Data Warehouse Research Articles

Protocol and research program of the European registry and biobank for interstitial lung diseases (eurILDreg)

Abstract Background and Aims Interstitial lung diseases (ILDs), encompassing both pediatric and adult cases, present a diverse spectrum of chronic conditions with variable prognosis. Despite limited therapeutic options beyond antifibrotic drugs and immunosuppressants, accurate diagnosis is challenging, often necessitating invasive procedures that may not be feasible for certain patients. Drawn against this background, experts across pediatric and adult ILD fields have joined forces in the RARE-ILD initiative to pioneer novel non-invasive diagnostic algorithms and biomarkers. Collaborating with the RARE-ILD consortium, the eurILDreg aims to comprehensively describe different ILDs, analyze genetically defined forms across age groups, create innovative diagnostic and therapeutic biomarkers, and employ artificial intelligence for data analysis. Methods The foundation of eurILDreg is built on a comprehensive parameter list developed and adopted by clinical experts, encompassing over 1,800 distinct parameters related to patient history, clinical examinations, diagnosis, lung function and biospecimen collection. This robust dataset is further enriched with daily assessments captured through the patientMpower app, including handheld spirometry and exercise tests, conducted on approximately 350 patients over the course of a year. This approach involves app-based daily assessments of quality of life, symptom tracking, handheld spirometry, saturation measurement, and the 1-min sit-to-stand test (1-STST). Additionally, pediatric data from the ChILD-EU registry will be integrated into the RARE-ILD Data Warehouse, with the ultimate goal of including a total of 4.000 ILD patients and over 100.000 biospecimen. Discussion The collaborative efforts within the consortium are poised to streamline research endeavors significantly, promising to advance patient-centered care, foster innovation, and shape the future landscape of interstitial lung disease research and healthcare practices. Trial Registration EurILDreg is registered in the German Clinical Trials Register (DRKS 00028968, 26.07.2022), and eurIPFreg is registered in ClinicalTrials.gov (NCT02951416).

Case Ascertainment in Pediatric Heart Failure Using International Classification of Disease Clinical Modification (ICD-CM) Codes.

Most epidemiological studies in pediatric heart failure (HF) use administrative database sources, defining patient cohorts by presence of a single HF ICD code. However, the ability of ICD codes to identify true HF patients is unknown in pediatrics.Here we describe the accuracy of HF ICD-10-CM code search algorithms, in identifying pediatric patients with HF from electronic data sources. Based on the adult HF literature, search algorithms were designed to incorporate HF ICD codes, imaging, and medications. Sensitivity, specificity, positive and negative predictive value and accuracy of the algorithms were tested among children in an advanced HF clinic ("Clinic cohort"). Top-performing algorithms were then tested in a large-scale regional electronic data warehouse (EDW), 01/2017 to 01/2020, generating the "EDW Cohort". False positive cases were identified and characterized by chart review. Within the Clinic Cohort, 78/378 patients (21%) had gold standard HF diagnoses. A search algorithm with one HF ICD coded visit was more sensitive but less specific than > 1 HF ICD coded visit, (sensitivity 94% and specificity 89% versus 69% and 97%, respectively). Correspondingly, > 1 ICD coded visit had a higher PPV than one ICD coded visit; 84% vs. 69%. Accuracy was similar (90% vs 91%). Presence of 1 HF ICD code combined with HF medication had high sensitivity, specificity, PPV, NPV and accuracy, all higher than the single ICD code algorithm. The "1 HF coded visit + any medication" algorithm resulted in highest accuracy (93%). Top-performing algorithms were tested in the EDW: the algorithm with > 1 HF ICD coded visit, and the algorithm with one HF ICD coded visit combined with HF medication. In the EDW Cohort, 133/248 (53.6%) patients had gold standard HF diagnoses though 115/248 (46.3%) were false positive cases; 41% of those had pulmonary over-circulation from congenital heart disease. Excluding children < 30days old and those with a history of an isolated VSD repair, complete AVSD repair, or PDA closure further reduced the proportion of false positives to 50/248 (20%). A search algorithm using a single HF ICD code can have acceptable sensitivity, specificity, PPV, NPV and accuracy in identifying children with HF from within electronic medical records. Similar to adult HF literature, specificity improves by including HF medication. When applied to large data sources, however, the search algorithms result in a high proportion of patients with pulmonary overcirculation related to congenital heart disease. To narrow the population to those with myocardial dysfunction, case identification may require use of ICD codes with linked of administrative, surgical, and/or imaging databases.

