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Objectives: Opioids play a significant role in the effective management of cancer-related pain. The COVID-19 lock down may have reduced access to opioids and caused a decline in the use of prescription of opioids among cancer survivors. This study compared opioid prescription rates among cancer survivors before and after the onset of COVID-19 pandemic using real-world electronic health records (EHR). Method(s): Cohort analyses of cancer patients using data from EHR database from the TriNetX, a global federated health research network across 76 healthcare organizations. We analyzed changes in prescription opioid use before (March 1, 2018, through March 1, 2019) and after onset of COVID-19 (April 01, 2020, through March 2021) among cancer survivors. The key outcome variable was any opioid prescription within 1 year of cancer diagnosis. One-to-one propensity score matching was used to balance the characteristics (age, sex, race, diagnoses including diabetes, hypertensive diseases, overweight, mood disorders, and visual disturbances) of the two cohorts. Data were analyzed using the TriNetX platform. Result(s): There were 1,502,143 cancer survivors before COVID-19 and 1,412,599 cancer survivors after the onset of COVID-19. The one-to-one propensity-score match yielded 1,382,561 cancer patients, mean age 64 at cancer diagnosis, and 73% were white. Percentage of opioid use among cancer patients declined from 35.6% before the COVID-19 to 35.1% after the onset of the pandemic (OR=0.976, 95% CI 0.971-0.981). Average number of opioid prescriptions within 1 year of cancer diagnosis declined from 5.7 before to 5.3 after the COVID-19 onset (p<0.001). Conclusion(s): Among cancer survivors, a small decline in prescription opioid use was observed after the onset of COVID-19 pandemic. Future studies are needed to distinguish the impact of revised guidelines, opioid prescription policy changes, and COVID-19 lock down on lower rates of prescription opioid use among cancer survivors.Copyright © 2023
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Objectives: This analysis was conducted to develop a comprehensive list of ICD-10 CM codes for underlying conditions identified by the CDC as being associated with high-risk of developing severe COVID-19 and assessed the consistency of these codes when applied to large US based datasets. Method(s): The comprehensive list of ICD 10-CM codes for CDC-defined high-risk underlying conditions were mapped from CDC references and FDA Sentinel code lists. These codes were subsequently applied to Optum's de-identified Clinformatics Data Mart Database (claims) and the Optum de-identified Electronic Health Record (EHR) database across 3 years (2018, 2019 and 2020) among continuously enrolled subjects >= 12 years of age to determine the performance and consistency in identifying these high-risk underlying conditions annually over these years. Result(s): A total of 10,276 ICD-10 codes were mapped to 21 underlying conditions. Within the claims data, 62.7% of subjects >= 12 years had >= 1 CDC-defined high-risk condition (excluding age) with 26.6% of patients >= 65 years while in the EHR data 38% had >= 1 high-risk underlying condition (excluding age) with 14.4% >= 65 years. These results were similar and consistent in both datasets across all years. Patients aged 12-64 years in the claims data had a higher rate of >=1 high risk underlying condition relative to the EHR data, 49.3% and 34%, respectively. The top 5 conditions among the >= 65 were identical across both databases: hypertension, immunocompromised status, heart conditions, diabetes (type 1 or 2), and overweight/obesity. The top 5 conditions among the 12-64 age group were also similar among the databases and included: immunocompromised status, hypertension, overweight/obesity, smoking (current or former), and mental health conditions. Conclusion(s): These findings present a comprehensive list of codes that can be used by researchers, clinicians and policy makers to identify and characterize patients that may be at high-risk for severe COVID-19 outcomes.Copyright © 2023
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COVID-19 pandemic brought up issues with healthcare costs, national economic development and welfare of the society in forefront. Nations across the globe followed different approaches to deal with COVID-19, such as zero tolerance, herd immunity, containment to build treatment capability. National healthcare became a contentious sociopolitical issue involving healthcare costs, technologies and societal health. In the United States even during the COVID-19 pandemic, the government approach was pursuing a sustainable improvement in patient care through adoption of medical and information technologies. The national healthcare policies are framed around technological interventions with the assumption that deployment of technologies could keep healthcare costs under control and at the same time improve health outcomes. However, evidences show that the healthcare costs are in the rise even with impressive progress in technological deployment. This article highlights some of the recent trends in healthcare costs, technological preparedness, medical technology developments in managing COVID-19 pandemic. The US government mandated electronic health record (EHR) systems implementation and assess its impact on healthcare costs and health outcomes. This article emphasizes the need for understanding the interconnectedness of costs, technology and societal health.
