Comprehensive Code List Associated with CDC-Defined High-Risk Underlying Medical Conditions Leading to Potential Progression to Severe COVID-19 Outcomes

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

Identifying Clinical Subtypes of Long COVID: An Unsupervised Machine Learning Approach

Objectives: The symptoms of patients with post-acute COVID-19 syndrome are heterogenous, impact multiple systems, and are often non-specific. To better understand the symptomatic profile of this population, this study used real-world data and unsupervised machine learning techniques to identify distinct groupings of long COVID patients. Method(s): Children/adolescents (age 0-17) and adults (age 18-64 and >=65) with >=2 primary diagnoses for U09.9 "Post COVID-19 condition" from 10/01/2021 (ICD-10 code introduction) until 03/31/2022 were selected from Optum's de-identified Clinformatics Data Mart Database, with the first diagnosis deemed index. Included patients had >=1 diagnosis for COVID-19 at least 4 weeks before index and continuous enrollment during the 12 months prior to index. Diagnoses recorded +/-2 weeks from index that were not present prior to the initial COVID-19 diagnosis were captured and used as patient features for k-means clustering. Final cluster assignments were selected based on silhouette coefficient and clinical relevancy of groupings. Result(s): 3,587 patients met eligibility criteria, yielding three clusters. Concurrent symptom domains surrounding index included breathing, fatigue, pain, cognitive, and cardiovascular diagnoses. The first cluster (N=2,578, 71.8%) was characterized by patients with only a single symptom domain (33% breathing, 33% cardiovascular, 20% fatigue, 11% cognitive). The second cluster (N=651, 18.1%) all presented with breathing symptoms accompanied by one additional domain (cardiovascular 40%, fatigue 28%, pain 18%). The final cluster (N=358, 9.9%) experienced breathing symptoms accompanied by two additional domains (fatigue and cardiovascular 34%, cardiovascular and cognitive 34%). Cluster 3 was slightly older than clusters 1 or 2 (mean age 66 vs. 58 years, respectively). Conclusion(s): Unsupervised machine learning identified distinct groups of long COVID patients, which may help inform multidisciplinary care needs. Our analysis suggests that many patients with long COVID may experience symptoms from only a single domain, and multi-system illness may generally include breathing complications accompanied by fatigue and/or cardiovascular complications.Copyright © 2023

Characteristics of Patients Diagnosed with Long COVID: Early Findings from Real-World Data

Objectives: While persistent and relapsing symptoms of COVID-19 are increasingly documented, limited data exist on the post-acute population. The objective of this analysis is to identify the characteristics of patients diagnosed with long COVID using real-world data. Method(s): Children/adolescents (age 0-17) and adults (age 18-39, 40-64 and >=65) with >=2 primary diagnoses for U09.9 "Post COVID-19 condition" from 10/01/2021 (ICD-10 code introduction) until 03/31/2022 were selected from Optum's de-identified Clinformatics Data Mart Database, with the first diagnosis deemed index. Included patients had >=1 diagnosis for COVID-19 and continuous enrollment 12 months prior to index (baseline). To ensure alignment with most institutional definitions, >=4 weeks between initial COVID-19 infection and index was required. Diagnoses recorded +/-2 weeks from index that were not present prior to the initial COVID-19 diagnosis were summarized. Newly prescribed treatments and total medical costs were evaluated during the month following index (continuous enrollment required). Result(s): 3,587 patients met eligibility criteria (mean age 59.02, 57.56% female) with a median time from initial COVID-19 infection to long COVID diagnosis of 83 days (IQR: 46-201 days). The most common concurrent diagnoses included breathing complications such as dyspnea (20.38%) and respiratory failure (15.23%);malaise and fatigue (15.31%);symptoms related to cognitive functioning/anxiety (11.35%);and chest pain (7.67%). Children/adolescents had the highest prevalence of chest pain, while patients >=65 years of age had the highest prevalence of issues with coordination. The average total medical cost during the month following long COVID diagnosis was $4,267 (SD $14,662), with common prescriptions including albuterol (4.42%), prednisone (3.51%), and methylprednisolone (2.01%). Conclusion(s): This retrospective analysis confirms clinically documented symptoms of long COVID in a large, real-world population. Once more data become available, further research on the long term economic and clinical outcomes among patients diagnosed with post-acute COVID-19 syndrome are warranted.Copyright © 2023

A global federated real-world data and analytics platform for research.

