Skip to main content

Red blood cell distribution width/platelet ratio on admission as a predictor for in-hospital mortality in patients with acute myocardial infarction: a retrospective analysis from MIMIC-IV Database

Abstract

Background

Red blood cell distribution width (RDW) to platelet ratio (RPR) is a novel inflammatory indicator. It integrates the risk prediction of RDW and platelet, which is associated with adverse outcomes. However, the predictive power of RPR in mortality for patients with acute myocardial infarction (AMI) remains uncertain. Thus, we aimed to explore the association between RPR and 180-day in-hospital mortality in patients with AMI.

Methods

Data on patients with AMI were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Patients were divided into two groups according to the optimal RPR cut-off value. The survival curve between high and low RPR groups was plotted via the Kaplan-Meier (KM) method. Univariate and multivariate Cox regression analyses were performed to determine the association between RPR on admission and 180-day in-hospital mortality.

Results

A total of 1266 patients were enrolled, of which 83 (6.8%) died within 180 days during the hospitalization. Compared with the survivor group, the non-survivor group had higher RPR on admission (0.11 ± 0.07 vs. 0.08 ± 0.06, P < 0.001). The KM curve indicated that the survival probability of low RPR group was higher than that of high RPR group. Multivariate Cox regression analysis demonstrated that higher RPR on admission was an independent and effective predictor of 180-day mortality in patients with AMI (hazard ratio [HR]: 2.677, 95% confidence interval [CI]: 1.159–6.188, P = 0.021).

Conclusion

Higher RPR was associated with higher in-hospital 180-day mortality in patients with AMI.

Peer Review reports

Introduction

With the increasing incidence and prevalence of coronary atherosclerosis, over 200,000 hospital visits each year are associated with myocardial infarction [1, 2]. The acute myocardial infarction (AMI) is characterized by the evidence of myocardial necrosis in a clinical setting consistent with acute myocardial ischemia [3]. Although the diagnostic criteria and major treatment strategies for AMI are well defined [4], the mortality rates for such patients remains high. Therefore, how to define high-risk patients is extremely important for the survival of patients with AMI.

Inflammation is one of the main factors contributing to coronary heart disease which leads to plaque instability and rupture [1]. Red blood cell distribution width (RDW) is associated with heterogeneous cell population [5], which can be used as a reliable indicator for anisocytosis [6]. Recent studies had suggested that inflammation, neurohormone and adrenergic system activation may affect erythrocyte maturation by interfering with erythrocyte membrane, resulting in elevated RDW [5, 7, 8]. RDW has been found to be an extremely effective prognostic predictor in patients with AMI [9, 10].

Platelets play a key role in the pathophysiology of AMI, associated with endothelial cell activation and, together with fibrin, form thrombus within coronary arteries [11]. Platelets not only participate in the formation of blood clots, but also release mediators that form and maintain local inflammatory responses. Several previous studies [12, 13] had shown that a decreased platelet count was associated with the severity of MI, death and reinfarction. However, the relationship between platelet counts and the prognosis of MI was controversial [14,15,16,17,18].

The RDW-to-platelets ratio (RPR), a combination of two independent parameters, has been established as a new index to reflect the severity of inflammation [19, 20]. RPR had been proved as a prognostic marker in breast cancer [19, 21], cardiovascular diseases [22, 23], acute pancreatitis [24, 25] and chronic hepatic [26, 27]. While RPR is related to the clinical prognosis of patients with cardiovascular diseases [22, 23], the predictive power of RPR in mortality for AMI patients remains uncertain. The aim of the present study was to investigate the relationship between RPR on admission and short-term mortality within 180 days in patients with AMI.

