Process Metrics Research Articles

Recommendations for Centers Performing Pediatric Heart Surgery in the United States.

Care and outcomes for the more than 40,000 patients undergoing pediatric and congenital heart surgery in the United States annually are known to vary widely. While consensus recommendations have been published across numerous fields as one mechanism to promote a high level of care delivery across centers, it has been more than two decades since the last pediatric heart surgery recommendations were published in the United States. More recent guidance is lacking, and collaborative efforts involving the many disciplines engaged in caring for these children have not been undertaken to date. The present initiative brings together professional societies spanning numerous care domains and congenital cardiac surgeons, pediatric cardiologists, nursing, and other healthcare professionals from diverse programs around the country to develop consensus recommendations for United States centers. The focus of this initial work is on pediatric heart surgery, and it is recommended that future efforts focus in detail on the adult congenital population. We describe the background, rationale, and methodology related to this collaborative effort, and recommendations put forth for Essential Care Centers (essential services necessary for any program), and Comprehensive Care Centers (services to optimize comprehensive and high-complexity care), encompassing structure, process, and outcome metrics across 14 domains.

Implementation evaluation ofa medical student-led intervention to enhance students' engagement with research: Findings and lessons learned.

Medical colleges globally have student organizations that serve to enable students' involvement in research. However, details of their approach and activities are seldom published to serve as learning for student organizations in other settings. The Student Research Forum (SRF), a student organization based at a private medical school in Pakistan aims to facilitate students in acquiring research skills. Following the observation of a downward trajectory of student initiative and interest, SRF leadership restructured the organization and improve its impact. This study describes the development and implementation evaluation of the interventions. The operational framework was revised using the Theory of Change by the core group. Major interventions included enhanced social media and outreach coordination, research workshops, journal clubs, and mentorship to increase research output, mentorship opportunities, and knowledge of medical research; ultimately improving quality in research. The outcomes generated over the course of the study's duration from July 2019 to September 2021 were analyzed using the process metrics of reach, adoption, and efficacy. As a result of the interventions, SRF expanded its reach by conducting a total of 41 events during the duration of the study, facilitated by social media growth on each of SRF's online platforms, with a 300% increase in followers on Facebook, and a nationwide network of 91 student ambassadors. An annual workshop series taught research skills to more than 3800 participants. Students leading their own events, SRF featuring international speakers, and the abstracts submitted to SRF's annual conference, along with the conference's reach of 10,000 students, are seen as improvements in the ToC-informed interventions' adoption. The efficacy of the interventions manifested as the REACH program allocated 56 research projects to vetted applicants. The applied interventions have accelerated SRF's progress towards achieving its long-term outcome of increased quality in research as translated by increased research output quantity, mentorship, and knowledge of medical research. Further evaluation is required to assess the success of the ToC. As SRF continues to grow, a continued analysis of the implementation outcomes is imperative to gauge its effectiveness.

نموذج مقترح لإدارة المخاطر في الشركات ذات المشروعات المتعددة

Recently, the importance of risk management has increased in the field of project management, especially software projects. The importance of risk management is because it has direct effects on the success of the project. Therefore, software organizations face a major challenge in achieving effective and efficient risk management. Therefore, measuring risks in software development is a key activity to be undertaken in risk management. Risk measurement is also one of the important topics that need more effort, especially in a work environment with multiple software projects. A multi-project environment involves multiple projects sharing the same available resources. This research aims to conduct a literature review to discover patterns or trends in software development to measure project risks and to reveal aspects of the measurement process, risk indicators, and various metrics for software projects. This research also aims to present a proposed framework for assessing risks in a multi-project work environment with shared resources for different levels in organizations with different capabilities.

