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Background
Chronic Obstructive Pulmonary Disease (COPD) patients are prone to thrombogenic events, particularly during exacerbations. Fractal dimension (df), a functional biomarker of clot microstructure is useful in assessing thrombogenicity in other diseases. The aim of this study was to compare the changes in df during acute exacerbation of COPD with stable disease.
Methods
This prospective study recruited 30 patients with stable disease from the chest clinic and 85 patients with acute exacerbations from the Emergency Department. The stable group had blood samples taken at one time point, whilst the exacerbation group were sampled at four time points (0 hours, 4-6 hours, 24 hours and 3-7 days). Biomarkers of inflammation, haemostasis and rheology were determined.
Results
At presentation, the acute exacerbation group had significantly elevated df when compared to the stable group (1.71 ± 0.06 vs 1.69 ± 0.05, p=0.03) but with no significant changes in df over the four time points (p=0.28) sampled. The patients who died in the acute exacerbation group had significantly elevated df when compared to those who survived (1.76 ± 0.03 vs 1.71 ± 0.06, p=0.02) with additional logistic regression analysis confirming that dfwas a significant predictor of mortality (p=0.024).
Conclusions
Clot microstructural analysis demonstrates that COPD patients during acute exacerbation are more thrombogenic when compared to those with stable disease. This thrombogenic state is not aggravated with appropriate treatment on admission. Patients who died during exacerbations were in a significantly enhanced thrombogenic state when compared to those who survived as demonstrated by significantly elevated df.
Background
Chronic obstructive pulmonary disease (COPD) is a chronic and progressive inflammatory respiratory disease, the severity of which is determined by a staged progression. It is the fourth leading cause of death worldwide [1] and is associated with significant morbidity. With 1.3 million people diagnosed and 30,000 deaths per year, the economic burden to the National Health System (NHS) in United Kingdom was estimated to be approximately £1.9 billion [2, 3]. COPD patients have an abnormal inflammatory response to smoking and air pollutants. The resulting lung tissue damage can manifest as chronic bronchitis, emphysema or bronchiolitis which then lead to chronic airflow obstruction and impairment of gas exchange [4]. The inflammatory markers released during this process can cause systemic inflammation [5]. In addition, damage to vascular endothelium releases tissue factor that can trigger coagulation pathways [6]. Several studies have shown that there is high incidence of venous thromboembolism in COPD particularly during exacerbations [7]. A recent systematic review showed the prevalence of pulmonary embolism in COPD to be 11% [8]. As the disease progresses, COPD patients develop frequent flare ups known as exacerbations. These exacerbations can be triggered by an infection or other factors (non-infective) which can be life threatening [9].
The tendency to form blood clots (thrombogenicity) in COPD has been studied extensively using a range of biomarkers [10, 11]. However, there is no single biomarker that helps to assess the global haemostasis accurately and thereby assess thrombogenicity in COPD patients [7]. Point of care tests such as rotational thromboelastometry (ROTEM) and thromboelastography (TEG) are becoming increasingly used to assess global haemostasis [12, 13]. However, such tests fail to provide any microstructural information of the underlying (fibrin) clot network. A relatively new biomarker of clot microstructure (the fractal dimension-df) has shown to be reliable in assessing thrombogenicity in inflammatory conditions such as sepsis. In this study, it was demonstrated that higher values of df reflect tighter and denser clot microstructure and lower values mean less dense and weaker blood clots [14]. This study was the first to assess thrombogenicity in COPD patients during exacerbation through the evaluation of clot microstructure using fractal dimension analysis. We hypothesise that increased inflammation during acute disease will lead to changes in df, reflecting tighter and denser clots which have been shown to be associated with thrombogenic disease states.
Methods
Study design
This study was a single centre prospective observational study carried out in a large teaching tertiary hospital. The study recruited two groups of patients; the stable group (SCOPD) from the Chest Clinic and Pulmonary Rehabilitation programme and the acute exacerbation group (AECOPD) from a busy Emergency Department.