Objective: This article describes a scalable, performant, sustainable global network of electronic health record data for biomedical and clinical research. Materials and Methods: TriNetX has created a technology platform characterized by a conservative security and governance model that facilitates collaboration and cooperation between industry participants, such as pharmaceutical companies and contract research organizations, and academic and community-based healthcare organizations (HCOs). HCOs participate on the network in return for access to a suite of analytics capabilities, large networks of de-identified data, and more sponsored trial opportunities. Industry participants provide the financial resources to support, expand, and improve the technology platform in return for access to network data, which provides increased efficiencies in clinical trial design and deployment. Results: TriNetX is a growing global network, expanding from 55 HCOs and 7 countries in 2017 to over 220 HCOs and 30 countries in 2022. Over 19 000 sponsored clinical trial opportunities have been initiated through the TriNetX network. There have been over 350 peer-reviewed scientific publications based on the network's data. Conclusions: The continued growth of the TriNetX network and its yield of clinical trial collaborations and published studies indicates that this academic-industry structure is a safe, proven, sustainable path for building and maintaining research-centric data networks.

Pediatric Cardiac Screening Data Warehouse: A National Public Health Resource

Background: Cardiac screening of youth for prevention of sudden cardiac death in the young (SCDY) has been debated due to the absence of large population-specific screening data with outcomes. Despite years of screening by US public screening groups (PSG), there is minimal coordination of effort and no standardized methods for real-world data collection. Objective(s): To understand the methods, quality, outcomes, and best practices of youth screening, the Cardiac Safety Research Consortium Pediatric Cardiology Working Group, in collaboration with FDA and PSGs, developed and enabled a scalable system to collect a uniform pediatric cardiac screening dataset including digital ECGs and post-screening electronic follow-up data. Method(s): Front end data collection (figure) was developed to include use of a universal unique ID system to align paper/digital collection of health and ECG data. PSGs use secure data transfer portals for digital ECG data upload for conversion to device-agnostic standardized FDA format to store in the national pediatric cardiac screening data warehouse. Follow-up data are obtained at designated post-screening intervals (one week, one and 3 months for pilot study) using initial text message contact followed by electronic consent (REDCap) and answering online health surveys. Result(s): Fourteen PSGs in ten states participated in the pilot study. PSG warehouse data include 33840 retrospective ECG datasets collected from 2010 to 2021 containing limited screened history/symptoms but demographics similar to US census as follows: Age 13-30y, Male/Female 57/43%, Asian 6%, Black 19%, Native American <1%, Pacific Islander <1%, White 68%, Other 4%;Hispanic/Non-Hispanic 27%/79%. Individual PSG site demographics reflected local populations. Prospective data collection since 2021 include >4000 uniform screening datasets (age, sex, race, ethnicity, ht, wt, screening H&P, COVID history, medications, digital ECG with results, screening outcome, and, if applicable, ECHO results). Follow up participation allowing initial cellular contact was high (avg 73%, range 51-91%/screening). Conclusion(s): Establishment of a national pediatric cardiac data warehouse enables large-scale aggregation of pediatric cardiac screening information to address deficits in the understanding and prevention of SCDY. This large real-world dataset will help establish normative data for pediatric ECGs which can facilitate development of new diagnostic tools such as machine learning and support pediatric drug and device development. [Formula presented]Copyright © 2023

Covid-19 Outcome in Patients with and without Cirrhosis

Background: Severe outcomes of COVID-19 are associated with advancing age, and multiple medical comorbidities. The impact of COVID-19 on the clinical course of patients with cirrhosis has not been well studied. We determined the effect of SARS-CoV-2 infection on the hospitalization and survival rates of patients with cirrhosis. Method(s): Using ICD-10-CM codes, we identified all Veterans with a diagnosis of cirrhosis in the VA Corporate Data Warehouse and COVID-19 Shared Data Resource. Study cohort included Veterans who were tested for SARS-CoV-2 and had no history of organ transplantation or malignancies. Each SARS-CoV-2 positive case was propensity-score matched by demographics and comorbidities with up to two SARS-CoV-2 negative controls. The primary endpoints were acute care hospitalization, admission to intensive care, respiratory support, or death. Result(s): Of 1,115,037 individuals tested for SARS-CoV-2, 31,680 were noted to have cirrhosis and among them 5,047 (16%) were SARS-CoV-2 positive. After exclusions and propensity-score matching, 5,047 SARS-CoV-2 positive and 9,913 propensity score matched SARS-CoV-2 negative individuals were included in the analysis cohort. Median age was 67 years, 95% were men and 25% were of black race. Median BMI was 30 and history of hypertension, diabetes, cardiovascular and chronic pulmonary disease was noted among 81%, 54%, 56% and 32% respectively. Among all cirrhotic individuals, SARS-CoV-2 positive individuals less frequently progressed to hepatic decompensation (3.1% vs 4.8%, P< 0.0001) or hospitalization (35.7% vs 38.2%, P=0.002), but more frequently required ICU admission 15% vs 12.2%, P< 0.0001) or respiratory support (7.3% vs 8.4%, P=0.01). Among those admitted, length of hospital stay was longer among SARS-CoV-2 positive individuals (7 vs 4 days, P< 0.0001). In Cox regression analysis, SARS-CoV-2 positivity was associated with a higher risk of all-cause mortality (HR 1.37, 95% CI 1.19,1.56). Conclusion(s): Although patients with cirrhosis and COVID-19 were less often hospitalized, they had longer duration of hospitalization and were at higher risk of severe or critical illness and death. (Figure Presented).

