Mortality in ischaemic stroke patients without standard modifiable risk factors: An analysis of the Riksstroke registry
Bibliographic record
Abstract
INTRODUCTION: Little is known of the long-term prognosis of patients with acute ischaemic stroke in the absence of standard modifiable stroke risk factors (SMoRFs). In acute coronary syndromes, patients without modifiable risk factors have a higher mortality rate. We analysed data from the Swedish Stroke Register to determine survival of patients without SMoRFs following an ischaemic stroke. PATIENTS AND METHODS: We identified adult patients with first-presentation acute ischaemic stroke between 2010 and 2020. Patients were considered to possess a SMoRF if they had one of: hypertension, diabetes, hyperlipidaemia, atrial fibrillation or an active smoking history. We compared mortality in patients with and without SMoRFs following first-presentation ischaemic stroke using cox regression models. We also assessed the combined endpoint death and dependency (mRS 3-6) at 3 months via logistic regression models. RESULTS: Of 152,588 patients with ischaemic stroke, hypertension (58.7%) and atrial fibrillation (27.3%) were the most common risk factors. 34,019 patients (22.3%) had no SMoRFs. After a first-presentation ischaemic stroke, patients without SMoRFs had a lower risk of death than patients with one or more SMoRFs (HR 0.58 [95% CI 0.57-0.59]). The absence of SMoRFs was associated with lower odds of death and dependency at 3 months in logistic regression models (OR 0·60 [95% CI 0.58-0.62]). CONCLUSION: One in five patients with acute ischaemic stroke had no standard modifiable stroke risk factors. These patients have lower risk of death compared to patients with one or more SMoRFs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| 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".