Method

Study design and patient selection

Data were extracted from the database of Medical Information Mart for Intensive Care IV (MIMIC-IV). The MIMIC-IV database contains clinical data of patients admitted to the Intensive Care Unit (ICU) of Beth Israel Deaconess Medical Center (BIDMC) from 2008 to 2019 inclusive. All patients’ identifiers were removed and patients’ privacy was well protected so that the requirement for informed consent was waived. The MIMIC-IV database is a freely available database, one of the authors who completed the online training course of Data or Specimens Only Research included in Human Research (certification number: 50,765,560) extracted the data. Data extraction was performed using PostgreSQL software (version 6.11).

AMI is defined as being present when blood levels of cardiac troponin (cTn) are increased above the 99th percentile upper reference limit and at least one of the following: 1) Symptoms of MI; 2) New ischaemic electrocardiogram changes; 3) Development of pathological Q waves; 4) Imaging evidence of new loss of viable myocardium or new regional wall motion abnormality in a pattern consistent with an ischaemic aetiology; 5) Identification of a coronary thrombus by angiography or autopsy [28].

Patients who met the diagnosis of AMI were enrolled in the study. Exclusion criteria were as follows: 1) age < 18 years old; 2) patients who were diagnosed with infection, cancer (including hematologic malignancies); 3) RPR on admission records were not available. 4) patients who stayed in the ICU for less than 24 h or without ICU stay record. For patients with multiple admissions, only the first admission data were recorded.

Data collection

Baseline data including gender, age, ethnicity, and concomitant disease (such as hypertension, diabetes mellitus, cardiac arrhythmia) were extracted. Clinical parameters including Troponin T, Creatine Kinase-MB (CK-MB), blood routine examination and other data were also included in the analysis. The RPR, the main factor we intended to study, was calculated as follows: RPR = RDW / platelet count. We also recorded patients’ surgical procedures including percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG). Missing values greater than 30% of parameters were excluded due to potential bias.

Endpoint event

The endpoint of this study was all-cause in-hospital mortality within 180 days.

Statistical analyses

The normality of continuous variables was analyzed using the skewness kurtosis normality test. Continuous variables were expressed as the mean with standard deviation (for normal distribution) or median with interquartile range (for non-normal distribution), while categorical variables were presented as numbers and percentages. Independent samples t-tests were performed to compare the differences between the group of survivor and non-survivor group. If continuous variables were normally distributed, otherwise, Mann-Whitney test were used. Chi-square test was used for comparison of categorized variables. Receiver operating characteristic (ROC) curves were used to determine the optimal cut-off value of RPR on admission. Patients in this study were separated into a high admission RPR group and a low admission RPR group according to the optimum cut-off value. The Kaplan-Meier (KM) method was used to plot survival curves of two groups with different RPR on admission, with statistical significance examined by the log-rank test. Univariate and multivariate Cox regression analyses were performed to determine the association between RPR on admission and in-hospital mortality. For variables whose P value was less than 0.05 in univariate analysis were considered as potential risk factors and were selected to the multivariate Cox regression model. In addition, the collinearity of variable was tested by variance inflation factors and the equal proportional-hazards assumption also be determined. Interaction analysis was used to determine whether there is an interaction between other variables and RPR. A two-sided P < 0.05 was considered statistically significant.

Results

Baseline characteristics on enrolled patients

A total of 1269 patients were enrolled in the study (Fig. 1), of which 843 (66.4%) were men. A comparison of baseline characteristics between the survivor and non-survivor groups based on prognosis within 180 days of admission to ICU was shown in Table 1. The average age of the patients was 68.8 ± 12.6 years. 83 patients died within 180 days of admission (mean length of stay: 122.4 ± 57.9 days). We found that patients in the non-survivor group had higher RPR on admission than patients in the survivor group (0.11 ± 0.07 vs. 0.08 ± 0.06, P < 0.001). In addition, other variables such as platelet, RDW, troponin T and creatinine showed significant differences between the two groups. There was no significant difference between the two groups for concomitant disease, such as hypertension, diabetes mellitus, cardiac arrhythmia.

