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Record W2988018214 · doi:10.1182/blood-2019-122053

Pulmonary Embolism Home Treatment: What GP Want?

2019· article· en· W2988018214 on OpenAlexaff
C. Calais, Grégoire Mercier, A. Meusy, I. Quéré, J.‐P. Galanaud

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicinePulmonary embolismPediatricsClinical trialFamily medicineEmergency medicineOutpatient clinicInternal medicine

Abstract

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Introduction: In routine clinical practice, in most countries, patients with pulmonary embolism (PE) are hospitalized a few days for clinical surveillance and to start anticoagulant treatment. Clinical trials and guidelines suggest that patients with stable PE could safely be treated as outpatients. This shift in management may increase general practitioner (GP) role in PE early management. However, GP opinion and suggestion regarding PE home treatment has never been assessed. Methods: Phone survey conducted in France among a random sample of 360 GP working in 6 regions representative of national access to French healthcare system namely, Brittany, Centre, Ile de France (Paris), Languedoc-Roussillon, Nord Pas de Calais, Provence Alpes Côte d'Azur. Primary objective was to assess GP' acceptability to PE home treatment. Secondary objectives included GP' preferences in terms of PE outpatient pathway, their current exposure to and management of PE as well as their personal experience with PE initial outpatient management. Results: During two pre-specified campaigns of recruitment (March-June 2015 and June-August 2017), 564 GP were contacted in order to reach study expected sample size (participation rate: 64%, 180 GP recruited during in each campaign). Most GP (95%) stated that they manage suspicion of PE less than once every 6 months. Current PE patient pathway is presented in Figure 1. 21% of GP (n=76) have already managed at least one patient with acute PE in the outpatient setting and 87% of GP (n=312) were favorable to home treatment of stable PE. This latter rate was similar during the 2 campaigns of recruitment. GP practicing alone were less likely to accept outpatient management of PE than those who worked in collaboration with other physicians: OR=0.3 [0.1 - 0.9]. Main reasons for their refusal were perceived seriousness of disease (90% of cases) and bleeding risk (60%). Figure 2 summarizes GP' suggestions and agreement regarding PE outpatient pathway. As per GP', conditions for discharge from emergency room (ER) department should include a medical report immediately available at time of patient's discharge (100% of GP agreed with), absence of social and medical facility isolation of the patient (99% of GP agreed with) patient's (99% of GP agreed with) and GP's (74% of GP agreed with) consents and a phone call to GP (74% of GP agreed with) to communicate initial treatment (56% of GP agreed with) and to provide the phone number of the hospitalist on call (56% of GP agreed with). After patient's discharge, 86% (n=309) of GP felt that outpatient pathway should be collaborative with a thrombosis specialist and should include follow-up visits with a thrombosis specialist at one week (89%, n=290), 3-6 months (80%) and when anticoagulant treatment is stopped (97%). A similar proportion of GP wanted PE patients to be managed either exclusively by themselves (8%, n=27) or by a thrombosis specialist (7%, n=24). 61% (n=219) of GP felt that direct oral anticoagulants (DOAC) should facilitate development of PE home treatment (16 did not, p<0.05) and that this should improve patient's quality of life. Conclusion: The vast majority of GP are favorable to stable PE home treatment if a formal outpatient pathway is established. DOAC are perceived as another key for the success of the development of PE home treatment. Disclosures Galanaud: BMS Pfizer: Consultancy; Servier: Consultancy; Sanofi: Consultancy; Aspen: Consultancy; Bayer: Research Funding.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.073
GPT teacher head0.424
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations25
Published2019
Admission routes1
Has abstractyes

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