Trends in Stress Urinary Incontinence Procedures from 2012 to 2020 from the Uda Project

INTRODUCTION AND OBJECTIVE: Stress urinary incontinence (SUI) is a major quality of life problem for many people. In women, SUI is associated with pelvic organ prolapse (POP) and in men after a radical prostatectomy. A safety review started in 2011 by the FDA of POP mesh resulted in the 2019 recall. This study reviews the prevalance and procedure trends for SUI between 2012 to 2020. METHOD(S): Using the 100% Optum Clinformatics Data Mart data and 100% national Medicare Fee-for-Service data, we identified subjects claims for urinary incontinence (UI) and any procedures performed for UI. Results reported as mean +/- standard deviation. RESULT(S): From 2012 - 2020, the mean prevalence of any UI in the 18-64 age group was 37,529 +/- 3292 or 0.62% of the population. In the Medicare population (aged 65+), it was 1,439,221 +/- 90507, or 5.7% of the group. The Female to Male ratio in the <65 yr group was 2.52:1 and in the 65+ was 5.31:1. The Medicare mean SUI prevalence was 212223 +/- 14292 (0.84% population), and the Optum group was 13,179 +/- 1,535 (0.22%).38,677 Medicare patients received procedures for UI in 2012. This increased to 54,122 by 2019, falling to 45,667 during COVID. In 2012, 12,286 patients received SUI procedures, which plateaued at 8,670/yr for 2015 to 2019. In 2020, 6508 patients had a SUI procedure. (Breakdown in Figure 1A). 4020 patients with UI aged 18-64 got a procedure in 2012, which decreased by 45% to 2635 in 2019 with a dip for COVID to 2020. The numbers plateaued from 2014 to 2019 at 2500 patients/yr approximately. The SUI patient numbers for this group decreased from 2501 in 2012 to 967 in 2020, plateaued between 2014 and 2019 at 1250 patients/yr approximately. (Breakdown in Figure 1B) There was a 50% decrease in patients getting sling procedures. Patients obtaining artificial urinary sphincter, and injectables remained constant. Men receiving artificial urinary sphincter, slings, and injectables has remained even in the 65+ age group. However, in the 18-64 age group, men obtaining slings decreased. CONCLUSION(S): The claims prevalence for UI has increased in older age and decreased in younger patients. Sling use has decreased in all female patients and younger men. The overall decrease in procedures for SUI, appears partly due to decreased reporting of incontinence claims in the younger population, together decreased Sling procedures in female patients.

