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Predictive value of systemic immune-inflammation index combined with N-terminal pro-brain natriuretic peptide for contrast-induced acute kidney injury in patients with STEMI after primary PCI

  • Nephrology - Original Paper
  • Published:
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Abstract

Objective

To investigate the relationship between the incidence of contrast-induced acute kidney injury (CI-AKI) after emergency percutaneous coronary intervention (PCI) and preoperative systemic immune-inflammation index (SII) and N-terminal pro-brain natriuretic peptide (NT-proBNP) levels in patients with acute ST-segment elevation myocardial infarction (STEMI), and to further analyze the predictive value of the combination of SII and NT-proBNP for CI-AKI.

Methods

The clinical data of 1543 patients with STEMI who underwent emergency PCI in our hospital from February 2019 to December 2022 were retrospectively analyzed. All patients were divided into training cohort (n = 1085) and validation cohort (n = 287) according to chronological order. The training cohort was divided into CI-AKI (n = 95) and non-CI-AKI (n = 990) groups according to the 2018 European Society of Urogenital Radiology definition of CI-AKI. Multivariate Logistic regression analysis was used to determine the independent risk factors for CI-AKI. Restricted cubic spline (RCS) was used to explore the relationship between SII, NT-proBNP, and the risk of CI-AKI. The receiver operating characteristic (ROC) curve was used to evaluate the predictive value of SII, NT-proBNP, and their combination in CI-AKI.

Results

The incidence of CI-AKI was 8.8% (95/1085). Multivariate logistic regression analysis showed that SII, NT-proBNP, age, baseline creatinine, fasting blood glucose, and diuretics were independent risk factors for CI-AKI. RCS analysis showed that SII > 1084.97 × 109/L and NT-proBNP > 296.12 pg/mL were positively associated with the incidence of CI-AKI. ROC curve analysis showed that the area under the curve of SII and NT-proBNP combined detection in predicting CI-AKI was 0.726 (95% CI 0.698–0.752, P < 0.001), the sensitivity was 60.0%, and the specificity was 77.7%, which were superior to the detection of SII or NT-proBNP alone.

Conclusion

Preprocedural high SII and NT-proBNP are independent risk factors for CI-AKI after emergency PCI in patients with STEMI. The combined detection of SII and NT-proBNP can more accurately predict CI-AKI risk than the single detection.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Guillon B et al (2018) Incidence, predictors, and impact on six-month mortality of three different definitions of contrast-induced acute kidney injury after coronary angiography. Am J Cardiol 121(7):818–824

    Article  PubMed  Google Scholar 

  2. Schmucker J et al (2018) Predictors of acute kidney injury in patients admitted with ST-elevation myocardial infarction—results from the Bremen STEMI-Registry. Eur Heart J Acute Cardiovasc Care 7(8):710–722

    Article  PubMed  Google Scholar 

  3. Lei L et al (2020) Population attributable risk estimates of risk factors for contrast-induced acute kidney injury following coronary angiography: a cohort study. BMC Cardiovasc Disord 20(1):289

    Article  PubMed  PubMed Central  Google Scholar 

  4. Abe D et al (2014) Clinical predictors of contrast-induced acute kidney injury in patients undergoing emergency versus elective percutaneous coronary intervention. Circ J 78(1):85–91

    Article  CAS  PubMed  Google Scholar 

  5. Nash K, Hafeez A, Hou S (2002) Hospital-acquired renal insufficiency. Am J Kidney Dis 39(5):930–936

    Article  PubMed  Google Scholar 

  6. Mehran R, Dangas GD, Weisbord SD (2019) Contrast-associated acute kidney injury. N Engl J Med 380(22):2146–2155

    Article  CAS  PubMed  Google Scholar 

  7. Yang YL et al (2020) Systemic immune-inflammation index (SII) predicted clinical outcome in patients with coronary artery disease. Eur J Clin Invest 50(5):e13230

    Article  CAS  PubMed  Google Scholar 

  8. Geng Y et al (2016) Systemic Immune-Inflammation Index Predicts Prognosis of Patients with Esophageal Squamous Cell Carcinoma: A Propensity Score-matched Analysis. Sci Rep 6:39482

