Antiplatelet therapy and clinical outcomes in cardiovascular diseases

Cardiovascular diseases (CVD) is a leading cause of death in the world. Despite effective treatment regimens for ischaemic heart disease (IHD) and ischaemic stroke, mortality and recurrence rates remain high. Antiplatelet therapy is on effective treatment and reduces the risk of recurrent heart attack and stroke. Nevertheless, there are patients who stopped or interrupted their antiplatelet therapy for certain reasons or some patients may be resistant or poor responders to antiplatelet therapy. Furthermore, there is evidence of rebound effect in platelet activity after antiplatelet cessation and this may associate with increased risk of cardiovascular event. This thesis is divided into five main chapters (chapters 3 to 7) which attempt to provide data to help resolve the uncertainty. Chapter 1 highlights the background of cardiovascular diseases and the global burden of cardiovascular and cerebrovascular diseases. The metabolism of platelets, antiplatelet therapy and current antiplatelet therapy guidelines are described, followed by discussion of the risk of cardiovascular event and changes in antiplatelet therapy. Chapter 2 describes the data source from Virtual International Stroke Trial Archive (VISTA) and National Health Service Greater Glasgow and Clyde (NHSGGC) Safe Haven, followed by definition of outcome measures. In chapter 3, Virtual International Stroke Trial Archive (VISTA) data was examined to test whether continue with the same antiplatelet therapy or changing to a new antiplatelet regimen reduces the risk of subsequent events in patients who experience a stroke whilst taking antiplatelet therapy. The findings indicate that subjects who switch to a new antiplatelet regimen after stroke did not have a lower early recurrence rate than subjects who continued with the same antiplatelet therapy. Observations on bleeding complications were similar in both groups. However, changing antiplatelet regimen after stroke was associated with more favourable functional outcome across a full scale modified Rankin Scale (mRS) at 90 days. In chapter 4, association between early or later initiation of antiplatelet with a recurrent ischaemic stroke and bleeding complications was assessed using VISTA data. The findings indicate that there was no association between a recurrent ischaemic stroke and timing of initiation of antiplatelet drug after stroke. However, early initiation was associated with increased risk of bleeding. In terms of functional outcomes, this study demonstrated that the mid-time and late initiation of antiplatelet therapy after acute stroke are associated with better functional outcomes compared with early initiation. In chapter 5, a nested case-control study was performed to explore the rate of antiplatelet cessation and interruption in a sample of patients with recent ischaemic stroke and to assess the risk of cardiovascular events associated with cessation and interruption of antiplatelet. It was found that there was no increased risk of cardiovascular event among patients who had early cessation or interrupted/stopped antiplatelet therapy within 90 days following acute ischaemic stroke. In chapter 6, the incidence and predictors of cardiovascular events after DAPT cessation were evaluated. The incidence of cardiovascular event while taking DAPT and following discontinuation of DAPT was 15.7% and 16.7% respectively. This study found that increasing age was associated with an increased risk of cardiovascular event, whereas, revascularization-treated patients and longer duration of DAPT, were each associated with a decreased risk. The duration of DAPT six months and less was associated a significantly higher risk for cardiovascular event. In chapter 7, an untargeted metabolomics analysis was performed while on DAPT (aspirin plus ticagrelor) and once they stopped ticagrelor to identify metabolite changes associated with cardiovascular events after stopping DAPT. Ten ACS patients were recruited in this study and data were analysed for seven patients. Three hundred eleven putative metabolites were identified. This study found 16 putative metabolites significantly altered following ticagrelor cessation. Of these, seven metabolites were from lipid pathway and down-regulated some up to 3-fold. On the other hand, adenosine, from nucleotide metabolism was upregulated up to 2.6-fold. It concluded that there are changes in numerous pathways following DAPT discontinuation and whether these changes differ in patients who have cardiovascular event after stopping DAPT warrant further investigation. In chapter 8, a summary of the findings of this thesis are presented as well as the future directions of research in this area.

Tenecteplase and alteplase in acute ischaemic stroke thrombolysis: clinical and imaging study

