Association Between White Blood Cell Counts at Diagnosis and Clinical Outcomes in Venous Thromboembolism　― From the COMMAND VTE Registry-2 ―

Abstract

Background: White blood cell (WBC) counts were reported to be a risk factor for acute adverse events in patients with venous thromboembolism (VTE). However, there are limited data on VTE patients without active cancer.

Methods and Results: The COMMAND VTE Registry-2 was a multicenter study enrolling 5,197 consecutive patients with acute symptomatic VTE. We divided 3,668 patients without active cancer into 4 groups based on WBC count quartiles (Q1–Q4) at diagnosis: Q1, ≤5,899 cells/μL; Q2, 5,900–7,599 cells/μL, Q3, 7,600–9,829 cells/μL; and Q4, ≥9,830 cells/μL. Patients in Q4 more often presented with pulmonary embolism (PE) than patients in Q1, Q2, and Q3 (68% vs. 37%, 53%, and 61%, respectively; P<0.001). The proportion of massive PEs among all PEs was higher in Q4 than in Q1, Q2, and Q3 (21% vs. 3.4%, 5.8%, and 11%, respectively; P<0.001). Compared with Q1, Q2, and Q3, patients in Q4 had a higher cumulative 5-year incidence of all-cause death (17.0%, 15.2%, 16.1%, and 22.8%, respectively; P<0.001) and major bleeding (10.9%, 11.0%, 10.3%, and 14.4%, respectively; P=0.002). The higher mortality risk of Q4 relative to Q2 was consistent regardless of the presentations of VTEs.

Conclusions: An elevated WBC count on VTE diagnosis was associated with a higher risk of mortality and major bleeding regardless of VTE presentation, suggesting the potential usefulness of WBC counts for further risk stratification.

From the perspective of the pathogenesis of the development of venous thromboembolisms (VTE), various blood cells, including red blood cells, platelets, and white blood cells (WBC), have been reported to play a crucial role in contributing to the formation of clots.¹ In fact, a previous study reported that elevated WBC counts were associated with an increased risk of VTE occurrence among hospitalized patients.² In addition, like anemia^3,4 and thrombocytopenia,^5,6 an elevated WBC count on diagnosis has been reported to be a potential predictor of mortality in patients with pulmonary embolisms (PEs), including patients with active cancer,^7–10 or in VTE patients with active cancer.¹¹ Conversely, because active cancer could have a considerable effect on mortality in VTE patients,^12,13 as well as on WBC counts through chemotherapy and cancer type, comprehensive data are lacking regarding the effect of WBC counts at diagnosis on mortality in VTE patients without active cancer.

In addition to mortality, elevated WBC counts have been reported to be a risk factor for bleeding and thromboses in patients with coronary artery disease,^14,15 ischemic stroke,¹⁶ and cancer-associated VTEs.¹¹ However, there remains a gap in understanding as to whether the WBC count has a similar effect on bleeding and VTE recurrence in VTE patients without active cancer. The WBC count at diagnosis could be useful in determining optimal management strategies for these patients. Thus, the aim of the present study was to investigate the association between the WBC count at diagnosis and clinical outcomes in VTE patients without active cancer using a large observational database in Japan.

Methods

Study Population

The COMMAND VTE (COntemporary ManageMent AND outcomes in patients with Venous ThromboEmbolism) Registry-2 was a physician-initiated retrospective multicenter cohort study enrolling consecutive patients with acute symptomatic VTE confirmed by objective imaging examinations or autopsy across 31 centers in Japan between January 2015 and August 2020. The design of the registry has been reported in detail elsewhere.¹² The relevant review boards or ethics committees of all 31 participating centers (Supplementary Appendix 1) approved the research protocol. The requirement for written informed consent from each patient was waived because we used clinical information obtained in routine clinical practice and none of the patients refused to participate in the study when contacted for follow-up. This method is concordant with the guidelines for epidemiological studies issued by the Ministry of Health, Labor, and Welfare in Japan (https://www.mhlw.go.jp/content/001077424.pdf).

After excluding 1,505 patients with active cancer, the present study population consisted of 3,668 patients without active cancer. These patients were divided into 4 groups based on WBC count quartiles (Q1–Q4) at diagnosis: Q1, ≤5,899 cells/μL; Q2, 5,900–7,599 cells/μL, Q3, 7,600–9,829 cells/μL; and Q4, ≥9,830 cells/μL (Figure 1). We compared the clinical characteristics, management strategies, and long-term outcomes across these quartiles.

Figure 1.

Study flow chart. Venous thromboembolism (VTE) includes pulmonary embolism and/or deep vein thrombosis. Q1–Q4, quartiles 1–4. WBC, white blood cell.

Data Collection and Definitions of Patient Characteristics

Data for baseline characteristics were collected from hospital charts or hospital databases according to prespecified definitions. The physicians at each institution were responsible for data entry into an electronic case report form in a web-based database system. Data were automatically checked for missing or contradictory inputs and values out of the expected range. Additional monitoring for data quality was performed at the Registry’s general office.

The severity of PEs was classified as massive, submassive, or low risk, as proposed by the American Heart Association (AHA).¹⁷ Thrombolysis therapy for PEs was defined as treatment with a tissue plasminogen activator.¹⁸ Hypoxemia was defined as an arterial partial pressure of oxygen of <60 mmHg or hemoglobin oxygen saturation <90%.¹⁹ Shock was defined as systolic blood pressure <90 mmHg for at least 15 min, a pressure drop of ≥40 mmHg for at least 15 min, or a requirement for inotropic support.¹⁹ Cardiac arrest or collapse was recorded as a cardiopulmonary arrest.¹⁹ The definitions of other characteristics are provided in Supplementary Appendix 2.

Clinical Follow-up and Endpoints

Follow-up information was collected primarily via a review of hospital charts. Additional follow-up information was obtained by contacting patients, their relatives, and/or referring physicians by telephone and/or mail with questions regarding vital status, recurrent VTE, bleeding, invasive procedures, and status of anticoagulation therapy.