CLINICAL OUTCOMES BY SARS-CoV-2 VARIANT AT A SINGLE ACADEMIC MEDICAL CENTER

Background: SARS-CoV-2 continues to change over time due to genetic mutations and viral recombination.1 Given the changing landscape of COVID-19 variants and availability of COVID-19 vaccinations, disease severity during acute infection has also been variable. However, most research related to COVID-19 to date has not focused on evaluating differences in outcomes by the dominant variant and the impact it might have on post-acute sequalae of COVID-19 (PASC). Method(s): We developed a data mart of electronic health record data pertaining to COVID-19 in a single North American metropolitan health system (RUSH University Medical Center). Patients were selected for analysis if they had at least one documented infection of COVID-19. Date ranges were established per dominant variant, and the date of diagnosis was matched to variant. Variants were determined by the most prominent variant of concern (VOC) circulating in the city of Chicago. Variants were categorized by the following by date ranges: Wildtype+D614G (3/7/20-3/20/21), Alpha (3/21/21-6/19/21), Delta (6/20/21-12/11/21), Omicron BA.1 (12/12/21-3/19/22), Omicron BA.2 (3/20/22- 6/18/22), and Omicron BA.4/BA.5 (6/19/22-present (9/30/22). Subsequent clinical outcomes were examined, including hospitalization, intensive care unit admission, or death. We characterized our sample by conducting descriptive statistics including frequency and percent of outcome by variant. Result(s): 44,499 patients were included in this analysis with 30.23% requiring hospitalization, 4.25% being admitted to intensive care unit (ICU), and 2.35% resulting in death. The greatest percentage of hospitalizations occurred with the Alpha variant at 41.88% (N=928), and the greatest percentage of ICU admissions (6.43%) and death (3.15%) occurred with the Delta variant. The latest Omicron variant (Wave 6) showed an increase in hospitalizations (35.18%), as compared to early Omicron waves (Wave 4 and 5) but maintained similar ICU rates. Death rates continued to decline during the Omicron waves (Table 1). Conclusion(s): Although Alpha and Delta variants seem to have more severe outcomes compared to other variants, it is important to note that COVID-19 prevention, treatment access, and management continues to change, potentially influencing how outcomes may differ over time. Future work should determine factors to adjust for when examining variant-level differences.

Severe COVID-19 infection predisposes patients to greater burden of hair loss

There is a growing body of evidence suggesting a link between COVID-19 infection and certain forms of hair loss, such as telogen effluvium. The present study aims to determine the prevalence of hair loss following COVID-19 infection and ascertain the role of COVID-19 severity as a risk factor for its development. A retrospective study was conducted using patient data from the Northwestern Medicine Enterprise Data Warehouse with institutional review board approval from Northwestern University. Patients aged >= 18 years with COVID-19 diagnoses between January 2020-June 2022 and >= 1 encounter with dermatology providers within 180 days post-infection were included in the study. History of COVID-19 and documented hair loss diagnoses were recorded along with demographic data. COVID-19 severity was classified based on whether the patient was given outpatient or inpatient/emergency care for COVID-19. Time-to-alopecia onset was calculated relative to the nearest preceding COVID-19 diagnosis. Pearson's chi-squared and Kaplan-Meier analysis were performed to evaluate differences in incidence and time-to-alopecia onset by severity of COVID-19 infection. Analyses were conducted using R 4.2.1. In total, 10,861 patients met the inclusion criteria for the study. Patients were more commonly female (N = 6,974, 64.2%) and White (N = 8,301, 76.4%) with a mean age of 48 years at COVID-19 diagnosis. Overall, 6.5% of COVID-19 patients treated in inpatient/emergency settings developed hair loss compared to 4.7% in outpatient settings (P = 0.009). Patients with outpatient care had a median time to alopecia diagnosis of 73 days, compared to 99 days for patients with inpatient/emergency care (P = 0.019). Our findings demonstrate hair loss following COVID-19 infection as a notable sequela of infection. Clinicians should closely monitor patients following hospitalization for COVID-19, as they may be predisposed to hair loss following infection due to psychological or physiological stress. Future studies should aim to validate our findings and explore this relationship on a larger scale.Copyright © 2023

COVID-19 patient diagnosis and treatment data mining algorithm based on association rules.

Association rules are used in different data mining applications, including Web mining, intrusion detection, and bioinformatics. This study mainly discusses the COVID-19 patient diagnosis and treatment data mining algorithm based on association rules. General data The key time interval during the main diagnosis and treatment process (including onset to dyspnea, first diagnosis, admission, mechanical ventilation, death, and the time from first diagnosis to admission, etc.), the cause of death by laboratory examination, and so forth. The frequency of drug use was counted and association rule algorithm was used to analyse and study the effect of drug treatment. The results could provide reference for rational drug use in COVID-19 patients. In this study, in order to improve the efficiency of data mining in data processing, it is necessary to pre-process these data. Secondly, in the application of this data mining, the main objective is to extract association rules of COVID-19 complications. So its properties for mining should be various diseases. Therefore, it is necessary to classify individual disease types. During the construction of association rules database, the data in the data warehouse is analysed online and the association rules data mining is analysed. The results are stored in the knowledge base for decision support. For example, the prediction results of the decision tree can be displayed at this level. After the construction of the mining model, the display interface can be mined, and the decision-maker can input the corresponding attribute value and then predict it. 0.76% of people had both COVID-19, CHD and hypertension, while 46.5% of people with COVID-19 and CHD were likely to have hypertension. This study is helpful to analyse the imaging factors of COVID-19 disease.

