The "invisible ceiling" of bystander CPR in three Asian countries: Descriptive study of national OHCA registry.

Background: Bystander cardiopulmonary resuscitation (CPR) for out-of-hospital cardiac arrest (OHCA) has increased in Singapore, Japan, and South Korea following the implementation of several public health, bystander-focused interventions, such as dispatcher-assisted CPR and community CPR training. It is unclear whether bystander CPR prevalence will continue on this trajectory over time. This study aimed to investigate the temporal trends of bystander CPR prevalence over a ten-year period in these three Asian countries.

Reduction of Postradiation Therapy Urinary Toxicity Via Intrafractional Megavoltage-Kilovoltage Prostate Location Monitoring.

Purpose: We hypothesized that an in-house developed system using megavoltage and kilovoltage image guidance (MKIG) to ensure correct prostate positioning during stereotactic body radiation therapy (SBRT) could potentially avoid unwanted doses to nontarget tissues, leading to reduced toxicities.

Methods And Materials: We built a 3-dimensional MKIG platform that accurately tracks prostate implanted fiducials in real time and clinically translated the system to replace a commercial approach, intrafraction motion review (IMR), which only tracks fiducials in the 2-dimensional kilovoltage views. From 2017 to 2019, 150 patients with prostate cancer were treated with SBRT and monitored using MKIG.

Validation of the NULL-EASE Score for Predicting Survival in a Multiethnic Asian Cohort of Out-of-Hospital Cardiac Arrest.

Background: NULL-PLEASE is a simple and accurate clinical scoring system developed in a Western cohort of patients with out-of-hospital cardiac arrest (OHCA). The need for blood test results limits its use in early stages of care. We adapted and validated the NULL-EASE score (without laboratory tests) in an independent, multiethnic Asian cohort of patients with out-of-hospital cardiac arrest.

Variable importance analysis with interpretable machine learning for fair risk prediction.

Machine learning (ML) methods are increasingly used to assess variable importance, but such black box models lack stability when limited in sample sizes, and do not formally indicate non-important factors. The Shapley variable importance cloud (ShapleyVIC) addresses these limitations by assessing variable importance from an ensemble of regression models, which enhances robustness while maintaining interpretability, and estimates uncertainty of overall importance to formally test its significance. In a clinical study, ShapleyVIC reasonably identified important variables when the random forest and XGBoost failed to, and generally reproduced the findings from smaller subsamples (n = 2500 and 500) when statistical power of the logistic regression became attenuated.