747 Burn Mortality Prediction Model and Communication Tool for Healthcare Providers
Notice bibliographique
Résumé
Abstract Introduction Communication is a key competency – the effective exchange of information is essential to a physician’s role. Currently, our centre does not have a communication tool to help guide point of care discussions between healthcare providers and during family meetings. Prognostic relationship of the BAUX score and ABSI index should be determined. An objective communication tool that shows predicted mortality, length of stay (LOS), and number of operations, specifically in our hospital using BAUX index and ABSI score will allow patients and healthcare providers to better understand prognosis, course in hospital, and develop appropriate expectations for outcomes in our centre. Methods In this cross-sectional study, all burn patients admitted to our Centre from 2012 to June 2022 were retrospectively recruited. Our burn registry was used to extract complete data of patient information including age, gender, %TBSA, burn depth, presence of inhalational injury, need for ventilator support, intensive care unit (ICU) admission days, hospital LOS, BAUX score, rBAUX score, and ABSI index. Patients were divided into three cohorts: all patients without an inhalation injury, a subgroup of smokers with and without an inhalation injury, and all patients with an inhalation injury. For each cohort, a mean LOS in hospital and/or ICU, number of burn operations, and mortality rate per incremental BAUX score and ABSI index was computed. Results A total of 839 patients were included, 725 without and 114 (13.6%) with an inhalation injury. A subgroup of patients (n=286) smokers with and without inhalational injury were separately analyzed. With severity of burn and presence of inhalational injury, both BAUX score and ABSI index show an incremental increase in LOS in hospital, number of operations, and increased mortality. Smokers with rBAUX ≥110 had the longest LOS in hospital, 78.4 hospital days and 35 ICU days. Interestingly, all patients with ABSI 6-7 had the longest LOS/%TBSA and those patients who additionally had an inhalational injury had the highest LOS/%TBSA (5.1 days/%TBSA). Increase in BAUX and ABSI did not correlate with increase in LOS/%TBSA. For BAUX ≥ 90 and ABSI ≥ 8, number of operations and mortality exponentially increased. Conclusions Patients with BAUX ≥ 90 and ABSI ≥ 8 should be counselled on a complex course in hospital. Higher BAUX and ABSI correlate with increased mean LOS and number of operations but not LOS/%TBSA. Using our Centre’s burn registry, we can predict course in hospital and provide this to patients, family, and healthcare providers before tertiary centre transfer and admission. Applicability of Research to Practice An objective communication model showing the burn centre’s mean LOS, LOS/%TBSA, number of operations, and risk of mortality, can help guide physician-patient and physician-healthcare provider communication. This tool will provide realistic burn recovery expectations at admission, family meetings, and when discussing consent.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».