We present the multi-band dual triply irreducible local expansion (D-TRILEX) approach to interacting electronic systems and discuss its numerical implementation.
This method is designed for a ...self-consistent description of multi-orbital systems that can also have several atoms in the unit cell.
The current implementation of the D-TRILEX approach is able to account for the frequency- and channel-dependent long-ranged electronic interactions.
We show that our method is accurate when applied to small multi-band systems such as the Hubbard-Kanamori dimer.
Calculations for the extended Hubbard, the two-orbital Hubbard-Kanamori, and the bilayer Hubbard models are also discussed.
Abstract only Introduction: Inclusion of diverse patients in clinical trials is essential to represent patient populations and to ensure that results are generalizable. Analysis of this topic is ...motivated by prior reports of clinical trials underrepresenting historically disadvantaged groups. We aimed to understand if the clinical trial population is representative of the overall stroke population in our community. Methods: We obtained clinical and demographic data from hospital administrative records including all acute ischemic stroke patients between March 2019 and February 2020. Consent was counted when a signed consent for study participation was documented in the inpatient record. Enrollment in studies with a primary inpatient population at the time of consent (MOST, TIMELESS, ARCADIA, SleepSMART, and STRONG) were included. Patients with consent (CP) and without consent (NCP) were compared using chi-square analysis and t-test probability with SPSS. Results: During the study period, 504 patients met the above criteria; 55 were consented to participate in clinical trials. Overall CP did not differ from NCP in % women (45.5% vs 41.2%, not significant), Hispanic ethnicity, discharge medications, hypertension, diabetes, heart failure, drug and alcohol abuse, atrial fibrillation, and payer (Medicare versus non-Medicare). Median age was lower in CP compared to NCP (63 ± 12.8 vs. 67 ± 13.9, p=.046). The mean NIHSS was lower in CP (6.37 ± 5.95 vs. 8.21 ± 8.91, p=.048). The CP group had lower incidence of prior stroke (9.1% vs. 20.9%, p=.037), less dyslipidemia (12.7% vs. 30.1%, p=.007), fewer comorbidities on average (1.9 ± 1.1 vs. 2.2 ± 1.5, p=.044), was less likely to identify as Asian (0% vs. 8.4%, p=.015) and more likely to identify as white (59.3% vs. 45.7%, p=.045). The CP group was more likely to have migraine (12.7% vs. 1.8% p<.001) and were discharged on more stroke risk-factor modifying medications (2.2 ± .558 vs. 1.9 ± .902, p=.001). Conclusions: While the study population is largely representative, they are more likely to be White, with lower stroke severity, fewer medical comorbidities and less likely to be Asian. We plan to expand this analysis to include more study centers and to guide future clinical trial design.
Abstract only Background: Patients with Acute Ischemic Stroke recover function better when discharged to inpatient rehabilitation (IRF) over skilled nursing facilities (SNF). We aimed to increase ...discharge to IRF over SNF at two Comprehensive Stroke Centers (CSC) within our care network. Methods: We reviewed the discharge pattern at our CSCs compared to comparable centers using GWTG benchmarking and designated a multidisciplinary task force aiming to meet discharge patterns at our centers to nationwide trends. The task force used Lean Methodology to identify barriers for IRF discharge recognition and placement. Results: The taskforce identified non-modifiable barriers such as socioeconomic determinants, insurance status, family/social support; and modifiable barriers such as inconsistencies in therapist recommendations and notations, limited access to case management, and lack of provider knowledge about IRF admission criteria. Beginning November 1, 2018, the following interventions were used to achieve increased IRF referral and admission rates: educating therapists to provide more specific and consistent documentation; thorough therapist, case management, and provider education on IRF admission criteria; cohorting patients on dedicated neurology units; and daily multidisciplinary team meetings (consisting of a therapist, case management, and the primary provider) on the neurology units. IRF admission rates were then collected retrospectively. Between Novembers 1, 2018 to April 30, 2019, the rate of admissions to IRF for the UCSD Health System increased from 7.7% to 13.5% (see Table). Conclusions: Using Lean Methodology we identified and reduced barriers for IRF referral after stroke. This suggests IRFs are underutilized when disposition is not effectively streamlined. Further studies are needed to understand which interventions had the highest impact of increasing IRF admission and referral rate.
