Letter: Trends in In-Hospital Mortality and Neurological Deficit Rates Following Ischemic Stroke in Low- and Middle-Income Countries
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".