Platelet-Lymphocyte Ratio as a New Predictor of In-hospital Mortality in Cardiac Intensive Care Unit Patients

DOI: https://doi.org/10.21203/rs.3.rs-763264/v1

Abstract

Background: It has been discovered that both inflammation and platelet aggregation could cause crucial effect on the occurrence and development of cardiovascular diseases. As a combination of platelet and lymphocyte, platelet-lymphocyte ratio (PLR) was proved to be correlated with the severity as well as prognosis of cardiovascular diseases. Exploring the relationship between PLR and in-hospital mortality in cardiac intensive care unit (CICU) patients was the purpose of this study.

Method: PLR was calculated by dividing platelet count by lymphocyte count. All patients were grouped by PLR quartiles and the primary outcome was in-hospital mortality. The independent effect of PLR was determined by binary logistic regression analysis. The curve in line with overall trend was drawn by local weighted regression (Lowess). Subgroup analysis was used to determine the relationship between PLR and in-hospital mortality in different subgroups.

Result: We included 5577 CICU patients. As PLR quartiles increased, in-hospital mortality increased significantly (Quartile 4 vs Quartile 1: 13.9 vs 8.3, P <0.001). After adjusting for confounding variables, PLR was proved to be independently associated with increased risk of in-hospital mortality (Quartile 4 vs Quartile 1: OR, 95% CI: 1.99, 1.46-2.71, P<0.001, P for trend <0.001). The Lowess curves showed a positive relationship between PLR and in-hospital mortality. The subgroup analysis revealed that patients with low Acute Physiology and Chronic Health Evaluation IV (APACHE IV) or with less comorbidities had higher risk of mortality for PLR. Further, PLR quartiles had positive relation with length of CICU stay (Quartile 4 vs Quartile 1: 2.7, 1.6-5.2 vs 2.1, 1.3-3.9, P<0.001), and the length of hospital stay (Quartile 4 vs Quartile 1: 7.9, 4.6-13.1 vs 5.8, 3.3-9.8, P<0.001).

Conclusion: PLR was independently associated with in-hospital mortality in CICU patients. 

1. Introduction

The concept of the Coronary Artery Care Unit (CCU) was first proposed in the early 1960s and quickly gained widespread support, which benefitted from rapid technological advances in cardiovascular medicine [1]. At the beginning, the primary purpose of these specialized wards which developer in coronary care established was to reduce mortality in patients with acute myocardial infarction (MI) [2]. Nevertheless, over the past few decades, the CCU's landscape has changed. Mortality of acute coronary syndromes decreased steadily over time [34], while the occurrence of other severe cardiovascular diseases appeared to increase [5]. Because of the greater diversity of diseases among patients admitted to CCU units, the concept of cardiac intensive care unit (CICU) had been used to represent this complex care environment more accurately. It has emerged to provide more targeted services for patients with critical heart diseases at present [6]. Nowadays, CICU has taken on a more important position. And easily accessible prognostic indicators for CICU patients are always welcomed by clinicians.

Previous studies have demonstrated that increased peripheral blood platelet count caused the rise of adverse cardiovascular outcomes, in patients with acute myocardial infarction (AMI), higher platelet count was proved to be associated with mortality and reinfarction within the first year after primary percutaneous intervention (PCI) [710]. Low amount of peripheral blood lymphocyte count which reflects inflammatory state, was also confirmed to increase adverse clinical outcomes in patients with cardiovascular diseases, such as congestive heart failure, advanced heart failure, coronary artery disease and unstable angina pectoris [1115]. As a new prognostic marker, platelet-lymphocyte ratio (PLR) was the combination of the two indexes which provides the concept of platelet aggregation and inflammatory pathways. In clinical practice, elevated PLR was shown to be associated with adverse outcomes. In the area of non-cardiovascular disease, PLR was proved to be an important inflammatory marker that predicted mortality in cancer population [1618], critical limb ischemia in peripheral artery disease [19] and neonate early-onset sepsis [20]. In cardiovascular disease, PLR was proved to be positively correlated with the occurrence of no-reflow after PCI [21]. Moreover, PLR has been proven to be independently associated with long-range survival rate in patients with STEMI and NSTEMI respectively [2223]. However, no research has demonstrated that how PLR affects patients with severe cardiovascular disease. Thus, investigating the relationship between PLR and in-hospital mortality of patients in CICU was the target of this research.

2. Method

2.1. Population selection criteria. 

Patients admitted to CICU were included. Adult patients (≥18 years) hospitalized for more than 2 days at their first admission were available. Patients meeting the following criteria were excluded: (1) hospital admission for non-heart disease; (2) lymphocyte and platelet data missing; (3) individual data missing ≥5%; (4) hematologic malignancy such as: leukemia and lymphoma. A total of 5577 patients were included (Figure 1). 

2.2. Data extraction. 

The data used in this study was from eICU Collaborative Research Database [24], which collected information on 20,859 admissions from 208 hospitals in the United States between 2014 and 2015. This database is available at: https://doi.org/10.13026/C2WM1R. And the author was approved to access to the database through Protecting Human Research Participants exam (certificate number: 9728458).

Following data were collected: demographics (age, gender and race), vital signs (blood pressure, heart rate, respiration rate, oxygen saturation), body mass index, diagnoses and comorbidities (congestive heart failure, coronary artery disease, acute coronary syndrome, ST-elevation myocardial infarction(STEMI), non-ST-elevation myocardial infarction(NSTEMI), arrhythmias, cardiac arrest, bradycardia, atrial fibrillation, ventricular arrhythmias, atrioventricular block, cardiomyopathy, valve disease, shock, pulmonary embolism, pulmonary hypertension, hypertension, diabetes, hypercholesterolemia, chronic obstructive pulmonary disease(COPD), respiratory failure, chronic kidney disease, acute kidney injury, malignancy, stroke, sepsis), laboratory parameters(white blood cell, lymphocyte, monocyte and neutrophil percentage, red blood cell platelet, hemoglobin, hematocrit, glucose, creatinine, blood nitrogen urea, sodium, potassium), medication use( antiplatelet, oral anticoagulants, beta-blockers, angiotensin-converting enzyme inhibitor/angiotensin receptor blocker(ACEI/ARB), statin), acute physiology score(APS) and Acute Physiology and Chronic Health Evaluation IV (APACHE IV)[25]. 

