ABSTRACT
BackgroundPsoriasis (PsO) and psoriatic arthritis (PsA) can greatly impact quality of life and result in substantial personal and societal costs. Complete and up to date data on the prevalence and incidence of these conditions and whether these change over time and vary by age is important for healthcare service planning so that specialist care and funding can be appropriately allocated.ObjectivesTo determine the prevalence and incidence of PsO and PsA in males and females from 2009-2019 across all age groups in England.MethodsWe used Clinical Practice Research Datalink AURUM, a primary care electronic health record database, including 20% of the English population. The codes used to identify patients with PsO and PsA were selected by rheumatologists and dermatologists and cross-checked with published code lists from other studies to ensure inclusion of all relevant codes. All included patients must have data for at least 1 year before their diagnosis. The annual incidence and point prevalence were calculated from 2009-2019 and stratified by age/sex. The study period ended in 2019 to avoid COVID-19 pandemic affecting results.ResultsThe prevalence of PsO and PsA in males and females increased annually, peaking in 2019 (PsO males 2.41% [95% confidence interval (CI) 2.40, 2.42];PsO females 2.60% [95% CI 2.59-2.61];PsA males 0.20% [95% CI 0.20-0.20];PsA females 0.21% [95% CI 0.21- 0.22]), as illustrated in Table 1. In 2019, the prevalence of PsO and PsA was highest in the over 65 years age group;PsO 4.25% [95% CI 4.22-4.28] and PsA 0.38% [95% CI 0.37-0.38]. The annual incidence (per 100,000 person years) of PsO has gradually decreased in males (from 168 (164-171) in 2009 to 148 (145-151) in 2019) but in females it has been stable with a slight annual decrease (from 180 (177-184) in 2009 to 173 (170-176) in 2019). The annual incidence for PsA has increased in both males and females (13 (12-14) in 2009 and 15 (14-16) in 2019 for males and 12 (11-13) in 2009 and 18 (17-19) in 2019 for females).ConclusionThe increasing prevalence of PsO and PsA highlights the importance of organising healthcare services to meet this need, particularly in the elderly population.ReferencesNIL.Table 1.Prevalence of PsO and PsA from 2009-2019 in EnglandYear20092010201120122013201420152016201720182019Population (n)1073383110910802110318501118036711343299112249341137842211657996119336261223432512420998PsO (n)216841229106239819250667259988268032276804286499295712304568311104PsO prevalence (%, 95%CI)-Male1.98 (1.96-1.99)2.06 (2.05- 2.07)2.13 (2.12-2.14)2.19 (2.18-2.20)2.24 (2.23- 2.25)2.33 (2.32- 2.34)2.37 (2.36- 2.38)2.39 (2.38- 2.40)2.40 (2.39- 2.41)2.40 (2.39- 2.42)2.41 (2.40- 2.42)-Female2.07 (2.05- 2.08)2.14 (2.13- 2.16)2.22 (2.21- 2.23)2.29 (2.28- 2.31)2.35 (2.33- 2.36)2.45 (2.43- 2.46)2.50 (2.49- 2.51)2.53 (2.52- 2.54)2.56 (2.54- 2.57)2.58 (2.56- 2.59)2.60 (2.59- 2.61)PsO incidence (100,000 person years)-Male168 (164-171)158 (155- 162)161 (158-165)153 (150-157)161 (157- 164)156 (153- 159)155 (152- 159)154 (151- 157)153 (150-156)150 (147-153)148 (145-151)-Female180 (177-184)176 (172-179)181 (177-184)171 (167-174)175 (171-178)176 (172-180)179 (176-183)178 (174-181)177 (174-181)174 (170-177)173 (170-176)PsA (n)1444515443164681752218545196182072021994232572451425683PsA prevalence (%, 95%CI)-Male0.14 (0.14- 0.14)0.15 (0.14- 0.15)0.15 (0.15- 0.16)0.16 (0.16- 0.16)0.17 (0.16- 0.17)0.18 (0.17- 0.18)0.18 (0.18- 0.19)0.19 (0.18- 0.19)0.19 (0.19- 0.20)0.20 (0.19- 0.20)0.20 (0.20- 0.20)-Female0.13 (0.13- 0.13)0.14 (0.13- 0.14)0.15 (0.14- 0.15)0.15 (0.15- 0.16)0.16 (0.16- 0.16)0.17 (0.17- 0.18)0.18 (0.18- 0.18)0.19 (0.19- 0.19)0.20 (0.19- 0.20)0.20 (0.20- 0.21)0.21 (0.21- 0.22)PsA incidence (100,000 person years)-Male13 (12- 14)12 (11- 13)13 (12- 14)12 (11- 13)13 (12-14)14 (13- 15)14 (13- 15)14 (13-15)1514-16)14(13- 15)15 (14-16)-Female12 (11- 13)13 (12- 14)13 (12- 14)14 (13-15)14 (13-15)15 (14-16)17 (16- 18)16 (15- 17)17 (16- 18)18 (17-19)18 (17-19)Acknowledgements:NIL.Disclosure of InterestsArani Vivekanantham: None declared, Edward Burn: None dec ared, Marta Pineda-Moncusí: None declared, Sara Khalid Grant/research support from: SK has received research