Evidence map›Paper›PMID 36847304›Full record

ArticleInternational journal of stroke : official journal of the International Stroke Society2023

Heterogeneity in the diagnosis and prognosis of ischemic stroke subtypes: 9-year follow-up of 22,000 cases in Chinese adults.

Matthew Chun, Haiqiang Qin, Iain Turnbull, Sam Sansome, Simon Gilbert, Alex Hacker, Neil Wright, Tingting Zhu, David Clifton, Derrick Bennett and 11 more

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of stroke : official journal of the International Stroke Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
1.9field-weighted citation impact, top 14% of its field
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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Large artery atherosclerotic versus cardioembolism subtypes of large hemispheric infarction in the middle cerebral artery.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025
    Article
  5. Clinical insights on "characteristics and impacts of large artery atherosclerosis (LAA) versus cardioembolism (CE) subtypes in large hemispheric infarction (LHI) of the middle cerebral artery (MCA)".Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025
    Article
  6. Article
  7. Article
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

21 authors at 4 institutions in 2 countries.

Matthew ChunClinical Trial Service Unit and Epidemiological Studies, Nuffield Department of Population Health and Big Data Institute, University of Oxford, Oxford, UK.
Haiqiang QinDepartment of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Iain TurnbullClinical Trial Service Unit and Epidemiological Studies, Nuffield Department of Population Health and Big Data Institute, University of Oxford, Oxford, UK.
Sam SansomeClinical Trial Service Unit and Epidemiological Studies, Nuffield Department of Population Health and Big Data Institute, University of Oxford, Oxford, UK.ORCID 0000-0002-9434-4482
Simon GilbertClinical Trial Service Unit and Epidemiological Studies, Nuffield Department of Population Health and Big Data Institute, University of Oxford, Oxford, UK.
Alex HackerClinical Trial Service Unit and Epidemiological Studies, Nuffield Department of Population Health and Big Data Institute, University of Oxford, Oxford, UK.
Neil WrightClinical Trial Service Unit and Epidemiological Studies, Nuffield Department of Population Health and Big Data Institute, University of Oxford, Oxford, UK.
Tingting ZhuDepartment of Engineering Science, University of Oxford, Oxford, UK.
David CliftonDepartment of Engineering Science, University of Oxford, Oxford, UK.
Derrick BennettClinical Trial Service Unit and Epidemiological Studies, Nuffield Department of Population Health and Big Data Institute, University of Oxford, Oxford, UK.
Yu GuoFuwai Hospital, Chinese Academy of Medical Sciences, Beijing, China.
Pei PeiPeking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China.
Jun LvDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Sciences Center, Beijing, China.
Canqing YuDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Sciences Center, Beijing, China.
Ling YangClinical Trial Service Unit and Epidemiological Studies, Nuffield Department of Population Health and Big Data Institute, University of Oxford, Oxford, UK.
Liming LiDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Sciences Center, Beijing, China.
Yan LuNCDs Prevention and Control Department, Suzhou CDC, Suzhou, China.
Zhengming ChenClinical Trial Service Unit and Epidemiological Studies, Nuffield Department of Population Health and Big Data Institute, University of Oxford, Oxford, UK.
Benjamin J CairnsClinical Trial Service Unit and Epidemiological Studies, Nuffield Department of Population Health and Big Data Institute, University of Oxford, Oxford, UK.
Yiping ChenClinical Trial Service Unit and Epidemiological Studies, Nuffield Department of Population Health and Big Data Institute, University of Oxford, Oxford, UK.
Robert ClarkeClinical Trial Service Unit and Epidemiological Studies, Nuffield Department of Population Health and Big Data Institute, University of Oxford, Oxford, UK.ORCID 0000-0002-9802-8241
University of Oxford · GBPeking University · CNCapital Medical University · CNChinese Academy of Medical Sciences & Peking Union Medical College · CN

Funding

British Heart Foundation CH/1996001/9454Cancer Research UK 29186Medical Research Council MC_U137686851Medical Research Council MC_UU_00017/1Wellcome Trust 212946/Z/18/Z
6 · The paper itself

Abstract

backgroundReliable classification of ischemic stroke (IS) etiological subtypes is required in research and clinical practice, but the predictive properties of these subtypes in population studies with incomplete investigations are poorly understood.

aimsTo compare the prognosis of etiologically classified IS subtypes and use machine learning (ML) to classify incompletely investigated IS cases.

methodsIn a 9-year follow-up of a prospective study of 512,726 Chinese adults, 22,216 incident IS cases, confirmed by clinical adjudication of medical records, were assigned subtypes using a modified Causative Classification System for Ischemic Stroke (CCS) (large artery atherosclerosis (LAA), small artery occlusion (SAO), cardioaortic embolism (CE), or undetermined etiology) and classified by CCS as "evident," "probable," or "possible" IS cases. For incompletely investigated IS cases where CCS yielded an undetermined etiology, an ML model was developed to predict IS subtypes from baseline risk factors and screening for cardioaortic sources of embolism. The 5-year risks of subsequent stroke and all-cause mortality (measured using cumulative incidence functions and 1 minus Kaplan-Meier estimates, respectively) for the ML-predicted IS subtypes were compared with etiologically classified IS subtypes.

resultsAmong 7443 IS subtypes with evident or probable etiology, 66% had SAO, 32% had LAA, and 2% had CE, but proportions of SAO-to-LAA cases varied by regions in China. CE had the highest rates of subsequent stroke and mortality (43.5% and 40.7%), followed by LAA (43.2% and 17.4%) and SAO (38.1% and 11.1%), respectively. ML provided classifications for cases with undetermined etiology and incomplete clinical data (24% of all IS cases; n = 5276), with area under the curves (AUC) of 0.99 (0.99-1.00) for CE, 0.67 (0.64-0.70) for LAA, and 0.70 (0.67-0.73) for SAO for unseen cases. ML-predicted IS subtypes yielded comparable subsequent stroke and all-cause mortality rates to the etiologically classified IS subtypes.

conclusionThis study highlighted substantial heterogeneity in prognosis of IS subtypes and utility of ML approaches for classification of IS cases with incomplete clinical investigations.

Indexed as

AtherosclerosisBrain IschemiaEmbolismIschemic StrokeStrokeAdultEast Asian PeopleFollow-Up StudiesHumansPrognosisProspective StudiesRisk FactorsChinaclassificationetiologyIschemic strokemachine learning

Identifiers

PMID36847304
PMCPMC10374992
OpenAlexW4322494998

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.