The outcomes evaluated in this study were all-cause death, recurrent VTEs, and major bleeding. Recurrent VTEs were defined as PEs and/or deep vein thromboses (DVTs) with symptoms accompanied by confirmation of a new thrombus or exacerbation of the thrombus by objective imaging examinations or autopsy.²⁰ Major bleeding was defined as International Society of Thrombosis and Hemostasis (ISTH) major bleeding, which consisted of a reduction in the hemoglobin level by at least 2 g/dL, transfusion of at least 2 units of blood, or symptomatic bleeding in a critical area or organ.²¹

An independent clinical event committee (Supplementary Appendix 3), unaware of patient characteristics, reviewed all study outcomes and classified the causes of death as due to PE, bleeding events, cardiac causes, cancer, other causes, or unknown causes.²²

Statistical Analysis

Categorical variables are presented as numbers and percentages, and continuous variables are presented as the mean±SD or median with interquartile range depending on their distribution. Categorical variables were compared using the Chi-squared test. Continuous variables were compared using analysis of variance (ANOVA) or the Kruskal-Wallis test depending on their distribution.

The Kaplan-Meier method was used to estimate cumulative incidence, with the significance of differences assessed using the log-rank test. To adjust for clinically relevant confounders, we used a multivariable Cox proportional hazard model to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the risks of clinical outcomes for patients in Q1, Q3, and Q4 relative to those in Q2 because WBC counts in Q2 were within the normal range (4,000–8,000) for healthy individuals. Consistent with our previous reports,^23–28 and considering clinical relevance, we selected 17 risk-adjusting variables for all-cause death, 9 risk-adjusting variables for recurrent VTEs, and 13 risk-adjusting variables for major bleeding (Table 1;²⁹ Supplementary Table 1). To visualize continuous changes in the risk of outcomes according to the WBC count, we also described natural cubic spline models for the adjusted HRs of the outcomes with 5 degrees of freedom.

Table 1.

Patient Characteristics According to WBC Quartiles (Q1–Q4) at Diagnosis of VTE

	Q1 (WBC ≤5,899/μL; n=891)	Q2 (WBC 5,900–7,599/μL; n=942)	Q3 (WBC 7,600–9,829/μL; n=918)	Q4 (WBC ≥9,830/μL; n=917)	P value
WBC count at diagnosis (/μL)	4,900 [4,265–5,400]	6,700 [6,255–7,148]	8,555 [8,000–9,200]	11,900 [10,800–14,000]
Baseline characteristics
Age (years)A,B,C	69.7±15.6	68.6±16.3	67.0±17.0	64.8±17.5	<0.001
Female sexA,B,C	589 (66)	554 (59)	524 (57)	537 (59)	<0.001
Body weight (kg)	56.8±13.1	60.1±14.2	61.5±15.3	61.3±16.3	<0.001
BMI (kg/m2)	23.1±4.2	23.6±4.2	24.1±4.6	24.0±5.2	<0.001
BMI ≥30 kg/m2 A,B	37 (4.2)	51 (5.4)	86 (9.4)	85 (9.3)	<0.001
Time from symptom onset to diagnosis (days)	5 (2–14)	5 (2–10)	3 (1–7)	2 (0–5)	<0.001
Comorbidities
Chronic lung disease	85 (9.5)	87 (9.2)	111 (12)	121 (13)	0.014
Chronic obstructive pulmonary disease	17 (1.9)	17 (1.8)	20 (2.2)	18 (2.0)	0.95
Asthma	43 (4.8)	36 (3.8)	46 (5.0)	43 (4.7)	0.62
HypertensionA,C	387 (43)	448 (48)	432 (47)	378 (41)	0.02
DiabetesA	118 (13)	160 (17)	151 (16)	156 (17)	0.09
Dyslipidemia	238 (27)	266 (28)	258 (28)	219 (24)	0.12
Chronic kidney diseaseA,C	155 (17)	192 (20)	167 (18)	199 (22)	0.08
Dialysis	10 (1.1)	8 (0.8)	13 (1.4)	12 (1.3)	0.69
History of cancer	125 (14)	118 (13)	100 (11)	93 (10)	0.0502
Heart failure	41 (4.6)	53 (5.6)	44 (4.8)	42 (4.6)	0.69
Atrial fibrillation	45 (5.1)	42 (4.5)	38 (4.1)	48 (5.2)	0.66
History of MI	18 (2.0)	13 (1.4)	36 (3.9)	18 (2.0)	0.002
History of strokeA,C	97 (11)	92 (9.8)	89 (9.7)	98 (11)	0.77
Liver cirrhosisA,C	9 (1.0)	5 (0.5)	10 (1.1)	3 (0.3)	0.16
Varicose veinA,B	70 (7.9)	47 (5.0)	39 (4.2)	25 (2.7)	<0.001
History of VTEA,B	78 (8.8)	83 (8.8)	65 (7.1)	52 (5.7)	0.03
History of major bleedingA,C	51 (5.7)	51 (5.4)	64 (7.0)	86 (9.4)	0.003
Autoimmune disorderA,B,C	122 (14)	119 (13)	113 (12)	160 (17)	0.005
Use of oral corticosteroids at diagnosis	68 (7.6)	81 (8.6)	108 (12)	141 (15)	<0.001
Transient risk factors for VTEsA,B,C	317 (36)	286 (30)	305 (33)	350 (38)	0.003
Major transient risk	153 (17)	119 (13)	119 (13)	125 (14)	<0.001
Minor transient risk	205 (23)	202 (21)	216 (24)	282 (31)
Unprovoked	452 (51)	537 (57)	500 (54)	407 (44)	<0.001
Presentation
Site of VTE
PE with or without a DVT	327 (37)	501 (53)	559 (61)	626 (68)	<0.001
DVT onlyA,B,C	564 (63)	441 (47)	359 (39)	291 (32)
Severity of PE
Hypoxemia	95/327 (29)	198 /501 (40)	274/559 (49)	359/626 (57)	<0.001
Shock	11/327 (3.4)	29/501 (5.8)	62/559 (11)	134/626 (21)	<0.001
Cardiac arrest/collapse	5/327 (1.5)	9/501 (1.8)	27/559 (4.8)	77/626 (12)	<0.001
Classification of severity of PE
MassiveA,B,C	11/327 (3.4)	29 /501 (5.8)	62/559 (11)	134/626 (21)	<0.001
SubmassiveA,B,C	87/327 (27)	194/501 (39)	222/559 (40)	209/626 (33)
Low riskA,B,C	229/327 (70)	278/501 (56)	275/559 (49)	283/626 (45)
Laboratory tests at diagnosis
Hemoglobin (g/dL)	11.8±2.3	12.5±2.1	12.6±2.3	12.4±2.5	<0.001
AnemiaA,C	501 (56)	399 (42)	390 (42)	428 (47)	<0.001
Platelet count (×103/μL)	20.9±8.2	21.6±7.9	22.1±8.5	22.7±10.0	<0.001
Thrombocytopenia (platelet count <100×109/L)A,C	40 (4.5)	26 (2.8)	29 (3.2)	49 (5.3)	0.015
AST (IU/L)	21.0 [18.0–28.0]	22.0 [17.0–29.5]	24.0 [18.0–34.0]	26.0 [18.0–45.0]	<0.001
Creatinine clearance (mL/min)	63.9 [45.3–91.6]	67.6 [47.7–92.8]	68.8 [46.6–98.0]	66.2 [43.4–102.6]	0.14
D-dimer (μg/mL)	6.2 [2.8–11.6]	8.6 [4.0–15.9]	9.4 [4.8–17.7]	12.4 [6.0–25.9]	<0.001
Troponin I (ng/mL; n=803)	0.010 [0.006–0.025]	0.020 [0.010–0.081]	0.045 [0.010–0.218]	0.088 [0.020–0.329]	<0.001
Troponin T (ng/mL; n=321)	0.010 [0.005–0.025]	0.019 [0.007–0.043]	0.023 [0.008–0.058]	0.035 [0.013–0.075]	<0.001
ThrombophiliaA,B	30 (3.4)	47 (5.0)	38 (4.1)	41 (4.5)	0.38
Treatment in the acute phase
Initial parenteral anticoagulation therapy	334 (37)	466 (49)	546 (59)	619 (68)	<0.001
Heparin	322 (36)	453 (48)	533 (58)	611 (67)	<0.001
Single injection at diagnosis	27/322 (8.4)	34/453 (7.5)	34/533 (6.4)	30/611 (4.9)	0.16
Continuous injection	295/322 (92)	419/453 (92)	499/533 (94)	581/611 (95)
Fondaparinux	13 (1.5)	10 (1.1)	14 (1.5)	7 (0.8)	0.40
Thrombolysis therapy with t-PA for PEs	33 (3.7)	64 (6.8)	94 (10)	99 (11)	<0.001
Mechanical ventilation	5 (0.6)	11 (1.2)	29 (3.2)	77 (8.4)	<0.001
PCPS	1 (0.1)	6 (0.6)	15 (1.6)	48 (5.2)	<0.001
Anticoagulation therapy beyond the acute phase	825 (92)	894 (95)	864 (94)	826 (91)	<0.001
Warfarin	111 (12)	113 (12)	121 (13)	152 (17)	<0.001
Direct oral anticoagulant	714 (80)	781 (83)	743 (81)	674 (74)
Concomitant medications at discharge
Corticosteroids	86 (9.7)	104 (11)	116 (13)	177 (19)	<0.001
Antiplatelet agents	87 (9.8)	87 (9.2)	92 (10)	71 (7.7)	0.33
Non-steroidal anti-inflammatory drugs	76 (8.5)	66 (7.0)	72 (7.8)	49 (5.3)	0.052
Proton pump inhibitors	379 (43)	421 (45)	425 (46)	486 (53)	<0.001
H2-receptor blockers	35 (3.9)	34 (3.6)	44 (4.8)	43 (4.7)	0.52
Statins	174 (20)	174 (18)	179 (19)	144 (16)	0.11

