Increased Resource Utilization Research Articles

Medicaid payer status is associated with increased 90-day morbidity and resource utilization following primary shoulder arthroplasty: a propensity score-matched analysis.

Medicaid payer status has been shown to affect risk-adjusted patient outcomes and health care utilization across multiple medical specialties and orthopedic procedures. However, there is a paucity of data regarding the impact of Medicaid payer status on 90-day morbidity and resource utilization following primary shoulder arthroplasty (reverse total shoulder arthroplasty [rTSA], anatomic total shoulder arthroplasty [aTSA], and hemiarthroplasty [HA]). The purpose of this study was to examine 90-day readmissionand reoperationrates, hospital length of stay (LOS), and direct cost following primary shoulder arthroplasty in the Medicaid population. The National Readmission Database was queried for all patients undergoing primary aTSA, rTSA, and HA from 2011 to 2016. Medicaid or non-Medicaid payer status was determined. Patient demographic characteristics and comorbidities, along with 90-day readmission, 90-day reoperation, LOS, and inflation-adjusted cost, were queried. Propensity score matching was used to control for baseline differences in cohorts that could be acting as confounders in the exposure-outcome relationship. This was achieved with 1-to-1 propensity score matching between Medicaid and non-Medicaid patients. Odds ratios (ORs) and 95% confidence intervals (CIs) for 90-day readmission and reoperation rates were calculated, and a comparison of LOS and cost was performed between the propensity score-matched cohorts. A total of 4667 Medicaid and 161,147 non-Medicaid patients were identified from the 2011-2016 National Readmission Databases. Propensity score analysis was performed, and 4637 Medicaid patients were matched to 4637 non-Medicaid patients; each group comprised 1504 rTSAs (32.4%), 1934 aTSAs (41.7%), and 1199 HAs (25.9%). Patients with Medicaid payer status yielded significant increases in the 90-day all-cause readmission rate of 11.6% vs. 9.3% (P<.001; OR, 1.28 [95% CI, 1.12-1.46]), 90-day shoulder-related readmission rate of 3.3% vs. 2.3% (P=.004; OR, 1.44 [95% CI, 1.12-1.85]), and 90-day reoperation rate of 2.0% vs. 1.3% (P=.008; OR, 1.54 [95% CI, 1.12-1.94]). Furthermore, there was an increased risk of an extended LOS (ie, LOS > 2 days) (28.4% vs. 25.7%; P=.004; OR, 1.14 [95% CI, 1.04-1.25]) along with increased direct cost (median, $17,612 vs. $16,775; P<.001). This study demonstrates that Medicaid payer status is independently associated with increased 90-day readmission and reoperation rates, LOS, and direct cost following primary shoulder arthroplasty. Providers may have a disincentive to treat patient populations who require increased resource utilization following surgery. Risk adjustment models accounting for Medicaid payer status will be necessary to ensure good access to care for this patient population by avoiding penalties for physicians and hospital systems.

Prospect Theory-Based Two-Sided Satisfied and Stable Matching Mechanism for the Shared Parking Slots Problem

The contradiction between the dramatic increase in the aggregate number of automobiles and the short supply of parking spaces leads to parking difficulties. Sharing mode helps improve the efficiency of existing parking spaces, increase resource utilization, and alleviate the difficulty of parking. This paper focuses on the matching mechanism in the shared parking slots problem, which involves three agents: shared parking suppliers, shared parking demanders, and shared parking platform. We propose a prospect theory-based two-sided satisfied and stable matching model (PT-TSSM) with two objectives to maximize the satisfaction degree of both shared parking demanders and shared parking suppliers. Numerical experiments are illustrated to demonstrate the efficiency of the proposed model. Moreover, the PT-TSSM model is compared with the other two shared parking mechanisms. The proposed model considers the satisfaction degrees of both shared parking demanders and suppliers, while first book first serve (FBFS) cares only one side of the participants. And compared with deferred acceptance (DA), our model not only takes two-sided stable matching into account but also considers the satisfaction degree of all the demand and supply participants, which obtain a two-sided satisfied and stable matching scheme.

