Data Warehouse Research Articles

8139 Examining the Association of GLP1-RA or DPP4i with Bullous Skin Diseases in Veterans

Abstract Disclosure: S. Saad-Omer: None. M. Kinaan: None. N. Sami: None. I. Mansi: None. Introduction: Pemphigus vulgaris (PV) and bullous pemphigoid (BP) are Autoimmune Blistering Disorders (AIBDs). GLP-1 Receptor Agonists (GLP1-RA) and Dipeptidyl peptidase 4 inhibitors (DPP4i) have been noted in various case reports and case control studies to be linked with certain AIBDs; however, their overall role in these diseases is not completely understood, despite the exponential increase in use of these medications. This study aims to be the first study to investigate the association of DPP-4 inhibitors and GLP-1 agonists with incidence of AIBD, PV, and BP in a large cohort of American veterans. Methods: This retrospective cohort study of veterans enrolled in the Veterans Health Administration (VHA) used Corporate Data Warehouse (CDW). We extracted data of patients from fiscal years (FY) 2006 to end of FY 2021 who filled either GLP1-RA or DPP4i prescriptions. CDW is a comprehensive data source that encompasses vital status, demographic data, inpatient and outpatient diagnoses and procedure codes, vital signs, laboratory data, and pharmacy fill data. The study groups included: 1) GLP1-RA group: patients who initiated GLP1-RA; and 2) DPP4i group: patients who initiated DPP4i. Our primary outcome was the incidence of AIBDs. We compared baseline characteristics of patients who experienced the outcome and those who did not among GLP1-RA or DDP4i users. We, thereafter, examined factors associated with incidence of AIBD, PV, and BP among GLP1-RA users and DPP4i users. Results: The initial cohort of GLP1-RA or DPP4i users comprised a total of 415,895 patients, from which we excluded 397 (0.095%) patients with prior bullous skin diseases before starting GLP1-RA or DPP4i medications. Overall, a total of 154 patients developed PV/BP disease after initiation of either DPP4i or GLP1-RA. Of these 154 patients, 123 of these patients were on DPP4i and 31 of these patients were on GLP1-RA. The mean age (SD) of PV/BP patients was 72 (11) years compared to an average age of 66 (10) years in patients who did not develop PV/BP. Patients were also found to be predominately white (77.9%). Factors associated with increased risk of BP were use of DPP4i (Odds Ratio [OR] 1.90, 95% confidence interval [95% CI: 1.14-3.47, p=<0.01), and older age (OR: 1.07, 95%CI: 1.04-1.09, p<0.01). Use of GLP1-RA or DPP4i was not associated with PV incidence. (OR: 1.31, 95% CI: 0.65-2.62, p=<0.45). Conclusion: In conclusion, our study supports existing literature that DPP4i use is associated with an increased risk of developing BP. Our data does not find association with GLP-1 agonists nor DPP4 inhibitors in increasing the risk of PV. Presentation: 6/3/2024

6851 Rapidly Increasing Use of Anti-Obesity Medications among Veterans in the Veterans Health Administration (VA)

Abstract Disclosure: V. Vimalananda: None. S. Qian: None. R. Bolton: None. A. Hung: None. V. Smith: None. M. Maciejewski: None. Introduction: Obesity is a prevalent and costly chronic disease that compromises quality of life; increases risks of cardiometabolic disease, osteoarthritis, and several cancers; and accounts for 28% of U.S. health care costs. Anti-obesity medications (AOMs) may fill the treatment gap given the low uptake of bariatric surgery and limited effectiveness of behavioral interventions, and are guideline-recommended as part of a comprehensive obesity treatment plan. The landscape for obesity treatment has changed with introduction of the highly effective and in-demand glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and dual GLP-1 + GIP receptor agonists (GIP/GLP-1 RAs). Given limited knowledge of use patterns, we describe use of AOMs among patients receiving care in the Veterans Health Administration (VA), which is the largest integrated health system in the United States. Methods: In a retrospective cohort study of VA patients from 7/1/2021-6/30/2023, we examined data from the VHA Corporate Data Warehouse separately for patients on AOMs without and with diagnosed diabetes, since the newer AOMs can be used at lower doses for the diabetes indication. Results: There were 31,106 patients without diabetes and at least one AOM prescription in the two year study period, who were 60% male and 62% White. AOM prescriptions from Year 1 to Year 2 among this group increased 215%. Overall, prescriptions were: semaglutide 34%, liraglutide 18%, orlistat 18%, phentermine/topiramate 14%, bupropion/naltrexone 12%, phentermine 4% and tirzepatide 0.03%. Prescribers were physicians 60%, LIPs 25%, and pharmacists 13%. There were 188,017 patients with diagnosed diabetes and at least one AOM prescription, and they were 92% male and 67% white. AOM prescriptions from Year 1 to Year 2 among this group increased 14% and were primarily (87%) for semaglutide. Prescribers were physicians 63%, licensed independent practitioners (LIPs) 23%, and pharmacists 10%. Conclusions AOM use is increasing in VA across all AOM classes, particularly among patients without diabetes, indicating growing interest in medical treatment of obesity. As AOM use spreads, real-world evidence on weight loss and clinical outcomes is needed among patients like those in VA who tend to be older and less healthy than the highly selected patients included in randomized clinical trials. Data is especially needed to guide practice among patients with obesity and without diabetes, given the dramatic increase in use among that group over the past 2 years. Presentation: 6/1/2024

