The development and implementation of a nurse practitioner sepsis screening team: Impact on transfer mortality
Notice bibliographique
Résumé
Background: Sepsis is a potentially deadly but treatable condition that occurs as a result of the systemic manifestations of infection. Despite large healthcare expenditures, patient outcomes can be poor, and survivors may still suffer from permanent organ damage, cognitive impairment, and physical disability. Failure to recognize and implement early goal-directed therapy leads to increased mortality. A review of hospital mortality identified that sepsis among inbound transfer patients to acute care units significantly contributed to the overall hospital mortality. As part of a multipronged, multidisciplinary approach, a nurse practitioner sepsis screening team was implemented to improve early diagnosis and treatment of sepsis and decrease mortality in this high-risk population. Methods: A large academic medical facility located in the Texas Medical Center in Houston accepts a significant number of transfer patients requiring a higher level of care from other institutions. A nurse practitioner sepsis screening team was created to focus on this highly vulnerable group. A validated, electronic screening tool was utilized to screen patients and facilitate early identification and treatment of sepsis. The nurse practitioner team screened and evaluated 3,268 inbound transfer patients from 10/01/2009 to 06/30/2012. When a high suspicion for sepsis was appreciated, or another acute condition was identified, the nurse practitioner collaborated with the attending physician and initiated appropriate treatment. The data analyzed were part of an Institutional Review Board (IRB) approved prospectively collected data set. The data were collected over a 57 month period spanning from 09/30/2007 through 06/30/2012 on all inbound transfer patients to the facility, which include pre-screening baseline statistics. Basic demographics including the patient’s age, gender, and race were collected. The outcome variable was status at discharge from the facility (alive or dead). After verifying assumptions of the chi-square test were met, a Pearson’s chi-square was run against the data set. All data were analyzed using IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp. Results: There was a significant association between inbound transfer patients who were evaluated upon arrival at this institution by the nurse practitioner sepsis screening team and mortality in this population regardless of their diagnoses (χ 2 (1) 115.04, p < .001). A patient not screened by the team was more likely to die during the hospitalization than a transfer patient that was screened. Conclusion: In this institution, the development and implementation of a nurse practitioner sepsis screening team has contributed to reducing mortality among the inbound acute care patient transfer population regardless of diagnoses. Further investigation is needed to understand the exact mechanisms that have contributed to this outcome.
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 | 0,006 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».