Abstract 1122‐000163: Stroke Center Accessibility Study in the U.S. Using Geospatial Analysis and Machine Learning

2021 ◽  
Vol 1 (S1) ◽  
Author(s):  
Ahsan Ali ◽  
Randall Edgell
Keyword(s):  
United States ◽  
Machine Learning ◽  
Acute Stroke ◽  
Gulf Coast ◽  
Systems Of Care ◽  
The United States ◽  
Geospatial Analysis ◽  
Stroke Mortality ◽  
Stroke Center ◽  
Zip Code

Introduction : Background: Several accrediting bodies certify the level of stroke care hospitals provide. The Joint Commission on Hospital Accreditation (JC) is the largest accrediting body in the United States. There is no open source Geographic Information Systems (GIS) dataset showing the distribution of JC accredited centers by ZIP code. Objective: to create a stroke center accessibility and stroke center desert system using geospatial analysis and machine learning which provides real‐time assessment of stroke center availability, distribution and access to care. Methods : Geospatial data layers of JC accredited stroke centers were compiled using data sources including U.S. Census Bureau and CDC. Map layers corresponding to the levels of JC accredited stroke hospitals geolocated using ZIP code were created as follows: 1) Acute Stroke Ready 2) Primary 3) Thrombectomy Capable 4) Comprehensive Stroke Center. A GIS dataset displaying stroke mortality by region was obtained from the ArcGIS Living Atlas. Stroke center deserts are analyzed using a 4.5 hour drive map along with population and diversity. Machine learning models were implemented to estimate stroke mortality as a function of distance to care centers and capability levels of the stroke centers. Results : Stroke centers are highly concentrated within large urban centers. There are geographic regions that have poor access to stroke centers. Such regions include the Gulf Coast States of Louisiana, Mississippi, and Alabama that have large areas with poor stroke center access while having some of the highest stroke mortality in the country. (Figure 1 ‐ Stroke Center Distribution in the United States) Dot Symbols: Blue = Acute Stroke Ready; Green = Primary; Yellow = Thrombectomy Capable; Red = Comprehensive Raster Data: Stroke Mortality by ZIP Code; White to Purple Scale with Purple = Highest Mortality Conclusions : There are regional variations in stroke center availability. There are certain regions with high stroke mortality with very little stroke center access. Geospatial AI tools can be utilized to improve stroke systems of care.

Abstract 1122‐000121: Integrated Geomapping Tool of Certified Stroke Centers in United States: A SVIN MT2020+ Committee Collaboration

2021 ◽  
Vol 1 (S1) ◽  
Author(s):  
Evan Kolesnick ◽  
Evan Kolesnick ◽  
Alfredo Munoz ◽  
Kaiz Asif ◽  
Santiago Ortega‐Gutierrez ◽  
...  
Keyword(s):  
United States ◽  
Acute Stroke ◽  
Clinical Outcomes ◽  
Healthcare Disparities ◽  
Systems Of Care ◽  
Stroke Care ◽  
The United States ◽  
Time Dependent ◽  
Stroke Systems Of Care ◽  
Stroke Center

Introduction : Stroke is a leading cause of morbidity, mortality and healthcare spending in the United States. Acute management of ischemic stroke is time‐dependent and evidence suggests improved clinical outcomes for patients treated at designated certified stroke centers. There is an increasing trend among hospitals to obtain certification as designated stroke centers. A common source or integrated tool providing both information and location of all available stroke centers in the US irrespective of the certifying organization is not readily available. The objective of our research is to generate a comprehensive and interactive electronic resource with combined data on all geographically‐coded certified stroke centers to assist in pre‐hospital triage and study healthcare disparities in stroke including availability and access to acute stroke care by location and population. Methods : Data on stroke center certification was primarily obtained from each of the three main certifying organizations: The Joint Commission (TJC), Det Norske Veritas (DNV) and Healthcare Facilities Accreditation Program (HFAP). Geographic mapping of all stroke center locations was performed using the ArcGIS Pro application. The most current data on stroke centers is presented in an interactive electronic format and the information is frequently updated to represent newly certified centers. Utility of the tool and its analytics are shown. Role of the tool in improving pre‐hospital triage in the stroke systems of care, studying healthcare disparities and implications for public health policy are discussed. Results : Aggregate data analysis at the time of submission revealed 1,806 total certified stroke centers. TJC‐certified stroke centers represent the majority with 106 Acute Stroke Ready (ASR), 1,040 Primary Stroke Centers (PSCs), 49 Thrombectomy Capable Centers (TSCs) and 197 Comprehensive Stroke Centers (CSCs). A total of 341 DNV‐certified programs including 36 ASRs, 162 PSCs, 16 PSC Plus (thrombectomy capable) and 127 CSCs were identified. HFAP‐certified centers (75) include 16 ASRs, 49 PSCs, 2 TSCs and 8 CSCs. A preliminary map of all TJC‐certified CSCs and TSCs is shown in the figure (1). Geospatial analysis reveals distinct areas with currently limited access to certified stroke centers and currently, access to certified stroke centers is extremely limited to non‐existent in fe States (for example: Idaho, Montana, Wyoming, New Mexico and South Dakota). Conclusions : Stroke treatment and clinical outcomes are time‐dependent and prompt assessment and triage by EMS directly to appropriate designated stroke centers is therefore critical. A readily available electronic platform providing location and treatment capability for all nearby certified centers will enhance regional stroke systems of care, including enabling more rapid inter‐hospital transfers for advanced intervention. Identifying geographic areas of limited access to treatment can also help improve policy and prioritize the creation of a more equitable and well‐distributed network of stroke care in the United States.

