L’e-santé est à la mode, mais les éléments cliniques démontrant son utilité sont rares et se limitent à du reporting de symptômes sans analyse spécifique. La théorie du chaos est applicable à la dynamique du cancer et amène des possibilités d’utilisation de ses propriétés pour développer des outils pratiques de surveillance des patients pertinents et validés.
Abstract: Survival and 2.0 surveillance of cancer: from (chaos) theory to clinical practice
Chaos theory was developed to improve the assessment of the dynamic of systems which have too complex equations and unpredictable behavior subsequently to a high dependence to initial conditions. Its use in oncology is recent and shows strong analogies with tumor dynamic. Interactions between tumor cells, host cells, immune cells and endothelial cells lead to a chaotic dynamic. Prey/predator and competition models are chaotic models. Observability theory, which allows to determine the most relevant variable in this model to better assess dynamic, shows that host cells (that can be represented by patients symptoms and weight variation) is the most relevant variable to follow. The first clinical applications to detect relapse of lung cancer were validated in 4 prospective trials (containing 1 phase 3 randomized trial that showed survival benefit). Symptoms monitoring of cancer patients may improve survival. We developed a web-application for an early detection of symptomatic relapse or complications and also allowing early supportive care in high-risk lung cancer patients between visits. A dynamical analysis of the weekly self-reported symptoms automatically triggered physician visit. We performed a multi-institutional phase III randomized study to compare survival between 1. web-application follow-up (experimental arm) for which patient’s self-scored symptoms that were weekly sent (between planned visits) to the oncologist and 2. a clinical routine assessment with a CT-scan (every 3-6 months or at investigator’s discretion – standard arm). High-risk lung cancer patients without progression after an initial treatment were included. Maintenance chemotherapy or TKI therapy were allowed. Internet access was required. In the experimental arm, an email alert was sent to the oncologist when some predefined clinical criteria were fulfilled: an imaging was then quickly prescribed. Early supportive cares were provided if adequate. Secondary outcomes were quality of life and performance status at relapse. 133 patients were randomized and 121 patients were included in the intent-to-treat analysis (96% stage III/IV): 60 in the experimental arm and 61 in standard arm. Median follow-up was 9 months. Median overall survival was 19 months vs 12 in favor of experimental arm (p=0.0014 – hazard ratio, 0.325 95% CI 0.157 to 0.672 ; p=0.0025) and the performance status at the first relapse was 0-1 for 76% of the patients in the experimental and 33% in standard arm (p < .001). One-year survival was 75 versus 49% in experimental and standard arms respectively (p=0.0025). Quality of life was improved in experimental arm (FACT-L, FACT-L TOI, FACT-G, p=0.02, p=0.01, p=0.04 respectively). This trial showed a significant survival and QOL improvement using symptom self-reporting follow-up by this web-application that allowed better PS at relapse and earlier supportive care. It validates observability theory and chaos theory approach in clinical practice.
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