PLR was obtained by dividing platelet count by lymphocyte count.

2.3. Grouping and outcomes. 

According to PLR quartiles, all enrolled patients were divided into four groups. The primary outcome was in-hospital mortality. Secondary outcomes were length of CICU stay and length of hospital stay.

2.4. Statistical analysis. 

Normally distributed continuous variables were expressed as mean ± standard deviation (SD) and compared between groups using analysis of variance. Skewed data were expressed as median and interquartile range(IQR) and compared using Kruskal–Wallis test. Categorical variables were expressed as number (percentage) and compared between groups using Chi-square test.

The relationship between PLR and in-hospital mortality was identified by binary logistic regression analysis and the results were expressed as odds ratio (OR) and 95% confidence interval (CI). P for trend was calculated. Covariates were selected by statistical analysis and clinical doubt to modulate the outcome. The curves that conformed to the general trend were plotted through local weighted regression (Lowess). Subgroup analysis was used to determine the relationship between PLR and in-hospital mortality in different subgroups, P for interaction was calculated. All tests were two-sided, P<0.05 was considered statistically significant. All methods were carried out in accordance with relevant guidelines and regulations. All data analysis were performed by Stata V.15.1.

3. Result

3.1. Subjects and baseline characteristics. 

5577 patients admitted to CICU were analyzed (Fig. 1). According to PLR quartiles, all patients were divided into four groups: PLR < 104.8 (n = 1392), 104.8 ≤ PLR < 167.0 (n = 1399), 167.0 ≤ PLR < 271.0 (n = 1392), PLR ≥ 271.0 (n = 1394). The characteristics of different PLR groups were summarized in Table 1, patients with higher PLR had the following characteristics: elder, male, Caucasian, lower systolic, mean and diastolic blood pressure, lower oxygen saturation, and higher heart rate, respiration rate, body mass index. Patients in higher PLR quartiles also tended to present more diagnoses and comorbidities of congestive heart failure, arrhythmias, atrial fibrillation, valve disease, shock, COPD, respiratory failure, chronic kidney disease, acute kidney injury, malignancy, sepsis and less coronary artery disease, acute coronary syndrome, STEMI, NSTEMI, cardiac arrest, ventricular arrhythmias, atrioventricular block, hypertension. Moreover, patients in higher PLR quartiles presented higher white blood cell, neutrophil percentage, platelet, glucose, creatinine, blood nitrogen urea, potassium values and lower lymphocyte, monocyte percentage, red blood cell, hemoglobin, hematocrit, sodium values. Patients in higher PLR quartiles received more oral anticoagulant and less antiplatelet, ACEI/ARB, Beta-blockers, statin treatment. Last but not the least, patients in the highest PLR quartile had highest APS and APACHE IV scores which were used to evaluate the severity of ICU patients and predict their prognosis.

3.2. Association between PLR and outcomes. 

Overall, in-hospital mortality rate was 10.7%. As PLR quartiles increased, in-hospital mortality increased significantly (Quartile 4 vs Quartile 1: 13.9 vs 8.3, P < 0.001) (Table 2). In unadjusted logistic regression analysis, there was a positive correlation between PLR and in-hospital mortality (Quartile 4 vs Quartile 1: OR, 95% CI: 1.77, 1.39–2.25, P < 0.001, P for trend < 0.001). In model 2, after adjusting for age, gender and ethnicity, higher PLR quartiles were still associated with increased risk of in-hospital mortality (Quartile 4 vs Quartile 1: OR, 95% CI: 1.63, 1.28–2.09, P < 0.001, P for trend < 0.001). In model 3, adjusted for more confounding variables, PLR was still independently related to the increased risk of in-hospital mortality (Quartile 4 vs Quartile 1: OR, 95% CI: 1.99, 1.46–2.71, P < 0.001, P for trend < 0.001). Furthermore, when patients were stratified by PLR quintiles, after adjusting for confounding variables, we reached the same conclusion that PLR was an independent risk factor for in-hospital mortality (Quintile 5 vs Quintile 1: OR, 95% CI: 1.95, 1.40–2.73, P < 0.001, P for trend < 0.001) (Table 3). 

Table 1

Characteristics of patients stratified by PLR quartiles.

Characteristics

Total

(n = 5577)

Quartiles of PLR

P Value

Quartile 1(n = 1392)

PLR < 104.8

Quartile 2(n = 1399)

104.8 ≤ PLR < 167.0

Quartile 3(n = 1392)

167.0 ≤ PLR < 271.0

Quartile 4(n = 1394)

PLR ≥ 271.0

Age(years)

66.1 ± 15.3

63.2 ± 15.3

66.0 ± 15.3

67.1 ± 15.2

68.3 ± 15.0

< 0.001

Gender, n(%)

         

0.002

Male

3027(54.3)

817(58.7)

739(52.8)

723(51.9)

748(53.7)

 

Female

2550(45.7)

575(41.3)

660(47.2)

669(48.1)

646(46.3)

 

Ethnicity, n(%)

         

< 0.001

Caucasian

3958(71.0)

937(67.3)

952(68.1)

999(71.8)

1070(76.8)

 

African American

914(16.4)

272(19.5)

241(17.2)

233(16.7)

168(12.0)

 

Other

705(12.6)

183(13.2)

206(14.7)

160(11.5)

156(11.2)

 

Vital signs

           

Systolic blood pressure(mmHg)

122.6 ± 19.0

123.2 ± 19.1

123.5 ± 19.7

123.7 ± 19.2

120.1 ± 17.9

< 0.001

Diastolic blood pressure(mmHg)

66.0 ± 11.3

67.3 ± 11.4

66.7 ± 11.6

65.7 ± 11.2

64.2 ± 10.7

< 0.001

Mean blood pressure(mmHg)

82.2 ± 12.9

83.6 ± 12.9

82.8 ± 13.1

82.1 ± 13.2

80.1 ± 12.0

< 0.001

Heart rate(beats/min)

89.2 ± 22.3

85.7 ± 21.8

86.6 ± 21.5

90.5 ± 22.8

94.0 ± 22.1

< 0.001

Respiration rate(beats/min)

21.1 ± 6.3

20.0 ± 6.0

20.8 ± 6.0

21.2 ± 6.2

22.4 ± 6.8

< 0.001

Oxygen saturation(%)