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With the development of science and technology, electronic information technology has penetrated into many aspects of society now. Electronic informed consent is an effective way to adapt to development clinical trial. China is still at an early stage in this field. Affected by the outbreak of COVID-19 in 2020, the demand for electronic informed consent in clinical trial has become more urgent. Based on my own work experience, the author wants to analyze the problems in the traditional informed consent process and the current situations of electronic informed consent in China and explores the feasibility of electronic informed consent in clinical trials.Copyright © 2021 Drug Evaluation Research. All rights reserved.
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Objectives: Post-COVID conditions (PCC) are increasingly reported in people who had COVID. Certain racial or socioeconomic groups may be at greater risk for PCC and less likely to seek care. We examined the uptake of the new ICD-10-CM diagnosis code for PCC in routine clinical practice in the United States and how it varied by race and payer group. Method(s): Using the Optum de-identified Electronic Health Record (EHR) dataset, we identified patients with an ICD-10-CM code for PCC (U09.9) between October 1, 2021, through March 31, 2022, with 6 months of prior EHR activity. The earliest diagnosis defined the index date. All concurrent diagnoses were measured on the index date. Prior COVID diagnosis was assessed using all available data before the index date. Result(s): There were 23,647 patients: 9.9% were African American, 12.1% had Medicaid, and 2.4% were uninsured. There was an overrepresentation of white patients among those with PCC (78.6% compared with 69.6% of the overall EHR in 2021). More African American (24.1%), Medicaid (23.1%), and uninsured (27.5%) patients were diagnosed in the inpatient setting or emergency department than whites (14.0%) and commercially insured patients (10.0%). Among racial groups, African Americans had the highest percentage of documented prior COVID diagnosis at 63.6%. Of concurrent diagnoses, shortness of breath and acute respiratory failure with hypoxia were higher among African Americans (13.9% and 6.1%, respectively) than whites (11.5% and 4.3%, respectively). The same pattern was seen when comparing Medicaid and uninsured to commercial payors. Conclusion(s): The PCC code was used differently across racial groups and payor types and captures varying manifestations of PCC. The differences in diagnosis locations underscore the importance of using data capturing all care settings when conducting studies using this code. Subgroup analyses are important for future studies using U09.9 due to variability in code application.Copyright © 2023
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Background: The interaction between checkpoint inhibitors (CPI) and Sars-COV-2 vaccines has been understudied. One potential complication in pts receiving CPI is immune-mediated adverse events (irAEs) resulting from overactivation of the immune system. It is unknown whether concurrent CPI and Sars-COV-2 vaccine administration increases the risk of irAEs. This retrospective study examined the incidence of severe irAEs in cancer patients receiving CPI therapy at the time of vaccination against Sars CoV-2. Method(s): Following IRB approval, pts with solid tumors who received any approved CPI since FDA authorization of the COVID-19 vaccine in December 2020 were identified via institutional electronic health record. Pts who received one or more doses of an authorized vaccine within 60 days of CPI treatment were included. The primary endpoint was to evaluate the incidence of severe irAE (one or more of the following: grade 3 AE or above, multi-system involvement, need for hospitalization). Secondary endpoints included time between CPI and