Abstract
Non-ST elevation myocardial infarction (NSTEMI) has a higher risk of long-term mortality than ST-elevation myocardial infarction; thus, identifying such high-risk patients is essential. Red cell distribution width (RDW) recently emerged as a strong predictor of mortality in several cardiovascular diseases, however, it is scarcely known whether RDW has a prognostic value in NSTEMI patients, therefore, this study aims to elucidate this issue. 421 consecutive patients with NSTEMI between January 2020 and June 2022 were prospectively enrolled. Patients were divided into 2 groups by the optimal cutoff value of RDW using time-dependent receiver operating characteristic curves. The optimal cutoff value of RDW for predicting all-cause mortality was 13.4 and the study population was divided into low RDW (≤13.4) and high RDW (>13.4). The primary endpoint of this study was long-term all-cause mortality. The secondary endpoint was the association between RDW and long-term adverse events, including heart failure, gastrointestinal hemorrhage, stroke events, re-infarction rate, cardiovascular mortality, and major adverse cardiovascular events. The association of RDW with the outcome was analyzed by Cox regression analysis. Patients with high RDW tended to be older, had a higher history of previous MI, a higher history of percutaneous coronary intervention, a higher level of neutrophil, high-sensitivity C-reactive protein, a lower level of albumin, and a lower level of ejection fraction (all P < .05). During a median follow-up of 720 days (IQR, 534–913 days), the all-cause mortality was significantly higher in the high RDW group than in the low RDW group (24.8% vs 6.3%, P < .001). In the multivariate Cox proportional hazard analysis, RDW > 13.4 was an independent predictor for long-term all-cause mortality [hazard ratio 3.008; 95% confidence interval 1.005, 9.003, P = .049]. Admission RDW could be used as a new biomarker for predicting long-term mortality in patients with NSTEMI, and high RDW was associated with an increased risk of all-cause mortality.
1. Introduction
Acute myocardial infarction (AMI) is one of the leading causes of morbidity and mortality worldwide.[1] Studies found that at least 5% to 10% of survivors die within the first 12 months post-MI, and about 50% need hospitalization within the same year.[2] AMI is categorized into 2 distinct presentations according to their initial electrocardiogram they are ST-elevation myocardial infarction (STEMI) and non-STEMI (NSTEMI). Although both have similar reperfusion strategies, the pathophysiology and outcomes differ between them. NSTEMI patients tend to have a higher risk of long-term mortality, present with multi-morbidity and multivessel coronary disease, but have lower early mortality than STEMI patients.[3,4] Such high mortality and burden have made identifying high-risk patients essential for AMI patients’ survival.
Inflammation can induce plaque disorder and rupture, which in turn induces the formation of atherosclerosis.[5] Recent studies demonstrated that inflammation can further affect red cell distribution width (RDW) by disrupting the red blood cell (RBC) membrane, interfering with RBC maturation.[6,7] Given that, several studies have found a positive association between elevated RDW and various cardiovascular diseases, such as heart failure,[8] AMI,[9] STEMI,[10] and atrial fibrillation.[11] Furthermore, population-based studies have indicated that RDW may predict all-cause mortality[12] and cardiovascular mortality.[13] Unfortunately, studies focusing on whether RDW may play a role in the long-term outcome of NSTEMI patients remain scarce, therefore, the present work aimed to elucidate this issue.
2. Methods
Study Population: Data of consecutive patients with NSTEMI between January 2020 and June 2022 were collected. Due to the retrospective nature of the study, the patient consent was waived. This study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Institutional Ethics Review Board of the First Affiliated Hospital of Chongqing Medical University.
Outcome definitions: The diagnosis of AMI was defined as an increase or decrease in cardiac troponin (cTn) with at least one value above the 99th percentile, the upper reference limit, and at least one of the following: symptoms of myocardial ischemia, new ischemic electrocardiographic changes, the presence of pathologic Q waves, imaging evidence of new loss of viable myocardium, or new regional wall motion abnormalities consistent with an ischemic cause.[14] NSTEMI is diagnosed in patients determined to have symptoms consistent with acute coronary syndrome, with elevation of cardiac troponin in combination with depressed ST-segment or T-wave inversion in a 12-lead ECG.[15] Major adverse cardiovascular events (MACE) was defined as the composite of myocardial infarction, coronary revascularization, stroke, cerebral hemorrhage, and heart failure.
Management: After the patients were diagnosed with NSTEMI, a comprehensive assessment of the patient was performed. Urgent coronary angiography or percutaneous coronary intervention is generally recommended as long as there are no contraindications or refusals. At the same time, according to the AMI guide routine use of antiplatelet agents, low fat, diuretic and other comprehensive treatments.[16]
Data collection: Basic information such as complications, coronary angiography results, history of cardiovascular diseases, echocardiography results, and vital signs were collected. The degree of coronary artery stenosis was categorized based on the percentage of the cross-sectional area of stenosis, in which occlusion of 0% to 49% was considered mild stenosis, 50% to 69% was considered moderate stenosis, 70% to 99% was regarded as severe stenosis, and total occlusion was assumed if there was an interruption of lumen continuity and disappearance of anterograde blood flow during coronary angiography.