Fig. 1
figure 1

Schematic summary of study and patient selection flow

Table 1 Baseline characteristics of the study population

Clinical outcomes

The ROC curve showed the optimal cut-off value of RPR on admission was 0.11. There were 221(17.4%) patients with RPR ≥ 0.11 on admission. Figure 2 showed KM curves for in-hospital mortality among patients stratified by RPR on admission. And we found that compared to the low RPR group, the high RPR group had a significantly higher rate of in-hospital mortality (hazard ratio [HR]: 2.750, 95% confidence interval [CI]: 1.766–4.276, P < 0.001). Figure 3 showed that the rate of in-hospital mortality of patients in high RPR group was about three times higher than that of patients with low RPR group.

Fig. 2
figure 2

Kaplan–Meier survival analysis for survival probability stratified by RPR

Fig. 3
figure 3

The proportion of mortality of patients with RPR on admission between the high and low groups

Univariate regression analysis demonstrated that many variables were associated with acute phase mortality within 180 days of patients with AMI (Table 2). Clinical characteristics including gender, age, ethnicity and other factors as well as laboratory parameters such as BUN, creatinine and so on were associated with in-hospital mortality in patient with AMI patients. Interestingly, concomitant diseases such as heart failure, hypertension, and diabetes were not shown to be associated with the endpoint event. Multivariate Cox regression analysis revealed that a higher RPR on admission was an independent risk factor for in-hospital mortality in patients with AMI (HR: 2.677,95% CI: 1.159–6.188, P = 0.021), despite confounding factors such as age, ethnicity other factors. We also observed that the older age (HR: 1.031, 95% CI: 1.005–1.507, P = 0.019), higher CK-MB (HR: 1.003, 95% CI: 1.000–1.006, P = 0.04), and higher blood glucose (HR: 1.004, 95 CI%: 1.002–1.006, P < 0.001), were associated with higher acute mortality, while white patients (HR: 0.403, 95% CI: 0.230–0.705, P = 0.001) and patients undergoing CABG (HR: 0.084, 95% CI: 0.011–0.623, P = 0.015) had a lower risk of death.

Table 2 Univariate and multivariate analysis of prognostic variables

Discussion

For the first time, the relationship between RPR and mortality in patients with AMI was evaluated. We compared the survivor and non-survivor groups of patients admitted to the ICU with AMI. The main findings of this study are as follows: 1) patients with AMI who died within 180 days had higher RPR on admission; 2) the rate of in-hospital mortality was significantly higher in patients with high RPR than in those with low RPR; 3) RPR on admission was an independent predictor of in-hospital mortality within 180 days in patients with AMI.

Recent studies had shown that elevated RDW was related to higher mortality in patients with AMI [29, 30]. A study of 3100 patients from the MIMIC-IV database showed that a higher RDW was associated with increased risks of in-hospital mortality in patients with AMI [31]. Another study enrolling 763 patients with AMI undergoing a primary PCI proved that a high RDW level on admission was associated with an increased risk for intrahospital cardiovascular mortality [32]. Coronary atherosclerosis is a chronic disease with stable and unstable periods. If the blood vessel wall is irritated by inflammation, a patient who in an unstable period may deteriorate to MI. Increased systemic and local inflammation play a key role in the pathophysiology of Acute coronary syndrome (ACS) [33]. Elevated RDW has been shown to be associated with severe inflammation [34]. Inflammatory factors desensitize bone marrow erythroid progenitor cells, blocking the anti-apoptotic and pro-maturation effects of erythropoietin. Resulting in a large number of reticulocytes to enter the peripheral blood and an increase in RDW [35]. It also led to the change of red blood cells (RBCs) lipid structure, and the destruction of RBCs degeneration ability. As a result, the oxygen carrying capacity of RBCs decreases and the viscosity of whole blood increases, which increases the risk of death in patients with MI [36, 37].