7490 Comparative Clinical Analysis of DPP-4 inhibitors and SGLT2 inhibitors; Switch Study in Real-World Setting

Abstract Disclosure: H. Choe: None. M. Kwak: None. J. Lee: None. Y. Choi: None. E. Hong: None. Objective: Dipeptidyl peptidase-4 inhibitors (DPP-4i) and sodium-glucose cotransporter-2 inhibitors (SGLT2i) represent two distinct pharmacological pathways in the management of type 2 diabetes, both aiming to improve glycemic control without risk of hypoglycemia. Metabolic implications and glycemic control associated with the interchange between SGLT2i and DPP-4i are scarce. This study aimed to evaluate the effect of switching between SGLT2i and DPP4i on various clinical parameters in Korean patients with type 2 diabetes. Methods: We conducted a retrospective cohort study at Hallym University Dongtan Sacred Heart Hospital, using clinical data warehouse. Baseline measurements and follow-up data at 3 months were collected for parameters including hemoglobin A1c (HbA1c), blood pressure, lipid profile, liver enzymes, renal function, and other metabolic markers. Results: Between 2015 and 2023, 104 participants switched from SGLT2 inhibitors to DPP4 inhibitors, while 321 switched in the opposite direction. Initially, those who switched from SGLT2 inhibitors to DPP4 inhibitors presented with lower baseline HbA1c levels (7.22 ± 0.73% versus 7.42 ± 0.76%, P = 0.017) and higher estimated glomerular filtration rates (eGFR) (95.2 ± 23.1 versus 85.9 ± 23.7 mL/min/1.73m², P = 0.001) compared to those who switched from DPP4 inhibitors to SGLT2 inhibitors. At three months of follow-up, the SGLT2 inhibitors to DPP4 inhibitors group exhibited a more significant reduction in HbA1c (adjusted β 0.22, 95% CI 0.03 to 0.40, P = 0.023), especially in patients with reduced eGFR (<60 mL/min/1.73 m²) (β 1.2, 95% CI 0.30 to 2.11, P = 0.013; P for interaction = 0.005). On the other hand, the DPP4 inhibitors to SGLT2 inhibitors group showed a greater decrease in fasting glucose levels (β -9.73, 95% CI -17.6 to -1.86, P = 0.016), along with more pronounced reductions in liver enzymes AST (β -4.15, 95% CI -6.49 to -1.81, P < 0.001) and ALT (β -6.42, 95% CI -9.58 to -3.27, P < 0.001), and a significant increase in hematocrit (β 3.31, 95% CI 2.36 to 4.26, P < 0.001) compared to the initial group. Conclusions: The study concludes that in Korean patients with type 2 diabetes, switching from SGLT2 inhibitors to DPP4 inhibitors is associated with a greater reduction in HbA1c, particularly in those with reduced renal function. Conversely, switching from DPP4 inhibitors to SGLT2 inhibitors results in more substantial improvements in fasting glucose and liver enzyme levels, suggesting differential benefits depending on the direction of the medication switch. Presentation: 6/3/2024

Impact of COVID-19 Pandemic on Emergency Department Visits for Opioid Use Disorder Across University of California Health Centers

