Next Article in Journal
Prognostic Role of Inflammatory and Nutritional Indices in NSCLC Patients Treated with Immune Checkpoint Inhibitors: Retrospective, Multicenter, Turkish Oncology Group Study of Overall and Elderly Populations
Previous Article in Journal
Risk of Osteonecrosis of the Jaw in Patients Treated with Zoledronic or Alendronic Acid: A Systematic Review
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Medicina
Volume 61
Issue 7
10.3390/medicina61071158
medicina-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Stress Hyperglycemia Ratio as a Predictor of In-Hospital Stent Thrombosis in STEMI Patients Undergoing Primary PCI: A Retrospective Cohort Study

by
Evliya Akdeniz
1,*,
Cennet Yıldız
1,
Mehmet Karaca
2,
Mehmet Pişirici
1,
Hasan Ali Sinoplu
1,
Onur Akpınar
1,
Atakan Arpac
1,
Didem Mirgün Manioğlu
1,
Dilay Karabulut
1 and
Fatma Nihan Turhan Çağlar
1
1
Department of Cardiology, Bakırköy Dr. Sadi Konuk Training and Research Hospital, 34147 Istanbul, Turkey
2
Department of Cardiology, Ataşehir Memorial Hospital, 34750 Istanbul, Turkey
*
Author to whom correspondence should be addressed.
Medicina 2025, 61(7), 1158; https://doi.org/10.3390/medicina61071158
Submission received: 31 May 2025 / Revised: 15 June 2025 / Accepted: 24 June 2025 / Published: 26 June 2025
(This article belongs to the Section Cardiology)
Browse Figure
Versions Notes

Abstract

Background and Objectives: and Objectives: Admission hyperglycemia (AH) is common in acute ST-elevation myocardial infarction (STEMI) and linked to poor prognosis. The stress hyperglycemia ratio (SHR) reflects relative hyperglycemia and may more accurately predict outcomes. This study examined AH, SHR, and in-hospital stent thrombosis (ST) in STEMI patients undergoing primary percutaneous coronary intervention (p-PCI). Material and Methods: Retrospective analysis included 1034 patients. AH was defined as glucose ≥ 11.1 mmol/L at admission. SHR was calculated as admission glucose divided by estimated average glucose derived from hemoglobin A1c (HbA1c). The primary outcome was in-hospital stent thrombosis. Patients were grouped by the occurrence of in-hospital ST. Univariable, multivariable, and LASSO (Least Absolute Shrinkage and Selection Operator) logistic regression identified predictors of ST. Results: In-hospital ST occurred in 1.5% of patients. ST patients had higher Killip class, heart rate, white blood cell, platelet counts, creatinine, AH, and SHR. SHR was an independent predictor of ST (OR 3.15, 95% CI 1.88–5.27, p < 0.001), whereas AH was not (p = 0.182). Neutrophil count, correlated with WBC, was also a significant risk factor. ROC analysis showed SHR ≥ 1.26 as an optimal cutoff predicting ST. Conclusions: SHR is a strong independent predictor of in-hospital ST after STEMI, superior to AH. Monitoring and managing stress-induced hyperglycemia play a crucial role in the setting of STEMI. Further studies are needed.

1. Introduction

It is now well established that admission hyperglycemia (AH) in patients presenting with acute ST-elevation myocardial infarction (STEMI) is associated with unfavorable clinical outcomes. This detrimental effect of AH has been demonstrated regardless of diabetes status [1,2,3]. Although the reported incidence of AH varies among different studies, it has been observed in up to 60% of patients with acute myocardial infarction (MI), highlighting its clinical relevance and the need for early recognition and management [4,5].
The concept of relative hyperglycemia (RH), calculated using the stress hyperglycemia ratio (SHR), was first introduced by Roberts et al. as a novel measure of prognosis in patients with critical illness. Roberts et al. showed that in critically ill patients, the relative hyperglycemic response is a more clinically significant prognostic parameter than absolute hyperglycemia [6]. In the following years, clinical studies were conducted showing that the relative hyperglycemic response predicted adverse outcomes in both diabetic and non-diabetic patients with different clinical conditions [7,8,9]. Despite technological advancements and improvements in operator experience in contemporary interventional cardiology, the incidence of stent thrombosis (ST) remains as high as 2–4.5% [10,11,12]. In addition to this relatively high incidence, in-hospital mortality in patients with ST is also approximately 5%, which might be considered a significantly high rate [13]. Considering both its incidence and the severity of its clinical outcomes, ST plays a pivotal role in cardiology practice. According to the definition of the Academic Research Consortium, early ST is defined as ST occurring between 0 and 30 days after stent implantation [14]. Risk factors for early ST include several parameters such as STEMI, multi-vessel coronary artery disease, reduced left ventricle ejection fraction (LVEF), Killip classification, diabetes mellitus (DM), smoking, anemia, thrombocytosis, non-compliance with dual antiplatelet therapy, poor pre-procedural TIMI flow, thrombus burden, inflammatory status, and malignancy [15,16,17,18].
Intensive glycemic control is no longer routinely used in hospitalized patients due to the increased risk of hypoglycemia and mortality shown in the NICE-SUGAR trial. As a result, the American Diabetes Association recommends a more conservative target blood glucose range of 140–180 mg/dL. This shift in practice raises important questions about how to manage relative hyperglycemia in acute cardiovascular settings such as STEMI [19,20].
In our study, we evaluated the relationship between AH and SHR, and in-hospital ST among STEMI patients undergoing primary percutaneous coronary intervention (p-PCI).