Using Real World Evidence (Rwe) to Assess Drug-Drug Interaction (Ddi) between Paxlovid and the Top 100 Prescribed Drugs in the Us

BACKGROUND: Paxlovid (nirmatrelvir/ritonavir) has received a US Emergency Use Authorization for patients >=12 years with mild-to- moderate COVID-19 at high-risk of progression to severe disease. DDI studies conducted with Paxlovid implicate the PK enhancer ritonavir as the main perpetrator of DDIs. Ritonavir is a potent inhibitor of CYP3A4, CYP2D6, and P-gp. The Paxlovid Fact Sheet1 identifies contraindicated drugs and those with a potentially important interaction. METHOD(S): A retrospective analysis was conducted using RWE from the Optum Clinformatics Data Mart. Patients were identified based on CDC criteria for high-risk COVID-19 and confirmed continuous insurance enrollment from Jan 1 to Dec 31, 2019 with >=1 prescription claim. Excluding non-drug claims (e.g., vaccines), the top 100 drugs were selected and ranked based on total patient counts. DDI potential with Paxlovid was evaluated using US Prescribing and DailyMed Information or relevant literature for each drug. RESULT(S): Of the top 100, 70 drugs are not expected to have a DDI with Paxlovid. These drugs are eliminated unchanged in urine, cleared by enzymes other than CYP3A4 or CYP2D6, are not P-gp substrates, or are cleared by multiple pathways. The remaining 30 drugs expected to have a DDI are represented in the Paxlovid Fact Sheet. The top four drug classes expected to interact with Paxlovid include corticosteroids, narcotic analgesics, anticoagulants, and statins. One drug, simvastatin, is contraindicated. The mechanism of interaction with Paxlovid, or lack thereof, will be presented in detail for each drug. CONCLUSION(S): Paxlovid DDI management is important to ensure the right patients receive this antiviral. This analysis provides an understanding of Paxlovid interactions with the top 100 drugs likely to be used in high-risk COVID-19 patients and serves as an additional DDI management resource.

Effect of COVID-19 on non-cystic fibrosis bronchiectasis exacerbation rates: a retrospective claims study

Objective: Non-cystic fibrosis bronchiectasis (NCFB) is associated with respiratory symptoms and exacerbations. The aetiology of exacerbations is poorly studied. This retrospective study sought to understand the impact of the COVID-19 pandemic on exacerbation frequency in those with NCFB. Method(s): Health insurance claims data (Mar. 2018-Feb 2020;Mar 2019-Feb 2021) from US Optum Clinformatics DataMart, which includes claims covering 68.8 million US patients, were used. Eligible patients had >=1 NCFB diagnosis code, >=1 exacerbation in each of the 2 time windows, and no other respiratory diseases. Descriptive analyses and chi square tests were used to test differences (exacerbations & patients per category). Result(s): The study included 905 patients in the 2018-2020 evaluation and 954 patients in the 2019-2021 evaluation. Fewer patients had an increased exacerbation rate in 2019-2021 than 2018-2020 (29% vs 43%) and a greater number had a decreased exacerbation rate (63% vs 45%). Total exacerbations changed by -43% in 2019-2021 vs -3% in 2018-2020, as did inpatient visits (-37% vs -5%) and antibiotic use (-44% vs -3%). A higher proportion of patients with exacerbations at baseline had no exacerbations in 2020-2021 (57% vs 39%;Figure). Conclusion(s): Public health measures taken during the COVID-19 pandemic were associated with lower NCFB exacerbation rates, potentially by decreasing exposure to viruses.

