Safety and effectiveness of low-dose aspirin for the prevention of gastrointestinal cancer in adults without atherosclerotic cardiovascular disease: a population-based cohort study

OBJECTIVE: To assess the association between low-dose aspirin and the incidence of colorectal cancer (CRC), gastric cancer (GC), oesophageal cancer (EC) and gastrointestinal bleeding (GIB) in adults without established atherosclerotic cardiovascular disease. DESIGN: Cohort study with propensity score matching of new-users of aspirin to non-users. SETTING: Clinical Data Analysis and Reporting System database, Hong Kong. PARTICIPANTS: Adults ≥40 years with a prescription start date of either low-dose aspirin (75-300 mg/daily) or paracetamol (non-aspirin users) between 1 January 2004 to 31 December 2008 without a history of atherosclerotic cardiovascular disease. MAIN OUTCOME MEASURES: The primary outcome was the first diagnosis of gastrointestinal cancer (either CRC, GC or EC) and the secondary outcome was GIB. Individuals were followed from index date of prescription until the earliest occurrence of an outcome of interest, an incident diagnosis of any type of cancer besides the outcome, death or until 31 December 2017. A competing risk survival analysis was used to estimate HRs and 95% CIs with death as the competing risk. RESULTS: After matching, 49 679 aspirin and non-aspirin users were included. The median (IQR) follow-up was 10.0 (6.4) years. HRs for low-dose aspirin compared with non-aspirin users were 0.83 for CRC (95% CI, 0.76 to 0.91), 0.77 for GC (95% CI, 0.65 to 0.92) and 0.88 for EC (95% CI, 0.67 to 1.16). Patients prescribed low-dose aspirin had an increased risk of GIB (HR 1.15, 95% CI, 1.11 to 1.20), except for patients prescribed proton pump inhibitors or histamine H2-receptor antagonists (HR 1.03, 95% CI, 0.96 to 1.10). CONCLUSION: In this cohort study of Chinese adults, patients prescribed low-dose aspirin had reduced risks of CRC and GC and an increased risk of GIB. Among the subgroup of patients prescribed gastroprotective agents at baseline, however, the association with GIB was attenuated

Determining propensity for sub-optimal low-density lipoprotein cholesterol response to statins and future risk of cardiovascular disease

Background: Variability in low-density lipoprotein cholesterol (LDL-C) response to statins is underappreciated. We characterised patients by their statin response (SR), baseline risk of cardiovascular disease (CVD) and 10-year CVD outcomes.Methods and Results: A multivariable model was developed using 183,213 United Kingdom (UK) patients without CVD to predict probability of sub-optimal SR, defined by guidelines as <40% reduction in LDL-C. We externally validated the model in a Hong Kong (HK) cohort (n=170,904). Patients were stratified into four groups by predicted SR and 10-year CVD risk score: [SR1] optimal SR & low risk; [SR2] sub-optimal SR & low risk; [SR3] optimal SR & high risk; [SR4] sub-optimal SR & high risk; and 10-year hazard ratios (HR) determined for first major adverse cardiovascular event (MACE).Our SR model included 12 characteristics, with an area under the curve of 0.70 (95% confidence interval [CI] 0.70–0.71; UK) and 0.68 (95% CI 0.67–0.68; HK). HRs for MACE in predicted sub-optimal SR with low CVD risk groups (SR2 to SR1) were 1.39 (95% CI 1.35–1.43, p<0.001; UK) and 1.14 (95% CI 1.11–1.17, p<0.001; HK). In both cohorts, patients with predicted sub-optimal SR with high CVD risk (SR4 to SR3) had elevated risk of MACE (UK HR 1.36, 95% CI 1.32–1.40, p<0.001: HK HR 1.25, 95% CI 1.21–1.28, p<0.001). Conclusions: Patients with sub-optimal response to statins experienced significantly more MACE, regardless of baseline CVD risk. To enhance cholesterol management for primary prevention, statin response should be considered alongside risk assessment

Bostonia: The Boston University Alumni Magazine. Volume 17

Founded in 1900, Bostonia magazine is Boston University's main alumni publication, which covers alumni and student life, as well as university activities, events, and programs

Development and validation of risk prediction model for recurrent cardiovascular events among Chinese: the Personalized CARdiovascular DIsease risk Assessment for Chinese model

Aims: Cardiovascular disease (CVD) is a leading cause of mortality, especially in developing countries. This study aimed to develop and validate a CVD risk prediction model, Personalized CARdiovascular DIsease risk Assessment for Chinese (P-CARDIAC), for recurrent cardiovascular events using machine learning technique. Methods and results: Three cohorts of Chinese patients with established CVD were included if they had used any of the public healthcare services provided by the Hong Kong Hospital Authority (HA) since 2004 and categorized by their geographical locations. The 10-year CVD outcome was a composite of diagnostic or procedure codes with specific International Classification of Diseases, Ninth Revision, Clinical Modification. Multivariate imputation with chained equations and XGBoost were applied for the model development. The comparison with Thrombolysis in Myocardial Infarction Risk Score for Secondary Prevention (TRS-2°P) and Secondary Manifestations of ARTerial disease (SMART2) used the validation cohorts with 1000 bootstrap replicates. A total of 48 799, 119 672 and 140 533 patients were included in the derivation and validation cohorts, respectively. A list of 125 risk variables were used to make predictions on CVD risk, of which 8 classes of CVD-related drugs were considered interactive covariates. Model performance in the derivation cohort showed satisfying discrimination and calibration with a C statistic of 0.69. Internal validation showed good discrimination and calibration performance with C statistic over 0.6. The P-CARDIAC also showed better performance than TRS-2°P and SMART2. Conclusion: Compared with other risk scores, the P-CARDIAC enables to identify unique patterns of Chinese patients with established CVD. We anticipate that the P-CARDIAC can be applied in various settings to prevent recurrent CVD events, thus reducing the related healthcare burden