Blood test: Blood sample was collected immediately after admission and was assayed at a central laboratory. Routine blood tests with automatic hematology analyzer (Sysmex XN-1000, Shanghai). RDW was calculated automatically by hematological analyzers by simply dividing the standard deviation of the mean corpuscular volume by the mean corpuscular volume and multiplying by 100 to yield a percentage value.
Endpoint: The primary end-point was long-term all-cause mortality. The secondary endpoint was the association between RDW and long-term adverse events, including heart failure, gastrointestinal hemorrhage, stroke events, re-infarction rate, cardiovascular mortality, and MACE.
Statistical analysis: All data were expressed as mean ± standard deviation. Baseline characteristic variables were compared using Pearson X2 test. The area under the curve of long-term all-cause mortality was calculated by the receiver operating characteristic (ROC) to determine the predictive value of RDW. Furthermore, patients were then divided into 2 groups according to the cutoff value of RDW. Kaplan–Meier curves were constructed, followed by the log-rank test. A P value of <.05 was considered statistically significant.
Logistic and linear regression analysis were used to analyze the association between RDW with several cardiac markers, such as myoglobin, creatinine kinase-MB, and cardiac troponin I. Cox regression analysis was performed to analyze the independent association between RDW with the prognosis of NSTEMI patients. The univariate Cox regression model was constructed based on the RDW cutoff value, and the multivariate Cox regression model was adjusted for variables considered clinically relevant or with a P value <.05 in the univariate analysis. The corrected hazard ratio (HR) and its 95% confidence interval (CI) were calculated. Statistical significance was defined as a bilateral P value < .05. HR > 1.0 and P < .05 were harmful associations, and HR < 1.0 and P < .05 were protective associations. SPSS 25.0 (IBM, USA), MedCalc statistical software 19.2.6 and GraphPad Prism 8.4.3 were used for data analysis.
3. Results
From January 2020 to June 2022, a total of 432 patients were diagnosed with NSTEMI, among which 11 patients had incomplete data or loss of follow-up, and the remaining 421 NSTEMI patients were included in this study.
The present study aimed to evaluate the predictive value of RDW in NSTEMI patients; therefore, ROC curve analysis was performed. As showed in Figure 1, the RDW presented with a modest predictive value [area under the curve 0.718; 95% CI 0.642, 0.794]. Based on the ROC curve, we found that the optimal cutoff value for RDW was 13.4, with a sensitivity of 70.9% and a specificity of 66.4%. Furthermore, we divided the study population into 2 groups, low RDW (≤13.4) and high RDW (>13.4).
Figure 1.
Receiver operating characteristic (ROC) analysis of red cell distribution width (RDW) for predicting long-term all-cause mortality.
Tables 1 and 2 showed the baseline demographic characteristics between the 2 groups. The patients in the high RDW group tended to be older, had a higher history of chronic kidney disease, a higher history of stroke, a higher history of previous MI, and a higher history of undergoing percutaneous coronary intervention (all P < .05). In terms of admission vital signs, patients in the high RDW group presented with higher heart rates (mean 83.0 vs 80.0 bpm, P = .027), while systolic blood pressure and diastolic blood pressure were comparable between the 2 groups. As for laboratory parameters, the high RDW group presented with a lower level of RBCs, albumin, hemoglobin, and procalcitonin, a higher level of d-dimer, myoglobin, neutrophil, urea, creatinine, uric acid, and high-sensitivity C-reactive protein (all P < .05), while creatinine kinase-MB, cardiac troponin I, low-density lipoprotein cholesterol, and leukocyte were comparable between the 2 groups (all P > .05). In terms of echocardiography, the high RDW group tended to have a larger left atrium, larger right atrium, larger left ventricle, and lower left ventricular ejection fraction (all P < .05). As for treatment, we found that the high RDW group had lower usage of aspirin, ticagrelor, statins, and angiotensin-converting enzyme inhibitors/angiotensin receptor blocker (all P < .05), but higher usage of clopidogrel, diuretics, and inotropes (all P < .05). Additionally, based on the coronary angiography results, the high RDW group was prone to develop multivessel disease (64.7% vs 55.6%, P < .001) and presented with severe stenosis and total occlusion in the left coronary artery descending (62.1% vs 58.2% and 9.2% vs 6.7%, respectively) and left circumflex artery (48.4% vs 42.9% and 14.4% vs 10.4%, respectively) compared to low RDW group (all P < .05). Correlations between RDW and cardiac biomarkers (myoglobin, CK-MB, and cardiac troponin I) were presented in Figure 2. Correlation analysis showed that RDW positively correlated with myoglobin (R2 = 0.044, P < .001) but negatively correlated with CK-MB (R2 = 0.002, P = .337) and cardiac troponin I (R2 = 0.003, P = .271).