grant funding from the UKRI and Alan Turing Institute outside this work. SK's research group has received grant support from Amgen and UCB Biopharma., Daniel Prieto-Alhambra Grant/research support from: DPA's department has received grant/s from Amgen, Chiesi-Taylor, Lilly, Janssen, Novartis, and UCB Biopharma. His research group has received consultancy fees from Astra Zeneca and UCB Biopharma. Amgen, Astellas, Janssen, Synapse Management Partners and UCB Biopharma have funded or supported training programmes organised by DPA's department., Laura Coates Speakers bureau: LC has been paid as a speaker for AbbVie, Amgen, Biogen, Celgene, Eli Lilly, Galapagos, Gilead, Janssen, Medac, Novartis, Pfizer and UCB., Consultant of: LC has worked as a paid consultant for AbbVie, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Eli Lilly, Gilead, Galapagos, Janssen, Novartis, Pfizer and UCB., Grant/research support from: LC has received grants/research support from AbbVie, Amgen, Celgene, Eli Lilly, Novartis and Pfizer.
ABSTRACT
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.
ABSTRACT
BackgroundPatients with autoimmune inflammatory rheumatic diseases are at higher risk for coronavirus disease (COVID)-19 hospitalization and worse clinical outcomes compared with the general population. However, data on the association between COVID-19 outcomes and gout, or gout-related medications are still lacking.ObjectivesWe aimed to compare COVID-19 related clinical outcomes in gout vs. non-gout patients.MethodsWe conducted a retrospective cohort study using the electronic health record-based databases of Seoul National University hospital (SNUH) from January 2021 to April 2022 mapped to a common data model. Patients with gout and without gout were matched using a large-scale propensity score (PS) algorithm. The clinical outcomes of interest were COVID-19 infection, severe COVID-19 outcomes defined as the use of mechanical ventilation, tracheostomy or extracorporeal membrane oxygenation, and death within 30 days of COVID-19 diagnosis. The hazard ratio (HR) for gout vs. non-gout patients derived by Cox proportional hazard models were estimated utilizing a 1:5 PS-matched cohort.Results2,683 patients with gout and 417,035 patients without gout were identified among the patients who visited SNUH. After 1:5 PS matching, 1,363 gout patients and 4,030 non-gout patients remained for the analysis. The risk of COVID-19 infection was not significantly different between patients with gout and those without gout (HR 1.07 [95% CI 0.59-1.84]). Within the first month after the COVID-19 diagnosis, there was also no significant difference in the risk of hospitalization (HR 0.57 [95% CI 0.03-3.90], severe COVID-19 outcomes (HR 2.90 [95% CI 0.54-13.71]), or death (HR 1.35 [95% CI 0.06-16.24]).ConclusionPatients with gout did not have an increased risk of COVID-19 infection or worse clinical outcomes. Updates of temporal trends of COVID-19 outcomes in gout patients are yet warranted as new SARS-CoV-2 variants emerge.References[1]Shin YH, et al. Autoimmune inflammatory rheumatic diseases and COVID-19 outcomes in South Korea: a nationwide cohort study. Lancet Rheumatol. 2021 Oct;3(10):e698-e706.[2]Topless RK, et al. Gout and the risk of COVID-19 diagnosis and death in the UK Biobank: a population-based study. Lancet Rheumatol. 2022 Apr;4(4):e274-e281.[3]Xie D, et al. Gout and Excess Risk of Severe SARS-CoV-2 Infection Among Vaccinated Individuals: A General Population Study. Arthritis Rheumatol.2023 Jan;75(1):122-132.Table 1.Clinical outcomes of COVID-19 infection in patients with goutOutcomesUnmatched populationPopulation with PS stratification using 10 strata1:5 PS matched populationHazard ratio (95% CI)p-valueHazard ratio (95% CI)p-valueHazard ratio (95% CI)p-valueCOVID-19 infection1.68 (1.03-2.57)0.031.20 (0.72-1.87)0.461.07 (0.59-1.84)0.82Hospitalization due to COVID-191.92 (0.32-6.05)0.391.63 (0.26-5.77)0.540.57 (0.03-3.90)0.66Severe COVID-19 infection4.72 (1.44-11.28)<0.014.22 (1.17-12.21)0.022.90 (0.54-13.71)0.20Death due to COVID-191.15 (0.07-5.18)0.900.77 (0.04-3.81)0.821.35 (0.06-16.24)0.84Acknowledgements:NIL.Disclosure of InterestsNone Declared.
