Increased Resource Utilization Research Articles

Association Between Delayed/Forgone Medical Care and Resource Utilization Among Women with Breast Cancer in the United States.

Although high treatment costs of breast cancer care are well documented, the relationship between delayed/forgone (D/F) care and resource utilization among patients with breast cancer is unknown. This study sought to investigate the relationship between D/F care, resource use, and healthcare expenditures among patients with breast cancer. Data on adult female patients with breast cancer were obtained from the Medical Expenditure Panel Survey to assess resource utilization and expenditures in the United States from 2007 to 2017. Weighted proportions of patients with ≥1 emergency department, ≥1 inpatient, ≥1 outpatient, and >5 office-based encounters were compared between those experiencing D/F care versus those who did not using Rao-Scott adjusted chi-squared tests. Annual, per capita total, out-of-pocket, emergency department, inpatient, outpatient, office-based visit, and prescription medication expenditures were compared by using two-part econometric models. Five percent of patients with breast cancer experienced D/F care, and 42.9% of patients cited financial barriers as the primary reason for D/F care. In unweighted estimates, there were higher proportions of patients with ≥1 hospitalizations (37% vs. 16%, P<0.001) among those experiencing D/F care versus those who did not. Patients with D/F care had $5372 (95% CI $35-$10,709, P=0.04) higher per capita inpatient expenditures than patients without D/F care. Delayed/forgone care is associated with increased resource utilization and healthcare spending among breast cancer patients. Further work is needed to address the root causes of D/F breast cancer care, with a view to mitigating disparate outcomes and increasing costs.

Unclear Trajectory and Uncertain Benefit: Creating a Lexicon for Clinical Uncertainty in Patients with Critical or Advanced Illness Using a Delphi Consensus Process.

Clinical uncertainty is associated with increased resource utilization, worsened health-related quality of life for patients, and provider burnout, particularly during critical illness. Existing data are limited, because determining uncertainty from notes typically requires manual, qualitative review. We sought to develop a consensus list of descriptors of clinical uncertainty and then, using a thematic analysis approach, describe how respondents consider their use in intensive care unit (ICU) notes, such that future work can extract uncertainty data at scale. We conducted a Delphi consensus study with physicians across multiple institutions nationally who care for critically ill patients or patients with advanced illnesses. Participants were given a definition for clinical uncertainty and collaborated through multiple rounds to determine which words represent uncertainty in clinician notes. We also administered surveys that included open-ended questions to participants about clinical uncertainty. Following derivation of a consensus list, we analyzed participant responses using thematic analysis to understand the role of uncertainty in clinical documentation. Nineteen physicians participated in at least 2 of the Delphi rounds. Consensus was achieved for 44 words or phrases over 5 rounds of the Delphi process. Clinicians described comfort with using uncertainty terms and used them in a variety of ways: documenting and processing the diagnostic thinking process, enlisting help, identifying incomplete information, and practicing transparency to reflect uncertainty that was present. Using a consensus process, we created an uncertainty lexicon that can be used for uncertainty data extraction from the medical record. We demonstrate that physicians, particularly in the ICU, are comfortable with uncertainty and document uncertainty terms frequently to convey the complexity and ambiguity that is pervasive in critical illness. Question: What words do physicians caring for critically ill patients use to document clinical uncertainty, and why?Findings: A consensus list of 44 words or phrases was identified by a group of experts. Physicians expressed comfort with using these words in the electronic health record.Meaning: Physicians are comfortable with uncertainty words and document them frequently to convey the complexity and ambiguity that is pervasive in critical illness.