2. Materials and Methods

2.1. Study Population

Between 2022 and 2025, patients who were hospitalized with a diagnosis of STEMI and treated with p-PCI after emergency coronary angiography were included in our study, retrospectively. The inclusion criteria were age to be older than 18 years and to be treated with p-PCI. Exclusion criteria were refusal to participate, renal failure with a glomerular filtration rate of less than 30 mL/min, failed p-PCI or balloon angioplasty only, or patients in whom intracoronary thrombus could not be demonstrated by coronary angiography due to sudden death while being monitored in the coronary intensive care unit after p-PCI.
Baseline demographic features and clinical and laboratory variables were obtained from hospital information system. Laboratory data, including glucose and HbA1c levels, were obtained from tests performed at the time of admission. LVEF was obtained by echocardiography with the Modified Simpson’s method after the p-PCI procedure while the patients were in coronary intensive care unit. The study cohort was categorized into two groups: in-hospital ST group and control group. The predictors of in-hospital ST were subsequently analyzed and determined.
Ethical approval for this study was granted by the Ethics Committee of our tertiary care institution. The research was carried out in compliance with the principles outlined in the Declaration of Helsinki.

2.2. Definitions

AH was defined as a blood glucose level of ≥11.1 mmol/L (≥200 mg/dL) measured at admission. RH was defined by the SHR, which was calculated by dividing the admission blood glucose by the estimated average glucose (eAG) [6]. eAG serves as a surrogate indicator of the average blood glucose level during the preceding 2–3 months and is commonly derived from HbA1c values. Nathan et al. [21] proposed an equation to estimate eAG from HbA1c levels. The equation is as follows: eAG = (1.59 × HbA1c) − 2.59.
In-hospital ST was defined in patients monitored following p-PCI is characterized by the onset of ischemic symptoms and/or clinical signs and electrocardiographic changes suggesting of MI, accompanied by angiographic evidence of a peri-stent thrombus formation [14].

2.3. Statistical Analysis

For continuous variables, data were reported as mean ± standard deviation, and categorical variables were described using the number and percentage. Patients with and without in-hospital ST were compared using an independent Student’s t-test and a Mann–Whitney U test. A receiver operating characteristic (ROC) curve was generated to assess the predictive performance of SHR and AH for in-hospital ST. Univariable logistic regression analysis was performed to identify independent predictors of in-hospital ST. Variables that were statistically significant in the univariable analysis were entered into the multivariable logistic regression analysis. Since white blood cell (WBC) and Neutrophil (NEU) counts were positively correlated, only the NEU count was entered into the multivariable analysis. LASSO (Least Absolute Shrinkage and Selection Operator) regression was performed to identify the most important predictors of in-hospital ST to prevent overfitting and improve predictive performance. All analyses were performed using SPSS statistical software version 25.0 and R statistical software version 4.5.0.

2.4. Endpoint

The primary endpoint of our study was in-hospital ST.

3. Results

This retrospective study included 1034 consecutive patients with a mean age of 59.92 ± 12.87 years; 21.1% were female. Hypertension and DM were present in 48% and 28.3% of patients, respectively, while 52.2% were active smokers. The in-hospital ST rate was 1.5% of the cohort. The baseline characteristics, along with the clinical and laboratory features of the study population and comparison between groups, are summarized in Table 1.
Upon comparison of the group with ST to the control group, non-significant differences were observed in terms of age, gender, hypertension, DM, or smoking status. Killip classification, heart rate, WBC, and platelet (PLT) count, and creatinine level were significantly higher in the ST group. In the ST group, admission glucose levels were higher (17.8 ± 9.5 mmol/L vs. 9.1 ± 5.0 mmol/L, p < 0.001), with a greater proportion of patients having glucose ≥ 11.1 mmol/L (56.3% vs. 20.8%, p = 0.002). The SHR levels were significantly higher in the ST group (2.17 ± 0.98 vs. 1.09 ± 0.30, p < 0.001). Both AH and SHR were higher in the ST group compared to the control group. The comparative data of the two groups are presented in Table 1.
Univariate analysis identified several significant factors associated with ST. KILLIP class 3–4 was strongly linked to ST (OR: 7.332, 95% CI: 2.596–20.713, p < 0.001). Additionally, higher heart rate, WBC, PLT, admission glucose level, and serum creatinine were associated with increased ST risk. Glucose levels ≥ 11.1 mmol/L (OR: 4.888, 95% CI: 1.800–13.277, p = 0.002) and the SHR (OR: 23.232, 95% CI: 8.900–60.644, p < 0.001) seemed to be predictors of in-hospital ST (Table 2). Multivariate logistic regression analysis was conducted to identify independent predictors of in-hospital ST. While AH was not found to be a statistically significant predictor (OR: 2.210, 95% CI: 0.690–7.076, p = 0.182), SHR emerged as an independent and significant predictor of in-hospital ST (OR: 22.301, 95% CI: 5.471–90.908, p < 0.001). Furthermore, it was revealed that NEU, which correlates with WBC, is also a significant risk factor (Table 3). Accordingly, we conducted LASSO regression to identify the most important predictors of in-hospital ST, aiming to reduce overfitting and improve predictive performance. SHR was verified as a strong and significant predictor, with an Odds Ratio of 3.15 (95% CI: 1.88–5.27, p < 0.001) (Table 4).
ROC curve analysis for the SHR demonstrated a strong predictive ability for in-hospital ST, with an area under the curve (AUC) of 0.879 (95% CI: 0.800–0.958, p < 0.001). The optimal cut-off value of SHR for in-hospital ST was determined to be ≥ 1.26, yielding a sensitivity of 75% and a specificity of 77.8% (Figure 1).

