Screening for Cognitive Impairment and Depression in Sickle Cell Disease: How Are We Doing?
Notice bibliographique
Résumé
Background: Psychiatric disorders such as depression and anxiety are prevalent in sickle cell disease (SCD). There is wide variation in the reported prevalence due to methodological differences in data collection, such as the use of billing codes, and inconsistent screening. The relationship between depression, anxiety and pain has been well established. Adults with SCD with depressive or anxious symptoms have increased daily pain episodes and overall poorer quality of life. The relationship between mood and cognition has yet to be defined. In 2020, American Society of Hematology (ASH) produced guidelines for prevention, diagnosis, and treatment of cerebrovascular disease in SCD, which provides recommendations for cognition screening. The panel recommends monitoring for cognitive impairment in adults using simplified signaling questions. There are no recommendations for mood disorder screening in SCD. However, the ASH SCD guidelines: management of acute and chronic pain in SCD provides a good practice statement for routine screening for depression and anxiety. Knowledge of the practical implementation of these guidelines is limited. In this study we aimed to investigate the implementation rates for mooddisorder and cognitive deficit screening in adults with SCD and assess screening pre- and post-ASH SCD guideline publications. 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. Medical history, mood and cognitive screening were confirmed via manual chart review. Mood screening was evaluated based on the presence of validated measures such as Patient Health Questionnaire (PHQ) testing. Cognitive screening was defined as 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.” Results: Overall, 81.5% were screened at least once for a mood or psychiatric disorder, present in 35.7% of those screened. The majority (98.6%) of screenings were for depression. 43.2% underwent screening for depression or anxiety pre- publication and 70.8% had screening post- publication (χ2= 64.78, p< 0.001). Predictors of mood screening were male sex (OR=2.564, p<0.001), and existing or previous mood or psychiatric disorder (OR= 5.092, p<0.001). Age, insurance type, income, and disease-modifying therapy were not significant predictors. 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 screening from pre- to post- 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). Insurance status trended toward significance (p=0.081). Age, sex, and disease-modifying therapy were not predictors of screening. Conclusion: In our study, we find significant improvement in screening rates for mood disorders following publication of ASH guidelines. Cognitive screening remained suboptimal and heavily reliant on informal methods. The increase in mood screening post publication suggests that structured recommendations or practice standards can effectively enhance clinical practice. However, the ASH guidelines were published in 2020 when there was increased awareness and sensitivity to mental health as a result of the COVID19 pandemic, which could have prompted the increase in mood disorder screening. Use of a brief validated screening tool, such as PHQ allows for ease of administration by any member of the healthcare team. The decrease in cognitive surveillance post-guideline indicates a gap in practical implementation of recommendations. Future studies will explore barriers to implementation, strategies to promote screening, and prospective studies to expand our knowledge on mood and cognition in SCD.
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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 | 0,002 | 0,004 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».