Automated Virtual Machine Migration with Load Shifting Technique Avoiding Latency in Cloud Environment

Efficient management of Virtual Machines (VMs) in cloud computing is one key aspect to achieve the best utilization of resources and performance. The study is about improving Automated Virtual Machine Migration with a Load Shifting Technique in order to reduce latency through the cloud domain. This methodology dynamically distribute the load across multiple VMs by migrating their workloads to other physical hosts determined according certain resource utilization metrics available in real-time. The dynamic and unpredictable nature of cloud workloads requires a solution that is more responsive in the way it manages VMs. Live migration and pre-copy migration offer suboptimal performance in terms of time to migrate, downtime due to the prolonged copying period as heaps swell/collapse during live migrations. Utilizing sophisticated algorithms and real-time monitoring to avoid these pitfalls, our methodology allows us for proper load balancing with minimal latency. The primary elements of the proposed framework are a detailed monitoring module, an intelligent load shifting algorithm, high-performance migration method and feedback loop for continuous optimization. The monitoring module is responsible for collecting on-the-fly information about CPU, memory, network traffic and disk I/O such that can be exploited by the load shifting algorithm. The algorithm processes this data to decide which VMs should be migrated with the purpose of ensuring load balancing and decreasing latency achieved using these migrations. In order to test the proposed approach, comprehensive simulations and comparatives analyses utilizing other models were performed. This results in better latency reduction, increased resource utilization and improved overall system performance. In fact, the proposed method can handle not only current challenges in cloud computing such as load balancing and latency issued but also a scalable and effective approach for general cloud infrastructure usage. Finally, the Automated Virtual Machine Migration with Load Shifting Technique proves to be a reliable and successful solution for assisting in managing dynamic cloud workloads. Through real-time monitoring, advanced algorithms and continuous optimization this method has now been able to improve cloud services performance and reliability thereby allowing it to better serve service level agreements as well meet up with user satisfaction. We hope to adapt the algorithm and generalize it for even more advanced predictive models in future iterations, as well as evaluation of practical implementation on cloud-based deployment environments.

Monte Carlo Simulation As Precision Predictive Tools to Find Isodose Curve of Gamma Irradiator: A Preliminary Study

&lt;span lang="EN-AU"&gt;Dose distribution mapping is critical for guaranteeing correct sample irradiation, with both experimental and simulation methods playing important roles. Simulations are an effective way to forecast dose distribution patterns, lowering costs and increasing resource utilization. The geometry, source configuration, and measurement locations are fundamental to determine. The gamma irradiator has 48 source areas, each containing two cobalt-60 pencils measuring 8.15 cm, as well as a 4.7 cm stainless steel dummy. Alanine dosimeters were used for dose assessments, and stability varied by less than 1% over six months at 6°C and up to 5% at 50°C. The study's findings revealed a 2.25% disparity in relative dosage between experimental measurements and PHITS models. This result is a major improvement over prior research that found a 10% difference. Furthermore, dosage mapping along the XY and Z axes revealed the most uniform zone on the Z-axis, measuring 7.5 cm to 12.5 cm and with a radius of no more than 5 cm. These findings contribute to our understanding of dose distribution in gamma irradiation and highlight the utility of Monte Carlo simulations for optimizing irradiation operations. The study implies that this model can be used to improve the arrangement of cobalt-60 pencils in the irradiator, improving homogeneity and radiation outcomes.&lt;/span&gt;

Merchants of Death: The Effect of Credit Supply Shocks on Hospital Outcomes

This study examines the link between credit supply and hospital health outcomes. We use bank stress tests as exogenous shocks to credit access for hospitals that have lending relationships with tested banks. We find that affected hospitals shift their operations to increase resource utilization following a negative credit shock but reduce the quality of their care to patients across a variety of measures, including a significant increase in risk-adjusted readmission and mortality rates. The results indicate that access to credit can affect the quality of health care hospitals deliver, pointing to important spillover effects of credit market frictions on health outcomes. (JEL G21, G32, I11, I18)

The Impact of Socioeconomic Factors on 90-day Postoperative Readmissions and Cost in Shoulder Arthroplasty Patients

