Notice bibliographique
Résumé
pulmonary embolism: pulmonary embolismFigureFigureChanging practice can be difficult. Not only do we have to learn a new way of doing things, we also have to unlearn old ways and unprogram heuristics that may be well established. Translation of new evidence into practice can take years. Take, for example, managing acute hemodynamically stable pulmonary embolism (PE). Disposing this patient has always been simple: anticoagulate and admit. From the EP's mindset, this formula seems like a win-win: clean disposition and a clean conscience that the patient would be safe in the hospital. But what is a win for one might not be for the other. The other in this case is the hospitalist who has one more patient to round on, the hospital supervisor who has one fewer inpatient bed available, the hospital administrator who may have to write off unreimbursed admission costs. And data suggest that lower-risk PE patients who have a longer length of stay also have a much higher rate of hospital-acquired complications, most notably pneumonia. (PLoS One 2017;12[10]:e0185022; http://bit.ly/2PqtZQh.) Is our longstanding clinical practice of admitting acute PE patients really the winner that it has seemed? Sending Patients Home Outpatient management of ED patients with PE is nothing new. Several hospitals in Canada launched an outpatient pathway in the late 1990s, publishing their reassuring results in 2000, and a number of prospective studies followed. (Ann Emerg Med 2012;60[5]:651; http://bit.ly/2yawtM8.) The most rigorous was a multinational trial of low-risk ED patients who lacked contraindications to home care and who were stratified using the PE Severity Index (PESI). Patients were randomized to outpatient v. inpatient care and followed for 90 days. (Lancet 2011;378[9785]:41.) Patients not hospitalized were observed for about 24 hours from presentation to home discharge; hospitalized patients stayed a mean of 4.5 days. The outcomes at 90 days for the outpatient cohort were found to be noninferior to their inpatient counterparts: symptomatic recurrent venous thromboembolism (VTE) (0.6% v. 0%), major bleeding (1.8% v. 0%), and all-cause mortality (0.6% v. 0.6%). The prospective evidence supporting safe outpatient management of adults with acute PE continues to mount. One prospective study of an outpatient treatment pathway included 200 explicitly defined low-risk patients with acute PE, and their 90-day results were excellent: No patients had a recurrent VTE or died, and only one suffered a major bleed. (Chest 2018;154[2]:249.) Despite this evidence, the prevalence of home discharge in most medical centers around the world is low, ranging from one percent to eight percent. Managing PE patents without hospitalization can be accomplished using several approaches to risk stratification. The best-studied validated tool is the PESI, which estimates 30-day all-cause mortality, which, when linked with outpatient exclusion criteria, has been used successfully to select patients for home care. (Lancet 2011;378[9785]:41; Chest 2018;154[2]:249; Am J Respir Crit Care Med 2005;172[8]:1041; http://bit.ly/2QBPEoA.). The PESI can be used strictly, where only low-risk classes (I & II) are deemed home-eligible, or more loosely, when the risk classes simply inform a more holistic assessment of eligibility. (Vinson, et al. Ann Intern Med [Epub Nov. 14, 2018].) A simplified PESI has been studied but underperforms the original. Another approach to patient identification uses the Hestia criteria, a list of contraindications to outpatient management that builds on the earlier Ottawa criteria. (J Thromb Haemost 2011;9[8]:1500; Thromb Haemost 2000;83[2]:209.) The two tools, however, address different facets of risk and are perhaps best integrated to inform clinical judgment. (Vinson, et al. Ann Intern Med [Epub Nov. 14, 2018].) High Rate of Satisfaction A body of evidence certainly suggests that home discharge of low-risk PE patients is safe and cost-effective, so the challenge is changing practice. We at Kaiser Permanente Northern California developed an electronic clinical decision support tool called RISTRA (RIsk STRAtification) to facilitate practice change. The tool automatically populates the PESI with patient-specific data and provides a list of outpatient contraindications (including the Hestia criteria). (Images.) Use of the tool, which was supported by clinical champions at 10 facilities, was high (nearly 70% of eligible patients) and well received by physicians. It was also associated with a 60 percent increase in home discharge and short stay ED observation compared with concurrent controls (28% v. 17%, p=0.007 for the difference in differences analysis) without an increase in major adverse events after discharge. (Ann Intern Med 2018 Nov 13; doi: 10.7326/M18-1206.) A follow-up phone survey revealed high rates of satisfaction among patients who were admitted, discharged, or had a short stay. (West J Emerg Med 2018;19[6]:938; http://bit.ly/2Rxg8sr.) Using these tools, shared decision-making, and a safety net for follow-up are practice-changers, and we should be able to find some acute PE patients—maybe one in three—who can safely go home. This may lead to a less heursitic and more contemplative dispostion dynamic, but we can take heart in having the support of evidence-based medicine.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,076 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».