Canalicular laceration repair using a novel bicanalicular silicone stent versus traditional bicanalicular stent with nasal fixation.

PURPOSE: To compare the clinical effects of two types of lacrimal stents in the repair of canalicular lacerations. METHODS: A retrospective analysis was conducted on patients with canalicular lacerations between January 2017 and December 2022. The canalicular reconstruction was performed using either the Runshi-RS bicanalicular silicone stent or the traditional bicanalicular silicone stent with nasal fixation, under a surgical microscope. The stent was placed for 3 months, and patients were followed up for more than 3 months after extubation. The anatomical and functional success rates were compared between the two groups. Anatomical success was assessed through diagnostic probing and irrigation of lacrimal passage, while functional success was determined by the patient's subjective symptoms of epiphora. RESULTS: The study included 315 patients (315 eyes) undergoing canalicular laceration repair. The Runshi-RS stent was utilized in 147 patients (46.7%), while the traditional stent with nasal fixation was employed in 168 patients (53.3%). The anatomical success rates (99.3% vs 98.8%, P = 0.642) and functional success rates (87.2% vs 88.1%, P = 0.926) were similar between the RS group and the traditional stent group. Postoperative complications were fewer (4.1% vs 10.1%, P = 0.04) and the operation time was shorter (67.1 ± 35.3 min vs 86.1 ± 43.4 min, P < 0.001) in the RS group. CONCLUSION: The Runshi-RS tube demonstrates favorable surgical outcomes for the repair of canalicular lacerations. Compared to the traditional stent with nasal fixation, the RS stent allows for shorter operation times and fewer postoperative complications in the repair of canalicular lacerations.

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.