vaccination, need for immunosuppressive therapy, and rate of discontinuation of CPI due to irAE. Data was analyzed using descriptive statistics. Result(s): 290 pts with bladder, head/neck, liver, skin (melanoma, SCC), renal, and gynecologic cancer were included in analysis. The median age was 67 years (IQR: 59.0-74.0) and 66% pts were male. At the time of vaccination, 201 pts (69.3%) received CPI monotherapy, 53 pts (18.3%) received combination (combo) CPI therapy, and 36 pts (12.4%) received other therapies (chemo, TKIs, etc.) with CPI. The vaccine manufacturer was Pfizer Bio-N-Tech in 162 pts (55.9%), Moderna in 122 pts (42.1%), and Johnson and Johnson in 6 pts (2.1%). The number of vaccinations received was >/= 3 in 214 pts, 2 in 64 pts, and 1 in 11 pts. 30 pts (11.5%) experienced severe irAEs following vaccination. The rate of severe irAEs was 10.3% (30/290) in the total population [6% (12/201) with CPI monotherapy, 19% (10/53) with combo CPI, and 22% (8/36) in the combo CPI-other group]. Severe irAEs occurred after the first vaccine dose in 5 pts (16.7%), second dose in 16 pts (53.3%), and third dose in 9 pts (30%) pts. The median time between CPI treatment and vaccination in pts who experienced irAE was15.5 days (IQR: 10.2-23.0). Hospitalization was required for 19 patients (63.3%). 24 pts (80.0%) required immunosuppressive therapy with a median therapy duration of 98.5 days (IQR 40.2-173.0). 16 pts (53.5%) discontinued CPI therapy following severe irAEs Conclusion(s): In this retrospective study, we observed a 10.3% rate of severe irAE in cancer pts receiving CPI concurrently with COVID-19 vaccines. Further investigation in pts with additional cancer types is warranted to help determine best practice guidelines for COVID-19 vaccination in cancer patients receiving CPI.
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Objectives: Develop a coding system to extract EHR data and establish research validity to lessen need for manual data extraction Methods: As part of a data collection project for COVID + patients requiring ICU care, we established data elements able to be extracted from the Epic electronic health record (EHR). Collaboration between Information Technology (IT), research and clinical personnel established where data elements were located within the EHR and what data could be extracted with minimal manual assistance and uploaded to a research database. Coding was developed using Structured Query Language (SQL) with best practices (includes indexes, execution plans, optimized range keys, avoiding large reads inside read-write transactions as instructed by the Epic consultant). Accuracy of extracted data was evaluated by manual validation of data against Epic records via random selection of patient data within the cohort. Result(s): From July-December 2022, coding was developed which extracted over 130 fields of data from 3093 COVID patients across 5 INOVA ICU sites (demographic, physiologic, lab, interventions, outcome). Prior efforts at data extraction of these elements from research personnel (ZS) who previously performed this task noted an average of 4 hours/patient to complete coded fields. Coded data was also noted to be more accurate when accessed by the same personnel to manually extracted fields. Assuming 4 hrs/pt, manual extraction would require 12,372 hours, which equates to over 6 full time human research personnel. Data coding required 446 hours. Coded data extraction can be almost immediate once fields requested are established, decreasing personnel costs and effort significantly. Conclusion(s): Reduction in need for manual data collection using automated coding extraction can reduce costs, personnel time and enhance research efforts. Sharing coding mapping to other EPIC sites or use of similar methods may improve timeliness of ongoing data extraction and will be useful to develop earlywarning and patient-centered care algorithms to improve care.
