Cardiovascular events and venous thromboembolism after primary malignant or non-malignant brain tumour diagnosis: a population matched cohort study in Wales (United Kingdom)

No formal PPI was undertaken for this study. However, the James Lind Alliance priority setting partnership on brain and spinal cord tumour identified long-term physical and cognitive effects of surgery and radiotherapy as one of the top 10 priorities. This study estimated the cardiovascular risks and VTE after brain tumour diagnosis compared to people without any cancer diagnosis. We also present these risks among brain tumour patients stratified by surgery status.

This is a retrospective matched cohort study based in Wales (UK) using data from the Secure Anonymised Information Linkage (SAIL) Databank. Wales has a population of 3.1 million. The SAIL Databank contains whole-population anonymised individual-level routine healthcare data linkable to disease registries and national statistics registers via pseudonymised unique identifiers. SAIL datasets used in this study included primary care, hospital care, cancer registry and administrative datasets. All datasets have national coverage except the primary care dataset, which covers approximately 75% of the Welsh population.

We used the cancer registry in SAIL Databank to identify adult patients aged ≥ 18 years with an incident primary intracranial tumour. Patients were eligible if their tumour was diagnosed between 2000 and 2014, and they had primary care data available from at least 1 year before index brain tumour diagnosis. Each patient must have had at least one matched control available, where the date of brain tumour diagnosis was not the date of death. Matched controls were people without a diagnosis of any cancer who had an active registration at a general practitioner practice during 2000 and 2014. We used the cancer registry to confirm no cancer diagnosis in the control population. Controls needed to have primary care data available at least 1 year before study entry, which was the time of tumour diagnosis of the matched patient with a brain tumour. Matching variables included date of birth within 5 years, sex and GP practice at a ratio of one brain tumour patient to up to five controls without replacement.

Code lists for all outcome and data variables are available on Open Science Framework (https://​doi.​org/​10.​17605/​OSF.​IO/​3FMY5). The primary outcome of interest was fatal and non-fatal major vascular events and VTE (deep vein thrombosis and pulmonary embolism). Major vascular events included haemorrhagic stroke (intracerebral haemorrhage and subarachnoid haemorrhage), ischaemic stroke, unspecified stroke, ischaemic heart disease (angina and myocardial infarction) and aortic and peripheral vascular disease. Events were defined as fatal if they were the direct cause of death on the death certificate or death occurred within 30 days of the outcome. We collected time-to-event data from date of study entry to date of event for all outcome variables. Patients may have more than one outcome event.

Data items included the following: demographics (age at study entry, sex, Welsh Index of Multiple Deprivation [WIMD] quintile, year of study entry), medical history (previous stroke, VTE, ischaemic heart disease (IHD), hypertension, hyperlipidaemia, diabetes mellitus) and medications (antihypertensives, antiplatelet, anticoagulants, lipid-lowering drugs). For people with brain tumours, we also collected tumour behaviour (malignant or non-malignant), tumour types (glioblastoma and World Health Organisation [WHO] grade 1 non-malignant meningioma) and surgery status. WIMD is a postcode-based measure of relative deprivation in Wales published by the Welsh Government. Lower WIMD represents higher deprivation. Participants were recorded as being on a medication if there were at least 6 prescriptions of that class of medication before the date of study entry. For medical history of hyperlipidaemia, we combined those with coding of hyperlipidaemia with those on lipid lowering drugs. Diabetes status used both coding of diabetes and antidiabetic drugs. Tumour behavioural code in the International Classification of Diseases for Oncology Third Edition (ICD-O-3) determined malignancy where /3 denoted malignant tumours and /0–1 denoted non-malignant tumours. The binary surgery status was determined by data from cancer registry, procedural codes and histological diagnosis as surgery is the only means for histological diagnosis. The end of follow-up is defined as date of death, date of deregistration from primary care or end of study period (31 December 2018) for those alive and registered with primary care.

