The clinical value and cost-effectiveness of treatments for patients with coronary artery disease.

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.

Impact of Cardiac Arrest Centers on the Survival of Patients With Nontraumatic Out-of-Hospital Cardiac Arrest: A Systematic Review and Meta-Analysis.

Background The role of cardiac arrest centers (CACs) in out-of-hospital cardiac arrest care systems is continuously evolving. Interpretation of existing literature is limited by heterogeneity in CAC characteristics and types of patients transported to CACs. This study assesses the impact of CACs on survival in out-of-hospital cardiac arrest according to varying definitions of CAC and prespecified subgroups. Methods and Results Electronic databases were searched from inception to March 9, 2021 for relevant studies. Centers were considered CACs if self-declared by study authors and capable of relevant interventions. Main outcomes were survival and neurologically favorable survival at hospital discharge or 30 days. Meta-analyses were performed for adjusted odds ratio (aOR) and crude odds ratios. Thirty-six studies were analyzed. Survival with favorable neurological outcome significantly improved with treatment at CACs (aOR, 1.85 [95% CI, 1.52-2.26]), even when including high-volume centers (aOR, 1.50 [95% CI, 1.18-1.91]) or including improved-care centers (aOR, 2.13 [95% CI, 1.75-2.59]) as CACs. Survival significantly increased with treatment at CACs (aOR, 1.92 [95% CI, 1.59-2.32]), even when including high-volume centers (aOR, 1.74 [95% CI, 1.38-2.18]) or when including improved-care centers (aOR, 1.97 [95% CI, 1.71-2.26]) as CACs. The treatment effect was more pronounced among patients with shockable rhythm (P=0.006) and without prehospital return of spontaneous circulation (P=0.005). Conclusions were robust to sensitivity analyses, with no publication bias detected. Conclusions Care at CACs was associated with improved survival and neurological outcomes for patients with nontraumatic out-of-hospital cardiac arrest regardless of varying CAC definitions. Patients with shockable rhythms and those without prehospital return of spontaneous circulation benefited more from CACs. Evidence for bypassing hospitals or interhospital transfer remains inconclusive.

Effective Treatment Recommendations for Type 2 Diabetes Management Using Reinforcement Learning: Treatment Recommendation Model Development and Validation.

BACKGROUND: Type 2 diabetes mellitus (T2DM) and its related complications represent a growing economic burden for many countries and health systems. Diabetes complications can be prevented through better disease control, but there is a large gap between the recommended treatment and the treatment that patients actually receive. The treatment of T2DM can be challenging because of different comprehensive therapeutic targets and individual variability of the patients, leading to the need for precise, personalized treatment. OBJECTIVE: The aim of this study was to develop treatment recommendation models for T2DM based on deep reinforcement learning. A retrospective analysis was then performed to evaluate the reliability and effectiveness of the models. METHODS: The data used in our study were collected from the Singapore Health Services Diabetes Registry, encompassing 189,520 patients with T2DM, including 6,407,958 outpatient visits from 2013 to 2018. The treatment recommendation model was built based on 80% of the dataset and its effectiveness was evaluated with the remaining 20% of data. Three treatment recommendation models were developed for antiglycemic, antihypertensive, and lipid-lowering treatments by combining a knowledge-driven model and a data-driven model. The knowledge-driven model, based on clinical guidelines and expert experiences, was first applied to select the candidate medications. The data-driven model, based on deep reinforcement learning, was used to rank the candidates according to the expected clinical outcomes. To evaluate the models, short-term outcomes were compared between the model-concordant treatments and the model-nonconcordant treatments with confounder adjustment by stratification, propensity score weighting, and multivariate regression. For long-term outcomes, model-concordant rates were included as independent variables to evaluate if the combined antiglycemic, antihypertensive, and lipid-lowering treatments had a positive impact on reduction of long-term complication occurrence or death at the patient level via multivariate logistic regression. RESULTS: The test data consisted of 36,993 patients for evaluating the effectiveness of the three treatment recommendation models. In 43.3% of patient visits, the antiglycemic medications recommended by the model were concordant with the actual prescriptions of the physicians. The concordant rates for antihypertensive medications and lipid-lowering medications were 51.3% and 58.9%, respectively. The evaluation results also showed that model-concordant treatments were associated with better glycemic control (odds ratio [OR] 1.73, 95% CI 1.69-1.76), blood pressure control (OR 1.26, 95% CI, 1.23-1.29), and blood lipids control (OR 1.28, 95% CI 1.22-1.35). We also found that patients with more model-concordant treatments were associated with a lower risk of diabetes complications (including 3 macrovascular and 2 microvascular complications) and death, suggesting that the models have the potential of achieving better outcomes in the long term. CONCLUSIONS: Comprehensive management by combining knowledge-driven and data-driven models has good potential to help physicians improve the clinical outcomes of patients with T2DM; achieving good control on blood glucose, blood pressure, and blood lipids; and reducing the risk of diabetes complications in the long term.