Platelet was associated with the activation of endothelial cells and was important in the pathogenesis of ACS [33]. Several studies had reported the evidence that decreased platelet counts were related to increased MI size and severity and mortality or reinfarction rates [12, 13]. A study demonstrated that decreased platelet counts at hospital admission in post-ACS patients had relation to a significant almost two times higher 5-year mortality rate [38]. Another study including 10,793 patients with ST-segment elevation myocardial infarction (STEMI) from the Thrombolysis In Myocardial Infarction trials database demonstrated that compared with baseline values, a greater decrease in follow-up platelet counts was independently associated with an increased risk of reinfarction at 30 days (OR: 1.44, 95% CI:1.13–1.82, P = 0.03) [39] These results were consistent with our findings, that the non-survivor group had lower platelet counts on admission compared with the survivor group.

RPR, as a potential indicator of inflammatory processes, is calculated as the ratio of RDW to platelet count. RPR was considered to be an effective prognostic factor for many diseases [19, 20, 40,41,42,43]. One study performed by Lehmann et al. proved that patients after deep-seated intracerebral hemorrhage (ICH) with a low baseline RPR were distinguished by significantly lower case-fatality at 90 days compared to patients with a high baseline RPR (27 vs. 57%; P = 0.003, OR: 3.6, 95% CI: 1.6–8.3) [20]. Zhu et al. demonstrated that RPR was an effective predictor of cardiovascular events in hemodialysis patients, with the areas under the ROC curves of 0.88 [41]. Another study including 470 STEMI patients with PCI, demonstrated that higher RPR was associated with an increased risk for in-hospital and long-term major adverse cardiovascular events (MACE), all-cause mortality and cardiovascular mortality [23]. In the development of atherosclerosis, unstable atherosclerotic plaque and formation of clots on the plaque surface, the inflammatory process plays an important role [44]. RPR was a combination of two inflammatory parameters that had been proved have a better ability to predict the prognosis of the disease. A study conducted by Xi et al. confirmed that compared with RDW, RPR presented better diagnostic value of hemophagocytic lymphohistiocytosis (HLH). Moreover, RPR had a better ability to monitor the effects of treatment on HLH [43]. Studies have shown that Troponin T and CKMB are positively correlated with AMI extent [45], which was consistent with our study. In our study, Troponin T and CKMB were both higher in the non-survivor group than in the survivor group, accompanied by higher RPR, suggesting that RPR may also be a predictor of poor outcomes in patients with AMI. RPR, as a readily available and economical blood routine parameter, has been neglected in clinical practice. Our study demonstrated the importance of RPR, as a potential indicator of inflammation, in predicting in-hospital mortality within 180 days in patients with AMI. After adjusting sex, age, concomitant disease, and treatment, RPR remained an independent predictor of mortality in patients with AMI. However, there are few studies on RPR at present. In the current practical application, RPR should be combined with other clinical indicators to better verify the clinical prognosis of AMI.

Limitations

As a single center, retrospective, small cohort study, there may still be uncontrollable factors affecting the outcome even if adjusted by analysis. We did not conduct subgroup analysis according to the severity of MI, but only analyzed and discussed according to the survival status of patients. A piece of missing data, such as leukocytes and neutrophils, were deleted in this study, these values may affect the outcome. Meanwhile, indicators such as smoking, drinking, and patient medication history, which may have a significant impact on patient outcomes, were not available in the database. The predictive role of RPR for all-cause mortality in patients with AMI in long-term follow-up could not be demonstrated for unavailable follow-up data in the MIMIC-IV database.

Conclusion

Based on the findings of this study, RPR, as a potential indicator of inflammatory processes, could effectively and independently predict 180-day in-hospital mortality in patients with AMI. More prospective randomized controlled studies are needed for further investigation on the effect of RPR in patients with AMI.

Data availability

The data that support the findings of this study are available from MIMIC-IV (Published: March 16th, 2021. Version: 1.0) public database. But restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the Massachusetts Institute of Technology (Cambridge, MA) and Beth Israel Deaconess Medical Center (Boston, MA).