Understanding the impact of Social Drivers of Health (SDOH) on shoulder arthroplasty is pivotal for the development of equitable value-based payment models that enhance the quality of patient care. This investigation aims to understand the influence of Area Deprivation Index (ADI) on hospital admissions, readmissions, and associated costs post-shoulder arthroplasty. We conducted an analysis using US Medicare claims data from 2019 to 2021, identifying patients who received shoulder arthroplasty in either an inpatient or outpatient setting using Current Procedural Terminology (CPT) codes. Our primary outcomes were 90-day unplanned readmissions and post-procedure 90-day care costs. The ADI was our primary exposure variable, calculated at the census block level. Our analysis adjusted for multiple factors using a stepwise modeling approach including patient demographics, 29 Elixhauser comorbid conditions, Medicaid-dual eligibility, and end-stage renal disease status using logistic regression models. A total of 145,435 patients were included in our study. The average age of patients in our cohort was 74.5 years old, with 59.5% being female. Patients in the high ADI group had a higher readmission rate than the lowest ADI group (8.5% vs. 6.0%, p<0.001).When controlling for confounding factors, there was an indepdentent association between high ADI and readmission (OR: 1.28 [95% CI 1.12, 1.46]). Overall, we saw a dose-dependent relationship between ADI and readmission, with the association growing stronger as ADI increased. Conversely, we found a negative association with ADI and high-costs. (High ADI Group OR: 0.80 [95% CI 0.70, 0.91]). Patients in the high SES group had higher healthcare contact days during the 90-day follow-up period, with a median of 16 visits (IQR 8, 23), compared to 13 visits (IQR 6, 22) in the middle SES group and 10 visits (IQR 5, 20) in the low SES group (p<0.001). Socioeconomic disparities significantly influence the outcomes of primary shoulder arthroplasty, as indicated by higher readmission rates for low SES patients. Notably, our analysis shows a strong, independent association between ADI and readmission. Moreover, patients with higher SES incurred greater costs within a 90-day period potentially due to increased resource utilization.

Using Quality Improvement to Optimize Blood Product Utilization in a Pediatric Cardiac Catheterization Laboratory.

Packed red blood cells (PRBC) are frequently ordered for cardiac catheterization procedures, which increases resource utilization and patient charges. We applied the Plan-Do-Study-Act (PDSA) principle in order to optimize the ordering of PRBC for pediatric cardiac procedures and reducing charges. Our primary aim was to increase adherence to ordering guidelines to greater than 97%, with a global aim to reduce resource utilization. The existing PRBC ordering guidelines were revised and procedure reports were updated to include administration of PRBC. The rate of pre-procedure PRBC orders, adherence to the new protocol guidelines, presence of documentation and rate of blood transfusion within 24h post-procedure, frequency of emergency release blood during a procedure were monitored. During our study period, there was an increased adherence from 86 to 100% adherence following implementation of the updated guidelines. With improved adherence, we decreased PRBC ordering and hospital charges to the patient in low-risk cardiac catheterization procedures, without an increase in blood transfusions.

Attendance, Wellness, and Engagement: The AWE Model of Workplace Satisfaction.

The Attendance, Wellness, and Engagement (AWE) Model of Workplace Satisfaction is an innovative approach to promoting a sustainable, healthy, and engaged workforce. Implemented at Louis Brier Home and Hospital, the AWE Model encapsulates a people strategy aimed at nurturing a supportive and fulfilling work environment. Attendance promotion is accomplished by acknowledging absences while providing a comprehensive support system to address personal challenges faced by healthcare workers. The wellness component is underscored by increasing resource utilization, offering on-site health services, and cultivating social groups to enhance holistic well-being. Additionally, engagement is characterized by staff recognition rituals, community-building initiatives, and celebratory events. Importantly, this article presents a compelling position that the AWE Model creates a positive impact on reducing absenteeism, enhancing staff satisfaction, and transforming organizational culture. As health leaders grapple with workforce challenges, the AWE Model serves as a pragmatic framework to cultivate environments where employees regularly attend work healthy and engaged.

Independent Risk Factors and Economic Burden Associated With Delayed Extubation Following Pediatric Liver Transplantation.

