Abstract
Influenza infection is associated with a risk of thrombosis. Whether factors associated with reduced thrombosis might also be associated with reduced risk in patients with severe influenza is unknown. To investigate risk factors associated with thrombosis in patients with severe influenza. We used a cohort data set to identify adults diagnosed with severe influenza. Univariable and multivariable logistic regression models explored potential risk factors for thrombosis events in patients with severe influenza. Cox regression analysis was used to examine the risk factors for mortality in patients with severe influenza. A total of 854 patients with severe influenza were included in the analysis. The incidence of VTE was 9.37% (80/854). Multivariable regression analysis showed that previous aspirin medication (OR: 0.37; 95%CI: 0.14-0.84; P = .029) could reduce the risk factor of thrombosis in patients with severe influenza. Compared with patients in the non-thrombosis group, patients in the thrombosis group required more mechanical ventilation (P < .001), tracheostomy (P < .001), ECMO (P = .046), and high-frequency ventilation (P = .004). The incidence of co-infection was higher in the thrombosis group compared to the non-thrombosis group (P = .025). Univariable Cox regression analysis showed that previous aspirin medication (HR 0.52, 95%CI: 0.33-0.82, P = .005) and previous statin medication (HR 0.54, 95%CI: 0.34-0.87, P = .011) were risk factors for 60-day mortality in patients with severe influenza. Patients with severe influenza are at high risk for thrombosis. The effect of aspirin on thrombosis in patients with severe influenza needs further investigation.
Keywords: thrombosis, influenza, antiplatelet, incidence, risk factor
Introduction
In recent years, there have been frequent public health events associated with respiratory viruses, particularly influenza and COVID-19. Influenza is a common viral infection that affects millions of people worldwide each year, with a significant impact on morbidity and mortality. In addition to its well-known respiratory complications and organ dysfunction, emerging evidence suggests an association between influenza infection and an increased risk of thrombotic events, particularly venous thromboembolism (VTE).1,2
The incidence of VTE has been estimated to be as high as 115-269 per 100 000 population. 3 Viral and bacterial infections are one of the risk factors for VTE due to systemic immune inflammation. 4 Previous studies have shown that COVID-19 infection can lead to venous thromboembolism.5–7 Coagulation complications secondary to influenza, resulting in the activation of tissue factors and the coagulation system, have previously been attributed to respiratory tract inflammation. 8 Meanwhile, influenza has been associated with an increased risk of acute myocardial infarction, 9 ischemic stroke, 10 and venous thromboembolism (VTE).11,12 However, there are insufficient studies to explain the association between influenza and VTE.
Therefore, a prospective cohort study was designed to investigate the incidence of venous thrombosis in patients with severe influenza and to explore the risk factors for VTE.
Methods
Study Design and Patients
This was a cohort study of patients from the Novel Influenza A Surveillance Registry (H1N1). The purpose of the surveillance registry was to characterize the demographics, clinical characteristics, outcomes, and resource utilization of patients with H1N1 influenza infection who required intensive care. Local Institutional Review Board (IRB) and informed consent approvals were obtained. Anonymous data were available at the Biologic Specimen and Data Repository Information Coordinating Center (https://biolincc.nhlbi.nih.gov).
All patients enrolled in the registry were included. The exclusion criteria were: (1) Age < 18-year-old; (2) Pregnancy. A confirmed influenza case was defined as a positive test result for pH1N1 using reverse transcriptase-polymerase chain reaction (RT-PCR) or viral culture. A probable influenza case was defined as a positive diagnostic test for influenza A (RT-PCR, viral culture, rapid diagnostic test, or immunofluorescence) that was otherwise not subtyped.
Data Collection
Baseline demographics including age, race, sex, ethnicity, height, weight, comorbidities, and admission medications will be collected from the medical record. Presenting clinical features will also be collected, such as symptoms, hospital admission dates, and intensive care unit (ICU) admission dates. The Acute Physiology and Chronic Health Evaluation II (APACHE II) score and the Glasgow Coma Score (GCS) will be calculated for data available in the medical record at enrollment. Baseline laboratory values for organ function (ie, renal, hepatic, hematologic, respiratory, and musculoskeletal) will be collected as well as the worst value during a patient's hospitalization. Crude outcomes including survival status, need for intensive care, dialysis, mechanical ventilation, co-infections, and ECMO will be collected.
