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Enregistrement W4417016883 · doi:10.1182/blood-2025-4373

How should fever be managed in adults with sickle cell disease? a quality metrics study and mapping of essential elements to improve care delivery

2025· article· en· W4417016883 sur OpenAlexaffabout
Qianqian Zhou, Jules Mercier‐Ross, Michaël Desjardins, Nazila Bettache, Alex Bourguignon, Nicolas Mercure-Corriveau, Bernard Lemieux, Anne‐Sophie Lemay, Benjamin Rioux‐Massé, Yves Pastore, Anais Elodie Arethuse, Rachel Blot-Passchier, Ève Camirand, Marie‐France Vachon, Bénédicte Koukoui, Stéphanie Forté

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueHemoglobinopathies and Related Disorders
Établissements canadiensCentre Hospitalier Universitaire Sainte-JustineUniversité de MontréalCentre Hospitalier de l’Université de Montréal
Organismes subventionnairesnon disponible
Mots-clésTriageEmergency departmentEtiologyFebrile neutropeniaPopulationDiseaseYoung adultRetrospective cohort studyHealth care

Résumé

récupéré en direct d'OpenAlex

Abstract Background: Adults with sickle cell disease (SCD) are at high risk for life-threatening bacterial infections due to functional or anatomic asplenia. Guidelines from the CDC and NHLBI classify fever in this population as a medical emergency. However, no universally validated, adult-specific benchmark exists for assessment and empiric antibiotic initiation. Many centres therefore extrapolate pediatric SCD and febrile neutropenia standards, targeting a door-to-antibiotic time of <30–60 minutes. At our institution, feedback from patients and providers revealed inconsistencies and delays in the management of febrile episodes. Objectives: To evaluate and improve the quality of fever care in adults with SCD at our centre by measuring key process indicators (door-to-antibiotic time, triage code, protocol utilization), identifying the etiologies of febrile episodes, assessing outcomes based on patient characteristics and the care timeline, and identifying actionable quality improvement targets. Methods: We conducted a retrospective study at the Centre Hospitalier de l'Université de Montréal (CHUM), including all individuals with SCD who presented to the emergency department (ED) with fever (≥38.5°C or 101.3°F) between July 2022 and December 2023. Clinical data included genotype, vaccination status, triage level (P2 = to be seen ≤15 min; P3 = ≤30 min), care time intervals, and outcomes (length of stay [LOS], ICU admission, mortality, 30-day ED return rate). Febrile etiologies were classified according to discharge diagnoses. Findings were presented to multidisciplinary stakeholders to guide fever care redesign. Results: 56 febrile episodes in 45 patients were included. Median age was 29 years (range: 17–50); 73% were women. Genotypes included 34 SS/Sβ0 and 22 SC/Sβ+. Fever was the only presenting symptom in 11%. Other symptoms included musculoskeletal pain (32%), upper respiratory symptoms (21%), and dyspnea (11%). Vaccination status was noted in the file in 23%. Process Metrics (median time): ED arrival-to-nurse-triage: 23min (6min–6h33). Triage-to-physician: 34min (0–16h07); 2 patients left without being seen. Triage was P2 in 54% (fever documented in 47%) and P3 in 46% (fever in 23%). Time-to-physician was shorter in P2 (29min) than P3 (52min; p=0.001). Antibiotics were given in 80%. No non-treated patients had bacterial infections. Assessment-to-antibiotic time: 2h46 (P2) vs. 3h39 (P3; p=0.123). Door-to-antibiotic time: 4h34 overall (3h10–22h31), shorter in P2 (3h36) than P3 (6h20; p=0.022). Fever/ACS protocols were used in 31%. Etiologies: Infectious causes were most common: viral infections (21 cases) and suspected pneumonia (8). Blood cultures were positive in 6 cases: Streptococcus (S. anginosus, S. pyogenes, 2), Staph. hominis (1), and E. coli (3). One patient had malaria. No cause was found in 7 cases. When fever was the only symptom, diagnoses included URI (4), dental abscess (1), and unknown (1). Outcomes: 23 were discharged home, while 31 required hospitalizations (4 acute chest syndrome (ACS), 13 vaso-occlusive crises (VOC), 9 infections, 5 non-hematological conditions), with a median LOS of 2.17 days (0.13–13.12). ACS was diagnosed in 9/31 hospitalized cases, 5 were transfused. One patient required ICU admission; no deaths were reported. The 30-day ED return rate was 11% (1 pleural effusion, 1 pulmonary embolism, 1 VOC, 3 reinfections). Time to antibiotic administration was not significantly associated with these outcomes. LOS did not vary significantly by genotype, age, sex, or presence of fever at triage but was significantly longer in ACS (5.07 vs. 1.27 days; p < 0.001). Quality Improvement Targets: more consistent triage classification of febrile SCD visits and better use and faster initiation of standardized fever/ACS protocols. Limitations: Small sample size and incomplete vaccination data limited ability to determine predictors of LOS. Milder febrile episodes managed outside the ED and ED avoidance were not captured. Conclusion: This study revealed delays and process gaps in ED fever care for adults with SCD at our centre. Universally assigning all febrile visits to P2 may help but would be insufficient to achieve timelines. This should be paired with additional quality improvement strategies. Nurse-activated protocols and risk-adapted strategies, similarly to neutropenic fever management, are under development. These interventions aim to ensure timely, high-quality care while reducing ED burden.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,009
score de la tête « metaresearch » (Gemma)0,024
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,037
Score d'incertitude au seuil0,073

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0090,024
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0020,004
Études des sciences et des technologies0,0010,000
Communication savante0,0010,002
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,010
Tête enseignante GPT0,257
Écart entre enseignants0,246 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission2
Résumé présentoui

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