Obesity is not associated with increased resource utilization or morbidity in patients undergoing appendectomy

Obesity is a growing public health concern that places patients at risk of morbidity and mortality following surgery. We sought to determine whether obesity influences our resource utilization and postoperative outcomes for patients who present with appendicitis. Charts were reviewed for patients age 1-18 years identified from a prospective registry who presented with a diagnosis of appendicitis from 2017 to 2020. Patients who underwent appendectomy were eligible. Charts were reviewed for demographics, imaging studies, laboratory studies, length of stay, operative times and thirty-day postoperative adverse events defined as return to the emergency room, re-admission, postoperative abscess or return to the operating room. A multivariate logistic regression analysis was performed to identify differences in resource utilization and outcome. A total of 451 patients were identified. There were 126 obese patients (27.9%). Obese patients were not more likely to present with perforated appendicitis and were not more likely to undergo computed tomography scans. All patients underwent laparoscopic appendectomy. Although intraoperative times were significantly longer for Black patients and older patients, BMI did not influence length of surgery. Length of stay was significantly higher for younger patients (p=0.019). Adverse events were seen in 38 patients (8.4%). There was no association between BMI and adverse events. Within our standardized management pathway, obesity does not influence management or patient outcomes for the treatment of appendicitis. Furthermore, obese patients did not require additional resource utilization. III.

1: ULTRASOUND SCREENING FOR VOCAL CORD DYSFUNCTION AFTER CONGENITAL HEART SURGERY IN CHILDREN

Introduction: Vocal cord dysfunction (VCD) is a common complication following congenital cardiac surgery. VCD is more frequently diagnosed in aortic arch repairs due to the risk of recurrent laryngeal nerve injury. We previously identified VCD as a key variable in feeding dysfunction leading to increased resource utilization in children with congenital heart surgery. The current diagnostic to identify VCD is Fiberoptic Endoscopy (FOE), however, Vocal Cord Ultrasound (VCUS) has been described as a screening tool to identify VCD in high-risk populations. We describe the performance of VCUS versus FOE in children following congenital cardiac surgery. Methods: Patients undergoing surgeries known to be associated with VCD were prospectively included. Patients underwent evaluation with FOE per our institutional protocol once they were weaned off of positive pressure ventilation. In addition to the FOE, four operators performed a bedside VCUS on high-risk patients. The results of the VCUS were compared against FOE for both diagnostic yield and patient tolerance. Patient tolerance was defined as changes in vital signs (i.e. bradycardia defined as heart rate < 100bpm, hypoxia defined as SpO2 decrease >10%, hypotension defined as fall in blood pressure by >10 mmHg, and respiratory distress defined as rate increase by 10. Results: Over two months, we screened 13 patients, of which 12 patients received FOE and VCUS. Eight patients (61%) were diagnosed with VCD by FOE. Qualitative interpretation of VCUS, with performers blinded to FOE results, had 100% sensitivity and specificity vs. FOE. VCUS had a lower incidence of respiratory distress (1 vs. 5, p=0.03) and hypoxia (0 vs. 2, p=0.06). No patients had bradycardia or hypotension. Conclusions: Preliminary data indicates that VCUS is very specific and sensitive in screening for VCD. In addition, VCUS was well tolerated by patients with a low incidence of complications. Further investigation is necessary to determine if VCUS can replace FOE as a screening tool for VCD in children following congenital cardiac surgery. Implementation of VCUS as the primary screening tool may prove important in resource-limited centers.

Prone Positioning During Venovenous Extracorporeal Membrane Oxygenation for Acute Respiratory Distress Syndrome-Better Together?

Prone Positioning During Venovenous Extracorporeal Membrane Oxygenation for Acute Respiratory Distress Syndrome-Better Together?

Decision Tree with Pearson Correlation-based Recursive Feature Elimination Model for Attack Detection in IoT Environment

The industrial revolution in recent years made massive uses of Internet of Things (IoT) applications like smart cities’ growth. This leads to automation in real-time applications to make human life easier. These IoT-enabled applications, technologies, and communications enhance the quality of life, quality of service, people’s well-being, and operational efficiency. The efficiency of these smart devices may harm the end-users, misuse their sensitive information increase cyber-attacks and threats. This smart city expansion is difficult due to cyber attacks. Consequently, it is needed to develop an efficient system model that can protect IoT devices from attacks and threats. To enhance product safety and security, the IoT-enabled applications should be monitored in real-time. This paper proposed an efficient feature selection with a feature fusion technique for the detection of intruders in IoT. The input IoT data is subjected to preprocessing to enhance the data. From the preprocessed data, the higher-order statistical features are selected using the proposed Decision tree-based Pearson Correlation Recursive Feature Elimination (DT-PCRFE) model. This method efficiently eliminates the redundant and uncorrelated features which will increase resource utilization and reduces the time complexity of the system. Then, the request from IoT devices is converted into word embedding using the feature fusion model to enhance the system robustness. Finally, a Deep Neural network (DNN) has been used to detect malicious attacks with the selected features. This proposed model experiments with the BoT-IoT dataset and the result shows the proposed model efficiency which outperforms other existing models with the accuracy of 99.2%.