4. Discussion

The key results of our retrospective cohort study are summarized as follows: First, the SHR is a robust and independent predictor of in-hospital ST in patients with STEMI undergoing p-PCI. Second, although the AH rate appeared markedly higher in the ST group compared to the control group (56.3% vs. 20.8%, respectively), it was not found to be a statistically significant independent predictor of in-hospital ST. Third, an SHR value ≥ 1.26 independently and strongly predicts the risk of in-hospital ST. The latter data are in line with the findings of the study by Chu et al., where an SHR value of ≥1.19 was identified as a predictor of intracoronary thrombus burden in STEMI patients [22]. Although HbA1c is an important marker that provides information about glucose metabolism over the past three months, it can be affected by both acute and chronic conditions such as acute blood loss, blood transfusion, severe renal failure, and chronic liver failure [23,24]. This should also be taken into consideration in patients with acute myocardial infarction.
In the overall cohort population, the proportion of diabetic patients was 28.3%, and this rate was similar between the ST group and the control group. Although diabetic patients tend to have a greater predisposition to hyperglycemia compared to non-diabetics, multiple studies have reliably identified a strong association between the SHR and negative clinical outcomes among patients presenting with acute coronary syndrome, across both diabetic and non-diabetic cohorts [25,26,27]. These scientific findings constitute the main rationale for not stratifying our entire study cohort based on the presence or absence of diabetes. Although KILLIP class did not reach statistical significance as an independent predictor of in-hospital ST in the multivariate analysis, the proportion of patients presenting with KILLIP class 3 and 4 was higher in the ST group compared to the control group—3.6% vs. 12.5% and 3.9% vs. 25%, respectively. Following acute MI, diastolic dysfunction characterized by elevated left ventricular filling pressures can lead to dysfunction of the coronary microcirculation. As a result, the flow velocity in the epicardial coronary arteries may be relatively reduced, which can explain the higher incidence of ST observed in KILLIP class 3–4 patients [28].
Although the underlying pathophysiological mechanisms by which hyperglycemia serves as a poor prognostic marker in STEMI patients are not clearly understood, several potential mechanisms can be suggested. Firstly, Chu et al. [22] demonstrated that the SHR could predict a large thrombus burden in patients with STEMI. Considering that acute hyperglycemia has been scientifically proven to augment thrombin formation and promote platelet activation, it appears plausible that stress hyperglycemia—rather than chronic hyperglycemia—may contribute to a higher thrombus burden. In this context, as demonstrated in our study, the hypothesis that SHR facilitates in-hospital ST is consistent with existing scientific evidence [29,30]. Another parameter that may explain the association between hyperglycemia and poor prognosis is infarct size. Lønborg et al., using cardiac magnetic resonance imaging, demonstrated that acute hyperglycemia is associated with a larger infarct size and a greater area of myocardium at risk [2]. The third potential mechanism contributing to the pathogenesis may be the enhancement of oxidative stress and microvascular dysfunction induced by acute hyperglycemia [31]. It is also well known that stress hyperglycemia induces a proinflammatory state in patients with AMI [32].
When all of the aforementioned mechanisms are considered together, the prothrombotic and proinflammatory effects of stress hyperglycemia—along with its propensity to enhance oxidative stress and predispose to microvascular dysfunction, which may facilitate the development of the no-reflow phenomenon (a condition that has been shown to be associated with stress hyperglycemia)—may lead to an expansion of the infarcted myocardial area [33]. This combination of pathophysiological mechanisms offers a reasonable explanation for the link between acute hyperglycemia and poor clinical outcomes in patients with MI. SHR, as an independent predictor of in-hospital ST, may reflect the adverse effects of acute hyperglycemia—namely, the induction of a prothrombotic and proinflammatory state along with microvascular dysfunction—which together may lead to impaired coronary blood flow and, subsequently, increase the risk of thrombotic complications.
In recent years, metabolic and immunity-based biomarkers have gained significant importance in cardiology practice [34]. Based on the concept that stress-induced hyperglycemia is associated with poor clinical outcomes due to its prothrombotic and proinflammatory effects, provide a rationale for future clinical studies investigating whether the close monitoring and management of stress hyperglycemia may lead to improved clinical outcomes.

