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Record W4388566314 · doi:10.1227/neu.0000000000002745

Letter: Trends in In-Hospital Mortality and Neurological Deficit Rates Following Ischemic Stroke in Low- and Middle-Income Countries

2023· letter· en· W4388566314 on OpenAlexaboutno aff
Alexa R. Lauinger, Anant Naik, Momodou G. Bah, Minnatallah Eltinay, Michael M. Covell, Joshua S. Catapano, Andrew W. Grande, Paul M. Arnold

Bibliographic record

VenueNeurosurgery · 2023
Typeletter
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineScopusStroke (engine)Low and middle income countriesPsychological interventionMEDLINEIncidence (geometry)Global healthNeurological deficitDeveloped countryIschemic strokeMeta-analysisDeveloping countryGerontologyEnvironmental healthPublic healthPopulationInternal medicineSurgeryPathologyIschemiaPsychiatry

Abstract

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To the Editor: Stroke is a leading cause of death and disability worldwide with an incidence of more than 12 million in 2019, a global cost estimated more than US $891 billion, and a disability-adjusted life-years loss of more than 143.00 million.1 Between 1990 and 2019, there has been a substantial increase in the number of cases and the number of deaths from strokes.2 Low- and middle-income countries (LMICs) are disproportionately affected, which may reflect differences in the ischemic stroke prevention guidelines or environmental risk factors between LMICs and high-income countries (HICs).1,3,4 Because of the lack of stroke studies completed in these countries, it is difficult to assess and generalize the different risk factors for ischemic strokes and to implement interventions to improve outcomes.3,4 This meta-analysis pools the stroke data from 25 countries to compare changes in in-hospital mortality and neurological deficit rates from 1984 to 2022 in LMICs. This analysis will help elaborate on the current obstacles to care for patients with stroke in LMICs. METHODS A preregistered literature search was completed adherent to 2020 PRISMA guidelines. We queried PUBMED, Web of Knowledge, and Scopus for MesH and non-MesH terms related to ischemic strokes and LMICs (search strategies have been detailed in Supplemental Digital Content 1, https://links.lww.com/NEU/D1000). This was an unplanned analysis of a previously registered study (CRD42023404915). Systematic reviews, meta-analyses, and other literature reviews were excluded but were used for citation matching based on the inclusion criteria. The Newcastle Ottawa Scale was used to determine the quality of included studies. The main outcomes of interest were in-hospital mortality and neurological deficit rate of patients with an incidence of ischemic stroke. To calculate this, the outcome rate was assumed to be equal for the study period listed. Then, for each year, a pooled proportional meta-analysis was performed across all studies using the inverse-variance method to ascertain a pooled mortality or neurological deficit rate per year. RESULTS Our search yielded 153 unique articles (Figure 1) that fit the inclusion criteria for a total of 65 009 participants from 1984 to 2022 (38 years) (Supplemental Digital Content 2, https://links.lww.com/NEU/E2). Data were collected from 25 countries, and the studies were most commonly completed in India or Iran. Based on the pooled publication data, there has been a decrease in the in-hospital mortality rate but an increase in the neurological deficit rate of patients with ischemic stroke in LMICs (Figure 2). There has been a 0.2% decline in mortality since 1990. The neurological deficit rate has increased by 1% overall, but it has been stagnant for the past decade.FIGURE 1.: PRISMA diagram of included studies.FIGURE 2.: Plots of in-hospital mortality and neurological deficit rates for pooled low- and middle-income countries data. The data points represent the mean and standard deviation for in-hospital mortality and neurological deficit rates for each year. The number over the bar plot indicates the total sample size for that year.DISCUSSION Although, there has been an increase in overall incidence of stroke and death from strokes in the past 30 years,2 our analysis demonstrates a decrease in the in-hospital mortality rate of patients with ischemic stroke in LMICs over the past 38 years. A similar trend was noted in China between 2007 and 20105 and in the United States between 2010 and 2017.6 There are several hypotheses that explain this trend, but it is likely that greater access technology played a role. Regardless of this change, LMICs still bear a greater burden from ischemic stroke. In 2019, the mortality rate for ischemic stroke was 1.3-fold higher in LMICs than HICs.2 Further improvement in the technology in LMICs would help improve mortality rate, and a focus on making this technology accessible for LMICs sooner would narrow the mortality rate gap between them and HICs. However, the increase in neurological deficit rate may be due to more patients having access to hospital care and thus getting treatment for more strokes that previously would have resulted in death. Age and severity of stroke are 2 factors that may contribute to poorer functional outcomes after ischemic stroke.7 In HICs, functional outcome rates have been improving because of improvement in stroke prevention programs.8 A focus on stroke prevention programs in LMICs will likely improve the functional outcome rate after ischemic stroke. The next step of this analysis is to explore the predictors for in-patient mortality and neurological deficits in LMICs, which could inform physicians of possible targets to further improve the health outcome of patients with stroke. Limitations of this study include intrinsic limitations in a meta-analysis approach and the limited number of publications about stroke from LMICs. Because data were only included from 25 countries and most of the studies were completed at a single health center, the outcomes may not be generalizable to all LMICs. This study also relies on the reporting of data from published studies, which introduces heterogeneity in the reported outcomes and an inability to account for stroke severity or recurrences in studies that did not report these factors.

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)0.005
metaresearch head score (Gemma)0.054
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.054
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0030.001
Research integrity0.0110.007
Insufficient payload (model declined to judge)0.0130.009

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 head0.026
GPT teacher head0.269
Teacher spread0.244 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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