Implementation of American Society of Hematology (ASH) Neuro-Related Guidelines at a Sickle Cell Center: How Are We Doing?
Notice bibliographique
Résumé
Background: Neurologiccomplications are a devastating consequence of sickle cell disease (SCD), and include stroke, silent cerebral infarct (SCI), and cognitive impairment. By age 18, 39% of children with SCD will have had a SCI. For adults, the prevalence of SCI is >50%. Clinically apparent stroke and SCI have been shown to affect cognition in SCD.As such, in 2020 American Society of Hematology (ASH) produced guidelines for the prevention, diagnosis, and treatment of cerebrovascular disease in children and adults with SCD, which provides recommendations for SCI and cognition screening. These guidelines recommend (1) using simplified signaling questions to monitor for cognitive impairment (strong recommendation based on low certainty of the evidence) and (2) at least 1 brain MRI in adulthood to detect SCI for adults with HbSS and HbSβ-null genotypes (conditional recommendation based on low certainty in evidence). However, knowledge about the practical implementation of these guidelines is limited. In this study we aim to investigate the implementation rates of screening for SCI and cognitive deficits in adults with SCD and assess screening pre- and post-ASH SCD guideline publication. Methods: We conducted a retrospective study of 443 adult patients with ICD-10 diagnosis of SCD (D57*) with an inpatient or outpatient encounter within the Yale-New Haven Health System between October 2018 to May 2024. Emergency room visits and sickle cell trait (D57.3*) were excluded. Brain MRI and cognitive screening were confirmed via manual chart review. Cognitive screening was defined by the use of a validated tool (i.e. Montreal Cognitive Assessment (MoCA), neuropsychological testing) or detailing a cognitive domain such as memory recall and excluding vague statements such as “alert & oriented” or “cognition was normal.” Data analysis was conducted in SPSS. Results: 87.4% of identified patients had at least 1 outpatient hematology visit within the Yale-New Haven Health System, with 39.1% of patients receiving at least one brain MRI. 16.9% of patients had a history of a clinically apparent stroke. 72% of patients received imaging due to symptoms. Excluding patients with a history of stroke, 2.3% of patients had SCI on imaging. There was no statistical difference in imaging from pre- to post-ASH guideline publication (29.78% vs. 26.85%, χ2 =0.490, p=0.48). Predictors of obtaining a brain MRI were history of stroke (OR= 8.66, p <0.001), cognitive disorder (OR=1.999, p=0.02), mood disorder (OR=3.15, p <0.001), and genotype Sβ+ (OR=5.034, p=0.038). Cognitive screening occurred at least once in 42.9% of patients. 17.4% were found to have a cognitive disorder. 66.1% of screenings were by informal surveillance (i.e. without the use of a validated tool). 9.1% of patients were screened using MoCA and 21.5% received formal neuropsychological testing (mean age at testing was 12). There was a decrease in cognitive surveillance from pre- to post-ASH guideline publication (34.9% vs. 25.3%, χ2=9.21, p=0.002). Predictors for cognitive screening were history of stroke (OR= 2.346, p=0.007), cognitive disorder (OR=6.453, p<0.001), and mood disorder (OR=3.707, p <0.001). Age, sex, insurance, income, and disease-modifying therapy were not significant predictors of screening. Conclusions: In this single center study, we found low implementation rates of the ASH cerebrovascular guideline screening for SCI and cognition in SCD. There was a non-significant decline in imaging and a significant decline in cognition screening between pre-and post-ASH guideline publication, likely a result of the COVID19 pandemic when most visits were virtual and imaging resources were scarce. Most cognitive screening was completed using informal surveillance, which lends itself to bias. Additionally, relying on reported chart history may incorrectly estimate the prevalence of cognitive disorders in the absence of validated screening methods. Although a small portion of patients were noted to have SCI on MRI screening, most MRI reports did not include measurements of abnormal T2/FLAIR signals. While the data is not generalizable, we anticipate expanding to include other sites for evaluation. Future studies that assess the determinants to implementation of the ASH guidelines and the development of interventions that mitigate these determinants are critical in improving outcomes in this historically marginalized population.
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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 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| 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) | 0,002 | 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 ».