Table 1.
A comparison of baseline characteristics of the 2 groups.
| Baseline Characteristics | RDW ≤ 13.4 | RDW > 13.4 | Z | P |
|---|---|---|---|---|
| n = 268 | n = 153 | |||
| Age (yr) | 65.5 (56.0–73.0) | 71.0 (60.5–78.0) | −4.149 | <.001 |
| BMI (kg/m2) | 24.22 (21.71–26.85) | 23.44 (20.31–25.88) | −2.121 | .034 |
| Admission vital signs | ||||
| SBP (mm Hg) | 138.0 (121.25–151.00) | 134.0 (117.50–151.00) | −1.231 | .218 |
| DBP (mm Hg) | 82.0 (72.0–90.0) | 79.0 (69.50–90.00) | −1.585 | .113 |
| Heart rate (bpm) | 80.0 (70.0–90.0) | 83.0 (72.00–96.50) | −2.428 | .015 |
| Laboratory parameters | ||||
| Lactate (mmol/L) | 1.4 (0.9–2.1) | 1.6 (0.9–2.2) | −0.826 | .409 |
| d-dimer (ng/mL) | 356.0 (191.0–616.0) | 560.0 (290.0–939.0) | −3.895 | <.001 |
| Myoglobin (ng/mL) | 82.0 (49.25–154.75) | 130.0 (71.75–287.25) | −4.417 | <.001 |
| CK-MB (ng/mL) | 7.70 (3.10–22.25) | 8.0 (3.1–24.0) | −0.100 | .920 |
| Cardiac troponin I (ng/mL) | 0.32 (0.11–1.29) | 0.45 (0.14–1.47) | −1.484 | .138 |
| Red blood cells (×1012/L) | 4.50 (4.14–4.87) | 4.18 (3.79–4.71) | −3.698 | <.001 |
| Hemoglobin (g/L) | 140.0 (127.25–150.00) | 124.0 (112.0–138.0) | −6.566 | <.001 |
| Leukocyte (×109/L) | 8.22 (6.49–9.50) | 8.17 (6.68–10.43) | −0.961 | .336 |
| Neutrophil (%) | 69.25 (63.33–76.10) | 70.9 (64.05–78.70) | −1.972 | .049 |
| Platelet (×109/L) | 208.0 (164.25–247.75) | 210.5 (157.5–258.0) | −0.263 | .793 |
| Urea (mmol/L) | 6.0 (5.0–7.7) | 7.1 (5.4–9.9) | −3.877 | <.001 |
| Creatinine (μmol/L) | 79.0 (66.0–96.0) | 91.0 (68.25–133.75) | −4.095 | <.001 |
| Uric Acid (μmol/L) | 339.0 (279.5–403.5) | 371.0 (310.50–452.75) | −3.073 | .002 |
| Albumin (g/L) | 42.0 (38.5–45.0) | 40.0 (37.0–43.0) | −3.563 | <.001 |
| Total cholesterol (mmol/L) | 4.18 (3.53–4.98) | 4.09 (3.43–4.90) | −0.665 | .506 |
| Triglyceride (mmol/L) | 1.51 (1.14–2.32) | 1.46 (0.99–2.04) | −1.443 | .149 |
| HDL-C (mmol/L) | 0.98 (0.84–1.20) | 1.08 (0.85–1.27) | −2.146 | .032 |
| LDL-C (mmol/L) | 2.57 (1.95–3.21) | 2.51 (1.78–3.22) | −0.907 | .364 |
| hs-CRP (mg/L) | 2.65 (1.12–6.63) | 3.98 (1.45–8.79) | −2.380 | .017 |
| Procalcitonin (ng/mL) | 0.45 (0.03–0.08) | 0.06 (0.03–0.14) | −2.333 | .020 |
| Echocardiography | ||||
| Right atrium (mm) | 35.0 (33.0–36.0) | 36.0 (33.0–38.0) | −2.746 | .006 |
| Right ventricle (mm) | 20.0 (19.0–21.0) | 20.0 (20.0–21.0) | −0.867 | .386 |
| Left atrium (mm) | 33.0 (30.0–36.0) | 35.0 (31.0–40.0) | −3.462 | .001 |
| Left ventricle (mm) | 49.0 (46.0–52.0) | 50.0 (47.0–56.0) | −3.506 | <.001 |
| Intraventricular septum (mm) | 11.0 (10.0–12.0) | 11.0 (10.0–12.0) | −0.938 | .348 |
| Ejection fraction (%) | 59.0 (52.0–62.0) | 54.5 (40.25–61.0) | −4.323 | <.001 |
BMI = body mass index, BPM = beats per minute, CK-MB = creatinine kinase MB, DBP = diastolic blood pressure, HDL-C = high-density lipoprotein cholesterol, hs-CRP = high-sensitivity C-reactive protein, LDL-C = low-density lipoprotein cholesterol, RDW = red cell distribution width, SBP = systolic blood pressure.