ABSTRACT
Objectives Human leukocyte antigens (HLA) are highly diverse transmembrane proteins that present viral peptides to T cells and launch pathogen-specific immune responses. We aim to investigate the correlation between HLA evolutionary divergence (HED), a surrogate for the capacity to present different peptides, and the outcomes of SARS-CoV-2 infection in a cohort from the St. Louis Metropolitan area. Methods We enrolled adult patients with SARS-CoV-2 infection confirmed by RT-PCR who were hospitalized at two tertiary hospitals in St. Louis between March and July 2020. Genomic DNA was extracted from peripheral blood and genotyped by next-generation sequencing (NGS). HLA alleles were assigned based on key-exon sequences (G group) and limited to the 2-field resolution. HED was calculated by Grantham distance, which considers the difference in composition, polarity, and molecular volume between each pair of amino acids from maternal and paternal HLA. The HED score was obtained for HLA class I (HLA-A, -B, and -C) genotypes using the HLAdivR package in R. Clinical data were collected retrospectively from electronic medical records. A poor outcome was defined as an admission to the intensive care unit (ICU), a need for mechanical ventilation, or death. A favorable outcome was defined as the absence of the above poor outcomes. Results A total of 234 patients were enrolled in this study, 96 being females (41%). The median age and BMI were 66 years old and 28.30 kg/m2, respectively. African Americans comprised 71.4% of the cohort. Only 19 patients (8.1%) presented with no comorbidity;the rest had one or more comorbidities, with cardiovascular diseases being the most common. A total of 137 (58.5%) patients had poor outcomes from SARS-CoV-2 infection, while 97 (41.5%) patients had a favorable outcome. We detected a significant association between higher HLA-B HED and favorable outcomes, with each 1-point increase in HLA-B HED associated with 8% increased probability for the composite endpoint (OR 1.08, 95% CI=1.01-1.16, P = 0.04). The HED scores calculated for HLA-A or HLA-C were not significantly different between patients with favorable or poor outcomes. In a multivariate logistic regression analysis, increased HLA-B HED score, younger age, and no comorbidity were independently associated with favorable outcomes (P = 0.02, P = 0.01, and P = 0.05, respectively). Conclusion Our study shows a significant correlation between lower HLA-B HED scores and poor outcomes after SARS-CoV-2 infection. This finding suggests that maximizing the presentation of diverse SARS-CoV-2 peptides by HLA-B alleles may improve the clearance of SARS-CoV-2. Further studies are warranted to understand the functional and mechanistic implications of this finding.
ABSTRACT
The deadly complication Scientists failed to find evidence that COVID-19 causes a "cytokine storm” leading to death in patients with COVID-19 but they did find that secondary bacterial pneumonia that does not resolve was a key driver of death in patients with COVID-19 and may have exceeded death rates from the viral infection itself. The approach grouped similar ICU patient-days into clinical states based on electronic health record data and allowed the scientists to discover how complications such as bacterial pneumonia impacted the course of illness. For more on complications in COVID19 see J Clin Investig 2023;published online April 27. https://doi.org/10.1172/JCI170682 For more on efficacious monoclonal antibodies see Ann Intern Med 2023;published online April 18. https://doi.org/10.7326/M22-3428 For more on targets for herpes virus see Sci Adv 2023;9: eadf3977 For more on an RSV vaccine in pregnancy see N Engl J Med 2023;388: 1451–64 For more on Pillar[5]arene see Nat Commun 2023;14: 2141 For more on doxycycline for STIs see N Engl J Med 2023;388: 1296–306 For more on immunity in tuberculosis see Nat Immunol 2023;24: 753–54
ABSTRACT
