Evidence map›Paper›PMID 39254195›Full record

ArticleInternational journal of stroke : official journal of the International Stroke Society2025

A lifetime economic model of mortality and secondary care use for patients discharged from hospital following acute stroke.

Peter McMeekin, Stephen McCarthy, Andrew McCarthy, Jennifer Porteous, Michael Allen, Anna Laws, Phil White, Martin James, Gary A Ford, Lisa Shaw and 1 more

Abstract read
In one paragraph

Article in International journal of stroke : official journal of the International Stroke Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Peter McMeekinDepartment of Nursing, Midwifery & Health, Northumbria University, Newcastle upon Tyne, UK.
Stephen McCarthyDepartment of Nursing, Midwifery & Health, Northumbria University, Newcastle upon Tyne, UK.ORCID 0000-0003-1473-776X
Andrew McCarthyDepartment of Nursing, Midwifery & Health, Northumbria University, Newcastle upon Tyne, UK.
Jennifer PorteousDepartment of Nursing, Midwifery & Health, Northumbria University, Newcastle upon Tyne, UK.
Michael AllenUniversity of Exeter Medical School, Exeter, UK.
Anna LawsUniversity of Exeter Medical School, Exeter, UK.
Phil WhiteTranslational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK.
Martin JamesPeninsula Applied Research Collaboration (PenARC), University of Exeter, Exeter, UK.
Gary A FordOxford University Hospitals NHS Foundation Trust and Medical Sciences Division, University of Oxford, Oxford, UK.
Lisa ShawPopulation Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK.ORCID 0000-0002-3435-9519
Christopher I PricePopulation Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe long-term health-economic consequences of acute stroke are typically extrapolated from short-term outcomes observed in different studies, using models based on assumptions about longer-term morbidity and mortality. Inconsistency in these assumptions and the methods of extrapolation can create difficulties when comparing estimates of lifetime cost-effectiveness of stroke care interventions.

aimsTo develop a long-term model consisting of a set of equations to estimate the lifetime effects of stroke care interventions to promote consistency in extrapolation of short-term outcomes.

methodsData about further admissions and mortality were provided for acute stroke patients discharged between 2013 and 2014 from a large English service. This was combined with data from UK life tables to create a set of parametric equations in a model that use age, sex, and modified Rankin Scores to predict the lifetime risk of mortality and secondary care resource utilization including ED attendances, non-elective admissions, and elective admissions. A cohort of 1509 (male 51%; mean age 74) stroke patients had median follow-up of 7 years and represented 7111 post-discharge patient years. A logistic model estimated mortality within 12 months of discharge, and a Gompertz model was used over the remainder of the lifetime. Hospital attendances were modeled using a Weibull distribution. Non-elective and elective bed days were both modeled using a log-logistic distribution.

resultsMortality risk increased with age, dependency, and male sex. Although the overall pattern was similar for resource utilization, there were different variations according to dependency and gender for ED attendances and non-elective/elective admissions. For example, 65-year-old women with a mRS at discharge of 1 would gain an extra 6.75 life years compared to 65-year-old women with a mRS at discharge of 3. Over their lifetime, 65-year-old women with an mRS at discharge of 1 would experience 0.09 less ED attendances, 2.12 less non-elective bed days, and 1.28 additional elective bed days than 65-year-old women with an mRS at discharge of 3.

conclusionsUsing long-term follow-up publicly available data from a large clinical cohort, this new model promotes standardized extrapolation of key outcomes over the life course and potentially can improve the real-world accuracy and comparison of long-term cost-effectiveness estimates for stroke care interventions. DATA ASSESS STATEMENT: Data are available upon reasonable request from third parties.

Indexed as

Models, EconomicPatient DischargeSecondary Health CareStrokeAgedAged, 80 and overCohort StudiesCost-Benefit AnalysisFemaleHumansMaleMiddle AgedUnited Kingdomburden of diseasecost factorsEconomicsmodelingmortalitystroke

Identifiers

PMID39254195
PMCPMC11669260

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.