Table 2.
A comparison of laboratory parameters, coronary angiography, and treatment of the 2 groups.
| RDW ≤ 13.4 | RDW > 13.4 | x2 | P | |
|---|---|---|---|---|
| n = 268 | n = 153 | |||
| Male (%) | 204 (76.1) | 105 (68.6) | 2.800 | .095 |
| Smoking (%) | 170 (63.5) | 86 (56.2) | 2.622 | .240 |
| Alcohol drinking (%) | 118 (44.1) | 62 (40.5) | 1.005 | .354 |
| Multivessel disease (%) | 149 (55.6) | 99 (64.7) | 12.734 | <.001 |
| Medical histories (%) | ||||
| Hypertension | 181 (67.5) | 65 (48.9) | 1.477 | .225 |
| Diabetes mellitus type 2 | 125 (46.6) | 75 (49.0) | 0.221 | .639 |
| Gastric ulcer or bleeding | 7 (2.6) | 9 (5.9) | 2.849 | .092 |
| Dyslipidemia | 61 (22.8) | 19 (12.4) | 6.769 | .009 |
| CKD | 27 (10.1) | 36 (23.5) | 13.856 | <.001 |
| Stroke | 12 (4.5) | 15 (9.8) | 4.604 | .032 |
| Previous MI | 40 (14.9) | 39 (25.5) | 7.131 | .007 |
| Previous PCI | 35 (13.1) | 38 (24.8) | 9.425 | .002 |
| Degree of coronary stenosis (%) | ||||
| Left main artery | 0.559 | .906 | ||
| Mild | 9 (3.4) | 5 (3.3) | ||
| Moderate | 9 (3.4) | 4 (2.6) | ||
| Severe | 21 (7.8) | 8 (5.2) | ||
| Total occlusion | 0 | 0 | ||
| Left coronary artery descending | 17.065 | .002 | ||
| Mild | 27 (10.1) | 4 (2.6) | ||
| Moderate | 34 (12.7) | 5 (3.3) | ||
| Severe | 156 (58.2) | 95 (62.1) | ||
| Total occlusion | 18 (6.7) | 14 (9.2) | ||
| Left circumflex artery | 15.226 | .004 | ||
| Mild | 16 (6.0) | 8 (5.2) | ||
| Moderate | 39 (14.6) | 7 (4.6) | ||
| Severe | 115 (42.9) | 74 (48.4) | ||
| Total occlusion | 28 (10.4) | 22 (14.4) | ||
| Right coronary artery | 3.587 | .465 | ||
| Mild | 25 (9.3) | 17 (11.1) | ||
| Moderate | 36 (13.4) | 18 (11.8) | ||
| Severe | 108 (40.3) | 56 (36.6) | ||
| Total occlusion | 42 (15.7) | 24 (15.7) | ||
| Treatment (n, %) | ||||
| Aspirin | 192 (71.6) | 77 (50.3) | 15.274 | <.001 |
| Clopidogrel | 91 (34.0) | 67 (43.8) | 4.019 | .045 |
| Ticagrelor | 169 (63.1) | 77 (50.3) | 6.501 | .011 |
| Statins | 263 (98.1) | 143 (93.4) | 6.183 | .013 |
| Beta-blocker | 210 (78.4) | 113 (73.9) | 1.942 | .379 |
| ACEI/ARB | 176 (66.7) | 95 (62.1) | 0.544 | .461 |
| Proton pump inhibitors | 230 (84.8) | 127 (83.0) | 0.598 | .439 |
| Diuretics | 58 (21.6) | 78 (51.0) | 38.336 | <.001 |
| Inotropes | 92 (34.3) | 72 (47.1) | 6.638 | .010 |
| IABP | 4 (1.5) | 4 (2.6) | 0.805 | .371 |
ACEI = angiotensin-converting enzyme inhibitor, ARB = angiotensin receptor blocker, CKD = chronic kidney disease, IABP = intra-aortic balloon pump, MI = myocardial infarction, PCI = percutaneous coronary intervention.
Figure 2.
Correlation analysis between RDW with cardiac troponin I (A), myoglobin (B) and creatinine kinase-MB (C). RDW = red cell distribution width.
During a median follow-up of 720 days (IQR, 534–913 days), all-cause mortality occurred in 57 (13.2%) patients, among which, cardiovascular mortality accounted for 38 (66.7%). Furthermore, in terms of long-term adverse events, 14 (3.2%) patients had gastrointestinal bleeding (G.I bleeding), 9 (2.1%) patients had stroke, 1 (0.2%) patient had cerebral hemorrhage, 86 (19.9%) patients complicated with heart failure, 36 (8.3%) patients had re-infarction, and during this follow-up period, the cumulative incidence of MACE was 27.5%. In Figure 3, we analyzed the association between RDW and long-term adverse events and found that the all-cause mortality rate was significantly higher in the high RDW group compared to the low RDW group (24.8% vs 6.3%, P < .001), moreover high RDW group was prone to a higher incidence of heart failure and MACE events (all P < .05), as for G.I bleeding, stroke, re-infarction, and cardiovascular mortality were comparable between the 2 groups (all P > .05).
