Thrombosis Occurrence in COVID-19 Compared With Other Infectious Causes of ARDS: A Contemporary Cohort *

Abstract

Thrombosis occurrence in coronavirus disease 2019 (COVID-19) has been mostly compared to historical cohorts of patients with other respiratory infections. We retrospectively evaluated the thrombotic events that occurred in a contemporary cohort of patients hospitalized between March and July 2020 for acute respiratory distress syndrome (ARDS) according to the Berlin Definition and compared those with positive and negative real-time polymerase chain reaction results for wild-type severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) using descriptive analysis. The association between COVID-19 and thrombotic risk was evaluated using logistic regression. 264 COVID-19-positive (56.8% male, 59.0 years [IQR 48.6-69.7], Padua score on admission 3.0 [2.0-3.0]) and 88 COVID-19-negative patients (58.0% male, 63.7 years [51.2-73.5], Padua score 3.0 [2.0-5.0]) were included. 10.2% of non-COVID-19 and 8.7% of COVID-19 patients presented≥1 clinically relevant thrombotic event confirmed by imaging exam. After adjustment for sex, Padua score, intensive care unit stay, thromboprophylaxis, and hospitalization length, the odds ratio for thrombosis in COVID-19 was 0.69 (95% CI, 0.30-1.64). We, therefore, conclude that infection-induced ARDS carries an inherent thrombotic risk, which was comparable between patients with COVID-19 and other respiratory infections in our contemporary cohort.

Keywords

COVID-19 hypercoagulation infection thrombosis pulmonary embolism deep venous thrombosis

Introduction

In advanced stages, respiratory tract infections can lead to acute respiratory distress syndrome (ARDS), a key feature of which is the progressive imbalance between inflammatory response and the coagulation system.^1-3 Initially, such imbalance is mainly concentrated in the lungs, where inflammatory processes following pathogen invasion become deregulated and locally activate the coagulation cascade, causing the development of pulmonary microthrombi.^1,4,5 As the disease progresses, extreme inflammation becomes systemic, and a widespread hypercoagulable state—further aggravated by tissue hypoxia due to poor pulmonary gas exchange—is established.^1,4,5 Hypercoagulability, combined with inflammatory or pathogen-mediated endothelial damage, is associated with an increased incidence of both arterial and venous macrothrombi in infection-induced ARDS.^1,4,5

That acute respiratory tract infections are related to a high thrombotic risk is well established in the literature, and novel evidence indicated that this risk may be even greater in coronavirus disease 2019 (COVID-19).^6-12 Recent studies reported that COVID-19 patients were 3 to 6 times more likely to develop any arterial or venous thrombosis compared to patients affected by other infectious respiratory illnesses.^13-16 Although extensive, most of the data indicating a higher thrombotic risk associated with COVID-19 has been gathered based on comparisons with cohorts of patients with other respiratory infections in the pre-COVID-19 era.^13-19 While studies of this kind have provided valuable initial insight into the hemostatic changes caused by COVID-19, an important limitation to the use of historical cohorts is the change in practice patterns over the years. As a result, differences in hospital protocol regarding disease management, thromboprophylaxis regimen, and diagnostic method for thrombosis to which COVID-19 and non-COVID-19 patients were subjected could impact the incidence of thrombotic events in these study groups, and thus influence how we interpret the thrombotic risk associated with the clinical conditions in question.

In order to address the possible influence of historical cohort use, we aimed to evaluate the occurrence of clinically relevant arterial and venous thrombotic events confirmed by imaging exam in a contemporary cohort of patients hospitalized for infection-induced ARDS during the first wave of COVID-19 in Brazil, comparing those positive and negative for wild-type severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) prior to the start of vaccination campaigns.

Patients and Methods

Study Population and Setting

We identified adult (age≥18 years) patients admitted between March 17 and July 31, 2020, for infection-induced ARDS to the UNICAMP Clinical Hospital, a public general hospital that served as a high-complexity reference center for COVID-19 cases belonging to the macro-region of Campinas, Brazil, which comprises 86 municipalities, and 6.5 million inhabitants.

To be included in our study, patients were required to both meet the Berlin Definition of ARDS³ and to be subjected to COVID-19 testing by real-time polymerase chain reaction (RT-PCR) during hospitalization.

Individuals met the Berlin Definition of ARDS if they presented a ratio of arterial partial pressure of oxygen to fraction of inspired oxygen (PaO₂/FiO₂)≤300 mmHg at least once throughout their hospitalization. They were then categorized as having mild (200<PaO₂/FiO₂≤300), moderate (100<PaO₂/FiO₂≤200), or severe ARDS (PaO₂/FiO₂≤100) based on the lowest PaO₂/FiO₂ measured during their hospital stay. Hypoxemia could not have been fully explained by cardiac failure or fluid overload as judged by the treating physician, and symptom onset or worsening (when previously existent) should have occurred 30 days before admission at the latest. For patients on high-flow nasal oxygen (HFNO),²⁰ FiO₂ was estimated using the conventional formula as follows: Estimated FiO₂= Atmospheric FiO₂+4*O₂ Flow Rate, with an atmospheric FiO₂ equal to 0.20.

One detectable result for SARS-CoV-2 by RT-PCR obtained from a sample taken at any given moment during hospitalization was sufficient to confirm COVID-19 as a diagnosis, while 2 or more undetectable results—with samples taken within a maximum interval of 48 h upon admission—were required to rule out COVID-19 as a diagnosis. In the latter case, patients were classified as having non-COVID-19 ARDS. If a previously improving non-COVID-19 patient presented a sudden clinical worsening during their hospital stay, new samples for RT-PCR testing were collected at least twice to investigate the possibility of an in-hospital SARS-CoV-2 infection. Any case of hospital-acquired COVID-19 was classified as COVID-19.