Design and Implementation of Simulation-Based Scheduling System with Reinforcement Learning for Re-Entrant Production Lines

Recently, manufacturing companies have been making efforts to increase resource utilization while ensuring the flexibility of production lines to respond to rapidly changing market environments and customer demand. In the high-tech manufacturing industry, which requires expensive manufacturing facilities and is capital-intensive, re-entrant production lines are used for efficient production with limited resources. In such a production system, a part visits a specific station repeatedly during the production period. However, a re-entrant production line requires an appropriate scheduling system because other parts with different processing requirements are processed at the same station. In this study, a re-entrant production line was modeled as a manufacturing environment via simulation, and an adaptive scheduling system was developed to improve its operational performance by applying deep reinforcement learning (DRL). To achieve this, a software architecture for integrating DRL with the simulation was developed and the states, actions, and rewards of the reinforcement learning (RL) agent were defined. Moreover, a discrete-event simulation control module was designed to collect data from the simulation model and evaluate the policy network trained via DRL. Finally, the applicability and effectiveness of the developed scheduling system were verified by conducting experiments on a hypothetical re-entrant production line.

Optimal Embedding of Heterogeneous RAN Slices for Secure and Technology-Agnostic RANaaS

A key challenge related to Radio Access Network (RAN) slicing is deciding how to efficiently map radio resources from the physical radio to realise RAN slices, known as the virtual wireless network embedding problem. To the best of our knowledge, this is the first paper to model and derive an analytical solution for embedding heterogeneous RAN slices with different waveforms, numerologies and Radio Access Technologies (RATs) with resources isolated down to the Physical (PHY) layer, ultimately enabling secure technology-agnostic RAN as a Service (RANaaS). First, we assess how current virtual wireless network embedding solutions model the allocation of radio resources to realise RAN slices. Then, we propose a graph-based model for embedding heterogeneous RAN slices that considers the guard bands required to ensure isolation in the frequency domain. This approach is transparent to the type and granularity of radio resources of each RAN slice, and can be extended to support RAN slices with new waveforms, numerologies and RATs. Next, we introduce a resource management optimisation problem solved at the Network Provider (NP) to determine the optimal embedding of RAN slices that maximises the total useful bandwidth occupied by tenants; and we propose three different heuristic algorithms to obtain solutions in near real-time. We compare their performance against the analytical solution using different metrics, and our results show that the best heuristic depends on the NP’s business model, e.g., using the Greedy Algorithm (GA) to increase resource utilisation or the Nearest Neighbour Algorithm (NNA) to increase the number of allocated RAN slices.

Prediction of total healthcare cost following total shoulder arthroplasty utilizing machine learning.

Given the increase in demand in treatment of glenohumeral arthritis with anatomic total (aTSA) and reverse shoulder arthroplasty (RTSA), it is imperative to improve quality of patient care while controlling costs as private and federal insurers continue its gradual transition toward bundled payment models. Big data analytics with machine learning shows promise in predicting health care costs. This is significant as cost prediction may help control cost by enabling health care systems to appropriately allocate resources that help mitigate the cause of increased cost. The Nationwide Readmissions Database (NRD) was accessed in 2018. The database was queried for all primary aTSA and RTSA by International Classification of Diseases, Tenth Revision (ICD-10) procedure codes: 0RRJ0JZ and 0RRK0JZ for aTSA and 0RRK00Z and 0RRJ00Z for RTSA. Procedures were categorized by diagnoses: osteoarthritis (OA), rheumatoid arthritis (RA), avascular necrosis (AVN), fracture, and rotator cuff arthropathy (RCA). Costs were calculated by utilizing the total hospital charge and each hospital's cost-to-charge ratio. Hospital characteristics were included, such as volume of procedures performed by the respective hospital for the calendar year and wage index, which represents the relative average hospital wage for the respective geographic area. Unplanned readmissions within 90 days were calculated using unique patient identifiers, and cost of readmissions was added to the total admission cost to represent the short-term perioperative health care cost. Machine learning algorithms were used to predict patients with immediate postoperative admission costs greater than 1 standard deviation from the mean, and readmissions. A total of 49,354 patients were isolated for analysis, with an average patient age of 69.9±9.6 years. The average perioperative cost of care was $18,843±$10,165. In total, there were 4279 all-cause readmissions, resulting in an average cost of $13,871.00±$14,301.06 per readmission. Wage index, hospital volume, patient age, readmissions, and diagnosis-related group severity were the factors most correlated with the total cost of care. The logistic regression and random forest algorithms were equivalent in predicting the total cost of care (area under the receiver operating characteristic curve=0.83). After shoulder arthroplasty, there is significant variability in cumulative hospital costs, and this is largely affected by readmissions. Hospital characteristics, such as geographic area and volume, are key determinants of overall health care cost. When accounting for this, machine learning algorithms may predict cases with high likelihood of increased resource utilization and/or readmission.