MDRepo-an open data warehouse for community-contributed molecular dynamics simulations of proteins.

Molecular Dynamics (MD) simulation of biomolecules provides important insights into conformational changes and dynamic behavior, revealing critical information about folding and interactions with other molecules. The collection of simulations stored in computers across the world holds immense potential to serve as training data for future Machine Learning models that will transform the prediction of structure, dynamics, drug interactions, and more. Ideally, there should exist an open access repository that enables scientists to submit and store their MD simulations of proteins and protein-drug interactions, and to find, retrieve, analyze, and visualize simulations produced by others. However, despite the ubiquity of MD simulation in structural biology, no such repository exists; as a result, simulations are instead stored in scattered locations without uniform metadata or access protocols. Here, we introduce MDRepo, a robust infrastructure that provides a relatively simple process for standardized community contribution of simulations, activates common downstream analyses on stored data, and enables search, retrieval, and visualization of contributed data. MDRepo is built on top of the open-source CyVerse research cyber-infrastructure, and is capable of storing petabytes of simulations, while providing high bandwidth upload and download capabilities and laying a foundation for cloud-based access to its stored data.

Abstract 4115684: Outcomes Associated with Remote Monitoring without In-person Evaluations for Patients with Cardiovascular Implantable Electronic Devices

Introduction: Traditionally, patients with cardiovascular implantable electronic devices (CIEDs: pacemakers and implantable cardioverter-defibrillators) attend in-person visits at least annually, paired with remote monitoring (RM). Because similar data are obtained through RM and in-person visits, it is unclear whether routine in-person visits are necessary. Two small trials conducted outside the U.S. found no difference in clinical outcomes without routine in-person visits for patients engaged in RM, but their generalizability is uncertain. Research Aim: To determine whether routine visits for CIED care are associated with major adverse cardiac events (MACE) among patients in the U.S.’ largest public integrated health system, the Veterans Health Administration (VHA). Methods: We classified Veterans who received their CIED care within VHA and sent ≥1 RM transmission between 7/1/20-6/30/21 into one of three groups based on type of clinical encounters for CIED care: (1) any in-person, (2) virtual-only (telephone and/or videoconference), or (3) no in-person or virtual visits. Data were obtained from the VHA National Cardiac Device Surveillance Program and Corporate Data Warehouse. The primary outcome was MACE, a composite of all-cause mortality, stroke, and cardiac hospitalization, between 7/1/21-6/30/22. We performed multivariable logistic regression, adjusting for patient and device characteristics. Results: Of 40,366 patients, 27,298 (68%) had at least one in-person visit, 9,268 (23%) had only virtual visits, and 3,800 (9%) had neither. Mean age was 73 years, 97% were male, and 37% lived in rural areas. There were 6522 (23.9%) MACE events among patients who had in-person visits, 2116 (22.8%) among those who had only virtual visits, and 947 (24.9%) among those who had no visits. After multivariable adjustment, there was no significant association between encounter type and MACE [odds ratio 0.93 (95% CI: 0.85-1.01) for patients who had any in-person or virtual visits compared to those without]. Conclusion: Among patients engaged in RM, the odds of MACE was similar regardless of whether patients had in-person or virtual visits versus no visits. Although there could be unmeasured confounding, these results suggest routine in-person or virtual visits may no longer be necessary for CIED care with RM. Patients, clinicians, and healthcare systems could benefit by reducing low-value visits and re-focusing resources on visits that yield actionable interventions.