Limitations

Our study has a number of limitations that must be taken into account. First, its retrospective design might facilitate potential biases, including selection and information bias. Second, the relatively small number of patients who developed in-hospital ST may have limited the statistical power to detect other potential predictors or interactions. Third, our study focused exclusively on in-hospital ST; therefore, due to the absence of early and long-term post-discharge follow-up data, the potential impact of SHR on the risk of ST after hospital discharge could not be evaluated. Additionally, in-hospital glycemic control strategies were not evaluated, and thus, the potential impact of therapeutic interventions could not be taken into account. Although the prevalence of ST is reported to be between 2 and 4.5% worldwide, it was found to be 1.5% in our study, which is slightly lower but still comparable to those reported in the literature. However, since the number of patients who developed in-hospital ST in our study was only 16, subgroup analysis dividing ST patients into diabetic and non-diabetic groups could not be performed. Another important limitation to address is that our study included only patients who underwent successful p-PCI. However, potential confounding factors such as stent type and size, high thrombus burden, dissection, or multivessel disease should be considered, as they may contribute to the development of in-hospital ST.

5. Conclusions

In conclusion, we identified the SHR as a strong and objective risk factor of in-hospital ST in patients with STEMI undergoing p-PCI. To the best of our knowledge, few studies have specifically evaluated the relationship between SHR and in-hospital ST. An SHR threshold of ≥ 1.26 was significantly associated with increased incidence of in-hospital ST, underscoring the clinical applicability of SHR within this group of patients. While AH was more prevalent among patients who developed ST, it did not independently predict this outcome. Therefore, our findings emphasize the prognostic relevance of SHR and suggest that closer glycemic monitoring and control during the acute phase of STEMI may be beneficial in reducing in-hospital ST. Furthermore, although an SHR cutoff of ≥ 1.26 was identified as predictive of in-hospital ST, this threshold requires validation in larger, prospective, and multi-center cohorts to ensure its generalizability.