Nutritional Risk Screening 2002 predicts surgical outcomes in 1532 patients undergoing total joint arthroplasty: A retrospective cohort study.

Limited information exists about the predictive effect of Nutritional Risk Screening (NRS) 2002 on orthopedic surgery. The aim of the present study is to explore the role of NRS 2002 in postoperative complications and resource utilization in patients with total joint arthroplasty (TJA). We retrospectively collected the demographics and surgical results of nearly 2000 TJA patients admitted from 2016 to 2020 and assessed the differences in short- and long-term complications and resource utilization parameters. Multivariate linear, logistic regression, and subgroup analysis were subsequently used to control for potential confounders. Survival analysis was performed to further verify the cumulative incidence of postoperative complications. We identified 1532 patients receiving TJA, 8.7% of which were at nutrition risk (NRS 2002 score ≥3 out of 7). Preoperative nutrition risk was associated with an increased risk of systemic complications, incisional complications, surgical site infection (SSI), incisional SSI, periprosthetic joint infection, dislocation, and periprosthetic fracture after TJA (odds ratio [OR], 3.62-31.99; all P < 0.05). Preoperative nutrition risk was further associated with an increased risk of cardiac complications, respiratory complications, urinary complications, and arthroplasty-related reoperation (OR, 3.16-12.29; all P < 0.01). Moreover, preoperative nutrition risk was associated with increased costs and length of stay, and increased risk of unplanned intensive care unit admission, arthroplasty-related readmission, infection-related readmission, and SSI-related readmission. NRS 2002 is associated with an elevated risk of postoperative complications and increased resource utilization, following TJA. Thus, routine screening is recommended to identify nutrition risk statuses of patients undergoing elective TJA.

Improved League Championship Algorithm (ILCA) for Load Balancing in Cloud Computing

You can’t obtain the outcomes you need without planning, thus it’s at the heart of cloud computing. This article’s major goal is to decrease value-added time, increase resource utilisation, and make cloud services viable for a single activity. In recent years, metaheuristic algorithms have drew attention to the correct functioning of work scheduling algorithms among the many job scheduling techniques. With sports leagues, the algorithm based on the League Championship (LCA) is fascinating because it can be used to identify the best team/task for programming.This article uses the Improved League Championship Algorithm (ILCA) to schedule tasks, reducing deployment time, cloud usage, and cost. The ILCA is implemented through the Cloudsim simulator and the Java programming language with a nonpreventive planning strategy. ILCA also enhances economies of scale and minimises the value of using the cloud. As it has proven to be versatile in terms of time to manufacture, resource usage and economics, ILCA could be a good candidate for a cloud broker as it has proven to be versatile in termsof time to manufacture, resource usage and economics usage.

The Impact of Depression and Anxiety on Perioperative Outcomes and Patient-Reported Outcomes Measurement Information System Physical Function After Thoracolumbar Surgery.