The Impact of the Covid19 Pandemic on Patients with Peripheral Artery Disease

Background The impact of COVID-19 on major adverse lower extremity (MALE) and cardiovascular events (MACE) in patients with peripheral artery disease (PAD) is unknown. Methods Using the VA Corporate Data Warehouse, Veterans with PAD were identified. Rates of MALE (amputation or lower extremity revascularization [LER]), and MACE (death, MI, or coronary revascularization) were assessed in pre-pandemic (3/11/2019-3/10/2020), early-pandemic (3/11/2020-3/10/2021), and late-pandemic (3/11/2021-3/10/2022) periods. Outcomes were compared using Kaplan-Meier method. Results Of 418,042 Veterans (mean age 72 yrs) with PAD, 76.7% were white and 96.8% male. Furthermore, 89.2% had HTN, 60.4% diabetes, 49.3% CAD, 21.6% heart failure, and 20.5% atrial fibrillation. From 3/11/2019 to 3/10/2022, 3,100 had amputation, 8,187 had LER, & 2,229 had MACE. Amputation rates declined and continued to decline in early- and late-pandemic period (306 to 268 to 235;p<0.001;rates per 100k). Rates of LER declined initially and stabilized in late-pandemic period (951 to 587 to 609;p < 0.001;rates per 100k). MACE did not change significantly. (215 to 168 to 202;p<0.001;rates per 100k). Conclusion Amputation rates in Veterans with PAD did not increase despite a clinically significant decline in LER. Given the known efficacy of noninvasive therapies in PAD, these data suggest that there is a need to re-evaluate appropriate indications for LER and amputation. [Formula presented]Copyright © 2023 American College of Cardiology Foundation

An Analytical Survey of Difficulty Faced in an Online Lecture During COVID-19 Pandemic Using CRISP-DM

COVID-19 has greatly disturbed life in many ways and has changed the way we live. Various surveys have been conducted in different fields, and the teaching-learning process has been affected to a great extent. During this pandemic, various online tools and technologies have been available for guiding students without attending school. Many governments, corporations, and research fields have officially ordered to use of online media for the teaching-learning process. Platforms such as Google Meet, Microsoft Team, and Web-e-X have allowed and arranged for online video conferencing mediums to achieve the goal of the teaching-learning process. However, as mentioned above, there are some serious issues with the online teaching-learning process. These include problems with continuous network bandwidth during sessions, physical and mental presence in the class, difficulties handling mathematics classes, and the potential for non-sense activities that may disturb the entire class. In order to discover knowledge, I am using a new approach to data mining technology called CRISP-DM. This study addresses the effectiveness of online teaching mode and learning and the challenges faced by students and teachers who are taking online classes during COVID-19. According to this study, 88.2% of students did not have proper internet or technology facilities, 58.30% of students were not satisfied with online learning, 85.3% of students complained about eyesight issues from taking online classes on devices, and 50.01% of students were unable to manage university affairs © 2023 Manmohan Singh, Vinod Patidar, Shaheen Ayyub, Anita Soni, Monika Vyas Dharmendra Sharma and Amol Ranadive. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license

Analysis of the impact of the SARS-CoV-2 infection on the pediatric population hospitalized during the pandemic in the Greater Paris University Hospitals.

Background: The clinical characteristics, disease progression and outcome in children affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection appear significantly milder compared to older individuals. Nevertheless, the trends in hospitalization and clinical characteristics in the pediatric population seem to be different over time across the different epidemic waves. Objective: Our aim was to understand the impact of the different COVID-19 variants in the pediatric population hospitalized in the Pediatric Departments of the Public Hospital in the Greater Paris area by the analysis performed with the Assistance Publique-Hopitaux de Paris (AP-HP) Health Data Warehouse. Methods: This is a retrospective cohort study including 9,163 patients under 18 years of age, hospitalized from 1 March 2020 to 22 March 2022, in the Paris area, with confirmed infection by SARS-CoV-2. Three mutually exclusive groups with decreasing severity (Pediatric Inflammatory Multisystem Syndrome (PIMS), symptomatic infection, mild or asymptomatic infection) were defined and described regarding demography, medical history, complication of the SARS-CoV-2 infection, and treatment during admission. Temporal evolution was described by defining three successive waves (March-September 2020, October 2020-October 2021, and November 2021-March 2022) corresponding to the emergence of the successive variants. Results: In the study period, 9,163 pediatric patients with SARS-CoV-2 infection were hospitalized in 21 AP-HP hospitals. The number of patients with SARS-CoV-2 infection increased over time for each wave of the pandemic (the mean number of patients per month during the first wave was 332, 322 during the 2nd, and 595 during the third wave). In the medical history, the most associated concomitant disease was chronic respiratory disease. Patients hospitalized during the third wave presented a higher incidence of pulmonary involvement (10.2% compared to 7% and 6.5% during the first and second waves, respectively). The highest incidence of PIMS was observed during the first and second waves (4.2% in the first and second waves compared to 2.3% in the 3rd wave). Discussion: This analysis highlighted the high incidence of hospitalized children in the Greater Paris Area during the third wave of SARS-CoV-2 pandemic corresponding to the Omicron Covid-19 variant, which is probably an expression of a concomitant SARS-CoV-2, while a decreased incidence of PIMS complication was observed during the same period.