Downscaling Daily Satellite-Based Precipitation Estimates Using MODIS Cloud Optical and Microphysical Properties in Machine-Learning Models

This study proposes a method for downscaling the spatial resolution of daily satellite-based precipitation estimates (SPEs) from 10 km to 1 km. The method deliberates a set of variables that have close relationships with daily precipitation events in a Random Forest (RF) regression model. The considered variables include cloud optical thickness (COT), cloud effective radius (CER) an cloud water path (CWP), derived from MODIS, along with maximum and minimum temperature (Tx, Tn), derived from CHIRTS. Additionally, topographic features derived from ALOS-DEM are also investigated to improve the downscaling procedure. The approach consists of two main steps: firstly, the RF model training at the native 10 km spatial resolution of the studied SPEs (i.e., IMERG) using rain gauge observations as targets; secondly, the application of the trained RF model at a 1 km spatial resolution to downscale IMERG from 10 km to 1 km over a one-year period. To assess the reliability of the method, the RF model outcomes were compared with the rain gauge records not considered in the RF model training. Before the downscaling process, the CC, MAE and RMSE metrics were 0.32, 1.16 mm and 6.60 mm, respectively, and improved to 0.48, 0.99 mm and 4.68 mm after the downscaling process. This corresponds to improvements of 50%, 15% and 29%, respectively. Therefore, the method not only improves the spatial resolution of IMERG, but also its accuracy.

Race-Based Differences in ST-Segment-Elevation Myocardial Infarction Process Metrics and Mortality From 2015 Through 2021: An Analysis of 178 062 Patients From the American Heart Association Get With The Guidelines-Coronary Artery Disease Registry.

Systems of care have been developed across the United States to standardize care processes and improve outcomes in patients with ST-segment-elevation myocardial infarction (STEMI). The effect of contemporary STEMI systems of care on racial and ethnic disparities in achievement of time-to-treatment goals and mortality in STEMI is uncertain. We analyzed 178 062 patients with STEMI (52 293 women and 125 769 men) enrolled in the American Heart Association Get With The Guidelines-Coronary Artery Disease registry between January 1, 2015, and December 31, 2021. Patients were stratified into and outcomes compared among 3 racial and ethnic groups: non-Hispanic White, Hispanic White, and Black. The primary outcomes were the proportions of patients achieving the following STEMI process metrics: prehospital ECG obtained by emergency medical services; hospital arrival to ECG obtained within 10 minutes for patients not transported by emergency medical services; arrival-to-percutaneous coronary intervention time within 90 minutes; and first medical contact-to-device time within 90 minutes. A secondary outcome was in-hospital mortality. Analyses were performed separately in women and men, and all outcomes were adjusted for age, comorbidities, acuity of presentation, insurance status, and socioeconomic status measured by social vulnerability index based on patients' county of residence. Compared with non-Hispanic White patients with STEMI, Hispanic White patients and Black patients had lower odds of receiving a prehospital ECG and achieving targets for door-to-ECG, door-to-device, and first medical contact-to-device times. These racial disparities in treatment goals were observed in both women and men, and persisted in most cases after multivariable adjustment. Compared with non-Hispanic White women, Hispanic White women had higher adjusted in-hospital mortality (odds ratio, 1.39 [95% CI, 1.12-1.72]), whereas Black women did not (odds ratio, 0.88 [95% CI, 0.74-1.03]). Compared with non-Hispanic White men, adjusted in-hospital mortality was similar in Hispanic White men (odds ratio, 0.99 [95% CI, 0.82-1.18]) and Black men (odds ratio, 0.96 [95% CI, 0.85-1.09]). Race- or ethnicity-based disparities persist in STEMI process metrics in both women and men, and mortality differences are observed in Hispanic White compared with non-Hispanic White women. Further research is essential to evolve systems of care to mitigate racial differences in STEMI outcomes.

Features of Cheyne-Stokes respiration automatically extracted from CPAP airflow signal raw data: Identification of discriminating features to detect heart failure

