Aims The aim of this study is to compare the Hestia rule vs. the simplified Pulmonary Embolism Severity Index (sPESI) for triaging patients with acute pulmonary embolism (PE) for home treatment. Methods and results Normotensive patients with PE of 26 hospitals from France, Belgium, the Netherlands, and Switzerland were randomized to either triaging with Hestia or sPESI. They were designated for home treatment if the triaging tool was negative and if the physician-in-charge, taking into account the patient’s opinion, did not consider that hospitalization was required. The main outcomes were the 30-day composite of recurrent venous thrombo-embolism, major bleeding or all-cause death (non-inferiority analysis with 2.5% absolute risk difference as margin), and the rate of patients discharged home within 24 h after randomization (NCT02811237). From January 2017 through July 2019, 1975 patients were included. In the per-protocol population, the primary outcome occurred in 3.82% (34/891) in the Hestia arm and 3.57% (32/896) in the sPESI arm (P = 0.004 for non-inferiority). In the intention-to-treat population, 38.4% of the Hestia patients (378/984) were treated at home vs. 36.6% (361/986) of the sPESI patients (P = 0.41 for superiority), with a 30-day composite outcome rate of 1.33% (5/375) and 1.11% (4/359), respectively. No recurrent or fatal PE occurred in either home treatment arm. Conclusions For triaging PE patients, the strategy based on the Hestia rule and the strategy based on sPESI had similar safety and effectiveness. With either tool complemented by the overruling of the physician-in-charge, more than a third of patients were treated at home with a low incidence of complications.

Background: The Food and Drug Administration (FDA) in the United States and the European Medicines Agency (EMA) have recognized social media as a new data source to strengthen their activities regarding drug safety.Objective: Our objective in the ADR-PRISM project was to provide text mining and visualization tools to explore a corpus of posts extracted from social media. We evaluated this approach on a corpus of 21 million posts from five patient forums, and conducted a qualitative analysis of the data available on methylphenidate in this corpus.Methods: We applied text mining methods based on named entity recognition and relation extraction in the corpus, followed by signal detection using proportional reporting ratio (PRR). We also used topic modeling based on the Correlated Topic Model to obtain the list of the matics in the corpus and classify the messages based on their topics.Results: We automatically identified 3443 posts about methylphenidate published between 2007 and 2016, among which 61 adverse drug reactions (ADR) were automatically detected. Two pharmacovigilance experts evaluated manually the quality of automatic identification, and a f-measure of 0.57 was reached. Patient's reports were mainly neuro-psychiatric effects. Applying PRR, 67% of the ADRs were signals, including most of the neuro-psychiatric symptoms but also palpitations. Topic modeling showed that the most represented topics were related to Childhood and Treatment initiation, but also Side effects. Cases of misuse were also identified in this corpus, including recreational use and abuse.Conclusion: Named entity recognition combined with signal detection and topic modeling have demonstrated their complementarity in mining social media data. An in-depth analysis focused on methylphenidate showed that this approach was able to detect potential signals and to provide better understanding of patients' behaviors regarding drugs, including misuse.