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Objectives: To examine temporal trends of FDA-approved and off-label second-generation antipsychotic (SGA) prescribing for adolescents over time through the Covid-19 pandemic. Method(s): This is a new-user, retrospective longitudinal panel study using electronic health record data from a large, integrated health care system. Outpatient prescription orders for a new SGA (index date) for adolescents (age 10-17 years) during 2013-2021 were analyzed. Prescription orders were linked to diagnoses at time of encounter to examine prescribing behavior. A one-year lookback period was used for baseline inclusion and exclusion criteria, including one-year "washout" of SGAs and continuous insurance enrollment. FDA-approved use was determined by two outpatient diagnoses (one baseline diagnosis and the prescription order diagnosis) for autism, psychotic disorders, bipolar disorders, or Tourette's;the remaining proportion was considered potentially off-label. We report crude annual prescribing rates per 1,000 youths. Result(s): There were 8,145 unique patients with new SGA prescription orders, of which 5,828 (71.6%) had linked diagnoses available. Calendar year 2013 had the highest prescribing rate prior to Covid-19 onset (2.1 per 1,000) but then declined through 2016 (1.7 per 1,000). Prescribing rates in 2020 (2.0 per 1,000) and 2021 (2.2 per 1,000) were higher than those between 2017-2019. Across all study years, SGA prescriptions were mostly off-label and ordered for aripiprazole, quetiapine, or risperidone. The proportion of off-label indications was highest in 2013 (80.1%) and lowest (69.1%) in 2019. Off-label proportions increased again in 2020 (76.1%) and in 2021 (74.1%). At baseline, patients frequently had other psychotropic prescriptions (e.g., antidepressants 63.3%, stimulants 22.9%, and sedatives/hypnotics 20.7%). Conclusion(s): A general decline in SGA prescribing rates among adolescents was observed from 2013 to 2019, but then increased following Covid-19 onset. Despite known safety risks, off-label use of SGAs remains prominent. Future studies are needed to better understand prescribing outside of pediatric professional society guidelines.Copyright © 2023
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Objectives: To describe the IPT collaborative approach for peripartum women with COVID-19 on ECMO and report the intervention outcomes. Method(s): A retrospective electronic health record review was performed from January 2020 through January 2022. All peripartum women on ECMO with COVID19 admitted to the cardiothoracic intensive care unit (CTICU) were included. The IPT came together to coordinate peripartum care and delivery. An algorithm was created to outline the roles and workflow in the care of these patients. The outcomes evaluated included delivery method, timing, and location, maternal survival at discharge, maternal ICU length of stay (LOS), and neonatal survival Results: Thirteen Peropartum women were placed on ECMO (5 antepartum and 8 postpartum, ages 27-42). None had been vaccinated against COVID-19. All received femoral vessel cannulation (11 venovenous and 2 venoarterial). Four patients underwent Caesareansection delivery while on ECMO. Maternal survival to hospital discharge was 84.6%. All neonates survived with COVID-19 negative status. Conclusion(s): The collaborative IPT approach with a structured algorithm facilitated survival outcomes. This report adds to the limited literature on peripartum. ECMO and provides insights to consider in planning for the care of these patients.
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Objectives: To describe and compare real-world outcomes for patients with mild-to-moderate COVID-19 at high risk for progression to severe COVID-19, treated with sotrovimab versus untreated. Method(s): Electronic health records from the National COVID Cohort Collaborative were used to identify US patients (aged >=12 years) diagnosed with COVID-19 (positive test or ICD-10: U07.1) in an ambulatory setting (26 May 2021-30 April 2022) who met Emergency Use Authorization high-risk criteria. Patients receiving the monoclonal antibody (mAb) sotrovimab within 10 days of diagnosis were assigned to the sotrovimab cohort with an index date on the day of infusion. Untreated patients (no evidence of early mAb treatment or prophylaxis mAb or oral antiviral treatment) were assigned to the untreated cohort with an imputed index date based on the time distribution between diagnosis and sotrovimab infusion for the sotrovimab cohort. The primary endpoint was hospitalization or death (both all-cause) within 29 days of index, reported as descriptive rates and adjusted (via inverse-probability-of-treatment weighting [IPTW]) odds ratios (OR) and 95% confidence intervals (CI). Result(s): Of nearly 2.9 