BackgroundContinuous positive airway pressure (CPAP) telemonitoring data is collected daily from millions of sleep apnea patients. This huge amount of data can be used for the early detection of treatment failures and could reveal incident acute or chronic cardiovascular events. However, the available automatically computed metrics do not fully characterize sleep-disordered breathing. ObjectiveTo find methods to process metrics characterizing Cheyne-Stokes respiration (CSR) that are able to discriminate between CSR related to heart failure (HF) and CSR related to other underlying conditions. MethodsThe raw airflow signals recorded by CPAP devices were analysed. Change point detection methods were used to isolate each respiratory cycle and CSR cycle. Simple algorithms were implemented to extract key features from the signal. Binary logistic regression was performed to identify the characteristics of the CSR airflow signal that is associated with the presence of underlying HF. ResultsLonger CSR cycles, a longer CSR episode, a greater variation in the amplitude of inspiration, a smaller increase in big breaths, a lower inter-cycle variability and a shorter breath duration were features associated with the occurrence of CSR in the context of HF. ConclusionThe proposed automated computation of CSR characteristics presents a novel tool to include in the CPAP software or remote monitoring platforms for monitoring patients’ treatment and comorbidities; and a step towards cross-disciplinary patient management.

Considerations for the Scale‐up of in vitro Transaminase‐Catalyzed Asymmetric Synthesis of Chiral Amines

AbstractIn vitro transaminase catalyzed asymmetric synthesis is not a new phenomenon in the biocatalytic toolbox. Our group published a review on process consideration for these reactions in 2011, in which process metrics and the unfavorable reaction equilibrium was deduced to be the main bottlenecks at the time. Now, a decade later, this has been solved by different process methods presented in this review, with the development of enzymatic cascades one of the main solutions. Today, the new bottleneck is the downstream processing, due to the complexity of transaminase reactions, in which a ketone and an amine as substrates and a new ketone and a new amine as products. How to recover the produced amine from the resulting product mixture is considered and will be the key challenge for scale‐up of such systems in the near future.

BPEL process defects prediction using multi-objective evolutionary search

Web services are becoming increasingly popular technologies for modern organizations to improve their cooperation and collaboration through building new software systems by composing pre-built services. Such services are typically composed and executed through BPEL (Business Process Execution Language) processes. Like any other software artifact, such processes are frequently changed to add new or modify existing functionalities or adapt to environmental changes. However, poorly planned changes may introduce BPEL process design defects known as anti-patterns or defects. The presence of defects often leads to a regression in software quality. In this paper, we introduce an automated approach to predict the presence of defects in BPEL code using Multi-Objective Genetic Programming (MOGP). Our approach consists of learning from real-world instances of each service-based business process defect (i.e., anti-pattern) type to infer prediction rules based on the combinations of process metrics and their associated threshold values. We evaluate our approach based on a dataset of 178 real-world business processes that belong to various application domains, and a variety of BPEL process defect types such as data flow and portability defects. The statistical analysis of the achieved results shows the effectiveness of our approach in identifying defects compared with state-of-the-art techniques with a median accuracy of 91%.

Residence Time Section Evaluation and Feasibility Studies for One-Column Simulated Moving Bed Processes (1-SMB)

The simulated moving bed (SMB) is a well-established, fully continuous process for chromatographic separation of difficult tasks with overlapping peaks, but it is relatively complex. The 1-SMB, which uses only one column but includes residence time zones to preserve concentration profiles, is a simpler semi-continuous alternative. This work examines the possible design of these residence time zones. Simulation studies were conducted to investigate the dependence of process metrics, such as purity, yield, productivity, and eluent consumption, on fluid dynamics. No deterioration in purity was observed, and the other variables remained constant over a wide range of axial dispersion before decreasing sharply. Pilot-scale experiments were conducted with various devices, including coiled flow inverters, eluate recycling devices, packed columns, and tank arrangements, to validate possible apparatus implementations with fluid dynamic measurements. It was demonstrated that the 1-SMB offers similar performance to the 4-SMB, albeit with reduced yield and lower apparatus complexity.