Data sources used in this study included primary care general practitioner dataset, Welsh demographic service, Patient Episode Database for Wales, Welsh Cancer Intelligence and Surveillance Unit (cancer registry) and Annual District Death Extract. We used Clinical Terms Version 2 READ codes to extract information from primary care data. Diagnoses using the International Classification of Diseases version 10 (ICD-10) codes and procedures using the Office of Population Censuses and Surveys Classification of Surgical Operations (OPCS-4) codes were used to define variables and outcomes in hospital data. Where available, we used code lists from related publications to define our variables (https://​doi.​org/​10.​17605/​OSF.​IO/​3FMY5). All outcomes and variables were captured using both primary care and hospital data. There was no formal sample size calculation, but the study design allowed detection of a hazard ratio of 1.20 with 80% power and 5% type 1 error in subgroup analyses. We planned to use all data available.

In all analyses, follow-up started from study entry to the earliest occurrence of death, date of deregistration from primary care, end of study period or first occurrence of outcome of interest. We consider malignant and non-malignant tumours separately because of their different prognosis. Additionally, our analysis period was split into two: within 1 year of study entry and 1 year after study entry. This was because the rates of CVD events in patients with brain tumour were higher in the first year after brain tumour diagnosis. Therefore, our analyses were stratified by tumour behaviour and time periods.

We calculated crude incidences of each outcome for patients with brain tumours and their matched controls separately and generated the corresponding 95% confidence intervals (CI) using the Collett exact method. Cox regression was our primary statistical model to assess the association between brain tumour diagnosis and different CVDs. We initially fitted models including brain tumour diagnosis and the matching variables to account for the study design [12] and then adjusted for risk factors listed in Table 1. To compare incidence trends of CVD in brain tumour patients with their controls by age, we fitted flexible parametric survival models with four degrees of freedom using the same covariates in the fully adjusted Cox model and then generated predicted incidences at ages 50, 65 and 75 years.

We performed the same Cox models in subgroups of matched cohorts with glioblastoma and non-malignant meningioma for assessing whether incidence trends were reproducible in more homogeneous tumour groups. We did not examine effect modification because of anticipated low power in our dataset with relative few outcome events. Our primary multivariable analysis did not include body mass index (BMI) or smoking status because of relatively elevated levels of missingness (Additional File 1: Figure S1). The levels of missing for cases and controls were 20% and 13% in smoking status and 9% and 3% in BMI; these were higher (~ 40%) at the beginning of the study period. We therefore performed sensitivity analyses of multivariable Cox models that included BMI and smoking status to assess confounding effects of these variables. Separately, we repeated our models considering a stroke diagnosis occurring after 14 days of tumour diagnosis as valid. This was because brain tumour presentation can mimic stroke and hospital coding may not distinguish between them. We also performed adjusted competing risk analyses as sensitivity analyses to assess subhazard ratios and cumulative incidences in the presence of competing risk from death. We used complete case analysis throughout. In each time-to-event analysis, failure time was from study entry to the first occurrence of the specific outcome event. We presented the proportion of patients with major vascular events and VTE after tumour diagnosis stratified by tumour subgroups and antiplatelet status. No statistical analyses were applied to the outcome data stratified by tumour subgroups and antiplatelet status because this was exploratory and likely to be underpowered. Statistical analyses were performed in R version 4.1.1 using packages ‘epiR’ (v2.0.19), ‘survival’ (v3.2–13), ‘rstpm2’ (v1.5.1) and ‘cmprsk’ (v2.2–10).

This project has received approval from the Information Governance Review Panel in SAIL Databank (project number: 0918). We used the STROBE checklist when writing our report.

This study included 6800 patients with primary brain tumour diagnosed in Wales 2000–2014 and their 33,785 age-, sex- and GP practice-matched controls. Cohort selection is presented in Fig. 1. The total follow-up time for brain tumour patients and their controls were 32,453.9 person-year (median 3.5 years; interquartile range [IQR] 3.5–10.1) and 247,142.1 person-year (median 6.6 years; IQR 3.9–10.3), respectively. There were 2869 patients with malignant tumour, of which 1340 (46.1%) were glioblastoma. Among 3931 patients with non-malignant tumour, 1498 (38.1%) had non-malignant meningioma. Characteristics of the study cohort are presented in Table 1. Characteristics of matched cohorts for glioblastoma and meningioma are presented in Additional File 1: Table S1. Primary treatment data is available in Additional File 1 Table S2.