RWD161 Pharmacological Treatment Disruption in Women With Post-Partum Depression (PPD) During COVID-19: A Retrospective Cohort Study Using Claim Data

Objectives: Postpartum depression (PPD) has been described as "the thief that steals motherhood" by depriving women of the anticipated joy of a new infant. Through this study, we intend to see the incidence, treatment rates (TR), relative-treatment rate (TRR), absolute treatment rate (ATR), and number needed to treat (NNT) pre- and post-COVID-19 on treatment of women with PPD. Method(s): This retrospective cohort study included newly diagnosed patients with PPD in 2019 (1st Jan - 31st Dec [pre-pandemic]) and 2020 (1st Jan - 31st Dec [pandemic]) using ICD-10-CM codes from Optum's de-identified Clinformatics Data Mart. Only the patients having continuous eligibility between 12 months before (baseline period) to 12-months post (follow-up period) the first diagnosis of PPD (index date) were included in study. During the follow-up period, patients were then checked for pharmacological treatment received (SSRI, SNRI's and other anti-depressants) using NDC codes. To measure effects, percentages of patients getting treatment, TRR (TR in pandemic/TR in pre-pandemic), ATR (TR in pre-pandemic - TR in a pandemic), and NNT (1/ATR) were calculated before and during COVID. The significance of categorical variables was examined using the Chi-square test. Result(s): We observed 39% increase in incidence of PPD patients during pandemic (n=16,095) vs pre-pandemic (n=11,565). Only 51% TR (risk ratio) was observed during pandemic vs 60% TR (risk ratio) in pre-pandemic with any SSRI, SNRI, and anti-depressants (p<.01). Compared to patients receiving treatment during pandemic vs pre-pandemic: TRR was found to be 85% (relative risk) and ATR was 9% (absolute risk reduction). The NNT comparing pre- and during pandemic was 11. Conclusion(s): The results of the study demonstrated that treatment of women with PPD was impacted during pandemic vs pre-pandemic (9% women did not receive treatment during pandemic). Alternative methods or non-pharmacological treatments may be required to further alleviate non-treated patients and improve their condition. Copyright © 2022

RWD128 Exploring the Burden of COVID-19 on Post-Traumatic Stress Disorder (PTSD) in Pre- and During-COVID-19 Pandemic Using Real-World Data

Objectives: This study aimed to explore the impact of COVID-19 on patients with PTSD and the burden of resource utilization in the pre- and during the COVID-19 pandemic. Method(s): This retrospective observational study included patients diagnosed with PTSD between 1st January 2018 to 31st December 2020 using ICD-10-CM codes from Optum's de-identified Clinformatics Data Mart database. In the study duration, distinct patients were identified and further classified by age, gender, and location of service. To determine the influence in pre- and during COVID-19 for each of the stratification variables, a year-wise comparison was done. Chi-square was performed as test of significance for categorical variables. Result(s): Overall we observed the number of PTSD patients increased by 7% (n=206,741) during the pandemic (1st January 2020 - 31st December 2020) vs pre-pandemic (1st January 2019 - 31st December 2019). A significant increase was seen across all age groups (p<.05). In the case of teenagers, PTSD was found to have increased by 22% whereas in adults and the elderly an 8% and 3% increase was seen respectively. When broken down by gender, a significant increase was observed. Females (+9% [n=143,032]) were seen to have been affected more compared to males (+4% [n=63,625]) during the pandemic vs pre-pandemic. In healthcare resources utilization overall, there was an observed 24% increase. For both inpatients and office, PTSD decreased significantly (-3% and -4% respectively) (p<.05);while ER visits, increased only by 1% (p<.05). A significant increase in outpatient and telehealth services was observed (122% and 454% respectively) (p<.05). Conclusion(s): An increased exacerbation in PTSD was observed during the pandemic with respect to burden across various stratification and resource utilization;especially in outpatient and telehealth services. Better treatment, psychotherapy and alternative care programs may be required to curb this impact and decrease the overall burden across various care setting. Copyright © 2022

RWD95 Exploring the Burden of COVID-19 on Suicides and Suicidal Attempts (SSA) in Pre- and During-COVID-19 Pandemic Using Real-World Data