It should be highlighted that the period stipulated for hospital admission corresponds to the first wave of COVID-19 in Brazil, and therefore all positive cases can be attributed to an infection by the wild-type SARS-CoV-2 variant.²¹ In addition, no patients were yet vaccinated against SARS-CoV-2 at the time of hospitalization, given that the Brazilian vaccination campaign officially began on January 17, 2021.²²

Patients with incomplete medical records (ie, data corresponding to more than 50% of their hospital stay were missing) or at least 2 consecutive negative COVID-19 RT-PCR tests taken upon admission, but with a history of a positive test taken in the past 30 days prior to hospitalization for which its result was unavailable to the treating physician, were excluded from this study.

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the State University of Campinas (UNICAMP) (CAAE 30648520.6.0000.5404).

Other ARDS Etiologies

To determine the possible causes of ARDS among non-COVID-19 patients, data from all available laboratory diagnostic testing strategies were analyzed, including results from blood culture, respiratory-tract culture, RT-PCR test for Influenza A/B virus and cytomegalovirus, rapid molecular test for Mycobacterium tuberculosis, and IgM antibody capture enzyme-linked immunosorbent assay (MAC-ELISA) for Dengue virus. The first positive result obtained from any of the above-mentioned laboratory tests was considered the likely ARDS etiology for the non-COVID-19 patient in question.

Because patients were rarely extensively tested for possible causes of ARDS other than SARS-CoV-2 in the pandemic setting, the Infectious Diseases Society of America and the American Thoracic Society criteria for defining severe community-acquired pneumonia (CAP) were adopted.²³ According to these criteria, non-COVID-19 patients were considered to have CAP if they met either one major criterion or at least 3 minor criteria. Major criteria corresponded to septic shock with a need for vasoactive drugs and/or respiratory failure requiring mechanical ventilation. Minor criteria included: respiratory rate≥30 breaths/minute; PaO₂/FiO₂≤250 mmHg; multilobar infiltrates on chest imaging exam; confusion/disorientation; uremia (blood urea nitrogen level≥43 mg/dL); leukopenia due to infection alone (white blood cell count <4000 cells/μL); thrombocytopenia (platelet count <100 000 cells/μL); hypothermia (core temperature <36 °C); and/or hypotension requiring aggressive fluid resuscitation.

Data Collection

Clinical data were retrieved from electronic records on AGHUse System (Hospital de Clínicas Porto Alegre, Porto Alegre, Brazil) and retrospectively reviewed. Included patients were analyzed from the day of hospital admission or symptom onset (in the case of hospital-acquired COVID-19) until the day of discharge, transfer to another medical facility, or death.

If patients were hospitalized a second time within 14 days after their first discharge, data from both hospitalization periods were analyzed as one. This way, thromboembolic complications requiring medical care that were initiated during their first hospital stay but manifested only after discharge were also taken into consideration.

Thrombotic Risk Assessment and COVID-19 Management Protocol

Upon admission or symptom onset (in the case of hospital-acquired COVID-19), both non-COVID-19 and COVID-19 patients were directed to the UNICAMP Clinical Hospital's COVID-19 ward or ICU (according to clinical status) and subjected to the center's COVID-19 management protocol. If 2 negative RT-PCR results were obtained, with samples taken within a maximum interval of 48 h upon admission, patients were then transferred to the non-COVID-19 hospital wing for further medical assistance. Patients with an active SARS-CoV-2 infection, in turn, mostly stayed in the COVID-19 hospital wing until discharge, even after the standard 14-day isolation period had ended.

The baseline risk of hospital-associated thrombosis was assessed by the Padua Prediction Score as previously described.²⁴ Following hospital protocol, all patients with no contraindication to anticoagulant use received thromboprophylaxis with unfractionated heparin (UFH) or low molecular weight heparin (LMWH) from the day of admission, with standard risk patients receiving 40 mg/day of subcutaneous enoxaparin. UFH/LMWH was escalated to full dose only on the clinical suspicion of an acute thrombotic event and was suspended if risk factors for bleeding were observed. For the purpose of this study, only the first anticoagulant dose (prophylactic or full dose) received during hospitalization was taken into consideration.

With regard to glucocorticoid use, hospital protocol stipulated that ARDS patients with confirmed or suspected COVID-19 receive intravenous or oral dexamethasone 6 mg/day, and intravenous hydrocortisone 200 mg in continuous infusion or 50 mg every 6 h when in volume or vasoactive drug-refractory septic shock. Alternatives to dexamethasone included hydrocortisone (50 mg every 8 h), methylprednisolone (10 mg every 6 h), and prednisone (40 mg every 6 h). The type and dose of corticosteroids were chosen according to the attending physician's judgment, and the different therapeutic approaches proposed by the hospital protocol were not necessarily equivalent to one another.

Outcomes

The primary outcome of this study was the occurrence of clinically relevant arterial and venous thrombotic events confirmed by an imaging exam. Thrombotic events included acute pulmonary embolism (PE), deep vein thrombosis (DVT), venous thrombosis in other sites, ischemic stroke, acute myocardial infarction (AMI), and arterial thrombosis in other sites. Because no screening strategies were applied during hospitalization, the following appropriate imaging exams were conducted only upon clinical suspicion of thrombosis: computed tomography (CT) pulmonary angiography for suspected PE; compression ultrasonography for suspected DVT; and CT scan of the brain or CT angiography of the carotid and intracerebral arteries for suspected ischemic stroke. AMI was diagnosed by cardiac enzyme measurement (including troponin) or electrocardiography. In-hospital mortality was assessed in both COVID-19 and non-COVID-19 patients, regardless of whether they had been affected by thrombosis or not.

Statistical Analysis

Patient characteristics were described using standard descriptive analysis. Categorical variables are reported as counts and percentages and were compared with the use of t-tests. Continuous variables, in turn, are reported as median with interquartile range (IQR) and were compared using Mann-Whitney U testing. The association between COVID-19 and thrombotic risk was evaluated using 5 logistic regression models: (1) unadjusted; (2) adjusted for sex and Padua score; (3) adjusted for the same variables as Model 2 plus thromboprophylaxis use and ICU stay; (4) adjusted for the same variables as Model 3 plus hospitalization length; and (5) adjusted for the same variables as Model 4 plus glucocorticoid use. The choice of covariates for which the logistic regression models were adjusted was based on their established role as clinical markers of disease severity and as risk factors for the development of thrombosis in the setting of severe respiratory infections. Logistic regression results are presented as odds ratios (OR) with 95% confidence intervals (CI). A P value<.05 was considered statistically significant. All analyses were performed using SPSS Statistics version 25.0 (IBM Corp., Armonk, NY, USA).