Introduction: Coronavirus 2019 (COVID-19) has had a devastating impact on mental health and access to addiction treatment in the United States, including in California, which resulted in the highest rates of emergency department visits (ED) for opioid poisoning in 2020. As California slowly returns to pre-pandemic normalcy, it remains uncertain whether the rates of opioid-related events have slowed down over time. We hypothesized that the number of opioid-related ED visits were exacerbated after the period of the COVID-19 pandemic and continue at a high rate in the present. Methods: In this analysis we searched the University of California (UC) Health Data Warehouse—a database of electronic health records from six UC health centers—for opioid-related ED visits, identifiying using the following International Classification of Diseases, 10th Ed, Clinical Modification codes: F11 codes, and T40.0*, T40.1*, T40.2*, T40.3*, T40.4*, T40.6*. Opioid overdose-associated visits were classified by types of opioids involved: heroin (T40.1*); prescription opioids (T40.2* or T40.3*); and synthetic opioids (T40.4*). We performed interrupted time analysis to estimate the immediate (level) change and change-in-time trend (trend change), from before (January 2018–October 2019) and during the pandemic (April 2020–December 2022). Monthly visit rates were evaluated with negative binomial regression adjusted for first-order autoregression and using all-cause ED counts as the offset. We present effect sizes as rate ratios (RR) and 95% confidence intervals (CI), tested at α = .05. Results: Before COVID-19, a steadily increasing trend in ED visit rates was observed for all outcomes (P < 0.05) except synthetic opioids. Total opioid-related ED visit rates increased by 15% (RR 1.15, 95%CI 1.02–1.29, P = 0.20) immediately after March 2020 before decreasing by 0.5% every month, albeit without statistical significance (RR .995, 95% CI .991–1.00, P = 0.06). Similar trends were observed with prescription opioid overdoses, with a step increase of 44% (RR 1.44, 95% CI 1.10–1.89, P = .008) before plateauing after March 2020 (RR 1.01, 95% CI .998–1.02, P = 0.12). After March 2020, ED visit rates for synthetic opioid overdoses were increasing steadily by 4% every month (RR 1.04, 95% CI 1.02–1.06, P = .001), unlike with heroin, which was observed with an 8% monthly reduction (RR .92, 95% CI .90–.93, P < .001). No immediate increase in visit rates was observed for either opioid. Conclusion: While opioid-related ED admissions among the UC health centers showed an overall decrease, prescription and synthetic opioid overdoses remained significantly higher than pre-pandemic trends as of December 2022. A multilevel approach to improve awareness of new opioid health policies could ameliorate these alarming rises in the post-pandemic era.

A-236 Rapid multiplexed PCR (polymerase chain reaction) tests for respiratory disease improve process and time to diagnosis versus laboratory-based molecular testing

Abstract Background The Infectious Disease Society of America recommends molecular testing for symptomatic patients suspected of having COVID-19. Molecular tests can be performed at the point of care or in a centralized laboratory, using different methodologies, and can be singleplex or multiplex. Limited data are available examining the use of these tests in clinical practice and impact of different test types on patient outcomes; however, common antiviral treatments for both COVID-19 and influenza are more effective when taken earlier and are not indicated for use more than 5 and 2 days after symptom onset, respectively. The aim of this study was to describe the diagnostic journey for non-inpatients who received molecular testing for respiratory pathogens in the laboratory versus those who received testing using a rapid, multiplexed real-time RT-PCR test that can be performed at the point of care. Methods De-identified administrative claims were analyzed from July 1, 2020 through September 30, 2022 for commercially insured and Medicare Advantage enrollees in the Optum Labs Data Warehouse. Patients with ≥1 medical claim for a non-inpatient visit with a procedure code for testing for SARS-CoV-2 were identified; patients who received antigen testing at index were excluded. Patients were followed for up to 90 days. Two patient groups were selected for the final study sample: 1) Patients with ≥1 claim with CPT code 0240U or 0241U with a point-of-care or laboratory place of service on the index date (proprietary laboratory analyses codes for Xpert® Xpress CoV-2/Flu/RSV, or “Xpert Xpress”); and 2) Patients with other laboratory-based molecular testing on the index date. Standardized difference of >0.10 or <-0.10 was considered a significant imbalance in proportions or means. Results In total, 51,602 patients received testing with Xpert Xpress while 317,192 received laboratory testing. Patients who were tested with Xpert Xpress were significantly older (mean 49.82 vs 42.19, SD 0.3), had a higher Charlson Comorbidity Index (mean 0.7 vs 0.35, SD 0.29), and were more likely to have a respiratory risk factor (6.09% vs 2.38%, SD 0.19). On average, patients who were tested with Xpert Xpress had significantly fewer tests for SARS-CoV-2 on the index date (mean 1.2 vs 1.58, SD -0.55), and fewer tests for SARS-CoV-2, influenza, or RSV on the index date and through the end of the 90-day fixed follow-up period (mean 1.64 vs 2.57, SD -0.45). They were also significantly more likely to be diagnosed with COVID-19 on the index date (23.31% vs 16.53%, SD 0.17). Among patients with a diagnosis for COVID-19, mean time to diagnosis was significantly shorter among patients tested with Xpert Xpress (4.85 vs 8.86 days, SD -0.27; median 0 vs 4 days). This finding was also consistent for influenza (4.05 vs 20.28 days, SD -0.74; median 0 vs 5 days). Conclusions Testing with Xpert Xpress at index resulted in fewer total tests and faster diagnosis compared to testing in the laboratory. Rapid, multiplex PCR testing provides a faster time to diagnosis and may support more appropriate and effective treatment.

