Patient Characteristics, Disease Staging, and Diagnostic Testing Prior Initial Alzheimer’s Disease Diagnosis
Bibliographic record
Abstract
BACKGROUND: The recent developments in Alzheimer's disease (AD) treatments have elucidated the importance of the timely and accurate diagnosis of AD. As clinical guidelines for the diagnosis of AD are further developed, biomarker testing and neuroimaging will become increasingly important. The purpose of this study was to explore the rates of diagnostic testing in patients newly diagnosed with AD. METHOD: This retrospective observational study used de-identified administrative claims and electronic health records (EHR) from the Optum Market Clarity™ Integrated Clinical + Claims Database to identify US adult commercial and Medicare Advantage enrollees with ≥1 claims for AD (first claim=index date) between 01/01/2020 and 09/30/2024. This dataset was further enhanced by the Optum Alzheimer's Disease Enriched Clinical Database which leverages validated natural language processing (NLP) methods to extract relevant AD data including cognitive test results (Mini-Mental State Examination [MMSE] or Montreal Cognitive Assessment [MoCA]). All enrollees had ≥360 days of baseline enrollment. Demographics, AD stage (from cognitive assessments), and biomarker/imaging were captured during the baseline period. RESULT: 4,306 patients met all inclusion criteria: baseline enrollment, cognitive assessment before AD diagnosis, and no AD diagnosis during baseline. Twenty-three percent of patients had "normal" cognitive test results, 40% had "mild," 30% had "moderate," and 8% had "severe." African American patients were more likely to be diagnosed in the moderate (41%) or severe (11%) stages compared to Caucasian patients (27% moderate, 7% severe). Additionally, Hispanic patients were more likely to be diagnosed in the moderate (41%) and severe (16%) stages compared to non-Hispanic patients (29% moderate, 7% severe). Finally, a low number of patients received biomarker testing (1% overall) or neuroimaging (3% overall) prior to their AD diagnosis regardless of AD stage. CONCLUSION: Enriching claims data with NLP-derived clinical values added important depth to this retrospective analysis. By identifying cognitive test results, we found racial and ethnic disparities in AD staging preceding a new AD diagnosis. We also found a suboptimal number of patients that received imaging to confirm their AD diagnosis regardless of stage. Future research is needed to better assess the impacts of suboptimal diagnostic testing.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".