Results

Patient Clinical Characteristics

Of the 357 adult patients admitted for infection-induced ARDS in the stipulated period, 352 were included in our study, among which 264 had positive COVID-19 test results (exposed) and 88 had negative results (unexposed), as detailed in Figure 1. The remaining patients were excluded due to incomplete medical records (n=2) and unavailable COVID-19 test results (n=3).

Figure 1.

Flow diagram of patient inclusion and exclusion.

Characteristics of both study groups are summarized in Table 1. They were comparable with regard to sex, age, hypertension, diabetes, liver cirrhosis, chronic infectious disease, end-stage chronic kidney disease, and organ or tissue transplant. COVID-19 patients were more frequently obese than non-COVID-19 patients, which, in turn, had a higher rate of active cancer, previous pulmonary disease, and systemic autoimmune disorder.

Table 1.

Patient Demographic and Clinical Baseline Characteristics.

	Non-COVID-19 (N=88)	COVID-19 (N=264)	P
Male, n (%)	51 (58.0%)	150 (56.8%)	.85
Age, median in years (IQR)	63.7 (51.2-73.5)	59.0 (48.6-69.7)	.16
Hypertension, n (%)	45 (51.1%)	144 (54.5%)	.58
Diabetes, n (%)	27 (30.7%)	102 (38.6%)	.18
Liver cirrhosis, n (%)	1 (1.1%)	8 (3.0%)	.33
Chronic infectious disease,^a n (%)	4 (4.5%)	10 (3.8%)	.75
End-stage chronic kidney disease, n (%)	4 (4.5%)	11 (4.2%)	.88
Organ or tissue transplant,^b n (%)	9 (10.2%)	21 (8.0%)	.51
Obesity,^c n (%)	8 (9.1%)	76 (28.8%)	.004
Active cancer, n (%)	11 (12.5%)	18 (6.8%)	.09
Pulmonary disease,^d n (%)	21 (23.9%)	15 (5.7%)	<.0001
Systemic autoimmune disease,^e n (%)	11 (12.5%)	14 (5.3%)	.02
Previous VTE, n (%)	8 (9.1%)	15 (5.7%)	.25
AMI or ischemic stroke (≤4 weeks), n (%)	5 (5.7%)	6 (2.3%)	.11
Trauma or surgery (≤4 weeks), n (%)	3 (3.4%)	16 (6.1%)	.34
Reduced mobility,^f n (%)	19 (21.6%)	14 (5.3%)	<.0001
Padua score, median (IQR)	3.0 (2.0-5.0)	3.0 (2.0-3.0)	.07
Padua score≥4, n (%)	35 (39.8%)	58 (22.0%)	.001

Abbreviations: IQR interquartile range; VTE venous thromboembolism; AMI acute myocardial infarction.

Tuberculosis, Hansen's disease, syphilis, gonorrhea, HIV, chronic hepatitis C, and B.

Kidney, liver, and bone marrow.

BMI (body mass index)>30 kg/m².

COPD (chronic obstructive pulmonary disease), asthma, pulmonary hypertension, and recurrent pneumonia.

Gout, arthrosis, systemic lupus erythematosus, rheumatoid arthritis, systemic sclerosis, and myasthenia gravis.

Bedridden for≥3 days prior to admission.

The median time between symptom onset and hospital admission, in days, was equal to 6.0 (IQR 3.0-9.0) and 7.0 (IQR 5.0-10.0, P=.001) in the non-COVID-19 and COVID-19 groups, respectively (Table 2). Lung impairment was markedly worse among COVID-19 patients, as shown by their lower median oxygenation saturation on room air on admission, lower median PaO₂/FiO₂ ratio throughout hospitalization, increased frequency of bilateral opacities on chest imaging exams, and greater need for mechanical ventilation as compared to non-COVID-19 patients. No significant differences were noted between groups in regard to vasoactive drug use, ICU stay, total length of hospital stay, hospital readmission, and in-hospital mortality rates.

Table 2.

Clinical Parameters Associated With ARDS Severity.

	Non-COVID-19 (N=88)	COVID-19 (N=264)	P
Time between symptom onset and hospital admission, median in days (IQR)	6.0 (3.0-9.0)	7.0 (5.0-10.0)	.001
SpO₂ on room air on admission, median in % (IQR)	92.0% (88.0%-95.3%)	90.0% (87.0%-93.0%)	.02
Lowest PaO₂/FiO₂, median (IQR)	160.9 (96.8-236.0)	101.3 (71.3-165.4)	<.0001
ARDS category
Mild,^a n (%)	35 (39.8%)	50 (18.9%)	<.0001
Moderate,^b n (%)	31 (35.2%)	82 (31.1%)	.47
Severe,^c n (%)	22 (25.0%)	132 (50.0%)	<.0001
Bilateral opacities on chest imaging exam, n (%)	56 (63.6%)	240 (90.9%)	<.0001
Respiratory failure requiring mechanical ventilation, n (%)	29 (33.0%)	117 (44.3%)	.06
Duration of mechanical ventilation, median in days (IQR)	14.0 (5.0-19.0)	13.0 (8.0-21.0)	.52
Septic shock requiring vasoactive drug use, n (%)	31 (35.2%)	115 (43.6%)	.17
Duration of vasoactive drug use, median in days (IQR)	4.0 (2.0-13.0)	7.0 (4.0-13.3)	.21
ICU stay, n (%)	42 (47.7%)	148 (56.1%)	.18
Duration of ICU stay, median in days (IQR)	6.0 (2.5-16.0)	13.0 (6.0-22.5)	.001
Total length of hospital stay, median in days (IQR)	9.5 (5.0-20.0)	12.0 (6.0-22.8)	.18
Hospital readmission,^d n (%)	6 (6.8%)	11 (4.2%)	.32
In-hospital death, n (%)	21 (23.9%)	70 (26.5%)	.62

Abbreviations: IQR interquartile range; SpO₂ oxygen saturation; PaO₂/FiO₂ arterial partial pressure of oxygen to fraction of inspired oxygen; ICU intensive care unit.