97(95, 99)

98(96, 100)

97(95, 99)

97(95, 99)

97(94, 99)

< 0.001

Body mass index(kg/m2)

29.3 ± 8.3

29.9 ± 7.9

29.5 ± 8.1

29.6 ± 8.9

28.1 ± 8.3

< 0.001

Diagnoses and comorbidities, n(%)

           

Congestive heart failure

1256(22.5)

261(18.8)

308(22.0)

343(24.6)

344(24.7)

< 0.001

Coronary artery disease

1853(33.2)

551(39.6)

539(38.5)

407(29.2)

356(25.5)

< 0.001

Acute coronary syndrome

1085(19.5)

362(26.0)

316(22.6)

232(16.7)

175(12.6)

< 0.001

STEMI

384(6.9)

149(10.7)

115(8.2)

73(5.2)

47(3.4)

< 0.001

NSTEMI

403(7.2)

110(7.9)

120(8.6)

89(6.4)

84(6.0)

0.027

Arrhythmias

1865(33.4)

411(29.5)

444(31.7)

503(36.1)

507(36.4)

< 0.001

Cardiac arrest

407(7.3)

153(11.0)

88(6.3)

93(6.7)

73(5.2)

< 0.001

Bradycardia

224(4.0)

64(4.6)

59(4.2)

61(4.4)

40(2.9)

0.086

Atrial fibrillation

1117(20.0)

208(14.9)

259(18.5)

320(23.0)

330(23.7)

< 0.001

Ventricular arrhythmias

241(4.3)

82(5.9)

62(4.4)

58(4.2)

39(2.8)

0.001

Atrioventricular block

119(2.1)

47(3.4)

31(2.2)

23(1.7)

18(1.3)

0.001

Cardiomyopathy

365(6.5)

95(6.8)

100(7.2)

96(6.9)

74(5.3)

0.189

Valve disease

164(2.9)

34(2.4)

38(2.7)

42(3.0)

50(3.6)

0.318

Shock

1718(30.8)

331(23.8)

351(25.1)

425(30.5)

611(43.8)

< 0.001

Pulmonary embolism

129(2.3)

39(2.8)

30(2.1)

31(2.2)

29(2.1)

0.567

Pulmonary hypertension

69(1.2)

13(0.9)

20(1.4)

17(1.2)

19(1.4)

0.647

Hypertension

1689(30.3)

407(29.2)

458(32.7)

441(31.7)

383(27.5)

0.011

Diabetes

1133(20.3)

257(18.5)

302(21.6)

297(21.3)

277(19.9)

0.144

COPD

633(11.4)

105(7.5)

144(10.3)

170(12.2)

214(15.4)

< 0.001

Respiratory failure

1669(300)

365(26.2)

360(25.7)

416(29.9)

528(37.9)

< 0.001

Chronic kidney disease

856(15.4)

163(11.7)

203(14.5)

236(17.0)

254(18.2)

< 0.001

Acute kidney injury

1031(18.5)

216(15.5)

226(16.2)

276(19.8)

313(22.5)

< 0.001

Malignancy

272(4.9)

38(2.7)

53(3.8)

67(4.8)

114(8.2)

< 0.001

Stroke

223(4.0)

58(4.2)

71(5.1)

48(3.5)

46(3.3)

0.066

Sepsis

1301(23.3)

207(14.9)

236(16.9)

316(22.7)

542(38.9)

< 0.001

Laboratory parameters

           

White blood cell (109/L)

11.8 ± 6.5

12.0 ± 7.6

11.1 ± 5.7

11.7 ± 6.1

12.3 ± 6.4

< 0.001

Lymphocyte (109/L)

1.6 ± 1.3

2.9 ± 1.7

1.6 ± 0.6

1.2 ± 0.5

0.7 ± 0.3

< 0.001

Monocyte percentage (%)

7.5 ± 3.7

7.8 ± 3.2

8.2 ± 3.4

7.6 ± 3.5

6.6 ± 4.3

< 0.001

Neutrophil percentage(%)

74.7 ± 12.6

64.4 ± 13.2

72.5 ± 9.8

78.2 ± 8.5

83.7 ± 9.6

< 0.001

Red blood cell (109/L)

4.1 ± 0.8

4.2 ± 0.9

4.1 ± 0.8

4.0 ± 0.8

4.0 ± 0.8

< 0.001

Platelet (109/L)

231 ± 102

187 ± 81

214 ± 74

242 ± 93

282 ± 125

< 0.001

Hemoglobin (g/dL)

12.0 ± 2.5

12.7 ± 2.5

12.2 ± 2.4

11.8 ± 2.4

11.2 ± 2.4

< 0.001

Hematocrit (%)

36.4 ± 7.1

38.3 ± 7.2

37.0 ± 6.8

36.0 ± 7.0

34.4 ± 6.8

< 0.001

Glucose (mg/dL)

162.4 ± 98.3

167.7 ± 98.5

156.3 ± 92.3

161.1 ± 95.1

164.9 ± 106.4

0.014

Creatinine (mg/dL)

1.80 ± 1.76

1.65 ± 1.62

1.72 ± 1.71

1.90 ± 1.89

1.93 ± 1.80

< 0.001

Blood nitrogen urea (mg/dL)

30.1 ± 23.4

25.7 ± 19.4

28.0 ± 19.8

32.0 ± 24.9

34.8 ± 27.3

< 0.001

Sodium (mmol/L)

136.9 ± 6.0

137.8 ± 5.3

137.4 ± 5.9

136.8 ± 6.3

135.8 ± 6.4

< 0.001

Potassium (mmol/L)

4.2 ± 0.8

4.2 ± 0.7

4.2 ± 0.8

4.2 ± 0.8

4.3 ± 0.