Abbreviations

RDW:

Red blood cell distribution width

RPR:

RDW to platelet ratio

AMI:

Acute myocardial infarction

MIMIC-IV:

Medical Information Mart for Intensive Care IV

KM:

Kaplan-Meier

HR:

hazard ratio

CI:

confidence interval

ICU:

Intensive Care Unit

BIDMC:

Beth Israel Deaconess Medical Center

CK-MB:

Creatine Kinase-MB

PCI:

percutaneous coronary intervention

CABG:

coronary artery bypass grafting

ROC:

Receiver operating characteristic

RBCs:

red blood cells

ICH:

intracerebral hemorrhage

MACE:

cardiovascular events

HLH:

lymphohistiocytosis

STEMI:

ST-segment elevation myocardial infarction

ACS:

Acute coronary syndrome

BUN:

blood urea nitrogen

MCH:

mean corpuscular hemoglobin

MCHC:

mean corpuscular hemoglobin concentration

MCV:

mean corpuscular volume

cTn:

cardiac troponin

References

  1. Reed GW, Rossi JE, Cannon CP. Acute myocardial infarction. Lancet (London England). 2017;389(10065):197–210.

    Article  PubMed  Google Scholar 

  2. Núñez-Gil IJ, Riha H, Ramakrishna H. Review of the 2017 european Society of Cardiology’s Guidelines for the management of Acute myocardial infarction in patients presenting with ST-Segment Elevation and focused Update on Dual Antiplatelet Therapy in Coronary Artery Disease developed in collaboration with the European Association for Cardio-Thoracic surgery. J Cardiothorac Vasc Anesth. 2019;33(8):2334–43.

    Article  PubMed  Google Scholar 

  3. Thygesen K, Alpert JS, Jaffe AS, Simoons ML, Chaitman BR, White HD. Third universal definition of myocardial infarction. Nat Rev Cardiol. 2012;9(11):620–33.

    Article  PubMed  Google Scholar 

  4. Levine GN, Bates ER, Bittl JA, Brindis RG, Fihn SD, Fleisher LA, ACC/AHA/ACP/AATS et al. /PCNA/SCAI/STS Guideline for the Diagnosis and Management of Patients With Stable Ischemic Heart Disease, 2013 ACCF/AHA Guideline for the Management of ST-Elevation Myocardial Infarction, 2014 AHA/ACC Guideline for the Management of Patients With Non-ST-Elevation Acute Coronary Syndromes, and 2014 ACC/AHA Guideline on Perioperative Cardiovascular Evaluation and Management of Patients Undergoing Noncardiac Surgery. Circulation. 2016;134(10):e123-55.

  5. Salvagno GL, Sanchis-Gomar F, Picanza A, Lippi G. Red blood cell distribution width: a simple parameter with multiple clinical applications. Crit Rev Clin Lab Sci. 2015;52(2):86–105.

    Article  PubMed  Google Scholar 

  6. Hoffmann JJ, Nabbe KC, van den Broek NM. Effect of age and gender on reference intervals of red blood cell distribution width (RDW) and mean red cell volume (MCV). Clin Chem Lab Med. 2015;53(12):2015–9.

    Article  CAS  PubMed  Google Scholar 

  7. Cole J, Ertoy D, Lin H, Sutliff RL, Ezan E, Guyene TT, et al. Lack of angiotensin II-facilitated erythropoiesis causes anemia in angiotensin-converting enzyme-deficient mice. J Clin Investig. 2000;106(11):1391–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Förhécz Z, Gombos T, Borgulya G, Pozsonyi Z, Prohászka Z, Jánoskuti L. Red cell distribution width in heart failure: prediction of clinical events and relationship with markers of ineffective erythropoiesis, inflammation, renal function, and nutritional state. Am Heart J. 2009;158(4):659–66.

    Article  PubMed  Google Scholar 

  9. Tonelli M, Sacks F, Arnold M, Moye L, Davis B, Pfeffer M. Relation between red blood cell distribution Width and Cardiovascular Event Rate in People with Coronary Disease. Circulation. 2008;117(2):163–8.

