Vol 30, No 1 (2023)
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Incremental predictive value of the combined use of the neutrophil-to-lymphocyte ratio and systolic blood pressure difference after successful drug-eluting stent implantation

Yong Hoon Kim1, Ae-Young Her1, Scot Garg2, Eun-Seok Shin3
Pubmed: 33438177
Cardiol J 2023;30(1):91-104.

Abstract

Background: Previous work has highlighted the importance of the neutrophil-to-lymphocyte ratio (NLR) and the difference in the ward-to-catheterization laboratory systolic blood pressure (ΔSBP) in prognostic stratification after acute coronary syndrome. However, there is paucity of data regarding the added value of combining these two variables to predict 5-year major clinical outcomes after percutaneous coronary intervention. Methods: A total of 1188 patients were classified into four groups according to the NLR and ΔSBP (high vs. low) using cutoffs derived from an analysis of receiver operating characteristic curves. A NLR > 3.0 and a ΔSBP > 25 mmHg were considered high values. The primary endpoint was the composite of all-cause death, cardiac death, and non-fatal myocardial infarction. The secondary endpoint was the composite of target lesion revascularization, target vessel revascularization, and incidence of cerebrovascular accidents. Results: The incidence of the primary endpoint was significantly higher in the high NLR and ΔSBP group than in the other three groups (2.2% vs. 4.7% vs. 4.3% vs. 13.2%, p < 0.001). The incidence of the secondary endpoint was similar among the four groups. Incorporation of high NLR and high ΔSBP into a model with conventional and meaningful clinical and procedural risk factors increased the C-statistics in predicting the primary endpoint (0.575 to 0.635, p = 0.002). Conclusions: The power to predict the primary endpoint after drug-eluting stent implantation at the 5-year follow-up was improved by combining NLR and ΔSBP.

clinicAL CARDIOLOGY

original article

Cardiology Journal

2023, Vol. 30, No. 1, 91–104

DOI: 10.5603/CJ.a2021.0004

Copyright © 2023 Via Medica

ISSN 1897–5593

eISSN 1898018X

Incremental predictive value of the combined use of the neutrophil-to-lymphocyte ratio and systolic blood pressure difference after successful drug-eluting stent implantation

Yong Hoon Kim*1Ae-Young Her*1Scot Garg2Eun-Seok Shin3
1Division of Cardiology, Department of Internal Medicine, Kangwon National University School of Medicine, Chuncheon, Republic of Korea
2East Lancashire Hospitals NHS Trust, Blackburn, Lancashire, United Kingdom
3Division of Cardiology, Department of Internal Medicine, Ulsan University Hospital, Ulsan, Republic of Korea

Address for correspondence: Eun-Seok Shin, MD, PhD, 877, Bangeojinsunhwando-ro, Dong-gu, Ulsan 44033, Republic of Korea, tel: +82 52 250 7000, e-mail: sesim1989@gmail.com

Received: 1.03.2020 Accepted: 31.12.2020 Early publication date: 11.01.2021

*The first two authors (Yong Hoon Kim and Ae-Young Her) are equally contributed to this work.

This article is available in open access under Creative Common Attribution-Non-Commercial-No Derivatives 4.0 International (CC BY-NC-ND 4.0) license, allowing to download articles and share them with others as long as they credit the authors and the publisher, but without permission to change them in any way or use them commercially.

Abstract
Background: Previous work has highlighted the importance of the neutrophil-to-lymphocyte ratio (NLR) and the difference in the ward-to-catheterization laboratory systolic blood pressure (DSBP) in prognostic stratification after acute coronary syndrome. However, there is paucity of data regarding the added value of combining these two variables to predict 5-year major clinical outcomes after percutaneous coronary intervention.
Methods: A total of 1188 patients were classified into four groups according to the NLR and DSBP (high vs. low) using cutoffs derived from an analysis of receiver operating characteristic curves. A NLR > 3.0 and a DSBP > 25 mmHg were considered high values. The primary endpoint was the composite of all-cause death, cardiac death, and non-fatal myocardial infarction. The secondary endpoint was the composite of target lesion revascularization, target vessel revascularization, and incidence of cerebrovascular accidents.
Results: The incidence of the primary endpoint was significantly higher in the high NLR and DSBP group than in the other three groups (2.2% vs. 4.7% vs. 4.3% vs. 13.2%, p < 0.001). The incidence of the secondary endpoint was similar among the four groups. Incorporation of high NLR and high DSBP into a model with conventional and meaningful clinical and procedural risk factors increased the C-statistics in predicting the primary endpoint (0.575 to 0.635, p = 0.002).
Conclusions: The power to predict the primary endpoint after drug-eluting stent implantation at the 5-year follow-up was improved by combining NLR and DSBP. (Cardiol J 2023; 30, 1: 91–104)
Key words: blood pressure difference, drug-eluting stent, neutrophil-to-lymphocyte ratio, outcomes, percutaneous coronary intervention

Introduction

The fundamental mechanism of coronary artery disease (CAD) is stenosis caused by inflammation and atherosclerosis [1]. The earliest type of atherosclerotic lesion observed is a pure inflammatory lesion composed mainly of monocyte-derived macrophages and T-lymphocytes [2]. Arbel et al. [3] demonstrated that a high neutrophil-to-lymphocyte ratio (NLR) is significantly associated with higher rates of 5-year mortality; therefore, the NLR could potentially be used to formulate prognosis in addition to conventional risk factors. Many other reports also emphasize the valuable role of NLR in CAD [4–6].