200 mmHg < PaO₂/FiO₂≤300 mmHg.

100 mmHg < PaO₂/FiO₂≤200 mmHg.

PaO₂/FiO₂≤100 mmHg.

Within 14 days after the first hospital discharge.

The most frequently reported symptoms on admission by both COVID-19 and non-COVID-19 patients included dyspnea, persistent cough, and fever (Supplemental Material 1). By analyzing study groups separately, it is possible to observe that COVID-19 patients were more commonly affected by anosmia, ageusia, muscle pain, diarrhea, and headache, while non-COVID-19 patients presented higher rates of abdominal pain, mental confusion, excessive drowsiness, coryza, tachypnea, and pleuritic chest pain.

Analysis of laboratory parameters measured upon admission revealed that non-COVID-19 patients were hospitalized with median D-dimer levels twice as high as those from COVID-19 patients (2269.0 ng/mL [IQR 900.3-5400.3] vs 1098.0 [603.5-2800.0], P=.002), as demonstrated in Supplemental Material 2. Median fibrinogen levels, in turn, were markedly elevated in both groups, but even more so in COVID-19 patients. Lastly, although platelet consumption tended to be more heightened in COVID-19 patients, the median platelet count remained within the normal range in both study groups. No significant differences were noted between groups in regard to C-reactive protein (CRP).

With respect to drug treatment received during hospitalization, thromboprophylaxis with UFH/LMWH was administered more frequently to COVID-19 patients than to those affected by other ARDS etiologies (84.8% vs 67.0%, P<.0001, Supplemental Material 3). The former also received glucocorticoid at a higher rate (74.2% vs 46.6%, P<.0001), while the latter used antiviral agents more often (27.7% vs 48.9%, P<.0001). Antibiotics were broadly used in both groups, the most common being azithromycin (90.5% vs 79.5%, P=.01) and amoxicillin + clavulanate (70.5% vs 61.4%, P=.11). Lastly, aspirin and hydroxychloroquine use were similar between groups (aspirin: 14.8% vs 21.6%, P=.14; hydroxychloroquine: 8.0% vs 9.1%, P=.74).

Other ARDS Etiologies

69.3% of non-COVID-19 patients met the severe CAP criteria established by the Infectious Diseases Society of America and the American Thoracic Society,²³ and 87.5% were subjected to at least one laboratory diagnostic testing for identifying ARDS causes other than COVID-19. Overall, 33.0% of non-COVID-19 patients received a positive result (respiratory-tract culture: 18.2%; blood culture: 14.8%; RT-PCR test for cytomegalovirus: 4.5%). Bacteria were identified in 92.3% and 87.5% of positive blood and respiratory-tract cultures, while fungi were present in 7.7% and 43.8% of them, respectively. There were 31.3% of respiratory-tract specimens showing colonization of both bacterial and fungal agents.

Thrombotic Events

Non-COVID-19 patients were more likely to score≥4 points in the Padua Prediction Score²⁴ mainly because they suffered from active cancer twice as often as COVID-19 patients (Table 1). In addition, 21.6% of non-COVID-19 patients were bedridden for at least 3 days prior to admission, while the same was true for only 5.3% of COVID-19 patients. Although the latter were 3 times more likely to have a body mass index (BMI)>30 kg/m², this did not significantly increase their thrombotic risk at baseline, since obesity is assigned a lower weight compared to active cancer and reduced mobility in the Padua Prediction Score.

In total, 10.2% of non-COVID-19 and 8.7% of COVID-19 patients presented one or more clinically relevant thrombotic events confirmed by imaging exams, with 2 COVID-19 patients suffering 2 thrombotic events each. As illustrated in Figure 2A, most events corresponded to venous thromboembolism (VTE), encompassing PE, DVT, as well as upper extremity DVT. Arterial thromboembolism (ATE), in turn, was represented by AMI, ischemic stroke, abdominal aortic branch thrombosis, and arterial thrombosis in the upper and lower limbs.

Figure 2.

(A) Thrombotic events occurred during hospitalization in absolute and relative numbers. Events related to COVID-19 and non-COVID-19 patients are presented in dark blue and in light blue, respectively. (B) Odds ratio and 95% CI for thrombosis among COVID-19 patients as compared to non-COVID-19 patients. Five logistic regression models were made: (1) unadjusted; (2) adjusted for sex and Padua score; (3) adjusted for the same variables as Model 2 plus thromboprophylaxis use and ICU stay; (4) adjusted for the same variables as Model 3 plus hospitalization length; and (5) adjusted for the same variables as Model 4 plus glucocorticoid use. Logistic regression results are presented as odds ratios with 95% confidence intervals. Abbreviations: OR, odds ratio; CI, confidence interval.

In the unadjusted logistic regression model (Figure 2B), the OR for any thrombosis among COVID-19 patients as compared to non-COVID-19 patients was 0.84 (95% CI, 0.37-1.89). In the remaining models, adjustment for multiple factors provided the following OR: 0.91 (95% CI, 0.40-2.09) in Model 2, adjusted for sex and Padua score; 0.71 (95% CI, 0.30-1.66) in Model 3, adjusted for sex and Padua score plus thromboprophylaxis use and ICU stay; 0.69 (95% CI, 0.30-1.64) in Model 4, adjusted for sex, Padua score, thromboprophylaxis use and ICU stay plus hospitalization length; and 0.68 (95% CI, 0.28-1.66) in Model 5, adjusted for sex, Padua score, thromboprophylaxis use, ICU stay, and hospitalization length plus glucocorticoid use.

