Publications by authors named "N H Doctor"
Objectives: Ambulance diversion has emerged as a strategy to address Emergency Department (ED) overcrowding, but the question of when or whether diversion should be triggered is widely debated. Although the positive and adverse impacts of diversion have been primarily studied using quantitative data, little is known about the experience and perceptions of key stakeholders involved in diversions. Our study aims to explore the challenges and impacts of ambulance diversion as experienced by key stakeholders and their suggestions for improving the diversion process.
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- A 'Termination of Resuscitation' (TOR) protocol was introduced in Singapore in 2019, allowing emergency responders to pronounce death on-scene for out-of-hospital cardiac arrests (OHCA), rather than transporting patients to hospitals.
- A Markov model evaluated the cost-effectiveness of three approaches: No TOR, Observed TOR, and Full TOR, demonstrating that Full TOR resulted in fewer costs and QALYs (quality-adjusted life years).
- The study concluded that implementing the TOR protocol was cost-effective, suggesting its sustainable adoption can be justified based on economic evaluations.
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.
View Article and Find Full Text PDFBackground: Shock-refractory ventricular fibrillation (VF) or ventricular tachycardia (VT) is a treatment challenge in out-of-hospital cardiac arrest (OHCA). This study aimed to develop and validate machine learning models that could be implemented by emergency medical services (EMS) to predict refractory VF/VT in OHCA patients.
Methods: This was a retrospective study examining adult non-traumatic OHCA patients brought into the emergency department by Singapore EMS from the Pan-Asian Resuscitation Outcomes Study (PAROS) registry.
Objectives: With more elderly presenting with Out-of-Hospital Cardiac Arrests (OHCAs) globally, neurologically intact survival (NIS) should be the aim of resuscitation. We aimed to study the trend of OHCA amongst elderly in a large Asian registry to identify if age is independently associated with NIS and factors associated with NIS.
Methods: All adult OHCAs aged ≥18 years attended by emergency medical services (EMS) from April 2010 to December 2019 in Singapore was extracted from the Pan-Asian Resuscitation Outcomes Study (PAROS) registry.