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02 March 2022: Clinical Research  

Retrospective Study of the Association Between Platelet-to-Lymphocyte Ratio in Patients with Acute Coronary Syndrome on Admission to a Rural Referral Center in East Java, Indonesia, and the Incidence of New Symptomatic Heart Failure at 6 Months

Ryan Enast Intan1ACEF, Firas Farisi Alkaff ORCID logo23CEF*, Yudi Her Oktaviono1D, Ricardo Adrian Nugraha ORCID logo1ACD, Tan Nicko Octora4B, Michael Jonatan ORCID logo1AF, Dimas Rio Balti5ABEF, Fani Suslina Hasibuan6B, Basuni Radi ORCID logo78D, Anwar Santoso78AD

DOI: 10.12659/MSM.935002

Med Sci Monit 2022; 28:e935002

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Abstract

BACKGROUND: This was a retrospective study conducted at a rural referral center in East Java, Indonesia, to evaluate the association between the platelet-to-lymphocyte ratio (PLR) on hospital admission and the incidence of new symptomatic heart failure (HF) within 6 months in patients with acute coronary syndrome (ACS).

MATERIAL AND METHODS: The study population consisted of all ACS patients who were hospitalized between 1 January and 31 December 2018 at a non-percutaneous coronary intervention-capable secondary referral hospital and came for a routine follow-up until 6 months afterwards. The diagnosis of new symptomatic HF was based on International Classification of Diseases 10th revision code I50.9.

RESULTS: From 126 hospitalized patients, 92 patients were included in the analysis. The incidence rate of new symptomatic HF at 6 months was 70.65%. High PLR upon initial admission was significantly associated with new symptomatic HF incidence (odds ratio=1.70, P<0.001). PLR was also able to discriminate new symptomatic HF incidence at 6 months with area under the curve of 0.83 (P=0.001). Multivariate Cox regression analysis showed that PLR was an independent predictor for new symptomatic HF incidence (hazard ratio=4.5, P=0.001).

CONCLUSIONS: In a rural center in Indonesia, the PLR was independently correlated with the onset of new symptomatic HF in patients with ACS 6 months after hospital admission. The PLR may be a supplementary biomarker for clinical outcomes in patients with ACS for use in resource-limited regions.

Keywords: acute coronary syndrome, biomarkers, Heart Failure

Background

Acute coronary syndrome (ACS) is a leading cause of mortality worldwide. It is a group of conditions caused by reduction of blood flow to the heart, including ST elevation myocardial infarction (STEMI), non-ST elevation myocardial infarction (NSTEMI), and unstable angina [1]. ACS can cause a number of complications, and one of the major complications is heart failure (HF) [2,3]. It is associated with high in-hospital mortality and poor long-term survival [4]. A population-based study in Saudi Arabian patients revealed that in-hospital mortality and cardiogenic shock were significantly higher if HF was presented [5]. A prospective cohort registry in the UK that evaluated the long-term prognosis of HF patients after ACS found that new-onset heart failure was associated with a 4-fold increased risk of death at 6 months [6]. Moreover, even in a stable condition, HF could lead to a dimnished quality of life [7]. Thus, it is very important to predict symptomatic HF occurrence in patients who survived ACS.

It has been proposed that inflammation plays an important role in both ACS and HF [8,9]. Various types of inflammatory biomarkers have been developed to predict the outcome of these diseases, such as N-terminal prohormone of brain natriuretic peptide (NT pro-BNP), high-sensitivity C-reactive protein (hs-CRP), lipoprotein-associated phospholipase A2, and homocysteine [10–12]. Unfortunately, these biomarkers are still scarce and are even unaffordable in areas with limited resources. Therefore, a biomarker that is cheap and widely available is needed.

Platelet-to-lymphocyte ratio (PLR) is a new inflammatory biomarker that can be used to predict morbidity and mortality. It can be easily calculated from complete blood count analysis. Recent studies showed that higher PLR levels reflected a more severe inflammation process and was associated with higher in-hospital and long-term mortality and major adverse cardiovascular events (MACE) in ACS patients [13–15]. Another study also found that higher PLR was corelated with worse 6-month survival rate in patients with acute HF and might be a novel marker in AHF management [16]. However, to the best of our knowledge, only a few studies have used PLR to predict HF incidence following ACS [17,18]. Therefore, this retrospective study was conducted at a rural referral center in East Java, Indonesia, to evaluate the association between the PLR on hospital admission and the incidence of HF at 6 months in patients with ACS

Material and Methods

STATISTICAL ANALYSIS:

For descriptive analysis, continuous data were given as mean±standard deviation (SD) or median [interquartile range (IQR)] depending on data distribution, while categorical data were given as n (%). For univariate analysis, the chi-square test was used for analyzing categorical data, and the independent t test or Mann-Whitney U test was used for continuous data, as appropriate. Variables with P value <0.25 from univariate analysis were then included in multivariate logistic regression analysis using the backward selection method [25]. Model calibration was tested using the Hosmer-Lemeshow test. Area under the curve (AUC) was used to determine whether PLR can be used to predict outcome incidence at 6 months or not. To determine the best PLR cut-off value, Youden Index was used [26]. Cox proportional multivariate hazards regression model was used to determine the hazard ratio of the PLR cut-off for outcome incidence at 6 months. P value <0.05 was considered as statistically significant [27]. All statistical analysis were done using SPSS for Windows version 21.0 (IBM Corp., Armonk, New York).