Impact of COVID-19 on acute isolation bed capacity and nursing workforce requirements: A retrospective review.

AIM: To understand the impact of COVID-19 on isolation bed capacity requirements, nursing workforce requirements and nurse:patient ratios. BACKGROUND: COVID-19 created an increased demand for isolation beds and nursing workforce globally. METHODS: This was a retrospective review of bed capacity, bed occupancy and nursing workforce data from the isolation units of a tertiary hospital in Singapore from 23 January 2020 to 31 May 2020. R v4.0.1 and Tidyverse 1.3.0 library were used for data cleaning and plotly 4.9.2.1 library for data visualization. RESULTS: In January to March 2020, isolation bed capacity was low (=<203 beds). A sharp increase in bed capacity was seen from 195 to 487 beds during 25 March to 29 April 2020, after which it plateaued. Bed occupancy remained lower than bed capacity throughout January to May 2020. After 16 April 2020, we experienced a shortage of 1.1 to 70.2 nurses in isolation wards. Due to low occupancy rates, nurse:patient ratio remained acceptable (minimum nurse:patient ratio = 0.26). CONCLUSION: COVID-19 caused drastic changes in isolation bed capacity and nursing workforce requirements. IMPLICATIONS FOR NURSING MANAGEMENT: Building a model to predict nursing workforce requirements during pandemic surges may be helpful for planning and adequate staffing.

The relationship between workload and length of stay in Singapore.

Prior studies link higher workload with longer length of stay (LOS) in the US. Unlike U.S. hospitals, Singaporean hospitals, like other major hospitals in the Asia-Pacific, are partially occupied by patients with non-acute needs due to insufficient alternative facilities. We examined the association between workload and length of stay (LOS) and the impact of workload on 30-day re-hospitalization and inpatient mortality rates in retrospective cohort in this setting. We defined workload as the daily number of patients per physician team. 13,097 hospitalizations of 10,000 patients were included. We found that higher workload was associated with shorter LOS (coefficient, -0.044 [95%CI, -0.083, -0.01]), especially for patients with longer stays (hazard ratios, not significantly greater than 1 before Day 4, 1.04 [95%CI, 1.01, 1.07] at Day 4 and 1.16 [95%CI, 1.10, 1.24] at Day 10), without affecting inpatient mortality (odds ratio (OR), 1.03 [95%CI, 0.99, 1.05]) or 30-day re-hospitalization (OR, 1.01 [95%CI, 0.99, 1.04]). This result differs from studies in the US and may reflect regional differences in the use of acute hospital beds for non-acute needs.

Association between the elderly frequent attender to the emergency department and 30-day mortality: A retrospective study over 10 years.