Depression and anxiety are common within spine patient populations. The demand for surgical management of degenerative spine conditions and the prevalence of mental disorders are expected to increase as the general population ages. Concurrently, there is increasing pressure to demonstrate high-value care through improved perioperative outcome metrics and patient-reported outcome instruments. The purpose of this study was to evaluate the impact of common mental disorders on perioperative markers of high resource utilization and patient-reported outcomes measurement information system physical function (PROMIS-PF) following thoracolumbar (TL) spine surgery. A retrospective review of patients undergoing TL decompression alone or with fusion at a single institution. Data were collected using an administrative database for patient demographics. Outcomes of interest included length of stay, discharge disposition, 90-day return to the emergency department (ED), 90-day hospital readmission, 1-year complication rate, 1-year revision surgery rate, 1-year residual radiculopathy, and PROMIS-PF scores recorded preoperatively, at 0 to 1, 1 to 3, 3 to 6, and 6 to 12 months postoperatively. Univariate analysis and multiple linear regression were utilized to analyze results. A total of 596 patients were included in this study, of whom 205 (34%) had a history of depression or anxiety. Compared with patients with no history of a mental disorder, patients with depression or anxiety who underwent TL decompression alone had higher rates of 90-day ED visits (P = 0.019), 90-day readmissions (P = 0.031), and complications at 1 year (P = 0.012). After risk adjustment, the diagnosis of depression or anxiety had no significant effect on PROMIS-PF improvement from the preoperative to postoperative period. Our study suggests that a history of depression or anxiety is common among patients undergoing spine surgery but has no significant impact on PROMIS-PF improvement. Because some patients with depression or anxiety may be at higher risk of postoperative resource utilization, further study and effort are warranted to support at-risk groups and improve overall care value. Although patients with depression or anxiety are at risk for increased resource utilization after TL decompression or fusion, they can experience similar levels of functional improvement as patients without these conditions. Therefore depression or anxiety should not be considered contraindications to surgery, but additional attention should be paid to this population during the postoperative recovery period.

Trust Management With Fault-Tolerant Supervised Routing for Smart Cities Using Internet of Things

The Internet of Things (IoT) connects heterogeneous sensors with dynamic networks to monitor smart communication and collect real-time data. Such systems are well adapted to satisfy the needs of smart cities and facilitate remote locations. Many cloud-based solutions for effective routing along with scalable data storage have been presented for constraint IoT systems. However, because of the unpredictable nature of mobile networks and communication links, most of the solutions may not be suitable for realistic applications and usually result in path failure with increasing resource utilization. Hence, data forwarding is only reliable and valuable if the proposed algorithms are trust aware with low overheads and consume balanced energy among nodes. Therefore, this article proposed a fault-tolerant supervised routing (Trust-FTSR) model for trust management in the IoT network, to improve trustworthiness and collaborative communication in smart cities. Each node evaluates the behavior of its neighbors and establishes a direct trust for a reliable and optimized network structure. In addition, using a supervised machine-learning technique, a fault-tolerant relaying system is provided without imposing additional overheads. Moreover, it removes the additional load in determining the optimal decision and training the IoT system to balance the network cost. In the end, a secure algorithm is proposed to ensure the privacy and authentication of the relaying system in the presence of critical attacks with secured keys. The proposed model is tested and its performance has significant improvement as compared to existing work.

Abstract A020: Patients with pancreatic cancer reporting prediagnosis pain have greater pain intensity scores, symptom burden, and ER visits: need for improved, ongoing management in pancreatic pain and associated symptoms

