Abstract 276: A Qualitative Analysis of Shared Decision Making in Cardiac Surgery
Notice bibliographique
Résumé
OBJECTIVES Comprehension of risks, benefits, and alternative treatment options is poor among patients referred for cardiac interventions. We have previously demonstrated that frail, elderly patients undergoing cardiac surgery require complex procedures and are at markedly increased risk of postoperative death and prolonged institutional care. An effective informed consent process is critical in this population. We suggest this vulnerable patient population may benefit from the institution of a formalized shared decision making (SDM) process. METHODS Three focus groups were convened for CABG, Valve, or CABG +Valve patients over 70 who were either within two years post-op, within 4-8 weeks post-op or had had a complicated post-operative course. Two focus groups were convened for the caretaker group: IMCU nurses & ICU nurses and surgeons, anesthesiologists & cardiac intensivists. In a semi-structured interview format, groups were asked questions regarding personal experience with informed consent, comprehension of discussions prior to surgery, potential improvements to the consent process, and SDM in cardiac surgery. Transcribed audio data was analyzed to develop consistent and comprehensive themes. RESULTS Patient groups were supportive of changing standard consent by including patient-specific risk factors through graphics, reduced language complexity and increased font size as means to improve comprehension and discussion. Patient groups felt access to this information earlier on in their care would allow time to identify personal values and desires for treatment. Both care provider groups supported a consent process that would provide patients with information earlier through decisional aids presented in a structured SDM process. All groups were supportive of a dedicated RN employed as a decisional coach to meet with patients and families prior to surgery to discuss their values, concerns, and questions to facilitate SDM with the care team. CONCLUSIONS Data from these groups will aid in the development of decision aids that serve to educate patients about their disease, the procedure proposed, and its risks and alternatives. Utilizing validated risk prediction models from our own experience allows us to provide patient specific risks for in-hospital mortality, major morbidity, and prolonged institutional care as well as long term outcomes freedom from mortality and re-hospitalization for cardiac cause.
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 | 0,005 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| 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) | 0,000 | 0,000 |
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; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».