Categorical variables are presented as numbers and percentages, whereas continuous variables are presented as the mean±SD or median [interquartile range] depending on their distribution. Categorical variables were compared using the Chi-squared test. Continuous variables were compared using Student’s t-test or the Wilcoxon’s rank-sum test depending on their distribution. ^ARisk-adjusting variables for the multivariable Cox regression model to estimate the risk of all-cause death. ^BRisk-adjusting variables for the multivariable Cox regression model to estimate the risk of recurrent VTE. ^CRisk-adjusting variables for the multivariable Cox regression model to estimate the risk of major bleeding. Chronic kidney disease was diagnosed if there was persistent proteinuria or if the eGFR was <60 mL/min/1.73 m² for more than 3 months. eGFR values were calculated based on the equation reported by the Japan Association of Chronic Kidney Disease Initiative as follows: 194 × serum creatinine^−1.094 × age^−0.287 in men; 194 × serum creatinine^−1.094 × age^−0.287 × 0.739 in women.²⁹ Anemia was diagnosed if hemoglobin was <13 g/dL for men and <12 g/dL for women. Thrombophilia included a protein C deficiency, protein S deficiency, antithrombin deficiency, and antiphospholipid syndrome. Creatinine clearance was calculated based on the Cockcroft-Gault equation as follows: (140 − age) × body weight / (72 × serum creatinine) in men; 0.85 × (140 − age) × body weight / (72 × serum creatinine) in women. AST, aspartate aminotransferase; BMI, body mass index; DVT, deep vein thrombosis; eGFR, estimated glomerular filtration rate; MI, myocardial infarction; PCPS, percutaneous cardiopulmonary support; PE, pulmonary embolism; Q1–Q4, Quartiles 1–4; t-PA, tissue plasminogen activator; VTE, venous thromboembolism; WBC, white blood cell.