BackgroundD-dimer and fibrinogen elevation has been observed in severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) infection which is associated with higher incidence of venous thromboembolism (VTE) and higher mortality rates. [1-3]. Autoimmune Rheumatic Diseases (ARDs) are associated with higher rates of VTE compared to general population [4]. Whether patients with ARDs infected with SARS-CoV2 have similar D-dimer and fibrinogen trends compared to patients without ARDs is unknown.ObjectivesCompare D-dimer and fibrinogen levels in patients with ARDs infected with SARS-CoV2 to patients without ARDs.MethodsPatients with ARDs infected with SARS-CoV2 were identified retrospectively from the electronic medical records (EMR) of Hamad Medical Corporation and matched (age and sex) to controls (1:3). D-dimer and fibrinogen levels were extracted electronically from EMR and stratified into six-time intervals defined in table 1. Day 0 was defined as the date of positive nasopharyngeal polymerase chain reaction swab test. 2 Independent Samples test (Mann-Whitney U) was used to compare the median (25th - 75th interquartile range [IQR]) level of D-dimer and fibrinogen between both study groups at the defined intervals.ResultsThe study included 203 cases and 551 controls with a mean (SD) age of 45.3 (11.7) and 44 (12.5) years, females were (122 [60.1%] vs. 297 [53.9%], p = 0.129), respectively.Distribution of ARDs was rheumatoid arthritis 86 (42.4%), spondyloarthropathy 33 (16.1%) and systemic lupus erythematosus 31 (15.7%) cases. 67% were on conventional synthetic disease modifying anti-rheumatic drugs (Cs-DMARDs), 15.8% on biological DMARDs and 4.9% on rituximab. About 83% of the ARDs group were in remission or low disease activity and 13% were in moderate or high disease activity.The median (25th - 75th IQR) level of D-dimer and fibrinogen were comparable between study groups in all defined intervals with insignificant p values except at interval 4, fibrinogen was significantly higher in the cases, p 0.006. Table 1ConclusionThere was no significant difference in the trend of D-dimer and fibrinogen levels during SARS-CoV2 infection between patients with ARDs and those without ARDs. Additional studies are needed to quantify the actual risk of VTE in patients with ARDs during SARS-CoV2 in correlation with serum markers of VTE.References[1]Eljilany I, Elzouki AN. D-Dimer, Fibrinogen, and IL-6 in COVID-19 Patients with Suspected Venous Thromboembolism: A Narrative Review. Vasc Health Risk Manag. 2020;16:455-62.[2]Li JY, Wang HF, Yin P, Li D, Wang DL, Peng P, et al. Clinical characteristics and risk factors for symptomatic venous thromboembolism in hospitalized COVID-19 patients: A multicenter retrospective study. J Thromb Haemost. 2021;19(4):1038-48.[3]Zhan H, Chen H, Liu C, Cheng L, Yan S, Li H, et al. Diagnostic Value of D-Dimer in COVID-19: A Meta-Analysis and Meta-Regression. Clin Appl Thromb Hemost. 2021;27:10760296211010976.[4]Lee JJ, Pope JE. A meta-analysis of the risk of venous thromboembolism in inflammatory rheumatic diseases. Arthritis Res Ther. 2014;16(5):435.Table 1.Differences in D-dimer and fibrinogen during SARS-CoV2 infection between patients with ARDs and those without at the defined intervals.Case N = 203Control N = 551P valueMedian (25th - 75th IQR), D-dimer (mg/L)(0 to < 3 days)0.56 (0.34 – 1.31)0.86 (0.54 – 1.41)0.096(≤ 3 to < 6 days)0.67 (0.35 – 2.58)1.11 (0.44 – 1.11)0.340(≤ 6 to < 9 days)0.81 (0.33 – 5.12)1.12 (0.56 – 3.28)0.299(≤ 9 to 12 days)0.94 (0.72 – 5.44)5.20 (1.0 – 15.05)0.058(≤ 12 to < 15 days)2.88 (0.72 – 5.53)4.96 (0.57 – 9.98)0.681(≤ 15 to 18 days)1.81 (0.89 – 2.55)5.56 (2.60 – 15.1)0.086Median (25th – 75th IQR), fibrinogen (mg/L)(0 to < 3 days)6.53 (2.0 - 6.53)5.65 (3.75 – 7.17)1.000(≤ 3 to < 6 days)6.25 (3.72 – 8.3)4.6 (4.1 – 5.6)0.385(≤ 6 to < 9 days)3.53 (3.29 – 4.62)3.4 (3.2 – 3.92)0.328(≤ 9 to 12 days)4.3 (2.82 – 4.78)2.2 (1.65 – 3.05)0.006(≤ 12 to < 15 days)4.4 (2.37 – 5.13)3.1 (1.7 – 4.45)0.170(≤ 15 to 18 days)3.6 ( – 5.7)3.7 (2.0 – 4.88)0.524Acknowledgements:NIL.Disclosure of InterestsNone Declared.