Service-oriented model-based fault prediction and localization for service compositions testing using deep learning techniques

As service-oriented computing systems become more buoyant and complex, the occurrence of faults dramatically increases. Fault prediction plays a crucial role in the service-oriented computing paradigm, aiming to reduce testing cost while maximizing testing quality to utilize testing resources effectively and increase the reliability of service compositions. Although various fault prediction techniques were considered in software testing, service-oriented systems were less fortunate, in which most of the studies have focused on single web services testing rather than service compositions. Moreover, mainly the detection of faulty/non-faulty services was addressed, ignoring the estimate of faults count, their severity, as well as predicting when and where such faults would occur. In this paper, a multilateral model-based fault prediction and localization approach is proposed using deep learning techniques for web service compositions testing rather than single web service testing, which uniquely predicts not only faulty services, but also their count and severity level, location of faults, and time at which faults would occur. Three deep learning models are investigated: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and a proposed hybrid model based on both CNN and RNN. The proposed approach is language-independent, as it adopts process metrics rather than code metrics to overcome the code unavailability concern of services. The experimental analysis adopted main performance metrics on multiple public datasets to evaluate its efficiency and effectiveness. The results indicated that the hybrid CNN_RNN model achieves an average accuracy range of 84%–95.7%, where the RNN and CNN models individually achieve 75%–90% and 70%-79.3% respectively. Thus, the hybrid model increases the accuracy level by 5%–10% and 15%–20%, while achieving the least mean square error of 30% and 60% compared to the RNN and CNN models respectively. In terms of time, the RNN model consumes less average time as of 30–50 ms for the different datasets of variant sizes compared to the CNN and hybrid CNN_RNN models that consume 79–102 and 177–224 ms respectively. Thus, RNN model consumes around 50%–80% less time than those of the CNN and hybrid models respectively.

A Regional Approach to Hospital-Based Violence Intervention Programs Through LOV.

Community violence is an underaddressed public health threat. Hospital-based violence intervention programs (HVIPs) have been used to address the root causes of violence and prevent reinjury. In this article, we describe the methodology of the St Louis Region-wide HVIP, Life Outside Violence (LOV) program, and provide preliminary process outcomes. Life Outside Violence mentors intervene following a violent injury to decrease risk of subsequent victimization and achieve goals unique to each participant by providing therapeutic counseling and case management services to patients and their families. Eligible patients are victims of violent injury between the ages of 8 and 24 years, who are residents of St Louis, Missouri, and present for care at a LOV partner adult or pediatric level I trauma hospital. Enrolled participants receive program services for 6 to 12 months and complete an individual treatment plan. In this article, we report LOV operational methodology, as well as process metrics, including program enrollment, graduation, and qualitative data on program implementation. From August 15, 2018, through April 30, 2022, 1750 LOV-eligible violently injured patients presented to a partner hospital, 349 were approached for program enrollment, and 206 consented to enroll in the program. During this pilot phase, 91 participants graduated from the LOV program and have process output data available for analysis. Life Outside Violence has been implemented into clinical practice as the first HVIP to influence across an entire region through partnership with multiple university and hospital systems. It is our hope that methods shared in this article will serve as a primer for organizations hoping to implement and expand HVIPs to interrupt community violence at the regional level.

Integration of the Program Evaluation Standards into an Evaluation Toolkit for a Transformative Model of Care for Mental Health Service Delivery

Background: Stepped Care 2.0 (SC2.0) is a transformative model of mental health service delivery. This model was created by Stepped Care Solutions (SCS), a not-for-profit consultancy that collaborates with governments, public service organizations, and other institutions that wish to redesign their mental health and addictions systems of care. The SC2.0 model is based on 10 foundational principles and 9 core components that can be flexibly adapted to an organization’s or community’s needs. The model supports groups to reorganize and deliver mental health care in an evidence-informed, person-centric way. SCS partnered with evaluators from the Centre for Health Evaluation and Outcome Sciences (CHÉOS) to create a toolkit that provides evaluation guidance. The toolkit includes a theory of change, guidance on selecting evaluation questions and designs, and an evaluation matrix including suggested process and outcome metrics, all of which can be tailored to each unique implementation of the SC2.0 model. The objective of this resource is to support organizations and communities to conduct high-quality evaluations for the purpose of continuous improvement (a core component of the model of care) and to assess the model’s impact. Purpose: The purpose of this paper is to discuss the integration of the program evaluation standards (PES) into an evaluation toolkit for SC2.0. Setting: In this paper, we describe the toolkit development, focusing on how the PES were embedded in the process and tools. We explore how the integration of the PES into the toolkit supports evaluators to enhance the quality of their evaluation planning, execution, and meta-evaluation. Intervention: Not applicable Research Design: Not applicable Data Collection and Analysis: Not applicable Findings: In this paper, we describe the toolkit development, focusing on how the PES were embedded in the process and tools. We explore how the integration of the PES into the toolkit supports evaluators to enhance the quality of their evaluation planning, execution, and meta-evaluation. Keywords: program evaluation standards; evaluation; mental health