The COVID-19 pandemic has required the adaptation of hyperacute stroke care (including stroke code pathways) and hospital stroke management. There remains a need to provide rapid and comprehensive ...assessment to acute stroke patients while reducing the risk of COVID-19 exposure, protecting healthcare providers, and preserving personal protective equipment (PPE) supplies. While the COVID infection is typically not a primary cerebrovascular condition, the downstream effects of this pandemic force adjustments to stroke care pathways to maintain optimal stroke patient outcomes.
The University of California San Diego (UCSD) Health System encompasses two academic, Comprehensive Stroke Centers (CSCs). The UCSD Stroke Center reviewed the national COVID-19 crisis and implications on stroke care. All current resources for stroke care were identified and adapted to include COVID-19 screening. The adjusted model focused on comprehensive and rapid acute stroke treatment, reduction of exposure to the healthcare team, and preservation of PPE.
The adjusted pathways implement telestroke assessments as a specific option for all inpatient and outpatient encounters and accounts for when telemedicine systems are not available or functional. COVID screening is done on all stroke patients. We outline a model of hyperacute stroke evaluation in an adapted stroke code protocol and novel methods of stroke patient management.
The overall goal of the model is to preserve patient access and outcomes while decreasing potential COVID-19 exposure to patients and healthcare providers. This model also serves to reduce the use of vital PPE. It is critical that stroke providers share best practices via academic and vetted social media platforms for rapid dissemination of tools and care models during the COVID-19 crisis.
Abstract only Introduction: In the early months of COVID-19 pandemic, a decline in stroke hospital admissions were reported nationwide. In a large, diverse region of Southern California, a ...collaborative effort was made to collect real-time data trends in stroke code activations and to assess this impact locally. The San Diego (SD) County Stroke Receiving Centers demonstrated a notable decrease of 30% in stroke code activations from March-May 2020 as compared to the same timeframe in 2019, which motivated the group to dedicate time and resources to pursue a united community messaging focused on seeking emergency treatment for stroke. Methods: A unified marketing campaign was created in collaboration with SD County EMS and the SD region American Heart Association/American Stroke Association. A single graphic message was utilized that emphasized the importance of seeking emergency treatment when suffering signs of stroke, along with the slogan “We are here for you. Every minute matters.” Impact of the campaign was gauged by quantifying the number of times our message was viewed on social media and number of stroke code activations after the campaign ended. Results: The unified social media campaign was posted by 14 of the 18 SD County stroke receiving hospitals during the month of June 2020. The team utilized Facebook, Twitter, Instagram and LinkedIn to convey the message. The campaign yielded a total of 26,727 views. The median monthly stroke code activations in July 2020 increased to 34, as compared to 26.5 for March-May 2020. Conclusion: In a time when social distancing has become the norm, it is more important than ever to band together as a community. This endeavor demonstrates that virtual messaging serves as a viable option for community education during the COVID-19 pandemic and in the future. A unified social messaging campaign targeting the importance of seeking emergency care for stroke during the COVID-19 pandemic is an effective way to reach large numbers of people regionally.
Abstract only Introduction: In the early months of the COVID-19 pandemic, decreased numbers of stroke code activations were reported nationwide. In San Diego County, a diverse region that borders ...Mexico with over 4500 square miles and population 3.3 million, trends in COVID-19 cases varied geographically. We saw an overall decrease in stroke cases across our systems and aimed to better understand if high COVID infection rates in subregions affected stroke code activations. Methods: Stroke code activation data from 15 Stroke Receiving Centers were matched with COVID-19 case rates by patient home zip code. Patients arriving via emergency medical services (EMS) or private transportation were included. Patients with home zip codes outside of San Diego County were excluded. Data represented the cumulative rate of stroke codes and COVID-19 cases per 100,000 population per zip code for the period of March 1 through June 30, 2020. Results: We counted 1,927 stroke code activations across 106 zip codes in San Diego County. The average stroke code activation rate was 58.4 per 100,000 (range: 0-310.6) The median stroke code activation rate was 55.95 (IQR=32.1-73.1) per 100,000 population. The median COVID rate per zip code was 244.9 (IQR=177-448.4) per 100,000 population. There were 958 (49.7%) non-stroke diagnoses, 576 (29.9%) AIS, 272 (14.1%) TIA, 104 (5.4%) ICH and 17 (.9%) SAH. We did not identify a correlation between stroke code activation rates and COVID rates across zip codes (r=.17, p=.09, 95% CI(-.02, .35)). Conclusions: Across a large and diverse single-county region, no correlation was found between COVID positivity rate per zip code and stroke code activations. We found no decreases in stroke code activations in areas with high COVID rates.