Abstract 185: Trends in Care Delivery and Outcomes for Acute Stroke and Transient Ischemic Attacks Among Rural and Urban Medicare Beneficiaries, 2008-2017

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Kori S Zachrison ◽  
Andrew D Wilcock ◽  
Lee H Schwamm ◽  
Lori Uscher-Pines ◽  
Jose R Zubizarreta ◽  
...  
Keyword(s):  
Acute Stroke ◽  
Systems Of Care ◽  
Care Delivery ◽  
The United States ◽  
Medicare Beneficiaries ◽  
Rural And Urban ◽  
Using Data ◽  
Stroke Systems Of Care ◽  
Stroke Center ◽  
First 90 Days

Introduction: Over the last decade substantial investments have been made in implementing stroke systems of care to improve access and quality of care. We sought to determine if these interventions have narrowed the rural-urban disparities in care over time for patients with acute stroke or transient ischemic attack (TIA). Methods: Descriptive observational study using data from all traditional Medicare beneficiaries in the United States from 2008-17 who presented to a hospital emergency department and were admitted either under observation stay or inpatient admission. Patients were classified as rural or urban based on home zip code. The main outcomes were rates of presentation to a certified stroke center, neurology consultation during admission, IV altepase, 90-day mortality, days living independently in the first 90 days post stroke, and 90-day spending. Results: We identified 3.31 million hospital stays for TIA and stroke in the study period. Rural and urban patients had similar age, race, gender, Medicaid status and presence of chronic conditions. In 2008, 24.4% and 60.4% of rural and urban patients respectively were cared for at a certified stroke center (disparity -36.1%). By 2017 this disparity had narrowed by 8.6% points (95% CI 6.6%,10.7%) (Fig). Between 2008 and 2017, the disparity in neurologist evaluation during admission narrowed by 7.4% (5.2%, 9.6%). However, there was no substantive change in disparity in alteplase use -0.1% (95% CI -0.5%,0.3%), mortality at 90 days 0.4% (95% CI 0.1%, 0.7%), or days living independently within 90 days -0.7 days (95% CI -1.1, 0.2). Spending in the first 90 days differentially increased among rural patients by $867 (95% CI 85, 1649). Conclusions: In the last decade, rural residents are more likely to receive care at a certified stroke center and receive neurologist consultation. However, disparities in outcomes are persistent, highlighting more work is needed to equitably extend stroke expertise to all Americans.

scholarly journals Descriptors of Cytochrome Inhibitors and Useful Machine Learning Based Methods for the Design of Safer Drugs

2021 ◽  
Vol 14 (5) ◽  
pp. 472
Author(s):  
Tyler C. Beck ◽  
Kyle R. Beck ◽  
Jordan Morningstar ◽  
Menny M. Benjamin ◽  
Russell A. Norris
Keyword(s):  
United States ◽  
Machine Learning ◽  
Drug Interactions ◽  
The United States ◽  
Structural Features ◽  
Physiochemical Properties ◽  
Drug Dosing ◽  
Therapeutic Outcomes ◽  
Cyp Inhibition ◽  
Cyp Inhibitors

Roughly 2.8% of annual hospitalizations are a result of adverse drug interactions in the United States, representing more than 245,000 hospitalizations. Drug–drug interactions commonly arise from major cytochrome P450 (CYP) inhibition. Various approaches are routinely employed in order to reduce the incidence of adverse interactions, such as altering drug dosing schemes and/or minimizing the number of drugs prescribed; however, often, a reduction in the number of medications cannot be achieved without impacting therapeutic outcomes. Nearly 80% of drugs fail in development due to pharmacokinetic issues, outlining the importance of examining cytochrome interactions during preclinical drug design. In this review, we examined the physiochemical and structural properties of small molecule inhibitors of CYPs 3A4, 2D6, 2C19, 2C9, and 1A2. Although CYP inhibitors tend to have distinct physiochemical properties and structural features, these descriptors alone are insufficient to predict major cytochrome inhibition probability and affinity. Machine learning based in silico approaches may be employed as a more robust and accurate way of predicting CYP inhibition. These various approaches are highlighted in the review.

