Renal Impairment and Risk of Acute Stroke: The INTERSTROKE Study
Bibliographic record
Abstract
<b><i>Background:</i></b> Previous studies reported an association of renal impairment with stroke, but there are uncertainties underpinning this association. <b><i>Aims:</i></b> We explored if the association is explained by shared risk factors or is independent and whether there are regional or stroke subtype variations. <b><i>Methods:</i></b> INTERSTROKE is a case-control study and the largest international study of risk factors for first acute stroke, completed in 27 countries. We included individuals with available serum creatinine values and calculated estimated glomerular filtration rate (eGFR). Renal impairment was defined as eGFR &#x3c;60 mL/min/1.73 m<sup>2</sup>. Multivariable conditional logistic regression was used to determine the association of renal function with stroke. <b><i>Results:</i></b> Of 21,127 participants, 41.0% were female, the mean age was 62.3 ± 13.4 years, and the mean eGFR was 79.9 ± 23.5 mL/min/1.73 m<sup>2</sup>. The prevalence of renal impairment was higher in cases (22.9% vs. 17.7%, <i>p</i> &#x3c; 0.001) and differed by region (<i>p</i> &#x3c; 0.001). After adjustment, lower eGFR was associated with increased odds of stroke. Renal impairment was associated with increased odds of all stroke (OR 1.35; 95% CI: 1.24–1.47), with higher odds for intracerebral hemorrhage (OR 1.60; 95% CI: 1.35–1.89) than ischemic stroke (OR 1.29; 95% CI: 1.17–1.42) (<i>p</i><sub>interaction</sub> 0.12). The largest magnitudes of association were seen in younger participants and those living in Africa, South Asia, or South America (<i>p</i><sub>interaction</sub> &#x3c; 0.001 for all stroke). Renal impairment was also associated with poorer clinical outcome (RRR 2.97; 95% CI: 2.50–3.54 for death within 1 month). <b><i>Conclusion:</i></b> Renal impairment is an important risk factor for stroke, particularly in younger patients, and is associated with more severe stroke and worse outcomes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".