< 0.001

PLR

167.0(104.8, 271.0)

76.4(57.0, 90.7)

134.2(119.3, 148.7)

208.6(185.9, 236.6)

416.5(326.8, 584.0)

< 0.001

Medication use, n(%)

           

Antiplatelet

2659(47.7)

743(53.4)

711(50.8)

635(45.6)

570(40.9)

< 0.001

Oral anticoagulants

687(12.3)

141(10.1)

167(11.9)

186(13.4)

193(13.9)

0.013

Beta-blockers

2446(43.9)

647(46.5)

651(46.5)

591(42.5)

557(40.0)

0.001

ACEI/ARB

1490(26.7)

387(27.8)

407(29.1)

385(27.7)

311(22.3)

< 0.001

Statin

1717(30.8)

487(35.0)

460(32.9)

399(28.7)

371(26.6)

< 0.001

APS

41(28, 57)

36(25, 56)

38(26, 53)

42(30, 56)

46(34, 61)

< 0.001

APACHE IV

55(40, 72)

50(35, 70)

52(38, 67)

57(42, 72)

61(47, 77)

< 0.001

Continuous variables were presented as mean ± SD or median (IQR). Categorical variables were presented as number (percentage). Abbreviation: PLR: platelet-lymphocyte ratio STEMI: ST-elevation myocardial infarction; NSTEMI: non-ST-elevation myocardial infarction; COPD: chronic obstructive pulmonary disease; PLR: platelet-lymphocyte ratio; ACEI: angiotensin-converting enzyme inhibitor; ARB: angiotensin receptor blocker; APS: acute physiology score; APACHE IV: Acute Physiology and Chronic Health Evaluation IV.

 

Table 2

Outcomes of patients stratified by PLR quartiles

Outcomes

Total

(n = 5577)

Quartiles of PLR

P Value

Quartile 1(n = 1392)

PLR < 104.8

Quartile 2(n = 1399)

104.8 ≤ PLR < 167.0

Quartile 3(n = 1392)

167.0 ≤ PLR < 271.0

Quartile 4(n = 1394)

PLR ≥ 271.0

In-hospital mortality, n(%)

597(10.7)

116(8.3)

120(8.6)

168(12.1)

193(13.9)

< 0.001

Length of CICU stay (days)

2.3(1.4, 4.2)

2.1(1.3, 3.9)

2.2(1.3, 3.9)

2.5(1.4, 4.4)

2.7(1.6, 5.2)

< 0.001

Length of hospital stay (days)

6.3(3.9, 11.1)

5.8(3.3, 9.8)

5.8(3.5, 10.3)

6.4(4.0, 11.1)

7.9(4.6, 13.1)

< 0.001

Continuous variables were presented as median (IQR). Categorical variables were presented as number (percentage). P values were calculated using Kruskal–Wallis test or Chi-square test to compare differences in outcomes between different PLR quartiles. Abbreviation: PLR: platelet-lymphocyte ratio; CICU: cardiac intensive care unit.


Table 3

The association between PLR and in-hospital mortality

 

OR (95% CI)

P Value

P for trend

 

OR (95% CI)

P Value

P for trend

Model 1

   

< 0.001

Model 1

   

< 0.001

Quartile 1: PLR < 104.8

Reference

   

Quintile 1: PLR < 95.5

Reference

   

Quartile 2:104.8 ≤ PLR < 167.0

1.03(0.80–1.35)

0.817

 

Quintile 2: 95.5 ≤ PLR < 139.0

0.86(0.64–1.16)

0.320

 

Quartile 3:167.0 ≤ PLR < 271.0

1.51(1.18–1.94)

0.001

 

Quintile 3:139.0 ≤ PLR < 198.9

1.11(0.84–1.47)

0.466

 

Quartile 4: PLR ≥ 271.0

1.77(1.39–2.25)

< 0.001

 

Quintile 4:198.9 ≤ PLR < 314.7

1.35(1.02–1.77)

0.033

 
       

Quintile 5: PLR ≥ 314.7

1.74(1.34–2.27)

< 0.001

 

Model 2

   

< 0.001

Model 2

   

< 0.001

Quartile 1: PLR < 104.8

Reference

   

Quintile 1: PLR < 95.5

Reference

   

Quartile 2:104.8 ≤ PLR < 167.0

1.00(0.76–1.31)

0.999

 

Quintile 2: 95.5 ≤ PLR < 139.0

0.85(0.63–1.15)

0.285

 

Quartile 3:167.0 ≤ PLR < 271.0

1.43(1.11–1.84)

0.005

 

Quintile 3:139.0 ≤ PLR < 198.9

1.06(0.80–1.41)

0.677

 

Quartile 4: PLR ≥ 271.0

1.63(1.28–2.09)

< 0.001

 

Quintile 4:198.9 ≤ PLR < 314.7

1.27(0.97–1.68)

0.086

 
       

Quintile 5: PLR ≥ 314.7

1.60(1.23–2.09)

0.001

 

Model 3

   

< 0.001

Model 3

   

< 0.001

Quartile 1: PLR < 104.8

Reference

   

Quintile 1: PLR < 95.5

Reference

   

Quartile 2:104.8 ≤ PLR < 167.0

1.60(1.15–2.21)

0.005

 

Quintile 2: 95.5 ≤ PLR < 139.0

1.34(0.93–1.93)

0.118

 

Quartile 3:167.0 ≤ PLR < 271.0

2.21(1.62–3.02)

< 0.001

 

Quintile 3:139.0 ≤ PLR < 198.9

1.85(1.31–2.63)

0.001

 

Quartile 4: PLR ≥ 271.0

1.99(1.46–2.71)

< 0.001

 

Quintile 4:198.9 ≤ PLR < 314.7

1.86(1.32–2.62)

< 0.001

 
       

Quintile 5: PLR ≥ 314.7

1.95(1.40–2.73)

< 0.001

 
Models were derived from binary logistic regression analysis. P for trend was calculated using binary logistic analysis to determine whether there was a trend when PLR was included as a grouping variable in the model (Quartile 1–4 or Quintile1-5). When PLR was included as a grouping variable in the model, P values were calculated using binary logistic analysis to determine whether there was a relationship between PLR quartiles(quintiles) and in-hospital mortality with Quartile1 (Quintile 1) serving as the reference group. When PLR was included as a continuous variable in the model, P values were calculated using binary logistic analysis to determine whether there was a relationship between PLR and in-hospital mortality. Model 1: unadjusted. Model 2: adjusted for age, gender, ethnicity. Model 3: adjusted for age, gender, ethnicity, systolic blood pressure, diastolic blood pressure, mean blood pressure, heart rate, body mass index, respiration, coronary artery disease, acute coronary syndrome, congestive heart failure, NSTEMI, cardiac arrest, arrhythmias, atrial fibrillation, ventricular arrhythmias, atrioventricular block, respiratory failure, stroke, malignancy, cardiomyopathy, hypertension, diabetes, white blood cell, red blood cell, hematocrit, blood nitrogen urea, creatinine, sodium, potassium, oral anticoagulants, ACEI/ARB, beta-blockers, statin, APS and APACHE IV. Abbreviation: PLR: platelet-lymphocyte ratio; OR: odds ratio; CI: confidence interval.