Patient recruitment
Patients aged 35 or above with a confirmed diagnosis of COPD as defined by the Global Strategy for the Diagnosis, Management and Prevention of COPD, Global Initiative for Chronic Obstructive Lung Disease (GOLD) criteria were included in this study from November 2018 to June 2022. Those receiving any medical treatment that could affect coagulation (anticoagulant therapy such as Warfarin, Heparin, Rivaroxaban, Dabigatran and Apixaban or any other anticoagulants) were excluded from the study.
Blood sampling
Collection procedure: Blood was sampled from SCOPD group at one time point during their presentation to the chest clinic and from AECOPD group at four time points (0 h, 4–6 h, 24 h and 3–7 days). The blood was taken atraumatically and initial 3-5mls were discarded and samples were collected to a 9.0 ml vacuum sealed plain plastic tube for rheology, 4.0 ml vacuum sealed K2EDTA (EDTA-Ethylenediaminetetraacetic acid) tube (Greiner Bio-one, Stonehouse, UK) for full blood count (FBC), 2.7 ml vacuum-sealed tubes containing sodium citrate (3.2%) for coagulation profile, d-dimer and FXIII (Factor XIII) and ROTEM (rotational thromboelastometry), 5.0 ml SST II Clot activator & serum gel separator for CRP (C-reactive protein) and Procalcitonin (PCT) and 3.0 ml Multiplate® Hirudin Blood Tube (Double-Wall) for platelet aggregation.
Laboratory analysis: The blood samples for FBC, coagulation markers and inflammatory markers were sent to the hospital main laboratory for analysis. FBC was analysed using Sysmex XE2100 automated haematology analyser, coagulation markers using Sysmex CA1500 (Sysmex UK, Milton Keynes, UK), D-dimer using TriniLIA Auto-D-dimer@ is used to detect the D-dimer concentration and inflammatory markers using appropriate ELIZA (enzyme-linked immunosorbent assay) kit assay. Platelet aggregometry tests such as ADP (Adenine Diphosphate) and ASPI (Arachidonic acid test) were carried out using Roche Multiplate® analyser.
Viscoelastic assays (Rheology/ROTEM): To measure the rheological biomarkers such as fractal dimension (df) and time to Gel Point (TGP), 7mls of whole blood was transferred into a double concentric geometry of an AR-G2 Rheometer (TA instruments, New Castle, DE, USA). The phase angle (a measure of viscous to elastic response) was monitored during coagulation by the application of sequential frequency sweeps spanning a range of 0.2 to 2 Hz. The Gel Point (GP) was identified through the detection of a frequency independent phase angle which indicates the exact transition from viscoelastic liquid to viscoelastic solid behaviour i.e. the incipient fibrin clot. The time taken to obtain a GP was recorded (TGP) and, from the viscoelastic data at the GP, the df was calculated using a mathematical relationship of fractals [15, 16]. ROTEM tests were carried out using ROTEM® Delta.
Statistical analysis
This study aimed to compare df between the SCOPD and AECOPD groups on admission. Firstly, based on the assumption that SCOPD patients have df similar to healthy individuals which was 1.73 (± SD 0.04), using a 2-sample t-test to detect a difference in these two groups with α = 0.05, and a power of 0.8, the mean difference of 0.05 and a combined SD of 0.06, the number needed to recruit to this study was calculated as a minimum of 25. However, due to expected attrition, it was decided to recruit 30 patients in each group. Secondly, one-way ANOVA was utilised to determine if there are any changes in df at four time points post-admission in the AECOPD patients. Our previous studies supported the view that reduction in inflammation in response to therapy results in df changing by 0.04 (± 0.06). Using an α = 0.05 and a power of 0.8, and therefore to detect differences at four levels using one-way ANOVA, a minimum of 51 subjects would be required for this part of the study. As we expected a substantial attrition due to disease severity, we recruited 85 patients in AECOPD group and further supporting the application of logistic regression to investigate the presence of significant relationships with df, in the AECOPD patients. All statistical analysis was carried out on IBM Statistical Package for Social Sciences (SPSS) for Windows, version 22.0 (Armonk, NY: IBM Corp). The normality of the data was confirmed via Shapiro-Wilk test. The values are reported as means ± standard deviation (SD) or median (interquartile ranges [IQR]) where appropriate. Comparisons were done with two sample t-test for mean ± SD and Mann-Whitney U or Kruskal-Wallis test for Median (IQR) and data was deemed significant when p < 0.05.