Another important causative factor of CAD is systolic arterial hypertension, which is associated with adverse cardiac events including such as death; as it is also associated with stress and other known psychosocial risk factors for CAD, it elevates the risk of cardiovascular sequelae [7, 8]. Coronary angiography (CAG) or percutaneous coronary intervention (PCI) may be accompanied by stress in healthy individuals. A meta-analysis suggested that greater responsiveness to acute mental stress has an adverse effect on future cardiovascular risk status; for example, a composite of elevated blood pressure (BP), increased left ventricular mass, subclinical atherosclerosis, and clinical cardiac events [9]. It would be very informative if we could predict long-term prognosis of the patients who are going to have CAG or PCI before these operations begin. Her et al. [10] suggested that changes in peri-procedural BP may be significantly associated with major adverse cardiac events and reported that a difference in the ward-to-cathe­terization laboratory systolic BP (SBP) (DSBP) of > 20 mmHg was related to an increased rate of all-cause death and cardiac death (CD) after drug--eluting stent (DES) implantation. In actual clinical practice, minimally invasive or non-invasive, inexpensive diagnostic tools are preferred over invasive diagnostic tools in view of cost and patient safety [11]. In this regard, the NLR and DSBP are very useful non-invasive diagnostic tools for predicting adverse cardiac events. However, most previous studies [4–6, 10, 12] focused only on one of those two parameters. Moreover, data showing the complementary actions and combined usefulness of NLR and DSBP in patients diagnosed with acute coronary syndrome (ACS) are limited. Therefore, we investigated the additional predictive power of the NLR and DSBP in comparison with that of conventional clinical and procedural risk factors in predicting ٥-year major clinical outcomes after DES implantation.

Methods

Study design and population

This retrospective observational study enrolled 2023 consecutive eligible patients who underwent PCI for ACS between September 2002 and August 2007 at the Cardiovascular Center of Ulsan University Hospital, Ulsan, South Korea. Data on cardiovascular risk factors and medical histories were self-reported by the patients. Patients were excluded if they had (1) stable angina (n = 171, 8.5%); (2) any systemic diseases or treatment modality potentially affecting the white blood cells as shown in Figure 1 (n = 602, 29.8%); and (3) missing data or patients were lost to follow-up (n = 62, 3.1%). Finally, 1188 patients were included in the study (Fig. 1). The study protocol complied with the ethical guidelines of the 1975 Declaration of Helsinki and was approved by the Ulsan University Hospital Institutional Review Board. Written informed consent was obtained from all participants. The enrolled patients were required to visit the cardiology out-patient department at the end of the first month and every 3 to 6 months thereafter for 5 years for clinical follow-up data to be collected through face-to-face interviews, medical chart reviews, and telephone contact.

Figure 1. Flow chart; CABG — coronary artery bypass grafting; CKD — chronic kidney disease; CVA — cardiovascular accidents; NLR — neutrophil-to-lymphocyte ratio; PCI — percutaneous coronary intervention; DSBP — differences in ward-to-catherization laboratory systolic blood pressure.
Study method and medical treatment

During admission, venous blood samples were taken to assess the following: (1) complete blood cell counts, which included total white blood cells, differential counts (neutrophils, lymphocytes, monocytes, eosinophils), red blood cell, and platelet counts; (2) cardiac enzymes (creatine kinase myocardial band [CK-MB] and cardiac troponin T); and (3) other blood chemistry parameters (high-sensitivity C-reactive protein, serum creatinine, estimated glomerular filtration rate [eGFR], and lipid profiles). NLR was defined as the ratio of the neutrophil count to the lymphocyte count. The method of BP measurement has been described previously [10]. In brief, patients were measured with the resting right arm BP in the supine position in a ward setting, before transfer to the catheterization laboratory, where it was measured again after the patients had laid down on the catheterization laboratory table prior to any arterial puncture or PCI (ward--to-catheterization laboratory BP difference). Differences were estimated in the systolic and diastolic BP and heart rate obtained in the ward and catheterization laboratory, with the measurements taken by trained nurses using an auscultatory sphygmomanometer. The BP and heart rate were measured twice for each location, with at least a 1-minute interval between recordings and the mean values were used in the analysis. The stents were deployed using standard PCI techniques [13]. A successful PCI was defined as an angiographic residual stenosis diameter of < 30% in the presence of thrombolysis in myocardial infarction (TIMI) grade 3 flow. After DES implantation, a minimum of 1 year of dual antiplatelet therapy was administered (100 mg acetylsalicylic acid daily and 75 mg clopidogrel daily).

Study definitions and clinical endpoints

The primary endpoint was the composite of all-cause death, CD, and non-fatal myocardial infarction (MI). The secondary endpoint was the composite of target lesion revascularization (TLR), target vessel revascularization (TVR), and cerebrovascular accidents at 5-year follow-up. All-cause death was defined as either of CD or non-CD. Non-fatal MI was defined as the presence of clinical symptoms, electrocardiographic changes, or abnormal imaging findings of MI, combined with an increase in the CK-MB fraction above the upper normal limits or an increase in troponin-T/troponin-I to greater than the 99th percentile of the upper normal limit after index PCI [14–16]. The definitions of TLR and TVR have been previously described [17]. The mean eGFR was calculated using the Modification of Diet in Renal Disease equation [18].

Statistical analysis

All statistical analyses were performed using SPSS v20 (IBM; Armonk, NY, USA). For continuous variables, differences among the three groups were evaluated using the analysis of variance or the Jonckheere-Terpstra test, and post-hoc analysis between the two groups was carried out using the Hochberg test or Dunnett-T3 test. Data are expressed as the means ± standard deviations. For discrete variables, the differences between two out of the three groups were analyzed using the c2 test or the Fisher exact test, as appropriate; data are expressed as counts and percentages [19]. In a multivariable Cox proportional hazard regression analysis, the baseline confounding covariates were selected if they were significantly different (p < 0.001) among the four groups or between the two groups or had predictive values. The multivariable Cox proportional hazard regression analysis including baseline confounding factors, was used to compare the clinical endpoints among the four groups or between the two groups. Survival analysis among the four groups was performed using the Kaplan--Meier method, and differences between the two groups were assessed using the log-rank test. Receiver operating characteristic (ROC) curves were used to differentiate the ability of the NLR and DSBP to predict primary endpoint (Fig. 2). After evaluating the relationship of the NLR and DSBP with the clinical outcomes using Cox proportional hazard regression analysis, we compared the incremental value of combining a high NLR and a high DSBP into the context of conventional and meaningful clinical and procedural characteristics for prediction of the primary endpoint. Estimates of the C-statistics for the Cox regression models were computed using the method of Pencina and D’Agostino [20]. Differences in the C-statistics (with 95% confidence interval [CI]) after the addition of the high NLR and high DSBP to a model with conventional and meaningful clinical and procedural risk factors were obtained using the bootstrap percentile method (200 replicates) [21]. The statistical significance level was set at a p-value of < 0.05 using a two-tailed test.