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  9. Desai AS et al (2011) Association between cardiac biomarkers and the development of ESRD in patients with type 2 diabetes mellitus, anemia, and CKD. Am J Kidney Dis 58(5):717–728

    Article  CAS  PubMed  Google Scholar 

  10. Mehran R et al (2021) A contemporary simple risk score for prediction of contrast-associated acute kidney injury after percutaneous coronary intervention: derivation and validation from an observational registry. Lancet 398(10315):1974–1983

    Article  PubMed  Google Scholar 

  11. Thygesen K et al (2019) Fourth universal definition of myocardial infarction (2018). Eur Heart J 40(3):237–269

    Article  PubMed  Google Scholar 

  12. van der Molen AJ et al (2018) Post-contrast acute kidney injury—Part 1: definition, clinical features, incidence, role of contrast medium and risk factors: recommendations for updated ESUR Contrast Medium Safety Committee guidelines. Eur Radiol 28(7):2845–2855

    Article  PubMed  PubMed Central  Google Scholar 

  13. Levey AS, Stevens LA (2010) Estimating GFR using the CKD Epidemiology Collaboration (CKD-EPI) creatinine equation: more accurate GFR estimates, lower CKD prevalence estimates, and better risk predictions. Am J Kidney Dis 55(4):622–627

    Article  PubMed  PubMed Central  Google Scholar 

  14. Levey AS et al (2009) A new equation to estimate glomerular filtration rate. Ann Intern Med 150(9):604–612

    Article  PubMed  PubMed Central  Google Scholar 

  15. Gensini GG (1983) A more meaningful scoring system for determining the severity of coronary heart disease. Am J Cardiol 51(3):606

    Article  CAS  PubMed  Google Scholar 

  16. Guo W et al (2022) Systemic immune-inflammation index is associated with diabetic kidney disease in Type 2 diabetes mellitus patients: Evidence from NHANES 2011–2018. Front Endocrinol (Lausanne) 13:1071465

    Article  PubMed  Google Scholar 

  17. Erdoğan M et al (2020) Systemic immune-inflammation index is a novel marker to predict functionally significant coronary artery stenosis. Biomark Med 14(16):1553–1561

    Article  PubMed  Google Scholar 

  18. Akcay A, Nguyen Q, Edelstein CL (2009) Mediators of inflammation in acute kidney injury. Mediators Inflamm 2009:137072

    Article  PubMed  Google Scholar 

  19. Bolisetty S, Agarwal A (2009) Neutrophils in acute kidney injury: not neutral any more. Kidney Int 75(7):674–676

    Article  CAS  PubMed  Google Scholar 

  20. Cao C, Yao Y, Zeng R (2021) Lymphocytes: versatile participants in acute kidney injury and progression to chronic kidney disease. Front Physiol 12:729084

    Article  PubMed  PubMed Central  Google Scholar 

  21. Weller S, Varrier M, Ostermann M (2017) Lymphocyte function in human acute kidney injury. Nephron 137(4):287–293

    Article  PubMed  Google Scholar 

  22. Christiansen SC et al (2006) Inflammatory cytokines as risk factors for a first venous thrombosis: a prospective population-based study. PLoS Med 3(8):e334

    Article  PubMed  PubMed Central  Google Scholar 

  23. Dirkes S (2013) Sepsis and inflammation: impact on acute kidney injury. Nephrol Nurs J 40(2):125–132 (quiz 133)

    PubMed  Google Scholar 

  24. Alcidi G et al (2022) Brain natriuretic peptide biomarkers in current clinical and therapeutic scenarios of heart failure. J Clin Med 11(11):3192

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Tsutamoto T et al (2007) Direct comparison of transcardiac difference between brain natriuretic peptide (BNP) and N-terminal pro-BNP in patients with chronic heart failure. Eur J Heart Fail 9(6–7):667–673

    Article  CAS  PubMed  Google Scholar 

  26. Fabbian F et al (2012) Relationship between N-terminal pro-B-type natriuretic peptide plasma levels and renal function evaluated with different formulae in older adult subjects admitted because of dyspnea. Gerontology 58(1):50–55

    Article  CAS  PubMed  Google Scholar 

  27. Mo H et al (2021) A predictive model based on a new CI-AKI definition to predict contrast induced nephropathy in patients with coronary artery disease with relatively normal renal function. Front Cardiovasc Med 8:762576