Construction and Practice of Teaching Evaluation System Based on Data Mining

The teaching quality evaluation system based on data mining technology can accurately and fairly identify the core driving factors to improve teaching quality. This method adopts the analysis of big data correlation rules, including data collection and processing preparation steps, builds the data warehouse of association rules, and then generates an educational quality evaluation framework using the principle of data mining. Based on this, this paper analyzes the construction design and method of the teaching evaluation system under data mining, hoping to provide help for the improvement of the teaching evaluation system and the improvement of teaching quality.

De Novo Natural Language Processing Algorithm Accurately Identifies Myxofibrosarcoma From Pathology Reports.

Available codes in the ICD-10 do not accurately reflect soft tissue sarcoma diagnoses, and this can result in an underrepresentation of soft tissue sarcoma in databases. The National VA Database provides a unique opportunity for soft tissue sarcoma investigation because of the availability of all clinical results and pathology reports. In the setting of soft tissue sarcoma, natural language processing (NLP) has the potential to be applied to clinical documents such as pathology reports to identify soft tissue sarcoma independent of ICD codes, allowing sarcoma researchers to build more comprehensive databases capable of answering a myriad of research questions. (1) What proportion of patients with myxofibrosarcoma within the National VA Database would be missed by searching only by soft tissue sarcoma ICD codes? (2) Is a de novo NLP algorithm capable of analyzing pathology reports to accurately identify patients with myxofibrosarcoma? All pathology reports (10.7 million) in the national VA corporate data warehouse were identified from 2003 to 2022. Using the word-search functionality, reports from 403 veterans were found to contain the term "myxofibrosarcoma." The resulting pathology reports were manually reviewed to develop a gold-standard cohort that contained only those veterans with pathologist-confirmed myxofibrosarcoma diagnoses. The cohort had a mean ± SD age of 70 ± 12 years, and 96% (287 of 300) were men. Diagnosis codes were abstracted, and differences in appropriate ICD coding were compared. An NLP algorithm was iteratively refined and tested using confounders, negation, and emphasis terms for myxofibrosarcoma. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated for the NLP-generated cohorts through comparison with the manually reviewed gold-standard cohorts. The records of 27% (81 of 300) of myxofibrosarcoma patients within the VA database were missing a sarcoma ICD code. A de novo NLP algorithm more accurately (92% [276 of 300]) identified patients with myxofibrosarcoma compared with ICD codes (73% [219 of 300]) or basic word searches (74% [300 of 403]) (p < 0.001). Three final algorithm models were generated with accuracies ranging from 92% to 100%. An NLP algorithm can identify patients with myxofibrosarcoma from pathology reports with high accuracy, which is an improvement over ICD-based cohort creation and simple word search. This algorithm is freely available on GitHub (https://github.com/sarcoma-shark/myxofibrosarcoma-shark) and is available to facilitate external validation and improvement through testing in other cohorts. Level II, diagnostic study.

Trends in and Predictors of Patient Pharmacogenomic Test Uptake in a National Healthcare System

Better understanding patient uptake of pharmacogenomic (PGx) testing may inform its implementation and maximize the benefits that such testing can confer. This study examined patient and provider factors associated with PGx test ordering in a national healthcare system where panel-based testing was implemented as part of routine care. We used a retrospective matched cohort design and data from the Veterans Health Administration Corporate Data Warehouse. A conditional logistic model was used to identify factors associated with a PGx order receipt and estimate odds ratios and 95% confidence intervals. The following patient factors predicted receipt of a PGx test order: younger age, married status, rural residence, non-Hispanic Black or Hispanic race/ethnicity, PGx educational mailer receipt, depression diagnosis, allergy to a drug on the panel, prescriptions for drugs on the panel, and specialty care visits (p<0.05). Additionally, patients whose providers were female, younger, a nurse practitioner/physician assistant or pharmacist, or participated in an educational mailer program were more likely to receive an order (p<0.05). This study highlights factors that may facilitate or hinder the widespread and equitable implementation of PGx testing in a large national healthcare system. The information is being used to further refine the program.

Using real-world electronic health record data to predict the development of 12 cancer-related symptoms in the context of multimorbidity.

