RESUMO
BACKGROUND: The clinical value and cost-effectiveness of invasive treatments for patients with coronary artery disease is unclear. Invasive treatments such as coronary artery bypass grafting and percutaneous coronary intervention are frequently used as a starting treatment, yet they are much more costly than optimal medical therapy. While patients may transition into other treatments over time, the choices of starting treatments are likely important determinants of costs and health outcomes. The aim is to predict by how much costs and health outcomes will change from a decision to use different starting treatments for patients with coronary artery disease in an Asian setting. METHODS: A cost-effectiveness study using a Markov model informed by data from Singapore General Hospital was done. All patients with initial presentations of stable coronary disease and no acute coronary syndromes who received medical treatments and interventional therapies were included. We compare existing practice, where the starting treatment can be medical therapy or stent percutaneous coronary interventions or coronary artery bypass grafting, with alternate starting treatment strategies. RESULTS: When compared to 'existing practice' a policy of starting 14% of patients with coronary artery bypass grafting and 86% with optimal medical therapy showed savings of $1,743 per patient and 0.23 additional quality adjusted life years. A change to policy nationwide would save $10 million and generate 1,380 quality adjusted life years. CONCLUSIONS: Increasing coronary artery bypass grafting and use of medical therapy in the setting of coronary artery disease is likely to saves costs and improve health outcomes. A definitive study to address the question we investigate would be very difficult to undertake and so using existing data to model the expected outcomes is a useful tool. There are likely to be large and complex barriers to the implementation of any policy change based on the findings of this study.
RESUMO
OBJECTIVE: To develop a 2-stage discrete events simulation (DES) based framework for the evaluation of elective surgery cancellation strategies and resumption scenarios across multiple operational outcomes. MATERIALS AND METHODS: Study data was derived from the data warehouse and domain knowledge on the operational process of the largest tertiary hospital in Singapore. 34,025 unique cases over 43 operating rooms (ORs) and 18 surgical disciplines performed from 1 January 2019 to 31 May 2020 were extracted for the study. A clustering approach was used in stage 1 of the modelling framework to develop the groups of surgeries that followed distinctive postponement patterns. These clusters were then used as inputs for stage 2 where the DES model was used to evaluate alternative phased resumption strategies considering the outcomes of OR utilization, waiting times to surgeries and the time to clear the backlogs. RESULTS: The tool enabled us to understand the elective postponement patterns during the COVID-19 partial lockdown period, and evaluate the best phased resumption strategy. Differences in the performance measures were evaluated based on 95% confidence intervals. The results indicate that two of the gradual phased resumption strategies provided lower peak OR and bed utilizations but required a longer time to return to BAU levels. Minimum peak bed demands could also be reduced by approximately 14 beds daily with the gradual resumption strategy, whilst the maximum peak bed demands by approximately 8.2 beds. Peak OR utilization could be reduced to 92% for gradual resumption as compared to a minimum peak of 94.2% with the full resumption strategy. CONCLUSIONS: The 2-stage modelling framework coupled with a user-friendly visualization interface were key enablers for understanding the elective surgery postponement patterns during a partial lockdown phase. The DES model enabled the identification and evaluation of optimal phased resumption policies across multiple important operational outcome measures. LAY ABSTRACT: During the height of the COVID-19 pandemic, most healthcare systems suspended their non-urgent elective surgery services. This strategy was undertaken as a means to expand surge capacity, through the preservation of structural resources (such as operating theaters, ICU beds, and ventilators), consumables (such as personal protective equipment and medications), and critical healthcare manpower. As a result, some patients had less-essential surgeries postponed due to the pandemic. As the first wave of the pandemic waned, there was an urgent need to quickly develop optimal strategies for the resumption of these surgeries. We developed a 2-stage discrete events simulation (DES) framework based on 34,025 unique cases over 43 operating rooms (ORs) and 18 surgical disciplines performed from 1 January 2019 to 31 May 2020 captured in the Singapore General Hospital (SGH) enterprise data warehouse. The outcomes evaluated were OR utilization, waiting times to surgeries and time to clear the backlogs. A user-friendly visualization interface was developed to enable decision makers to determine the most promising surgery resumption strategy across these outcomes. Hospitals globally can make use of the modelling framework to adapt to their own surgical systems to evaluate strategies for postponement and resumption of elective surgeries.
