Predicting institutionalization and mortality across the spectrum of Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background Patients and care partners express a desire for accurate prognostic information. Previous studies predicting institutionalization and mortality focused on the dementia stage. Yet, Alzheimer’s disease (AD) is characterized by a long pre‐dementia stage. We aimed to predict institutionalization and mortality in patients across the cognitive spectrum of AD. Method We included n=1418 non‐demented patients with Subjective Cognitive Decline (SCD) or Mild Cognitive Impairment (MCI) (age 63±7, 38%F, Mini Mental State Examination (MMSE) 27±2) and n=1179 patients with AD dementia (age 65±7, 53%F, MMSE 20±5) from the Amsterdam Dementia Cohort. The dates of institutionalization (admission to a nursing home; follow‐up 3.4±2.7 years) and mortality (follow‐up 4.5±2.7 years) were derived from Statistics Netherlands. We constructed models, stratified by dementia status, to predict institutionalization and mortality using Cox regression. We evaluated the following determinants: MMSE, Neuropsychiatric Inventory (NPI), Charlson Comorbidity Index (CCI) and APOE e4 status, MRI (medial temporal lobe atrophy (MTA), global cortical atrophy (GCA), white matter hyperintensities (WMH)) and CSF biomarker levels (Aβ42 and p‐tau). Determinants were selected using backward selection (p<0.10). All models included age and sex. In addition, we evaluated the discriminative performance of models without MRI and CSF biomarkers. Discriminative performance of the models was assessed with Harrell’s C statistics. Result In non‐demented patients, n=123 (9%) patients died and n=74 (5%) patients were institutionalized. In AD dementia, n=413 (35%) patients died and n=453 (38%) patients were institutionalized. Table 1 shows the optimal prediction models for institutionalization and mortality. Discriminative performance was better in non‐demented patients than in those in the dementia stage: institutionalization (Harrell’s C 0.81 vs 0.68) and mortality (0.78 vs 0.66). Although model fit was better for the full model, discriminative performance was comparable for the model without MRI for institutionalization and without CSF for mortality in non‐demented patients (Table 1). Conclusion We constructed prediction models to predict institutionalization and mortality in patients across the spectrum of AD. Highest discriminative performance was found in models for non‐demented patients with SCD or MCI. CSF biomarkers contributed most to predict institutionalization and MRI biomarkers to predict mortality.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".