Author Contributions

Conceptualization, E.A. and C.Y.; data curation, E.A., C.Y., O.A. and A.A.; formal analysis, E.A., C.Y. and M.K.; methodology, E.A., C.Y., D.K. and F.N.T.Ç.; software, M.K., M.P., H.A.S. and D.M.M.; supervision, F.N.T.Ç.; writing—original draft, E.A.; writing—review and editing, E.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study protocol was approved by the ethics committee of the Bakırköy Dr. Sadi Konuk Training and Research Hospital, University of Health Sciences (Number: 2025/69-2025-05-03, date of approval: 5 March 2025).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Worthley, M.I.; Shrive, F.M.; Anderson, T.J.; Traboulsi, M. Prognostic implication of hyperglycemia in myocardial infarction and primary angioplasty. Am. J. Med. 2007, 120, 643.e1–643.e7. [Google Scholar] [CrossRef] [PubMed]
  2. Lønborg, J.; Vejlstrup, N.; Kelbæk, H.; Nepper-Christensen, L.; Jørgensen, E.; Helqvist, S.; Holmvang, L.; Saunamäki, K.; Bøtker, H.E.; Kim, W.Y.; et al. Impact of acute hyperglycemia on myocardial infarct size, area at risk, and salvage in patients with STEMI and the association with exenatide treatment: Results from a randomized study. Diabetes 2014, 63, 2474–2485. [Google Scholar] [CrossRef] [PubMed]
  3. Ferreira, J.A.; Baptista, R.M.; Monteiro, S.R.; Gonçalves, F.M.; Monteiro, P.F.; Gonçalves, L.M. Admission hyperglycemia and all-cause mortality in diabetic and non-diabetic patients with acute myocardial infarction: A tertiary center analysis. Intern. Emerg. Med. 2021, 16, 2109–2119. [Google Scholar] [CrossRef] [PubMed]
  4. Kim, E.J.; Jeong, M.H.; Kim, J.H.; Ahn, T.H.; Seung, K.B.; Oh, D.J.; Kim, H.S.; Gwon, H.C.; Seong, I.W.; Hwang, K.K.; et al. Clinical impact of admission hyperglycemia on in-hospital mortality in acute myocardial infarction patients. Int. J. Cardiol. 2017, 236, 9–15. [Google Scholar] [CrossRef]
  5. Trongtorsak, A.; Kewcharoen, J.; Thangjui, S.; Yanez-Bello, M.A.; Sous, M.; Prasai, P.; Navaravong, L. Admission hyperglycemia in acute myocardial infarction is associated with an increased risk of arrhythmias: A systematic review and meta-analysis. J. Arrhythm. 2022, 38, 307–315. [Google Scholar] [CrossRef]
  6. Roberts, G.W.; Quinn, S.J.; Valentine, N.; Alhawassi, T.; O’Dea, H.; Stranks, S.N.; Burt, M.G.; Doogue, M.P. Relative hyperglycemia, a marker of critical illness: Introducing the stress hyperglycemia ratio. J. Clin. Endocrinol. Metab. 2015, 100, 4490–4497. [Google Scholar] [CrossRef]
  7. Huang, Y.W.; Yin, X.S.; Li, Z.P. Association of the stress hyperglycemia ratio and clinical outcomes in patients with stroke: A systematic review and meta-analysis. Front. Neurol. 2022, 13, 999536. [Google Scholar] [CrossRef]
  8. Lee, T.F.; Drake, S.M.; Roberts, G.W.; Bersten, A.; Stranks, S.N.; Heilbronn, L.K.; Mangoni, A.A.; Burt, M.G. Relative hyperglycemia is an independent determinant of in-hospital mortality in patients with critical illness. Crit. Care Med. 2020, 48, e115–e122. [Google Scholar] [CrossRef]
  9. Ding, L.; Zhang, H.; Dai, C.; Zhang, A.; Yu, F.; Mi, L.; Qi, Y.; Tang, M. The prognostic value of the stress hyperglycemia ratio for all-cause and cardiovascular mortality in patients with diabetes or prediabetes: Insights from NHANES 2005–2018. Cardiovasc. Diabetol. 2024, 23, 84. [Google Scholar] [CrossRef]
  10. Dangas, G.D.; Caixeta, A.; Mehran, R.; Parise, H.; Lansky, A.J.; Cristea, E.; Brodie, B.R.; Witzenbichler, B.; Guagliumi, G.; Peruga, J.Z.; et al. Harmonizing Outcomes With Revascularization and Stents in Acute Myocardial Infarction (HORIZONS-AMI) Trial Investigators. Frequency and predictors of stent thrombosis after percutaneous coronary intervention in acute myocardial infarction. Circulation 2011, 123, 1745–1756. [Google Scholar] [CrossRef]
  11. Lim, S.; Koh, Y.S.; Kim, P.J.; Kim, H.Y.; Park, C.S.; Lee, J.M.; Kim, D.B.; Yoo, K.D.; Jeon, D.S.; Her, S.H.; et al. Incidence, implications, and predictors of stent thrombosis in acute myocardial infarction. Am. J. Cardiol. 2016, 117, 1562–1568. [Google Scholar] [CrossRef] [PubMed]
  12. Van Werkum, J.W.; Heestermans, A.A.; Zomer, A.C.; Kelder, J.C.; Suttorp, M.J.; Rensing, B.J.; Koolen, J.J.; Brueren, B.R.; Dambrink, J.H.; Hautvast, R.W.; et al. Predictors of coronary stent thrombosis: The Dutch Stent Thrombosis Registry. J. Am. Coll. Cardiol. 2009, 53, 1399–1409. [Google Scholar] [CrossRef] [PubMed]
  13. Ohno, Y.; Yamaji, K.; Kohsaka, S.; Inohara, T.; Amano, T.; Ishii, H.; Kadota, K.; Nakamura, M.; Nakazawa, G.; Yoshimachi, F.; et al. Incidence and in-hospital outcomes of patients presenting with stent thrombosis (from the Japanese Nationwide Percutaneous Coronary Intervention Registry). Am. J. Cardiol. 2020, 125, 720–726. [Google Scholar] [CrossRef] [PubMed]
  14. Cutlip, D.E.; Windecker, S.; Mehran, R.; Boam, A.; Cohen, D.J.; van Es, G.A.; Steg, P.G.; Morel, M.A.; Mauri, L.; Vranckx, P.; et al. Academic Research Consortium. Clinical end points in coronary stent trials: A case for standardized definitions. Circulation. 2007, 115, 2344–2351. [Google Scholar] [CrossRef]
