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IntroductionRevascularisation in patients with symptomatic stable coronary artery disease compared to optimal medical therapy remains controversial and practice is variable between clinicians. National guidance (NICE) recommends referral for revascularisation only if a patient is on maximal tolerated dose of two anti-anginal agents. ESC guidance however stipulates that in patients with chronic coronary syndrome, revascularisation should be an adjunct to optimal guideline directed medical therapy in those who remain symptomatic or whom revascularisation will improve prognosis.Our objective was to review local practice at our centre and evaluate whether patients referred for elective percutaneous coronary intervention (PCI) due to stable anginal symptoms are on optimal medical therapy (OMT) prior to attempted coronary revascularisation. MethodsPatients were retrospectively identified from the British Cardiovascular Intervention Society (BCIS) database and included if they attended for elective PCI over a 12 month period. Patients were excluded if they had PCI for an acute coronary syndrome. Baseline demographic data were collected, procedural details and details of anti-anginal therapy pre angiography.Results134 patients were included in this study, 108 (81%) were male and median age was 71 years. 17 (13%) patients had functional testing prior to PCI including stress echocardiography or stress CMR. Most patients had single vessel PCI (94%) (table 1). The majority of patients, 106 (79%) were on at least one first-line anti-anginal agent at baseline – defined as either a beta blocker, calcium- channel blocker or long-acting nitrate (table 2). 28 (21%) patients were not on any anti-anginal medication. 44 (33%) patients were on 3 anti-anginal agents and only 2 patients (1%) were on 4 agents at baseline. Following outpatient review, prior to planned PCI, only 21 patients (16%) had initiation of anti-anginal therapy or up-titration of existing therapy. In 10/21 patients a nitrate was added or increased and in 7/21 patients beta-blocker therapy was added or up-titrated.ConclusionsOur study demonstrates that the majority of patients referred for elective revascularisation in the context of stable angina and chronic coronary artery disease were established one or two anti-anginal agents hence failed medical therapy. Of note, 21% patients were not on any anti-anginal therapy prior to PCI. The question remains as to whether further up-titration prior to PCI changes outcomes or symptom severity and whether a guideline directed OMT strategy should be pursued prior to revascularisation in this patient subgroup.Abstract 226 Table 1Baseline patient characteristics n % cohort Male 108 81% Age (years) 40–60 34 25% 61–80 78 58% >80 22 16% PCI to LMS 5 4% PCI to LAD 62 46% PCI to RCA 53 40% PCI to LCx 19 14% LMS: Left main stem, LAD: Left Anterior Descending, RCA: Right Coronary artery, LCx: Left Circumflex arteryAbstract 226 Table 2Established anti-anginals at baseline n % cohort Beta blocker 79 59% Calcium channel blocker 53 40% Nitrate 49 37% Nicorandil 6 4% Ranolazine 1 1% Conflict of InterestNone

Uncomplicated type B aortic dissection (uTBAD) accounts for a significant proportion of TBAD cases, but large-scale data on their prognosis remain limited. This study aims to evaluate real-world management and outcomes of uTBAD. Patients aged 20 or older admitted for acute TBAD between 1 April 2013 and 30 September 2020 were included in the analysis set. They were classified as uTBAD 1 month after admission. Data were sourced from the Shizuoka Kokuho Database, a regional claims database. The primary outcomes were all-cause mortality and aortic events (death, type A dissection, rupture or surgery). Cumulative event rates were estimated using the Kaplan-Meier method. Outcomes of patients treated with thoracic endovascular aortic repair (TEVAR) versus medical therapy were compared using inverse probability weighting. A total of 1292 uTBAD patients were identified. Sixty-seven patients underwent TEVAR within 12 months, with a cumulative TEVAR rate of 5.4%. The cumulative mortality was significantly higher in comparison to the age- and sex-adjusted general population (1 year: 15.0% vs 6.7%, 3 years: 28.6% vs 18.6%, P < 0.001). Aortic events occurred in 22.1%, 30.0% and 36.7% at 1, 2 and 3 years, respectively. TEVAR within 12 months was associated with a trend towards lower mortality (adjusted hazard ratio 0.53, 95% confidence interval 0.27-1.04) and fewer aortic events (adjusted hazard ratio 0.54, 95% confidence interval 0.29-1.01) compared to medical therapy. uTBAD patients have poorer survival and higher rates of aortic events compared to the general population. TEVAR within 12 months can potentially improve patient outcomes.