    Article  PubMed  Google Scholar 

  10. Anderson JL, Ronnow BS, Horne BD, Carlquist JF, May HT, Bair TL, et al. Usefulness of a complete blood count-derived risk score to predict incident mortality in patients with suspected cardiovascular disease. Am J Cardiol. 2007;99(2):169–74.

    Article  PubMed  Google Scholar 

  11. Falk E, Nakano M, Bentzon JF, Finn AV, Virmani R. Update on acute coronary syndromes: the pathologists’ view. Eur Heart J. 2013;34(10):719–28.

    Article  CAS  PubMed  Google Scholar 

  12. Roh JW, Lim S, Hwang Y, Lee KY, Choo EH, Choi IJ et al. Ischemic and Bleeding Events Associated with Thrombocytopenia and Thrombocytosis after Percutaneous Coronary Intervention in Patients with Acute Myocardial Infarction. J Clin Med. 2020;9(10).

  13. Patel SJ, Ajebo G, Kota V, Guddati AK. Outcomes of hospitalized patients with myocardial infarction and immune thrombocytopenic purpura: a cross sectional study over 15 years. Am J blood Res. 2020;10(5):210–6.

    PubMed  PubMed Central  Google Scholar 

  14. Quan XQ, Ji HY, Jiang J, Huang JB, Zhang CT. Prognostic utility of the combination of platelet count with neutrophil-to-lymphocyte ratio in aged patients with Acute Myocardial Infarction undergoing percutaneous coronary intervention. Emerg Med Int. 2021;2021:4023472.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Fawzy A, Anderson JA, Cowans NJ, Crim C, Wise R, Yates JC, et al. Association of platelet count with all-cause mortality and risk of cardiovascular and respiratory morbidity in stable COPD. Respir Res. 2019;20(1):86.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Patti G, Di Martino G, Ricci F, Renda G, Gallina S, Hamrefors V, et al. Platelet indices and risk of Death and Cardiovascular events: results from a large Population-Based Cohort Study. Thromb Haemost. 2019;119(11):1773–84.

    Article  PubMed  Google Scholar 

  17. Song PS, Ahn KT, Jeong JO, Jeon KH, Song YB, Gwon HC et al. Association of baseline platelet count with all-cause mortality after acute myocardial infarction. European heart journal Acute cardiovascular care. 2020:2048872620925257.

  18. Sharif D, Abu-Salem M, Sharif-Rasslan A, Rosenschein U. Platelet counts on admission affect coronary flow, myocardial perfusion and left ventricular systolic function after primary percutaneous coronary intervention. Eur heart J Acute Cardiovasc care. 2017;6(7):632–9.

    Article  PubMed  Google Scholar 

  19. Takeuchi H, Abe M, Takumi Y, Hashimoto T, Miyawaki M, Okamoto T, et al. Elevated red cell distribution width to platelet count ratio predicts poor prognosis in patients with breast cancer. Sci Rep. 2019;9(1):3033.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Lehmann F, Schenk LM, Bernstock JD, Bode C, Borger V, Gessler FA, et al. Elevated red cell distribution width to platelet ratio is Associated with poor prognosis in patients with spontaneous, deep-seated Intracerebral Hemorrhage. Front Neurol. 2021;12:751510.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Takeuchi H, Abe M, Takumi Y, Hashimoto T, Kobayashi R, Osoegawa A, et al. The prognostic impact of the platelet distribution width-to-platelet count ratio in patients with breast cancer. PLoS ONE. 2017;12(12):e0189166.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Celık T, Balta S, Demır M, Yıldırım AO, Kaya MG, Ozturk C, et al. Predictive value of admission red cell distribution width-platelet ratio for no-reflow phenomenon in acute ST segment elevation myocardial infarction undergoing primary percutaneous coronary intervention. Cardiol J. 2016;23(1):84–92.