ABSTRACT
The impact of the 2019 coronavirus disease (COVID-19) pandemic is still being revealed, and little is known about the effect of COVID-19-induced outpatient and inpatient losses on hospital operations in many counties. Hence, we aimed to explore whether hospitals adopted profit compensation activities after the 2020 first-wave outbreak of COVID-19 in China. A total of 2,616,589 hospitalization records from 2018, 2019, and 2020 were extracted from 36 tertiary hospitals in a western province in China; we applied a difference-in-differences event study design to estimate the dynamic effect of COVID-19 on hospitalized patients' total expenses before and after the last confirmed case. We found that average total expenses for each patient increased by 8.7% to 16.7% in the first 25 weeks after the city reopened and hospital admissions returned to normal. Our findings emphasize that the increase in total inpatient expenses was mainly covered by claiming expenses from health insurance and was largely driven by an increase in the expenses for laboratory tests and medical consumables. Our study documents that there were profit compensation activities in hospitals after the 2020 first-wave outbreak of COVID-19 in China, which was driven by the loss of hospitalization admissions during this wave outbreak.
ABSTRACT
COVID-19 is currently a major global public health challenge. In the battle against the outbreak of COVID-19, how to manage and share the COVID-19 Electric Medical Records (CEMRs) safely and effectively in the world, prevent malicious users from tampering with CEMRs, and protect the privacy of patients are very worthy of attention. In particular, the semi-trusted medical cloud platform has become the primary means of hospital medical data management and information services. Security and privacy issues in the medical cloud platform are more prominent and should be addressed with priority. To address these issues, on the basis of ciphertext policy attribute-based encryption, we propose a blockchain-empowered security and privacy protection scheme with traceable and direct revocation for COVID-19 medical records. In this scheme, we perform the blockchain for uniform identity authentication and all public keys, revocation lists, etc are stored on a blockchain. The system manager server is responsible for generating the system parameters and publishes the private keys for the COVID-19 medical practitioners and users. The cloud service provider (CSP) stores the CEMRs and generates the intermediate decryption parameters using policy matching. The user can calculate the decryption key if the user has private keys and intermediate decrypt parameters. Only when attributes are satisfied access policy and the user's identity is out of the revocation list, the user can get the intermediate parameters by CSP. The malicious users may track according to the tracking list and can be directly revoked. The security analysis demonstrates that the proposed scheme is indicated to be safe under the Decision Bilinear Diffie-Hellman (DBDH) assumption and can resist many attacks. The simulation experiment demonstrates that the communication and storage overhead is less than other schemes in the public-private key generation, CEMRs encryption, and decryption stages. Besides, we also verify that the proposed scheme works well in the blockchain in terms of both throughput and delay.
ABSTRACT
OBJECTIVES/GOALS: Missed appointments (MAs) negatively impact the health outcomes of adults living with type 2 diabetes mellitus (T2DM), causing disruptions in clinic operation and added financial cost to healthcare providers and systems. This study aimed to identify risk factors for MAs in both in-person and telehealth settings among adults living with T2DM. METHODS/STUDY POPULATION: Using a sequential multi-method design guided by the modified Quality-Caring Model, the quantitative phase of this study used electronic health records (EHR) data in Calendar Years 2019 and 2020 with 7,276 encounters made by 2,235 patients with T2DM from four diabetes clinics within a tertiary academic medical center in Baltimore, MD. Multivariable random effect logistic regression were used to examine the association between MAs and included predictors (i.e., patient characteristics [e.g., age, race, health status], health provider factors [e.g., types of provider], and health system factors [e.g., scheduling lag]). Based on the results of the quantitative phase, a purposive sample of 23 adults with T2DM and 10 providers were then interviewed individually via phone or zoom. RESULTS/ANTICIPATED RESULTS: The EHR data found that the following variables decreased the odds of MAs: having an activated patient portal account, patients with age over 46 or with white race. Telehealth was associated with 50% decreased odds of MAs during COVID (after 3/23/2020). On the other hand, longer scheduling lag increased the odds of MAs. Qualitative interviews revealed that MAs were often related to social needs, such as lack of/limited health-related transportation and its associated financial burden. Telehealth helped break these barriers for some adults with T2DM, but technical challenges in telehealth persisted for those with low digital health literacy and people who did not have a digital device and/or with unstable internet connection. Providers worried that these challenges might undermine the quality of diabetes care. DISCUSSION/SIGNIFICANCE: Disparities in MAs by age and race were noted, which might reflect the impact of unmeasured social needs in EHR. Perceived convenient telehealth may reduce MAs in T2DM care. However, the persistent technical challenges of telehealth should be addressed to optimize the quality of diabetes care and to promote care continuity for underserved populations.