Results
Baseline characteristics of SCOPD and AECOPD patients
The study recruited 30 SCOPD and 85 AECOPD patients. The four sampling points in AECOPD group were AECOPD-A, AECOPD-B, AECOPD-C and AECOPD-D. Both the groups were matched for demographics such as age, sex and body mass index. There were more current smokers in the AECOPD group as expected. Hypertension was significantly higher (p = 0.02) and a significant number of patients died in one year in the AECOPD group (p = 0.02) (Table 1).
Table 1. Background characteristics of SCOPD and AECOPD patients (BMI- body mass Index, HTN- Hypertension, IHD- ischemic heart Disease, VTE- venous Thromboembolism, CVA- cerebral vascular Disease, ICU- intensive care Unit, FEV1- forced expiratory volume at 1 s)
SCOPD (n = 30) | AECOPD (n = 85) | p value | |
|---|---|---|---|
Age (years) | 67 ± 10 | 70 ± 10 | 0.19 |
Sex (M/F) | 15:15 | 41:44 | 0.87 |
BMI | 27.5 ± 7.7 | 29.2 ± 8 | 0.46 |
Current smoker | 9/30 (30%) | 36/81 (44%) | 0.43 |
HTN | 5/30 (17%) | 30/85 (35%) | 0.02* |
Diabetes mellitus | 6/30 (20%) | 18/85 (21%) | 0.89 |
IHD | 4/30 (13%) | 2/85 (2%) | 0.12 |
CVA | 1/30 (3%) | 8/85 (9%) | 0.10 |
Heart failure | 2/30 (7%) | 5/85 (6%) | 0.88 |
Previous VTE | 2/30 (7%) | 1/85 (1%) | 0.26 |
Cancer | 2/30 (7%) | 7/85 (8%) | 0.40 |
FEV1 (% predicted) | 55 ± 24 | 50 ± 20 | 0.42 |
Hospital length of stay (days) | - | 6 (2–14) | - |
ICU length of stay (days) | - | 11 (3–31) | - |
ICU admission | - | 9/85 (11%) | - |
Died in this admission | - | 10/85 (12%) | - |
Died within 1 year | 4/30 (13%) | 25/85 (30%) | 0.02* |
Investigated for VTE | - | 8/85 (9%) | - |
VTE during admissions | - | 1/85 (1%) | - |
SCOPD and AECOPD blood results at presentation
The inflammatory markers and leukocyte counts were significantly elevated in the AECOPD group at presentation to the ED when compared to the SCOPD group. Markers of haemostasis were all higher in the AECOPD group when compared to the SCOPD group, specifically Fibrinogen, FXIII and D-dimer were significant. The rheological markers, including df, were significantly different in AECOPD group compared to SCOPD group (Table 2).