Abstract Objective: This study assessed the pancreatic cancer (PC) patient-reported burdens of PC pain from 2016-2020 in an online patient-reported outcomes (PRO) survey supported by the Pancreatic Cancer Action Network (PanCAN). Assessments of PC symptoms, PC pain intensity, physical and mental functioning, associated symptom management and resource utilization were analyzed between PC patients reporting prediagnosis PC pain vs those who did not. Methods: Through years 2016 - 2020, PC patient volunteers filled out an online PRO survey composed of independent surveys and standalone items in this cross-sectional study. PC patient responses to Patient Information, PC Experience, General Health Assessment and Pain Management surveys were collected to assess PC pain and related factors in activities and associated symptoms. Comparisons between PC patient group responses to two survey items were assessed: 1) presence or absence of prediagnosis PC pain; 2) pain intensity, scored by an 11-point numeric rating scale (NRS). Patient groups: NRS=0-3 (none to mild pain) and NRS=5-8 (moderate to severe pain). Descriptive statistics were used for numbers and frequencies and all bivariate analyses were performed using Chi-square or Fisher’s Exact tests. Results: Survey responses to 108 items ranging from 5-100% participation were collected and analyzed from 1978 PC patient volunteers. Significant differences were noted in postdiagnosis average pain intensities based on presence or absence of prediagnosis pain: NRS 2.64+2.54 vs NRS 1.56+2.01, respectively, p=0.0039. The presence of PC pain (abdominal and/or back pain) was the most frequent prediagnosis symptom reported (62%, N=1222) followed by digestive problems (55.32%, p=0.0001), weight loss and/or loss of appetite (54.88%, p=0.0001) and fatigue (46.54%, p=0001). Significantly greater impairment of those with prediagnostic pain was noted in 45% of 20 health assessment statements, including pain interference&amp;gt;physical functioning &amp;gt;anxiety &amp;gt;depression &amp;gt;fatigue, and increased resource utilization for PC pain management was noted (ER visits N=42 vs N=8 respectively, p=0.018 and/or hospitalizations, N=30 vs N=4, respectively, p=NS). Patient responses to pain ranged from NRS 0-8 (N=155), and group NRS 5-8 scores reported had significantly greater impairment in 85% of 20 health assessment statements. No improvements in average pain intensity scores were noted between yrs 2018-2020 vs yrs prior to 2018. Conclusions: PC pain is the most frequent prediagnostic PC symptom and is associated with an increase in postdiagnosis average pain intensity, greater impairment in physical function, and an increase in ER visits compared to those who do not report prediagnostic pain. The results suggest that PC pain is not adequately managed early in the course of the disease. There is a need to better assess symptoms, QOL, and therapeutic interventional outcomes to determine the contribution of improvements in tumor reduction and survival with patient symptom reduction and better physical and mental functioning. Citation Format: Terry A. McNearney, Biai D.E. Digbeu, Dennis Ladnier, Kawther Abdilleh, Sudheer Doss, Lynn M. Matrisian, Jacques G. Baillargeon. Patients with pancreatic cancer reporting prediagnosis pain have greater pain intensity scores, symptom burden, and ER visits: need for improved, ongoing management in pancreatic pain and associated symptoms [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A020.

Abstract 9873: Erroneous Electrocardiographic Interpretations and Its Clinical Implications

Introduction: Advancement of artificial intelligence (AI) has aided clinicians in the interpretation of electrocardiograms (ECGs) serving as an essential tool to provide rapid triage and care. However, in some cases, AI can misinterpret an ECG and may mislead the interpreting physician. Therefore, we aimed to describe the rate of ECG misinterpretation and its potential clinical impact in patient's management Methods: We performed a retrospective descriptive analysis of misinterpreted ECGs and its clinical impact from May 28, 2020 to May 9, 2021. An electrophysiologist screened ECGs with confirmed diagnosis of atrial fibrillation (AF), sinus tachycardia (ST), sinus bradycardia (SB), intraventricular conduction delay (IVCD), and premature atrial contraction (PAC) that were performed in the emergency department. We then classified the misinterpreted ECGs as pseudo-AF, ST, SB, IVCD, or PAC into the correct diagnosis, and reviewed the misinterpreted ECGs and medical records to evaluate inappropriate use of antiarrhythmic drugs (AAD), beta-blockers (BB), calcium channel blockers (CCB), anticoagulation, or resource utilization of cardiology and/or electrophysiology (EP) consultation. Results: A total of 4,969 ECGs were screened with diagnoses of AF (2,282), IVCD (296), PAC (972), SB (895), and ST (638). Among these, 101 ECGs (2.02%) were misinterpreted. Pseudo-AF (58%) was the most common followed by pseudo-PAC (15%), pseudo-ST (13%), pseudo-IVCD (8%) and pseudo-SB (6%). Patients with misinterpreted ECGs were aged 76.6±11.6yr with male (52.5%) predominance and hypertension being the most prevalent (83.2%) comorbid condition. The misinterpretation of ECGs led to the inappropriate use of BB (19.8%), CCB (5.0%), AAD therapy (7.9%), anticoagulation (30.7%) in patients with pseudo-AF, as well as inappropriate resource utilization including cardiology (41.6%) and EP (8.9%) consultations. Conclusions: Misinterpretation of ECGs may lead to inappropriate medical therapies and increased resource utilization. Therefore, it is essential to encourage physicians to carefully examine AI interpreted ECG’s, especially those interpreted as having AF.

