Implementation of American Society of Hematology (ASH) Neuro-Related Guidelines at a Sickle Cell Center: How Are We Doing?
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".