To evaluate short- and long-term clinical outcomes, we conducted a landmark analysis at 90 days, as described previously.³⁰ The specific landmark point of 90 days was selected by the minimum duration of anticoagulation therapy, which is recommended by current guidelines for all patients with VTE.³¹ In addition, because there could be different risks of mortality across the WBC quartiles, a competing risks regression analysis was then used to calculate the cumulative incidence function of recurrent VTEs and major bleeding to take competing risks of mortality into account. Furthermore, we conducted a stratified analysis according to the presentations of VTEs, and divided the entire cohort into 4 subgroups: massive PEs, submassive PEs, low-risk PEs, and DVTs only.³¹

To confirm the robustness of the main analysis, we also conducted the following sensitivity analyses. First, we used different cut-off values of the WBC count based on a previous paper¹¹ (i.e., namely low=≤3,999 cells/μL; normal=4,000–10,999 cells/μL; and high=≥11,000 cells/μL) and evaluated clinical outcomes. Second, because acute therapeutic strategies for critical PEs could have a certain effect on bleeding risk, we evaluated clinical outcomes after excluding 340 patients who received percutaneous cardiopulmonary support (PCPS) and/or thrombolysis. Third, the use of corticosteroids could increase WBC counts and modulate inflammation, and is common in patients with autoimmune disease. To avoid multicollinearity, we constructed a multivariable model including the risk-adjusting variable of “use of oral corticosteroids” instead of “autoimmune disease”.

All statistical analyses were conducted using R version 4.3.2 software. All P values reported are 2-tailed, and P<0.05 was considered statistically significant.

Results

Patient Characteristics

Among the 3,668 patients, there were 891 (24%), 942 (26%), 918 (25%), and 917 (25%) patients in Q1, Q2, Q3, and Q4, respectively (Figure 1). A detailed distribution of WBC counts at diagnosis is shown in Supplementary Figure 1. Baseline characteristics differed across the 4 quartiles in several aspects: patients in Q4 were younger than those in Q1, Q2, and Q3 (64.8 vs. 69.7, 68.6, and 67.0 years, respectively; P<0.001); the median time from symptom onset to diagnosis was shorter for patients in Q4 than for those in Q1, Q2, and Q3 (2 vs. 6, 5, and 3 days, respectively; P<0.001); patients in Q4 more often presented with PEs with or without DVTs than those in Q1, Q2, and Q3 (68% vs. 37%, 53%, and 61%, respectively; P<0.001); and, in patients with PEs, the proportion of massive PEs was higher in Q4 than in Q1, Q2, and Q3 (21% vs. 3.4%, 5.8%, and 11%, respectively; P<0.001).

Treatment Strategies

Compared with patients in Q1, Q2, and Q3, patients in Q4 were more frequently treated with initial parenteral anticoagulation therapy (37%, 49%, 59%, and 68%, respectively; P<0.001), thrombolysis (3.7%, 6.8%, 10%, and 11%, respectively; P<0.001), mechanical ventilation (0.6%, 1.2%, 3.2%, and 8.4%, respectively; P<0.001), and PCPS (0.1%, 0.6%, 1.6%, and 5.2%, respectively; P<0.001; Table 1). The cumulative discontinuation rate of anticoagulation therapy was higher among patients in Q1 than in Q2, Q3, and Q4 (53.5%, 46.9%, 46.9%, and 49.2%, respectively, at 3 years; P=0.005; Supplementary Figure 2.

Clinical Outcomes

The cumulative 5-year incidence of all-cause death was significantly higher in Q4 than in than in Q1, Q2, and Q3 (22.8% vs. 17.0%, 15.2%, 16.1%, respectively; P<0.001; Figure 2A). After adjusting for confounders, the risk of all-cause death remained significantly higher for Q4 relative to Q2 for the entire follow-up period (adjusted HR 1.70; 95% CI 1.31–2.22; P<0.001; Table 2). The natural spline curve models showed that the risk of mortality was lowest when WBC counts were within the normal range (4,000–8,000 cells/μL) of healthy individuals (Supplementary Figure 3A). Furthermore, there was a correlation between an increase in WBC counts and a higher risk of mortality. Landmark analysis at 90 days showed that the cumulative 90-day incidence of all-cause death was significantly higher in Q4 than in Q1, Q2, and Q3 (7.9% vs. 2.5%, 1.5%, and 3.6%, respectively; P<0.001); however, there was no significant difference in the cumulative 5-year incidence of all-cause death beyond 90 days across WBC quartiles (14.9%, 13.9%, 13.0%, and 16.2% for Q1–Q4, respectively; P=0.70; Figure 2B).

Figure 2.

Kaplan-Meier curves for all-cause death according to white blood cell (WBC) quartiles (Q1–Q4) at diagnosis of venous thromboembolism (A) over the entire follow-up period and (B) within and beyond 3 months (90 days) in the landmark analysis at 90 days. Venous thromboembolism includes pulmonary embolism and/or deep vein thrombosis.

Table 2.

Clinical Outcomes Over the Entire Follow-up Period According to White Blood Cell Quartiles

	No. patients (%) with events during the follow-up period (cumulative 5-year incidence)	Crude HR (95% CI)	P value	Adjusted HR (95% CI)	P value
All-cause death
Quartile 2	92 (15.2)	Ref.	–
Quartile 1	94 (17.0)	1.13 (0.85–1.49)	0.40	1.01 (0.76–1.34)	0.95
Quartile 3	102 (16.1)	1.21 (0.92–1.59)	0.17	1.19 (0.90–1.57)	0.21
Quartile 4	147 (22.8)	1.84 (1.43–2.38)	<0.001	1.70 (1.31–2.22)	<0.001
Major bleeding
Quartile 2	61 (11.0)	Ref.	–
Quartile 1	57 (10.9)	1.02 (0.71–1.47)	0.90	0.96 (0.66–1.38)	0.82
Quartile 3	71 (10.3)	1.32 (0.94–1.85)	0.11	1.23 (0.87–1.73)	0.24
Quartile 4	90 (14.4)	1.74 (1.26–2.41)	<0.001	1.35 (0.96–1.90)	0.080
Recurrent venous thromboembolisms
Quartile 2	62 (11.8)	Ref.	–
Quartile 1	38 (7.0)	0.69 (0.47–1.02)	0.065	0.74 (0.50–1.09)	0.12
Quartile 3	45 (8.1)	0.75 (0.51–1.08)	0.13	0.73 (0.50–1.06)	0.10
Quartile 4	45 (9.3)	0.80 (0.55–1.17)	0.26	0.80 (0.54–1.18)	0.26

The cumulative 5-year incidence was estimated using the Kaplan-Meier method. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated by the Cox proportional hazard models using Quartile 2 as the reference.