Thrombosis
Thrombosis was defined as any patient with confirmed deep vein thrombosis or pulmonary embolism during hospitalization. We divided the population into 2 groups: the thrombosis group (patients with confirmed DVT or PE) and the non-thrombosis group (patients without DVT or PE).
Statistical Analysis
Statistical analysis was performed with IBM SPSS Statistics version 26.0 (IBM SPSS Statistics for Macintosh, Armonk, New York). Continuous variables were reported as mean and standard deviation (SD) or median and interquartile range (IQR) after testing for normality. Categorical variables were presented as numbers and percentages. Differences between thrombosis and non-thrombosis patients were compared using the chi-squared test or Fisher's exact test for categorical variables and the nonparametric Mann-Whitney U test for continuous variables.
Multivariable logistic regression models were used to investigate the potential risk factors for thrombosis events in patients with severe influenza. Covariates were selected using univariate analysis with a significance level of 0.05. The final model was constructed using the stepwise backward elimination method based on the likelihood ratio. The results of univariate and multivariate analysis were reported as odds ratios (ORs) with 95% confidence intervals (CIs). Cox regression analysis was used to examine the risk factors for mortality in patients with severe influenza. Results were reported as hazard ratio (HR) with 95% confidence intervals (CIs).
A two-tailed P value of <.05 was considered to be statistically significant.
Results
Severe Influenza Patients
A total of 854 patients with severe influenza were included in the analysis (Figure 1). The cumulative incidence of VTE was 9.37% (80/854). The mean age of the participants was 47 ± 16 years and the mean BMI was 32 ± 10 kg/m2. Of the participants, 47.89% were male and the majority were white (67.80%).
Figure 1.
Study flowchart.
Characteristics and Baseline for Both Patient Cohort
The non-thrombosis group and the thrombosis group were comparable about BMI (32 ± 10 vs 35 ± 10 kg/m2, P = .014), aspirin (143 vs 6, P = .014), and statin (106 vs 5, P = .036) medication (Table 1).
Table 1.
Baseline and Clinical Characteristics of Patients with Severe Influenza. Values are Presented as Number (%) or Mean (SD) Unless Otherwise Indicated.
| Characteristic | Total n = 854 |
Non-Thrombosis n = 774 |
Thrombosis n = 80 |
P |
|---|---|---|---|---|
| Age, y | 47 ± 16 | 47 ± 16 | 48 ± 17 | .759 |
| Male | 409 (47.89%) | 379 (48.97%) | 30 (37.50%) | .051 |
| BMI, kg/m2 | 32 ± 10 | 32 ± 10 | 35 ± 10 | .014 |
| APACHE Ⅱ score | 22 ± 9 | 21 ± 9 | 23 ± 10 | .134 |
| Race | ||||
| Asian | 24 (2.81%) | 20 (2.58%) | 4 (5.00%) | .271 |
| White | 579 (67.80%) | 528 (68.22%) | 51 (63.75%) | .416 |
| Black | 197 (23.07%) | 174 (22.48%) | 23 (28.75%) | .205 |
| Other race | 55 (6.44%) | 53 (6.85%) | 2 (2.50%) | .132 |
| Symptoms | ||||
| Fever | 587 (68.74%) | 535 (69.12%) | 52 (65.00%) | .449 |
| Cough | 628 (73.54%) | 569 (73.51%) | 59 (73.75%) | .964 |