Successful early extubation (EE) after liver transplant (LT) has been shown to reduce intensive care unit (ICU) and hospital length of stay and infectious, vascular, and sedation-related complications in adults. EE may not always be feasible in children, and many may require prolonged mechanical ventilation. Limited data exists regarding the candidacy of EE, risk factors, consequences, and hospital costs of delayed extubation (DE) in pediatric LT. We conducted a retrospective review to investigate predictive factors and associated costs of EE and DE in infants and children after orthotopic LT at our institution between 2011 and 2021. Of 338 LT (median age 39 months, 54% females), 246 (73%) had EE (within 24 h of LT), while 27% had DE. Age < 1 year (p = 0.0019), diagnosis of biliary atresia (0.02), abnormal pre-LT echocardiogram (0.02), and patients with ongoing hospital admission before LT (0.0001) were independently associated with DE. Hospital costs were significantly (∼3-fold) higher (p < 0.0001) in the DE group. In addition, factors associated with increased total hospital costs were age < 1 year and hospitalization before LT. EE post-LT is feasible and merits a trial. The prevalence of DE though modest is associated with increased resource utilization and hospital costs. Children who can be extubated early and those at risk for DE can be identified pre-operatively for optimal planning and allocation of resources.

Optimized irrigation practices with fertilizer utilization strategies to improve photo-fluorescence efficiency, vascular bundles and maize production in semi-arid regions

The mechanism of irrigation models combined with fertilizer utilization strategies under the biodegradable film mulching could greatly promote crop photosynthesis, vascular bundles structure; resource utilization and maize production are unclear in semi-arid areas. Unfortunately, this mechanism provides a scientific basis for improving irrigation and fertilizer utilization. A field study was carried out during 2021–2022 years. Seven treatments were established: two nitrogen levels: low-N (150 kg N ha−1) and high-N (300 kg N ha−1) combined with three different irrigation models: drip irrigation (DI), ridge irrigation (RI) and border irrigation (BI) under the biodegradable film mulching with (CK) treatment have no irrigation, fertilizer and mulching. Our results revealed that DIH treatment considerably increased soil water storage, enhanced photosynthesis rate (Pn) of maize by mainly to facilitate stomatal opening compared to the rest of all treatments. In addition, it also enhances the differentiation of the vascular bundle system and maintains its post silk function under better environmental conditions, greatly improving nitrogen storage in soil and plants, and enhancing maize productivity. DIH and RIH treatments significantly increased net photosynthesis rate (Pn), stomatal conductance (Gs), transpiration rate (Tr), maximum quantum yield of PSII photochemistry (Fv/Fm), and effective quantum yield of PSII photochemistry (ΦPSII), photochemical quenching (qP), non-photochemical quenching (NPQ), and yield were observed, but evapotranspiration (ET) decreased at different growth stages. The results showed that DIH treatment was an effective tillage strategy, which increased biomass yield by 32.6 %, grain yield by 46.0 %, water use efficiency (WUE) by 46.2 %, and nitrogen use efficiency (NUE) by 86.4 % compared to other treatments. Given these results, thus we recommend the drip irrigation combined with a high-N level under a biodegradable film mulching increase photo-fluorescence efficiency, maize production and resource utilization efficiency in semi-arid regions.

Feasibility Study of Marigold as Intercrop in Drumstick Cultivation for Interim Income Generation