Table 2. Inflammatory markers, markers of haemostasis, rheological markers and other markers in SCOPD and AECOPD patients at presentation (ADP- adenosine Diphosphate, ASPI- arachidonic acid test, Hb%- Haemoglobin, HCT- Haematocrit)
SCOPD | AECOPD | p value | |
|---|---|---|---|
WBC (×109/L) | 9.5 ± 3.5 | 15.1 ± 8.1 | < 0.001* |
Neutrophils (×109/L) | 6.9 ± 3.8 | 12.6 ± 7.5 | < 0.001* |
CRP (mg/L) | 0 (0–6) | 38 (12–75) | < 0.001* |
PCT (ug/L) | 0.04 (0.02–0.05) | 0.11 (0.05–0.57) | < 0.001* |
Markers of primary haemostasis | |||
Platelets (×109/L) | 265 ± 63 | 301 ± 117 | 0.36 |
ADP (platelet aggregation) | 51 ± 29 | 55 ± 33 | 0.74 |
ASPI (platelet aggregation) | 72 ± 35 | 81 ± 45 | 0.25 |
Markers of secondary haemostasis | |||
PT (sec) | 10.8 ± 1.0 | 11.1 ± 1.2 | 0.22 |
APTT (sec) | 23.4 ± 1.9 | 24.6 ± 4.3 | 0.13 |
Fibrinogen (g/L) | 3.4 ± 1.0 | 4.6 ± 1.2 | 0.001* |
FXIII (IU/dL) | 138 ± 21 | 132 ± 65 | 0.02* |
Markers of tertiary haemostasis | |||
D-dimer (< 500 ug/L) | 445 (323–726) | 870 (393–1980) | 0.003* |
Rheological markers | |||
df | 1.69 ± 0.05 | 1.71 ± 0.06 | 0.03* |
TGP | 316 ± 101 | 275 ± 73 | 0.004* |
Hb%, HCT | |||
Haemoglobin (g/L) | 143 ± 15 | 137 ± 22 | 0.25 |
Haematocrit (L/L) | 0.43 ± 0.04 | 0.43 ± 0.06 | 0.80 |
Changes in biomarkers of AECOPD group at four time points
There were significant changes in the inflammatory markers and leukocyte counts across the four time points investigated. However, the markers of haemostasis and rheological markers did not show any significant changes. Both Hb% and HCT significantly reduced across the four time points. There were no changes in ROTEM parameters across four time points (Table 3).
Table 3. Inflammatory markers, markers of haemostasis, rheological markers in AECOPD patients over four time points
AECOPD-A | AECOPD-B | AECOPD-C | AECOPD-D | P value | |
|---|---|---|---|---|---|
Inflammatory markers | |||||
WBC | 15.1 ± 8.1 | 12.9 ± 6.2 | 11.6 ± 4.4 | 10.7 ± 3.9 | 0.001* |
Neutrophils | 12.6 ± 7.5 | 11.6 ± 5.9 | 9.7 ± 4.0 | 8.8 ± 3.7 | 0.005* |
CRP | 38 (12–75) | 47 (21–111) | 44 (23–134) | 15 (7–51) | 0.004* |
PCT | 0.11 (0.05–0.57) | 0.24 (0.10–2.30) | 0.35 (0.08–1.77) | 0.15 (0.06–0.76) | 0.01* |
Markers of primary haemostasis | |||||
Platelets | 301 ± 117 | 261 ± 94 | 257 ± 93 | 268 ± 96 | 0.05 |
ADP | 55 ± 33 | 64 ± 31 | 51 ± 25 | 52 ± 29 | 0.15 |
ASPI | 81 ± 45 | 95 ± 42 | 76 ± 44 | 68 ± 39 | 0.05 |
Markers of secondary haemostasis | |||||
PT | 11.1 ± 1.2 | 11.1 ± 1.9 | 11.3 ± 1.2 | 10.9 ± 0.9 | 0.71 |
APTT | 24.6 ± 4.3 | 25.1 ± 2.5 | 25.1 ± 3.4 | 23.3 ± 3.9 | 0.12 |
Fibrinogen | 4.6 ± 1.2 | 4.6 ± 1.1 | 4.6 ± 1.1 | 4.2 ± 1.0 | 0.36 |
FXIII | 132 ± 65 | 126 ± 25 | 117 ± 30 | 116 ± 23 | 0.19 |
Markers of tertiary haemostasis | |||||
D-dimer (< 500 ug/L) | 870 (393–1980) | 661 (361–1755) | 688 (442–1851) | 970 (414–1483) | 0.675 |
Thromboprophylaxis | |||||
Anti-Xa (units/mL) | 0.01 (0–0.04.04) | 0.03 (0.01–0.05) | 0.08 (0.02–0.16) | 0.08 (0–0.10.10) | < 0.001* |
Rheological markers | |||||
df | 1.71 ± 0.06 | 1.70 ± 0.52 | 1.70 ± 0.07 | 1.71 ± 0.07 | 0.275 |
TGP | 275 ± 73 | 256 ± 84 | 291 ± 142 | 308 ± 170 | 0.114 |
Hb%, HCT | |||||
Hb (g/L) | 137 ± 22 | 130 ± 23 | 123 ± 25 | 129 ± 19 | 0.005* |
HCT (L/L) | 0.43 ± 0.06 | 0.40 ± 0.07 | 0.39 ± 0.06 | 0.40 ± 0.06 | 0.003* |
Comparison of AECOPD group that died and survived the admission and dfas predictor of mortality
There was no significant difference in the demographics of AECOPD patients who died. Those who died were more acidotic and had significantly elevated pCO2 levels (p < 0.001). None of the patients who died were investigated for VTE when compared to those who survived (11%). In addition, there was no difference in inflammatory and haemostatic biomarkers. AECOPD patients who died had significantly elevated df when compared to those who survived (1.76 ± 0.03 vs. 1.71 ± 0.06, p = 0.02) (Fig. 1). Binary regression analysis showed that df was a significant predictor of mortality (p = 0.024).
