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.

Antibiotic use in hospitalized patients with COVID-19: a population-based study in Hong Kong.

Background: Hong Kong experienced four epidemic waves caused by the ancestral strain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2020-2021 and a large Omicron wave in 2022. Few studies have assessed antibacterial prescribing for coronavirus disease 2019 (COVID-19) inpatients throughout the pandemic. Objectives: To describe inpatient antibacterial prescribing and explore factors associated with their prescription. Methods: Electronic health records of patients with COVID-19 admitted to public hospitals in Hong Kong from 21 January 2020 to 30 September 2022 were used to assess the prevalence and rates of inpatient antibacterial drug use (days of therapy/1,000 patient days [DOT/1,000 PD]). We used multivariable logistic regression to investigate potential associations between patients' baseline characteristics and disease severity and prescription of an antibacterial drug during hospital admission. Results: Among 65,810 inpatients with COVID-19, 54.0% were prescribed antibacterial drugs (550.5 DOT/1,000 PD). Compared to waves 1-2 (46.7%; 246.9 DOT/1,000 PD), the prescriptions were lowest during wave 4 (28.0%; 246.9; odds ratio (OR): 0.39, 95% CI: 0.31-0.49) and peaked in early wave 5 (64.6%; 661.2; 0.82, 0.65-1.03). Older age (≥80 years: OR 2.66, 95% CI, 2.49-2.85; 60-79 years: 1.59, 1.51-1.69, compared with 20-59 years), more severe disease (fatal: 3.64, 3.2-4.16; critical: 2.56, 2.14-3.06, compared with severe), and COVID-19 vaccine doses (two doses: 0.74, 0.69-0.78; three doses: 0.69, 0.64-0.74; four doses: 0.52, 0.44-0.62, compared with unvaccinated) were associated with inpatient antibacterial drug use. Conclusions: Antibacterial prescribing changed over time for hospitalized patients with confirmed COVID-19 and was potentially related to patients' demographics, medical conditions, and COVID-19 vaccination status as well as healthcare capacity during epidemic waves.

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.

Antipsychotics and Risk of Neuroleptic Malignant Syndrome: A Population-Based Cohort and Case-Crossover Study.

BACKGROUND: Neuroleptic malignant syndrome (NMS) is a rare and acute adverse drug reaction associated with antipsychotic therapy. However, few data on the risk and epidemiology of NMS are available. OBJECTIVES: The aim of this study was to ascertain the incidence risk and all-cause mortality of NMS associated with antipsychotic use, and to assess the association of recent antipsychotic exposure and NMS. METHODS: We did a population-based study using data from the Hong Kong Hospital Authority's Clinical Data Analysis and Reporting System database. Cases had a first diagnosis of NMS between 1 January 2004 and 30 November 2017. A case-crossover analysis was used to compare antipsychotic exposure 30 days before the diagnosis of NMS (index date) and a reference period 91-120 days before the index date. To adjust for potential time trends in antipsychotic exposure, we sampled from cases to match current cases and future cases, and further adjusted for select medications and acute medical conditions. RESULTS: 297,647 patients were prescribed antipsychotics, and the incidence risk of NMS was 0.11%. Of the 336 cases included in the case-crossover analysis, 20 (6%) died within 30 days after the index date; only one case had NMS recorded as the primary cause of death. When compared with the reference period, cases were more frequently prescribed multiple antipsychotics (15.8% vs 26.8%; standardized mean difference [SMD] 0.27) and short-acting injectable antipsychotics (3.6% vs 13.7%; SMD 0.37) during the 30 days prior to the diagnosis of NMS. Odds ratios for antipsychotic exposure in the case-crossover, case-crossover adjusted for time trend, and case-crossover adjusted for time trend and potential confounders analysis were 8.00 (95% confidence interval 3.42-18.69), 5.88 (2.46-14.04), and 4.77 (1.95-11.66). CONCLUSIONS: Our results suggest that recent use of antipsychotics is associated with NMS. Although a case-only design inherently controls for confounding by time-invariant factors, residual confounding by acute medical conditions with similar presentations to NMS cannot be fully excluded.