Introduction: Intravenous thrombolysis in acute ischaemic stroke with alteplase improves clinical outcomes, but it has limited efficacy and is associated with increased risk of intracranial haemorrhage. An improved tissue plasminogen activator, tenecteplase, was evidenced to be at least equally effective with lower risk of haemorrhage in acute myocardial infarction thrombolysis. To date, two completed phase II randomised controlled studies comparing tenecteplase and alteplase in acute ischaemic strokes showed variable results. Methods: A literature review of thrombolytic agents used in myocardial infarction and acute ischaemic stroke was performed, followed by a retrospective investigation of the bolus-to- infusion delay of alteplase administration. The main focus of this thesis is the report of our single centre phase II randomised controlled trial that compared tenecteplase (0.25mg/kg, maximum 25mg) and alteplase (0.9mg/kg, maximum 90mg, 10% as the initial bolus, following by one hour infusion with the rest of the dose) in acute ischaemic stroke thrombolysis using advanced imaging as biomarkers. Imaging comprised baseline computed tomography (CT), CT perfusion (CTP) and CT angiography (CTA), and CT+CTA at 24-48 hours. The primary end-point was penumbral salvage (CTP-defined penumbra volume minus follow-up CT infarct volume). A sub-study of coagulation and fibrinolysis analysis of the two agents was performed by comparing a group of coagulation variables measured pre-treatment, 3-12 hours, and 24±3 hours post thrombolysis. An individual patient data (IPD) meta-analysis was carried out using all three completed tenecteplase/alteplase comparison studies in stroke thrombolysis. We compared clinical outcomes including modified Rankin scale at 3 months, early neurological improvement at 24 hours, intracerebral haemorrhage rate and mortality at 3 months between all three tenecteplase doses (0.1mg/kg, 0.25 mg/kg, and 0.4mg/kg) examined and standard alteplase. Imaging outcomes including penumbra salvage, recanalisation rates were also compared using the data from the two studies that had advance imaging carried out. Results: Delay between the initial bolus and the subsequent infusion in administration of alteplase is common. This may reduce the likelihood of achieving a good functional outcome. Among the 104 patients recruited in ATTEST trial, 71 contributed to the imaging primary outcome. No significant differences were observed for penumbral salvage [68 (SD 28) % tenecteplase vs 68 (SD 23) % alteplase], mean difference 1% (95% confidence interval -10%, 12%, p=0·81) or for any secondary end-point. The SICH incidence (1/52, 2% vs 2/51, 4%, by SITS-MOST definition, p=0·55; by ECASS-2 definition, 3/52, 6% tenecteplase vs 4/51, 8% alteplase, p=0.59) did not differed significantly. There was a trend towards lower ICH risk in the tenecteplase group (8/52 tenecteplase, 15% vs 14/51 alteplase, 29%, p=0·091). Compared to baseline, alteplase caused significant hypofibrinogenaemia (p=0.002), prolonged Prothrombin Time (PT) (p=0.011), hypoplasminogenaemia (p=0.001) and lower Factor V (p=0.002) at 3-12 hours after administration with persistent hypofibrinogenaemia at 24h (p=0.011), while only minor hypoplasminogenaemia (P=0.029) was seen in the tenecteplase group. Tenecteplase consumed less plasminogen (p<0.001) and fibrinogen (p=0.002) compared with alteplase. In a pooled analysis, tenecteplase 0.25mg/kg had the greatest odds to achieve early neurological improvement (OR [95%CI] 3.3 [1.5, 7.2], p=0.093), excellent functional outcome (mRS 0-1) at three months (OR [95%CI] 1.9 [0.8, 4.4], p= 0.28), with reduced odds of ICH (OR [95%CI] 0.6 [0.2, 1.8], P=0.43) compared with alteplase. Only 19 patients were treated with tenecteplase 0.4mg/kg, which showed increased odds of SICH compared with alteplase (OR [95% CI] 6.2 [0.7, 56.3]). In the two studies where advanced imaging was performed, the imaging outcomes did not differ in the IPD analysis. Conclusion: Tenecteplase 0.25 mg/kg has the potential to be a better alternative to alteplase. It can be given as a single bolus, does not cause disruption to systemic coagulation, and is possibly safer and more effective in clot lysis. Further phase III study to compare tenecteplase and alteplase in acute ischaemic stroke is warranted.

Exploring the potential role of allostatic load biomarkers in risk assessment of patients presenting with depressive symptoms