Objectives: Chronic heart failure is associated with reduced quality of life (QoL) and poor prognosis. Support via a left ventricular assist device (LVAD) is an alternative to optimised medical management for patients with advanced heart failure. This study evaluated health-related QoL with both therapy options. Methods: In this consecutive cross-sectional study, patients with LVAD support (n = 50) and patients with optimised medical management (n = 50) were interviewed comprehensively about various domains of QoL, emotional stress, perceived self-efficacy, social support, life satisfaction, and communication. Results: LVAD patients had a better overall QoL (KCCQ, clinical summary: MW: 67.4 vs. 52.9). Patients with medical management reported increased emotional stress stemming from depressed mood (HADS-D, MW: 7.1 vs. MW: 6.0). Depressed mood proved to be the most significant negative predictor for health-related QoL as well as for emotional well-being. Conclusions: Although they had a worse clinical situation preoperatively, LVAD patients had a significantly better QoL in both physical dimensions and functional competencies as well as significantly less psychological stress through depressed mood and anxiety. Fragestellung: Die chronische Herzinsuffizienz geht einher mit reduzierter Lebensqualität und schlechter Prognose. Die Implantation eines Linksherzunterstützungssystems (LVAD) gilt als Alternative zur optimalen medikamentösen Therapie bei fortgeschrittener Herzinsuffizienz. Diese Studie evaluiert vergleichend die gesundheitsbezogene Lebensqualität der beiden Therapieoptionen. Methode: In dieser konsekutiven Querschnittsstudie wurden Patienten mit LVAD-Therapie (n = 50) und Patienten mit optimaler medikamentöser Therapie (n = 50) umfassend zu verschiedenen Dimensionen der Lebensqualität, psychischen Belastung, Selbstwirksamkeitserwartung, sozialen Unterstützung, Lebenszufriedenheit und Kommunikation befragt. Ergebnisse: Die bessere allgemeine Lebensqualität erreichten die Patienten unter LVAD-Therapie (KCCQ, Klinische Zusammenfassung: MW: 67.4 vs. 52.9). Die Patienten mit medikamentöser Therapie berichteten von erhöhter psychischer Belastung durch Depressivität (HADS-D, MW: 7.1 vs. MW: 6.0). Die Depressivität erwies sich als wichtigster signifikant negativer Prädiktor sowohl für die gesundheitsbezogene Lebensqualität als auch für das psychische Wohlbefinden. Diskussion: Patienten unter LVAD-Therapie hatten trotz primär ungünstigerer präoperativer klinischer Situation eine signifikant bessere Lebensqualität in den physischen Dimensionen und funktionalen Kompetenzen und weniger psychische Belastungen durch Depressivität und Angst.

Before the development of transcatheter interventions, patients with mitral regurgitation (MR) and high surgical risk were often conservatively treated and subject to poor prognoses. We aimed to assess the therapeutic approaches and outcomes in the contemporary era. The study participants were consecutive high-risk MR patients from April 2019 to October 2021. Among the 305 patients analyzed, 274 (89.8%) underwent mitral valve interventions, whereas 31 (10.2%) received medical therapy alone. Of the interventions, transcatheter edge-to-edge mitral repair (TEER) was the most frequent (82.0% of overall), followed by transcatheter mitral valve replacement (TMVR) (4.6%). In patients treated with medical therapy alone, non-optimal morphologies for TEER and TMVR were shown in 87.1% and 65.0%, respectively. Patients undergoing mitral valve interventions experienced less frequent heart failure (HF) rehospitalization compared to those with medical therapy alone (18.2% vs. 42.0%, p < 0.01). Mitral valve intervention was associated with a lower risk of HF rehospitalization (HR 0.36 [0.18–0.74]) and an improved New York Heart Association class (p < 0.01). Most high-risk MR patients can be treated with mitral valve interventions. However, approximately 10% remained on medical therapy alone and were considered as unsuitable for current transcatheter technologies. Mitral valve intervention was associated with a lower risk of HF rehospitalization and improved functional status.

We conduct an empirical investigation of the impact of queue management on patients’ average wait time and length of stay (LOS). Using an emergency department’s (ED) patient-level data from 2007 to 2010, we find that patients’ average wait time and LOS are longer when physicians are assigned patients under a pooled queuing system with a fairness constraint compared to a dedicated queuing system with the same fairness constraint. Using a difference-in-differences approach, we find the dedicated queuing system is associated with a 17% decrease in average LOS and a 9% decrease in average wait time relative to the control group—a 39-minute reduction in LOS and a four-minute reduction in wait time for an average patient of medium severity in this ED. Interviews and observations of physicians suggest that the improved performance stems from the physicians’ increased ownership over patients and resources that is afforded by a dedicated queuing system, which enables physicians to more actively manage the flow of patients into and out of ED beds. Our findings suggest that the benefits from improved flow management in a dedicated queuing system can be large enough to overcome the longer wait time predicted to arise from nonpooled queues. We conduct additional analyses to rule out alternate explanations for the reduced average wait time and LOS in the dedicated system, such as stinting and decreased quality of care. Our paper has implications for healthcare organizations and others seeking to reduce patient wait time and LOS without increasing costs. This paper was accepted by Serguei Netessine, operations management.