| Shortness of breath | 632 (74.00%) | 578 (74.68%) | 54 (67.50%) | .164 |
| Smoking | .902 | |||
| Past | 145 (16.98%) | 133 (17.18%) | 12 (15.00%) | |
| Current | 269 (31.50%) | 242 (31.27%) | 27 (33.75%) | |
| None | 436 (51.05%) | 395 (51.03%) | 41 (51.25%) | |
| Comorbidities | ||||
| Heart disease | 151 (17.68%) | 141 (18.22%) | 10 (12.50%) | .202 |
| Diabetes | 236 (27.63%) | 219 (28.29%) | 17 (21.25%) | .180 |
| Hypertension | 358 (41.92%) | 329 (42.51%) | 29 (36.25%) | .280 |
| COPD | 154 (18.03%) | 140 (18.09%) | 14 (17.50%) | .896 |
| Asthma | 152 (17.80%) | 137 (17.70%) | 15 (18.75%) | .815 |
| Other lung disease | 72 (8.43%) | 65 (8.40%) | 7 (8.75%) | .914 |
| Cancer | 33 (3.86%) | 30 (3.88%) | 3 (3.75%) | >.999 |
| Hematologic malignancy | 41 (4.80%) | 39 (5.04%) | 2 (2.50%) | .418 |
| HIV | 31 (3.63%) | 25 (3.23%) | 6 (7.50%) | .061 |
| Renal failure | 38 (4.45%) | 35 (4.52%) | 3 (3.75%) | >.999 |
| Previous drug history | ||||
| Aspirin | 149 (17.45%) | 143 (18.48%) | 6 (7.50%) | .014 |
| Non-steroidal anti-inflammatories | 101 (11.83%) | 92 (11.89%) | 9 (11.25%) | .867 |
| Statin | 133 (15.57%) | 127 (16.41%) | 6 (7.50%) | .036 |
| Corticosteroids | 111 (13.00%) | 106 (13.70%) | 5 (6.25%) | .059 |
| Immunosuppression | 70 (8.20%) | 63 (8.14%) | 7 (8.75%) | .850 |
| ACEI | 134 (15.69%) | 124 (16.02%) | 10 (12.50%) | .410 |
| Anti-influenzals | 54 (6.32%) | 50 (6.46%) | 4 (5.00%) | .610 |
Corticosteroids：> 20 mg/day prednisone equivalent for adults and >0.3 mg/kg/day for patients < 18 years old for any duration within 6 months of ICU admission.
COPD: Chronic Obstructive Pulmonary Disease, ACEI: Angiotensin-converting enzyme inhibitors.
There was no difference in baseline laboratory values for both groups, including white blood cells, platelets, creatine, bilirubin, and creatinine phosphokinase. However, for the worst value, there was a significant difference in creatine (1.4[0.94, 3.00] vs 1.71[1.2, 4.12] mg/dL, P = .012), PLT (144[92 201] vs 94[58, 166] ×109/ml, P < .001) and bilirubin (0.80[0.50, 1.30] vs 1.10[0.70, 2.10] mg/dL, P < .001) (Table 2).
Table 2.
Laboratory Test Characteristics of Patients with Severe Influenza. Values are Presented as Number (%) or Mean (SD) Unless Otherwise Indicated.
| Characteristic | Total n = 854 |
Non-Thrombosis n = 774 |
Thrombosis n = 80 |
P |
|---|---|---|---|---|
| Baseline | ||||
| WBC(×103/uL) | 100 (11, 500) | 105 (11, 500) | 7 (7, 7) | .273 |
| Neu (%) | 83 (74, 89) | 83 (73, 89) | 82 (75, 90) | .528 |
| Lym (%) | 9 (5, 14) | 9 (5, 14) | 10 (4, 15) | .854 |
| PLT (×109 / ml) | 203 ± 106 | 203 ± 106 | 201 ± 98 | .829 |
| Creatinine(mg/dL) | 1.00 (0.76, 1.58) | 1.01 (0.76, 1.60) | 0.99 (0.74, 1.39) | .484 |
| Total Bilirubin(mg/dL) | 0.60 (0.40, 1.00) | 0.60 (0.40, 1.00) | 0.70 (0.50, 1.23) | .107 |
| CPK (U/L) | 116 (51, 292) | 120 (50, 289) | 106 (64, 413) | .558 |
| HR (bpm) | 104 ± 23 | 104 ± 23 | 104 ± 24 | .867 |
| RR (bpm) | 25 ± 9 | 25 ± 9 | 26 ± 9 | .351 |
| SBP (mm Hg) | 120 (105, 139) | 120 (105, 139) | 120 (105, 137) | .848 |