Figure 3.
Long-term adverse events according to the RDW. *P < .01. RDW = red cell distribution width.
Figure 4 shows the K–M curves between the 2 groups. The cumulative mortality in the high RDW group was significantly higher than in the low RDW group (log-rank P < .001). Cox regressions were performed to determine the association between RDW and primary outcomes (Table 3). Several covariates that were considered clinically relevant were selected, they were age, multivessel disease, history of hypertension, history of diabetes mellitus type 2, cardiac troponin I, procalcitonin, hs-CRP (Chigh-sensitivity C-reactive protein), and RDW > 13.4. In the univariate Cox proportional hazard analysis, age, history of diabetes mellitus type 2, hs-CRP, and RDW > 13.4 were associated with all-cause mortality (all P < .05). Meanwhile, in the multivariate Cox proportional hazard analysis, only age [HR 1.096; 95% CI 1.027, 1.171, P = .006] and RDW > 13.4 [HR 3.008; 95% CI 1.005, 9.003, P = .049] remain as an independent factor for all-cause mortality.
Figure 4.
Kaplan–Meier curves for all-cause mortality in patients with NSTEMI. NSTEMI = non-ST elevation myocardial infarction.
Table 3.
Univariate and multivariate cox regression analysis of long-term mortality.
| β | SE | Wald | HR | 95%CI | P | |
|---|---|---|---|---|---|---|
| Univariable | ||||||
| Age (1 yr increase) | 0.089 | 0.016 | 29.311 | 1.093 | 1.058–1.129 | <.001 |
| Multivessel disease | 0.762 | 0.426 | 3.203 | 2.143 | 0.930–4.938 | .074 |
| History of hypertension | 0.394 | 0.345 | 1.306 | 1.483 | 0.755–2.913 | .253 |
| History of diabetes mellitus type 2 | 0.268 | 0.292 | 0.843 | 1.308 | 0.737–2.320 | <.001 |
| Cardiac troponin I | 0.028 | 0.030 | 0.855 | 1.028 | 0.970–1.090 | .355 |
| Procalcitonin | 0.135 | 0.100 | 1.800 | 1.144 | 0.940–1.393 | .180 |
| hs-CRP | 0.020 | 0.009 | 5.665 | 1.021 | 1.004–1.038 | .017 |
| RDW > 13.4 | 1.501 | 0.321 | 21.926 | 4.486 | 2.393–8.408 | <.001 |
| Multivariable | ||||||
| Age (1 yr increase) | 0.092 | 0.033 | 7.549 | 1.096 | 1.027–1.171 | .006 |
| Multivessel disease | ||||||
| History of hypertension | ||||||
| History of diabetes mellitus type 2 | ||||||
| Cardiac troponin I | 0.094 | 0.031 | 9.378 | 1.099 | 1.035–1.167 | .002 |
| Procalcitonin | ||||||
| hs-CRP | ||||||
| RDW > 13.4 | 1.101 | 0.559 | 3.875 | 3.008 | 1.005–9.003 | .049 |
hs-CRP = high-sensitivity C-reactive protein, HR = hazard ratio, RDW = red cell distribution width.
4. Discussion
The main findings from the present study are as follows. First, RDW can be used as a simple and reliable parameter to identify NSTEMI patients at high risk of death rapidly. Second, high admission RDW was associated with an increased risk of long-term all-cause mortality, heart failure incidence, and MACE events. Third, after multivariate adjustment, high RDW was an independent risk factor for all-cause mortality in NSTEMI patients.
Inflammation has been known to play an important role in the pathophysiological process involved in injury, repair, and remodeling of the infarcted heart. At the onset of MI, inflammation is initiated with the inflammatory cell infiltration, accompanied by activation of both innate and adaptive immune responses.[17] The initial pro-inflammatory response is then followed by an anti-inflammatory reparative phase, which allows wound healing and scar formation. A strong correlation between elevated levels of inflammatory biomarkers and mortality in patients with AMI has been documented; with studies finding that traditional inflammatory indexes such as C-reactive protein,[18] serum amyloid,[19] interleukin-6,[20] and tumor necrosis factor[21] may have an important prognostic value. RDW due to its simplicity, inexpensive, and rapid calculation, has recently become a promising inflammatory parameter, with studies demonstrating that RDW may be an effective prognostic predictor in patients with AMI[9] and STEMI.[10] Unfortunately, there exists a different inflammatory pattern between STEMI and NSTEMI patients,[22,23] with the former having an inflammatory state higher than the latter; therefore, in this study, we decided to focus on the prognostic value of RDW in NSTEMI patients alone to ensure uniformity in analysis.