Subgroup Analysis

In order to assess the possible clinical characteristics that may have contributed to the occurrence of thrombotic events, 3 subgroup comparisons were made: first, between non-COVID-19 and COVID-19 patients with thrombosis; secondly, between non-COVID-19 patients with and without thrombosis; and thirdly, between COVID-19 patients with and without thrombosis (Supplemental Material 4).

Among those affected by thrombosis, 66.7% of non-COVID-19 and 73.9% of COVID-19 patients were men. Their median age, in years, was 68.1 (IQR 54.3-75.1) and 53.8 (47.3-66.8), respectively. The majority of both subgroup participants were in the ICU at the time of thrombosis diagnosis and had received thromboprophylaxis prior to this. Interestingly, non-COVID-19 patients presented median D-dimer levels that were 3 times higher than those of COVID-19 patients on admission, which may have been due to the shorter time between admission and thrombosis diagnosis among the former. This might also explain why non-COVID-19 patients tended to have a higher Padua score on admission. On the other hand, COVID-19 patients with thrombosis had a median PaO₂/FiO₂ ratio twice as low as that of non-COVID-19 patients.

By analyzing non-COVID-19 patients with and without thrombosis, it can be noted that those affected by thrombotic events were more frequently treated with anticoagulants and tended to have higher median D-dimer levels on admission. COVID-19 patients with thrombosis, in turn, tended to be predominantly men and more often admitted to the ICU in comparison to COVID-19 patients without thrombotic manifestations. They also differed importantly in terms of D-dimer levels on admission, PaO₂/FiO₂ ratio, and total length of hospital stay, with those affected by thrombosis showing a higher degree of hypercoagulability and lung impairment requiring longer hospital assistance. No significant differences were observed with regard to in-hospital mortality in the 3 comparisons.

Discussion

In our contemporary cohort of patients with ARDS due to COVID-19 and other respiratory infections, we observed that thrombotic events in COVID-19 patients occurred at a rate that was both dangerously high and comparable to patients with other types of infection-induced ARDS. This finding persisted even after accounting for the fact that: (1) COVID-19 patients received thromboprophylaxis more often and tended to evolve with more severe lung impairment, and (2) non-COVID-19 patients were more frequently affected by comorbidities and were more likely to score≥4 points in the Padua Prediction Score. An important difference between patients with thrombosis in both study groups was the time between hospital admission and thrombosis diagnosis: while non-COVID-19 patients tended to seek medical assistance most likely when the thrombotic event was already in place, COVID-19 patients mainly developed thrombosis throughout their hospitalization. This is a possible explanation as to why such higher median D-dimer levels were noted among non-COVID-19 patients in comparison to COVID-19 patients on admission.

Thrombosis Occurrence in COVID-19 and in Other Respiratory Infections

The notion that acute respiratory infections can be linked to an increase in the risk of thromboembolic events is supported by evidence accumulated over the past 2 decades. Early studies testing this hypothesis were mostly focused on the occurrence of arterial events: in 1998, Meier et al²⁵ reported a case-control study using the General Practice Research Database (GPRD) and concluded that a respiratory infection within the previous month was associated with an increased risk of AMI for a period of 2 weeks in people without a history of clinical risk factors for AMI (OR 1-5, 6-10, 11-15, and 16-30 days before index date: 3.6 [95% CI 2.2-5.7], 2.3 [1.3-4.2], 1.8 [1.0-3.3], and 1.0 [0.7-1.6]). This finding was further corroborated by a much larger case-control study conducted by Smeeth et al²⁶ using the same GPRD database, which also reported an association between respiratory-tract infections and stroke. To test whether the GPRD findings for both AMI and stroke could be replicated in a similar but separate database, Clayton et al²⁷ carried out a case-control study in the IMS Disease Analyzer Mediplus (IMS) database and found strong evidence of an increased risk of both events in the 7 days following infection (OR for AMI, 2.10 [95% CI, 1.38-3.21]; OR for stroke, 1.92 [95% CI, 1.24-2.97]).

Later, the risk of VTE was also proven to increase after acute infections. In 2006, Smeeth et al²⁸ studied the risk of DVT and PE after acute infection using the self-controlled case-series method in the UK's Health Improvement Network database and concluded that the risk of DVT was increased up to 6 months following a respiratory infection, with an IR of 1.91 (95% CI, 1.49-2.44) in the first 2 weeks. The authors argued that no reliable estimate of the risk of PE after respiratory infection could be reached given that early presentations of PE might have been misdiagnosed as respiratory infections. Using the IMS database once again, Clayton et al²⁹ reported an increased risk of DVT in the month following a respiratory infection (OR 2 .64 [95% CI, 1.62-4.29]), as well as an increased risk of PE in the 3 months following infection (OR 2.50 [95% CI, 1.33-4.72]). Such increased risk of DVT and PE persisted for up to a year. Accordingly, in a case-crossover study using the Health and Retirement Study databases and Medicare files, infection was the most common trigger of hospitalization for VTE in the United States.³⁰

Despite the many studies demonstrating the association between respiratory infections and thromboembolic complications, there is no established consensus on the absolute risk of thrombosis in respiratory infections. Prior reports that aim to address this matter are mostly limited in size and duration of follow-up and have rarely provided data on both arterial and venous events from the same cohort. Also, there are no clinical trials on antithrombotic strategies for non-COVID-19 respiratory illnesses to this day, even though they represent a leading cause of death worldwide.³¹ Such lack of robust data concerning thrombotic risk in other respiratory infections hinders accurate comparisons to COVID-19, leaving room for overestimation.