Automated Drawing Process Metrics for Evaluating Cognitive Impairment from Human Drawing Behavior (S15.004)

<h3>Objective:</h3> To evaluate four novel tablet-based drawing assessments for detecting cognitive impairment (CI) using automated analysis of traditional and Boston Process Approach (BPA) features. <h3>Background:</h3> The digital Clock Drawing Task extracts cognitive- and motor-related features, enabling automatic CI detection in 3 minutes. Here, we applied the BPA to 4 other assessments to expand upon that capability. <h3>Design/Methods:</h3> Novel process-based metrics captured during drawing/writing of Trails A/B, Symbol Digit Modalities Test (SDMT), Spiral Tracing, and Pathfinding were analyzed in two independent datasets: (1) 170 cognitively normal (CN) subjects to calculate test-retest reliability of features, and (2) 1000 CN and cognitively impaired (CI) subjects to quantify their group differences. <h3>Results:</h3> Average reliability was good across features; SDMT: r=0.54 (s.d.=0.2, range=−0.02–0.92), Trails B: r=0.42 (s.d.=0.25, range=−0.19–0.87), Spiral Tracing: r=0.5 (s.d.=0.19, range=−0.07–0.78), Pathfinding: r=0.47 (s.d.=0.2, range=0.07–0.78). Trails: CN drew faster and spent less time in each circle (|T|’s&gt;2.8, p’s&lt;0.001); SDMT: CI spent less time thinking/pausing (T=−4.01, p&lt;0.0001) and more time writing (T=9.73, p&lt;0.001); Pathfinding: CI drew faster with higher acceleration (T=3.51, p&lt;0.0001) and drew less (T=−6.48, p&lt;0.0001); Spiral Tracing: CN deviated less (T=−2.88, p&lt;0.0001), had lower velocity (T=−5.22, p&lt;0.0001), and spent a larger portion of time drawing (T=11.57, p&lt;0.0001) with less variability (T=−3.83, p&lt;0.0001). <h3>Conclusions:</h3> Our results demonstrate the value of process-based metrics during neuropsychological tasks in distinguishing between CN and CI individuals with fair-to-good test-retest reliability, thereby identifying specific processes that can lead to actionable recommendations. <b>Disclosure:</b> Dr. Toro-Serey has received personal compensation for serving as an employee of Linus Health. Mr. Higgins has received personal compensation for serving as an employee of Infosys Ltd. Mr. Banks has received personal compensation for serving as an employee of Linus Health Inc. Dr. Jannati has received personal compensation for serving as an employee of Linus Health. Dr. Jannati has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Boston Children’s Hospital, Harvard Medical School. Dr. Gomes-Osman has received personal compensation for serving as an employee of Linus Health. Dr. Gomes-Osman has received personal compensation in the range of $500-$4,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Journal of Neurologic Physical Therapy. Dr. Pascual-Leone has received personal compensation for serving as an employee of Linus Health. Dr. Pascual-Leone has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Magstim. Dr. Pascual-Leone has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Neuroelectrics. Dr. Pascual-Leone has received personal compensation in the range of $500-$4,999 for serving as a Consultant for TetraNeuron. Dr. Pascual-Leone has received personal compensation in the range of $500-$4,999 for serving as a Consultant for MedRhythms. Dr. Pascual-Leone has received personal compensation in the range of $100,000-$499,999 for serving as an officer or member of the Board of Directors for Linus Health. Dr. Pascual-Leone has received personal compensation in the range of $5,000-$9,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Annals of Neurology. Dr. Pascual-Leone has stock in TI Solutions. The institution of Dr. Pascual-Leone has received research support from NIH. The institution of Dr. Pascual-Leone has received research support from NSF. The institution of Dr. Pascual-Leone has received research support from Brightfocus Foundation . Dr. Pascual-Leone has received intellectual property interests from a discovery or technology relating to health care. Dr. Pascual-Leone has received publishing royalties from a publication relating to health care. Dr. Bates has received personal compensation for serving as an employee of Linus Health. Dr. Bates has received personal compensation in the range of $10,000-$49,999 for serving as an officer or member of the Board of Directors for TMCity Foundation. Dr. Bates has received stock or an ownership interest from Linus Health, Inc.. Dr. Bates has received intellectual property interests from a discovery or technology relating to health care. Dr. Bates has received intellectual property interests from a discovery or technology relating to health care. Dr. Showalter has received personal compensation for serving as an employee of Linus Health. Dr. Showalter has stock in Linus Health. Dr. Tobyne has received personal compensation for serving as an employee of Linus Health. Dr. Tobyne has received personal compensation for serving as an employee of Charles River Analytics. Dr. Tobyne has received stock or an ownership interest from Charles River Analytics. The institution of Dr. Tobyne has received research support from National Institutes of Health. The institution of Dr. Tobyne has received research support from Department of Defense.

