Relevance of Medical Big Data Analysis Depends on Clinical Accuracy: The Q Fever Paradigm

Clinical efficacy of chloroquine derivatives in COVID-19 infection: comparative meta-analysis between the big data and the real world

In the context of the current coronavirus disease 2019 (COVID-19) pandemic, we conducted a meta-analysis on the effects of chloroquine derivatives in patients, based on unpublished and published reports available publicly on the internet as of 27 May 2020. The keywords 'hydroxychloroquine', 'chloroquine', 'coronavirus', 'COVID-19' and 'SARS-Cov-2' were used in the PubMed, Google Scholar and Google search engines without any restrictions as to date or language. Twenty studies were identified involving 105 040 patients (19 270 treated patients) from nine countries (Brazil, China, France, Iran, Saudi Arabia, South Korea, Spain and the USA). Big data observational studies were associated with conflict of interest, lack of treatment dosage and duration, and absence of favourable outcome. Clinical studies were associated with favourable outcomes and details on therapy. Among clinical studies, three of four randomized controlled trials reported a significant favourable effect. Among clinical studies, a significant favourable summary effect was observed for duration of cough (OR 0.19, p 0.00003), duration of fever (OR 0.11, p 0.039), clinical cure (OR 0.21, p 0.0495), death (OR 0.32, p 4.1 × 10-6) and viral shedding (OR 0.43, p 0.031). A trend for a favourable effect was noted for the outcome 'death and/or intensive care unit transfer' (OR 0.29, p 0.069) with a point estimate remarkably similar to that observed for death (∼0.3). In conclusion, a meta-analysis of publicly available clinical reports demonstrates that chloroquine derivatives are effective to improve clinical and virological outcomes, but, more importantly, they reduce mortality by a factor of 3 in patients with COVID-19. Big data are lacking basic treatment definitions and are linked to conflict of interest. The retraction of the only big data study associated with a significantly deleterious effect the day after (June 5, 2020) the acceptance of the present work (June 4, 2020) confirms the relevance of this work.