In addition, we believe that a significant heterogeneity between study populations may have contributed to the high variability of thrombotic rates reported among COVID-19 patients. In one of the earliest reports, Cui et al³² retrospectively collected data on 81 patients hospitalized with severe COVID-19 in a single institution in China and reported that a quarter of ICU patients developed VTE. No thromboprophylaxis was administered to these patients. A similar VTE rate was reported by a Dutch cohort study early on in the pandemic: despite the use of anticoagulant agents in prophylactic dose, 27% of the 184 patients with severe COVID-19 developed VTE while in the ICU.³² On the other hand, reports with larger sample sizes showed an overall VTE risk of 3.0% among COVID-19 cases, and clinical trials on strategies for antithrombotic therapy in COVID-19 found that major thrombotic events affected 1.6% and 9.1% of patients with moderate and critical illness, respectively.^33-35

These numbers, though no less worthy of clinical caution, are considerably smaller than those reported by retrospective studies on COVID-19, and very much comparable to the currently available data on acute respiratory infections in the pre-COVID-19 era.^9-12,29 In a matched case-control study including more than 1000 patients treated for VTE, a 4.0% incidence of venous events was found in patients with pneumonia following respiratory tract infections, while a crossover-cohort analysis reported that, among more than 5 million patients discharged after a respiratory infection, 1.5% were readmitted within 180 days with AMI, and 1.7% with VTE.^12,29 Differences in sample size, patient selection, setting (ward/ICU), individual institutions’ thromboprophylaxis strategy, and event screening procedures make it difficult not only to analyze the occurrence of thrombosis in COVID-19 and non-COVID-19 patients from a same cohort, but also to compare findings between studies.^{9-12,29,33-35}

To our knowledge, only 2 prior studies evaluated thrombotic events in a cohort of COVID-19 and non-COVID-19 patients hospitalized in the same time period.^36,37 In the study of Mei et al,³⁶ its unexposed population consisted of patients diagnosed with CAP after 2 negative RT-PCR results for COVID-19, whereas in that of Chaudhary et al,³⁷ the inclusion of non-COVID-19 patients was based solely on negative RT-PCR results, regardless of their reason for hospital admission and of the presence of respiratory symptoms. Despite differences in inclusion criteria, both reports also concluded that the incidence of thromboembolism was similar between study populations: Mei et al³⁶ found an overall VTE rate of 2.0% in COVID-19 and 3.6% in CAP patients (P=.23); Chaudhary et al³⁷ reported that 5.9% of COVID-19-positive and 10.2% of COVID-19-negative patients suffered from arterial or venous thrombosis during their hospital stay (P=.16).

Even though the 2 studies’ results are in line with our findings, they possess particular traits that merit discussion. For instance, it is possible that the analysis conducted by Mei et al was undermined by the fact that patients with COVID-19 were younger and healthier than those with CAP, a difference for which the performed statistical analyses were not adjusted.³⁸ Chaudhary et al, in turn, followed COVID-19-positive patients for only a brief 6-week period, since the study was conducted from January until the beginning of May 2020, and the first COVID-19 cases in the Mayo Clinic enterprise began to emerge at the end of March.³⁹

In this regard, our study has strengths for establishing precise inclusion criteria that allowed for the enrollment of COVID-19 and non-COVID-19 patients presenting similar clinical manifestations consistent with infection-induced ARDS and subjected to the same COVID-19 laboratory testing strategy. Thus, there was only an 11-day interval between the admission date of the first ARDS patients who tested negative and positive for COVID-19 in the UNICAMP Clinical Hospital. Additionally, because all ARDS patients were first directed to the COVID-19 hospital wing, the initial clinical management of COVID-19 and non-COVID-19 patients followed the same treatment protocol.

Limitations

It is important to note that our study has limitations due to its retrospective and single-centered design, and because more confounding factors may exist than those employed in the odds ratio adjustment calculation. It should also be acknowledged that non-COVID-19 patients differed from COVID-19 patients in terms of comorbidities and Padua Prediction Score. To prevent such differences from serving as potential sources of bias, they were adjusted in the multivariable regression analysis.

Furthermore, certain cases of subclinical and postdischarge thrombosis might have been missed since no systematic screening was applied during hospitalization, and only thromboembolic complications requiring hospitalization within 14 days after the patient's first discharge were analyzed. Still, we were interested in in-hospital, clinically relevant thrombotic events confirmed by imaging exam and believe that this possible underestimation is unlikely to impact our results, given the lowering threshold to suspect thrombosis in COVID-19 and the higher mortality rate related to in-hospital thrombosis compared to postdischarge thrombosis.⁴⁰

Finally, it is possible that false-negative RT-PCR results led to the wrong allocation of some COVID-19 patients in the non-COVID-19 group. In order to reduce such risk, we established that non-COVID-19 patients should test negative in 2 consecutive rounds of RT-PCR—the gold standard for COVID-19 diagnosis—, and that samples should be taken within a maximum interval of 48 h upon admission. The 6-day median time between symptom onset and hospital admission in the non-COVID-19 group indicates that most of these patients were tested within the recommended window period of 3 to 10 days following the start of symptoms, further increasing the accuracy of the RT-PCR results. By using this diagnostic approach, the specificity of the RT-PCR test has been proven to be 97%,⁴¹ and so we expect < 3% of our non-COVID-19 patients to have been possibly misdiagnosed. Although no laboratory testing for infectious agents other than SARS-CoV-2 was routinely conducted on ARDS patients at the UNICAMP Clinical Hospital, the 33% rate of detection of any infectious agent in our non-COVID-19 group is in line with previous evidence showing that up to 60% of ARDS pathogens remain unknown even after the most extensive laboratory investigation is conducted.⁴² This supports the notion that the difficulty of specifying which pathogen is causing ARDS can be attributed to limitations pertaining not only to our retrospective study design but the ability of existing diagnostic tests to successfully detect infectious agents as well.

Conclusion

In conclusion, the similarity in thrombosis frequency seen in our contemporary cohort of COVID-19 and other ARDS patients speaks in favor of a prothrombotic tendency that is likely common to ARDS in general, and not as heightened in COVID-19 as previously suggested. In this sense, our results point towards the importance of dedicating time and resources to the study of respiratory illnesses beyond COVID-19, so that we can better understand the mechanisms behind the hemostatic changes they cause, as well as develop antithrombotic treatment methods to reduce disease burden and mortality.