From Lowess curve in Fig. 2A, we found that mortality was lowest when PLR was about equal to 60. Specifically, when PLR was less than 60, PLR was inversely associated with mortality, while when the PLR was greater than 60, in-hospital mortality increased with the increase of the PLR. Besides, as shown in Fig. 2B, as PLR increased, in-hospital mortality increased with a range of 10 to 90 percent of PLR.

In addition, increased PLR quartiles were associated with prolonged length of CICU stay (Quartile 4 vs Quartile 1: 2.7, 1.6–5.2 vs 2.1, 1.3–3.9, P < 0.001) and hospital stay (Quartile 4 vs Quartile 1: 7.9, 4.6–13.1 vs 5.8, 3.3–9.8, P < 0.001) (Table 2).

3.3. Subgroup analysis. 

No significant interactions were observed in most subgroups. The risk of in-hospital mortality decreased in patients with higher heart rate. Patients with more comorbidities such as coronary artery disease, acute coronary syndrome, hypertension had higher risk of in-hospital mortality for PLR. But patients with cardiac arrest, shock, acute kidney injury had lower risk of in-hospital mortality. Increased risk of in-hospital mortality for PLR was also confirmed in patients with high red blood cell and low glucose, APS, APACHE IV. Besides, patients who received antiplatelet, oral anticoagulants, beta-blocker, ACEI/ARB therapy had higher risk of mortality for PLR (Table 4).

 
Table 4

Subgroup analysis of associations between in-hospital mortality and PLR.

Subgroups

N

Quartile 1

Quartile 2

Quartile 3

Quartile 4

P

for interaction

Age (years)

         

0.932

< 67

2687

Reference

0.96(0.64–1.45)

1.47(1.00-2.15)

1.78(1.23–2.60)

 

≥67

2890

Reference

1.02(0.72–1.46)

1.43(1.03-2.00)

1.60(1.16–2.22)

 

Gender

         

0.519

Male

3027

Reference

1.12(0.79–1.59)

1.59(1.14–2.21)

1.94(1.41–2.67)

 

Female

2550

Reference

0.94(0.62–1.41)

1.43(0.98–2.09)

1.57(1.08–2.29)

 

Ethnicity

         

0.677

Caucasian

3958

Reference

1.11(0.80–1.52)

1.47(1.09–1.98)

1.84(1.38–2.45)

 

African American

914

Reference

0.64(0.34–1.19)

1.58(0.94–2.66)

1.60(0.91–2.82)

 

Other

705

Reference

1.72(0.71–4.15)

1.77(0.71–4.45)

1.66(0.65–4.24)

 

Body mass index (kg/m2)

         

0.447

<27.9

2793

Reference

1.43(0.97–2.12)

1.76(1.21–2.57)

1.95(1.36–2.81)

 

≥27.9

2784

Reference

0.76(0.53–1.11)

1.35(0.96–1.88)

1.69(1.21–2.36)

 

Systolic blood pressure (mmHg)

         

0.257

<120

2734

Reference

0.99(0.71–1.38)

1.53(1.12–2.10)

1.54(1.14–2.08)

 

≥120

2843

Reference

1.11(0.71–1.74)

1.52(1.00-2.32)

1.96(1.29–2.99)

 

Diastolic blood pressure (mmHg)

         

0.496

<65

2768

Reference

0.95(0.68–1.34)

1.32(0.96–1.80)

1.53(1.13–2.07)

 

≥65

2809

Reference

1.14(0.73–1.77)

1.70(1.12–2.58)

1.87(1.23–2.84)

 

Mean blood pressure (mmHg)

         

0.907

<81

2859

Reference

1.10(0.79–1.55)

1.59(1.16–2.17)

1.64(1.21–2.23)

 

≥81

2718

Reference

0.89(0.57–1.38)

1.22(0.80–1.86)

1.57(1.04–2.39)

 

Heart rate (beats/min)

         

0.038

<87

2744

Reference

0.98(0.64–1.49)

1.58(1.06–2.34)

2.08(1.40–3.08)

 

≥87

2833

Reference

1.07(0.75–1.51)

1.36(0.98–1.88)

1.42(1.04–1.94)

 

Respiration rate (beats/min)

         

0.053

<20

2313

Reference

1.22(0.78–1.89)

1.79(1.17–2.73)

2.13(1.40–3.24)

 

≥20

3264

Reference

0.93(0.67–1.30)

1.33(0.98–1.81)

1.53(1.13–2.06)

 

Oxygen saturation (%)

         

0.563

<97

2174

Reference

1.20(0.78–1.85)

1.69(1.13–2.53)

2.04(1.38–3.02)

 

≥97

3403

Reference

0.94(0.67–1.32)

1.39(1.01–1.92)

1.58(1.15–2.16)

 

Congestive heart failure

         

0.661

Yes

1256

Reference

1.39(0.79–2.44)

1.94(1.15–3.29)

2.07(1.23–3.49)

 

No

4321

Reference

0.93(0.69–1.27)

1.37(1.03–1.83)

1.67(1.27–2.20)

 

Coronary artery disease

         

0.030

Yes

1853

Reference

1.54(0.94–2.53)

2.26(1.38–3.70)

2.77(1.70–4.53)

 

No

3724

Reference

0.87(0.63–1.19)

1.23(0.92–1.65)

1.41(1.07–1.87)

 

Acute coronary syndrome

         

0.036

Yes

1085

Reference

1.96(1.09–3.55)

2.58(1.41–4.72)

3.44(1.86–6.35)

 

No

4492

Reference

0.86(0.64–1.16)

1.31(1.00-1.72)

1.51(1.15–1.96)

 

STEMI

         

0.526

Yes

384

Reference

1.33(0.55–3.18)

1.76(0.70–4.47)

2.57(0.97–6.84)

 

No

5193

Reference

1.01(0.76–1.33)