  15. Gori, T.; Polimeni, A.; Indolfi, C.; Räber, L.; Adriaenssens, T.; Münzel, T. Predictors of stent thrombosis and their implications for clinical practice. Nat. Rev. Cardiol. 2019, 16, 243–256. [Google Scholar] [CrossRef]
  16. Beinart, R.; Abu Sham’a, R.; Segev, A.; Hod, H.; Guetta, V.; Shechter, M.; Boyko, V.; Behar, S.; Matetzky, S. The incidence and clinical predictors of early stent thrombosis in patients with acute coronary syndrome. Am. Heart J. 2010, 159, 118–124. [Google Scholar] [CrossRef]
  17. Krychtiuk, K.A.; Bräu, K.; Schauer, S.; Sator, A.; Galli, L.; Baierl, A.; Hengstenberg, C.; Gangl, C.; Lang, I.M.; Roth, C.; et al. Association of Periprocedural Inflammatory Activation With Increased Risk for Early Coronary Stent Thrombosis. J. Am. Heart Assoc. 2024, 13, e032300. [Google Scholar] [CrossRef]
  18. Claessen, B.E.; Henriques, J.P.; Jaffer, F.A.; Mehran, R.; Piek, J.J.; Dangas, G.D. Stent thrombosis: A clinical perspective. JACC Cardiovasc. Interv. 2014, 7, 1081–1092. [Google Scholar] [CrossRef]
  19. NICE-SUGAR Study Investigators; Finfer, S.; Chittock, D.R.; Su, S.Y.; Blair, D.; Foster, D.; Dhingra, V.; Bellomo, R.; Cook, D.; Dodek, P.; et al. Intensive versus conventional glucose control in critically ill patients. N. Engl. J. Med. 2009, 360, 1283–1297. [Google Scholar] [CrossRef]
  20. American Diabetes Association Professional Practice Committee. 16. Diabetes Care in the Hospital: Standards of Care in Diabetes—2024. Diabetes Care 2024, 47 (Suppl. S1), S295–S306. [Google Scholar] [CrossRef]
  21. Nathan, D.M.; Kuenen, J.; Borg, R.; Zheng, H.; Schoenfeld, D.; Heine, R.J. A1c-Derived Average Glucose Study Group. Translating the A1C assay into estimated average glucose values. Diabetes Care 2008, 31, 1473–1478. [Google Scholar] [CrossRef]
  22. Chu, J.; Tang, J.; Lai, Y.; Gao, Y.; Ye, Z.; Guan, C.; Ding, K.; Yao, Y.; Chen, F.; Liu, X. Association of stress hyperglycemia ratio with intracoronary thrombus burden in diabetic patients with ST-segment elevation myocardial infarction. J. Thorac. Dis. 2020, 12, 6598–6608. [Google Scholar] [CrossRef]
  23. Little, R.R.; Rohlfing, C.L.; Tennill, A.L.; Hanson, S.E.; Connolly, S.; Higgins, T.; Wiedmeyer, C.E.; Weykamp, C.W.; Krause, R.; Roberts, W. Measurement of HbA1c in patients with chronic renal failure. Clin. Chim. Acta 2013, 418, 73–76. [Google Scholar] [CrossRef] [PubMed]
  24. Mian, F.J.; Jacob, K. HbA1c is not always reliable in estimating glycaemic control. Pract. Diabetes 2016, 33, 317–319. [Google Scholar] [CrossRef]
  25. He, H.M.; Wang, Z.; Xie, Y.Y.; Zheng, S.W.; Li, J.; Li, X.X.; Jiao, S.Q.; Yang, F.R.; Sun, Y.H. Maximum stress hyperglycemia ratio within the first 24 h of admission predicts mortality during and after the acute phase of acute coronary syndrome in patients with and without diabetes: A retrospective cohort study from the MIMIC-IV database. Diabetes Res. Clin. Pract. 2024, 208, 111122. [Google Scholar] [CrossRef]
  26. Liu, J.; Zhou, Y.; Huang, H.; Liu, R.; Kang, Y.; Zhu, T.; Wu, J.; Gao, Y.; Li, Y.; Wang, C.; et al. Impact of stress hyperglycemia ratio on mortality in patients with critical acute myocardial infarction: Insight from American MIMIC-IV and the Chinese CIN-II study. Cardiovasc. Diabetol. 2023, 22, 281. [Google Scholar] [CrossRef]
  27. Ma, X.; Chu, H.; Sun, Y.; Cheng, Y.; Zhang, D.; Yang, L.; Wang, Z.; Liu, X.; Zhou, Y. Prognostic significance of stress hyperglycemia ratio in patients with type 2 diabetes mellitus and acute coronary syndromes. Thromb. J. 2025, 23, 47. [Google Scholar] [CrossRef] [PubMed]
  28. Kumar, R.; Ammar, A.; Saghir, T.; Sial, J.A.; Shah, J.A.; Kumar, A.; Shaikh, A.H.; Achakzai, A.S.; Qamar, N.; Karim, M. Incidence, predictors, and outcomes of acute and sub-acute stent thrombosis after emergency percutaneous coronary revascularization with drug-eluting stents: A prospective observational study. Glob. Heart 2022, 17, 24. [Google Scholar] [CrossRef] [PubMed]
  29. Undas, A.; Wiek, I.; Stêpien, E.; Zmudka, K.; Tracz, W. Hyperglycemia is associated with enhanced thrombin formation, platelet activation, and fibrin clot resistance to lysis in patients with acute coronary syndrome. Diabetes Care 2008, 31, 1590–1595. [Google Scholar] [CrossRef]
  30. Gresele, P.; Guglielmini, G.; De Angelis, M.; Ciferri, S.; Ciofetta, M.; Falcinelli, E.; Lalli, C.; Ciabattoni, G.; Davì, G.; Bolli, G.B. Acute, short-term hyperglycemia enhances shear stress-induced platelet activation in patients with type II diabetes mellitus. J. Am. Coll. Cardiol. 2003, 41, 1013–1020. [Google Scholar] [CrossRef]
  31. Takei, Y.; Tomiyama, H.; Tanaka, N.; Yamashina, A. Close relationship between sympathetic activation and coronary microvascular dysfunction during acute hyperglycemia in subjects with atherosclerotic risk factors. Circ. J. 2007, 71, 202–206. [Google Scholar] [CrossRef] [PubMed]
  32. Marfella, R.; Siniscalchi, M.; Esposito, K.; Sellitto, A.; De Fanis, U.; Romano, C.; Portoghese, M.; Siciliano, S.; Nappo, F.; Sasso, F.C.; et al. Effects of stress hyperglycemia on acute myocardial infarction: Role of inflammatory immune process in functional cardiac outcome. Diabetes Care 2003, 26, 3129–3135. [Google Scholar] [CrossRef] [PubMed]