million patients diagnosed with COVID-19 during the analysis time period, 4,992 met the criteria for the sotrovimab cohort and 541,325 were included in the untreated cohort. Patients in the sotrovimab cohort were older (60 versus 54 years), more likely to be male (40% versus 38%) and White (85% versus 75%), and met more EUA criteria (3 versus 2) versus the untreated cohort. The 29-day hospitalization or mortality rates were 3.5% (176/4,992) and 4.5% (24,163/541,325) in the sotrovimab and untreated cohorts respectively (unadjusted OR [95% CI]: 0.77 [0.67,0.90];p=0.001;IPTW-adjusted OR [95% CI]: 0.74 [0.61,0.91];p=0.004). Conclusion(s): Sotrovimab demonstrated clinical effectiveness in preventing severe outcomes (hospitalization, mortality) between 26 May 2021-30 April 2022, which included the Delta variant and early surge of Omicron BA.1/BA.2. Funding(s): GSK (Study 219020)Copyright © 2023
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Background: We reviewed patients with COVID-19 ARDS managed with VV-ECMO support at our center from March 2020 until February 2022. Material(s) and Method(s): We extracted data from electronic health records (Metavision and DIPS). We registered premorbid health status, ventilator-settings before initiation of ECMO, the time-course, and hospital mortality. Result(s): Thirty patients were managed at our hospital, with a median age of 57.2 years (28-65) and median BMI 28 (22-40). No patient had any serious comorbidity. Twenty-two patients received non-invasive ventilation prior to intubation (1-10 days). The median time on ventilator were 8.0 days (1-19) prior to ECMO and median tidal volume was 5.8 mL/kg PBW (3.1-7.5). Hypoxemia (median PaO2-FiO2 ratio 8 kPa, range 6-12 kPa) and hypercapnia (median PaCO2 11.9 kPa, range 4.2-18.5) [SEP1] despite lung protective ventilation were the main indications for VV-ECMO. Two patients had severe respiratory acidosis without hypoxemia. 18 patients developed serious complications while managed with ECMO (acute renal failure, clinically significant bleeding, sepsis, right ventricular heart failure, dislocation of cannulae). Seven patients received renal replacement therapy. Sixteen patients (53%) died. Thirteen patients (43%) died on ECMO, three (10%) after weaning, Twelve (40%) were discharged from hospital, two are currently in ICU (7%). The median duration of ECMO and ventilator treatment, was 27 (6-50) and 37 (9-78) days, respectively. Conclusion(s): Management of patients with COVID-19 ARDS with VV-ECMO is very resource-intensive, and accompanied by serious complications and high mortality. In-hospital mortality in our cohort was 53%, which is comparable with reports from other centers. However, the duration of ECMO, and pre-ECMO mechanical ventilation, were longer than typically reported.
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The Zero Suicide (ZS) approach to health system quality improvement (QI) aspires to reduce/eliminate suicides through enhancing risk detection and suicide prevention services. This first report from our randomized trial evaluating a stepped care for suicide prevention intervention within a health system conducting ZS-QI describes (1) our screening and case identification process, (2) variation among adolescents versus young adults, and (3) pandemic-related patterns during the first COVID-19 pandemic year. Between April 2017 and January 2021, youths aged 12-24 years with elevated suicide risk were identified through an electronic health record (EHR) case-finding algorithm followed by direct assessment screening to confirm risk. Eligible/enrolled youth were evaluated for suicidality, self-harm, and risk/protective factors. Case finding, screening, and enrollment yielded 301 participants showing suicide risk indicators: 97% past-year suicidal ideation, 83% past suicidal behavior;and 90% past non-suicidal self-injury (NSSI). Compared to young adults, adolescents reported more past-year suicide attempts (47% vs. 21%, p <.001) and NSSI (past 6 months, 64% vs. 39%, p <.001);less depression, anxiety, posttraumatic stress, and substance use;and greater social connectedness. Pandemic onset was associated with lower participation of racial-ethnic minority youths (18% vs. 33%, p <.015) and lower past-month suicidal ideation and behavior. Results support the value of EHR case-finding algorithms for identifying youths with potentially elevated risk who could benefit from suicide prevention services, which merit adaptation for adolescents versus young adults. Lower racial-ethnic minority participation after the COVID-19 pandemic onset underscores challenges for services to enhance health equity during a period with restricted in-person health care, social distancing, school closures, and diverse stresses.Copyright © 2023 Society of Clinical Child and Adolescent Psychology.