1.49(1.15–1.93)

1.73(1.34–2.22)

 

NSTEMI

         

0.081

Yes

403

Reference

4.37(1.42–13.44)

5.37(1.71–16.83)

6.24(2.00-19.44)

 

No

5174

Reference

0.91(0.69–1.21)

1.39(1.07–1.80)

1.63(1.27–2.10)

 

Arrhythmias

         

0.089

Yes

1865

Reference

1.41(0.85–2.34)

2.19(1.37–3.49)

2.51(1.58–3.97)

 

No

3712

Reference

0.92(0.67–1.26)

1.28(0.95–1.74)

1.52(1.14–2.04)

 

Cardiac arrest

         

0.004

Yes

407

Reference

0.89(0.52–1.52)

1.44(0.86–2.42)

1.12(0.64–1.96)

 

No

5170

Reference

1.62(1.13–2.31)

2.37(1.69–3.32)

3.21(2.32–4.44)

 

Bradycardia

         

0.162

Yes

224

Reference

7.13(0.83–61.1)

6.87(0.80–58.9)

11.12(1.29–96.18)

 

No

5353

Reference

0.98(0.75–1.29)

1.46(1.14–1.88)

1.69(1.32–2.16)

 

Atrial fibrillation

         

0.281

Yes

1117

Reference

1.03(0.54–1.96)

1.91(1.08–3.38)

2.11(1.20–3.71)

 

No

4460

Reference

1.03(0.77–1.38)

1.38(1.04–1.83)

1.65(1.26–2.17)

 

Ventricular arrhythmias

         

0.516

Yes

241

Reference

1.14(0.47–2.76)

1.85(0.80–4.27)

1.16(0.42–3.19)

 

No

5336

Reference

1.04(0.79–1.38)

1.52(1.17–1.97)

1.86(1.45–2.40)

 

Atrioventricular block

         

0.711

Yes

119

Reference

3.33(0.57–19.4)

3.38(0.52–21.79)

2.81(0.37–21.66)

 

No

5458

Reference

1.00(0.76–1.31)

1.48(1.15–1.90)

1.74(1.36–2.22)

 

Cardiomyopathy

         

0.464

Yes

365

Reference

1.24(0.44–3.48)

2.15(0.83–5.58)

2.43(0.91–6.53)

 

No

5212

Reference

1.02(0.77–1.34)

1.47(1.14–1.90)

1.73(1.35–2.23)

 

Valve disease

         

0.425

Yes

164

Reference

0.28(0.28–2.82)

3.67(0.93–14.43)

1.97(0.48–8.03)

 

No

5413

Reference

1.06(0.81–1.38)

1.45(1.12–1.87)

1.76(1.37–2.25)

 

Shock

         

0.005

Yes

1718

Reference

1.02(0.67–1.56)

1.29(0.88–1.91)

1.10(0.76–1.60)

 

No

3859

Reference

1.01(0.71–1.44)

1.52(1.10–2.12)

2.05(1.48–2.84)

 

Pulmonary embolism

         

0.746

Yes

129

Reference

3.70(0.66–20.59)

1.98(0.31–12.67)

2.13(0.33–13.69)

 

No

5448

Reference

1.00(0.76–1.31)

1.50(1.17–1.93)

1.76(1.37–2.25)

 

Pulmonary hypertension

         

0.805

Yes

69

Reference

0.61(0.75–4.98)

1.18(0.17–8.33)

0.65(0.79–5.29)

 

No

5508

Reference

1.04(0.79–1.36)

1.51(1.18–1.95)

1.79(1.40–2.29)

 

Hypertension

         

0.021

Yes

1689

Reference

2.26(1.10–4.61)

2.91(1.45–5.84)

4.08(2.06–8.09)

 

No

3888

Reference

0.92(0.68–1.23)

1.39(1.06–1.82)

1.51(1.16–1.96)

 

Diabetes

         

0.415

Yes

1133

Reference

1.10(0.61-2.00)

1.45(0.82–2.57)

2.18(1.26–3.77)

 

No

4444

Reference

1.01(0.75–1.37)

1.53(1.16–2.01)

1.67(1.27–2.19)

 

Hypercholesterolemia

         

0.369

Yes

411

Reference

0.26(0.53–1.28)

2.07(0.79–5.42)

1.83(0.68–4.94)

 

No

5166

Reference

1.09(0.83–1.43)

1.47(1.14–1.91)

1.76(1.37–2.26)

 

COPD

         

0.447

Yes

633

Reference

0.63(0.25–1.62)

1.87(0.87–4.04)

1.79(0.85–3.79)

 

No

4944

Reference

1.08(0.82–1.43)

1.44(1.11–1.88)

1.74(1.34–2.25)

 

Respiratory failure

         

0.061

Yes

1669

Reference

0.98(0.67–1.43)

1.28(0.90–1.82)

1.25(0.90–1.75)

 

No

3908

Reference

1.12(0.75–1.66)

1.66(1.15–2.41)

1.95(1.35–2.82)

 

Chronic kidney disease

         

0.807

Yes

856

Reference

1.11(0.59–2.09)

1.15(0.62–2.11)

1.72(0.97–3.05)

 

No

4721

Reference

1.00(0.74–1.34)

1.57(1.19–2.06)

1.71(1.31–2.25)

 

Acute kidney injury

         

0.005

Yes

1031

Reference

0.95(0.59–1.53)

1.06(0.68–1.67)

1.03(0.67–1.60)

 

No

4546

Reference

1.06(0.76–1.47)

1.65(1.22–2.24)

2.04(1.52–2.75)

 

Malignancy

         

0.017

Yes

272

Reference

0.50(0.18–1.41)

0.67(0.26–1.73)

0.56(0.23–1.34)

 

No

5305

Reference

1.07(0.81–1.41)

1.56(1.20–2.02)

1.85(1.44–2.39)

 

Sepsis

         

0.063

Yes

1301

Reference

1.20(0.70–2.06)

1.56(0.95–2.56)

1.19(0.74–1.90)

 

No

4276

Reference

0.96(0.70–1.31)

1.37(1.02–1.84)

1.84(1.37–2.48)

 

Stroke

         

0.882

Yes

223

Reference

2.38(0.79–7.11)

3.15(1.01–9.83)

1.90(0.56–6.44)