Figure 2. Receiver operating characteristic curve of neutrophil-to-lymphocyte ratio (NLR) and difference in the ward--to-catheterization laboratory systolic blood pressure (DSBP) for primary endpoints.

Results

Cutoff values for the NLR and DSBP

Analysis of the ROC curve was performed to detect the best NLR cutoff value that predicted the primary endpoint. This yielded a cutoff NLR of 3.0, with a sensitivity of 60.0%, a specificity of 47.2%, and an area under the ROC curve of 0.605 (95% CI: 0.518–0.682) (Fig. 2). Therefore, NLRs of > 3.0 (n = 253, 21.3%) and ≤ 3.0 (n = 935, 78.7%) were considered to be high and low values, respectively. Thereafter, the subjects were sub-divided according to DSBP. In the same manner, an ROC curve analysis was used to detect the best cutoff value of DSBP for predicting the primary endpoint. This yielded a cutoff DSBP of 25 mmHg, with a sensitivity of 63.0%, a specificity of 52.0%, and an area under the ROC curve of 0.612 (95% CI: 0.528–0.691; Fig. 2). DSBP > 25 mmHg (n = 474, 39.9%) and ≤ 25 mmHg (n = 714, 60.1%) were considered to be high and low values, respectively. The patients were classified into four groups according to the NLR and DSBP (high vs. low) using the cutoffs derived from the analysis of the ROC curves: group A (low NLR [≤ 3.0] and low DSBP [≤ 25 mmHg], n = 552, 46.5%), group B (high DSBP [> 25 mmHg] and low NLR [≤ 3.0], n = 383, 32.2%), group C (high NLR [> 3.0] and low DSBP [≤ 25 mmHg], n = 162, 13.6%), and group D (high DSBP [> 25 mmHg] and high NLR [> 3.0], n = 91, 7.7%).

Baseline clinical and angiographic characteristics

The baseline laboratory and angiographic characteristics according to the NLR and DSBP are summarized in Table 1. The mean age of the total study population was 60.5 ± 10.3 years, and the oldest patient was included in group D. The mean left ventricular ejection fraction (LVEF) of the total study population was 61.6 ± 10.3%. The number of patients with hypertension and unstable angina was the highest in group B. The number of acute MI was the highest in group C. The mean serum creatinine level was the highest in group D. However, the number of patients with a history of diabetes mellitus, dyslipidemia, and MI; number with left anterior descending artery, left circumflex artery, and right coronary artery as the treated vessels; American College of Cardiology/ /American Heart Association lesion type; extent of CAD; number of deployed stents; mean diameter of deployed stents; and mean length of deployed stents were not significantly different among the four groups.

Table 1. Baseline clinical, laboratory, angiographic and procedural characteristics.

Variables

Group A

Low NLR ≤ 3.0

Low DSBP
≤ 25 mmHg
(n = 552)

Group B

High DSBP
> 25 mmHg

Low NLR ≤ 3.0
(n = 383)

Group C

High NLR > 3.0

Low DSBP
≤ 25 mmHg
(n = 162)

Group D

High DSBP
> 25 mmHg

High NLR > 3.0
(n = 91)

P value

Group A vs. B

Group A vs. C

Group A vs. D

Group
B vs. C

Group
B vs. D

Group C vs. D

Group A vs. B
vs. C vs. D

Men

164 (29.7%)

168 (43.9%)

47 (29.0%)

32 (35.2%)

< 0.001

0.922

0.326

0.001

0.156

0.325

< 0.001

Age [years]

58.9 ± 10.1

62.4 ± 9.9

60.2 ± 11.5

62.8 ± 10.0

< 0.001

0.214

0.001

0.039

0.704

0.063

< 0.001

BMI [kg/m2]

24.4 ± 2.9

24.7 ± 3.0

24.0 ± 3.2

23.9 ± 3.3

0.066

0.271

0.280

0.023

0.046

0.837

0.036

Hypertension

248 (44.9%)

228 (59.5%)

78 (46.9%)

52 (57.1%)

< 0.001

0.655

0.032

0.008

0.722

0.149

< 0.001

Diabetes mellitus

137 (24.8%)

91 (23.8%)

38 (23.5%)

21 (23.0%)

0.757

0.756

0.793

0.939

0.890

0.945

0.956

Dyslipidemia

170 (30.8%)

136 (35.5%)

40 (24.7%)

28 (30.8%)

0.137

0.142

0.996

0.016

0.462

0.305

0.090

Previous MI

9 (1.6%)

8 (2.1%)

4 (2.5%)

2 (2.2%)

0.623

0.505

0.660

0.756

0.948

0.892

0.898

Current smokers

114 (20.7%)

79 (20.6%)

18 (11.1%)

16 (17.6%)

0.992

0.006

0.574

0.007

0.563

0.179

0.041

Unstable angina

420 (76.1%)

308 (80.4%)

67 (41.3%),

53 (58.2%)

0.128

< 0.001

0.001

< 0.001

< 0.001

0.013

< 0.001

NSTEMI

87 (15.8%)

52 (13.6%)

55 (34.0%),

21 (23.1%)

