Systemic inflammation response index as a clinical outcome evaluating tool and prognostic indicator for hospitalized stroke patients: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Stroke, which is the main element of cerebrovascular disease (CVD), has become the foremost reason for death and disability on a global scale. The systemic inflammation response index (SIRI), a newly developed and comprehensive indicator, has demonstrated promise in forecasting clinical results for diverse ailments. Nevertheless, the uncertainty surrounding the assessment and prediction of clinical outcomes for stroke patients by SIRI persists, and the conflicting findings from the limited studies conducted on this matter further complicate the situation. Consequently, we performed a thorough systematic review and meta-analysis to explore the correlation between SIRI and the clinical results in individuals suffering from stroke. METHODS: This research was registered in PROSPERO and carried out following the PRISMA guidelines. A thorough investigation was carried out on PubMed, Embase, the Cochrane Library, Web of Science, and Scopus databases. Furthermore, we conducted a manual search in Chinese databases, such as China national Knowledge Infrastructure (CNKI), WanFang, VIP, and China Biology Medicine (CBM). We assessed the potential for bias in the studies included by utilizing the Newcastle-Ottawa Scale (NOS) tool. Adverse clinical outcomes were the main focus of the study, with secondary endpoints including mortality, the predictive value of SIRI, SIRI values across various endpoints, and clinical parameters associated with subarachnoid hemorrhage (SAH) in relation to low and high SIRI group. RESULTS: Following rigorous evaluation, a grand total of 22 investigations, encompassing a populace of 12,737 individuals, were considered suitable for incorporation in the final analysis. The findings from our meta-analysis indicate a strong and consistent correlation between elevated SIRI levels and adverse functional outcomes, irrespective of the method used to evaluate unfavorable outcomes. Furthermore, increased SIRI values have a strong correlation with mortality rates in both the short and long term. Besides, SIRI is a useful indicator of the severity of SAH. SIRI demonstrates strong predictive ability in identifying unfavorable outcomes and stroke-related pneumonia (SAP), as higher SIRI values are typically linked to negative endpoints. Nevertheless, the meta-analysis indicated that there was no significant increase in the risk of early neurological deterioration (END) and acute hydrocephalus (AHC) in high SIRI group when comparing to low SIRI. CONCLUSION: This study could potentially pave the way for groundbreaking insights into the relationship between SIRI and stroke patient outcomes, as it appears to be the first meta-analysis to explore this association. Given the critical role of the inflammatory response in stroke recovery, closely monitoring patients with high SIRI levels could represent a promising strategy for mitigating brain damage post-stroke. Thus, further investigation into SIRI and its impact on clinical outcomes is essential. While our initial findings offer valuable insights into this area, continued research is necessary to fully elucidate the potential of SIRI, ideally through dynamic monitoring and large-scale, multi-center studies. Ultimately, this research has the potential to inform clinical decision-making and improve patient outcomes following stroke. SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/prospero/ ; Identifier CRD42023405221.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.186 | 0.319 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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; both teacher heads agree on what is shown here.
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".