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INTRODUCTION: Prior data have suggested that suboptimal antibiotic prescribing in the emergency department (ED) is common for uncomplicated lower respiratory tract infections (LRTI), urinary tract infections (UTI), and acute bacterial skin and skin structure infections (ABSSSI). The objective of this study was to measure the effect of indication-based antibiotic order sentences (AOS) on optimal antibiotic prescribing in the ED. METHODS: This was an IRB-approved quasi-experiment of adults prescribed antibiotics in EDs for uncomplicated LRTI, UTI, or ABSSSI from January to June 2019 (pre-implementation) and September to December 2021 (post-implementation). AOS implementation occurred in July 2021. AOS are lean process, electronic discharge prescriptions retrievable by name or indication within the discharge order field. The primary outcome was optimal prescribing, defined as correct antibiotic selection, dose, and duration per local and national guidelines. Descriptive and bivariate statistics were performed; multivariable logistic regression was used to determine variables associated with optimal prescribing. RESULTS: A total of 294 patients were included: 147 pre-group and 147 post-group. Overall optimal prescribing improved from 12 (8%) to 34 (23%) (P < 0.001). Individual components of optimal prescribing were optimal selection at 90 (61%) vs 117 (80%) (P < 0.001), optimal dose at 99 (67%) vs 115 (78%) (P = 0.036), and optimal duration at 38 (26%) vs 50 (34%) (P = 0.13) for pre- and post-group, respectively. AOS was independently associated with optimal prescribing after multivariable logistic regression analysis (adjOR, 3.6; 95%CI,1.7-7.2). A post-hoc analysis showed low uptake of AOS by ED prescribers. CONCLUSIONS: AOS are an efficient and promising strategy to enhance antimicrobial stewardship in the ED.
Subject(s)
Antimicrobial Stewardship , Respiratory Tract Infections , Urinary Tract Infections , Adult , Humans , Anti-Bacterial Agents/therapeutic use , Respiratory Tract Infections/drug therapy , Emergency Service, Hospital , Urinary Tract Infections/drug therapy , Practice Patterns, Physicians' , Inappropriate PrescribingABSTRACT
To support health care and public health in managing the array of information available about patients and populations, health systems have adopted a variety of information and communications technologies (ICT). Examples include electronic health record systems that document patient symptoms, diseases, and medications as well as health care processes. Yet, many ICT systems operate as islands unto themselves, unable to connect or share information with other ICT systems. Such fragmentation of data and information is an impediment to achieving the goal of efficient, coordinated health care delivery. It was further a major challenge during the COVID-19 pandemic when information was rapidly needed yet challenging to access. Health information exchange (HIE) seeks to address the challenges of connecting disparate ICT systems, enabling information to be available when and where it is needed by clinicians, administrators, and public health authorities. This chapter robustly defines HIE, including its core components and various forms. This chapter further discusses the role of HIE in supporting care delivery and public health. © 2023 Elsevier Inc. All rights reserved.
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Background/Aims During the COVID-19 pandemic rheumatology services were advised to limit face to face contact, with remote telemedicine used instead. Although suitable for some people, issues have been highlighted with telemedicine. The frequency and proportion of remote appointments during the pandemic has not been described, or the socio-demographic characteristics of those accessing remote or in-person rheumatology care. This study aims to describe rheumatology healthcare utilisation and mode of appointment (remote/in-person) in people with rheumatoid arthritis (RA), prior to, and during the pandemic in England. Methods A retrospective prevalent cohort study of people with RA, identified using a validated algorithm, as of 1st April 2019 using electronic health record data (OpenSAFELY). Outpatient rheumatology appointments between 1st April 2019 and 31st March 2022 were identified. For each year, the number of outpatient appointments, mode of appointment (remote/in-person) and patient socio-demographic characteristics were described. Results 130,884 people with RA were identified. Since the start of the pandemic, the proportion of people without any appointments in a 12-month period increased from 28.5% in 2019/20 to 33.3% in 2020/ 21 and has not recovered. Older people were most frequently not seen (51% of people >80 years in 2020/21 and 2021/22). Of appointments where mode was known, 54.4% of people with appointments in the year from April 2020 were only seen remotely, reducing to 35.1% in the year from April 2021 (Table 1). The proportion with all remote appointments increased with increasing age, comprising 62% of people >80 years in 2020. This age gradient persisted in 2021, though proportions of those >80 years with all-remote appointments was lower (44%). Compared to urban dwellers, a higher proportion of those living in rural areas had all remote appointments in 2020 (58% vs 53%) and 2021 (38% vs 34%). Conclusion During the pandemic, one third of people with RA were not seen at all over a 12-month period and these were more frequently older people. Over half of people were only seen remotely in 2020, decreasing to one-third in 2021. Given the limitations of remote appointments it is unknown whether this increased frequency of remote appointments will impact long-term outcomes.