An interactive dashboard for analyzing user interaction patterns in the i2b2 clinical data warehouse.

Clinical data warehouses provide harmonized access to healthcare data for medical researchers. Informatics for Integrating Biology and the Bedside (i2b2) is a well-established open-source solution with the major benefit that data representations can be tailored to support specific use cases. These data representations can be defined and improved via an iterative approach together with domain experts and the medical researchers using the platform. To facilitate these discussions, it is important to understand how users interact with the system. The objective of this work was to develop metrics for describing user interactions with clinical data warehouses in general and i2b2 in particular. Moreover, we aimed to develop a dashboard featuring interactive visualizations that inform data engineers and data stewards about potential improvements. We first identified metrics for different data usage dimensions and extracted the relevant metadata about previous user queries from the i2b2 database schema for further analysis. We then implemented associated visualizations in Python and integrated the results into an interactive dashboard using Dash. The identified categories of metrics include frequency of use, session duration, and use of functionality and features. We created a dashboard that extends our local i2b2 data warehouse platform, focusing on the latter category, further broken down into the number of queries, frequently queried concepts, and query complexity. The implementation is available as open-source software. A range of metrics can be derived from metadata logged in the i2b2 database schema to provide data engineers and data stewards with a comprehensive understanding of how users interact with the platform. This can help to identify the strengths and limitations of specific instances of the platform for specific use cases and aid their iterative improvement.

Real-world use of multiplex point-of-care molecular testing or laboratory-based molecular testing for influenza-like illness in a 2021 to 2022 US outpatient sample.

While molecular testing is recommended for symptomatic patients suspected of having coronavirus disease 2019 (COVID-19), limited data are available examining real-world use of tests for severe acute respiratory syndrome coronavirus (SARS-CoV-2) and the impact of SARS-CoV-2 testing on patient outcomes. In this retrospective cohort study using de-identified administrative claims data in the Optum Labs Data Warehouse, we identified 2 groups of patients with ≥1 outpatient claims with a procedure code for SARS-CoV-2 testing between January 2021 and September 2022. Group 1 had ≥1 claims with CPT code 0240U or 0241U ("Xpert Xpress") (N = 51,602); Group 2 had ≥1 claims for laboratory-based molecular testing (N = 317,192). Outcomes assessed on the identification date and through the 90-day follow-up included claims evidence of use of SARS-CoV-2, influenza, and respiratory syncytial virus (RSV) tests, diagnosis of active COVID-19, influenza, or RSV, and use of treatments (antivirals for COVID-19, influenza, and RSV and other treatments for COVID-19 and RSV). Patients in Group 1 had fewer tests for SARS-CoV-2, influenza, or RSV (mean ± standard deviation 1.6±1.4 versus 2.6±2.6, standardized difference -0.45), faster time to diagnosis of COVID-19 (median 0 versus 4 days, standardized difference -0.27) or influenza (median 0 versus 5 days, standardized difference -0.74), and faster time to treatment of COVID-19, influenza, or RSV (median 1 versus 5 days, standardized difference 0.16) than patients in Group 2. In this nationwide real-world study of outpatient testing, use of point-of-care molecular multiplex SARS-CoV-2 testing resulted in fewer claims for SARS-CoV-2, influenza, and RSV tests, faster time to diagnosis, and faster time to treatment than laboratory-based molecular testing.

Continuation versus Interruption of Buprenorphine/Naloxone in Adult Veterans Undergoing Surgery: Examination of Postoperative Pain and Opioid Utilization in a National Retrospective Cohort Study.