ABSTRACT
OBJECTIVES/GOALS: Analysis and modeling of large, complex clinical data remain challenging despite modern advances in biomedical informatics. We aim to explore the potential of topological data analysis (TDA) to address such challenges in the context of COVID-19 outcomes using electronic health records (EHRs). METHODS/STUDY POPULATION: In this work, we develop TDA approaches to characterize subtypes and predict outcomes in patients with COVID-19 infection. First, data for >70,000 COVID-19 patients were extracted from the OneFlorida EHR database. Next, enhancements to the TDA algorithm Mapper were designed and implemented to adapt the technique to this type of data. Clinical variables, including patient demographics, vital signs, and lab values, were then used as input to conduct a population-level exploratory analysis with an emphasis on identifying phenotypic subtypes at increased risk of adverse outcomes such as major adverse cardiovascular events (MACE), mechanical ventilation, and death. RESULTS/ANTICIPATED RESULTS: Preliminary Mapper experiments have produced visual representations of the COVID-19 patient population that are well-suited to exploratory analysis. Such visualizations facilitate easy identification of phenotypic subnetworks that differ from the general population in terms of baseline variables or clinical outcomes. In this and subsequent work, we aim to fully characterize and quantify differences between these subnetworks to identify factors that may confer increased risk (or protection from) adverse outcomes. We also plan to validate and rigorously compare the efficacy of this TDA-based approach to common alternatives such as clustering, principal component analysis, and machine learning. DISCUSSION/SIGNIFICANCE: This work demonstrates the potential utility of TDA for the characterization of complex biomedical data. Mapper provides a novel means of exploring EHR data, which are otherwise difficult to visualize and can aid in identifying or characterizing patient subtypes in diseases such as COVID-19.
ABSTRACT
OBJECTIVES/GOALS: During the pandemic, alcohol related deaths increased by 25%. To help understand how we might mitigate this negative outcome, we sought to examine the association of new diagnosis of alcohol use disorder (AUD) with SARS-CoV2 through two years of the pandemic. METHODS/STUDY POPULATION: Using a non-date-shifted TriNetX database, we conducted a retrospective cohort analysis of electronic health records of patients age ≥12 years who had been diagnosed either with COVID-19 (n=1,359,817) or other respiratory infections with no record of COVID-19 (n=2,013,031). Patients were then matched for propensity score risk for AUD, and results were analyzed by three-month intervals from January 2020 through January 2022, in blocks numbered 1-8. Results were expressed as hazard ratios (HR) and 95% confidence intervals (CI) for diagnosis of AUD from two weeks to six months following COVID-19 diagnosis. RESULTS/ANTICIPATED RESULTS: There was significant excess risk compared to control cohorts of AUD following COVID-19 diagnoses made during the first three months of the pandemic (HR (CI)): block 1: 2.41(1.89,3.08);no excess risk was seen for the remainder of 2020 (blocks 2-4) (HR1.01-1.14, NS). The excess risk increased again in 2021 as the delta and omicron variants emerged (HR and 95% CI): block 5 were: 1.26(1.11, 1.43));block 6: 1.88(1.62-2.18));block 7: 1.24(1.10,1.41);block 8: 1.12(1.0-1.25). COVID-19 diagnosis was associated with clinically-evident AUD under some pandemic circumstances. DISCUSSION/SIGNIFICANCE: COVID-19 early in the pandemic (block 1) was associated with substantial excess risk for new diagnosis of AUD, with smaller excess risk after COVID-19 during 2021 (blocks 5-7), and no excess risk otherwise. Diagnosis of COVID-19 and pandemic contextual factors are associated with increased risk for AUD.
ABSTRACT
This paper introduces a prototype for clinical research documentation using the structured information model HL7 CDA and clinical terminology (SNOMED CT). The proposed solution was integrated with the current electronic health record system (EHR-S) and aimed to implement interoperability and structure information, and to create a collaborative platform between clinical and research teams. The framework also aims to overcome the limitations imposed by classical documentation strategies in real-time healthcare encounters that may require fast access to complex information. The solution was developed in the pediatric hospital (HP) of the University Hospital Center of Coimbra (CHUC), a national reference for neurodevelopmental disorders, particularly for autism spectrum disorder (ASD), which is very demanding in terms of longitudinal and cross-sectional data throughput. The platform uses a three-layer approach to reduce components' dependencies and facilitate maintenance, scalability, and security. The system was validated in a real-life context of the neurodevelopmental and autism unit (UNDA) in the HP and assessed based on the functionalities model of EHR-S (EHR-S FM) regarding their successful implementation and comparison with state-of-the-art alternative platforms. A global approach to the clinical history of neurodevelopmental disorders was worked out, providing transparent healthcare data coding and structuring while preserving information quality. Thus, the platform enabled the development of user-defined structured templates and the creation of structured documents with standardized clinical terminology that can be used in many healthcare contexts. Moreover, storing structured data associated with healthcare encounters supports a longitudinal view of the patient's healthcare data and health status over time, which is critical in routine and pediatric research contexts. Additionally, it enables queries on population statistics that are key to supporting the definition of local and global policies, whose importance was recently emphasized by the COVID pandemic.