  33. Khalfallah, M.; Maria, D.A.; Allaithy, A. Impact of Stress Hyperglycemia on No-Reflow Phenomenon in Patients with ST Elevation Myocardial Infarction Undergoing Primary Percutaneous Coronary Intervention. Glob. Heart 2022, 17, 23. [Google Scholar] [CrossRef] [PubMed]
  34. Bilgin, M.; Akkaya, E.; Dokuyucu, R. The Role of Triglyceride/HDL Ratio, Triglyceride–Glucose Index, and Pan-Immune-Inflammation Value in the Differential Diagnosis of Acute Coronary Syndrome and Predicting Mortality. J. Clin. Med. 2024, 13, 4832. [Google Scholar] [CrossRef]
Medicina 61 01158 g001
Figure 1. Receiver operator characteristic (ROC) curve for SHR predicting in-hospital ST. AH: admission hyperglycemia, AUC: Area Under Curve, SHR: stress hyperglycemia ratio. AUC = 0.879 (95% CI: 0.800–0.958), cutoff ≥ 1.26 yielded 75% sensitivity and 77.8% specificity.
Figure 1. Receiver operator characteristic (ROC) curve for SHR predicting in-hospital ST. AH: admission hyperglycemia, AUC: Area Under Curve, SHR: stress hyperglycemia ratio. AUC = 0.879 (95% CI: 0.800–0.958), cutoff ≥ 1.26 yielded 75% sensitivity and 77.8% specificity.
Medicina 61 01158 g001
Table 1. Baseline characteristics of the study population and comparison of clinical characteristics between control group and in-hospital ST group.
Table 1. Baseline characteristics of the study population and comparison of clinical characteristics between control group and in-hospital ST group.
VariableOverall (n = 1018)
(Mean ± SD or n (%))		Control Group (n = 1018)In-Hospital ST Group (n = 16)p-Value
Age (years)59.92 ± 12.8759.93 ± 12.8355.94 ± 14.350.130
Female gender218 (21.1%)215 (21.1%)3 (18.8%)0.818
HTN496 (48%)490 (48.1%)6 (37.5%)0.395
DM293 (28.3%)290 (28.5%)3 (18.7%)0.666
Smoking540 (52.2%)530 (52.1%)10 (62.5%)0.084
Killip Class <0.001
Killip 1936 (90.5%)926 (91%)10 (62.5%)
Killip 215 (1.5%)15 (1.5%)0 (0%)
Killip 339 (3.8%)37 (3.6%)2 (12.5%)
Killip 444 (4.3%)40 (3.9%)4 (25%)
SBP (mmHg)146.65 ± 30.13136.97 ± 30.03119.87 ± 31.670.073
Heart Rate (bpm)82.85 ± 20.9282.66 ± 20.7995.00 ± 250.021
LVEF (%)45.76 ± 11.1545.82 ± 11.1643.57 ± 8.640.320
HDL (mg/dL)35.62 ± 10.1735.58 ± 10.1838.25 ± 9.720.284
LDL (mg/dL)113.91 ± 39.07113.88 ± 38.78115.57 ± 53.540.876
HbA1c (%)6.69 ± 1.786.69 ± 1.786.73 ± 1.660.757
WBC (×103/µL)12.52 ± 4.8612.39 ± 4.5420.51 ± 12.510.004
HGB (g/dL)13.61 ± 1.9813.61 ± 1.9714.31 ± 2.500.064
PLT (×103/µL)237.50 ± 65.99236.55 ± 65.63293.69 ± 66.620.001
hs-cTnT (ng/L)1799.66 ± 7928.451765.12 ± 7903.884224.41 ± 9644.200.334
Baseline Glucose
(mmol/L)		9.3 ± 5.29.1 ± 5.017.8 ± 9.5<0.001
Creatinine (mg/dL)1.00 ± 0.680.99 ± 0.671.37 ± 0.940.011
AH221 (21.3%)212 (20.8%)9 (56.3%)0.002
SHR1.12 ± 0.361.09 ± 0.302.17 ± 0.98<0.001
AH: admission hyperglycemia, DM: diabetes mellitus, HbA1c: hemoglobin A1c, HDL: High-Density Lipoprotein, HGB: hemoglobin, hs-cTnT: High Sensitive Cardiac Troponin T, HTN: hypertension, LDL: Low-Density Lipoprotein, LVEF: Left Ventricular Ejection Fraction, PLT: platelet, SBP: Systolic Blood Pressure, SHR: stress hyperglycemia ratio, ST: stent thrombosis, WBC: white blood cell.
Table 2. Univariable binary logistic regression analysis of predictors for in-hospital ST events.
Table 2. Univariable binary logistic regression analysis of predictors for in-hospital ST events.
VariableOR95% CIp
Killip 3–47.3322.596–20.713<0.001
Heart Rate1.0241.004–1.0440.020
WBC1.1631.100–1.230<0.001
PLT1.0101.004–1.0150.001
Baseline Glucose1.0081.005–1.011<0.001
Creatinine1.3861.001–1.9200.049
AH4.8881.800–13.2770.002
SHR23.2328.900–60.644<0.001
AH: admission hyperglycemia, CI: Confidence Interval, OR: Odds Ratio, PLT: platelet, SHR: stress hyperglycemia ratio, WBC: white blood cell.
Table 3. Multivariable binary logistic regression analysis of predictors for in-hospital ST events—Model A and Model B.
Table 3. Multivariable binary logistic regression analysis of predictors for in-hospital ST events—Model A and Model B.
VariableModel AModel B
OR95% CIpOR95% CIp
Killip 3–42.6840.754–9.5490.1273.0590.462–20.2410.246
Heart Rate0.9990.977–1.0210.9270.9780.948–1.0100.176
PLT1.0060.999–1.0130.1141.0091.000–1.0180.063
NEU1.1241.046–1.2080.0021.0110.902–1.1330.852
Creatinine1.1190.635–1.9710.6980.8060.232–2.7980.734
AH2.2100.690–7.0760.182———
SHR———22.3015.471–90.908<0.001
AH: admission hyperglycemia, CI: Confidence Interval, NEU: Neutrophil, OR: Odds Ratio, PLT: platelet, SHR: stress hyperglycemia ratio.
Table 4. LASSO regression analysis results for in-hospital ST events.
Table 4. LASSO regression analysis results for in-hospital ST events.
VariableOR95% CIp
Killip Class (3–4)1.270.84–1.930.251
Heart Rate0.640.34–1.220.175
PLT1.770.98–3.220.060
NEU1.030.62–1.710.917
Creatinine0.890.43–1.820.746
SHR3.151.88–5.27<0.001
CI: Confidence Interval, NEU: Neutrophil, PLT: platelet, OR: Odds Ratio, SHR: stress hyperglycemia ratio.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Akdeniz, E.; Yıldız, C.; Karaca, M.; Pişirici, M.; Sinoplu, H.A.; Akpınar, O.; Arpac, A.; Manioğlu, D.M.; Karabulut, D.; Turhan Çağlar, F.N. Stress Hyperglycemia Ratio as a Predictor of In-Hospital Stent Thrombosis in STEMI Patients Undergoing Primary PCI: A Retrospective Cohort Study. Medicina 2025, 61, 1158. https://doi.org/10.3390/medicina61071158