Prescription rates for buprenorphine in opioid use disorder are increasing, and recent guidelines recommend its continuation during and after surgery; however, evidence from clinical outcome studies is limited. We tested the hypotheses that 1) perioperative continuation of buprenorphine does not result in higher pain scores and 2) that this approach does not result in higher supplemental postoperative opioid requirements. The Veterans Affairs Corporate Data Warehouse was queried for patients who underwent surgery while being prescribed buprenorphine/naloxone for opioid use disorder between 2010 and 2020. Analysis of the prescription record was used to infer buprenorphine management, and a 3:1 matched control set of patients without buprenorphine prescriptions was generated. We examined patients who continued buprenorphine, patients who had buprenorphine interrupted, and control patients. The primary outcome was time-weighted average postoperative pain scores from inpatient and outpatient sources within 72 hours of surgery. The secondary outcome was postoperative average daily morphine equivalent opioid requirements within two weeks of surgery. A total of 1,881 surgical procedures in 1,673 patients taking buprenorphine for opioid use disorder were included; these procedures were matched to 5,748 control patients (5,775 procedures) without a buprenorphine prescription. Among the 1,881 procedures, 1,186 (63%) continued buprenorphine through the perioperative period while 695 (37%) interrupted buprenorphine. Pain scores were clinically similar for all three groups (4.1 ± 1.9 control [n = 3284], 4.9 ± 2.0 continued buprenorphine [n = 662], and 5.5 ± 1.7 interrupted buprenorphine [n = 419]; P < 0.001).Patients who continued buprenorphine did not require significantly more supplemental opioids as compared to controls (39.7 mg morphine equivalents/day ± 1.9 versus 36.5 ± 0.7, P = 0.23), and patients who interrupted buprenorphine received more supplemental opioids than those who continued it (74.2 ± 4.5 mg morphine equivalents/day versus 39.7 ± 1.9,respectively; P < 0.001). Continuation of buprenorphine is not associated with higher average pain scores or postoperative opioid requirements, supporting recently published guidelines.

Centralized Data Warehouse vs. Data Mesh: A Comparative Analysis of Modern Data Management Paradigms

This comprehensive article explores the evolving landscape of enterprise data management, focusing on comparing traditional centralized data warehouses and the emerging data mesh paradigm. As organizations grapple with exponential data growth, projected to reach 180 zettabytes by 2025 [1], the choice of data architecture has become increasingly critical. The article examines both approaches' core principles, advantages, and challenges, highlighting how centralized data warehouses offer strong consistency and governance [2, 8] but face scalability issues [3]. In contrast, data mesh provides enhanced flexibility and domain-specific optimization [4] at the cost of more complex governance [5]. Through a detailed comparative analysis, the study investigates the impact of these architectures on organizational control, scalability, adaptability to business needs, and data quality management. Furthermore, it explores the potential of hybrid approaches that aim to leverage the strengths of both paradigms. The research considers various factors influencing the selection of data management strategies, including organization size, data complexity, and industry-specific requirements. By synthesizing current literature and industry trends, this article provides data professionals and decision-makers with a nuanced understanding of modern data management paradigms, enabling them to make informed choices aligned with their specific business contexts and long-term data strategies.

BUILDING DATA WAREHOUSING FOR MACHINE LEARNING AND CLIENT SCORING

In modern business, data is a critical resource that can significantly increase the company's competitiveness. One of the key tasks associated with data is the creation of data warehouses that provide effective storage, processing and analysis of information. In particular, structured data warehouses play a critical role in the development of machine learning and customer scoring systems. In this article, we will look at the stages of building data warehouses, integrating them with machine learning systems, and the importance of structured data for customer scoring. At the end of the article, we will present a mini-project on implementing a data warehouse using SQL and PySpark, as well as an example of machine learning for customer scoring. Keywords: data warehouse, SQL and PySpark, customer scoring, data structuring, structured data warehouses.

Combating Antimicrobial Resistance Through a Data-Driven Approach to Optimize Antibiotic Use and Improve Patient Outcomes: Protocol for a Mixed Methods Study.