 

No

5354

Reference

0.97(0.73–1.27)

1.46(1.13–1.88)

1.76(1.38–2.26)

 

Antiplatelet

         

0.011

Yes

2659

Reference

1.28(0.85–1.94)

1.97(1.33–2.92)

2.52 (1.71–3.71)

 

No

2918

Reference

0.86(0.61–1.23)

1.19(0.86–1.65)

1.30(0.95–1.78)

 

Oral anticoagulants

         

< 0.001

Yes

687

Reference

5.22(0.62–43.86)

9.66(1.24–75.16)

26.79(3.61–75.16)

 

No

4890

Reference

1.01(0.77–1.32)

1.47(1.14–1.89)

1.54(1.20–1.98)

 

Beta-blockers

         

0.001

Yes

2446

Reference

2.16(1.31–3.56)

2.50(1.52–4.12)

3.73(2.31–6.02)

 

No

3131

Reference

0.73(0.53–1.02)

1.20(0.90–1.61)

1.22(0.91–1.63)

 

ACEI/ARB

         

0.001

Yes

1490

Reference

2.32(1.00-5.36)

3.43(1.53–7.68)

6.01(2.74–13.15)

 

No

4087

Reference

0.94(0.71–1.25)

1.36(1.04–1.78)

1.42(1.09–1.84)

 

Statin

         

0.091

Yes

1717

Reference

1.77(1.01–3.10)

2.00(1.14–3.52)

2.91(1.69–4.99)

 

No

3860

Reference

0.86(0.63–1.17)

1.34(1.01–1.77)

1.47(1.11–1.93)

 

White blood cell (109/L)

         

0.232

<10.3

2761

Reference

0.91(0.58–1.44)

1.80(1.19–2.71)

2.49(1.67–3.73)

 

≥10.3

2816

Reference

1.15(0.82–1.60)

1.35(0.98–1.85)

1.36(1.00-1.85)

 

Neutrophil percentage (%)

         

0.671

<76

2736

Reference

0.77(0.52–1.12)

1.25(0.84–1.86)

1.97(1.26–3.09)

 

≥76

2841

Reference

0.88(0.59–1.33)

0.90(0.62–1.32)

0.89(0.62–1.29)

 

Red blood cell (109/L)

         

0.049

<4.1

2796

Reference

0.98(0.68–1.41)

1.20(0.85–1.69)

1.40(1.01–1.94)

 

≥4.1

2781

Reference

1.02(0.69–1.51)

1.79(1.25–2.57)

2.01(1.38–2.92)

 

Platelet (109/L)

         

0.151

<217

2770

Reference

1.18(0.84–1.66)

1.73(1.24–2.42)

2.09(1.48–2.96)

 

≥217

2807

Reference

0.84(0.54–1.30)

1.28(0.87–1.89)

1.51(1.04–2.20)

 

Hemoglobin (g/dL)

         

0.021

<12.1

2753

Reference

0.81(0.56–1.18)

1.08(0.76–1.53)

1.24(0.89–1.72)

 

≥12.1

2824

Reference

1.20(0.82–1.75)

1.90(1.33–2.72)

2.20(1.52–3.20)

 

Hematocrit (%)

         

0.119

<36.9

2781

Reference

0.86(0.58–1.26)

1.14(0.80–1.62)

1.34(0.96–1.87)

 

≥36.9

2796

Reference

1.16(0.80–1.69)

1.85(1.30–2.64)

2.12(1.47–3.08)

 

Glucose (mg/dL)

         

< 0.001

<132

2739

Reference

1.31(0.84–2.04)

2.44(1.62–3.68)

2.21(1.45–3.37)

 

≥132

2838

Reference

0.94(0.67–1.33)

1.11(0.80–1.53)

1.56(1.16–2.11)

 

Creatinine (mg/dL)

         

0.321

<1.18

2780

Reference

1.29(0.81–2.07)

1.99(1.28–3.10)

2.13(1.37–3.32)

 

≥1.18

2797

Reference

0.92(0.66–1.27)

1.25(0.92–1.69)

1.48(1.10–1.99)

 

Blood nitrogen urea (mg/dL)

         

0.060

<23

2762

Reference

0.97(0.65–1.49)

1.55(1.04–2.29)

1.57(1.05–2.36)

 

≥23

2815

Reference

1.00(0.70–1.42)

1.32(0.95–1.83)

1.57(1.15–2.15)

 

Sodium (mmol/L)

         

0.632

<138

2758

Reference

1.11(0.75–1.67)

1.65(1.14–2.39)

1.75(1.22–2.52)

 

≥138

2819

Reference

0.97(0.68–1.39)

1.40(0.99–1.96)

1.83(1.31–2.57)

 

Potassium (mmol/L)

         

0.295

<4.1

2500

Reference

0.88(0.58–1.34)

1.33(0.91–1.95)

1.46(0.99–2.13)

 

≥4.1

3077

Reference

1.13(0.79–1.60)

1.62(1.16–2.25)

1.94(1.41–2.67)

 

APS

         

< 0.001

<41

2729

Reference

2.52(1.15–5.51)

4.61(2.19–9.70)

6.16(2.93–12.92)

 

≥41

2848

Reference

0.87(0.65–1.18)

1.04(0.79–1.38)

1.04(0.79–1.36)

 

APACHE IV

         

< 0.001

<55

2747

Reference

2.55(1.17–5.57)

3.96(1.85–8.48)

6.61(3.16–13.81)

 

≥55

2830

Reference

0.85(0.63–1.15)

1.02(0.77–1.35)

0.99(0.76–1.30)

 
Binary logistic regression analysis was used and results were presented as OR(odds ratio) and 95% CI(confidence interval). P for interaction was calculated using binary logistic analysis to determine whether there is interaction between different subgroups and PLR quartiles. Abbreviation: STEMI: ST-elevation myocardial infarction; NSTEMI: non-ST-elevation myocardial infarction; COPD: chronic obstructive pulmonary disease; PLR: platelet-lymphocyte ratio; ACEI: angiotensin-converting enzyme inhibitor; ARB: angiotensin receptor blocker; APS: acute physiology score; APACHE IV: Acute Physiology and Chronic Health Evaluation IV.