This study uses electronic health record (EHR) data to predict 12 common cancer symptoms, assessing the efficacy of machine learning (ML) models in identifying symptom influencers. We analyzed EHR data of 8156 adults diagnosed with cancer who underwent cancer treatment from 2017 to 2020. Structured and unstructured EHR data were sourced from the Enterprise Data Warehouse for Research at the University of Iowa Hospital and Clinics. Several predictive models, including logistic regression, random forest (RF), and XGBoost, were employed to forecast symptom development. The performances of the models were evaluated by F1-score and area under the curve (AUC) on the testing set. The SHapley Additive exPlanations framework was used to interpret these models and identify the predictive risk factors associated with fatigue as an exemplar. The RF model exhibited superior performance with a macro average AUC of 0.755 and an F1-score of 0.729 in predicting a range of cancer-related symptoms. For instance, the RF model achieved an AUC of 0.954 and an F1-score of 0.914 for pain prediction. Key predictive factors identified included clinical history, cancer characteristics, treatment modalities, and patient demographics depending on the symptom. For example, the odds ratio (OR) for fatigue was significantly influenced by allergy (OR = 2.3, 95% CI: 1.8-2.9) and colitis (OR = 1.9, 95% CI: 1.5-2.4). Our research emphasizes the critical integration of multimorbidity and patient characteristics in modeling cancer symptoms, revealing the considerable influence of chronic conditions beyond cancer itself. We highlight the potential of ML for predicting cancer symptoms, suggesting a pathway for integrating such models into clinical systems to enhance personalized care and symptom management.

Disparities in mental health screening for veteran women with a new diagnosis of breast cancer.

222 Background: Compared to the general population, mental health disorders, including depression, are higher for Veteran women. Breast cancer (BC) is the most commonly diagnosed cancer in this population and up to 50% of women will experience depressive symptoms after diagnosis, associated with poor prognosis and survival. An increasing number of Veteran women live in rural areas, where access to behavioral health care may be limited. This study sought to evaluate rural disparities in mental health screening and follow-up evaluation after a new BC diagnosis. Methods: Women Veterans with a new BC diagnosis were identified between 2017 and 2021. Data for mental health screenings [Patient Health Questionnaire (PHQ)-2, PHQ-9, General Anxiety Disorder-7] were collected subsequent to the first BC diagnosis. Rurality was defined per the USDA Rural-Urban Commuting Area (RUCA) codes framework to determine rurality based on population density and commuting patterns. Data were abstracted from several large VA and non-VA datasets, including the Corporate Data Warehouse (CDW) Outpatient files, non-VA Care Medical and Pharmacy System files, Program Integrity Tool, and VA Informatics and Computing Infrastructure for Oncology. Rurality data was specified using CDW designations combined with ZIP-code-level USDA RUCA information. All categorical variables were analyzed using chi-square tests. Means were tested using t-tests. Results: 14,225 Veteran women with a new BC diagnosis were identified. The mean age was 63.4 (SD 11.7). The most common documented race was White (30.6%), followed by Black (15.7%). 59.6% lived in an urban setting as opposed to rural setting (40.4%). In the entire population, only 14% had a mental health screening within 3 months of a cancer diagnosis. Veteran women living in an urban setting were more likely to be screened compared to those living in a rural setting (17.4% vs 9.2%, p &lt; 0.001). For women screened within 3 months, most screened negative, regardless of rurality (76.1% rural, 75.7% urban). Of women who had a positive mental health screen within 3 months, only approximately one-third (31.8% rural, 34.2% urban) had additional screening regardless of rurality. However, for those with a negative initial screen, urban Veterans were more likely to have any additional screening (17% vs 10.2%, p = 0.001). PHQ-2 was the most common mental health screening tool used (73.8%) followed by a combination of PHQ-9 and GAD-7 (9.6%). Conclusions: Mental health screening within 3 months of breast cancer diagnosis is low for Veteran women. There is a significant difference in screening between rural and urban populations. It is critical to both improve mental health screening overall in this population and bridge the disparity gap between screening in rural and urban Veteran women. This may impact not only mental health but also breast cancer outcomes during the period encompassing treatment and survivorship.

Identifying Patients With Primary Biliary Cholangitis and Cirrhosis Using Administrative Data in a National Cohort.