ABSTRACT
Each year, Telehealth and Medicine Today asks experts in the field to share their insights into the future and predict how telehealth will influence uptake and healthcare in the new year.
ABSTRACT
Medical devices and Electronic Medical Records (EMR) have been technologically integrated, transforming their independent structure and functionality. There is a significant increase in medical device deployment in healthcare institutions. However, the integration exposes them to cyber threats, which can undermine effective care delivery and threaten patient safety. The World Health Organization has noticed a significant rise in cyberattacks following the COVID-19 outbreak. This paper reviews the literature on cyber threats affecting the medical device integration with EMR (MDI-EMR). It highlights the cyber threats to the MDI-EMR and the effectiveness of control mechanisms. The most common cyber threats to MD include phishing, ransomware attacks, data breaches, Distributed Denial of Service attacks, and SQL injection. Security challenges associated with the EMR and medical devices are also threating their confidentiality, integrity, and availability. The review enables researchers to better understand safety, security and privacy issues related to the MD-EMR, as well as available solutions. © 2022 IEEE.
ABSTRACT
Health information technology (HIT) holds potential to transform Home Health Care (HHC), yet, little is known about its adoption in this setting. In the context of infection prevention and control, we aimed to: (1) describe challenges associated with the adoption of HIT, for example, electronic health records (EHR) and telehealth and (2) examine HHC agency characteristics associated with HIT adoption. We conducted in-depth interviews with 41 staff from 13 U.S. HHC agencies (May-October 2018), then surveyed a stratified random sample of 1506 agencies (November 2018-December 2019), of which 35.6% participated (N = 536 HHC agencies). We applied analytic weights, generating nationally-representative estimates, and computed descriptive statistics, bivariate and multivariable analyses. Four themes were identified: (1) Reflections on providing HHC without EHR;(2) Benefits of EHR;(3) Benefits of other HIT;(4) Challenges with HIT and EHR. Overall, 10% of the agencies did not have an EHR;an additional 2% were in the process of acquiring one. Sixteen percent offered telehealth, and another 4% were in the process of acquiring telehealth services. In multivariable analysis, EHR use varied significantly by geographic location and ownership, and telehealth use varied by geographic location, ownership, and size. Although HIT use has increased, our results indicate that many HHC agencies still lack the HIT needed to implement technological solutions to improve workflow and quality of care. Future research should examine the impact of HIT on patient outcomes and the impact of the COVID-19 pandemic on HIT use in HHC.
ABSTRACT
PURPOSE OF THE STUDY: Respiratory viruses, air pollutants, and aeroallergens are all implicated as triggers for pediatric asthma symptoms. The current study sought to determine whether changes in respiratory viruses, air pollutants, or aeroallergens during the coronavirus disease 2019 (COVID-19) pandemic were associated with decreased asthma exacerbations. In a prior study, the authors found that during the first months following public health interventions to limit the spread of COVID-19, asthma visits and steroid prescriptions decreased by more than 80%, with a corresponding decrease in rhinovirus infections, without noted changes in air pollution. STUDY POPULATION: The authors reviewed asthma patient encounter data from January 1 to December 31 for the years 2015 through 2020 from the Children's Hospital of Philadelphia Care Network, including 48 outpatient primary care and specialty care sites, 4 urgent care sites, 15 community hospitals, and a 557-bed quaternary care center. Demographic data for outpatient, inpatient, and video visits were characterized by patient sex, race, ethnicity, birth year cohort, and payer type. 2020 data for 28 157 patients were compared with 2015 to 2019 data for 88 039 patients. METHODS: Health care utilization and respiratory viral testing data for the period between January 1, 2015 and December 31, 2020 were extracted from the Children's Hospital of Philadelphia Care Network electronic health record. Air pollution data, including particulate pollution, ozone, and nitrogen dioxide, were obtained from US Environmental Protection Agency databases. Tree, grass, weed, and mold aeroallergen data were obtained from a National Allergy Bureau-certified monitoring station. Summary statistics for rates of encounters and asthma-related prescriptions from 2020 were compared with those from 2015 to 2019. Comparisons were made between prelockdown, lockdown, and phased reopening periods for public health measures in Philadelphia and surrounding counties. RESULTS: During the COVID-19 pandemic, weekly positive tests for influenza A, influenza B, RSV, and rhinovirus were lower than 2015 to 2019 historical averages. Air pollution and aeroallergen trends did not substantially change during the COVID-19 pandemic compared with historic and seasonal average data. CONCLUSIONS: Viral respiratory infections are a primary driver of pediatric asthma exacerbations.
