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Record W4387729230 · doi:10.1177/17474930231210397

Association of multimorbidity with mortality after stroke stratified by age, severity, etiology, and prior disability

2023· article· en· W4387729230 on OpenAlexfundno aff
Matthew B. Downer, Ramón Luengo-Fernández, Lucy E Binney, Sergei A. Gutnikov, L E Silver, Aubretia McColl, Peter M. Rothwell

Bibliographic record

VenueInternational Journal of Stroke · 2023
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
FundersClarendon FundRhodes ScholarshipsUniversity of OxfordWolfson FoundationCanadian Institutes of Health ResearchNational Institute for Health and Care ResearchWellcome Trust
KeywordsMedicineStroke (engine)EtiologyInternal medicineComorbidityAtrial fibrillationDepression (economics)Modified Rankin ScalePopulationPediatricsIschemic stroke

Abstract

fetched live from OpenAlex

Background: Multimorbidity is common in patients with stroke and is associated with increased medium- to long-term mortality, but its value for clinical decision-making and case-mix adjustment will depend on other factors, such as age, stroke severity, etiological subtype, prior disability, and vascular risk factors. Aims: In the absence of previous studies, we related multimorbidity to long-term post-stroke mortality with stratification by these factors. Methods: In patients ascertained in a population-based stroke incidence study (Oxford Vascular Study; 2002–2017), we related pre-stroke multimorbidity (weighted/unweighted Charlson comorbidity index (CCI)) to all-cause/vascular/non-vascular mortality (1/5/10 years) using regression models adjusted/stratified by age, sex, predicted early outcome (THRIVE score), stroke severity (NIH stroke scale (NIHSS)), etiology (Trial of Org 10172 in Acute Stroke Treatment (TOAST)), premorbid disability (modified Rankin Scale (mRS)), and non-CCI risk factors (hypertension, hyperlipidemia, atrial fibrillation, smoking, deprivation, anxiety/depression). Results: Among 2454 stroke patients (M/SD age: 74.1/13.9 years; 48.9% male; M/SD NIHSS: 5.7/7.0), 1375/56.0% had ⩾ 1 CCI comorbidity and 685/27.9% had ⩾ 2. After age/sex adjustment, multimorbidity (unweighted CCI ⩾ 2 vs 0) predicted (all ps < 0.001) mortality at 1 year (aHR = 1.57, 95% CI = 1.38–1.78), 5 years (aHR = 1.73, 95% CI = 1.53–1.96), and 10 years (aHR = 1.79, 95% CI = 1.58–2.03). Although multimorbidity was independently associated with premorbid disability (mRS > 2: aOR = 2.76, 2.13–3.60) and non-CCI risk factors (hypertension: 1.56, 1.25–1.95; hyperlipidemia: 2.58, 2.03–3.28; atrial fibrillation: 2.31; 1.78–2.98; smoking: 1.37, 1.01–1.86), it predicted death after adjustment for all measured confounders (10-year-aHR = 1.56, 1.37–1.78, p < 0.001), driven mainly by non-vascular death (aHR = 1.89, 1.55–2.29). Predictive value for 10-year all-cause death was greatest in patients with lower expected early mortality: lower THRIVE score (p int < 0.001), age < 75 years (aHR = 2.27, 1.71–3.00), NIHSS < 5 (1.84, 1.53–2.21), and lacunar stroke (3.56, 2.14–5.91). Results were similar using the weighted CCI. Conclusion: Pre-stroke multimorbidity is highly prevalent and is an independent predictor of death after stroke, supporting its inclusion in case-mix adjustment models and in informing decision-making by patients, families, and carers. Prediction in younger patients and after minor stroke, particularly for non-vascular death, suggests potential clinical utility in targeting interventions that require survival for 5–10 years to achieve a favorable risk/benefit ratio. Data access statement: Data requests will be considered by the Oxford Vascular Study (OXVASC) Study Director (P.M.R.-peter.rothwell@ndcn.ox.ac.uk).

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.019
GPT teacher head0.312
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2023
Admission routes1
Has abstractyes

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