BACKGROUND: To determine if elderly frequent attenders are associated with increased 30-day mortality, assess resource utilization by the elderly frequent attenders and identify associated characteristics that contribute to mortality. METHODS: Retrospective observational study of electronic clinical records of all emergency department (ED) visits over a 10-year period to an urban tertiary general hospital in Singapore. Patients aged 65 years and older, with 3 or more visits within a calendar year were identified. Outcomes measured include 30-day mortality, admission rate, admission diagnosis and duration spent at ED. Chi-square-tests were used to assess categorical factors and Student t-test was used to assess continuous variables on their association with being a frequent attender. Univariate and multivariate logistic regressions were conducted on all significant independent factors on to the outcome variable (30-day mortality), to determine factor independent odds ratios of being a frequent attender. RESULTS: 1.381 million attendance records were analyzed. Elderly patients accounted for 25.5% of all attendances, of which 31.3% are frequent attenders. Their 30-day mortality rate increased from 4.0% in the first visit, to 8.8% in the third visit, peaking at 10.2% in the sixth visit. Factors associated with mortality include patients with neoplasms, ambulance utilization, male gender and having attended the ED the previous year. CONCLUSION: Elderly attenders have a higher 30-day mortality risk compared to the overall ED population, with mortality risk more marked for frequent attenders. This study illustrates the importance and need for interventions to address frequent ED visits by the elderly, especially in an aging society.

Data-Driven Approach to Defining the Emergency Department Frequent Attender Using a Cohort of 10 Years.

AIMS: To identify, based on the measure of resource utilization, the number of visits per calendar year that defines the emergency department (ED) frequent attender; and examine for significant trends in patient characteristics and outcomes which may support the use of our definition. MATERIALS AND METHODS: We conducted a retrospective observational study of electronic clinical records of all ED visits over a 10-year period from January 2005 to December 2014 to an urban tertiary general hospital. We defined the ED frequent attender based on the number of ED attendances per calendar year which would yield a patient group representing more than 20% of all patient visits. Chi-square tests were conducted on each categorical factor individually to assess if they were independent of time, and the Student's t-test was used to assess continuous variables on their association with being a frequent attender. RESULTS: 1.381 million attendance records were analyzed. Patients who attended three or more times per year accounted for about 22.1% of all attendances and were defined as frequent attenders. They were associated with higher triage acuity, complex chronic illnesses, greater 30-day mortality for patients with three to six visits, and increased markers of resource utilization, such as ambulance use (15.5% vs. 11.6%), time to disposition (180 vs. 155 minutes), admissions rate (47.4% vs. 30.7%) and inpatient length of stay (6 days vs. 4 days). All p values were statistically significant (p < 0.001). CONCLUSION: We have demonstrated a data-driven approach to defining an ED frequent attender. Frequent attenders are associated with increased resource utilization, more complex illness and may be associated with greater 30-day mortality rates.

Nurse workforce scheduling in the emergency department: A sequential decision support system considering multiple objectives.

AIM: We propose a nurse scheduling framework based on a set of performance measures that are aligned with multiple outcome measures. A case study for the emergency department is presented. METHODS: A total of 142,564 emergency department attendances over 1 year were included in this study. Operational requirements, constraints and historical workload data were translated into a mixed-integer sequential goal programming model, which considers the following outcome measures: (1) nurse-patient ratios; (2) number of favourable/unfavourable shifts; and (3) dispersion of rest days. Computational studies compared the performance of the mixed-integer sequential goal programming results with manually generated historical nurse schedules. RESULTS: The maximum nurse-patient ratio deviation against the target was approximately 10% compared to 47% generated by the historical rosters (a 10% deviation translates to approximately two nurses). An on-line decision support system, which integrates shift preferences, staff databases and a workload forecasting module, was also developed. CONCLUSION: A decision support system based on the mixed-integer sequential goal programming modelling framework was proposed. The application of the model in a case study for an emergency department demonstrated improvements over existing manual scheduling methods. IMPLICATIONS FOR NURSING MANAGEMENT: This study demonstrates a mathematical, programming-based decision support system, which allows for managerial priorities and nurse preferences to be jointly considered in the automatic generation of nurse rosters.