It is projected that drug-resistant infections will lead to 10 million deaths annually by 2050 if left unabated. Despite this threat, surveillance data from resource-limited settings are scarce and often lack antimicrobial resistance (AMR)-related clinical outcomes and economic burden. We aim to build an AMR and antimicrobial use (AMU) data warehouse, describe the trends of resistance and antibiotic use, determine the economic burden of AMR in Uganda, and develop a machine learning algorithm to predict AMR-related clinical outcomes. The overall objective of the study is to use data-driven approaches to optimize antibiotic use and combat antimicrobial-resistant infections in Uganda. We aim to (1) build a dynamic AMR and antimicrobial use and consumption (AMUC) data warehouse to support research in AMR and AMUC to inform AMR-related interventions and public health policy, (2) evaluate the trends in AMR and antibiotic use based on annual antibiotic and point prevalence survey data collected at 9 regional referral hospitals over a 5-year period, (3) develop a machine learning model to predict the clinical outcomes of patients with bacterial infectious syndromes due to drug-resistant pathogens, and (4) estimate the annual economic burden of AMR in Uganda using the cost-of-illness approach. We will conduct a study involving data curation, machine learning-based modeling, and cost-of-illness analysis using AMR and AMU data abstracted from procurement, human resources, and clinical records of patients with bacterial infectious syndromes at 9 regional referral hospitals in Uganda collected between 2018 and 2026. We will use data curation procedures, FLAIR (Findable, Linkable, Accessible, Interactable and Repeatable) principles, and role-based access control to build a robust and dynamic AMR and AMU data warehouse. We will also apply machine learning algorithms to model AMR-related clinical outcomes, advanced statistical analysis to study AMR and AMU trends, and cost-of-illness analysis to determine the AMR-related economic burden. The study received funding from the Wellcome Trust through the Centers for Antimicrobial Optimisation Network (CAMO-Net) in April 2023. As of October 28, 2024, we completed data warehouse development, which is now under testing; completed data curation of the historical Fleming Fund surveillance data (2020-2023); and collected retrospective AMR records for 599 patients that contained clinical outcomes and cost-of-illness economic burden data across 9 surveillance sites for objectives 3 and 4, respectively. The data warehouse will promote access to rich and interlinked AMR and AMU data sets to answer AMR program and research questions using a wide evidence base. The AMR-related clinical outcomes model and cost data will facilitate improvement in the clinical management of AMR patients and guide resource allocation to support AMR surveillance and interventions. PRR1-10.2196/58116.

Feasibility of automated surveillance of implantable devices in orthopaedics via clinical data warehouse: the Studio study

BackgroundTotal hip, knee and shoulder arthroplasties (THKSA) are increasing due to expanding demands in ageing population. Material surveillance is important to prevent severe complications involving implantable medical devices (IMD) by taking appropriate preventive measures. Automating the analysis of patient and IMD features could benefit physicians and public health policies, allowing early issue detection and decision support. The study aimed to demonstrate the feasibility of automated cohorting of patients with a first arthroplasty in two hospital data warehouses (HDW) in France.MethodsThe study included adult patients with an arthroplasty between 2010 and 2019 identified by 2 data sources: hospital discharge and pharmacy. Selection was based on the health insurance thesaurus of IMDs in the pharmacy database: 1,523 distinct IMD references for primary THSKA. In the hospital discharge database, 22 distinct procedures for native joint replacement allowing a matching between IMD and surgical procedure of each patient selected. A program to automate information extraction was implemented in the 1st hospital data warehouse using natural language processing (NLP) on pharmacy labels, then it was then applied to the 2nd hospital.ResultsThe e-cohort was built with a first arthroplasty for THKSA performed in 7,587 patients with a mean age of 67.4 years, and a sex ratio of 0.75. The cohort involved 4,113 hip, 2,630 knee and 844 shoulder surgical patients. Obesity, cardio-vascular diseases and hypertension were the most frequent medical conditions.DiscussionThe implementation of an e-cohort for material surveillance will be easily workable over HDWs France wild. Using NLP as no international IMD mapping exists to study IMD, our approach aims to close the gap between conventional epidemiological cohorting tools and bigdata approach.ConclusionThis pilot study demonstrated the feasibility of an e-cohort of orthopaedic devices using clinical data warehouses. The IMD and patient features could be studied with intra-hospital follow-up and will help analysing the infectious and unsealing complications.