0.400

< 0.001

0.096

< 0.001

0.035

0.086

< 0.001

STEMI

45 (8.1%)

23 (6.0%)

40 (24.7%),

17 (18.7%)

0.249

< 0.001

0.003

< 0.001

< 0.001

0.347

< 0.001

LVEF [%]

62.1 ± 6.9

62.2 ± 6.7

59.3 ± 8.4,

60.0 ± 7.3

0.866

< 0.001

0.010

< 0.001

0.009

0.487

< 0.001

White blood cell109/L]:

7.5 ± 2.3

7.3 ± 2.2

10.2 ± 3.3,

9.6 ± 3.2

0.040

< 0.001

< 0.001

< 0.001

< 0.001

0.160

< 0.001

Neutrophil [%]

54.9 ± 8.4

54.8 ± 8.5

75.5 ± 6.3,

75.1 ± 7.2

0.787

< 0.001

< 0.001

< 0.001

< 0.001

0.634

< 0.001

Lymphocyte [%]

33.9 ± 7.7

34.0 ± 7.7

16.6 ± 4.4,

16.7 ± 4.4

0.816

< 0.001

< 0.001

< 0.001

< 0.001

0.923

< 0.001

Monocyte [%]

5.8 ± 2.4

5.6 ± 2.0

4.6 ± 2.2,

4.4 ± 1.8

0.096

< 0.001

< 0.001

< 0.001

< 0.001

0.400

< 0.001

Eosinophil [%]

3.3 ± 3.0

3.4 ± 3.0

1.9 ± 1.7,

2.4 ± 2.8

0.949

< 0.001

0.003

< 0.001

0.003

0.100

< 0.001

Hemoglobin [g/dL]

14.0 ± 1.6

13.4 ± 1.7

13.9 ± 2.8

13.1 ± 2.1

< 0.001

0.690

< 0.001

0.062

0.110

0.009

< 0.001

Hematocrit [%]

41.0 ± 15.6

38.9 ± 4.7

39.8 ± 4.9

38.0 ± 6.0

0.003

0.119

0.001

0.042

0.158

0.011

0.011

Platelet [×109/L]

254.8 ± 67.9

256.4 ± 62.6

260.8 ± 82.0

258.1 ± 73.5

0.716

0.402

0.692

0.546

0.839

0.792

0.803

NLR

1.75 ± 0.60

1.74 ± 0.59

5.26 ± 3.20

5.41 ± 3.31

0.754

< 0.001

< 0.001

< 0.001

< 0.001

0.735

< 0.001

Ward SBP [mmHg]

131.3 ± 18.8

126.3 ± 19.5

125.4 ± 18.4

125.3 ± 19.0

< 0.001

0.001

0.006

0.598

0.644

0.972

< 0.001

Cath lab SBP [mmHg]

140.0 ± 20.6

162.3 ± 24.4

131.4 ± 21.0

159.4 ± 22.4

< 0.001

< 0.001

< 0.001

< 0.001

0.277

< 0.001

< 0.001

DSBP [mmHg]

12.4 ± 7.5

42.2 ± 14.5

11.1 ± 7.5†

41.1 ± 12.3

< 0.001

0.060

< 0.001

< 0.001

0.462

< 0.001

< 0.001

hs-CRP [mg/dL]

0.55 ± 2.09

0.40 ± 0.93

1.79 ± 3.55

1.51 ± 3.23

0.234

< 0.001

0.019

< 0.001

0.006

0.581

< 0.001

Serum creatinine [mg/L]

1.1 ± 0.3

1.1 ± 0.2

1.1 ± 0.4

1.3 ± 1.1

0.389

0.170

0.028

0.064

0.021

0.072

< 0.001

eGFR [mL/min/1.73 m2]

71.4 ± 16.0

68.6 ± 14.8

70.2 ± 16.6

64.7 ± 19.5

0.005

0.423

0.002

0.272

0.078

0.024

0.001

Peak CK-MB [mg/dL]

18.7 ± 2.9

14.7 ± 2.8

78.1 ± 8.0

68.4 ± 5.6

0.166

< 0.001

0.003

< 0.001

0.001

0.632

< 0.001

Peak troponin-T [ng/mL]

0.87 ± 0.15

0.71 ± 0.09

3.88 ± 1.65

2.46 ± 0.43

0.311

< 0.001

0.007

< 0.001

0.003

0.064

< 0.001

Total cholesterol [mg/dL]

193.5 ± 39.3

195.1 ± 44.2

192.0 ± 43.4

186.3 ± 42.5

0.569

0.693

0.133

0.449

0.080

0.311

0.329

Triglyceride [mg/L]

150.0 ± 86.1

151.9 ± 101.7

129.7 ± 87.9

116.2 ± 62.0

0.777

0.012

< 0.001

0.013

< 0.001

0.162

0.001

HDL cholesterol [mg/L]

41.8 ± 16.2

41.5 ± 10.3

46.0 ± 53.4

40.8 ± 53.4

0.702

0.324

0.480

0.294

0.626

0.245

0.184

LDL cholesterol [mg/L]

114.5 ± 36.1

117.3 ± 37.7

111.0 ± 34.7

110.1 ± 34.7

0.286

0.276

0.281

0.070

0.093

0.855

0.194

Discharge medications:

Acetylsalicylic acid

548 (99.3%)

378 (98.7%)

162 (100.0%)

89 (97.8%)

0.499

0.579

0.203

0.329

0.624

0.128

0.265

Clopidogrel

544 (98.6%)

377 (98.4%)

161 (99.4%)

90 (98.9%)

0.885

0.692

0.792

0.680

0.740

0.678

0.832

Beta-blocker

220 (39.9%)

152 (39.7%)

56 (34.6%)

42 (46.2%)

0.959

0.234

0.300

0.289

0.286

0.081

0.339

CCB

121 (21.9%)