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The ability of a health information exchange (HIE) to consolidate information, collected from multiple, disparate information systems, into a single, person-centric health record can provide a comprehensive and longitudinal representation of an individual's medical history. Shared, longitudinal health records can be leveraged to enhance the delivery of individual clinical care and provide opportunities to improve health outcomes at the population level. This chapter describes the clinical benefits imparted by the shared health record (SHR) component an HIE infrastructure. It also characterizes the potential public health benefits of the aggregate level, population health indicators calculated, stored, and distributed by a health management information system (HMIS) component. Tools for visualizing health indicators from the HMIS, including disease surveillance systems developed during the COVID-19 pandemic, are also described. Postpandemic components such as the SHR and HMIS will likely play critical roles in strengthening health information infrastructures in states and nations. © 2023 Elsevier Inc. All rights reserved.
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Background: Within the coronavirus 2019 (COVID-19) pandemic, literature has found worsened patient outcomes and increased virus transmissibility associated with reduced air quality. This factor, a structural social determinant of health (SDOH), has shown great promise as a link between air quality and patient outcomes during the COVID-19 pandemic. Researching SDOH within our patient populations is often difficult and limited by poor documentation or extensive questionnaires or surveys. The use of demographic data derived from the electronic health record (EHR) to more accurately represent SDOH holds great promise. The use of area-level determinants of health outcomes has been shown to serve as a good surrogate for individual exposures. We posit that an area level measure of air quality, the county-level Air Quality Index (AQI), will be associated with disease worsening in intensive care unit (ICU) patients being treated for COVID-19. Method(s): We will calculate AQI using a combination of open-source records available via the United States Environmental Protection Agency (EPA) and manual calculations using geospatial informatics systems (GIS) methods. Subjects will be identified as adult (> 18 years) patients admitted to Vanderbilt University Medical Center's ICUs between January 1, 2020, and March 31, 2022 with a positive SARS-CoV-2 laboratory analysis result. We will exclude patients without a home address listed. Patient demographic and hospital data from ICU admission to 28 days following admission will include: age, sex, home address, race, insurance type, primary language, employment status, highest level of education, and hospital course data. Together these will be collated to produce our primary outcome variable of WHO Clinical Progression Scale score. These validated scores range from 0 (uninfected) to 10 (dead) to track clinically meaningful progression of COVID-19 infected patients. Our AQI variable will be obtained from the EPA available county-level monitoring station spatial data combined with open-source state/county center point spatial data. These data contain historic cataloguing to determine air quality at both specific time points and averages over time. Where a county's average yearly AQI is not available due to lack of a monitoring station, we will use spatial data tools to calculate an average based on data from nearby stations. We will utilize yearly averages of AQI in the year prior to COVID-19 diagnosis to describe overall impact of air quality on patients' respiratory outcomes as opposed to single day exposures. Linkage of patient data to AQI database will be performed using patient addresses. Discussion(s): By combining area level data with electronic health record (EHR) data, we will be positioned to understand the contribution of environmental and social determinants of health on patient outcomes. Our long-term goal is to elucidate which social and environmental determinants of health are associated with worse outcomes from COVID-19 and other respiratory viruses, using data extracted from the EHR.