    Article  PubMed  Google Scholar 

  23. Pusuroglu H, Cakmak HA, Akgul O, Erturk M, Surgit O, Akkaya E, et al. The prognostic value of admission red cell distribution width-to-platelet ratio in patients with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention. Revista portuguesa de cardiologia: orgao oficial da sociedade portuguesa de cardiologia = portuguese. J Cardiol : official J Portuguese Soc Cardiol. 2015;34(10):597–606.

    Google Scholar 

  24. İlhan M, İlhan G, Gök AF, Bademler S, Verit Atmaca F, Ertekin C. Evaluation of neutrophil-lymphocyte ratio, platelet-lymphocyte ratio and red blood cell distribution width-platelet ratio as early predictor of acute pancreatitis in pregnancy. The journal of maternal-fetal & neonatal medicine: the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies. Int Soc Perinat Obstet. 2016;29(9):1476–80.

    Google Scholar 

  25. Cetinkaya E, Senol K, Saylam B, Tez M. Red cell distribution width to platelet ratio: new and promising prognostic marker in acute pancreatitis. World J Gastroenterol. 2014;20(39):14450–4.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Karagöz E, Tanoğlu A, Ülçay A, Erdem H, Turhan V, Kara M, et al. Mean platelet volume and red cell distribution width to platelet ratio for predicting the severity of hepatic fibrosis in patients with chronic hepatitis C. Eur J Gastroenterol Hepatol. 2016;28(7):744–8.

    Article  PubMed  Google Scholar 

  27. Taefi A, Huang CC, Kolli K, Ebrahimi S, Patel M. Red cell distribution width to platelet ratio, a useful indicator of liver fibrosis in chronic hepatitis patients. Hep Intl. 2015;9(3):454–60.

    Article  Google Scholar 

  28. Thygesen K, Alpert JS, Jaffe AS, Chaitman BR, Bax JJ, Morrow DA, et al. Fourth universal definition of myocardial infarction (2018). Eur Heart J. 2019;40(3):237–69.

    Article  PubMed  Google Scholar 

  29. Ferreira JP, Lamiral Z, Bakris G, Mehta C, White WB, Zannad F. Red cell distribution width in patients with diabetes and myocardial infarction: an analysis from the EXAMINE trial. Diabetes Obes Metab. 2021;23(7):1580–7.

    Article  CAS  PubMed  Google Scholar 

  30. Lin G, Dai C, Xu K, Wu M. Predictive value of neutrophil to lymphocyte ratio and red cell distribution width on death for ST segment elevation myocardial infarction. Sci Rep. 2021;11(1):11506.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Huang S, Zhou Q, Guo N, Zhang Z, Luo L, Luo Y, et al. Association between red blood cell distribution width and in-hospital mortality in acute myocardial infarction. Medicine. 2021;100(15):e25404.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. İlhan E, Güvenç TS, Altay S, Çağdaş M, Çalik AN, Karaca M, et al. Predictive value of red cell distribution width in intrahospital mortality and postintervention thrombolysis in myocardial infarction flow in patients with acute anterior myocardial infarction. Coron Artery Dis. 2012;23(7):450–4.

    Article  PubMed  Google Scholar 

  33. Budzianowski J, Pieszko K, Burchardt P, Rzeźniczak J, Hiczkiewicz J. The role of hematological indices in patients with Acute Coronary Syndrome. Dis Markers. 2017;2017:3041565.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Tsimikas S, Willerson JT, Ridker PM. C-reactive protein and other emerging blood biomarkers to optimize risk stratification of vulnerable patients. J Am Coll Cardiol. 2006;47(8 Suppl):C19–31.

    Article  CAS  PubMed  Google Scholar 

  35. Afsar B, Saglam M, Yuceturk C, Agca E. The relationship between red cell distribution width with erythropoietin resistance in iron replete hemodialysis patients. Eur J Intern Med. 2013;24(3):e25–9.

    Article  CAS  PubMed  Google Scholar 

  36. Xanthopoulos A, Giamouzis G, Dimos A, Skoularigki E, Starling RC, Skoularigis J et al. Red Blood Cell Distribution Width in Heart Failure: Pathophysiology, Prognostic Role, Controversies and Dilemmas. J Clin Med. 2022;11(7).