RDW is calculated by dividing the standard deviation of the mean corpuscular volume and multiplying by 100 to yield a percentage value on behalf of the RBCs size heterogeneity. At first, RDW was used to evaluate different types of anemia; however, studies have found that acute and chronic cardiovascular diseases are often associated with a high degree of anisocytosis.[8–11] In addition, studies found that high RDW was associated with a higher prevalence of hypertension, diabetes mellitus, history of myocardial infarction, and heart failure.[24] The exact mechanisms of how RDW was associated with AMI remain unclear, with studies suggesting that chronic inflammatory state desensitizes bone marrow erythroid progenitor cells, blocking the anti-apoptotic and maturation effects of erythropoietin, changing RBCs lipid structure and the destruction of degeneration ability, eventually cause oxygen-carrying capacity of RBCs decreases and the viscosity of the whole blood increases, which increases the risk of death in patients with MI.[25] In line with this hypothesis, several pro-inflammatory cytokines have been proven effective in inhibiting erythropoietin secretion and RBCs maturation, thus enhancing anisocytosis.[26] This finding was also reflected in our study, as we found that the high RDW group tended to present with a higher level of hs-CRP, a higher level of neutrophil, a higher level of leukocyte, and a lower level of albumin, indicating a more severe inflammatory response presented in the high RDW group. In addition, d-dimer, myoglobin, creatinine, and multivessel disease were significantly higher in the high RDW group, which were all risk factors for poor outcomes in MI patients.
Only 2 studies have evaluated the prognostic value of RDW in NSTEMI patients. Azab et al[27] explored the predictive value of RDW on short- and long-term mortality among 619 NSTEMI patients and found that RDW > 14 is an independent predictor of all-cause long-term mortality. Meanwhile, Gul et al[28] performed a prospective study including 310 patients and reported that RDW at admission was a significant predictor of adverse clinical outcomes in patients with NSTEMI and unstable angina. These findings aligned with our study, as RDW > 13.4 may serve as an independent long-term prognostic biomarker in NSTEMI patients. Furthermore, it is noteworthy that in the multivariate adjustment, we have added traditional inflammatory biomarkers, such as hs-CRP and procalcitonin, however, only RDW remains an independent predictor of all-cause mortality. To date, there is no universal reference range for RDW, mostly due to variations in RBCs size measurement methods, instrumentation, standards, and statistical approaches across different laboratories.[29] The optimal RDW cutoff value in our study was 13.4, which is lower than most other studies.[27,28] The possible explanation may be because the treatment of NSTEMI continues to evolve with the introduction of P2Y12 inhibitors, lipid-lowering therapy, and drug-eluting stents. Not only do they have anti-inflammatory properties,[30–32] but also significantly improve the prognosis of patients with NSTEMI. In accordance with this hypothesis, we compared the baseline treatment between previous studies[27,28] with ours and found that not only did most patients undergo coronary revascularization, but also the use of antiplatelet agents and statins was more frequent in our studies.
Our present study has several clinical implications. First, although previous studies have observed the relationship between RDW in NSTEMI patients, however, most of the studies were conducted before the introduction of several breakthrough findings in AMI fields, such as routine dual antiplatelet therapy, intravascular ultrasound, PCSK9 inhibitors, and polymer-free drug-eluting stent. Second, NSTEMI and STEMI have different inflammatory patterns, however, inflammatory response remains essential for cardiac repair and remodeling, thus, the prognostic value of RDW should be taken into consideration in high-risk patients. Third, the present study demonstrated that RDW could serve as a simple risk stratification tool for patients with NSTEMI due to its positive relationship with long-term all-cause mortality. Therefore, this easily accessible biomarker should be emphasized in clinical practice.
5. Limitations
There are limitations to this study that should be noted. First, this is a single-center retrospective study and thus has its limitations in nature. Second, RDW is a common but not a specific inflammatory biomarker in the NSTEMI and its level may be influenced by many factors, such as age, sex, nutritional status, genetic factors, renal function, and dyslipidemia. Other factors that may affect RDW should be considered. Third, we only analyzed RDW, hs-CRP, and procalcitonin as markers of inflammation and did not evaluate other markers, such as IL-1, IL-6, and TNF-α, which in combination may further elucidate the role of RDW in NSTEMI patients. Furthermore, we only used RDW at admission and did not do a series of examinations that could provide more information on the relationship between RDW and outcome. In addition, the sample size of our present study is relatively small, and considering that the cutoff value of RDW is based on the study sample size, more studies with large sample sizes are needed to confirm our findings.
6. Conclusions
Admission RDW could be used as a new biomarker for predicting long-term mortality in patients with NSTEMI, and high RDW was associated with an increased risk of all-cause mortality.
Author contributions
Conceptualization: Yuce Peng, Byran Richard Sasmita.
Data curation: Suxin Luo.