Much of prior work in the area of service operations management has assumed service rates to be exogenous to the level of load on the system. Using operational data from patient transport services and cardiothoracic surgery—two vastly different health-care delivery services—we show that the processing speed of service workers is influenced by the system load. We find that workers accelerate the service rate as load increases. In particular, a 10% increase in load reduces length of stay by two days for cardiothoracic surgery patients, whereas a 20% increase in the load for patient transporters reduces the transport time by 30 seconds. Moreover, we show that such acceleration may not be sustainable. Long periods of increased load (overwork) have the effect of decreasing the service rate. In cardiothoracic surgery, an increase in overwork by 1% increases length of stay by six hours. Consistent with prior studies in the medical literature, we also find that overwork is associated with a reduction in quality of care in cardiothoracic surgery—an increase in overwork by 10% is associated with an increase in likelihood of mortality by 2%. We also find that load is associated with an early discharge of patients, which is in turn correlated with a small increase in mortality rate.

This work examines the impact of discharge decisions under uncertainty in a capacity-constrained high-risk setting: the intensive care unit (ICU). New arrivals to an ICU are typically very high-priority patients and, should the ICU be full upon their arrival, discharging a patient currently residing in the ICU may be required to accommodate a newly admitted patient. Patients so discharged risk physiologic deterioration, which might ultimately require readmission; models of these risks are currently unavailable to providers. These readmissions in turn impose an additional load on the capacity-limited ICU resources. We study the impact of several different ICU discharge strategies on patient mortality and total readmission load. We focus on discharge rules that prioritize patients based on some measure of criticality assuming the availability of a model of readmission risk. We use empirical data from over 5,000 actual ICU patient flows to calibrate our model. The empirical study suggests that a predictive model of the readmission risks associated with discharge decisions, in tandem with simple index policies of the type proposed, can provide very meaningful throughput gains in actual ICUs while at the same time maintaining, or even improving upon, mortality rates. We explicitly provide a discharge policy that accomplishes this. In addition to our empirical work, we conduct a rigorous performance analysis for the family of discharge policies we consider. We show that our policy is optimal in certain regimes, and is otherwise guaranteed to incur readmission related costs no larger than a factor of \\documentclass{aastex} \\usepackage{amsbsy} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{bm} \\usepackage{mathrsfs} \\usepackage{pifont} \\usepackage{stmaryrd} \\usepackage{textcomp} \\usepackage{portland,xspace} \\usepackage{amsmath,amsxtra} \\pagestyle{empty} \\DeclareMathSizes{10}{9}{7}{6} \\begin{document} $(\\hat{\\rho}+1)$ \\end{document} of an optimal discharge strategy, where \\documentclass{aastex} \\usepackage{amsbsy} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{bm} \\usepackage{mathrsfs} \\usepackage{pifont} \\usepackage{stmaryrd} \\usepackage{textcomp} \\usepackage{portland,xspace} \\usepackage{amsmath,amsxtra} \\pagestyle{empty} \\DeclareMathSizes{10}{9}{7}{6} \\begin{document} $\\hat{\\rho}$ \\end{document} is a certain natural measure of system utilization.

