Measurement of Uncertainty in Prediction of No-Reflow Phenomenon after Primary Percutaneous Coronary Intervention Using Systemic Immune Inflammation Index: The Gray Zone Approach

Systemic immune-inflammation index (SII), which is a good predictive marker for coronary artery disease, can be calculated by using platelet, neutrophil, and lymphocyte counts. The no-reflow occurrence can also be predicted using the SII. The aim of this study is to reveal the uncertainty of SII for diagnosing ST-elevation myocardial infarction (STEMI) patients who were admitted for primary percutaneous coronary intervention (PCI) for the no-reflow phenomenon. A total of 510 consecutive acute (STEMI) patients with primary PCI were reviewed and included retrospectively. For diagnostic tests which are not a gold standard, there is always an overlap between the results of patients with and without a certain disease. In the literature, for quantitative diagnostic tests where the diagnosis is not certain, two approaches have been proposed, named “grey zone” and “uncertain interval”. The uncertain area of the SII, which is given the general term “gray zone” in this article, was constructed and its results were compared with the “grey zone” and “uncertain interval” approaches. The lower and upper limits of the gray zone were found to be 611.504–1790.827 and 1186.576–1565.088 for the grey zone and uncertain interval approaches, respectively. A higher number of patients inside the gray zone and higher performance outside the gray zone were found for the grey zone approach. One should be aware of the differences between the two approaches when making a decision. The patients who were in this gray zone should be observed carefully for detection of the no-reflow phenomenon.

[1]  A. Kurtul,et al.  Systemic immune-inflammation index predicts no-reflow phenomenon after primary percutaneous coronary intervention , 2021, Acta cardiologica.

[2]  E. Navarese,et al.  State of the Art: No-Reflow Phenomenon. , 2020, Cardiology clinics.

[3]  T. Behl,et al.  No-Reflow after PPCI—A Predictor of Short-Term Outcomes in STEMI Patients , 2020, Journal of clinical medicine.

[4]  H. Landsheer Uncertain Interval Methods for Three-Way Cut-Point Determination in Test Results [R package UncertainInterval version 0.6.0] , 2020 .

[5]  R. Irizarry ggplot2 , 2019, Introduction to Data Science.

[6]  T. Morita,et al.  P589Prognostic value of systemic immune-inflammation index in patients with chronic heart failure , 2018, European Heart Journal.

[7]  J. Fajar,et al.  The predictors of no reflow phenomenon after percutaneous coronary intervention in patients with ST elevation myocardial infarction: A meta-analysis , 2018, Indian heart journal.

[8]  Marco Valgimigli,et al.  2017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation: The Task Force for the management of acute myocardial infarction in patients presenting with ST-segment elevation of the European Society of Cardiology (ESC). , 2018, European heart journal.

[9]  Rachel V. Stankowski,et al.  Management of No-Reflow Phenomenon in the Catheterization Laboratory. , 2017, JACC. Cardiovascular interventions.

[10]  J. Landsheer Interval of Uncertainty: An Alternative Approach for the Determination of Decision Thresholds, with an Illustrative Application for the Prediction of Prostate Cancer , 2016, PloS one.

[11]  Sanjiv Gupta,et al.  No reflow phenomenon in percutaneous coronary interventions in ST-segment elevation myocardial infarction , 2016, Indian heart journal.

[12]  W. Guo,et al.  Systemic Immune-Inflammation Index Predicts Prognosis of Patients after Curative Resection for Hepatocellular Carcinoma , 2014, Clinical Cancer Research.

[13]  M. B. Demirçelik,et al.  Usefulness of the platelet-to-lymphocyte ratio in predicting angiographic reflow after primary percutaneous coronary intervention in patients with acute ST-segment elevation myocardial infarction. , 2014, The American journal of cardiology.

[14]  G. Gioia,et al.  No-Reflow Phenomenon , 2014, Angiology.

[15]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[16]  Michail I. Papafaklis,et al.  Angiographic thrombus burden classification in patients with ST-segment elevation myocardial infarction treated with percutaneous coronary intervention. , 2010, The Journal of invasive cardiology.

[17]  Hadley Wickham,et al.  ggplot2 - Elegant Graphics for Data Analysis (2nd Edition) , 2017 .

[18]  F. Burzotta,et al.  Myocardial no-reflow in humans. , 2009, Journal of the American College of Cardiology.

[19]  R. Jaffe,et al.  Microvascular Obstruction and the No-Reflow Phenomenon After Percutaneous Coronary Intervention , 2008, Circulation.

[20]  T. Hafez Modification of Diet in Renal Disease (MDRD) estimated glomerular filtration rate (eGFR) formula. , 2007, The American journal of cardiology.

[21]  D. Rott Advantage of percutaneous coronary intervention over medical therapy in angina relief and the placebo effect. , 2005, Journal of the American College of Cardiology.

[22]  Jacques Pouchot,et al.  A grey zone for quantitative diagnostic and screening tests. , 2003, International journal of epidemiology.

[23]  Y. Taniyama,et al.  Alternation in the coronary blood flow velocity pattern in patients with no reflow and reperfused acute myocardial infarction. , 1996, Circulation.

[24]  D B Matchar,et al.  Likelihood ratios for continuous test results--making the clinicians' job easier or harder? , 1993, Journal of clinical epidemiology.

[25]  J. Jamart Chance-corrected sensitivity and specificity for three-zone diagnostic tests. , 1992, Journal of clinical epidemiology.

[26]  A R Feinstein,et al.  The inadequacy of binary models for the clinical reality of three-zone diagnostic decisions. , 1990, Journal of clinical epidemiology.

[27]  W. Siegenthaler,et al.  Nonoperative dilatation of coronary-artery stenosis: percutaneous transluminal coronary angioplasty. , 1979, The New England journal of medicine.