Objectives: This study aimed to explore the impact of COVID-19 on patients with SSA and the burden of resource utilization in the pre- and during the COVID-19 pandemic. Method(s): This retrospective observational study included patients diagnosed with SSA between 1st January 2019 to 31st December 2020 using ICD-10-CM codes from Optum's de-identified Clinformatics Data Mart. In the study duration, distinct patients were identified and further classified by age, gender, and location of service. To determine the influence in pre- and during COVID-19 for each of the stratification variables, a year-wise comparison was done. Chi-square test was performed to check the significance of categorical variables. Result(s): Overall we observed the number of SSA patients increased by 2% (n=266,329) during the pandemic (1st January 2020 - 31st December 2020). A significant increase was seen across all age groups (p<.01). In the case of teenagers, SSA was found to have increased by 80% whereas in adults and elderly an 15% and 8% increase was seen respectively during pandemic (p<.01). When stratified by gender, a significant increase was observed only in females (+9% [n=174,647]) where in males (-3% [n=91,573]) decrease was observed during pandemic. In healthcare resources utilization overall, there was an observed 12% increase during pandemic. For inpatients, office, and outpatient, SSA decreased significantly (-4%, -8%, and -1% respectively) during pandemic (p<.01). A significant increase in outpatient and telehealth services was observed (34% and 1,299% respectively) (p<.01). Conclusion(s): An increased exacerbation in SSA was observed during the pandemic with telehealth and outpatient services being impacted the highest. This may be attributed to facing near-death scenarios, and the loss of loved ones amongst other factors. With the increase in cases, health care resource utilization across various settings is pressed. Better treatment and programs may be required to curb this impact and decrease the overall burden. Copyright © 2022

Spatiotemporal Data Warehousing for Event Tracking Applications

In this paper, we propose a multidimensional spatiotemporal modeling framework of data warehouse creation for tracing dynamic events in contemporary applications, like crowd contact tracing for Covid-19 prevention. Such a framework offers a natural and consistent solution for slowly changing dimension management. It provides a progressive evolution from traditional static data management to modern dynamic data analysis with spatiotemporal tracking capabilities for IoT applications. Based on such a framework, en-tity-centered resource integration and related business intelligence applications can be rig-orously developed, managed and properly tracked. © 2022 Institute of Information Science. All rights reserved.

Urine Culture Clinical Decision Support Menu for Reducing Inappropriate Lab Ordering

Background. Antibiotic treatment of asymptomatic bacteriuria (ASB) is unnecessary except in pregnant women or those undergoing invasive urologic procedures. Unnecessary treatment of ASB is an important driver of inappropriate antimicrobial use (IAU), leading to antimicrobial resistance, Clostridioides difficile infection, adverse drug events, and increased costs. Because ASB requires detection to be treated, unnecessary urine cultures (UC) are a key cause of IAU. Strong evidence supports not obtaining a UC from asymptomatic patients. Methods. To reduce unnecessary UC orders at the Minneapolis Veterans Affairs Health Care System (MVAHCS), UC orders within the electronic health record (EHR) were redirected to a UC clinical decision support (CDS) menu (Figure 1). Selection of an indication from the defined list is required to place a UC order and provides tracking. UC order data was obtained from the Corporate Data Warehouse (CDW), the VA's data program. Patient bed days were collected from a CDW dashboard developed by the Iowa City Veteran's Affairs Health Care System. Data was visualized using Microsoft Power BITM platform. Results. The UC CDS menu was implemented at the MVAHCS in September 2020. UC orders from 16 months prior to implementation (9/1/2018 - 12/31/2019) was compared to orders 16 months after implementation (9/1/2020 - 12/31/2021). Data from 1/1/2020 - 8/31/2020 was not included due to atypical patient care patterns during the COVID-19 pandemic.4 The monthly number of UC orders after implementation significantly decreased from an average of 765 to 564, a 26.3% reduction (P < .001;2-sided t-test) (Figure 2). The average patient bed days prior to and following implementation was not significantly different (Figure 3). Most UC orders came from the UC CDS menu (8103, 89.8%) compared to orders placed from other order menus or directly from the drug file (920, 10.2%). The most common indication selected was dysuria, frequency, and urgency (4050, 44.9%) followed by fever or sepsis (1230, 13.6%) then pre-operative urologic screening (1056, 11.7%) (Figure 4). Conclusion. Implementation of a UC CDS menu within the MVAHCS EHR resulted in significantly fewer UC orders. Most UC orders had an appropriate indication suggesting the decrease was primarily due to preventing unnecessary UC orders. (Figure Presented).