Impact of a Comprehensive ST‐Segment–Elevation Myocardial Infarction Protocol on Key Process Metrics in Black Americans

Impact of a Comprehensive ST‐Segment–Elevation Myocardial Infarction Protocol on Key Process Metrics in Black Americans

Relating process and outcome metrics for meaningful and interpretable cannulation skill assessment: A machine learning paradigm

Background and Objectives: The quality of healthcare delivery depends directly on the skills of clinicians. For patients on hemodialysis, medical errors or injuries caused during cannulation can lead to adverse outcomes, including potential death. To promote objective skill assessment and effective training, we present a machine learning approach, which utilizes a highly-sensorized cannulation simulator and a set of objective process and outcome metrics. Methods: In this study, 52 clinicians were recruited to perform a set of pre-defined cannulation tasks on the simulator. Based on data collected by sensors during their task performance, the feature space was then constructed based on force, motion, and infrared sensor data. Following this, three machine learning models– support vector machine (SVM), support vector regression (SVR), and elastic net (EN)– were constructed to relate the feature space to objective outcome metrics. Our models utilize classification based on the conventional skill classification labels as well as a new method that represents skill on a continuum. Results: With less than 5% of trials misplaced by two classes, the SVM model was effective in predicting skill based on the feature space. In addition, the SVR model effectively places both skill and outcome on a fine-grained continuum (versus discrete divisions) that is representative of reality. As importantly, the elastic net model enabled the identification of a set of process metrics that highly impact outcomes of the cannulation task, including smoothness of motion, needle angles, and pinch forces. Conclusions: The proposed cannulation simulator, paired with machine learning assessment, demonstrates definite advantages over current cannulation training practices. The methods presented here can be adopted to drastically increase the effectiveness of skill assessment and training, thereby potentially improving clinical outcomes of hemodialysis treatment.

A review of data-driven fault detection and diagnostics for building HVAC systems

With the wide adoption of building automation system, and the advancement of data, sensing, and machine learning techniques, data-driven fault detection and diagnostics (FDD) for building heating, ventilation, and air conditioning systems has gained increasing attention. In this paper, data-driven FDD is defined as those that are built or trained from data via machine learning or multivariate statistical analysis methods. Following this definition, this paper reviews and summarizes the literature on data-driven FDD from three aspects: process, systems studied (including the systems being investigated, the faults being identified, and the associated data sources), and evaluation metrics. A data-driven FDD process is further divided into the following steps: data collection, data cleansing, data preprocessing, baseline establishment, fault detection, fault diagnostics, and potential fault prognostics. Literature reported data-driven methods used in each step of an FDD process are firstly discussed. Applications of data-driven FDD in various HVAC systems/components and commonly used data source for FDD development are reviewed secondly, followed by a summary of typical metrics for evaluating FDD methods. Finally, this literature review concludes that despite the promising performance reported in the literature, data-driven FDD methods still face many challenges, such as real-building deployment, performance evaluation and benchmarking, scalability and transferability, interpretability, cyber security and data privacy, user experience, etc. Addressing these challenges is critical for a broad real-building adoption of data-driven FDD.