Diabetes, a chronic disease necessitating long-term treatment and self-management, presents significant challenges for patients who spend most of their treatment time outside of hospitals. The potential of digital therapeutics for diabetes has garnered recognition from different organizations. Although some prior studies have demonstrated successful reductions in patients' blood glucose levels and body weight through digital diabetes programs, many studies were limited by including patients with prediabetes, including patients treated with mostly premixed insulin, or evaluating user engagement outcomes rather than clinical outcomes. Consequently, limited evidence remains regarding the effectiveness of health management mobile apps specifically designed for patients with type 2 diabetes mellitus (T2DM) initiating basal insulin (BI). Based on this, a data-based and artificial intelligence management system named \"TRIO\" was developed to provide patients with more personalized intervention methods in stages, in groups, and around the clock. TRIO assists doctors and nurses in achieving better blood glucose controls, truly carries out standardized management around patients, and allows them to have a higher quality of life. TRIO represents the 3 essential pillars in comprehensive diabetes management: physician, nurse, and patient. This prospective observational study evaluated the effectiveness and safety of the TRIO optimal health management program for patients with T2DM initiating BI therapy in a real-world setting. Patients aged 18-85 years with inadequate glycemic control (baseline hemoglobin A [HbA ] ≥7%) starting BI therapy were enrolled in outpatient and inpatient settings. The study lasted 3 months, with health education and phone-based follow-up assessments. Data collected included patient characteristics, medical history, baseline diabetes conditions, treatment compliance, glycemic control, and safety indicators. A total of 199,431 patients were included, and 118,134 patients completed the 3-month follow-up between December 1, 2019, and December 31, 2021, involving 574 hospitals in China. The mean baseline HbA was 9.2%, the mean duration of diabetes was 7.3 years, and 80.4% (1,59,930/1,98,969) of patients were using BI with oral antihyperglycemic drugs. After the intervention, mean HbA decreased by -2.59% from baseline, with 55.6% (28,858/51,912) achieving the target HbA level of <7%. Patients who set lower fasting plasma glucose goals (<6.1 mmol/L) showed more significant HbA reductions (P<.001) and higher target achievement than those with fasting plasma glucose goals of ≥6.1 mmol/L. Factors such as complications, diabetes duration, and baseline HbA levels influenced the magnitude of HbA reduction. The presence of complications, shorter diabetes duration, and higher baseline HbA were significantly associated with increased hypoglycemia incidence risk (all P<.05). The TRIO optimal health management program effectively improved glycemic control in patients with T2DM initiating BI therapy. Individualized treatment approaches considering patient characteristics and glycemic goals are vital for optimal outcomes.

One key factor contributing to emergency department (ED) overcrowding is prolonged waiting time for admission to inpatient wards, also known as ED boarding time. To gain insights into reducing this waiting time, we study operations in the inpatient wards and their interface with the ED. We focus on understanding the effect of inpatient discharge policies and other operational policies on the time-of-day waiting time performance, such as the fraction of patients waiting longer than six hours in the ED before being admitted. Based on an empirical study at a Singaporean hospital, we propose a novel stochastic processing network with the following characteristics to model inpatient operations: (1) A patient's service time in the inpatient wards depends on that patient's admission and discharge times and length of stay. The service times capture a two-time-scale phenomenon and are not independent and identically distributed. (2) Pre-and post-allocation delays model the extra amount of waiting caused by secondary bottlenecks other than bed unavailability, such as nurse shortage. (3) Patients waiting for a bed can overflow to a nonprimary ward when the waiting time reaches a threshold, where the threshold is time dependent. We show, via simulation studies, that our model is able to capture the inpatient flow dynamics at hourly resolution and can evaluate the impact of operational policies on both the daily and time-of-day waiting time performance. In particular, our model predicts that implementing a hypothetical policy can eliminate excessive waiting for those patients who request beds in mornings. This policy incorporates the following components: a discharge distribution with the first discharge peak between 8 A.M. and 9 A.M. and 26% of patients discharging before noon, and constant-mean allocation delays throughout the day. The insights gained from our model can help hospital managers to choose among different policies to implement depending on the choice of objective, such as to reduce the peak waiting in the morning or to reduce daily waiting time statistics.

The COVID-19 has placed pandemic modeling at the forefront of the whole world’s public policymaking. Nonetheless, forecasting and modeling the COVID-19 medical waste with a detoxification center of the COVID-19 medical wastes remains a challenge. This work presents a Fuzzy Inference System to forecast the COVID-19 medical wastes. Then, people are divided into five categories are divided according to the symptoms of the disease into healthy people, suspicious, suspected of mild COVID-19, and suspicious of intense COVID-19. In this regard, a new fuzzy sustainable model for COVID-19 medical waste supply chain network for location and allocation decisions considering waste management is developed for the first time. The main purpose of this paper is to minimize supply chain costs, the environmental impact of medical waste, and to establish detoxification centers and control the social responsibility centers in the COVID-19 outbreak. To show the performance of the suggested model, sensitivity analysis is performed on important parameters. A real case study in Iran/Tehran is suggested to validate the proposed model. Classifying people into different groups, considering sustainability in COVID 19 medical waste supply chain network and examining new artificial intelligence methods based on TS and GOA algorithms are among the contributions of this paper. Results show that the decision-makers should use an FIS to forecast COVID-19 medical waste and employ a detoxification center of the COVID-19 medical wastes to reduce outbreaks of this pandemic.