Drumstick (Moringa oliefera Lam.) PKM-1(Annual) is an important perennial vegetable crop present cultivating in India not only as vegetable and for moringa leaf powder. India is the main producer of drumstick in the world because higher contribution is due to favourable temperature during flowering and fruit development stages. Even though encountered several production hurdles resulting greater yield loss due to existing climatic conditions during fruit developmental stages. But this crop gave them perfect assurance in giving good income in an average about 2-3 Lakhs rupees over a period of 2 years. Based on weather conditions farmers were being come forwarded by training programmes conducted by our KVK from 2017-till date, we have documented the farmers results by growing intercrops mainly marigold crop. Which gives good yield and good income to the farmers before pod formation of drumstick. Marigold is 110 days crop where drumstick gives first yield after 7 months. In between marigold crop not only giving accountable benefits to the farmers and it reduces the nematode problems in the field as well explained by Steiner, 1941 Marigold (Tagetes erecta) is one of the most widely studied plant genera due to its allelopathic potential against PPNs. Marigolds’ repressive impact on nematodes has been documented for over 50 years and some studies said that spiral (Helicotylenchus multicinctus) nematodes are responsible for yield losses of 30–60% in many banana-producing countries in the tropics (Davide, 1995, Speijer and Kajumba, 1996, Kashaija et al., 2004) . The partner crops of the intercropping system share space, water, and nutrients making the most efficient utilization of available resources (Xu et al., 2020). Earlier farmers were paddy growers where they didn’t see profits by selling paddy in IKP centers or as individual sales. So We conducted trails in Huzurabad Division and Errabelli villages of Karimnagar District (Old Karimnagar District), got successful results by doubling the farmer’s income. Raised marigold seedlings in KVK during June month and supplied to the farmers. Transplanted marigold and Drumstick in July month with 60cm x60cm and Drumstick spacing was 3mx3m where 444 plants were accommodated in one acre area, Intercropping is an agricultural system that improves land use efficiency through simultaneous cultivation of more different crops in the same field using functional complementarity to increase resources utilization, increase the productivity of crops and nutrient-use efficiency (Chifflot et al., 2006; Zhan and Li, 2003) Even went through many hurdles in marigold and drumstick crops in rainy season due to paddy growing soils and water logging conditions. In this, observed positive effects on soil carbon and nitrogen of intercropping found in previous intercropping studies (Makumba et al., 2007), [1]. Results are as follows we harvested 36.80Q per acre in 8 pickings and after drumstick pods harvested from February up to June 134 pods per tree. It shows C: B ratio as drumstick (PKM1) contribution 1:11.7 and marigold (Tanindo (Yellow and Orange) contribution 1:4.3 to the farmers. The demonstration plots reported that yield of marigold and drumsticks are 38.6Q and 49.58Q per acre. Farmers got good income Rs. 2, 74,160.00 through two crops in one acre area with Marigold as inter crop in Drumstick plantation. Based on this success story of the farmers, NABARD bank started giving loans to the farmers and they published this success story in their credit linkage plan 2018-19.

Hybrid group opportunistic maintenance strategy for offshore wind turbines considering maintenance resource allocation in harsh working environments

The intelligent maintenance of offshore wind turbines (OWTs) is vital for sustainable energy generation and utilization, as harsh working environments generally limit their application. As offshore wind farms expand into deeper waters, operational and maintenance costs continue to increase. Therefore, devising targeted maintenance strategies for offshore wind farms is crucial. Existing strategies often overlook the role of maintenance resources in controlling the operational and maintenance costs. To address this problem, this study proposes a hybrid group opportunistic maintenance strategy to implement resource allocation-based maintenance for OWTs under dynamic conditions. First, a failure rate model based on the life decline and system coupling is proposed to capture the evolution of failures in OWTs in harsh working environments, thereby enhancing the accuracy of fault maintenance. Subsequently, a hybrid linear and nonlinear deterioration model is proposed to identify the different deterioration trends owing to the various sensitivities of diverse components to harsh working environments, which enhances the precision of degradation maintenance. In addition, a maintenance resource allocation model is proposed to allocate resources in advance, thereby increasing resource utilization and reducing maintenance costs. A multi-objective optimization approach is proposed to maximize the mean maintenance level (MML) and minimize the per unit cycle expected maintenance cost rate (PECR) by considering harsh working environments. Finally, the nutcracker optimization algorithm is introduced into the maintenance model to generate the optimal solution in a multi-objective manner. The results show that the maintenance model is feasible and effective for practical adaptive maintenance applications of OWTs in harsh working environments in practical applications.

