Validitation of the Rowland Universal Dementia Assessment Scale (RUDAS) for the Detection of Cognitive Impairment Among Adults with Sickle Cell Disease
Bibliographic record
Abstract
Background Adults with sickle cell disease (SCD) are at higher risk of cognitive disorder compared to the general population. The American Society of Hematology recommends cognitive screening; however, optimal surveillance strategies have not been established. The Rowland Universal Dementia Assessment Scale (RUDAS) is a short questionnaire that was specifically developed in culturally diverse populations. Objective To determine whether the RUDAS is a valid screening tool to detect cognitive disorder among adults with SCD. Methods This cross-sectional study was conducted between 2018 and 2023 in 3 large centres of excellence of SCD (Centre Hospitalier de l'Université de Montréal [CHUM] in Montréal, and University Health Network [UHN] in Toronto, Canada, and APHP-Henri Mondor French Red Blood Cell Coordinating Referral Center, Créteil, France). All outpatients (pts), ≥18 years-old, of all SCD genotypes, with documented RUDAS screening were included, unless they were unable to provide informed consent. The French or the English versions of the RUDAS were used for cognitive screening, according to pts' preference. RUDAS was adjusted for education (+1 point) if the number of years of education was ≤12. At CHUM, pts were assessed comprehensively by a multidisciplinary neurovascular team. Diagnosis of major or mild cognitive disorder was determined according to VasCog diagnostic criteria. Multistep validation was performed by an expert in measures (SB), and included reliability of repeat testing and interrater reliability, the partial credit model, and “gold standard” (criterion) validation of the RUDAS against formal diagnosis of cognitive disorder. Sensitivity (Ss), specificity (Sp), positive predictive value (PPV) and negative predictive value (NPV) were calculated. For reliability testing, only 145 pts had repeat testing within 3-6 months and 92 had repeat scoring by an independent assessor. For the criterion validation, only the 117 pts evaluated at the CHUM were included in the analysis. Results A total of 440 pts met the inclusion criteria: 173 (39.3%) from CHUM, 171 from UHN (38.9%) and 96 from APHP-Henri Mondor (21.8 %); 248 were women (56.4 %) and the median [range] age was 34 [18-75] years old. Median [range] RUDAS score was 26 [16-30]. For reliability testing, the correlation coefficient for repeat RUDAS testing after 3 to 6 months was 0.6. Scores improved from a median of 26.0 [16-30] to 28.0 [19-20], suggesting a potential learning effect. The interrater reliability coefficient was 1.0 Latent class analyses using 2 classes had the best fit, compared to 3 or 4 classes, based on lowest Akaike information and Bayes information criterion values. The entropy value for this model was acceptable (0.89). RUDAS was able to discriminate two clinically distinct groups. Pts in one group had overall lower RUDAS performance (median=22 [16-27] vs. 27[18-30]), were also older, more likely to have a history of stroke, common vascular risk markers and factors, lower kidney function, lower educational attainment and higher unemployment. Clinical validity was tested by comparing the RUDAS to formal neurological and cognitive assessment in the CHUM group. The subgroup of patients who had a formal cognitive evaluation (N = 117) was divided into two groups: those with (N=42) and those without (N=75) a confirmed diagnosis of major or minor cognitive disorder. The mean RUDAS scores differed significantly in those two groups (26.5 ± 2.8 vs. 28.5 ± 2.6, t=3.5, p<0.001). A RUDAS cut-off of <27/30 detected cognitive disorder with a 50% Ss, 83% Sp, 62% PPV and a 75% NPV. When adjusted for education level, a RUDAS score of <27/30 had a 45% Ss, 89% Sp, 70% PPV and 74% NPV. Conclusion The RUDAS is an accessible, valid, and scalable screening tool for systematic cognitive surveillance of adults with SCD. It can be administered in 6 minutes by health care professionals (e.g., nurses, physicians). It has modest sensitivity, but good specificity, especially when adjusted for educational level. It could therefore assist healthcare professionals caring for adults with SCD in the detection of patients in need of specific interventions, comprehensive neurocognitive assessment, and educative or professional accommodations. Future research should evaluate the utility of the RUDAS for longitudinal surveillance of cognitive disorders and whether adaptations could improve its sensitivity in adults with SCD.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".