Incidentally discovered Covert Cerebrovascular Disease by CT versus MRI: Agreement and Prognostic Value for Stroke and Dementia in a Large Real-World Cohort
Bibliographic record
Abstract
Abstract Background Covert cerebrovascular disease (CCD), comprising covert brain infarction (CBI) and white matter disease (WMD), is common in older adults and linked to increased risk of stroke and dementia. While most CCD research relies on MRI, CT remains the predominant imaging modality in clinical care. The influence of imaging modality on detection and prognosis of incidentally discovered CCD remains unclear. Methods We identified 18,626 patients aged ≥50 years from Kaiser Permanente Southern California who underwent both CT and MRI brain scans within 30 days between 2009– 2022. Patients with known prior stroke or dementia were excluded. Natural language processing algorithms were applied to radiology reports to identify CBI and WMD status and WMD severity (none, mild, moderate, severe). We assessed prevalence, cross-modality agreement (Cohen’s kappa), and reclassification patterns. Prognostic associations with incident stroke or dementia were estimated using Cox Proportional Hazards regression adjusted for vascular and cognitive risk factors. Findings CBI prevalence was similar for CT (6.3%) and MRI (6.1%), but agreement was modest (κ=0.27). WMD was reported far more often on MRI (60.5%) than CT (24.4%). Among 15,551 patients with classifiable severity on both modalities, 47.9% (n=7,441) had discordant grades, with 92.3% upgraded on MRI. The incidence rates of stroke or dementia per 1,000 person-years were 12.7 (95% CI 11.5 - 14.0) for patients without WMD on either modality (36.3% of the cohort), 22.6 (21.0 - 24.2) for WMD detected on MRI only (39.2% of the cohort), and 52.2 (48.69 to 55.95) for WMD detected on both CT and MRI (21.2% of the cohort). In adjusted Cox models, WMD detected on MRI only was associated with a 23% higher hazard of stroke or dementia (HR=1.23, 95% CI 1.07–1.41) compared with no WMD on either modality, while WMD detected on both CT and MRI was associated with an 82% higher hazard (1.82, 1.58–2.11). Interpretation MRI detects substantially more WMD than CT; however, WMD visible on CT has stronger prognostic significance, despite CT’s low sensitivity. These findings emphasize modality-based diagnostic and prognostic differences and support the need for modality-specific approaches when translating CCD research into clinical risk assessment and patient counselling. Funding This work is funded by an Alzheimer’s Drug Discovery Foundation (ADDF) award (RC-202209-2024187) and National Institutes of Health (NIH) grants (2 RF1 NS102233-05, R01 NS134859-01). Research in context Evidence before this study Most knowledge about covert cerebrovascular disease (CCD), including covert brain infarction (CBI) and white matter disease (WMD), comes from MRI-based research cohorts. However, CT is the dominant imaging modality in routine care, where incidental CCD is identified. Previous studies identified from our literature search suggest that CT and MRI may differ in sensitivity, but no studies have directly compared diagnostic and prognostic differences across both modalities within the same patients in routinely-obtained neuroimaging. Added value of this study This is the first large-scale, within-subject analysis of CT and MRI CCD findings obtained in routine care. We found that MRI detects substantially more WMD, while CT-detected WMD is more strongly associated with future stroke and dementia. CBI prevalence was similar across modalities, but agreement was modest. Implications of all the available evidence Our findings suggest that clinically meaningful CCD is usually captured by CT scan; pragmatic research studies need to incorporate this modality.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".