The accuracy of administrative codes to capture patients with both primary biliary cholangitis (PBC) and cirrhosis could be challenging because of the potential for incorrect coding due to the old nomenclature "Primary Biliary Cirrhosis." Therefore, the aim of this study was to examine the positive predictive value (PPV) of International Classification of Diseases (ICD) codes for PBC and cirrhosis. This was a retrospective cohort study using data from the VA Corporate Data Warehouse. Eligibility criteria included adult patients diagnosed to have PBC and cirrhosis based on one inpatient or two outpatient ICD 9 or 10 codes, and were validated against chart review of each participant. We identified 1408 patients who were found to have ICD codes for both cirrhosis and PBC. The ICD 9/10 codes for PBC and cirrhosis had a PPV of 0.75 (95% CI 0.73-0.75) for cirrhosis, 0.75 for PBC (95% CI 0.73-0.78), and 0.52 (0.50-0.55) for PBC and cirrhosis. When portal hypertension was combined with ICD 9/10 codes, the PPV of cirrhosis improved to 0.92 (0.90-0.94), and that of PBC cirrhosis improved to 0.64 (0.60-0.67). By combining ICD 9/10 codes for portal hypertension and receipt of ursodeoxycholic acid (UDCA), the PPV for cirrhosis improved to 0.91 (0.88-0.94), PBC increased to 0.78 (0.74-0.82), and that for PBC cirrhosis to 0.69 (0.65-0.74). In a large national cohort, the use of ICD 9/10 codes had modest reliability for identifying participants with PBC and cirrhosis. The PPV for cirrhosis can be improved by incorporating ICD 9/10 codes for portal hypertension with receipt of UDCA.

Proposing a Model to Enhance Security in Online Blockchain-Based Banking Transactions

In the banking industry, technologies are changing rapidly. The banking industry is the first target to implement the new technology to provide better customer services. In the past, banking systems used the manual ledger system, which has many drawbacks. To overcome this, the banking industry implemented the computerised mechanism to implement the centralised system, in which data is stored in one place, like a data warehouse. However, this centralised, automated system has also led to increased threats and cybercrimes. There are various malicious software (ransomware, phishing) through which hackers can hack customers' private data and use it illegally. The urgency to address these challenges has led to the adoption of blockchain, a leading technology based on decentralised, distributed behaviour and its public ledger system. Implementing blockchain in the banking sector is not just a choice, but a necessity. It can increase security, immutability, faster transactions, a decentralised system, consensus manner, transparency, etc. We will be discussing blockchain and its functionality. Furthermore, we will present a theoretical framework that uses consortium blockchain to reduce security threats in online transactions. Our model, IVSMOT, is a crucial step in this direction, as it divides the private key into two shares, thereby addressing the urgent need to enhance security in online banking transactions.

Data resource profile: the ORIGINS project databank: a collaborative data resource for investigating the developmental origins of health and disease

IntroductionThe ORIGINS Project ("ORIGINS") is a longitudinal, population-level birth cohort with data and biosample collections that aim to facilitate research to reduce non-communicable diseases (NCDs) and encourage 'a healthy start to life'. ORIGINS has gathered millions of datapoints and over 400,000 biosamples over 15 timepoints, antenatally through to five years of age, from mothers, non-birthing partners and the child, across four health and wellness domains: 'Growth and development', 'Medical, biological and genetic', 'Biopsychosocial and cognitive', 'Lifestyle, environment and nutrition'. MethodsMothers, non-birthing partners and their offspring were recruited antenatally (between 18 and 38 weeks' gestation) from the Joondalup and Wanneroo communities of Perth, Western Australia from 2017 to 2024. Data come from several sources, including routine hospital antenatal and birthing data, ORIGINS clinical appointments, and online self-completed surveys comprising several standardised measures. Data are merged using the Medical Record Number (MRN), the ORIGINS Unique Identifier and the ORIGINS Pregnancy Number, as well as additional demographic data (e.g. name and date of birth) when necessary. ResultsThe data are held on an integrated data platform that extracts, links, ingests, integrates and stores ORIGINS' data on an Amazon Web Services (AWS) cloud-based data warehouse. Data are linked, transformed for cleaning and coding, and catalogued, ready to provide to sub-projects (independent researchers that apply to use ORIGINS data) to prepare for their own analyses. ORIGINS maximises data quality by checking and replacing missing and erroneous data across the various data sources. ConclusionAs a wide array of data across several different domains and timepoints has been collected, the options for future research and utilisation of the data and biosamples are broad. As ORIGINS aims to extend into middle childhood, researchers can examine which antenatal and early childhood factors predict middle childhood outcomes. ORIGINS also aims to link to State and Commonwealth data sets (e.g. Medicare, the National Assessment Program -- Literacy and Numeracy, the Pharmaceutical Benefits Scheme) which will cater to a wide array of research questions.