The cumulative 5-year incidence of major bleeding was significantly higher in Q4 than in Q1, Q2, and Q3 (14.4% vs. 10.9%, 11.0%, and 10.3%, respectively; P=0.002; Figure 3A). After adjusting for confounders, there was a numerically higher risk of major bleeding in Q4 relative to Q2 for the entire follow-up period (adjusted HR 1.35; 95% CI 0.96–1.90; P=0.08; Table 2). Even after adjusting for the competing risk of death, the excess risk of major bleeding in Q4 remained significant (10.1%, 10.2%, 9.7%, and 13.1%, in Q1–Q4, respectively; P=0.002; Supplementary Figure 4A). The natural spline curve model showed that the risk of major bleeding was lowest when WBC counts were within the normal range (4,000–8,000 cells/μL) of healthy individuals (Supplementary Figure 3B). Furthermore, an increase in the WBC count was correlated with an increased risk of mortality. Landmark analysis at 90 days showed that the cumulative 90-day incidence of major bleeding was significantly higher in Q4 than in Q1, Q2, and Q3 (6.4% vs. 1.7%, 2.3%, and 3.6%, respectively; P<0.001); however, there was no significant difference in the cumulative 5-year incidence of major bleeding beyond 90 days across quartiles (9.3%, 8.9%, 6.9%, and 8.4% for Q1–Q4, respectively; P=0.80; Figure 3B).

Figure 3.

Kaplan-Meier curves for major bleeding according to white blood cell (WBC) quartiles (Q1–Q4) at diagnosis of venous thromboembolism (A) over the entire follow-up period and (B) within and beyond 3 months (90 days) in the landmark analysis at 90 days. Venous thromboembolism includes pulmonary embolism and/or deep vein thrombosis.

There was no significant difference in the cumulative 5-year incidence of recurrent VTEs across quartiles (7.0%, 11.8%, 8.1%, and 9.3% for Q1–Q4, respectively; P=0.20; Supplementary Figure 5). After adjusting for confounders, there was no excess risk of recurrent VTEs in Q4 relative to Q2 (Table 2). After adjusting for the competing risk of death, there was no excess risk of recurrent VTEs in Q4 relative to Q2 (Supplementary Figure 4B).

Stratified and Sensitivity Analyses

The distribution of the WBC count at diagnosis according to the presentation of VTEs is shown in Supplementary Figure 6. The median WBC count at diagnosis in the DVT only, low-risk PE, submassive PE, and massive PE subgroups was 6,820, 7,750, 8,200, and 10,300 cells/μL, respectively (P<0.001). The results of stratified analysis according to the presentation of VTEs were consistent with those of the main analysis, regardless of the presentation of VTEs, with a higher risk of all-cause death in Q4 than in the other quartiles (Supplementary Figure 7). The detailed causes of death according to the presentation of VTEs are shown in Supplementary Figure 8.

The results of the sensitivity analysis using different cut-off values for WBC counts at diagnosis were fully consistent with the main analysis (Supplementary Figure 9). In addition, the sensitivity analysis after excluding patients with PCPS and/or thrombolysis therapy showed a numerically higher risk of major bleeding in Q4 relative to Q2 (Supplementary Figure 10; Supplementary Table 2). The results of different multivariate models adjusted for the use of oral corticosteroids instead of autoimmune disease were consistent with those of the main analysis (Supplementary Table 3).

Discussion

The main findings of the present study are that: (1) patients with a higher WBC count at diagnosis more often presented with severe PE; (2) higher WBC counts at diagnosis were associated with a higher mortality risk regardless of the presentation of VTEs; and (3) a higher WBC count at diagnosis was associated with a higher risk of major bleeding, but not of VTE recurrence.

Previous studies reported that an elevated WBC count was associated with the severity of cardiovascular disease and cancer, as well as the mortality from these diseases.^32,33 Similarly, previous studies, including those in patients with active cancer, also reported that higher WBC and neutrophil counts in PE patients were associated with the severity of PEs.^9,34 Trujillo-Santos et al. also reported that elevated WBC counts in VTE patients with active cancer were associated with the severity of PEs.¹¹ Because active cancer could have a considerable effect on the WBC count, we excluded patients with active cancer from the present study and found that the WBC count at diagnosis was associated with the severity of PE even in VTE patients without active cancer. Historically, the WBC count, especially the neutrophil count, has been recognized as a biomarker of inflammation. From a pathophysiological point of view, inflammation could play an important role in the development and progression of VTEs, as in other diseases such as coronary artery disease and cancer.^35–38 The causal relationship between VTEs and inflammation remains controversial. Nevertheless, suppressing inflammation would be a potential treatment target for patients with VTEs if the inflammation caused worsening of VTEs.³⁹

Previous studies also reported that higher WBC counts were associated with an increased mortality risk in PE patients, including those with active cancer.^7–10 In addition, it has been reported that elevated WBC counts in cancer-associated VTEs are associated with the risk of mortality.¹¹ In line with these previous studies, we found that higher WBC counts at diagnosis were associated with a higher mortality risk in patients with VTEs, which was consistent regardless of the presentations of VTEs. This association could partly reflect the patients’ poor health condition, including infectious diseases. However, even though the clinical severity of VTEs is a better predictor of mortality, an elevated WBC count at diagnosis could be an additional predictor of mortality, especially in the acute phase, which could be helpful in determining acute phase management strategies in patients with VTEs.