97 (25.3%)

36 (22.2%)

24 (26.4%)

0.239

0.915

0.345

0.513

0.894

0.538

0.564

Nitrate

266 (1.7%)

186 (1.6%)

60 (2.1%)

47 (2.3%)

0.947

0.015

0.572

0.014

0.641

0.034

0.047

ACEI

177 (32.1%)

143 (37.3%)

43 (26.5%)

33 (36.3%)

0.107

0.208

0.469

0.018

0.904

0.117

0.076

ARB

53 (9.6%)

45 (11.7%)

33 (20.4%)

16 (17.6%)

0.329

0.001

0.028

0.011

0.162

0.623

0.001

Lipid lowering agents

452 (81.9%)

314 (82.0%)

140 (86.4%)

70 (76.9%)

0.969

0.193

0.251

0.258

0.298

0.058

0.290

Treated vessels:

LAD

252 (45.7%)

197 (51.4%)

80 (49.4%)

51 (56.0%)

0.084

0.421

0.070

0.708

0.484

0.359

0.159

LCx

204 (37.0%)

130 (33.0%)

61 (37.7%)

32 (35.2%)

0.367

0.926

0.815

0.433

0.902

0.786

0.767

RCA

224 (1.7%)

146 (1.6%)

54 (2.1%)

35 (2.3%)

0.456

0.100

0.731

0.331

0.952

0.415

0.416

Left main

24 (4.3%)

20 (5.2%)

3 (1.9%)

4 (4.4%)

0.534

0.166

0.983

0.101

0.747

0.255

0.368

ACC/AHA lesion type:

Type B1

102 (18.5%)

77 (20.1%)

21 (13.0%)

19 (20.9%)

0.555

0.124

0.565

0.047

0.885

0.108

0.231

Type B2

159 (28.8%)

123 (32.1%)

52 (32.1%)

32 (35.2%)

0.278

0.434

0.218

0.997

0.619

0.677

0.516

Type C

114 (20.7%)

88 (23.0%)

30 (18.5%)

20 (22.0%)

0.419

0.280

0.781

0.258

0.890

0.515

0.670

Extent of CAD:

1 vessel disease

439 (79.5%)

310 (80.9%)

134 (82.7%)

68 (74.7%)

0.618

0.432

0.332

0.718

0.193

0.143

0.454

2 vessel disease

75 (13.0%)

42 (11.0%)

20 (12.3%)

15 (16.5%)

0.269

0.793

0.514

0.659

0.153

0.448

0.458

≥ 3 vessel disease

38 (6.9%)

31 (8.1%)

8 (4.9%)

8 (8.8%)

0.526

0.468

0.510

0.209

0.832

0.283

0.545

Number of stent

1.53 ± 0.81

1.58 ± 0.85

1.50 ± 0.76

1.72 ± 0.91

0.501

0.821

0.158

0.522

0.306

0.186

0.438

Stent diameter [mm]

3.19 ± 0.32

3.16 ± 0.30

3.18 ± 0.31

3.49 ± 2.77

0.305

0.789

0.441

0.741

0.398

0.423

0.104

Stent length [mm]

40.3 ± 25.1

41.3 ± 23.9

40.4 ± 21.9

45.6 ± 28.1

0.638

0.967

0.206

0.794

0.310

0.297

0.554

Median follow-up
duration [days]

1889.5 ± 828.5

1987.5 ± 870.5

1901.5 ± 791.8

1895.5 ± 948.5

0.100

0.412

0.542

0.114

0.112

0.640

0.132

Clinical outcomes

The cumulative incidence of the primary endpoint, all-cause death, CD, and secondary endpoint are summarized in Table 2, Figure 3, and Supplemental Online Material 1. After adjustment, the cumulative incidence of the primary endpoint in group D was significantly higher than that in group A (adjusted hazard ratio [aHR]: 1.920; 95% CI: 1.462–2.522; p < 0.001), group B (aHR: 1.751; 95% CI: 1.186–2.584; p = 0.005), and group C (aHR: 3.514; 95% CI: 1.481–9.640; p = 0.015) (Table 2, Fig. 3A). Similarly, the cumulative incidence of all-cause death was significantly higher in group D than in group A (aHR: 2.466; 95% CI: 1.721–3.532; p < 0.001), group B (aHR: 1.767; 95% CI: 1.168–2.603; p = 0.007), and group C (aHR: 3.191; 95% CI: 1.065–9.557; p = 0.038) (Table 2, Fig. 3B). The cumulative incidence of CD in group D was significantly higher than that in group A (aHR: 3.394; 95% CI: 1.627–7.079; p = 0.001) and group B (aHR: 3.185; 95% CI: 1.228–7.014; p = 0.017) (Table 2, Fig. 3C). The cumulative incidence of secondary endpoint was not significantly different among the four groups (Suppl. Online Material 1, Fig. 3D). Table 3 summarizes improvements in C-statistics in predicting the primary endpoint when high NLR and high DSBP were added into the model with conventional and meaningful clinical and procedural risk factors. The addition of high NLR and high DSBP led to significant improvements in C-statistics from 0.575 to 0.602 (p = 0.017) and 0.622 (p = 0.004), respectively. However, the greatest improvement in C-statistics was seen when both high NLR and high DSBP were combined into the model, with C-statistics increasing significantly to 0.635 (p = 0.002). Table 4 shows independent predictors of the primary and secondary endpoints at 5 years. Low LVEF (< 50%), hypertension, lymphocyte count, and catheterization laboratory SBP were found to be meaningful independent predictors of the primary endpoint. Additionally, diabetes mellitus was found to be a meaningful independent predictor of the secondary endpoint.

Table 2. Comparison of clinical outcomes at 5 years.