Supplemental Material

sj-docx-1-cat-10.1177_10760296231175656 - Supplemental material for Thrombosis Occurrence in COVID-19 Compared With Other Infectious Causes of ARDS: A Contemporary Cohort

Supplemental material, sj-docx-1-cat-10.1177_10760296231175656 for Thrombosis Occurrence in COVID-19 Compared With Other Infectious Causes of ARDS: A Contemporary Cohort by Andréa Coy-Canguçu, Gisele A. Locachevic, PhD, João Carlos S. Mariolano, Kaio Henrique De O. Soares, José Diogo Oliveira, Camila De Oliveira Vaz, MSc, Gislaine Vieira-Damiani, PhD, Bruna Mazetto, PhD, Joyce Maria Annichino-Bizzacchi, MD, PhD, Erich V. De Paula, MD, PhD, and Fernanda A. Orsi, MD, PhD in Clinical and Applied Thrombosis/Hemostasis

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the São Paulo Research Foundation—FAPESP (Grants 2020/12630-2 and 2016/14172-6). Andréa Coy-Canguçu received financial support (scholarship) from the São Paulo Research Foundation—FAPESP (Grant 2020/16105-0). The funders had no role in study design, data collection, and analysis, the decision to publish, or preparation of the manuscript.

ORCID iDs

Andréa Coy-Canguçu

Fernanda A. Orsi

Supplemental Material

Supplemental material for this article is available online.

References

MacKman

Antoniak

Wolberg

Kasthuri

Key

NS.

Coagulation abnormalities and thrombosis in patients infected with SARS-CoV-2 and other pandemic viruses. Arterioscler Thromb Vasc Biol. 2020;2033‐2044. doi:10.1161/ATVBAHA.120.314514

Fanelli

Ranieri

VM.

Mechanisms and clinical consequences of acute lung injury. Ann Am Thorac Soc. 2015;12:S3-S8. doi:10.1513/AnnalsATS.201407-340MG

Ranieri

Rubenfeld

Thompson

, et al. Acute respiratory distress syndrome. JAMA. 2012;307(23):2526-2533. doi:10.1001/jama.2012.5669

Frantzeskaki

Armaganidis

Orfanos

SE.

Immunothrombosis in acute respiratory distress syndrome: cross talks between inflammation and coagulation. Respiration. 2017;93(3):212-225. doi:10.1159/000453002

Matthay

Zemans

Zimmerman

, et al. Acute respiratory distress syndrome. Nat Rev Dis Primers. 2018;5(1). doi:10.1038/s41572-019-0069-0

Violi

Cangemi

Falcone

, et al. Cardiovascular complications and short-term mortality risk in community-acquired pneumonia. Clin Infect Dis. 2017;64(11):1486-1493. doi:10.1093/cid/cix164

Warren-Gash

Blackburn

Whitaker

McMenamin

Hayward

AC.

Laboratory-confirmed respiratory infections as triggers for acute myocardial infarction and stroke: a self-controlled case series analysis of national linked datasets from Scotland. Eur Respir J. 2018;51(3):AR. doi:10.1183/13993003.01794-2017.

Barnes

Heywood

Mahimbo

Rahman

Newall

MaCintyre

CR.

Acute myocardial infarction and influenza: a meta-analysis of case-control studies. Heart. 2015;101(21):1738-1747. doi:10.1136/heartjnl-2015-307691.

Suh

Hong

Ohana

, et al. Pulmonary embolism and deep vein thrombosis in COVID-19: a systematic review and meta-analysis. Radiology. 2021;298(2):E70-E80. doi:10.1148/RADIOL.2020203557.

10.

Di Minno

Ambrosino

Calcaterra

Di Minno

MND

. COVID-19 and venous thromboembolism: a meta-analysis of literature studies. Semin Thromb Hemost. 2020;46(7):763-771. doi:10.1055/s-0040-1715456.

11.

McBane

Torres Roldan

Niven

, et al. Anticoagulation in COVID-19: a systematic review, meta-analysis, and rapid guidance from Mayo Clinic. Mayo Clin Proc. 2020;95(11):2467-2486. doi:10.1016/j.mayocp.2020.08.030.

12.

Smilowitz

Subashchandran

Newman

, et al. Risk of thrombotic events after respiratory infection requiring hospitalization. Sci Rep. 2021;11(1):1-9. doi:10.1038/s41598-021-83466-9.

13.

Stals

MAM

Grootenboers

MJJH

van Guldener

, et al. Risk of thrombotic complications in influenza versus COVID-19 hospitalized patients. Res Pract Thromb Haemost. 2021;5(3):412-420. doi:10.1002/rth2.12496.

14.

Helms

Tacquard

Severac

, et al. High risk of thrombosis in patients with severe SARS-CoV-2 infection: a multicenter prospective cohort study. Intensive Care Med. 2020;46(6):1089-1098. doi:10.1007/s00134-020-06062-x.

15.

Smilowitz

Subashchandran

Yuriditsky

, et al. Thrombosis in hospitalized patients with viral respiratory infections versus COVID-19. Am Heart J. 2021;231:93-95. doi:10.1016/j.ahj.2020.10.075.

16.

Lachant

Roto

Rappaport

Prasad

Lachant

White

RJ.

Venous thromboembolism associates with SARS-CoV-2 more than seasonal influenza. Thromb Res. 2021;205:40-43. doi:10.1016/j.thromres.2021.07.003.

17.

Merkler

Parikh

Mir

, et al. Risk of ischemic stroke in patients with coronavirus disease 2019 (COVID-19) vs patients with influenza. JAMA Neurol. 2020;77(11):1366-1372. doi:10.1001/jamaneurol.2020.2730.

18.

Poissy

Goutay

Caplan

, et al. Pulmonary embolism in patients with COVID-19: awareness of an increased prevalence. Circulation. 2020;142(2):184-186. doi:10.1161/CIRCULATIONAHA.120.047430.

19.

Doyle

Hunt

Sanderson

, et al. A comparison of thrombosis and hemorrhage rates in patients with severe respiratory failure due to coronavirus disease 2019 and influenza requiring extracorporeal membrane oxygenation. Crit Care Med. Published online 2021:E663-E672. doi:10.1097/CCM.0000000000004971.

20.

Matthay

Thompson

Ware

LB.

The Berlin definition of acute respiratory distress syndrome: should patients receiving high-flow nasal oxygen be included?