[See PDF for image]
Fig. 1
AECOPD patients who died had significantly higher df when compared to those who survived (p = 0.02)
Relationship between df and clot mass
To understand the relationship between df and the clot mass, the mean df of SCOPD (1.69), AECOPD (1.71) and AECOPD patients who died (1.76) were calculated as previously described [14]. A normalised mass (M = 1) is set at the mean df of SCOPD (1.69). The graph demonstrates that a small increase in df was associated with a significant increase in clot mass i.e. a 26% increase in AECOPD group and 134% in AECOPD patients who died, compared to the SCOPD group (Fig. 2).
[See PDF for image]
Fig. 2
Displaying the relationship between df and normalised mass. The mean df of SCOPD, AECOPD and AECOPD patients who died were plotted. A normalised mass of 1.00 (M = 1) is set at the mean df of the SCOPD group which is 1.69. The graph visually demonstrates that small changes in df is associated with a significant change in a clot’s mass
Final clot imaging: scanning electron microscopy
SEM was used to image the mature clot of an AECOPD patient (78 A). The image (Fig. 3) shows extensive fibrin networks that was comparable to other inflammatory conditions [14].
[See PDF for image]
Fig. 3
SEM image of an AECOPD (78A) patient blood clot. The figure shows the crosslinking of the fibrin
Discussion
This is the first study that assesses changes in clot microstructure in COPD exacerbations utilising the functional biomarker of clot microstructure (df). Previous studies have demonstrated that df was sensitive to progressive haemodilution [17] and anticoagulation [18]. In addition, in a study of warfarinised patients, df was the only significant biomarker capable of distinguishing differences between VTE and non-VTE patients [19]. In sepsis, which is an inflammatory condition, higher values of df were associated with denser and tighter clot microstructure and lower values of df were associated with looser and weaker clot microstructure [14]. Therefore, this study hypothesised that AECOPD patients may have higher df when compared to SCOPD group.
During exacerbations, there was significant increase in the inflammatory markers agreeing with other studies [20, 21]. It is known that inflammation can trigger coagulation pathways [22]. In this study all the markers of haemostasis were higher in AECOPD group in particular fibrinogen. Hyperfibrinogenaemia can cause increased formation of fibrin networks [23]. The fibrin networks are then crosslinked and stabilised by FXIII [24]. The FXIII levels was significantly reduced in AECOPD group demonstrating the utilisation for fibrin crosslinks (Fig. 3). Fibrin formation activates fibrinolytic pathways [25, 26] which was evidenced by significantly elevated D-dimer. This might explain why there was denser and tighter clot microstructure (high df) in acute exacerbation group at presentation.