| DBP (mm Hg) | 67 (57, 79) | 66 (57, 79) | 68 (58, 82) | .314 |
| MAP (mm Hg) | 85 (74, 98) | 85 (74, 98) | 88 (73, 99) | .677 |
| P/F | 113 (73, 218) | 121 (74, 230) | 81 (67, 118) | <.001 |
| GCS score | 11.3 ± 4.6 | 11.5 ± 4.5 | 8.8 ± 5.2 | <.001 |
| Day 3 | ||||
| SBP (mm Hg) | 118 (106, 135) | 119 (106, 135) | 112 (104, 132) | .052 |
| DBP (mm Hg) | 64 (56, 73) | 65 (57, 74) | 56 (50, 68) | <.001 |
| MAP (mm Hg) | 82 (74, 93) | 83 (74, 93) | 75 (68, 90) | <.001 |
| P/F | 144 (90, 220) | 148 (91, 226) | 105 (82, 171) | .003 |
| Day 7 | ||||
| SBP (mm Hg) | 123 (111, 143) | 124 (111, 142) | 123 (112, 147) | .958 |
| DBP (mm Hg) | 67 (57, 78) | 68 (58, 78) | 64 (56, 74) | .085 |
| MAP (mm Hg) | 86 (77, 99) | 87 (77, 99) | 85 (75, 95) | .252 |
| P/F | 140 (97, 210) | 151 (103, 225) | 112 (83, 169) | .005 |
| Highest creatine(mg/dL) | 1.40 (0.97, 3.08) | 1.40 (0.94, 3.00) | 1.71 (1.20, 4.12) | 0.012 |
| Lowest PLT(×109 / ml) | 141 (87, 199) | 144 (92, 201) | 94 (58, 166) | <.001 |
| Highest bilirubin(mg/dL) | 0.80 (0.50, 1.40) | 0.80 (0.50, 1.30) | 1.10 (0.70, 2.10) | <.001 |
Abbreviations: WBC, white blood cell; PLT, platelet; Neu, neutrophils; Lym, lymphocyte; CPK, Creatinine phosphokinase; SBP, Systolic blood pressure; DBP, Diastolic blood pressure; MAP, Mean artery blood pressure; HR, heart rate; RR, Respiratory Rate; GCS, Glasgow Coma Scale; P/F, PaO2/FiO2. Data are presented as mean ± standard deviation (SD) or median, IQR.
Meanwhile, the thrombosis group had a lower PaO2/FiO2 ratio (P/F) at baseline (P < .001), day 3 (P < .001), and day 7 (P = .005) compared to the non-thrombosis group. The thrombosis group had a lower GCS score (11.5 ± 4.5 vs 8.8 ± 5.2, P < .001) (Table 2).
Special Treatment and Clinical Outcome
Table 3 showed no significant difference between the non-thrombosis group and the thrombosis group in dialysis, high-dose corticosteroids, prone ventilation, intravenous immunoglobulin, and plasma. Compared with non-thrombosis group patients, thrombosis group patients required more mechanical ventilation (510 vs 74, P < .001), tracheostomy (82 vs 30, P < .001), ECMO (23 vs 6, P = .046), and high-frequency ventilation (49 vs 12, P = .004).
Table 3.
Special Treatment for Patients with Severe Influenza.
| Characteristic | Total n = 854 |
Non-Thrombosis n = 774 |
Thrombosis n = 80 |
P |
|---|---|---|---|---|
| Mechanical ventilation | 584 (68.38%) | 510 (65.89%) | 74 (92.50%) | <.001 |
| Tracheostomy | 112 (13.11%) | 82 (10.59%) | 30 (37.50%) | <.001 |
| Dialysis | 98 (11.48%) | 85 (10.98%) | 13 (16.25%) | .159 |
| ECMO | 29 (3.40%) | 23 (2.97%) | 6 (7.50%) | .046 |
| High-frequency ventilation | 61 (7.14%) | 49 (6.33%) | 12 (15.00%) | .004 |
| High dose corticosteroids | 173 (20.26%) | 154 (19.90%) | 19 (23.75%) | .414 |
| Prone ventilation | 37 (4.33%) | 31 (4.01%) | 6 (7.50%) | .147 |
| Intravenous immune globulin | 20 (2.34%) | 19 (2.45%) | 1 (1.25%) | >.999 |
| Intravenous immune plasma | 6 (0.70%) | 6 (0.78%) | 0 (0.00%) | >.999 |
| Fresh frozen plasma | 64 (7.49%) | 56 (7.24%) | 8 (10.00%) | .371 |
Table 4.
