Alterations in clot microstructure in acute exacerbations of COPD

Abstract

Background

Chronic Obstructive Pulmonary Disease (COPD) patients are prone to thrombogenic events, particularly during exacerbations. Fractal dimension (d_f), 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 d_f 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 d_f when compared to the stable group (1.71 ± 0.06 vs 1.69 ± 0.05, p=0.03) but with no significant changes in d_f over the four time points (p=0.28) sampled. The patients who died in the acute exacerbation group had significantly elevated d_f 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 d_fwas 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 d_f.

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-d_f) has shown to be reliable in assessing thrombogenicity in inflammatory conditions such as sepsis. In this study, it was demonstrated that higher values of d_f 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 d_f, 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 (d_f) and time to Gel Point (T_GP), 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 (T_GP) and, from the viscoelastic data at the GP, the d_f 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 d_f between the SCOPD and AECOPD groups on admission. Firstly, based on the assumption that SCOPD patients have d_f 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 d_f 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 d_f 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 d_f, 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 d_f, 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 d_fas 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 d_f 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 d_f was a significant predictor of mortality (p = 0.024).

[See PDF for image]

Fig. 1

AECOPD patients who died had significantly higher d_f when compared to those who survived (p = 0.02)

Relationship between d_f and clot mass

To understand the relationship between d_f and the clot mass, the mean d_f 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 d_f of SCOPD (1.69). The graph demonstrates that a small increase in d_f 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 d_f and normalised mass. The mean d_f of SCOPD, AECOPD and AECOPD patients who died were plotted. A normalised mass of 1.00 (M = 1) is set at the mean d_f of the SCOPD group which is 1.69. The graph visually demonstrates that small changes in d_f 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 (d_f). Previous studies have demonstrated that d_f was sensitive to progressive haemodilution [17] and anticoagulation [18]. In addition, in a study of warfarinised patients, d_f 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 d_f were associated with denser and tighter clot microstructure and lower values of d_f were associated with looser and weaker clot microstructure [14]. Therefore, this study hypothesised that AECOPD patients may have higher d_f 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 d_f) 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 d_f. 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 d_f 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 d_f 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 d_f 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 d_f. It was shown in the previous studies that diabetic ketoacidosis (DKA) patients at presentation had densely organised clot microstructure as indicated by high d_f 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 d_f in patients who died in the exacerbation group might be multifactorial and unclear. Statistical analysis showed that d_f 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 d_f 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 d_f in AECOPD patients. When compared to stable group, acute exacerbations had significantly elevated d_f indicating that they were more thrombogenic. Furthermore, patients who died had significantly elevated d_f 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

COPD

Chronic obstructive pulmonary disease

VTE

venous thromboembolism

d_f

fractal dimension

CRP

C-reactive protein

PCT

procalcitonin

SCOPD

Stable chronic obstructive pulmonary disease

AECOPD

Acute exacerbation of chronic obstructive pulmonary disease

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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