A point field driven approach to process metrics based on laser powder bed fusion additive manufacturing models and in situ process monitoring

The widespread adoption of additive manufacturing (AM) in different industries has accelerated the need for quality control of these AM parts. Some of the complex and labor-intensive challenges associated with qualification and certification of AM parts are addressed by modeling and monitoring process conditions. Quantifying melt-track process conditions remains a significant computational challenge due to the large-scale differential between melt pool and part volumes. This work explores a novel point field (PF) driven AM model-based process metric (AM-PM) approach for calculating melt track resolved process conditions with maximal computational speed. A cylindrical Ti-6Al-4V test article with 16 equiangular zones having varied process parameters was built. The melt-track resolved AM-PMs were calculated and mapped to porosity existence for the 5.8-million-point PF of the test article. AM-PMs were calculated in 6.5 min, ~ 665 × faster than a similarly sized finite element calculation. This approach enables efficient prediction, assessment, and adjustment of AM builds.Graphical abstract

Seasonal variation in blood pressure control across US health systems.

We aimed to characterize seasonal variation in US population-based blood pressure (BP) control and BP-related metrics and evaluate the association between outdoor temperature and BP control variation. We queried electronic health records (EHRs) from 26 health systems, representing 21 states, to summarize BP metrics by quarters of 12-month periods from January 2017 to March 2020. Patients with at least one ambulatory visit during the measurement period and a hypertension diagnosis during the first 6 months or prior to the measurement period were included. Changes in BP control, BP improvement, medication intensification, average SBP reduction after medication intensification across quarters and association with outdoor temperature were analyzed using weighted generalized linear models with repeated measures. Among 1 818 041 people with hypertension, the majority were more than 65 years of age (52.2%), female (52.1%), white non-Hispanic (69.8%) and had stage 1/2 hypertension (64.8%). Overall, BP control and process metrics were highest in quarters 2 and 3, and lowest in quarters 1 and 4. Quarter 2 had the highest percentage of improved BP (31.95 ± 0.90%) and average SBP reduction after medication intensification (16 ± 0.23 mmHg). Quarter 3 had the highest percentage of BP controlled (62.25 ± 2.55%) and lowest with medication intensification (9.73 ± 0.60%). Results were largely consistent in adjusted models. Average temperature was associated with BP control metrics in unadjusted models, but associations were attenuated following adjustment. In this large, national, EHR-based study, BP control and BP-related process metrics improved during spring/summer months, but outdoor temperature was not associated with performance following adjustment for potential confounders.

Abstract Number ‐ 277: Mobile Stroke Unit Direct to Angiosuite Process Metrics and Clinical Outcomes