AMA Style

Akdeniz E, Yıldız C, Karaca M, Pişirici M, Sinoplu HA, Akpınar O, Arpac A, Manioğlu DM, Karabulut D, Turhan Çağlar FN. Stress Hyperglycemia Ratio as a Predictor of In-Hospital Stent Thrombosis in STEMI Patients Undergoing Primary PCI: A Retrospective Cohort Study. Medicina. 2025; 61(7):1158. https://doi.org/10.3390/medicina61071158

Chicago/Turabian Style

Akdeniz, Evliya, Cennet Yıldız, Mehmet Karaca, Mehmet Pişirici, Hasan Ali Sinoplu, Onur Akpınar, Atakan Arpac, Didem Mirgün Manioğlu, Dilay Karabulut, and Fatma Nihan Turhan Çağlar. 2025. "Stress Hyperglycemia Ratio as a Predictor of In-Hospital Stent Thrombosis in STEMI Patients Undergoing Primary PCI: A Retrospective Cohort Study" Medicina 61, no. 7: 1158. https://doi.org/10.3390/medicina61071158

APA Style

Akdeniz, E., Yıldız, C., Karaca, M., Pişirici, M., Sinoplu, H. A., Akpınar, O., Arpac, A., Manioğlu, D. M., Karabulut, D., & Turhan Çağlar, F. N. (2025). Stress Hyperglycemia Ratio as a Predictor of In-Hospital Stent Thrombosis in STEMI Patients Undergoing Primary PCI: A Retrospective Cohort Study. Medicina, 61(7), 1158. https://doi.org/10.3390/medicina61071158

Article Metrics

Back to TopTop