Outcomes

Group A
(n = 552)

Group D
(n = 91)

Log-rank

Unadjusted
HR (95% CI)

P value

Adjusted*
HR (95% CI)

P value

Primary endpoint:

12 (2.3%)

12 (13.9%)

< 0.001

1.869 (1.431–2.440)

< 0.001

1.920 (1.462–2.522)

< 0.001

All-cause death

5 (1.0%)

11 (12.7%)

< 0.001

2.420 (1.701–3.442)

< 0.001

2.466 (1.721–3.532)

< 0.001

Cardiac death

1 (0.2%)

5 (5.8%)

< 0.001

3.170 (1.550–6.486)

0.002

3.394 (1.627–7.079)

0.001

Non-fatal MI

7 (1.4%)

1 (1.4%)

0.970

1.041 (0.128–8.456)

0.970

1.061 (0.522–9.160)

0.869

Secondary endpoint:

52 (10.1%)

10 (12.8%)

0.464

1.088 (0.868–1.363)

0.466

1.121 (0.891–1.411)

0.330

TLR

39 (7.5%)

7 (9.4%)

0.705

1.053 (0.805–1.377)

0.706

1.111 (0.847–1.459)

0.446

TVR

49 (9.9%)

8 (11.2%)

0.780

1.036 (0.807–1.329)

0.783

1.086 (0.843–1.401)

0.522

CVA

3 (0.6%)

2 (2.4%)

0.083

1.623 (0.894–2.946)

0.112

1.765 (0.936–3.329)

0.079

Outcomes

Group B
(n = 383)

Group D
(n = 91)

Log–rank

Unadjusted
HR (95% CI)

P value

Adjusted*
HR (95% CI)

P value

Primary endpoint:

18 (5.1%)

12 (13.9%)

0.003

1.704 (1.182–2.455)

0.004

1.751 (1.186–2.584)

0.005

All-cause death

15 (4.3%)

11 (12.7%)

0.002

1.782 (1.208–2.630)

0.004

1.767 (1.168–2.603)

0.007

Cardiac death

2 (0.5%)

5 (5.8%)

< 0.001

3.271 (1.441–7.426)

0.005

3.185 (1.228–7.014)

0.017

Non-fatal MI

3 (0.8%)

1 (1.4%)

0.754

1.197 (0.386–3.712)

0.755

1.469 (0.407–5.308)

0.557

Secondary endpoint:

37 (10.8%)

10 (12.8%)

0.615

1.094 (0.771–1.551)

0.616

1.171 (0.811–1.692)

0.399

TLR

24 (6.9%)

7 (9.4%)

0.598

1.120 (0.735–1.706)

0.598

1.129 (0.725–1.759)

0.591

TVR

32 (10.0%)

8 (11.2%)

0.764

1.060 (0.719–1.561)

0.769

1.127 (0.750–1.694)

0.565

CVA

6 (1.8%)

2 (2.4%)

0.628

1.217 (0.547–2.709)

0.630

1.434 (0.616–3.342)

0.403

Outcomes

Group C
(n = 162)

Group D
(n = 91)

Log–rank

Unadjusted
HR (95% CI)

P value

Adjusted*
HR (95% CI)

P value

Primary endpoint:

7 (4.4%)

12 (13.9%)

0.007

3.378 (1.329–8.582)

0.011

3.514 (1.481–9.640)

0.015

All-cause death

5 (3.2%)

11 (12.7%)

0.003

4.311 (1.497–12.41)

0.007

3.191 (1.065–9.557)

0.038

Cardiac death

2 (1.2%)

5 (5.8%)

0.041

4.700 (0.912–24.24)

0.044

3.546 (0.594–21.18)

0.165

Non-fatal MI

2 (1.3%)

1 (1.4%)

0.990

1.015 (0.092–11.20)

0.990

4.323 (0.195–95.89)

0.355

Secondary endpoint:

14 (9.1%)

10 (12.8%)

0.326

1.498 (0.665–3.375)

0.329

1.508 (0.647–3.518)

0.342

TLR

7 (4.5%)

7 (9.4%)

0.164

2.071 (0.726–5.909)

0.173

2.392 (0.772–7.410)

0.131

TVR

9 (5.9%)

8 (11.2%)

0.165

1.924 (0.742–4.988)

0.178

2.311 (0.803–6.655)

0.120

CVA

3 (2.0%)

2 (2.4%)

0.742

1.349 (0.225–8.081)

0.743

1.273 (0.200–7.810)

0.798

Outcomes

Cumulative events according to the level of NLR

Low NLR (≤ 3.0)
Group A + B
(n = 935)

High NLR (> 3.0)
Group C +D
(n = 253)

Log–rank

Unadjusted
HR (95% CI)

P value

Adjusted
HR (95% CI)

P value

Primary endpoint:

30 (3.5%)

19 (7.8%)

0.003

2.315 (1.303–4.112)

0.004

2.499 (1.285–4.857)

0.007

All-cause death

20 (2.3%)

16 (6.5%)

0.001

2.917 (1.512–5.629)

0.001

2.816 (1.326–5.982)

0.007

Cardiac death

3 (0.2%)

7 (2.0%)

0.001

8.535 (2.207–33.00)

0.002

9.509 (2.238–40.41)

0.001

Non-fatal MI

10 (1.1%)

3 (1.6%)

0.517

1.463 (0.459–4.666)

0.520

1.833 (0.444–7.570)

0.403

Secondary endpoint:

89 (10.4%)

24 (10.4%)

0.915

1.025 (0.653–1.608)

0.915

1.034 (0.623–1.717)

0.896

TLR

63 (7.3%)

14 (6.1%)

0.448

1.251 (0.701–2.232)

0.449

1.163 (0.633–2.139)

0.626

TVR

81 (9.9%)

17 (7.6%)

0.274

1.331 (0.789–2.246)

0.283

1.002 (0.571–1.757)

0.996

CVA

9 (1.0%)

5 (2.1%)

0.188

2.051 (0.687–6.120)

0.198

1.229 (0.230–5.864)