Formal analysis: Yuce Peng, Byran Richard Sasmita.
Funding acquisition: Yuce Peng.
Investigation: Yuce Peng, Byran Richard Sasmita.
Methodology: Yuce Peng, Byran Richard Sasmita.
Resources: Yuce Peng, Byran Richard Sasmita, Suxin Luo.
Software: Byran Richard Sasmita, Suxin Luo.
Supervision: Yuce Peng, Byran Richard Sasmita, Suxin Luo.
Validation: Yuce Peng, Suxin Luo.
Visualization: Yuce Peng, Byran Richard Sasmita.
Writing – original draft: Yuce Peng, Byran Richard Sasmita.
Writing – review & editing: Yuce Peng, Suxin Luo.
Abbreviations:
- AMI
- acute myocardial infarction
- CI
- confidence interval
- HR
- hazard ratio
- MACE
- major adverse cardiovascular events
- NSTEMI
- non-ST elevation myocardial infarction
- RBCs
- red blood cells
- RDW
- red cell distribution width
- ROC
- receiver operating characteristic
- STEMI
- ST-elevation myocardial infarction
YP and BRS contributed equally to this work.
The authors have no funding and conflicts of interest to disclose.
All methods were carried out in accordance with relevant guidelines and regulations. This study was conducted in accordance with the Declaration of Helsinki and approved by the ethics committee of the First Affiliated Hospital of Chongqing Medical University. All the participants were informed of the study content and signed informed consent.
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
How to cite this article: Peng Y, Sasmita BR, Luo S. Prognostic value of red cell distribution width in non-ST elevation myocardial infarction: A cohort study. Medicine 2024;103:12(e37461).
Contributor Information
Yuce Peng, Email: China.pyc@live.com.
Byran Richard Sasmita, Email: 1400885752@qq.com.
References
- [1].World Health Organization. The 10 leading causes of death in the world. 2011. Available at: http://who.int/mediacentre/factsheets/fs310/en/. Access date May 31, 2014.
- [2].Mechanic OJ, Gavin M, Grossman SA. Acute myocardial infarction. [Updated 2022 Aug 8]. In: StatPearls. Treasure Island (FL): StatPearls Publishing; 2023. Available at: https://www.ncbi.nlm.nih.gov/books/NBK459269/ [Google Scholar]
- [3].Chan MY, Sun JL, Newby LK, et al. Long-term mortality of patients undergoing cardiac catheterization for ST-elevation and non-ST-elevation myocardial infarction. Circulation. 2009;119:3110–7. [DOI] [PubMed] [Google Scholar]
- [4].Steg PG, Goldberg RJ, Gore JM, et al. GRACE Investigators. Baseline characteristics, management practices, and in-hospital outcomes of patients hospitalized with acute coronary syndromes in the Global Registry of Acute Coronary Events (GRACE). Am J Cardiol. 2002;90:358–63. [DOI] [PubMed] [Google Scholar]
- [5].Reed GW, Rossi JE, Cannon CP. Acute myocardial infarction. Lancet (London England). 2017;389:197–210. [DOI] [PubMed] [Google Scholar]
- [6].Förhécz Z, Gombos T, Borgulya G, et al. 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;158:659–66. [DOI] [PubMed] [Google Scholar]
- [7].Salvagno GL, Sanchis-Gomar F, Picanza A, et al. Red blood cell distribution width: a simple parameter with multiple clinical applications. Crit Rev Clin Lab Sci. 2015;52:86–105. [DOI] [PubMed] [Google Scholar]
- [8].Felker GM, Allen LA, Pocock SJ, et al. Red cell distribution width as a novel prognostic marker in heart failure: data from the CHARM program and the duke data-bank. J Am Coll Cardiol. 2007;50:40–7. [DOI] [PubMed] [Google Scholar]
- [9].Dabbah S, Hammerman H, Markiewicz W, et al. Relation between red cell distribution width and clinical outcomes after acute myocardial infarction. Am J Cardiol. 2010;105:312–7. [DOI] [PubMed] [Google Scholar]
- [10].Odhaib SA, Alhumrani AR. The effect of red cell distribution width admission value on the outcome of patients with first-ever ST-elevation Myocardial Infarction in Basrah. Cureus. 2020;12:e7373. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [11].Adamsson Eryd S, Borné Y, Melander O, et al. Red blood cell distribution width is associated with incidence of atrial fibrillation. J Intern Med. 2014;275:84–92. [DOI] [PubMed] [Google Scholar]
- [12].Cavusoglu E, Chopra V, Gupta A, et al. Relation between red blood cell distribution width (RDW) and all-cause mortality at two years in an unselected population referred for coronary angiography. Int J Cardiol. 2010;141:141–6. [DOI] [PubMed] [Google Scholar]