Abstract 13451: Cardiovascular Complications During Delivery Admissions Associated With Assisted Reproductive Technology: A National Inpatient Sample Analysis (2008-2019)

Introduction: Persons who conceived through Assisted Reproductive Technology (ART) have an increased risk of obstetric-associated complications. Hypothesis: ART is associated with increased acute cardiovascular (CV) complications during delivery admissions. Methods: We used data from the National Inpatient Sample (2008-2019) while utilizing ICD-9 or ICD-10 codes to identify delivery hospitalizations and ART procedure. Results: A total of 45,867,086 weighted delivery cases were identified, of which 0.24% were among individuals who conceived through ART (n=108,542). Persons with an ART history were older at the time of delivery (median 35 vs. 28 years, p&lt;0.01) and had higher prevalence of hypertension, gestational diabetes, and dyslipidemia (all, p&lt;0.01). After adjustment for age, race/ethnicity, comorbidities, multiple gestation, insurance, and income, ART remained an independent predictor of peripartum CV complications including preeclampsia (aOR 1.48 [1.45-1.51], p&lt;0.01), heart failure (aOR 1.94 [1.10-3.40], p&lt;0.01), and cardiac arrhythmias (aOR 1.39 [1.30-1.48], p&lt;0.01) compared to natural conception. Likewise, the risk of acute kidney injury (aOR 2.57 [2.25-2.92], p&lt;0.01), stroke (aOR 1.57 [1.23-2.01], p&lt;0.01), pulmonary edema (aOR 2.29 [2.02-2.61], p&lt;0.01), and venous thromboembolism (aOR 1.92 [1.63-2.25], p&lt;0.01) were higher with ART. However, higher odds of developing peripartum cardiomyopathy or acute coronary syndrome were not associated with ART. Length of stay (3 vs. 2 days, p&lt;0.01) and cost of hospitalization ($5,903 vs. $3,922, p&lt;0.01) were higher for deliveries among persons with history of ART. Conclusions: Persons who conceived with ART had higher risk of preeclampsia, heart failure, cardiac arrhythmias, pulmonary edema, AKI, stroke, and VTE during their delivery hospitalizations. This may in part contribute to increased resource utilization seen in patients conceiving with ART

Energy-Efficient Task Scheduling and Resource Allocation for Improving the Performance of a Cloud–Fog Environment

Inadequate resources and facilities with zero latency affect the efficiencies of task scheduling (TS) and resource allocation (RA) in the fog paradigm. Only the incoming tasks can be completed within the deadline if the resource availability in the cloud and fog is symmetrically matched with them. A container-based TS algorithm (CBTSA) determines the symmetry relationship of the task/workload with the fog node (FN) or the cloud to decide the scheduling workloads (whether in the fog or a cloud). Furthermore, by allocating and de-allocating resources, the RA algorithm reduces workload delays while increasing resource utilization. However, the unbounded cloud resources and the computational difficulty of finding resource usage have not been considered in CBTSA. Hence, this article proposes an enhanced CBTSA with intelligent RA (ECBTSA-IRA), which symmetrically balances energy efficiency, cost, and the performance-effectiveness of TS and RA. Initially, this algorithm determines whether the workloads are accepted for scheduling. An energy-cost–makespan-aware scheduling algorithm is proposed that uses a directed acyclic graph (DAG) to represent the dependency of tasks in the workload as a graph. Workloads are prioritized and selected for the node to process the prioritized workload. The selected node for processing the workload might be a FN or cloud and is decided by an optimum efficiency factor that trades off the schedule length, cost, and energy. Moreover, a Markov decision process (MDP) was adopted to allocate the best resources using the reinforcement learning scheme. Finally, the investigational findings reveal the efficacy of the presented algorithms compared to the existing CBTSA in terms of various performance metrics.

GVMP: A multi-objective joint VM and vGPU placement heuristic for API remoting-based GPU virtualization and disaggregation in cloud data centers

The diverse needs of customers drive cloud providers to incorporate more GPU-enabled services. It is known that users barely utilize GPUs. Hence, GPU virtualization techniques are employed to enable GPU sharing and increase resource utilization. Among GPU virtualization techniques, API remoting leverages disaggregation for GPU provisioning, allowing GPUs to be accessed remotely by non-resident VMs. It enables a VM to access any available GPU in the data center which, in turn, provides more flexibility to reach a better placement. It also enables users to choose VM and vGPU instances separately based on computational needs as GPU-enabled VM instances with fixed configurations may not proportionally utilize allocated resources. However, an inefficient VM or vGPU placement may result in poor performance and increase in energy consumption and rejection ratio. There is also a network communication overhead for remotely allocated vGPUs. The main challenge is how to place VMs and vGPUs such that rejection ratio is decreased and the negative performance impacts caused by GPU sharing and remote access are kept at a minimum. In this work, we formally define the joint problem of VM and vGPU placement based on API remoting as a multi-objective ILP model and introduce gVMP as a heuristic to solve it. The proposed method is compared against state-of-the-art heuristics and commercial solutions that are based on API remoting and GRID technologies for the placement of GPU-enabled VMs. We study the effectiveness of the proposed gVMP algorithm for low and high arrival rates in slow and fast networks. Our results show that under different scenarios and in comparison to state-of-the-art policies, our heuristic improves average request duration, efficiency, and SLA by up to 40%, 25% and 29%, respectively.