Efficient resource allocation in cloud computing environments using AI-driven predictive analytics

This paper proposes an innovative AI-driven approach for efficient resource allocation in cloud computing environments using predictive analytics. The study addresses the critical challenge of optimizing resource utilization while maintaining high quality of service in dynamic cloud infrastructures. A hybrid predictive model combining XGBoost and LSTM networks is developed to forecast workload patterns across various time horizons. The model leverages historical data from a large-scale cloud environment, encompassing 1000 servers and over 52 million data points. A dynamic resource scaling algorithm is introduced, which integrates the predictive model outputs with real-time system state information to make proactive allocation decisions. The proposed framework incorporates advanced techniques such as workload consolidation, resource oversubscription, and elastic resource pools to maximize utilization efficiency. Experimental results demonstrate significant improvements in key performance indicators, including increasing resource utilization from 65% to 83%, reducing SLA violation rates from 2.5% to 0.8%, and enhancing energy efficiency, with PUE improving from 1.4 to 1.18. Comparative analysis shows that the proposed model outperforms existing prediction accuracy and resource allocation efficiency methods. The study contributes to the field by presenting a comprehensive, AI-driven solution that addresses the complexities of modern cloud environments and paves the way for more intelligent and autonomous cloud resource management systems.

K-medoid clustering containerized allocation algorithm for cloud computing environment

Load balancing is critical for container-based cloud computing environments for several reasons. A lack of appropriate load balancing techniques could result in a decrease in performance and possible service interruptions as some nodes get overloaded, while others are left underutilized. Cloud service providers can reduce latency and boost system performance by strategically placing containers using clustering algorithms. These techniques aid in efficiently using resources and load balancing by clustering related containers together according to their shared attributes. Clustering strategies are effective in allocating and controlling resources to meet the demands of a changing workload. Algorithms for clustering combine related workloads or containers into clusters, improving performance isolation and maximizing resource usage. One popular methodology for data clustering is the K-Medoid Clustering Algorithm. It is especially helpful when working with categorical data or when the dataset contains outliers. K-medoids is an unsupervised clustering approach where the core of the cluster is made up of data points known as “medoids.” A medoid is a location in the cluster whose total distance to every object in the cluster—also known as its dissimilarity—is as small as possible. Any appropriate distance function may be used, such as the Manhattan distance, the Euclidean distance, or another one. Thus, by choosing K medoids from our data sample, the K-medoids method splits the data into K clusters. This work presents the K-Medoid clustering technique for containers, which can enhance load balancing, decrease resource execution times, and increase resource utilization rates all at the same time. The results of the experiment show that the proposed method outperforms MACO and FCFS in terms of throughput by about 70% when number of cloudlets increased. The relative improvement of execution time of the proposed K-medoid algorithm w.r.t FCFS is about 50%.

Targeted preparation for hydrocarbons through catalytic hydrodeoxygenation of co-pyrolysis bio-oil using bimetal-loaded Nb2O5 catalyst

The co-pyrolysis of industrial enzymatic hydrolysis of lignin residue with waste plastic to produce bio-oil can increase resource utilization and reduce pollution. However, the high concentration of oxygenated compounds, particularly phenolic compounds, limits the large-scale application of crude bio-oil. Bimetallic-loaded bifunctional catalysts have been prepared by loading different transition metals and Ru onto Nb2O5. We explored their performances in crude co-pyrolysis bio-oil hydrodeoxygenation (HDO). Compared to other transition metals, the selectivities for aromatic hydrocarbons, phenolic compounds, and other oxygenated compounds in the upgraded oil obtained by the catalytic HDO of Ni and Ru bimetallic-loaded Nb2O5 (Ni-Ru@Nb2O5) was the lowest, whereas the aliphatic hydrocarbons selectivity was the highest. The selectivities for other oxygenated compounds, phenolic compounds, and aromatic hydrocarbons in the upgraded oil decreased as the Ni loading increased, whereas the aliphatic hydrocarbons selectivity increased. Ni-Ru@Nb2O5 catalyzed HDO of crude bio-oil showed a maximum selectivity of 86.30 % for hydrocarbons in the upgraded oil at a reaction time of 2 h and a reaction temperature of 280 °C. The degree of deoxygenation of upgraded oil reached 68.87 % under optimal conditions, and the molar ratio of H-to-C was increased from 1.31 (crude bio-oil) to 2.37 (upgraded oil). Ni-Ru@Nb2O5 is an effective catalyst method for the HDO of crude bio-oil.