Clinical Outcome for Patients with Severe Influenza.
| Characteristic | Total, n = 854 | Non-Thrombosis Patients, n = 774 | Thrombosis Patients, n = 80 | P |
|---|---|---|---|---|
| Co-infection | 288 (33.72%) | 252 (32.56%) | 36 (45.00%) | .025 |
| weaning | 404 (69.30%) | 359 (70.39%) | 45 (61.64%) | .130 |
| MV length, days | 7 (3, 14) | 6 (3, 13) | 13 (8, 19) | <.001 |
| 60-day mortality | 197 (23.09%) | 179 (23.16%) | 18 (22.50%) | .894 |
Compared to the non-thrombosis group, the incidence of co-infection was higher in the thrombosis group (32.56% vs 45.00%, P = .025) (Table 4). Meanwhile, patients in the thrombosis group required a longer duration of mechanical ventilation (6[3,13] days vs 13[8,19] days, P < .001) (Table 4). There was no significant difference in 60-day mortality (Table 4).
Risk Factors for Severe Influenza Patients
Risk Factors for Thrombosis in Patients with Severe Influenza
Univariable logistic analysis showed that previous aspirin medication, previous statin medication, mechanical ventilation, co-infection, and BMI were significantly different between the thrombosis group and the non-thrombosis group (P < .05) (Table 5). However, multivariable logistic analysis shows that only previous aspirin medication and previous statin medication were significantly different between the thrombosis group and the non-thrombosis group (P < .05) (Table 5).
Table 5.
Univariate and Multivariate Logistic Regression Analysis to Identify the Risk Factors.
| Univariable | Multivariable | |||||
|---|---|---|---|---|---|---|
| Characteristic | OR | 95% CI | P-Value | OR | 95% CI | P-Value |
| Male | 1.60 | 1.00, 2.59 | .052 | 1.77 | 1.05, 3.04 | .035 |
| Age | 1.00 | 0.99, 1.02 | .754 | 1.01 | 1.00, 1.03 | .082 |
| Aspirin | 0.36 | 0.14, 0.77 | .018 | 0.37 | 0.14, 0.84 | .029 |
| Statin | 0.41 | 0.16, 0.90 | .042 | 0.48 | 0.17, 1.14 | .124 |
| MV | 6.38 | 2.98, 16.62 | <.001 | 5.01 | 1.52, 19.82 | .012 |
| Co-infection | 1.69 | 1.06, 2.70 | .026 | 1.50 | 0.90, 2.50 | .119 |
| BMI | 1.03 | 1.00, 1.05 | .015 | 1.02 | 0.99, 1.04 | .121 |
| Tracheostomy | 5.06 | 3.05, 8.41 | <0.001 | 3.01 | 1.71, 5.30 | <.001 |
| ECMO | 2.65 | 1.04, 6.71 | .040 | 1.30 | 0.44, 3.86 | .638 |
| High-frequency ventilation | 2.61 | 1.32, 5.15 | .006 | 1.42 | 0.66, 3.08 | .371 |
Risk Factors for 60-day Mortality in Patients with Severe Influenza
Univariable Cox regression analysis showed that previous aspirin use (HR 0.52, 95%CI: 0.33-0.82, P = .005) and previous statin use (HR 0.54, 95%CI: 0.34-0.87, P = .011) were risk factors for 60-day mortality in patients with severe influenza (Table 6). In multivariable Cox regression analysis, however, there were no differences for either factor.
Table 6.