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During a health crisis and a pandemic, information technology, analytics, and clinical engineering departments within an acute-care hospital setting play a significant role in the delivery of healthcare services. Electronic health record systems have become equally as important as the technology infrastructure that underpins them and the healthcare service itself. Both healthcare workers and patients require network access for effective communications, both within the hospital and beyond. Collaboration tools within and across departments at all levels have become essential for business continuity and clinical care. During the COVID-19 crisis, the virtual workspace became the "new normal” for healthcare workers, and virtual care through telehealth platforms, for patients and caregivers, enabled a quality of care to be maintained while still protecting both patients and healthcare workers throughout the infectious pandemic surge. Providing such services required agile project planning, along with a collaborative team effort, to quickly and effectively respond to expanded patient capacity within SBH. This chapter documents how the IT department at SBH Health System was able to successfully adapt to the demanding requirements of the initial COVID-19 surge in New York City, and it further highlights the key lessons learned to help recognize the tools needed to assist enhanced clinical innovations during a health crisis, especially an infectious pandemic. © SBH Health System 2022.
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Background/Aims Tofacitinib and baricitinib were the first orally available, targeted synthetic Janus kinase (JAK) inhibitors approved for the treatment of rheumatoid arthritis (RA) in the UK. Evidence suggests that JAK inhibitors are as efficacious as biological DMARDs in the treatment of RA. Their safety profile has been demonstrated in long term extension studies and RCTs. However, real-world, long-term data remains as important in bridging the gap between controlled studies and routine practice. We report our initial real-world experience of a cohort of RA patients commenced on JAKi before the SARS-CoV-2 pandemic within a regional centre in the UK. Methods All patients commenced on JAKi for the treatment of RA between February 2018 and March 2020 were identified from our in-house database. Data was retrospectively collected from clinical notes and electronic health records from February 2018 up until April 2022. This included patient demographics, disease duration, serological status, concurrent csDMARD usage, history of bDMARD exposure, duration of use and reason for discontinuation of the drug if appropriate. DAS- 28 scores were recorded at baseline and quarterly. SPSS (version 22.0) was used for data analysis. Results One hundred thirty patients were treated with JAK inhibitors (Tofacitinib 22%, Baricitinib 78%);80% female, mean (S.D.) age 61.5 (12.3) years. 92 (70.8%) patients were seropositive. 70 (53.8%) patients were on concurrent csDMARDs and 23 (17.7%) on concurrent steroids. The mean number of previous bDMARDs was 1.8 +/- 1.7;41 (31.5%) were bDMARD naive. The mean baseline DAS-28 ESR (S.D.) score was 5.96 (0.96). There were significant differences in mean DAS- 28 ESR scores (compared with baseline) of 1.54, 1.96, 2.41, 2.33 and 1.80 at 3, 6, 12, 18 and 24 months respectively (p<0.0001). Mean DAS-28 ESR scores were not statistically significant between bDMARD naive patients and those that had previously received bDMARDs. Overall JAKi retention rate was 66.9% with a mean follow up duration of 27.4+/-13.1 months. Persistence was 88.5%, 76.9%, 73.2% and 68.5% at 6, 12, 18, and 24 months, respectively. Of the 38 patients who stopped JAK inhibitors, 11 stopped due to inefficacy (6, primary inefficacy). 3 patients were lost to follow-up and 6 deceased. Cause of death was sepsis (2), venous thromboembolism (1) and unknown (3). 18 patients stopped because of adverse events (AEs). The most common AEs were recurrent infections (11), gastrointestinal side effects (9), lymphopenia (7), thromboembolic events (6) and herpes zoster (5). In total 6 (4.1%) patients had thromboembolic events which included pulmonary embolism (4) and deep vein thrombosis (1) and central retinal artery thrombosis (1). Conclusion JAK inhibitors in this real-world population of RA patients were effective in reducing disease activity and patients had high persistence rates. Recurrent infections, herpes zoster and thrombo-embolism remain adverse events of concern.