Fluoroamphiphilic polymers exterminate multidrug-resistant Gram-negative ESKAPE pathogens while attenuating drug resistance.

ESKAPE pathogens are a panel of most recalcitrant bacteria that could "escape" the treatment of antibiotics and exhibit high incidence of drug resistance. The emergence of multidrug-resistant (MDR) ESKAPE pathogens (particularly Gram-negative bacteria) accounts for high risk of mortality and increased resource utilization in health care. Worse still, there has been no new class of antibiotics approved for exterminating the Gram-negative bacteria for more than 50 years. Therefore, it is urgent to develop novel antibacterial agents with low resistance and potent killing efficacy against Gram-negative ESKAPE pathogens. Herein, we present a class of fluoropolymers by mimicking the amphiphilicity of cationic antimicrobial peptides. Our optimal fluoroamphiphilic polymer (PD45HF5) displayed selective antimicrobial ability for all MDR Gram-negative ESAKPE pathogens, low resistance, high in vitro cell selectivity, and in vivo curative efficacy. These findings implied great potential of fluoroamphiphilic cationic polymers as promising antibacterial agents against MDR Gram-negative ESKAPE bacteria and alleviating antibiotic resistance.

Readmission Outcomes in Kidney Transplant Recipients With and Without Delayed Graft Function

Delayed graft function (DGF) is a common post-transplant event associated with increased resource utilization. As a center with experience in DGF, we aimed to assess differences in readmissions and post-transplant outcomes between patients with and without DGF. This was a retrospective review of deceased donor kidney transplant recipients at Mayo Clinic Arizona between 2015 and 2020. Recipients with at least one early readmission following kidney transplantation were included in the study. Two groups were identified: (1) recipients with DGF who required early readmission and (2) recipients without DGF who required early readmission. Among recipients with DGF, 43.9% (n = 405) required early readmission compared to 29.1% (n = 179) without DGF (P < .0001). There were no differences in the initial hospital length of stay (P = .08), and most recipients in both groups only required a single readmission (61.7% vs 72.1%, P = .02). Recipients with DGF were more likely to have ≥2 readmissions (P = .02) and a higher total readmission rate (P = .006). Recipients with DGF who required readmission also required more outpatient clinic visits (P = .003). When comparing recipients with and without DGF who required readmission, there were no differences in patient (P = .22) or death-censored (P = .72) graft survival. When comparing patients with and without DGF requiring one versus ≥2 readmissions, there were no differences in patient survival (P = .15), however patients with DGF and ≥2 readmissions had lower death-censored graft survival (P = .001). Recipients with DGF are at increased risk of readmission. Transplant center-level changes to reduce readmissions and infections could have an important impact on DGF outcomes.

Management and Clinical Outcomes of Neonatal Hypothermia in the Newborn Nursery.