Association of Homelessness and Unstable Housing With Cardiovascular Care Utilization Among Veterans.

Veterans are disproportionately more likely to experience homelessness and unstable housing (HUH) compared with the general population. Cardiovascular disease is the leading cause of death among Veterans experiencing HUH. We aimed to understand whether HUH status among Veterans with preexisting cardiovascular disease was associated with disparities in cardiovascular care access and utilization. Retrospective study of all Veterans with preexisting cardiovascular disease between 2017 and 2019 using Veterans Affairs Corporate Data Warehouse and Homeless registry data. Primary outcomes were annual outpatient visits for cardiovascular disease management and visits with cardiovascular disease-related specialists. Secondary outcomes included cardiovascular disease-related procedures and emergency department visits and hospitalizations. HUH status was determined based on response to a screener, diagnostic codes, or use of homelessness services, and outcomes were assessed in the first year HUH status was determined. After applying inverse probability of treatment weighting, negative binomial and logistic regression models were fit to estimate the association between experiencing HUH and the outcomes of interest. Among 1 357 973 Veterans (mean age, 71.6 [SD=10.6] years; 2.5% female) with preexisting cardiovascular disease, 56 093 were identified as experiencing HUH during the study period. Veterans experiencing HUH had fewer outpatient visits for cardiovascular disease management or with cardiovascular disease-related specialists (4.3% [95% CI, 2.5%-6.1%] and 14.1% [95% CI, 12.5%-15.8%], respectively) compared with housed Veterans. HUH status was associated with lower rates of receiving certain procedures including coronary artery bypass graft, lower extremity revascularization, and carotid artery stenosis interventions and higher rates of all-cause and cardiovascular emergency department visits and hospitalizations. Veterans with chronic cardiovascular conditions experiencing HUH had lower rates of outpatient visits for cardiovascular disease management and higher rates of emergency department visits and hospitalizations. Given the disproportionate burden of cardiovascular disease in this population, interventions to improve access to cardiovascular care are needed.

The hemodynamic effects of protamine in pediatric patients undergoing pulmonary artery reconstruction and unifocalization surgery: A Pilot Study.: Hemodynamic effects of protamine in children.

ObjectivesTo determine protamine administration increases pulmonary artery pressures in patients undergoing unifocalization or pulmonary artery reconstruction surgeries. Design: Retrospective database study. SettingA large pediatric heart center within an academic quaternary care facility. ParticipantsAll patients undergoing pulmonary artery reconstruction or unifocalization procedure identifiable within the data warehouse. InterventionsWe collected data from Stanford University's data repository, formatted it, and analyzed it using RStudio (v 2023.06.1+524). Measurements and Main ResultsOur primary outcome is the change in pulmonary artery pressure (PAP) after an administration of protamine. Secondary outcomes include changes in the mean arterial pressure, the ratio of systolic pulmonary artery to systemic artery pressure, right sided filling pressure, and left atrial pressure. After a protamine bolus, we found a difference in PAP (Friedman X2 = 49.46, p <0.001). When compared to two minutes before its administration, the PAP was higher at two minutes (29.00 mmHg versus 25.00 mmHg, p <0.001), five minutes (30.00 mmHg versus 25.00 mmHg, p <0.001) and ten minutes (31 mmHg versus 25 mmHg, p < 0.001). When co-administered with calcium, there was also a significant increase in PAP (Friedman X2 = 28.11, p < 0.001), with a higher PAP ten minutes after calcium administration when compared to two minutes before (32 mmHg versus 26 mmHg, p < 0.001) Conclusions: Protamine administration led to a small increase in PAP after separation from cardiopulmonary bypass in patients undergoing pulmonary artery reconstruction or unifocalization surgeries. Calcium co-administration did not lead to a greater increase in PAP.