- [13].Wang H, Wang J, Xia J, et al. Red cell distribution width to platelet ratio predicts liver fibrosis in patients with autoimmune hepatitis. Medicine (Baltim). 2020;99:e21408. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [14].Thygesen K, Alpert JS, Jaffe AS, et al. Fourth universal definition of myocardial infarction (2018). Circulation. 2018;138:e618–51. [DOI] [PubMed] [Google Scholar]
- [15].Gilutz H, Shindel S, Shoham-Vardi I. Adherence to NSTEMI guidelines in the emergency department: regression to reality. Crit Pathw Cardiol. 2019;18:40–6. [DOI] [PubMed] [Google Scholar]
- [16].Amsterdam EA, Wenger NK, Brindis RG, et al. 2014 AHA/ACC Guideline for the Management of Patients with Non-ST-Elevation acute coronary syndromes: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014;64:e139–228. Erratum in: J Am Coll Cardiol. 2014 Dec 23;64(24):2713-4. Dosage error in article text. PMID: 25260718. [DOI] [PubMed] [Google Scholar]
- [17].Nahrendorf M, Pittet MJ, Swirski FK. Monocytes: protagonists of infarct inflammation and repair after myocardial infarction. Circulation. 2010;121:2437–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [18].Sheikh AS, Yahya S, Sheikh NS, et al. C-reactive protein as a predictor of adverse outcome in patients with acute coronary syndrome. Heart Views. 2012;13:7–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [19].Casl MT, Surina B, Glojnarić-Spasić I, et al. Serum amyloid A protein in patients with acute myocardial infarction. Ann Clin Biochem. 1995;32 (Pt 2)(Pt 2):196–200. [DOI] [PubMed] [Google Scholar]
- [20].Ridker PM, Rane M. Interleukin-6 signaling and anti-interleukin-6 therapeutics in cardiovascular disease. Circ Res. 2021;128:1728–46. [DOI] [PubMed] [Google Scholar]
- [21].Tian M, Yuan Y-C, Li J-Y, et al. Tumor necrosis factor-α and its role as a mediator in myocardial infarction: a brief review. Chronic Dis Translat Med. 2015;1:18–26. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [22].Di Stefano R, Di Bello V, Barsotti MC, et al. Inflammatory markers and cardiac function in acute coronary syndrome: difference in ST-segment elevation myocardial infarction (STEMI) and in non-STEMI models. Biomed Pharmacother. 2009;63:773–80. [DOI] [PubMed] [Google Scholar]
- [23].Hjort M, Eggers KM, Lindhagen L, et al. Differences in biomarker concentrations and predictions of long-term outcome in patients with ST-elevation and non-ST-elevation myocardial infarction. Clin Biochem. 2021;98:17–23. [DOI] [PubMed] [Google Scholar]
- [24].Tonelli M, Sacks F, Arnold M, et al. Relation between red blood cell distribution width and cardiovascular event rate in people with coronary disease. Circulation. 2008;117:163–8. [DOI] [PubMed] [Google Scholar]
- [25].Pernow J, Mahdi A, Yang J, et al. Red blood cell dysfunction: a new player in cardiovascular disease. Cardiovasc Res. 2019;115:1596–605. [DOI] [PubMed] [Google Scholar]
- [26].Macdougall IC, Cooper A. The inflammatory response and epoetin sensitivity. Nephrol Dial Transplant. 2002;17(Suppl 1):48–52. [DOI] [PubMed] [Google Scholar]
- [27].Azab B, Torbey E, Hatoum H, et al. Usefulness of red cell distribution width in predicting all-cause long-term mortality after non-ST-elevation myocardial infarction. Cardiology. 2011;119:72–80. [DOI] [PubMed] [Google Scholar]
- [28].Gul M, Uyarel H, Ergelen M, et al. The relationship between red blood cell distribution width and the clinical outcomes in non-ST elevation myocardial infarction and unstable angina pectoris: a 3-year follow-up. Coron Artery Dis. 2012;23:330–6. [DOI] [PubMed] [Google Scholar]
- [29].Jin L, Gao Y, Ye J, et al. Clinical usefulness of measuring red blood cell distribution width in patients with hepatitis B virus-related acute-on-chronic liver failure. Clin Lab. 2017;63:1403–10. [DOI] [PubMed] [Google Scholar]
- [30].Evangelista V, Manarini S, Dell’Elba G, et al. Clopidogrel inhibits platelet-leukocyte adhesion and platelet dependent leukocyte activation. Thromb Haemost. 2005;94:568–77. [PubMed] [Google Scholar]
- [31].Diamantis E, Kyriakos G, Quiles-Sanchez LV, et al. The anti-inflammatory effects of statins on coronary artery disease: an updated review of the literature. Curr Cardiol Rev. 2017;13:209–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [32].Ozer N, Tangurek B, Firat F, et al. Effects of drug-eluting stents on systemic inflammatory response in patients with unstable angina pectoris undergoing percutaneous coronary intervention. Heart Vessels. 2008;23:75–82. [DOI] [PubMed] [Google Scholar]