In addition to the risk of mortality, the risk of recurrent VTEs and bleeding could be clinically relevant in determining optimal management strategies for VTEs. A previous study reported that higher WBC counts in patients with acute coronary syndrome were associated with an increased risk of bleeding and ischemic events in the acute phase.¹⁵ Another study reported that the neutrophil-to-lymphocyte ratio was associated with gastrointestinal bleeding in patients with cerebral bleeding.⁴⁰ Similarly, it has been reported that elevated WBC counts in cancer-associated VTEs are associated with the risk of recurrent VTEs and bleeding.¹¹ In the present study, higher WBC counts at diagnosis were associated with a higher risk of major bleeding, but not recurrent VTEs. Several risk scores for bleeding, including the VTE-BLEED score, do not include the WBC count at diagnosis.⁴¹ However, an elevated WBC count at diagnosis of VTE should be carefully noted considering the relatively high bleeding risk in the acute phase.

Study Limitations

This study has several limitations. First, this study was based on an observational study, which could be subject to various biases inherent to an observational study design. In particular, therapeutic decision making was left to the discretion of the attending physicians, which could be influenced by WBC counts upon diagnosis through the severity of VTEs. Second, there were several differences in patient characteristics, including the severity of PEs, across WBC quartiles. Although we adjusted for several confounders, there may still have been unmeasured confounders. There may be selection bias due to the unmeasured confounders such as comorbidities that were not collected as part of the data, which could have influenced mortality, bleeding events, and WBC count. Thus, the results of the present study are hypothesis-generating and should be interpreted with caution. Third, the time from symptom onset to diagnosis differed across WBC quartiles groups. Because WBC counts can change over time, fluctuations in the WBC count may have some effect on the interpretation of our results. Fourth, there could be useful biomarkers for predicting of the outcomes of VTEs, such as troponin I or troponin T. However, because there were many missing values for these biomarkers and many missing values causes selection bias, they were not included in the results in the main analysis (Supplementary Table 4). Fifth, detailed subtypes of WBC were not collected in the present study. Thus, we could not evaluate the effects and associations of WBC subtypes with outcomes. Finally, although a sensitivity analysis including the use of oral corticosteroids showed results that were consistent with those of the main analysis, we could not evaluate the influence of detailed corticosteroids use, including doses, on WBC counts.

Conclusions

An elevated WBC count at diagnosis was associated with a higher risk of mortality and major bleeding regardless of the presentation of VTEs, suggesting the potential usefulness of WBC counts for further risk stratification.

Acknowledgments

The authors appreciate the support and collaboration of the coinvestigators who participated in the COMMAND VTE Registry-2. The authors also express their gratitude to Mr. John Martin from Japan Lifeline Co., Ltd for his grammatical assistance with a draft of this manuscript.

Sources of Funding

The COMMAND VTE Registry-2 was supported, in part, by JSPS KAKENHI (Grant no. JP 21K16022). The research funding had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; or preparation, review, or approval of the manuscript.

Disclosures

Y.Y. has received lecture fees from Bayer Healthcare, Bristol-Myers Squibb, Pfizer, and Daiichi-Sankyo, and grant support from Bayer Healthcare and Daiichi-Sankyo. T.M. reports receiving lecturer fees from Abbott, AstraZeneca, Boehringer Ingelheim, Bristol-Myers Squibb, Daiichi Sankyo, Japan Lifeline, Pfizer, Tsumura and UCB, as well as manuscript fees from Pfizer; and being on the advisory board for GlaxoSmithKline, Novartis and Teijin. K. Kaneda has received lecture fees from Bristol-Myers Squibb, Pfizer, and Daiichi-Sankyo. Y.N. has received lecture fees from Bayer Healthcare, Bristol-Myers Squibb, Pfizer, and Daiichi-Sankyo. N.I. has received lecture fees from Bayer Healthcare, Bristol-Myers Squibb, and Daiichi-Sankyo. Sa.I. has received lecture fees from Bayer Healthcare, Bristol-Myers Squibb, and Daiichi-Sankyo. Y.O. has received lecture fees from Bayer Healthcare, Bristol-Myers Squibb, Pfizer, and Daiichi-Sankyo, and research funds from Bayer Healthcare and Daiichi-Sankyo. N.K. has received lecture fees from Bayer Healthcare and grant support from Pfizer. All other authors report that they have no relationships relevant to the contents of this paper to disclose.

IRB Information

The research protocol was approved by Kyoto University Hospital Ethics Committee (R3082). In addition, the research protocol was approved by the relevant review boards or ethics committees in all 31 participating centers (Supplementary Appendix 1). The study was conducted in accordance with the principles of the Declaration of Helsinki. The requirement for written informed consent from each patient was waived because we used clinical information obtained in routine clinical practice. This method is concordant with the guidelines for epidemiological studies issued by the Ministry of Health, Labor, and Welfare in Japan.

Data Availability

The data, analytic methods, and study materials will not be made available to other researchers for purposes of reproducing the results or replicating the procedure. However, if the relevant review board or ethics committee approves data sharing and all investigators of the COMMAND VTE Registry-2 provide consent, the deidentified participant data will be shared on a request basis through the principal investigator. The study protocol will also be available. The data will be shared as Excel files via email during the period that considered necessary for the research.

Supplementary Files

Please find supplementary file(s);