Neonatal hypothermia has been shown to be commonly detected among late preterm and term infants. In preterm and very low birth weight infants, hypothermia is associated with increased morbidity and mortality. Little is known about the clinical interventions and outcomes in hypothermic late preterm and term infants. This study fills this gap in the evidence. Single-center retrospective cohort study using electronic health record data on infants ≥35 weeks' gestation admitted to a newborn nursery from 2015 to 2021. Hypothermia was categorized by severity: none, mild (single episode, 36.0-36.4°C), and moderate or recurrent (<36.0°C and/or 2+ episodes lasting at least 2 hours). Bivariable and multivariable logistic regression examined associations between hypothermia and interventions or outcomes. Stratified analyses by effect modifiers were conducted when appropriate. Among 24 009 infants, 1111 had moderate or recurrent hypothermia. These hypothermic infants had higher odds of NICU transfer (adjusted odds ratio [aOR] 2.10, 95% confidence interval [CI] 1.68-2.60), sepsis evaluation (aOR 2.23, 95% CI 1.73-2.84), and antibiotic use (aOR 1.73, 95% CI 1.15-2.50) than infants without hypothermia. No infants with hypothermia had culture-positive sepsis, and receipt of antibiotics ≥72 hours (surrogate for culture-negative sepsis and/or higher severity of illness) was not more common in hypothermic infants. Hypothermic infants also had higher odds of blood glucose measurement and hypoglycemia, slightly higher percent weight loss, and longer lengths of stay. Late preterm and term infants with hypothermia in the nursery have potentially unnecessary increased resource utilization. Evidence-based and value-driven approaches to hypothermia in this population are needed.

An ecological population study of lampreys in the Lotka-Volterra tertiary trophic level based on sex differences

This study examines the adaptive changes in sex ratio on animal populations and its effects on ecosystem stability and interactions. The effects of sex ratio dynamics on the ecosystem were analyzed through mathematical modeling using lampreys as the study population. In this study, a two-stage tertiary trophic Lotka-Volterra mathematical model of ecological dynamics was developed, taking into account food preferences, ecological roles, and sex interactions of lampreys. The simulation results showed that a rapid adjustment in sex ratio led to a reduction in environmental loads, which triggered an early rebound increase in lampreys and predator populations. To assess the ability of lampreys populations to adjust sex ratios, this study used a combination of fuzzy integrated evaluation and hierarchical analysis methods. The results of the study revealed the importance of sex-adaptive traits for population development, including increased resource utilization efficiency and population fitness, as well as decreased reproductive success and reduced genetic diversity. Based on the RRT (resistance, resilience and temporal stability) method, this study constructed an ecosystem stability assessment model and investigated the effects of changes in the sex ratio of lampreys on ecosystem stability. The experimental results showed that ecosystem stability was highest when the sex ratio was 0.6 and lowest when the sex ratio was 0.2. Variation in sex ratio may affect the behavior and abundance of lampreys, thereby directly influencing their interactions with other species. Variability in sex ratio provides more resources for predators and parasites of lampreys, promotes their growth, and positively affects ecosystem balance. Simulation results showed that the model was highly stable under different interactions. In summary, this study reveals the important effects of sex ratio on ecosystem stability and interactions, and provides important insights into the understanding of adaptive evolutionary traits of species, ecosystem stability and ecological interactions.

DLRover-RM: Resource Optimization for Deep Recommendation Models Training in the Cloud

Deep learning recommendation models (DLRM) rely on large embedding tables to manage categorical sparse features. Expanding such embedding tables can significantly enhance model performance, but at the cost of increased GPU/CPU/memory usage. Meanwhile, tech companies have built extensive cloud-based services to accelerate training DLRM models at scale. In this paper, we conduct a deep investigation of the DLRM training platforms at AntGroup and reveal two critical challenges: low resource utilization due to suboptimal configurations by users and the tendency to encounter abnormalities due to an unstable cloud environment. To overcome them, we introduce DLRover, an elastic training framework for DLRMs designed to increase resource utilization and handle the instability of a cloud environment. DLRover develops a resource-performance model by considering the unique characteristics of DLRMs and a three-stage heuristic strategy to automatically allocate and dynamically adjust resources for DLRM training jobs for higher resource utilization. Further, DLRover develops multiple mechanisms to ensure efficient and reliable execution of DLRM training jobs. Our extensive evaluation shows that DLRover reduces job completion times by 31%, increases the job completion rate by 6%, enhances CPU usage by 15%, and improves memory utilization by 20%, compared to state-of-the-art resource scheduling frameworks. DLRover has been widely deployed at AntGroup and processes thousands of DLRM training jobs on a daily basis. DLRover is open-sourced and has been adopted by 10+ companies.