Univariate and Multivariate Cox Regression Analysis to Identify the Clinical Factors Influencing Survival.
| Univariable | Multivariable | |||||
|---|---|---|---|---|---|---|
| Characteristic | HR | 95% CI | P-Value | HR | 95% CI | P-Value |
| Male | 0.88 | 0.66, 1.16 | .355 | 0.98 | 0.61, 1.56 | .920 |
| Age | 0.99 | 0.98, 1.00 | .055 | 1.00 | 0.99, 1.02 | .781 |
| Aspirin | 0.52 | 0.33, 0.82 | .005 | 0.58 | 0.28, 1.20 | .139 |
| Statin | 0.54 | 0.34, 0.87 | .011 | 0.49 | 0.22, 1.09 | .082 |
| P/F | 1.00 | 1.00, 1.00 | .233 | 1.00 | 0.99, 1.00 | .017 |
| GCS score | 0.99 | 0.96, 1.02 | .408 | 1.00 | 0.95, 1.05 | .956 |
| MAP3 | 1.00 | 0.99, 1.01 | .769 | 1.00 | 0.98, 1.02 | .863 |
| P/F3 | 1.00 | 1.00, 1.00 | .667 | 1.00 | 1.00, 1.00 | .164 |
| MV | 1.00 | 0.74, 1.35 | .994 | 1.06 | 0.35, 3.25 | .912 |
| Co-infection | 1.07 | 0.80, 1.43 | .670 | 1.03 | 0.64, 1.67 | .902 |
| Tracheostomy | 0.94 | .63, 1.41 | .763 | 0.95 | 0.54, 1.69 | .868 |
| Inhaled NO | 0.83 | 0.53, 1.30 | .410 | 0.98 | 0.47, 2.05 | .966 |
| ECMO | 0.60 | 0.32, 1.13 | .111 | 0.42 | 0.16, 1.06 | .067 |
| High frequency ventilation | 0.78 | 0.47, 1.28 | .327 | 1.22 | 0.57, 2.59 | .611 |
| Highest creatine | 1.00 | 0.95, 1.06 | .921 | 1.02 | 0.93, 1.11 | .726 |
| Highest bilirubin | 1.01 | 0.98, 1.04 | .520 | 0.98 | 0.92, 1.04 | .459 |
| Lowest PLT | 1.00 | 1.00, 1.00 | .203 | 1.00 | 1.00, 1.00 | .835 |
| BMI | 1.01 | 1.00, 1.02 | .111 | 1.02 | 1.00, 1.04 | .106 |
| Thrombosis | 1.03 | 0.63, 1.67 | .913 | 1.13 | 0.58, 2.20 | .722 |
Discussion
In the large cohort linking severe influenza and thrombosis,the overall incidence of thrombosis was found to be 9.37%. The use of aspirin and statin medications has been demonstrated to reduce the risk of thrombosis in patients with severe influenza. Patients with severe influenza and thrombosis require more specialized treatment, including mechanical ventilation, tracheostomy, ECMO, and high-frequency ventilation. These findings collectively indicate that clinicians, particularly those in ICU, should prioritize the recognition and management of influenza-associated thrombosis.
Inflammation and coagulation are simultaneously activated in critical illnesses such as infection 13 and sepsis. 14 Severe influenza infection is characterized by excessive inflammation and tissue pathology in the lung. 15 Meanwhile, influenza infection could induce thrombin generation and activation of coagulation through increased tissue factor expression.16,17 Coagulation dysfunction is associated with increased mortality in respiratory viral infections such as COVID-19. 18 The glycocalyx regulates coagulation in response to inflammation and vascular pathophysiology in healthy individuals. 19 Taghavi et al found that the H1N1 virus could induce endothelial dysfunction by degrading endothelial glycocalyx in vitro. 20 Rademaker et al found that circulating levels of von Willebrand factor (vWF) and P-selectin, both molecules that spread on the endothelial surface upon stimulation to activate platelets and induce thrombosis, were increased in influenza patients. 21 Influenza virus infection can lead to a hypercoagulable state, 22 predisposing individuals to thrombotic events. In addition, the inflammatory response induced by influenza infection may contribute to the development of thrombosis by promoting endothelial dysfunction and activation of the coagulation pathway.