Background: Depression is a major health problem worldwide and the majority of patients presenting with depressive symptoms are managed in primary care. Current approaches for assessing depressive symptoms in primary care are not accurate in predicting future clinical outcomes, which may potentially lead to over or under treatment. The Allostatic Load (AL) theory suggests that by measuring multi-system biomarker levels as a proxy of measuring multi-system physiological dysregulation, it is possible to identify individuals at risk of having adverse health outcomes at a prodromal stage. Allostatic Index (AI) score, calculated by applying statistical formulations to different multi-system biomarkers, have been associated with depressive symptoms. Aims and Objectives: To test the hypothesis, that a combination of allostatic load (AL) biomarkers will form a predictive algorithm in defining clinically meaningful outcomes in a population of patients presenting with depressive symptoms. The key objectives were: 1. To explore the relationship between various allostatic load biomarkers and prevalence of depressive symptoms in patients, especially in patients diagnosed with three common cardiometabolic diseases (Coronary Heart Disease (CHD), Diabetes and Stroke). 2 To explore whether allostatic load biomarkers predict clinical outcomes in patients with depressive symptoms, especially in patients with three common cardiometabolic diseases (CHD, Diabetes and Stroke). 3 To develop a predictive tool to identify individuals with depressive symptoms at highest risk of adverse clinical outcomes. Methods: Datasets used: ‘DepChron’ was a dataset of 35,537 patients with existing cardiometabolic disease collected as a part of routine clinical practice. ‘Psobid’ was a research data source containing health related information from 666 participants recruited from the general population. The clinical outcomes for 3 both datasets were studied using electronic data linkage to hospital and mortality health records, undertaken by Information Services Division, Scotland. Cross-sectional associations between allostatic load biomarkers calculated at baseline, with clinical severity of depression assessed by a symptom score, were assessed using logistic and linear regression models in both datasets. Cox’s proportional hazards survival analysis models were used to assess the relationship of allostatic load biomarkers at baseline and the risk of adverse physical health outcomes at follow-up, in patients with depressive symptoms. The possibility of interaction between depressive symptoms and allostatic load biomarkers in risk prediction of adverse clinical outcomes was studied using the analysis of variance (ANOVA) test. Finally, the value of constructing a risk scoring scale using patient demographics and allostatic load biomarkers for predicting adverse outcomes in depressed patients was investigated using clinical risk prediction modelling and Area Under Curve (AUC) statistics. Key Results: Literature Review Findings. The literature review showed that twelve blood based peripheral biomarkers were statistically significant in predicting six different clinical outcomes in participants with depressive symptoms. Outcomes related to both mental health (depressive symptoms) and physical health were statistically associated with pre-treatment levels of peripheral biomarkers; however only two studies investigated outcomes related to physical health. Cross-sectional Analysis Findings: In DepChron, dysregulation of individual allostatic biomarkers (mainly cardiometabolic) were found to have a non-linear association with increased probability of co-morbid depressive symptoms (as assessed by Hospital Anxiety and Depression Score HADS-D≥8). A composite AI score constructed using five biomarkers did not lead to any improvement in the observed strength of the association. In Psobid, BMI was found to have a significant cross-sectional association with the probability of depressive symptoms (assessed by General Health Questionnaire GHQ-28≥5). BMI, triglycerides, highly sensitive C - reactive 4 protein (CRP) and High Density Lipoprotein-HDL cholesterol were found to have a significant cross-sectional relationship with the continuous measure of GHQ-28. A composite AI score constructed using 12 biomarkers did not show a significant association with depressive symptoms among Psobid participants. Longitudinal Analysis Findings: In DepChron, three clinical outcomes were studied over four years: all-cause death, all-cause hospital admissions and composite major adverse cardiovascular outcome-MACE (cardiovascular death or admission due to MI/stroke/HF). Presence of depressive symptoms and composite AI score calculated using mainly peripheral cardiometabolic biomarkers was found to have a significant association with all three clinical outcomes over the following four years in DepChron patients. There was no evidence of an interaction between AI score and presence of depressive symptoms in risk prediction of any of the three clinical outcomes. There was a statistically significant interaction noted between SBP and depressive symptoms in risk prediction of major adverse cardiovascular outcome, and also between HbA1c and depressive symptoms in risk prediction of all-cause mortality for patients with diabetes. In Psobid, depressive symptoms (assessed by GHQ-28≥5) did not have a statistically significant association with any of the four outcomes under study at seven years: all cause death, all cause hospital admission, MACE and incidence of new cancer. A composite AI score at baseline had a significant association with the risk of MACE at seven years, after adjusting for confounders. A continuous measure of IL-6 observed at baseline had a significant association with the risk of three clinical outcomes- all-cause mortality, all-cause hospital admissions and major adverse cardiovascular event. Raised total cholesterol at baseline was associated with lower risk of all-cause death at seven years while raised waist hip ratio- WHR at baseline was associated with higher risk of MACE at seven years among Psobid participants. There was no significant interaction between depressive symptoms and peripheral biomarkers (individual or combined) in risk prediction of any of the four clinical outcomes under consideration. Risk Scoring System Development: In the DepChron cohort, a scoring system was constructed based on eight baseline demographic and clinical variables to predict the risk of MACE over four years. The AUC value for the risk scoring system was modest at 56.7% (95% CI 55.6 to 57.5%). In Psobid, it was not possible to perform this analysis due to the low event rate observed for the clinical outcomes. Conclusion: Individual peripheral biomarkers were found to have a cross-sectional association with depressive symptoms both in patients with cardiometabolic disease and middle-aged participants recruited from the general population. AI score calculated with different statistical formulations was of no greater benefit in predicting concurrent depressive symptoms or clinical outcomes at follow-up, over and above its individual constituent biomarkers, in either patient cohort. SBP had a significant interaction with depressive symptoms in predicting cardiovascular events in patients with cardiometabolic disease; HbA1c had a significant interaction with depressive symptoms in predicting all-cause mortality in patients with diabetes. Peripheral biomarkers may have a role in predicting clinical outcomes in patients with depressive symptoms, especially for those with existing cardiometabolic disease, and this merits further investigation.