CIKM 2020 conference report

2021 ◽  
pp. 1-4
Author(s):  
Mathieu D'Aquin ◽  
Stefan Dietze
Keyword(s):  
United States ◽  
Machine Learning ◽  
Knowledge Management ◽  
Information Retrieval ◽  
Conference Report ◽  
The United States ◽  
Knowledge Based ◽  
The World ◽  
Science Conference ◽  
Information And Knowledge Management

The 29th ACM International Conference on Information and Knowledge Management (CIKM) was held online from the 19 th to the 23 rd of October 2020. CIKM is an annual computer science conference, focused on research at the intersection of information retrieval, machine learning, databases as well as semantic and knowledge-based technologies. Since it was first held in the United States in 1992, 28 conferences have been hosted in 9 countries around the world.

scholarly journals Slowdown in the decline of stroke mortality in the United States, 1978-1986.

Stroke ◽  
1990 ◽  
Vol 21 (9) ◽  
pp. 1274-1279 ◽  
Author(s):  
R Cooper ◽  
C Sempos ◽  
S C Hsieh ◽  
M G Kovar
Keyword(s):  
United States ◽  
The United States ◽  
Stroke Mortality

Quality of Life and the Carbon Footprint: A Zip-Code Level Study Across the United States

2021 ◽  
Vol 30 (4) ◽  
pp. 323-343
Author(s):  
Matthew Thomas Clement ◽  
Chad L. Smith ◽  
Tyler Leverenz
Keyword(s):  
Quality Of Life ◽  
United States ◽  
Carbon Footprint ◽  
Ecological Impact ◽  
Well Being ◽  
The United States ◽  
Alternative Measure ◽  
Level Data ◽  
Zip Code

Much sustainability scholarship has examined the environmental dimensions of subjective and objective well-being. As an alternative measure of human well-being, we consider the notion of quality of life and draw on a framework from the sustainability literature to study its association with ecological impact, specifically the carbon footprint. We conduct a quantitative analysis, combining zip-code level data on quality of life and the carbon footprint per household for the year 2012 across the continental United States ( n=29,953). Findings consistently show a significant, negative association between quality of life and the carbon footprint. Our findings point to the potential advantages of utilizing robust objective measures of quality of life that extends beyond economic well-being and life expectancy alone. Furthermore, our findings question the conventional wisdom that sustainability requires sacrifices, while suggesting opportunities for how increased levels of sustainability may be achieved while retaining high levels of quality of life.

scholarly journals COVID-19: Examining the case growth-rate due to Visitor vs Local Mobility using Machine Learning: A Study in the United States

2021 ◽  
Author(s):  
satya katragadda ◽  
ravi teja bhupatiraju ◽  
vijay raghavan ◽  
ziad ashkar ◽  
raju gottumukkala
Keyword(s):  
United States ◽  
Machine Learning ◽  
Statistical Significance ◽  
Disease Outbreaks ◽  
The United States ◽  
Mobile Phone Data ◽  
Infectious Disease Outbreaks ◽  
Local Mobility ◽  
Local Risk ◽  
The Impact

Abstract Background: Travel patterns of humans play a major part in the spread of infectious diseases. This was evident in the geographical spread of COVID-19 in the United States. However, the impact of this mobility and the transmission of the virus due to local travel, compared to the population traveling across state boundaries, is unknown. This study evaluates the impact of local vs. visitor mobility in understanding the growth in the number of cases for infectious disease outbreaks. Methods: We use two different mobility metrics, namely the local risk and visitor risk extracted from trip data generated from anonymized mobile phone data across all 50 states in the United States. We analyzed the impact of just using local trips on infection spread and infection risk potential generated from visitors' trips from various other states. We used the Diebold-Mariano test to compare across three machine learning models. Finally, we compared the performance of models, including visitor mobility for all the three waves in the United States and across all 50 states. Results: We observe that visitor mobility impacts case growth and that including visitor mobility in forecasting the number of COVID-19 cases improves prediction accuracy by 34. We found the statistical significance with respect to the performance improvement resulting from including visitor mobility using the Diebold-Mariano test. We also observe that the significance was much higher during the first peak March to June 2020. Conclusion: With presence of cases everywhere (i.e. local and visitor), visitor mobility (even within the country) is shown to have significant impact on growth in number of cases. While it is not possible to account for other factors such as the impact of interventions, and differences in local mobility and visitor mobility, we find that these observations can be used to plan for both reopening and limiting visitors from regions where there are high number of cases.

Sign in / Sign up

Export Citation Format

Share Document