Screening for Cognitive Impairment and Depression in Sickle Cell Disease: How Are We Doing?
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".