https://doi.org/10.1253/circj.CJ-24-0581

References

• 1.   Campos J, Brill A. The role of bone marrow-derived cells in venous thromboembolism. Int J Biochem Cell Biol 2020; 128: 105850, doi:10.1016/j.biocel.2020.105850.
• 2.   Zakai NA, Callas PW, Repp AB, Cushman M. Venous thrombosis risk assessment in medical inpatients: The medical inpatients and thrombosis (MITH) study. J Thromb Haemost 2013; 11: 634–641, doi:10.1111/jth.12147.
• 3.   Goto S, Turpie AGG, Farjat AE, Weitz JI, Haas S, Ageno W, et al. The influence of anemia on clinical outcomes in venous thromboembolism: Results from GARFIELD-VTE. Thromb Res 2021; 203: 155–162, doi:10.1016/j.thromres.2021.05.007.
• 4.   Yamashita Y, Morimoto T, Amano H, Takase T, Hiramori S, Kim K, et al. Influence of baseline anemia on long-term clinical outcomes in patients with venous thromboembolism: From the COMMAND VTE registry. J Thromb Thrombolysis 2019; 47: 444–453, doi:10.1007/s11239-018-1791-2.
• 5.   Yamashita Y, Morimoto T, Amano H, Takase T, Hiramori S, Kim K, et al. Influence of baseline platelet count on outcomes in patients with venous thromboembolism (from the COMMAND VTE Registry). Am J Cardiol 2018; 122: 2131–2141, doi:10.1016/j.amjcard.2018.08.053.
• 6.   Ito S, Inoko M, Yamashita Y, Morimoto T, Kimura T; COMMAND VTE Registry-2 Investigators. Thrombocytopenia and bleeding events in patients with venous thromboembolism. Eur J Intern Med 2024; 122: 132–134, doi:10.1016/j.ejim.2024.01.007.
• 7.   Huang CM, Lin YC, Lin YJ, Chang SL, Lo LW, Hu YF, et al. Risk stratification and clinical outcomes in patients with acute pulmonary embolism. Clin Biochem 2011; 44: 1110–1115, doi:10.1016/j.clinbiochem.2011.06.077.
• 8.   Jo JY, Lee MY, Lee JW, Rho BH, Choi WI. Leukocytes and systemic inflammatory response syndrome as prognostic factors in pulmonary embolism patients. BMC Pulm Med 2013; 13: 74, doi:10.1186/1471-2466-13-74.
• 9.   Venetz C, Labarère J, Jiménez D, Aujesky D. White blood cell count and mortality in patients with acute pulmonary embolism. Am J Hematol 2013; 88: 677–681, doi:10.1002/ajh.23484.
• 10.   Obradovic S, Dzudovic B, Subotic B, Salinger S, Matijasevic J, Benic M, et al. Association of blood leukocytes and hemoglobin with hospital mortality in acute pulmonary embolism. J Clin Med 2023; 12: 6269, doi:10.3390/jcm12196269.
• 11.   Trujillo-Santos J, Di Micco P, Iannuzzo M, Lecumberri R, Guijarro R, Madridano O, et al. Elevated white blood cell count and outcome in cancer patients with venous thromboembolism: Findings from the RIETE Registry. Thromb Haemost 2008; 100: 905–911.
• 12.   Kaneda K, Yamashita Y, Morimoto T, Chatani R, Nishimoto Y, Ikeda N, et al. Anticoagulation strategies and long-term recurrence in patients with venous thromboembolism in the era of direct oral anticoagulants. Eur J Intern Med 2023; 118: 59–72, doi:10.1016/j.ejim.2023.08.007.
• 13.   Nakano Y, Adachi S, Imai R, Yoshida M, Shimokata S, Murohara T, et al. Mortality, recurrent thromboembolism and major bleeding in cancer-associated and non-cancer pulmonary embolism patients treated with direct oral anticoagulants. Circ J 2024; 88: 243–250, doi:10.1253/circj.CJ-20-1247.
• 14.   Costa F, van Klaveren D, James S, Heg D, Räber L, Feres F, et al. Derivation and validation of the predicting bleeding complications in patients undergoing stent implantation and subsequent dual antiplatelet therapy (PRECISE-DAPT) score: A pooled analysis of individual-patient datasets from clinical trials. Lancet 2017; 389: 1025–1034, doi:10.1016/S0140-6736(17)30397-5.
• 15.   Alkhalfan F, Nafee T, Yee MK, Chi G, Kalayci A, Plotnikov A, et al. Relation of white blood cell count to bleeding and ischemic events in patients with acute coronary syndrome (from the ATLAS ACS 2-TIMI 51 Trial). Am J Cardiol 2020; 125: 661–669, doi:10.1016/j.amjcard.2019.12.007.
• 16.   Vo TP, Kristiansen MH, Hasselbalch HC, Wienecke T. Elevated white blood cell counts in ischemic stroke patients are associated with increased mortality and new vascular events. Front Neurol 2023; 14: 1232557, doi:10.3389/fneur.2023.1232557.
• 17.   Jaff MR, McMurtry MS, Archer SL, Cushman M, Goldenberg N, Goldhaber SZ, et al. Management of massive and submassive pulmonary embolism, iliofemoral deep vein thrombosis, and chronic thromboembolic pulmonary hypertension. Circulation 2011; 123: 1788–1830, doi:10.1161/cir.0b013e318214914f.
• 18.   Nishimoto Y, Yamashita Y, Morimoto T, Saga S, Amano H, Takase T, et al. Thrombolysis with tissue plasminogen activator in patients with acute pulmonary embolisms in the real world: From the COMMAND VTE registry. J Thromb Thrombolysis 2019; 48: 587–595, doi:10.1007/s11239-019-01913-x.
• 19.   Yamashita Y, Morimoto T, Kadota K, Takase T, Hiramori S, Kim K, et al. Severity of pulmonary embolism at initial diagnosis and long-term clinical outcomes: From the COMMAND VTE Registry. Int J Cardiol 2021; 343: 107–113, doi:10.1016/j.ijcard.2021.08.052.
• 20.   Hokusai-VTE Investigators; Büller HR, Décousus H, Grosso MA, Mercuri M, Middeldorp S, Prins MH, et al. Edoxaban versus warfarin for the treatment of symptomatic venous thromboembolism. N Engl J Med 2013; 369: 1406–1415, doi:10.1056/nejmoa1306638.
• 21.   Schulman S, Kearon C. Definition of major bleeding in clinical investigations of antihemostatic medicinal products in non-surgical patients. J Thromb Haemost 2005; 3: 692–694, doi:10.1111/j.1538-7836.2005.01204.x.