Our study identified trends in men as risk factors for VTE in patients with severe influenza, although there was no statistical significance. Rubino et al found that the incidence of VTE varies by sex across the lifespan. 23 After midlife, men have a faster increase in thrombosis incidence than women. 24 The average age of the participants was 47 years, consistent with the findings of the aforementioned studies.
We did not identify any clinical factor that increased the risk of thrombosis in patients with severe influenza. However, using logistic regression analysis, we found that previous aspirin use may reduce the risk. Platelets are known to play an active role in thrombosis and inflammation. An experimental influenza model demonstrated that modulation of platelet activation and its interactions with other immune cells may protect the lungs from influenza-induced pathogenesis. 15 However, the efficacy of antiplatelet therapy in severe diseases, such as sepsis and COVID-19, is highly controversial, with most results coming from retrospective studies.25,26,27,28 Kobayashi et al verified that pre-sepsis antiplatelet medication was associated with lower mortality rates through propensity score analysis. 25 As previously indicated, the results demonstrated that prior aspirin administration may potentially mitigate the risk of thrombosis in patients with severe influenza. It is noteworthy that the relevant mechanism requires further elucidation.
Statin has been widely used for cardiovascular disease. Birdal et al performed a network meta-analysis to investigate the effect of statin on thromboembolism. 29 Pairwise meta-analysis showed a significantly lower incidence of VTE with statins than with placebo (0.79% vs 0.99%, RR: 0.87, 0.77-0.98; P = .022), and rosuvastatin had clear advantages. 29 Mendelian randomization analysis showed the association between statin medication and the risk of thrombosis (OR: 0.999, 95%CI: 0.998-1.000, P = .004). 30 Interestingly, statin use was associated with reduced mortality in patients with influenza (OR = 0.68, 95% CI: 0.56, 0.82; P < .001). 30 This finding was confirmed for both 30-day and 90-day mortality. 30 The mechanism may involve several possibilities and further evidence is needed to confirm this clinical phenomenon.
It is well-established that obesity is associated with an increased risk of thrombosis. 31 It has been demonstrated that obesity represents a risk factor for thrombosis in a number of different populations.32,33 The present study revealed that the mean BMI of the thrombosis group was significantly higher than that of the non-thrombosis group. However, univariate logistic regression analysis indicated that high BMI was a risk factor for influenza combined with thrombosis. Conversely, multivariable logistic regression analysis did not yield the same result, potentially due to the high BMI observed in both groups (32 ± 10 vs 35 ± 10 kg/m²).
It is important to consider the limitations of our study when interpreting the results. Some data were absent due to the secondary analysis of the observational study. Firstly, this study did not examine coagulation indexes such as PT, APTT, and D-dimer. Secondly, ultrasound is an invaluable tool for evaluating cardiac function. However, this study lacks pertinent data regarding cardiac biomarkers. Third, as a result of the secondary analysis, the effects of anticoagulation treatment, including the use of anticoagulants, physical prophylaxis, and the incidence of bleeding side effects, could not be evaluated. In addition, the possibility exists that other confounding factors may have introduced bias into the results.
Conclusion
Clinicians should be aware of thrombosis in patients with severe influenza. Although thrombosis does not increase mortality, the thrombosis group in patients with severe influenza needs more special treatment. Basic anticoagulation therapy is added in time. Antiplatelets and statins may help reduce the occurrence of thrombosis in patients with severe influenza, and further studies are needed to confirm the effect.
Footnotes
Availability of Data and Materials: All data were available in the Biologic Specimen and Data Repository Information Coordinating Center (https://biolincc.nhlbi.nih.gov).
Author Contributions: XMQ: Conceptualization, Data curation, Writing original draft, and review & editing. MJL: Conceptualization, Writing original draft and review & editing. QZW: Methodology, Software. YKZ: Methodology, Validation. LK: Project administration, Supervision. LZ: Investigation, Writing – review & editing.
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The authors received no financial support for the research, authorship, and/or publication of this article.
ORCID iD: Xianming Qiu https://orcid.org/0000-0001-9809-6175
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