Phenome-wide profiling identifies genotype-phenotype associations in Phelan-McDermid syndrome using family-sourced data from an international registry

BackgroundPhelan-McDermid syndrome (PMS) is a rare neurodevelopmental disorder caused by 22q13 deletions that include the SHANK3 gene or pathogenic sequence variants in SHANK3. It is characterized by global developmental delay, intellectual disability, speech impairment, autism spectrum disorder, and hypotonia; other variable features include epilepsy, brain and renal malformations, and mild dysmorphic features. Here, we conducted genotype-phenotype correlation analyses using the PMS International Registry, a family-driven registry that compiles clinical data in the form of family-reported outcomes and family-sourced genetic test results.MethodsData from the registry were harmonized and integrated into the i2b2/tranSMART clinical and genomics data warehouse. We gathered information from 401 individuals with 22q13 deletions including SHANK3 (n = 350, ranging in size from 10 kb to 9.1 Mb) or pathogenic or likely pathogenic SHANK3 sequence variants (n = 51), and used regression models with deletion size as a potential predictor of clinical outcomes for 328 phenotypes.ResultsOur results showed that increased deletion size was significantly associated with delay in gross and fine motor acquisitions, a spectrum of conditions related to poor muscle tone, renal malformations, mild dysmorphic features (e.g., large fleshy hands, sacral dimple, dysplastic toenails, supernumerary teeth), lymphedema, congenital heart defects, and more frequent neuroimaging abnormalities and infections. These findings indicate that genes upstream of SHANK3 also contribute to some of the manifestations of PMS in individuals with larger deletions. We also showed that self-help skills, verbal ability and a range of psychiatric diagnoses (e.g., autism, ADHD, anxiety disorder) were more common among individuals with smaller deletions and SHANK3 variants.LimitationsSome participants were tested with targeted 22q microarrays rather than genome-wide arrays, and karyotypes were unavailable in many cases, thus precluding the analysis of the effect of other copy number variants or chromosomal rearrangements on the phenotype.ConclusionsThis is the largest reported case series of individuals with PMS. Overall, we demonstrate the feasibility of using data from a family-sourced registry to conduct genotype-phenotype analyses in rare genetic disorders. We replicate and strengthen previous findings, and reveal novel associations between larger 22q13 deletions and congenital heart defects, neuroimaging abnormalities and recurrent infections.

Research on the Application of Data Governance for College Teachers

In recent years, many universities have embarked on the construction of the One Form project through one-stop service halls to address the challenge of teachers difficulties in filling out forms. However, in practical applications, data quality issues are prominent due to factors such as non-uniform data standards and untimely data maintenance, severely impacting teachers data application experience. This paper, through analyzing the current situation of teachers data quality, proposes a solution for teachers data governance based on the one-stop service platform. By establishing data standards, building a standard data warehouse, and instituting a data management guarantee system, this paper aims to continuously improve teachers data quality and enhance their data service experience.

Innovation and Practice of Integrated Public Service System for Digitally Enabled Lifelong Learning for All People

Lifelong learning for all has gradually become a norm in developing education policies in various countries. Innovation and Practice of Integrated Public Service System for Digitally Enabled Lifelong Learning for All People attempts to analyze and address the issues above from the data continuous development and improvement perspective. These issues include Unequal distribution of resources, Rapid technological upgrading, Lack of appropriate platforms, and An urgent need for personalized learning, Data security and privacy. China has made some progress in promoting the construction of a digitally empowered learning society while facing some challenges and problems. The solutions include:●Strengthen the overall planning of the platform.●Integration of crucial application scenarios.●Implementing data governance to build a lifelong learning data warehouse.●Using platforms to strengthen the application of learning outcomes.●Construction of platform network security guarantee system.

Data Lakehouse: A survey and experimental study

Efficient big data management is a dire necessity to manage the exponential growth in data generated by digital information systems to produce usable knowledge. Structured databases, data lakes, and warehouses have each provided a solution with varying degrees of success. However, a new and superior solution, the data Lakehouse, has emerged to extract actionable insights from unstructured data ingested from distributed sources. By combining the strengths of data warehouses and data lakes, the data Lakehouse can process and merge data quickly while ingesting and storing high-speed unstructured data with post-storage transformation and analytics capabilities. The Lakehouse architecture offers the necessary features for optimal functionality and has gained significant attention in the big data management research community. In this paper, we compare data lake, warehouse, and lakehouse systems, highlight their strengths and shortcomings, identify the desired features to handle the evolving challenges in big data management and analysis and propose an advanced data Lakehouse architecture. We also demonstrate the performance of three state-of-the-art data management systems namely HDFS data lake, Hive data warehouse, and Delta lakehouse in managing data for analytical query responses through an experimental study.