4. Discussion

This study confirmed the relationship between PLR and in-hospital mortality in CICU patients. (1) As PLR quartiles increased, in-hospital mortality increased significantly. And after adjusting for possible confounding variables, PLR was still independently associated with in-hospital mortality. (2) The Lowess curves presented a positive relationship between PLR and in-hospital mortality. (3) Significant interactions were observed in several subgroups. (4) Length of CICU and hospital stay were prolonged as PLR increased.

In the previous study, inflammation has been proven to be strongly associated with development and prognosis of cardiovascular disease [26]. And there was evidence that lymphocytes played a key role in the regulation of inflammatory response at all levels during atherosclerosis. During the systemic inflammatory response, the lymphocyte count was proved to decrease because of increased lymphocyte apoptosis [27]. This may explain the underlying mechanism for the diagnostic and prognostic validity of low lymphocyte count in patients with acute coronary syndrome and stable coronary artery disease (CAD), respectively [14, 28]. The prethrombotic state is caused by increased megakaryocyte series proliferation and relative thrombocytosis, which reflects the body's persistent inflammatory state [7]. Moreover, some studies have demonstrated an increase in the incidence of adverse events as the platelet count increased [710]. Patients with higher platelet count corresponded to worse outcomes in ACS [29]. The reason may be that elevated levels of platelet mononuclear aggregation (PMA) in the blood of patients with coronary heart disease, which correlated with plaque stability [3031]. And high PMA levels in patients with NSTEMI have been proven to increase the risk of adverse outcomes [32]. As a member of systemic inflammatory response family, PLR is the combination of platelet and lymphocyte, which represents the situation of aggregation and inflammatory pathways, and is able to amplify changes in these two indicators, especially in cases where some clinicians tend to overlook such changes, such as when the indicator values are near the upper or lower limits of normal. Paying attention to PLR in clinical practice and improving the level of nursing and monitoring may improve the prognosis and reduce mortality.

As an available indicator, PLR has already been proven to be associated with severity and prognosis of cardiovascular disease. An observational study which enrolled 619 patients with NSTEMI confirmed that high PLR could independently predict the increased of long-term mortality [22]. And previous study included 636 patients with ST-elevated acute myocardial infarction showed that PLR was an independent predictor of cardiovascular mortality [23]. Moreover, PLR was proved to be a conventional risk factor in predicting severe atherosclerosis, and independently associated with increased Gensini score [33]. Besides, it was showed that high preoperative PLR level increased the incidence of no-reflow in patients after PCI [20].

Our research reached a similar conclusion that increased PLR was independently associated with in-hospital mortality in CICU patients, providing evidence for the use of PLR in patients with severe cardiovascular disease. In the subgroups of congestive heart failure, coronary artery disease, valvular disease, cardiomyopathy, arrhythmias, and shock, the same conclusion could be reached. These diseases almost covered most diseases in CICU, which confirmed the reliability of PLR application in CICU patients.

Through the Lowess curves, it has been demonstrated that when PLR was lower than 60, the mortality rate decreased with the increase of PLR, which suggested that we should be flexible when using PLR to judge the disease condition of CICU patients. When the PLR is very small, consideration should be given that whether the patient has other comorbidities that may increase mortality, such as diseases of the blood system. In patients with idiopathic thrombocytopenia, the platelet count is less than 100*109/L, and even less than 10*109/L in severe cases, resulting in an extremely small PLR value. In this study, we only excluded patients with hematologic malignancies from hematologic diseases. And due to retrospective studies, we could not rule out the possibility of missed diagnosis.

Therefore, when applying PLR in clinical judgment, it is unreasonable to think that the smaller the PLR, the better, and it is necessary to define a threshold value. The value of 10%-90%PLR was set as the reference range, and the Lowess curve showed that the mortality rate increased with the increase of PLR, that is, there was no inflection point. In this way, a more reasonable reference range for PLR was 69–460. When the PLR is below 69, the patients should be considered for other comorbidities that may increase mortality. When the PLR is greater than 460, we need to be aware that the patients' condition may be severe with a higher mortality rate.

In addition, as PLR quartiles increased, the length of hospital stay and the length of hospital stay significantly increased, which brought the psychological, physical, and financial burden on patients. Therefore, more attention should be paid to inexpensive, easily accessible indicators like PLR, which is more cost-effective, especially in some cases that more complex score could not be calculated, for example, the patient is unable to undergo complex examination or the patient is in a remote area without the condition to do so.

Independent association between PLR and in-hospital mortality in CICU patients was proved in this study, which is convenient for clinical use. The multi-center and large sample size made the conclusion more reliable. However, this study also had some limitations. First of all, bias is inevitable due to the retrospective study. Secondly some important indexes can’t be collected such as left ventricular ejection fraction, C-reactive protein, cholesterol. Generally speaking, the accuracy of the model is determined by the variables in the model, and the accuracy of the model in this study is affected to a certain extent due to the lack of the above variables. This will be improved in further research. The inability to dynamically analyze PLR was also the limitation.

5. Conclusion

To sum up, the results indicated that PLR was an independent predictor of CICU patient mortality in hospital. The in-hospital mortality rate increased significantly as PLR quartiles increased. Further, high PLR was related to prolonged CICU and hospital stay length. And patients with low APACHE IV or with less comorbidities had higher risk of mortality for PLR.

Declarations

Funding

This study was supported by grants from Beijing Municipal Health Commission (Grant No. PXM2020_026272_000002 and Grant No. PXM2020_026272_000014) and Natural Science Foundation of Beijing, China (Grant No. 7212027) to Yujie Zhou.

Declaration of Competing Interests

Authors declare no competing interests

Ethical Approval

This study was exempted from institutional review Board approval for the following reasons: (1) retrospective design, which was lack of direct patient intervention; (2) Privacert certification of reidentification risk conforming to safe harbor standards for security protocols (Cambridge, MA) (HIPAA Certification no. 1031219-2). 

Supplementary material

  The original data from the study is in the supplementary file.

Method statement

All methods were carried out in accordance with relevant guidelines and regulations.

Data Availability

The data used in this study was from eICU Collaborative Research Database [24], which is available at: https://doi.org/10.13026/C2WM1R. The author was approved to access to the database through Protecting Human Research Participants exam (certificate number: 9728458).

Authors’ Contributions

  Guangyao Zhai and Biyang Zhang contributed equally to this study.

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