0.809

Outcomes

Cumulative events according to the DSBP

Low DSBP
(≤ 25 mmHg)

Group A + C
(n = 714)

High DSBP
(> 25 mmHg)

Group B + D
(n = 474)

Log–rank

Unadjusted
HR (95% CI)

P value

Adjusted‡
HR (95% CI)

P value

Primary endpoint:

19 (2.8%)

30 (6.8%)

0.001

2.499 (1.407–4.440)

0.002

2.379 (1.262–4.218)

0.007

All-cause death

10 (1.5%)

26 (5.9%)

< 0.001

4.125 (1.989–8.554)

< 0.001

4.098 (1.819–7.471)

0.001

Cardiac death

3 (0.4%)

7 (1.6%)

0.046

3.620 (0.936–14.00)

0.062

5.763 (1.116–29.76)

0.037

Non-fatal MI

9 (1.4%)

4 (0.9%)

0.576

1.397 (0.430–4.538)

0.578

1.651 (0.425–6.409)

0.469

Secondary endpoint:

66 (9.9%)

47 (11.1%)

0.457

1.152 (0.793–1.675)

0.458

1.207 (0.810–1.799)

0.355

TLR

46 (6.8%)

31 (7.4%)

0.745

1.078 (0.684–1.701)

0.745

1.068 (0.659–1.731)

0.790

TVR

58 (9.0%)

40 (10.2%)

0.558

1.126 (0.752–1.684)

0.564

1.155 (0.753–1.772)

0.510

CVA

6 (0.9%)

8 (1.9%)

0.152

2.130 (0.739–6.139)

0.162

3.177 (0.775–13.03)

0.108

Figure 3. Kaplan-Meier analysis for primary outcome (A), all-cause death (B), cardiac death (C), and secondary end--point (D).
Table 3. C-statistics for Cox regression models for prediction of primary endpoint.

Meaningful risk factors, NLR and DSBP

C-statistics

Estimated difference
(95% CI)

P value

Meaningful clinical and procedural risk factors*

0.575

Reference

Reference

Meaningful clinical and procedural risk factors
plus high NLR

0.602

0.055 (–0.107 to 0.108)

0.017

Meaningful clinical and procedural risk factors
plus high DSBP

0.622

0.050 (–0.099 to 0.099)

0.004

Meaningful clinical and procedural risk factors
plus high NLR and high DSBP

0.635

0.048 (–0.093 to 0.094)

0.002

Table 4. Independent predictors for primary and secondary endpoints at 5 years.

Variables

Primary endpoint

Secondary endpoint

Univariate

HR (95% CI)

P value

Multivariate

HR (95% CI)

P value

Univariate

HR (95% CI)

P value

Multivariate

HR (95% CI)

P value

NLR

2.630 (2.021–3.422)

< 0.001

2.154 (1.948–3.125)

0.030

1.985 (0.982–2.563)

0.009

1.862 (0.672–2.874)

0.028

DSBP

2.288 (1.332–3.931)

< 0.001

2.018 (1.012–3.321)

0.018

2.036 (1.245–2.763)

0.002

1.961 (1.028–3.012)

0.010

Age ≥ 65 years

2.015 (1.153–3.521)

0.014

2.517 (1.264–5.011)

0.009

1.615 (1.073–2.432)

0.022

1.544 (0.921–2.590)

0.100

Gender (men)

1.654 (0.949–2.885)

0.076

1.309 (0.637–2.688)

0.464

1.073 (0.724–1.589)

0.727

1.543 (0.897–2.654)

0.117

LVEF < 50%

1.809 (0.814–4.021)

0.146

2.893 (1.196–6.997)

0.018

1.195 (0.642–2.227)

0.547

1.416 (0.602–3.331)

0.426

Hypertension

1.986 (1.014–3.125)

0.028

2.372 (1.105–5.984)

0.023

1.217 (0.840–1.763)

0.298

1.461 (0.899–2.372)

0.126

Diabetes mellitus

1.231 (0.664–2.283)

0.509

1.474 (0.744–2.921)

0.266

1.667 (1.128–2.462)

0.010

1.872 (1.150–3.048)

0.012

Dyslipidemia

2.226 (1.278–3.876)

0.005

1.374 (0.718–2.629)

0.337

1.695 (1.164–2.467)

0.006

1.037 (0.655–1.641)

0.878

Neutrophil

1.027 (1.003–1.051)

0.025

0.931 (0.859–1.008)

0.078

1.004 (0.988–1.020)

0.645

1.052 (0.977–1.133)

0.181

Lymphocyte

0.960 (0.933–0.987)

0.004

0.869 (0.787–0.959)

0.005

0.852 (0.681–0.954)

0.035

1.056 (0.971–1.148)

0.203

Cath. Lab. SBP

1.013 (1.002–1.024)

0.018

1.031 (0.998–1.025)

0.009

1.236 (1.023–1.432)

0.021

1.007 (0.997–1.017)

0.202

Ward SBP

1.005 (0.991–1.020)

0.468

1.005 (0.987–1.022)

0.604

1.001 (0.991–1.010)

0.904

0.998 (0.985–1.012)

0.796

Beta-blocker

1.431 (0.822–2.493)

0.205

1.056 (0.556–2.006)

0.869

1.004 (0.688–1.465)

0.985

1.216 (0.767–1.929)

0.405

Calcium channel blocker

1.407 (0.768–2.576)

0.269

1.836 (0.888–3.796)

0.101

1.121 (0.735–1.710)

0.595

1.088 (0.635–1.864)

0.760

Lipid-lowering agent

2.632 (1.466–4.726)

0.001

1.092 (0.569–2.095)

0.791

2.107 (1.399–3.173)

< 0.001

1.226 (0.766–1.962)

0.396

Current smoker

1.264 (0.663–2.411)

0.477

0.953 (0.444–2.044)

0.901

1.281 (0.799–2.054)

0.305

1.303 (0.761–2.232)

0.335

Discussion

The main findings of this study are the follows. The cumulative incidence of the primary endpoint in group D was significantly higher than that in the other three groups and the incorporation of high NLR and high DSBP into the model with conventional and meaningful clinical and procedural risk factors synergistically increased the ability to predict the primary endpoint.

