource utilization outcomes. It remains an open question whether the overall survival (OS) in these patients could be improved by using a web-mediated follow-up rather than classical scheduled follow-up and imaging.
Methods: Advanced-stage lung cancer patients without evidence of disease progression after or during initial treatment were randomly assigned in a multicenter phase III trial to compare a web-mediated follow-up algorithm (experimental arm), based on weekly self-scored patient symptoms, with routine follow-up with CT scans scheduled every three to six months according to the disease stage (control arm). In the experimental arm, an alert email was automatically sent to the oncologist when self-scored symptoms matched predefined criteria. The primary outcome was OS.
Results: From June 2014 to January 2016, 133 patients were enrolled and 121 were retained in the intent-to-treat analysis; 12 deemed ineligible after random assignment were not subsequently followed. Most of the patients (95.1%) had stage III or IV disease. The median follow-up was nine months. The median OS was 19.0 months (95% confidence interval [CI] = 12.5 to noncalculable) in the experimental and 12.0 months (95% CI = 8.6 to 16.4) in the control arm (one-sided P = .001) (hazard ratio = 0.32, 95% CI = 0.15 to 0.67, one-sided P = .002). The performance status at first detected relapse was 0 to 1 for 75.9% of the patients in the experimental arm and for 32.5% of those in the control arm (two-sided P < .001). Optimal treatment was initiated in 72.4% of the patients in the experimental arm and in 32.5% of those in the control arm (two-sided P < .001).
Conclusions: A web-mediated follow-up algorithm based on self-reported symptoms improved OS due to early relapse detection and better performance status at relapse.