All the patients in this study received standard treatment such as nebulisers, steroids and antibiotics if indicated. Steroids help to reduce inflammation [27] and antibiotics help to remove trigger caused by bacterial infection. The data showed significant reduction in the inflammatory markers at four time points in the AECOPD group. Furthermore, all patients in this study received prophylactic thromboprophylaxis as evidenced by significantly different anti-Xa levels at four time points. It was shown previously that progressive increase in the heparin [18] dosage results in decrease in df. However, in the AECOPD patients, the same thromboprophylaxis dosage was given on a daily basis. All the above might have contributed to no further increase in df meaning that the AECOPD patients becomes less thrombogenic over time with standard treatment. This was further evidenced by only 1% having VTE out of 9% investigated for VTE.
COPD exacerbation is associated with high inpatient mortality [28, 29] and one-year mortality [30] and this study agrees with those findings. This study performed a sub-analysis to investigate the changes in df and other biomarkers between those who died and survived. It is to be noted that none of the patients who died were investigated for VTE or were admitted to ICU. The patients who died were more acidotic and had significantly higher pCO2 levels indicating that they were having severe type 2 respiratory failure. However, the symptoms in COPD exacerbation mimics that of pulmonary embolism and there is a risk that the patients may be under investigated [31]. A novel finding of this study is that patients who died exhibited a significantly higher df compared to those who survived. Interestingly, none of the standard biomarkers for inflammation and haemostasis were significantly different between these two groups. This may indicate that there might be other factors that cause significantly elevated df. It was shown in the previous studies that diabetic ketoacidosis (DKA) patients at presentation had densely organised clot microstructure as indicated by high df which then normalises over the next 24 h with standard treatment. The possible reasons were severe dehydration and metabolic acidosis [32]. Therefore, the reasons for high df in patients who died in the exacerbation group might be multifactorial and unclear. Statistical analysis showed that df was a significant predictor of mortality and was a significant discriminator between patients who died and survived.
Limitations
This study had several limitations. Firstly, this was a single centre prospective observational study, therefore the results may not be generalisable. Secondly, this proof-of-concept study was mechanistic in design and although this study presents some outcome data, it was not powered for outcome. Thirdly, COPD patients included in this study had treatments started at varying times such as receiving steroids and antibiotics in the community (rescue packs) and in the ambulance. This study did not specifically investigate this variation in treatment times. Finally, the recruitment to the study was paused for several months because of COVID-19 pandemic and careful consideration was given not to include patients with COVID-19 infection when the study resumed. Furthermore, to validate the findings that df is a predictor of mortality there is a requirement of adequately powered outcome studies.
Conclusion
In this study, we investigated the changes in clot microstructure utilising the biomarker df in AECOPD patients. When compared to stable group, acute exacerbations had significantly elevated df indicating that they were more thrombogenic. Furthermore, patients who died had significantly elevated df when compared to those who survived. These findings reveal, for the first time, a potential association between clot microstructure and mortality risk in acute exacerbations of COPD. Further large-scale, adequately powered outcome studies are planned to explore the clinical utility of these biomarkers.
Acknowledgements
We are grateful for the invaluable advice and support of late Dr Kim Harrison (Consultant in Respiratory Medicine, Swansea Bay University Health Board) to this project.
Authors’ contributions
SP and PAE conceived, designed and coordinated the study as part of the PhD programme at Swansea University. SP and JZ recruited and collected blood samples from the participants. SP, ML, KH and JZ performed rheological testing. ML and KH provided rheological advice. SP, JZ and JW contributed to data collection. SP interpreted and performed statistical analysis and was validated by KM. All authors read and approved the final manuscript.
Funding
This study received no funding.
Data availability
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request subject to approval from the Research Ethics Committee 6 and Research and Development of both Swansea University and Swansea University Health Board.
Declarations
Ethics approval and consent to participate
The study had full ethical approval from Wales Research Ethics Committee 6 (REC reference- 17/WA//0123) and was conducted in accordance with the Declaration of Helsinki. Patients who had capacity signed a consent form and for those who did not have capacity, a consultee declaration form was obtained from the direct care team or legal representative.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Abbreviations
Chronic obstructive pulmonary disease
venous thromboembolism
fractal dimension
C-reactive protein
procalcitonin
Stable chronic obstructive pulmonary disease
Acute exacerbation of chronic obstructive pulmonary disease
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