Online Adaptive Radiation Therapy and Opportunity Cost

Changes in patient anatomy and tumor geometry pose a challenge to ensuring consistent target coverage and organ-at-risk sparing; online adaptive radiation therapy (ART) accounts for these interfractional changes by facilitating replanning before each treatment. This project explored the opportunity cost of computed tomography (CT)-based online ART by evaluating time and human resource requirements. Time-driven activity-based costing (TDABC) was employed to determine the cost of this time to assess if the dosimetric benefit is worthwhile. CT-based online ART was recently employed at our institution and has been used to treat pelvic disease sites (prostate, prostate bed, prostate with nodal coverage, bladder, rectum); data points from all adaptively treated patients (415 fractions) were used. Time taken for each adaptive fraction before treatment, which at our facility is best represented by the duration between 2 cone beam CT scans, was used as a broadly applicable and transferable metric, representing the additional time required for ART on top of standard image guided radiation therapy. Dosimetric effect was also considered by taking the difference of planning target volume V100% for the scheduled and adapted plans. Using recently validated TDABC at this facility, the per fraction cost of ART was determined, reflecting the added cost of ART on top of image guided radiation therapy. A median time of 15.97 (interquartile range, 13.23-18.83) additional minutes was required for each adaptive fraction. TDABC demonstrated an average minimum cost per adapted fraction of $103.58. Dosimetric differences between V100% of the scheduled versus adapted plan showed a mean dosimetric difference of 15.8%. Although online ART decreases the uncertainty of anatomic shifts, each adaptive fraction requires more staff time, delaying completion of other tasks and increasing resource utilization. Although toxicity benefits require further studies, the implementation of progressively complex radiation therapy technologies, like ART, requires consideration of the time and human resource requirements and subsequent opportunity cost.

Cardiovascular Complications During Delivery Admissions Associated With Assisted Reproductive Technology (from a National Inpatient Sample Analysis 2008 to 2019)

Women who conceive through assisted reproductive technology (ART) have a known increased risk of obstetric complications. However, whether ART is also associated with higher risk of developing cardiovascular complications during delivery admissions has not been well established. We used data from the National Inpatient Sample (2008 to 2019) and used the International Classification of Diseases codes to identify delivery hospitalizations and ART procedures. A total of 45,867,086 weighted delivery cases were identified, of which 0.24% were among women who conceived through ART (n=108,542). Women with an ART history were older at the time of delivery (median 35 vs 28years, p <0.01) and had a higher prevalence of hypertension, gestational diabetes, and dyslipidemia (all, p <0.01). After adjustment for age, race/ethnicity, co-morbidities, multiple gestation, insurance, and income, ART remained an independent predictor of peripartum cardiovascular complications, including pre-eclampsia/eclampsia (adjusted odds ratio [aOR] 1.48, 95% confidence interval [CI] 1.45 to 1.51), heart failure (aOR 1.94, 95% CI 1.10 to 3.40), and cardiac arrhythmias (aOR 1.39, 95% CI 1.30 to 1.48), compared with natural conception. Likewise, the risk of acute kidney injury (aOR 2.57, 95% CI 2.25 to 2.92), ischemic stroke (aOR 1.73, 95% CI 1.24 to 2.43), hemorrhagic stroke (aOR 1.63, 95% CI 1.27 to 2.11), pulmonary edema (aOR 2.29, 95% CI 2.02 to 2.61), and venous thromboembolism (aOR 1.92, 95% CI 1.63 to 2.25) were higher with ART. However, odds of developing peripartum cardiomyopathy or acute coronary syndrome were not associated with ART. Length of stay (3 vs 2 days, p <0.01) and cost of hospitalization ($5,903 vs $3,922, p <0.01) were higher for deliveries among women with a history of ART. In conclusion, women who conceived with ART had higher risk of pre-eclampsia, heart failure, arrhythmias, stroke, and other complications during their delivery hospitalizations. This may, in part, contribute to their increased resource utilization seen.