  37. Pernow J, Mahdi A, Yang J, Zhou Z. Red blood cell dysfunction: a new player in cardiovascular disease. Cardiovascular Res. 2019;115(11):1596–605.

    Article  CAS  Google Scholar 

  38. Małyszczak A, Łukawska A, Dyląg I, Lis W, Mysiak A, Kuliczkowski W. Blood platelet count at Hospital Admission Impacts Long-Term Mortality in patients with Acute Coronary Syndrome. Cardiology. 2020;145(3):148–54.

    Article  PubMed  Google Scholar 

  39. Ly HQ, Kirtane AJ, Murphy SA, Buros J, Cannon CP, Braunwald E, et al. Association of platelet counts on presentation and clinical outcomes in ST-elevation myocardial infarction (from the TIMI trials). Am J Cardiol. 2006;98(1):1–5.

    Article  PubMed  Google Scholar 

  40. Chen B, Ye B, Zhang J, Ying L, Chen Y. RDW to platelet ratio: a novel noninvasive index for predicting hepatic fibrosis and cirrhosis in chronic hepatitis B. PLoS ONE. 2013;8(7):e68780.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Zhu X, Li G, Li S, Gong Z, Liu J, Song S. Neutrophil-to-lymphocyte ratio and red blood cell distribution width-to-platelet ratio predict cardiovascular events in hemodialysis patients. Experimental and therapeutic medicine. 2020;20(2):1105–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Qiu L, Chen C, Li SJ, Wang C, Guo F, Peszel A, et al. Prognostic values of red blood cell distribution width, platelet count, and red cell distribution width-to-platelet ratio for severe burn injury. Sci Rep. 2017;7(1):13720.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Xi Y, Bai Y. Diagnostic value of red blood cell distribution width, platelet distribution width, and red blood cell distribution width to platelet ratio in children with hemophagocytic lymphohistiocytosis. J Clin Lab Anal. 2021;35(9):e23909.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Meng LB, Yu ZM, Guo P, Wang QQ, Qi RM, Shan MJ, et al. Neutrophils and neutrophil-lymphocyte ratio: inflammatory markers associated with intimal-media thickness of atherosclerosis. Thromb Res. 2018;170:45–52.

    Article  CAS  PubMed  Google Scholar 

  45. Byrne RA, Ndrepepa G, Braun S, Tiroch K, Mehilli J, Schulz S, et al. Peak cardiac troponin-T level, scintigraphic myocardial infarct size and one-year prognosis in patients undergoing primary percutaneous coronary intervention for acute myocardial infarction. Am J Cardiol. 2010;106(9):1212–7.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to thank the Massachusetts Institute of Technology and the Beth Israel Deaconess Medical Center for the MIMIC project.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

Authors

Contributions

Chen C created the concept and designed the study, Li L extracted the data from the database, Hong Y, RZ analyzed the data. Hong Y drafted the manuscript. Chen C revised the manuscript. All the authors had full access to all the data in the study, and took responsibility for the integrity and accuracy of the data.

Corresponding author

Correspondence to Chen Chen.

Ethics declarations

Ethics approval and consent to participate

The studies involving human participants were reviewed and approved by the Institutional Review Boards of the Beth Israel Deaconess Medical Center (BIDMC) and the Massachusetts Institute of Technology (MIT). Therefore, the First Affiliated Hospital of Wenzhou Medical University ethics committee waived the informed consent for the present study. The study was conducted in accordance with the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yao, H., Lian, L., Zheng, R. et al. Red blood cell distribution width/platelet ratio on admission as a predictor for in-hospital mortality in patients with acute myocardial infarction: a retrospective analysis from MIMIC-IV Database. BMC Anesthesiol 23, 113 (2023). https://doi.org/10.1186/s12871-023-02071-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12871-023-02071-7

Keywords