Results

BASELINE CHARACTERISTIC:

Patients’ baseline characteristic is presented based on the low level (<108) and high level of PLR value (≥108) based on median PLR level in this population (Table 1). Upon ACS presentation, patients with high PLR level were older and more of them had a history of hypertension compared to patients with low PLR level (P=0.041 and 0.001, respectively). Patients in the high PLR group also had higher GRACE score on admission (119±28.21 vs 102±34.68, P<0.001). All patients were treated regularly with optimal medication for ACS, including statin, anti-platelet, beta-blocker, and ace inhibitor. Thrombolytic was performed in all patients with STEMI that fulfilled the indication criteria for this procedure (34 patients), and was successful in 15 (44%) patients. Baseline TTE showed that 95% patients had diastolic dysfunction, 93% patients had LV dysfunction (LVEF <55%), and 99% patients had regional wall motion abnormality.

STUDY OUTCOMES:

There were no in-hospital outcome parameters associated with PLR (all P>0.05). For long-term outcomes, the incidence of 6-month new clinical symptomatic HF was significantly higher in patients with high PLR (odds ratio (OR)=1.70, 95% confidence interval=1.33–2.18, P<0.001) (Table 2).

The incidence rate of new clinical symptomatic HF at 6 months after ACS was 70% (65 patients). Among them, 41 patients (63%) developed early-onset symptomatic HF during the hospitalization (<7 days after ACS onset), and 24 patients (37%) developed late-onset symptomatic HF after hospitalization (≥7 days after ACS onset). The proportion of patients with diabetes mellitus or lower LVEF who develop symptomatic HF during hospitalization was significantly higher than after hospitalization (Table 3). On the contrary, the proportion of patients with hypertension who developed symptomatic HF during hospitalization was significantly lower. The PLR was significantly lower in patients who did not develop symptomatic HF compared to patients who developed acute-onset or late-onset symptomatic HF (both P<0.001) (Figure 1).

In the sub-analysis among patients with myocardial infarction, there was no significant difference between STEMI and NSTEMI in symptomatic HF incidence at 6-month follow-up (hazard ratio (HR)=1.15, 95% CI=0.81–1.57, P=0.685). However, the failure of thrombolytic treatment in STEMI-eligible patients was a significant predictor for the occurrence of symptomatic HF at 6-month follow-up (HR=1.83, 95% CI=1.07–3.14, P=0.007). Furthermore, when compared with the non-eligible and failed thrombolytic STEMI patients, those with successful thrombolytic treatments were less likely to develop new symptomatic HF in the follow-up period (50% vs 87.9% incidence rate, HR=0.24, 95% CI=0.09–0.69, P=0.015).

MULTIVARIATE ANALYSIS OF SYMPTOMATIC HF INCIDENCE PREDICTOR:

The association of different predictor variables with symptomatic HF incidence at 6 months was assessed in univariate and multivariate analyses (Table 4). In the multivariate logistic regression model for symptomatic HF incidence at 6 months after ACS, age, sex, myocardial infarction, in-hospital LV ejection fraction, circadian morning onset, admission PLR level, creatine kinase myocardial band (CKMB) level, hypertension status, smoking status, and family history of CVD were included. After adjusting for other factors, PLR level and LVEF remained significant (P<0.001). The Hosmer and Lemeshow test suggested the model had good fitness to the data (P=0.941). The model explained 65.9% (Nagelkerke R2) of the variance in symptomatic HF incidence and correctly classified 90.4% of the cases. As PLR level increase by 1 unit, the odds of having clinical symptomatic HF at 6 months also increased (adjusted OR=1.072, 95% CI=1.03–1.11, P<0.001). Meanwhile, higher baseline LVEF after ACS acted as a protective factor from symptomatic HF incidence at 6 months (adjusted OR=0.85, 95% CI=0.77–0.94, P=0.002). The final model showed that LVEF and PLR level had good discrimination for new symptomatic HF, with C statistic of 0.939 (P<0.001) (Figure 2).

