Logistic prognostic model predicts institutional residence following acute ischaemic strokeNew model predicts where stroke survivors will live after injury

medRxiv Published September 30, 2026 Study authors: Kubiak, A.; Haji Boloori, F.; Powers, N.; Wu, Z.; Tran, P.; Osikoya, O. DOI ↗ Editorial oversight: Dr. Amelia Tan, PhD · Internal Medicine & Chronic Disease

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Key Takeaway

Note that while the model predicts institutional residence, its performance is limited by local system variations.

This secondary analysis of a large randomized trial evaluated a logistic prognostic model designed to predict institutional residence for patients following an acute ischaemic stroke. The model incorporated multiple admission-day terms to determine the likelihood of a patient residing in a nursing home or similar facility six months post-event. The study compared this comprehensive model against a benchmark consisting only of age and stroke syndrome.

The results indicated that the full model achieved discrimination levels similar to the benchmark. However, the authors noted that the majority of the predictive signal was derived from age and stroke syndrome. Furthermore, the model's performance showed notable variation when validated across different countries, suggesting that local factors influence outcomes.

Limitations noted by the authors include the significant variability in model performance across different regions and the fact that most predictive power is driven by basic demographics and clinical severity. While the model provides a structured way to predict long-term care needs, its practical application may be constrained by the diversity of local healthcare systems. Clinicians should consider these findings as a tool for risk stratification, while remaining mindful of the impact of local infrastructure on actual placement outcomes.

When a person survives a stroke, one of the biggest questions for families is where the patient will live. Will they be able to stay at home, or will they need to move into a nursing home or assisted living facility? This uncertainty can make planning for the future very difficult.

A large study involving over 14,000 survivors across 36 countries tested a new prediction model. This model looked at 17 different factors from the first day of hospital admission. The results showed that this new model performed just as well as the current standard, which only looks at age and the severity of the stroke.

While the tool is helpful, it has some limits. Most of the information it uses comes from the patient's age and the type of stroke they had. Also, the accuracy of the tool changes depending on the country, because local rules for where people live after a stroke vary. It is a useful tool for doctors, but it works best when combined with local knowledge.

What this means for you:

A new model can predict if a stroke survivor needs a nursing home, but its accuracy varies by country.

Common questions

How accurate is this new prediction model?

The new model showed a performance score of 0.789. This is very similar to the current standard of using age and stroke severity alone, which had a score of 0.774. While it is a reliable tool, its accuracy can change depending on which country the patient is in.

What factors does the model use to make its prediction?

The model uses 17 different terms from the first day of hospital admission. However, it is important to note that most of the information used by the model comes from the patient's age and the specific type of stroke they suffered.

Who is this finding helpful for?

This finding helps doctors and families who are trying to plan for the long-term care of a person who has just suffered an ischemic stroke. It helps predict if the patient will need a nursing home or institutional residence six months after their stroke.

Study Details

Study typeRct

Sample sizen = 19,435

EvidenceLevel 2

PublishedSep 2026

View Original Abstract ↓

BACKGROUND: Existing models for discharge to long-term institutional care after stroke are developed within single health systems and pool to an area under the curve near 0.80, but none is accompanied by an estimate of how much of the outcome is a property of the health system rather than the patient. METHODS: Secondary analysis of the International Stroke Trial, a randomised trial in 19,435 patients with suspected acute ischaemic stroke recruited in 1991-1996 across 36 countries. Among 14,885 survivors to six months with residence recorded, we developed and internally validated a logistic prognostic model for residential or nursing-home residence using 17 admission-day terms, and compared it against a pre-specified benchmark of age and stroke syndrome alone. Between-country variation was estimated by a method-of-moments decomposition with a cluster bootstrap. RESULTS: Institutional residence was recorded for 1,984 of 14,885 survivors (13.3%); the model was fitted on 14,121 patients containing 1,935 events. In 10-fold cross-validation with the whole procedure refitted per fold, the out-of-fold c-statistic was 0.789, calibration slope 0.987, calibration-in-the-large 0.1371 predicted against 0.1370 observed, and Brier score 0.1013. A benchmark of age and stroke syndrome alone reached an optimism-corrected 0.774 against 0.790 for the full model, a corrected difference of 0.0160. Between-country variation gave an intraclass correlation of 0.0409 (0.0191 to 0.0673), and in leave-one-country-out validation the c-statistic ranged from 0.635 to 0.823 across the 14 countries with enough events to estimate it. CONCLUSIONS: Institutional residence after stroke can be stratified at admission with discrimination comparable to existing models and calibration that holds in unseen patients, but almost all of the signal is carried by age and stroke syndrome. Performance varies substantially between countries, pointing to placement systems as the limit on what a pooled model can do.

Logistic prognostic model predicts institutional residence following acute ischaemic strokeNew model predicts where stroke survivors will live after injury

Common questions

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