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Record W4285082632 · doi:10.1016/s2214-109x(22)00238-8

Hypertension in stroke survivors and associations with national premature stroke mortality: data for 2·5 million participants from multinational screening campaigns

2022· article· en· W4285082632 on OpenAlexaff
Queran Lin, Tingxi Ye, Pengpeng Ye, Claudio Borghi, Suzie Cro, Albertino Damasceno, Nadia Khan, Peter M Nilsson, Dorairaj Prabhakaran, Agustín J. Ramiréz, Markus P. Schlaich, Aletta E. Schutte, George S. Stergiou, Michael A. Weber, Thomas Beaney, Neil R Poulter

Bibliographic record

VenueThe Lancet Global Health · 2022
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsCentre for Health Evaluation and Outcome Sciences
FundersNational Institute for Health Research Applied Research Collaboration WestServierImperial College LondonUniversity of Maryland School of Public HealthNational Institute for Health and Care ResearchNIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer ResearchBritish Heart FoundationInternational Society of Hypertension
KeywordsMedicineStroke (engine)Blood pressureLogistic regressionInternal medicineIncidence (geometry)Cross-sectional studyPhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Blood pressure control has a pivotal role in reducing the incidence and recurrence of stroke. May Measurement Month (MMM), which was initiated in 2017 by the International Society of Hypertension, is the largest global blood pressure screening campaign. We aim to compare MMM participants with and without a previous history of stroke and to investigate associations between national-level estimates of blood pressure management from MMM and premature stroke mortality. METHODS: In this annual, global, cross-sectional survey, more than 2·5 million volunteers (≥18 years) from 92 countries were screened in May, 2017, and May, 2018. Three seated blood pressure readings and demographic, lifestyle, and cardiovascular disease data were collected. Associations between risk factors and stroke history were analysed with mixed-effects logistic regression, and associations between national-level estimates of blood pressure management and premature stroke mortality based on Global Burden of Disease data were investigated with linear regression. FINDINGS: 2 222 399 (88·4%) of 2 515 365 participants had recorded data on a history of stroke, of whom 62 639 (2·8%) reported a previous stroke. Participants with a history of stroke had higher rates of hypertension (77·0% vs 32·9%, p<0·0001) and of treated (90·2% vs 57·0%, p<0·0001) and controlled (55·9% vs 32·4%, p<0·0001) hypertension than those without a history of stroke. A third of participants with a history of stroke had either untreated hypertension or treated but uncontrolled hypertension (blood pressure ≥140/90 mm Hg). Strong positive associations were found between national premature stroke mortality and mean systolic blood pressure (84·3 [95% CI 38·8 to 129·9] years of life lost [YLL] per 100 000 people per mm Hg increase) and the percentage of participants with raised blood pressure (49·1 [22·6 to 75·6] YLL per 100 000 people per 1% increase). Strong negative associations were found between national premature stroke mortality and the percentage of participants with hypertension on treatment (-21·0 [-33·0 to -8·9] YLL per 100 000 people per 1% increase) and with controlled blood pressure (-31·6 [-43·8 to -19·4] YLL per 100 000 people per 1% increase). INTERPRETATION: Blood pressure control remains suboptimal worldwide among people with a history of stroke. National estimates of blood pressure management reflect national premature stroke mortality sufficiently to prompt policy makers to promote blood pressure screening and management. FUNDING: International Society of Hypertension and Servier Pharmaceuticals.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.137
GPT teacher head0.385
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations45
Published2022
Admission routes1
Has abstractyes

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