Among 2869 patients with malignant tumours, 252 (8.8%) patients had at least one cardiovascular event within 1 year of diagnosis. There were 89 patients with a major vascular event and 170 patients with VTE (Fig. 2). The crude incidence rates per 1000 person-year in patients with malignant brain tumour and their controls in the first year were 54.1 (95% CI 43.4–66.6) and 48.1 (95% CI 44.5–52.0) for major vascular outcomes and 106.7 (95% CI 91.3–124.0) and 4.9 (95% CI 3.8–6.2) for VTE, respectively. In the fully adjusted Cox models, malignant brain tumour was associated with VTE (hazard ratio [HR] 21.58, 95% CI 16.12–28.88, p < 0.001), haemorrhagic stroke (HR 6.63, 95% CI 3.75–11.70, p < 0.001), and ischaemic stroke (HR 1.88, 95% 95% CI 1.06–3.36, p = 0.032) but not IHD (HR 0.67, 95% CI 0.45–1.00, p = 0.051) or aortic and peripheral vascular disease (HR 1.17, 95% CI 0.56–2.47, p = 0.674). In multivariable analyses of patients surviving 1 year compared to controls, malignant brain tumour diagnosis was associated with VTE (HR 2.20, 95% CI 1.52–3.18, p < 0.001) and all stroke types combined (HR 1.45, 95% CI 1.00–2.10, p = 0.047) (Fig. 2). One-year survivors had lower risk of IHD compared with their controls (HR 0.58, 95% CI 0.37–0.89, p = 0.013).

In 3931 patients with non-malignant tumours, 329 (8.4%) patients had at least one cardiovascular event within 1 year of diagnosis. There were 265 patients with a major vascular event and 96 patients with VTE (Fig. 2). The incidence rates per 1000 person-year in patients with non-malignant tumour, and their controls in the first year were 78.8 (95% CI 69.6–88.8) and 40.3 (95% CI 37.5–43.3) for major vascular outcomes and 20.0 (95% CI 15.5–25.3) and 5.4 (95% CI 4.4–6.5) for VTE, respectively. In the multivariable analysis, a non-malignant tumour diagnosis was associated with VTE (HR 3.72, 95% CI 2.73–5.06, p < 0.001), haemorrhagic stroke (HR 4.18, 95% CI 2.59–6.74, p < 0.001), ischaemic stroke (HR 3.52, 95% CI 2.69–4.61, p < 0.001) and aortic and peripheral arterial disease (HR 2.09, 95% CI 1.26–3.48, p = 0.003). In those surviving 1 year after tumour diagnosis, non-malignant tumour compared to controls was associated with a lower risk of haemorrhagic stroke (HR 0.55, 95% CI 0.37–0.83, p = 0.004), ischaemic heart disease (HR 0.65, 95% CI 0.56–0.76, p < 0.001) and aortic and peripheral arterial disease (HR 0.60, 95% CI 0.44–0.84, p = 0.003) (Fig. 2).

There were 1340 patients with glioblastoma (Additional File 1: Table S1), and 137 (10.2%) of these patients had at least one cardiovascular event within 1 year of diagnosis. Thirty-six (2.7%) patients had a major vascular event, and 107 (8.0%) patients had VTE (Additional File 1: Figure S2). The incidence rates per 1000 person-year in glioblastoma patients and their controls in the first year were 50.7 (95% CI 35.5–70.3) and 52.6 (95% CI 47.1–58.6) for major vascular outcomes and 159.4 (95% CI 130.6–192.6) and 4.7 (95% CI 3.2–6.8) for VTE, respectively. In the adjusted Cox models, glioblastoma diagnosis was associated with VTE (HR 31.78, 95% CI 20.99–48.13, p < 0.001) and haemorrhagic stroke (HR 5.02, 95% CI 2.16–11.67, p < 0.001) (Additional File 1: Figure S2). We did not examine these associations in patients surviving 1 year because of the small number of outcomes observed. There were 291 patients with glioblastoma surviving 1 year without a cardiovascular event, of which 15 developed a CVD during the remaining follow-up time.