ABSTRACT
This report describes the development of a data-driven approach for identifying individuals who tested negative to a SARS-CoV-2 infection, despite their residence with individuals who had confirmed infections. Household studies have demonstrated efficiency in evaluating exposure to SARS-CoV-2. Leveraging earlier studies based on the household unit, our analysis utilized close contacts in order to trace chains of infection and to subsequently categorize TEFLONs, an acronym for Timely Exposed to Family members Leaving One Not infected. We used over one million anonymized electronic medical records, retrieved from Maccabi Healthcare Services' centralized computerized database from March 2020 to March 2022. The analysis yielded 252 TEFLONs, who were probably at very high risk of infection and yet, demonstrated clinical resistance. The exposure extent in each household positively correlated with household size, reflecting the in-house rolling transmission event. Our approach can be easily implemented in other clinical fields and should spur further research of clinical resistance to various infections.
ABSTRACT
P35 Figure 1ConclusionsUnplanned hospital attendances are common amongst cirrhotic patients, particularly those from lower socioeconomic groups. Over half of patients with at least one liver-related admission died during the follow up period, with two thirds of these deaths occurring during or shortly after discharge.The data shows the true burden of liver disease and highlights the need for improved in-patient care for this vulnerable patient group.
ABSTRACT
IntroductionDuring the COVID-19 pandemic, many elective services were discontinued, including day-case venesection for patients with haemochromatosis. As services resumed, prioritisation of patients for venesection was required according to clinical need. Venesection procedure codes are recorded within inpatient episodes in the hospital electronic health record (EHR). This study aimed to use analysis of these episodes to stratify patients requiring the most urgent venesection.MethodsUtilising a database of 540 patients with haemochromatosis, details of all inpatient episodes between January 2015 and March 2020 were obtained from the hospital’s Informatics Department. For each patient, the total number of venesections for each calendar year was obtained, along with the start date of venesection and the hospital site. Patients with cirrhosis were identified from analysis of diagnosis codes contained within the EHR. Those patients with the highest intensity venesection prior to the discontinuation of services, and those who had commenced venesection most recently, were considered the highest priority for venesection.ResultsBetween January and March 2020, 31 patients had started venesection for haemochromatosis. Among the 540 patients receiving treatment, those undergoing the most intense venesection included 29 patients who had more than 16 procedures in 2019 and 2020. A further 20 patients had received 12–15 venesection and 54 had undergone 8–11 procedures during this period. Patients were stratified according to their local treatment site and venesection was restarted according to clinical need identified by this analysis.ConclusionsAnalysis of EHRs has been used extensively in epidemiological research and various methodologies have been developed. This study demonstrates its utility in service development with a direct impact on patient care. This analysis enabled a rapid framework for identifying clinical need, prior to restarting routine monitoring with serum ferritin.Since venesection for haemochromatosis requires a day case admission, these episodes are captured nationally in the Hospital Episode Statistics (HES) database. This study also demonstrates the use of EHRs for future studies in patients with haemochromatosis, including the prevalence of cirrhosis and its complications, analysis of regional variation in service provision, and clinical outcomes.
ABSTRACT
Early prediction of patients at risk of clinical deterioration can help physicians intervene and alter their clinical course towards better outcomes. In addition to the accuracy requirement, early warning systems must make the predictions early enough to give physicians enough time to intervene. Interpretability is also one of the challenges when building such systems since being able to justify the reasoning behind model decisions is desirable in clinical practice. In this work, we built an interpretable model for the early prediction of various adverse clinical events indicative of clinical deterioration. The model is evaluated on two datasets and four clinical events. The first dataset is collected in a predominantly COVID-19 positive population at Stony Brook Hospital. The second dataset is the MIMIC III dataset. The model was trained to provide early warning scores for ventilation, ICU transfer, and mortality prediction tasks on the Stony Brook Hospital dataset and to predict mortality and the need for vasopressors on the MIMIC III dataset. Our model first separates each feature into multiple ranges and then uses logistic regression with lasso penalization to select the subset of ranges for each feature. The model training is completely automated and doesn't require expert knowledge like other early warning scores. We compare our model to the Modified Early Warning Score (MEWS) and quick SOFA (qSOFA), commonly used in hospitals. We show that our model outperforms these models in the area under the receiver operating characteristic curve (AUROC) while having a similar or better median detection time on all clinical events, even when using fewer features. Unlike MEWS and qSOFA, our model can be entirely automated without requiring any manually recorded features. We also show that discretization improves model performance by comparing our model to a baseline logistic regression model. © 2022 IEEE.