Abstract only Introduction: On March 16, 2020 San Diego County implemented a stay at home order in response to COVID-19 pandemic; followed by the state of California instituting a shelter in place ...order. Locally, San Diego County’s stroke receiving centers (SRC) determined a 30% drop in stroke code activations between March-April 2020 compared to the same time in 2019 indicating a possible delay in seeking care. Utilizing discharge data, we sought to understand the impact of the stay at home order on the timeliness of seeking care. Hypothesis: We hypothesized an increase in last known normal (LKN) to hospital arrival time and a decrease in alteplase (tPA) and endovascular therapy (EVT) treatment rates between March 16-June 30 2020 compared to March 16-June 30 2019. Methods: AIS patients presenting to one of 16 SRC in San Diego County between March 16-June 30 in 2019 and 2020, discharged from the hospital or treated in the ED and transferred to another facility were included. Patients arriving as transfers from another facility were excluded. Results: In 2019, of 1,342 AIS cases LKN time was recorded for 85.6% of cases; of 1,092 cases in 2020 86.4% of cases had a LKN. Average LKN to arrival was 20.5 hours in 2019 and 32.4 hours in 2020 (p = .001, 95% CI 4.79, 18.93). In 2019, 209 (15.6%) received tPA and 91 (6.8%) had EVT. In 2020, 144 (13.2%) received tPA and 75 (6.9%) had EVT. Odds that a case in 2019 received tPA was 1.21 times that of cases in 2020 (p=.09). Odds that a case in 2019 had EVT was .99 times that of cases in 2020 (p=.93). Conclusion: Ischemic stroke patients arriving between March 16-June 30, 2020 had a longer LKN to arrival time compared to the same time frame in 2019. The longer time to arrival may have been due to patients waiting longer to seek care, as anecdotal information from patients eluded to. The odds of receiving tPA or EVT treatment in 2020 compared to 2019 were not statistically significant. This may be due to patients experiencing acute symptoms accessing healthcare at the same rate in 2020 as 2019. Analysis of percent of patients arriving within 4 hours of LKN and average NIHSS are important next steps to determine this. Regardless, during a time of community crisis, it is important to broadcast community messaging focusing on the importance of seeking emergency care for stroke-like symptoms.
We present the multi-band dual triply irreducible local expansion (D-TRILEX) approach to interacting electronic systems and discuss its numerical implementation. This method is designed for a ...self-consistent description of multi-orbital systems that can also have several atoms in the unit cell. The current implementation of the D-TRILEX approach is able to account for the frequency- and channel-dependent long-ranged electronic interactions. We show that our method is accurate when applied to small multi-band systems such as the Hubbard-Kanamori dimer. Calculations for the extended Hubbard, the two-orbital Hubbard-Kanamori, and the bilayer Hubbard models are also discussed.
Deep learning has seen tremendous growth over the past decade. It has set new performance limits for a wide range of applications, including computer vision, speech recognition, and machinery health ...monitoring. With the abundance of instrumentation data and the availability of high computational power, deep learning continues to prove itself as an efficient tool for the extraction of micropatterns from machinery big data repositories. This study presents a comparative study for feature extraction capabilities using stacked autoencoders considering the use of expert domain knowledge. Case Western Reserve University bearing dataset was used for the study, and a classifier was trained and tested to extract and visualize features from 12 different failure classes. Based on the raw data preprocessing, four different deep neural network structures were studied. Results indicated that integrating domain knowledge with deep learning techniques improved feature extraction capabilities and reduced the deep neural networks size and computational requirements without the need for exhaustive deep neural networks architecture tuning and modification.
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