PLR AS AN INDEPENDENT PREDICTOR FOR DYMPTOMATIC HF INCIDENCE AT 6 MONTHS:

PLR was able to positively discriminate new clinical symptomatic HF incidence at 6 months, with AUC of 0.83 (95% CI=0.75–0.92, P<0.001). The best cut-off for PLR level to predict new clinical symptomatic HF was 87 (sensitivity 0.89 [95% CI=0.82 to 0.94], specificity 0.63 [95% CI 0.53 to 0.72], positive likelihood ratio 2.41, positive predictive value 0.71 [95% CI=0.65 to 0.76], and negative predictive value 0.85 [95% CI=0.76 to 0.91]) (Figure 3). Using this cut-off, 58 out of 68 patients (85.3%) with PLR ≥87 had new symptomatic HF, whereas only 7 out of 24 (29.2%) with PLR <87 had new symptomatic HF (OR=2.92, 95% CI=1.55–5.50, P=0.001). Multivariate Cox regression was then used to calculate the hazard ratio for symptomatic HF incidence. After controlling for other confounding factors, PLR level ≥87 was shown to be an independent predictor of 6 months symptomatic HF incidence after ACS (HR=4.5, 95% CI=1.8–11, P=0.001) (Figure 4).

SECONDARY ANALYSIS:

In secondary analysis using Pearson correlation, PLR was positively correlated with GRACE score (p=0.004, r=0.27) and negatively correlated with LVEF (p=0.001, r=−0.39) (Figure 5).

Discussion

In this study, we found that 44.5% of the patients developed new symptomatic HF during admission. This incidence is higher than the previous study in Saudi Arabia that had 20% admission HF incidence [4]. This might be due to the long pre-hospital delay time (median 6.75 hours) in our study. Overall outcome at 6 months showed a 70% incidence rate of new clinical HF. This finding is higher than the result of the GRACE study with 17.6% and UK registry with 6.5% of new HF incidence rate at 6 months [6,28]. The high number of new HF cases in our study population might have been caused by a suboptimal revascularization therapy due to the absence of primary PCI strategy in our hospital. This hypothesis is also supported by the data from the GRACE study that showed patients with ACS who developed new HF were less likely to undergo cardiac catheterization [28].

From the onset of HF, our findings were similar to the population in the GRACE study [28], but with different risk factors. In our study, patients with early-onset HF were more likely to have diabetes and lower LVEF, and were less likely to have hypertension compared to the late-onset HF, whereas in the GRACE study, patients admitted with HF tended to be older and were more likely to have a history of coronary artery disease and atrial fibrillation [28].

From the univariate analysis, the risk factor predictor for developing new HF has some similarities to previous studies [6,28]. New findings in our study are the addition of GRACE score and biomarker from complete blood count and CKMB as a predictor of HF incidence. However, after multivariate logistic regression analysis, only LVEF and PLR remained significant as predictors of HF at 6 months after ACS. While previous studies only analyzed PLR either as an independent predictor for ACS [13–15] or acute HF outcome [16], our study is the first to show that PLR can also be used to predict symptomatic HF incidence at 6 months after ACS.

In regard to MACE outcome, many studies have analyzed PLR as an independent predictor for MACE and mortality in ACS or HF patient populations, including a meta-analysis of 8 studies involving 6627 participants [13,29–31]. Our study also showed that PLR had a weak correlation with GRACE mortality risk score at admission, which is a predictor of MACE within 6 months after ACS, similar to a previous study of STEMI patients [32]. However, the result of our study showed that MACE was not associated with higher PLR value. However, some studies reported that PLR value was a predictor of MACE in ACS patients [13,30]. These varied results might be caused by the difference in follow-up duration. While the observation period in our study was only 6 months, the follow-up duration in other studies were up to 5 years [13].

The cut-off points of PLR value for biomarker predictor risk group stratification varies across studies, depending on the disease, inflammation status, and population characteristics [13,33,34]. In our study, the cut-off PLR value for discriminating new symptomatic HF incidence at 6 months was lower than in previous studies. We hypothesized that patients who had an ACS event in a non-PCI facility or in a rural area were more prone to develop new HF at 6-month follow-up, despite optimal medication.

It is still not clear how the exact mechanism of PLR level is associated with adverse outcomes in ACS patients. Higher PLR value has been reported to be correlated with increased inflammatory activity and prothrombotic status due to relative thrombocytosis and megakaryocytic proliferation in ACS patients [29,35]. Furthermore, lower lymphocyte level due to increased cortisol in response to physiological stress can also be correlated with worse clinical outcomes in ACS patients [36]. Hence, PLR calculated from numbers of platelets and lymphocytes may be a novel indicator of inflammatory and prothrombotic status, and might be utilized to predict the outcome of ACS, including HF development. The mechanism involved in this includes the release of proteolytic enzymes and pro-inflammatory cytokines, secondary to the increased inflammatory state in ACS [37,38]. A previous study has shown that higher PLR is positively correlated with increased inflammatory cytokines such as interleukin-1 (IL-1), interleukin-6 (IL-6), and tumor necrotizing factor-α [39], which can damage the myocardium, resulting in reduced left ventricular function and HF [40]. High levels of pro-inflammatory cytokines might also lead to myocardial remodeling and cardiac arrhythmia, resulting in cardiac dysfunction [41]. Our secondary analysis also shows that PLR is negatively correlated with LVEF during hospitalization, which may affect the mechanism of HF development. This finding was in line with a previous study by Bekler et al (2015), which showed that high PLR was associated with left ventricular systolic dysfunction (LVEF <40%) in NSTEMI patients [17].