Lancet Respir Med. 2021;9(8):933-936. doi:10.1016/S2213-2600(21)00105-3.

21.

Zeiser

Donida

da Costa

, et al. First and second COVID-19 waves in Brazil: a cross-sectional study of patients’ characteristics related to hospitalization and in-hospital mortality. The Lancet Regional Health - Americas. 2022;6:100107. doi:10.1016/j.lana.2021.100107.

22.

Bastos

LSL

Aguilar

Rache

, et al. Primary healthcare protects vulnerable populations from inequity in COVID-19 vaccination: an ecological analysis of nationwide data from Brazil. The Lancet Regional Health - Americas. Published online August 2022:100335. doi:10.1016/j.lana.2022.100335.

23.

Mandell

Wunderink

Anzueto

, et al. Infectious diseases society of America/American thoracic society consensus guidelines on the management of community-acquired pneumonia in adults. Clin Infect Dis. 2007;44(Suppl. 2). doi:10.1086/511159.

24.

Barbar

Noventa

Rossetto

, et al. A risk assessment model for the identification of hospitalized medical patients at risk for venous thromboembolism: the Padua prediction score. J Thromb Haemost. 2010;8(11):2450-2457. doi:10.1111/j.1538-7836.2010.04044.x.

25.

Meier

Jick

Derby

, et al. Acute respiratory-tract infections and risk of first-time acute myocardial infarction. The Lancet. 1998;351(9114):1467-1471. doi:10.1016/S0140-6736(97)11084-4.

26.

Smeeth

Thomas

Hall

Hubbard

Farrington

Vallance

Risk of myocardial infarction and stroke after acute infection or vaccination. N Engl J Med. 2004;351(25):2611-2618. doi:10.1056/NEJMoa041747.

27.

Clayton

Thompson

Meade

TW.

Recent respiratory infection and risk of cardiovascular disease: case-control study through a general practice database. Eur Heart J. 2007;29(1):96-103. doi:10.1093/eurheartj/ehm516.

28.

Smeeth

Cook

Thomas

Hall

Hubbard

Vallance

Risk of deep vein thrombosis and pulmonary embolism after acute infection in a community setting. The Lancet. 2006;367(9516):1075-1079. doi:10.1016/S0140-6736(06)68474-2.

29.

Clayton

Gaskin

Meade

TW.

Recent respiratory infection and risk of venous thromboembolism: case-control study through a general practice database. Int J Epidemiol. 2011;40(3):819-827. doi:10.1093/ije/dyr012.

30.

Rogers

MAM

Levine

Blumberg

Flanders

Chopra

Langa

KM.

Triggers of hospitalization for venous thromboembolism. Circulation. 2012;125(17):2092-2099. doi:10.1161/CIRCULATIONAHA.111.084467.

31.

Mayor

Acute respiratory infections are world’s third leading cause of death. Br Med J. 2010;341:c6360-c6360. doi:10.1136/bmj.c6360.

32.

Klok

Kruip

MJHA

van der Meer

NJM

, et al. Incidence of thrombotic complications in critically ill ICU patients with COVID-19. Thromb Res. 2020;191:145-147. doi:10.1016/j.thromres.2020.04.013.

33.

Gallastegui

Zhou

von Drygalski

Barnes

RFW

Fernandes

Morris

. Pulmonary embolism does not have an unusually high incidence among hospitalized COVID-19 patients. Clin Appl Thromb Hemost. 27:1076029621996471. doi:10.1177/1076029621996471.

34.

ATTACC Investigators, ACTIV-4a Investigators , REMAP-CAP Investigators , et al. Therapeutic anticoagulation with heparin in noncritically ill patients with COVID-19. N Engl J Med. 2021;385(9):790-802. doi:10.1056/NEJMoa2105911.

35.

REMAP-CAP Investigators, ACTIV-4a Investigators , ATTACC Investigators , et al. Therapeutic anticoagulation with heparin in critically ill patients with COVID-19. N Engl J Med. 2021;385(9):777-789. doi:10.1056/NEJMoa2103417.

36.

Mei

Fan

Yuan

, et al. Comparison of venous thromboembolism risks between COVID-19 pneumonia and community-acquired pneumonia patients. Arterioscler Thromb Vasc Biol. 2020;2332-2337. doi:10.1161/ATVBAHA.120.314779.

37.

Chaudhary

Padrnos

Wysokinska

, et al. Macrovascular thrombotic events in a mayo clinic enterprise-wide sample of hospitalized COVID-19-positive compared with COVID-19-negative patients. Mayo Clin Proc. 2021;96(7):1718-1726. doi:10.1016/j.mayocp.2021.04.022.

38.

Hamilton

Waite

AAC

Welters

ID.

Letter by Hamilton et al regarding article, “comparison of venous thromboembolism risks between COVID-19 pneumonia and community-acquired pneumonia patients”. Arterioscler Thromb Vasc Biol. 2020;40(11):e309-e310. doi:10.1161/ATVBAHA.120.315235.

39.

Bauer

PR.

Prothrombotic state in COVID-19: myth or reality?

Mayo Clin Proc. 2021;96(7):1694-1696. doi:10.1016/j.mayocp.2021.05.015.

40.

Stubbs

Assareh

Curnow

Hitos

Achat

HM.

Incidence of in-hospital and post-discharge diagnosed hospital-associated venous thromboembolism using linked administrative data. Intern Med J. 2018;48(2):157-165. doi:10.1111/imj.13679.

41.

Hanson

Caliendo

Arias

, et al. IDSA Guidelines on the Diagnosis of COVID-19: Molecular Diagnostic Testing. Infectious Diseases Society of America. Published 2020. Accessed January 23, 2022. https://www.idsociety.org/practice-guideline/covid-19-guideline-diagnostics/#

42.

Kao

Chiu

Hung

, et al. Coinfection and mortality in pneumonia-related acute respiratory distress syndrome patients with bronchoalveolar lavage: a prospective observational study. Shock. 2017;47(5):615-620. doi:10.1097/SHK.0000000000000802.

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