• 22.   Faller N, Limacher A, Méan M, Righini M, Aschwanden M, Beer JH, et al. Predictors and causes of long-term mortality in elderly patients with acute venous thromboembolism: A prospective cohort study. Am J Med 2017; 130: 198–206, doi:10.1016/j.amjmed.2016.09.008.
• 23.   Konstantinides SV, Meyer G, Becattini C, Bueno H, Geersing GJ, Harjola VP, et al. 2019 ESC guidelines for the diagnosis and management of acute pulmonary embolism developed in collaboration with the European Respiratory Society (ERS). Eur Heart J 2020; 41: 543–603, doi:10.1093/eurheartj/ehz405.
• 24.   Kakkos SK, Gohel M, Baekgaard N, Bauersachs R, Bellmunt-Montoya S, Black SA, et al. Editor’s choice: European Society for Vascular Surgery (ESVS) 2021 clinical practice guidelines on the management of venous thrombosis. Eur J Vasc Endovasc Surg 2021; 61: 9–82, doi:10.1016/j.ejvs.2020.09.023.
• 25.   Ortel TL, Neumann I, Ageno W, Beyth R, Clark NP, Cuker A, et al. American Society of Hematology 2020 guidelines for management of venous thromboembolism: Treatment of deep vein thrombosis and pulmonary embolism. Blood Adv 2020; 4: 4693–4738, doi:10.1182/bloodadvances.2020001830.
• 26.   Stevens SM, Woller SC, Kreuziger LB, Bounameaux H, Doerschug K, Geersing GJ, et al. Antithrombotic therapy for VTE disease: Second update of the CHEST guideline and expert panel report. Chest 2021; 160: e545–e608, doi:10.1016/j.chest.2021.07.055.
• 27.   den Exter PL, Woller SC, Robert-Ebadi H, Masias C, Morange PE, Castelli D, et al. Management of bleeding risk in patients who receive anticoagulant therapy for venous thromboembolism: Communication from the ISTH SSC Subcommittee on Predictive and Diagnostic Variables in Thrombotic Disease. J Thromb Haemost 2022; 20: 1910–1919, doi:10.1111/jth.15776.
• 28.   Nishimoto Y, Yamashita Y, Morimoto T, Chatani R, Kaneda K, Ikeda N, et al. Direct oral anticoagulant-associated bleeding complications in patients with gastrointestinal cancer and venous thromboembolism. Eur J Intern Med 2024; 127: 74–83, doi:10.1016/j.ejim.2024.04.012.
• 29.   Imai E, Yasuda Y, Makino H. Japan Association of Chronic Kidney Disease Initiatives (J-CKDI). JMAJ 2011; 54: 403–405.
• 30.   Kimura T, Morimoto T, Nakagawa Y, Tamura T, Kadota K, Yasumoto H, et al. Antiplatelet therapy and stent thrombosis after sirolimus-eluting stent implantation. Circulation 2009; 119: 987–995, doi:10.1161/circulationaha.108.808311.
• 31.   Japanese Circulation Society. Guidelines for diagnosis, treatment and prevention of pulmonary thromboembolism and deep vein thrombosis (JCS 2017). https://www.j-circ.or.jp/cms/wp-content/uploads/2017/09/JCS2017_ito_h.pdf (accessed October 9, 2024).
• 32.   Barron HV, Cannon CP, Murphy SA, Braunwald E, Gibson CM. Association between white blood cell count, epicardial blood flow, myocardial perfusion, and clinical outcomes in the setting of acute myocardial infarction: A thrombolysis in myocardial infarction 10 substudy. Circulation 2000; 102: 2329–2334, doi:10.1161/01.cir.102.19.2329.
• 33.   Shankar A, Wang JJ, Rochtchina E, Yu MC, Kefford R, Mitchell P. Association between circulating white blood cell count and cancer mortality: A population-based cohort study. Arch Intern Med 2006; 166: 188–194, doi:10.1001/archinte.166.2.188.
• 34.   Peng R, Yin W, Wang F, Cong X, Lu B, Hua L, et al. Neutrophil levels upon admission for the assessment of acute pulmonary embolism with intermediate- and high-risk: An indicator of thrombosis and inflammation. Thromb J 2023; 21: 28, doi:10.1186/s12959-023-00471-w.
• 35.   Kaptoge S, Di Angelantonio E, Lowe G, Pepys MB, Thompson SG, Collins R, et al. C-Reactive protein concentration and risk of coronary heart disease, stroke, and mortality: An individual participant meta-analysis. Lancet 2010; 375: 132–140, doi:10.1016/S0140-6736(09)61717-7.
• 36.   Kunutsor SK, Seidu S, Blom AW, Khunti K, Laukkanen JA. Serum C-reactive protein increases the risk of venous thromboembolism: A prospective study and meta-analysis of published prospective evidence. Eur J Epidemiol 2017; 32: 657–667, doi:10.1007/s10654-017-0277-4.
• 37.   Zhu M, Ma Z, Zhang X, Hang D, Yin R, Feng J, et al. C-reactive protein and cancer risk: A pan-cancer study of prospective cohort and Mendelian randomization analysis. BMC Med 2022; 20: 301, doi:10.1186/s12916-022-02506-x.
• 38.   Suzuki S, Katagiri R, Yamaji T, Sawada N, Imatoh T, Ihira H, et al. Association between C-reactive protein and risk of overall and 18 site-specific cancers in a Japanese case-cohort. Br J Cancer 2022; 126: 1481–1489, doi:10.1038/s41416-022-01715-8.
• 39.   Potere N, Abbate A, Kanthi Y, Carrier M, Toldo S, Porreca E, et al. Inflammasome signaling, thromboinflammation, and venous thromboembolism. JACC Basic Transl Sci 2023; 8: 1245–1261, doi:10.1016/j.jacbts.2023.03.017.
• 40.   Jiang X, Zhou L, Chen Y, Tang Y. Neutrophil-to-lymphocyte ratio on admission predicts gastrointestinal bleeding in acute basal ganglia hemorrhage. J Clin Lab Anal 2022; 36: e24679, doi:10.1002/jcla.24679.
• 41.   Klok FA, Hösel V, Clemens A, Yollo WD, Tilke C, Schulman S, et al. Prediction of bleeding events in patients with venous thromboembolism on stable anticoagulation treatment. Eur Respir J 2016; 48: 1369–1376, doi:10.1183/13993003.00280-2016.