Because previous work has highlighted the importance of the NLR and DSBP in predicting long-term major clinical outcomes in patients with ACS who underwent PCI, we investigated combined usefulness of these two non-invasive, inexpensive, relatively simple, not to mention the little time required employing the diagnostic tools in this study. The present study showed the additive value and combined usefulness of the NLR and DSBP in predicting the primary endpoint in patients with ACS after DES implantation. According to available research, this study is the first to report the additive benefit of the NLR and DSBP in predicting the 5-year follow-up of clinical outcomes in patients with ACS undergoing PCI with DES implantation.

Vascular inflammation plays a critical role in the initiation, evolution, and rupture of atherosclerotic plaques [22]. The circulating biomarkers of this process predict morbidity and mortality in patients with established CAD [23, 24]. To date, there has been diverse evidence regarding the role of the NLR, which suggests that it has an association with CAD [25, 26] and that it could predict adverse in-hospital mortality and long-term mortality up to 3 years [26]. In this study, the NLR had an additional good prognostic value for predicting the primary endpoint during the 5-year follow-up (increase in C-statistics from 0.575 to 0.602; p = = 0.017). In addition, the cumulative incidence of the primary endpoint was higher in the high NLR group than in the low NLR group (aHR: 2.499; 95% CI: 1.285–4.857; p = 0.007; Table 2). These results are compatible with those of previous reports [3, 5, 25]. In patients with ACS, a low lymphocyte count is common and can be explained by the elevated cortisol level, which induces apoptosis [27]. This low lymphocyte count (i.e., high NLR) is associated with adverse clinical outcomes [6]. In this study, the frequency of the primary endpoint was significantly higher in the high DSBP group than in the low DSBP group at 5 years (aHR: 2.379; 95% CI: 1.262–4.218; p = 0.007; Table 2). It is well known that target organ damage in patients with hypertension and cardiovascular complications is related to elevated BP, which is determined from the average of multiple BP readings (mean BP) [28]. DSBP may not be a good substitute of 24-hour ambulatory BP monitoring (ABPM). However, ABPM is an additional diagnostic test with inherent costs and takes more time to get results compared to DSBP. Therefore, we thought that DSBP may be the preferred technique for the patients with ACS. Moreover, BP measurement in the ward and before catheterization is already done in routine clinical practice.

Although the NLR and DSBP have a different pathophysiological mechanism, they both lead to accelerated atherosclerosis. In patients with ACS, an increased total leukocyte count predicts mortality and recurrence of MI [29, 30]. ACS is most commonly caused by disruption of atherosclerotic plaques with superimposed thrombus formation; thus, inflammation plays a crucial role in the pathogenesis of acute coronary events [31]. To date, the precise mechanism of acute transient BP elevation is less well known. Under stressful situations (e.g., CAG or PCI), increased activity of the hypothalamic-pituitary-adrenal axis culminating in release of with catecholamine play a vital role in initiating acute deviations from normal physiology such as hypertension. Through changes in the circulating catecholamine levels, emotional or psychosocial stress evokes negative effects on autonomic and hormonal homeostasis, which can lead to inflammation, metabolic abnormalities, endothelial dysfunction, hypertension, and insulin resistance [32]. From a different perspective, increased cardiac output and arterial stiffness under stressful circumstances are related to acute BP responses; the latter are associated with atherosclerosis [33, 34]. Her et al. [10] proposed that an acute elevation of BP under stress might contribute to an increased risk of adverse clinical outcomes, with this BP elevation representing significant arterial atherosclerosis and increased arterial stiffness. Another possible interaction between BP and NLR is that an elevated BP increases the formation of hydrogen peroxide and free radicals in the plasma [35], and these substrates cause decreased production of nitric oxide from the endothelium [36] and increased leukocyte adhesion [37]. Therefore, these two parameters are associated with each other. Because atherosclerosis is an inflammatory disease and an important causative factor of BP variations, early detection and modification of these reversible factors may reduce the frequency and severity of adverse cardiac events during long-term follow-up after implantation of DESs. In this regard, the combined use of the NLR and DSBP as a predictive tool for adverse cardiac events is a rational approach, especially in patients undergoing PCI. Finally, although in this study shows the additional combined usefulness of the NLR and DSBP in predicting long-term outcomes after implantation of DESs, other laboratory (e.g., GFR, hemoglobin), clinical features, or well-established risk assessment tools (e.g., Global Registries of Acute Coronary Events [GRACE] or TIMI risk scores) could also be used as alternatives for these two parameters in further studies.

Limitations of the study

This study has some limitations. First, the NLR was checked only once; thus, it was not known whether there was any change in its value. Second, even though there was an attempt to perform a multivariable Cox proportional hazard regression analysis including baseline confounding factors, as this study was a non-randomized retrospective single-center study with several exclusion criteria, selection bias cannot be excluded. Third, the sample size may not be sufficiently large enough to accurately estimate the study results. Larger randomized prospective studies are required to confirm these results. Fourth, group D was composed of a stringently selected population. Therefore, this may have led to selection bias. Fifth, because 24-hour ABPM was not performed in this study, assessment of any BP variability was not possible. Finally, the patients in this study were enrolled between September 2002 and August 2007; this limited study period can be considered to be the main limitation.

Conclusions

In conclusion, in this retrospective observational study of the patients with ACS, the incorporation of both high NLR and high DSBP into the model with conventional and meaningful clinical and procedural risk factors increased the ability to predict the primary endpoint during the 5-year follow-up period.

Conflict of interest: None declared

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