Offering services and self-service in intelligent public administration

Self-service and offering services in public administration is a new concept that should be the basis of its service in the future. Here, we present a concept that should enable not only more efficient service but also the satisfaction of service users. Especially because it would provide them with the option to choose. To implement this concept effectively, it is necessary to implement intelligent public administration that can provide it. For this, we need knowledge bases and intelligent software agents. We will list some of the tools for implementing knowledge bases, and in one tool, we will create an appropriate database. We will use it to present the process of offering services. Here, we will mention the process of forwarding the "Birth Certificate" - a birth certificate that is part of self-service. We will mention offering a "Criminal Record Certificate" and "Title deed" as an example of offering services. These are a unique public administration data warehouse and a necessary means for user identification. There are also necessary legal prerequisites for implementing and using self-service and offering services in intelligent public administration.

The association of azole antifungals with overall survival in patients with non-small cell lung cancer receiving immune checkpoint inhibitors.

Preclinical data suggest antifungal azole derivatives have antitumor efficacy that may modulate response to immune checkpoint inhibitors (ICIs). We aimed to evaluate the association of azole drugs with overall survival (OS) in a population of patients with non-small cell lung cancer (NSCLC) treated with ICI within the Veterans Health Administration (VHA). In this retrospective study, the VA Corporate Data Warehouse was queried for patients diagnosed with NSCLC and treated with ICI from 2010 to 2018. Concomitant oral azole use was defined as dispensation by a VA pharmacy within 90 days of the first ICI infusion. Patients who received azole after 30 days were excluded from the analysis to mitigate immortal time bias. OS was measured from the start of ICI. Cox regression and propensity score matching were used to adjust for confounders. We identified 3413 patients with NSCLC receiving ICI; 324 (9.5%) were exposed to concomitant azoles. As a group, azole use was not associated with OS (hazard ratio [HR] = 0.96; 95% CI, 0.84-1.09; P = .51). After stratification by azole type, clotrimazole had an association with better OS on univariable (HR = 0.75; 95% CI, 0.59-0.96; P = .024) and multivariable analysis (HR = 0.71; 95% CI, 0.56-0.91; P = .007). Propensity score matching of patients who received clotrimazole vs no azole yielded 101 patients per matched cohort. Clotrimazole was associated with improved OS, although this did not meet the threshold for statistical significance (HR = 0.74; 0.54-1.01; P = .058). This observational study demonstrated an association between clotrimazole and OS among patients with advanced NSCLC receiving ICI. These findings build upon preclinical evidence and support further investigation into the potential for clotrimazole as a repurposed FDA drug to improve cancer outcomes.

Demographic and pathogen characteristics of incident bacterial meningitis in infants in South Africa: A cohort study.

Bacterial meningitis is a major cause of death, with an approximate case fatality rate of 37% across all age groups in South Africa. This study aimed to describe the demographic and pathogen characteristics of incident meningitis in children aged <1 year in South Africa from 2014 through 2018, during a period when Haemophilus influenzae type b vaccine and pneumococcal conjugate vaccines (PCV) were both included in the expanded program on immunization (EPI). We conducted a cohort study of routine laboratory data in the National Health Laboratory Service Corporate Data Warehouse, which covers approximately 80% of the South African population. We defined a case of laboratory-confirmed bacterial meningitis as any person aged <1 year with meningitis diagnosed by culture and identification of a pathogen documented as being a common cause of meningitis in CSF. The cause-specific incidence risks were calculated by dividing the number of positive specimens in each age group and year by the corresponding mid-year population for children under 1 year old and those in the post-neonatal period (≥ 28 days to 365 days old). For children under 28 days old, the annual numbers of registered livebirths were used. We used Poisson regression to compare the incidence of meningitis by year. We identified 3575 (1.5%) cases of culture-confirmed bacterial meningitis from the 232,016 cerebrospinal fluid (CSF) specimens tested from 2014-2018. The highest number of cases were recorded in children aged <28 days (1873, 52.4%), male children (1800, 50.4%) as well as in the Gauteng Province (2014, 56.3%). Acinetobacter baumannii (14.9%), followed by Klebsiella pneumoniae (13.5%), and group B streptococcus (GBS) (10.7%), were the most common pathogens detected. Overall, A. baumannii had the highest incidence risk, occurring at 9.8 per 100,000 persons in children aged <1 year in 2018. Among neonates, A. baumannii peaked at 14.9 per 100,000 livebirths in 2018, while Streptococcus pneumoniae was most common in the post-neonatal period (≥ 28 days to 365 days old), peaking at 9.8 per 100,000 persons in 2014. There was an increase in the annual incidence of most pathogens over the study period. There was an increasing trend in the annual incidence of bacterial meningitis in infants caused by most pathogens, particularly A. baumannii, K. pneumoniae and GBS. In addition to increased uptake of vaccination, prevention measures to reduce nosocomial and mother-to-child transmission of bacteria could include antenatal screening for GBS in pregnant women, rigorous hygiene in the hospital environment as well as rational antibiotic use.