Introduction Mobile Stroke Units (MSUs) speed treatment with tPA, but did not affect time from alert to puncture time for endovascular therapy (EVT) in the Benefits of Stroke Treatment Using a Mobile Stroke Unit (BEST‐MSU), a prospective multicenter controlled trial comparing MSU with standard EMS management. The lack of improvement in EVT treatment time could be because the CTA to identify large vessel occlusions (LVOs), and alerting of the EVT team, were delayed in most MSU patients until after Emergency Department (ED) arrival. The impact of identifying LVOs by imaging on the MSU enabling a direct (from MSU) to angiosuite (DTAS) protocol has yet to be studied. Methods We conducted a pre‐specified substudy of tPA‐eligible stroke patients with LVOs on computed tomography (CT) and/or CT angiography (CTA) who were enrolled into the MSU arm of the BEST‐MSU study. We compared alert to puncture time and other process metrics, mean utility‐weighted modified Rankin Scale (uw‐mRS) and functional independence (mRS 0–2) at 90 days, and rate of early neurologic recovery (30% improvement in NIHSS score) at 24 hours in patients who were managed by DTAS vs post‐ED arrival diagnosis and alerting. Results A total of 169 MSU patients with LVOs were identified; 22 in the DTAS group and 111 in the non DTAS group. Data were not available for 36 LVO patients. Baseline characteristics including age, sex, ethnicity, prestroke mRS, and initial NIHSS were comparable between the groups. 100.0% of patients in the DTAS group vs 90.1% in the non DTAS received tPA (p = 0.264). EVT was performed on 85.0% of patients in the DTAS group vs 77.1% in the non DTAS group (p = 0.634). DTAS group had a faster alert to puncture time (108.00 min [94.75,124.75.] vs 150.50 min [121.25, 179.00], p&lt; 0.001) and door to puncture time (37.00 min [25.50,62.75 vs 86.50 min [62.50,116.00], p&lt; 0.001). The mean score on the uw‐mRS at 90 days was 0.626 ±0.367 in the DTAS group and 0.660 ±0.382 in the non DTAS group and after adjustment for age, baseline NIHSS, premorbid functional status, prior stroke/TIA and site, no significant difference was observed; (p = 0.54). In an unadjusted analysis, early neurologic recovery (72.7% vs 67.6%, (OR = 0.634, 95% CI [0.46,3.82]) and functional independence (50.0% vs 51.4%, p = 1.000) were comparable between DTAS and non DTAS patients. Patients in the DTAS group were less likely to receive general anesthesia (36.4% vs 71.2%, p = 0.004). Rates of recanalization (77.3% vs73,4%, p = 0.947) and post procedural PH‐2 hemorrhage (4.5% vs 0.9%, p = 0.746) were similar in the DTAS and non DTAS group respectively. Conclusions In tPA‐eligible LVO stroke patients, DTAS management resulted in shorter alert to puncture (42 minutes) and door to puncture times (50 minutes) with similar safety and clinical outcomes compared with non DTAS management. MSU DTAS represents an optimized pathway for LVO patients triaged and treated in the field.

Improving Compliance With Institutional Performance on Train of Four Monitoring.

We performed a multistep quality improvement project related to neuromuscular blockade and monitoring to evaluate the effectiveness of a comprehensive quality improvement program based upon the Multi-institutional Perioperative Outcomes Group (MPOG) Anesthesiology Performance Improvement and Reporting Exchange (ASPIRE) metrics targeted specifically at improving train of four (TOF) monitoring rates. We adapted the plan-do-study-act (PDSA) framework and implemented 2 PDSA cycles between January 2021 and December 2021. PDSA Cycle 1 (Phase I) and PDSA Cycle 2 (Phase II) included a multipart program consisting of (1) a departmental survey assessing attitudes toward intended results, outcomes, and barriers for TOF monitoring, (2) personalized MPOG ASPIRE quality performance reports displaying provider performance, (3) a dashboard access to help providers complete a case-by-case review, and (4) a web-based app spaced education module concerning TOF monitoring and residual neuromuscular blockade. Our primary outcome was to identify the facilitators and barriers to implementation of our intervention aimed at increasing TOF monitoring. In Phase I, 25 anesthesia providers participated in the preintervention and postintervention needs assessment survey and received personalized quality metric reports. In Phase II, 222 providers participated in the preintervention needs assessment survey and 201 participated in the postintervention survey. Thematic analysis of Phase I survey data aimed at identifying the facilitators and barriers to implementation of a program aimed at increasing TOF monitoring revealed the following: intended results were centered on quality of patient care, barriers to implementation largely encompassed issues with technology/equipment and the increased burden placed on providers, and important outcomes were focused on patient outcomes and improving provider knowledge. Results of Phase II survey data was similar to that of Phase I. Notably in Phase II a few additional barriers to implementation were mentioned including a fear of loss of individualization due to standardization of patient care plan, differences between the attending overseeing the case and the in-room provider who is making decisions/completing documentation, and the frequency of intraoperative handovers. Compared to preintervention, postintervention compliance with TOF monitoring increased from 42% to 70% (28% absolute difference across N = 10 169 cases; P < .001). Implementation of a structured quality improvement program using a novel educational intervention showed improvements in process metrics regarding neuromuscular monitoring, while giving us a better understanding of how best to implement improvements in this metric at this magnitude.