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Record W4294142638 · doi:10.3389/fneur.2022.999536

Association of the stress hyperglycemia ratio and clinical outcomes in patients with stroke: A systematic review and meta-analysis

2022· review· en· W4294142638 on OpenAlexaboutno aff
Yongwei Huang, Xiao-Shuang Yin, Zongping Li

Bibliographic record

VenueFrontiers in Neurology · 2022
Typereview
Languageen
FieldMedicine
TopicHyperglycemia and glycemic control in critically ill and hospitalized patients
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMeta-analysisOdds ratioModified Rankin ScaleInternal medicineStroke (engine)Cochrane LibraryConfidence intervalGlycated hemoglobinPublication biasRelative riskSystematic reviewIncidence (geometry)MEDLINEDiabetes mellitusIschemic strokeType 2 diabetesEndocrinology

Abstract

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Objective Stress hyperglycemia (SH) is common in patients with acute diseases, such as stroke and myocardial infarction. Stress hyperglycemia ratio (SHR) is calculated by glucose/glycated hemoglobin and has been widely used for evaluating SH. But whether SHR is associated with clinical outcomes in stroke patients remains unclear so far. Although many studies have shown that higher SHR means poor outcomes, there is still no absolute evidence that SHR plays a critical role in stroke patients. Hence, we performed a systematic review and meta-analysis aiming to investigate the association between SHR and clinical outcomes in stroke patients. Methods We performed a comprehensive literature search of the PubMed, Embase, Cochrane Library databases, Clinicaltrials.gov, and WHO-ICTRP. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), we performed our study. The Newcastle-Ottawa Scale (NOS) tool was used to examine the potential bias of included studies. The endpoints including poor outcome, mortality, neurological deficit, hemorrhagic transformation (HT), and infectious complications were statistically analyzed. Results Sixteen retrospective studies met the eligibility criteria, and a number of 183,588 patients were included. Our meta-analysis demonstrated a significant increase in the incidence of poor outcome, according to assessment by the modified Rankin Scale (mRS) ≥ 3 points [odds ratio (OR) 2.53, 95% confidence interval (CI) 1.99–3.22, P < 0.00001, I 2 = 68%], mortality (OR 1.96, 95% CI 1.58–2.44, P < 0.00001, I 2 = 61%), neurological deficit (OR 1.99, 95% CI 1.47–2.70, P < 0.00001, I 2 = 75%), hemorrhagic transformation (HT) (OR 3.70, 95% CI 2.69–5.08, P < 0.00001, I 2 = 0%), and infectious complications [(Pneumonia) OR 2.06, 95% CI 1.57–2.72, P < 0.00001, I 2 = 24%; (Urinary tract infection) OR 2.53, 95% CI 1.45–4.42, P = 0.001, I 2 = 57%] in stroke patients with higher SHR. However, no significant influence was observed for recanalization rate (OR 0.86, 95% CI 0.54–1.38, P = 0.53, I 2 = 0%). Conclusion With or without diabetes, no matter whether undergoing intravenous thrombolysis or mechanical thrombectomy, higher SHR significantly increased the occurrence of poor outcomes, mortality, neurological deficit, HT, and infectious complications. The recanalization rate was not statistically significant between the two groups. More attention must be paid in clinical practice to SH. Future investigation should focus on the diagnostic value of SHR and the early control of hyperglycemia. Meanwhile, whether SHR could become a novel and promising target for early intervention is worthy of attention in further research. Besides, the influence of the dynamic change of glucose-to-HbA1c ratio, namely SHR, on intracerebral hemorrhage outcomes requires further investigation in future research. Although no randomized double-blind studies have been conducted, the available massive sample studies reflect the actual situation in the clinic and assist clinical decision makers. Systematic review registration https://www.crd.york.ac.uk/prospero/ , identifier: CRD42022345587.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.902
Threshold uncertainty score0.783

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0090.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.314
Teacher spread0.287 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

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Citations51
Published2022
Admission routes1
Has abstractyes

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