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Mehran R et al (2004) A simple risk score for prediction of contrast-induced nephropathy after percutaneous coronary intervention: development and initial validation. J Am Coll Cardiol 44(7):1393–1399

    PubMed  Google Scholar 

  29. Liu Y et al (2015) Preprocedural N-terminal pro-brain natriuretic peptide (NT-proBNP) is similar to the Mehran contrast-induced nephropathy (CIN) score in predicting CIN following elective coronary angiography. J Am Heart Assoc 4(4):e001410

    Article  PubMed  PubMed Central  Google Scholar 

  30. Goussot S et al (2015) N-terminal fragment of pro B-type natriuretic peptide as a marker of contrast-induced nephropathy after primary percutaneous coronary intervention for ST-segment elevation myocardial infarction. Am J Cardiol 116(6):865–871

    Article  CAS  PubMed  Google Scholar 

  31. Hall C (2004) Essential biochemistry and physiology of (NT-pro)BNP. Eur J Heart Fail 6(3):257–260

    Article  MathSciNet  CAS  PubMed  Google Scholar 

  32. O’Donnell DH et al (2010) Contrast-induced nephrotoxicity: possible synergistic effect of stress hyperglycemia. AJR Am J Roentgenol 195(1):W45-49

    Article  PubMed  Google Scholar 

  33. Lin KY et al (2018) Association of preprocedural hyperglycemia with contrast-induced acute kidney injury and poor outcomes after emergency percutaneous coronary intervention. Angiology 69(9):770–778

    Article  CAS  PubMed  Google Scholar 

  34. Wang J et al (2020) Acute hyperglycemia may induce renal tubular injury through mitophagy inhibition. Front Endocrinol (Lausanne) 11:536213

    Article  PubMed  Google Scholar 

  35. Wang JY et al (2015) Renal tubular damage may contribute more to acute hyperglycemia induced kidney injury in non-diabetic conscious rats. J Diabetes Compl 29(5):621–628

    Article  Google Scholar 

  36. Mahesh B et al (2008) Does furosemide prevent renal dysfunction in high-risk cardiac surgical patients? Results of a double-blinded prospective randomised trial. Eur J Cardiothorac Surg 33(3):370–376

    Article  PubMed  Google Scholar 

  37. Duan N et al (2015) Furosemide with saline hydration for prevention of contrast-induced nephropathy in patients undergoing coronary angiography: a meta-analysis of randomized controlled trials. Med Sci Monit 21:292–297

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Malik AO et al (2021) Patient-centered contrast thresholds to reduce acute kidney injury in high-risk patients undergoing percutaneous coronary intervention. Am Heart J 234:51–59

    Article  PubMed  PubMed Central  Google Scholar 

  39. Huang C et al (2019) Development and validation of a model for predicting the risk of acute kidney injury associated with contrast volume levels during percutaneous coronary intervention. JAMA Netw Open 2(11):e1916021

    Article  PubMed  PubMed Central  Google Scholar 

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Funding

This work was supported by grants from the National Natural Science Foundation of China (Grant No. 81900216) and the Science and Technology Program of Xuzhou (KC21067).

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Author notes

  1. Guoqi Shen and Haiyan He have contributed equally to this work.

Authors and Affiliations

  1. Institute of Cardiovascular Diseases, Xuzhou Medical University, Xuzhou, 221000, China

    Guoqi Shen, Xudong Zhang, Linsheng Wang, Zhen Wang & Wenhua Li

  2. Department of Cardiology, Xuzhou Municipal Hospital Affiliated to Xuzhou Medical University, Xuzhou, 221000, China

    Haiyan He

  3. Department of Cardiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221000, Jiangsu, China

    Fangfang Li, Yuan Lu & Wenhua Li

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  1. Guoqi Shen
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All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

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Correspondence to Yuan Lu or Wenhua Li.

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Shen, G., He, H., Zhang, X. et al. Predictive value of systemic immune-inflammation index combined with N-terminal pro-brain natriuretic peptide for contrast-induced acute kidney injury in patients with STEMI after primary PCI. Int Urol Nephrol 56, 1147–1156 (2024). https://doi.org/10.1007/s11255-023-03762-3

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  • DOI: https://doi.org/10.1007/s11255-023-03762-3

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