There were several limitations to this study. This study was a single-center study conducted in a rural area, which may be different than in an urban clinical setting. The relatively small number of samples compared to other studies might have caused several potential biases. The follow-up period was only 6 months, which may also have contributed to the non-significant result for MACE. Another limitation was that we had no comparable data on other biomarkers such as hs-CRP, IL-1, IL-6, or NT pro-BNP. Analysis of the correlation of PLR with these substances may have strengthened our study. However, despite the limitations, this is the first study to evaluate use of PLR as an independent predictor for new symptomatic HF after ACS.

Conclusions

In a rural center in Indonesia, PLR was independently correlated with the onset of symptomatic heart failure in patients with ACS 6 months after hospital admission. PLR may be a supplementary biomarker for clinical outcomes in patients with ACS for use in resource-limited regions. In patients with high PLR level, strict control of cardiovascular risk factors, routine follow-up visits, and close monitoring should be encouraged.

Figures

Figure 1. Platelet-to-lymphocyte ratio (PLR) difference based on heart failure (HF) onset. Acute-onset=HF occurred during the hospitalization and/or <7 days after acute coronary syndrome (ACS); Late-onset=HF occurred later after ACS hospitalization (≥7 days-6 months). P value <0.05 (*) was considered statistically significant, and P value >0.05 (NS) was considered not statistically significant. Figure 2. Receiver operating characteristic curve of the final model consist of left ventricular ejection fraction and platelet-to-lymphocyte ratio to predict heart failure incidence at 6 months after acute coronary syndrome. AUC – area under curve. Figure 3. Receiver operating characteristic curve of platelet-to-lymphocyte ratio (PLR) as a predictor of heart failure (HF) incidence at 6 months. The best cut-off for PLR to discriminate 6-month HF is 87 (Sensitivity=89%, Specificity=63%, Odds ratio=2.92, 95% confidence interval 1.55–5.50, P<0.001). AUC – area under curve. Figure 4. Multivariate Cox regression hazard function analysis showing differences between patients with high and low platelet-to-lymphocyte ratio (PLR) associated with the outcome of HF occurring within 6 months after hospital admission for acute coronary syndrome. The hazard ratio for high PLR (PLR ≥87) is 4.5 (95% confidence interval=1.8–11, P=0.001). Figure 5. Scatter plots showing the association between (A) platelet-to-lymphocyte ratio (PLR) and Global Registry of Acute Coronary Events score (GRACE) risk score, (B) PLR and left ventricular ejection fraction (LVEF).

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Figures

Figure 1. Platelet-to-lymphocyte ratio (PLR) difference based on heart failure (HF) onset. Acute-onset=HF occurred during the hospitalization and/or <7 days after acute coronary syndrome (ACS); Late-onset=HF occurred later after ACS hospitalization (≥7 days-6 months). P value <0.05 (*) was considered statistically significant, and P value >0.05 (NS) was considered not statistically significant.Figure 2. Receiver operating characteristic curve of the final model consist of left ventricular ejection fraction and platelet-to-lymphocyte ratio to predict heart failure incidence at 6 months after acute coronary syndrome. AUC – area under curve.Figure 3. Receiver operating characteristic curve of platelet-to-lymphocyte ratio (PLR) as a predictor of heart failure (HF) incidence at 6 months. The best cut-off for PLR to discriminate 6-month HF is 87 (Sensitivity=89%, Specificity=63%, Odds ratio=2.92, 95% confidence interval 1.55–5.50, P<0.001). AUC – area under curve.Figure 4. Multivariate Cox regression hazard function analysis showing differences between patients with high and low platelet-to-lymphocyte ratio (PLR) associated with the outcome of HF occurring within 6 months after hospital admission for acute coronary syndrome. The hazard ratio for high PLR (PLR ≥87) is 4.5 (95% confidence interval=1.8–11, P=0.001).Figure 5. Scatter plots showing the association between (A) platelet-to-lymphocyte ratio (PLR) and Global Registry of Acute Coronary Events score (GRACE) risk score, (B) PLR and left ventricular ejection fraction (LVEF).

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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750