Of the 1498 patients with meningioma (Additional File 1: Table S1), 184 (12.3%) had at least one cardiovascular event within 1 year of diagnosis. Major vascular event and VTE occurred in 162 (10.8%) and 36 (2.4%) patients, respectively. The incidence rates per 1000 person-year in meningioma patients and their controls were 123.6 (95% CI 104.7–145.0) and 52.0 (95% CI 46.8–57.7) for major vascular events and 29.6 (95% CI 20.9–40.6) and 4.9 (95% CI 3.4–6.8), respectively. In multivariable models, meningioma diagnosis was associated with VTE (HR 5.90, 95% CI 3.71–9.38, p < 0.001), haemorrhagic stroke (HR 3.80, 95% CI 2.01–7.18, p < 0.001) and ischaemic stroke (HR 4.37, 95% CI 3.09–6.18, p < 0.001). Patients with meningiomas alive 1 year after tumour diagnosis had lower risk of haemorrhagic stroke (HR 0.37, 95% CI 0.17–0.80, p = 0.011), ischaemic heart disease (HR 0.72, 95% CI 0.55–0.93, p = 0.013), and aortic and peripheral arterial disease (HR 0.45, 95% CI 0.23–0.86, p = 0.015) (Additional File 1: Figure S2).

Using multivariable flexible parametric models, we estimated the incidence of major vascular events and VTE for tumour groups stratified by age (Fig. 3). The incidence of VTE showed a bimodal distribution with peaks at 1 month and 5 months in malignant tumours. People with non-malignant tumours had the highest incidence of major vascular events early after tumour diagnosis. Patients who underwent surgery for malignant brain tumour had highest risks of VTE at 1 and 5 months after diagnosis, but this was not observed in those not receiving surgery (Additional File 1: Figure S3). Incidence trends of outcome events in patients with malignant tumour stratified by surgery status was similar to the overall trends.

Analyses using multivariable Cox models, including BMI and smoking status, showed similar results to the main analyses (Additional File 1: Figure S4). Because a high proportion of stroke occurred shortly after tumour diagnosis, we removed brain tumour patients having a stroke within 14 days. In patients with malignant tumours surviving 14 days without a stroke, they had higher risks of all stroke types combined (including unspecified stroke) and haemorrhagic stroke (Additional File 1: Figure S5). In patients with non-malignant tumours surviving 14 days without a stroke, they had higher risk of all stroke subtypes (Additional File 1: Figure S6). The direction of subhazard ratios in the competing risk analyses were consistent with cause-specific analyses presented in Figs. 2 and 3 (Additional File 1: Table S3 and S4).

The absolute numbers of patients suffered cardiovascular outcomes within first year of diagnosis by tumour type, surgery status and use of antiplatelet are presented in Table 2. Some numbers are not presented due to data suppression to minimise disclosure risk. Across patient groups, the proportions of patients who had major vascular events were higher in those on an antiplatelet drug at the time of diagnosis, except for patients with meningioma who did not have surgery.

Strengths of this analysis include the availability of medical comorbidities associated with CVD. We demonstrated that risks observed for all non-malignant tumours can be applied to meningiomas; the risk profiles of malignant tumours and glioblastoma are different, suggesting that tumour subtypes should be considered separately. Our results add to the existing literature by describing the risk of CVDs from the time of brain tumour diagnosis. We were unable to include body mass index and smoking status in our primary analyses due to low-quality data availability. However, given similar effect estimates when restricted to the subpopulation with these data, it is unlikely to be a key confounder for the associations observed. This interpretation is supported by data from the English matched-cohort study [8], which found little confounding effect by these covariates [8]. We did not report chemotherapy and radiotherapy for our cohort because of insufficient quality. Treatment data routinely collected into the cancer registry lacks specificity about agents, doses and cycles. Data linkage with detailed treatment data may offer more clinically relevant analyses. Post-operative VTE prophylaxis may differ in regimen, timing and duration, which could affect our CVD estimates for surgical patients. Analyses incorporating this data could clarify the effect of VTE prophylaxis and the risk of bleeding. Lastly, routine healthcare data can provide a large dataset but may be subjected to misclassification and may lack relevant clinical details such as BMI, alcohol history and physical activity.