The primary cause of markedly elevated aminotransferases in hospitalized patients with cirrhosis in ischemic hepatitis.

Marked elevation in aminotransferases (≥1000 IU/l) is typically associated with acute liver injury. Here, we hypothesized that the cause of elevation in aminotransferases ≥1000 in patients with cirrhosis is likely due to a limited number of disorders and may be associated with poor outcomes. We aimed to investigate the most common etiologies of acute elevations in aminotransferases in patients with cirrhosis, and to examine their associated outcomes. From May 2012 to December 2022, all hospitalized patients with cirrhosis and an aspartate aminotransferase or alanine aminotransferase ≥ 1000 IU/l were identified through Medical University of South Carolina's Clinical Data Warehouse. Complete clinical data were abstracted for each patient, and in-hospital mortality was examined. The cohort was made up of 152 patients, who were 57 ± 12 years old, with 51 (34%) women. Underlying liver disease included mainly hepatitis C cirrhosis, alcohol-related cirrhosis, metabolic dysfunction-associated steatohepatitis cirrhosis, autoimmune cirrhosis, primary sclerosing cholangitis cirrhosis, and cryptogenic cirrhosis. The most common cause of marked elevation in aminotransferases in cirrhotic patients was ischemic hepatitis (71%), followed by chemoembolization (7%), autoimmune hepatitis (6%), drug-induced liver injury (3%), post-transjugular intrahepatic portosystemic shunt placement (3%), rhabdomyolysis (3%), and hepatitis C (2%). During hospitalization and over a 1-month follow-up period, the mortality rate in patients with ischemic hepatitis was 73% (79/108), while that for other causes of liver injury was 20% (9/44). Ischemic hepatitis is the leading cause of marked elevation of aminotransferases in patients with cirrhosis, with distinctive clinical characteristics than other etiologies, and significantly poorer outcomes.

The Veterans Affairs Rheumatoid Arthritis Registry: A Unique Population in Rheumatoid Arthritis Research

Background: As the largest integrated healthcare system in the U.S., the Veterans Affairs (VA) provides a unique context for the conduct of clinical and clinical-translational research in rheumatoid arthritis (RA). Objectives: To review attributes of the VA Rheumatoid Arthritis Registry (RA) and highlight its research contributions. Findings: With more than 3,600 participants enrolled from 19 VA medical centers across the U.S., VARA includes longitudinally collected clinical data and a central biorepository that includes serum, plasma, and DNA collected at enrollment. VARA research capacity is enhanced via active linkages with internal data including the VA's Corporate Data Warehouse and elements captured during oncology care. This capacity is further enabled via active linkages with the National Death Index and Centers for Medicare & Medicaid Services (CMS) data. Conclusion: As a highly unique study population with comprehensive data annotation available to researchers, VARA is poised to continue address impactful questions in RA for years to come.

Impact of Home Telehealth Expansion on High-Cost Utilization Among Veterans Health Administration Patients with Diabetes.

Home telehealth (HT) services have rapidly expanded in the Veterans Health Administration (VHA), but little is known about the real-world impact of the expansion of these services on utilization. To evaluate the impact of expanding HT diabetes services in VHA on high-cost utilization. This cohort study used cross-temporal propensity score matching to identify patients with diabetes who would receive HT services (treatment group) with program expansion (treatment group) with a comparison group of patients with diabetes who did not receive HT services (control group) between 2010 and 2018. A difference-in-differences design was used to compare pre-post changes in high-cost utilization between propensity-matched groups. Data used was from the VHA's Corporate Data Warehouse. VHA patients with diabetes, n=7214 in the treatment group who received HT services, and n=1,067,138 in the control group who did not receive HT services. Emergency department (ED) visits, all-cause hospitalization, and hospitalizations for ambulatory care sensitive conditions (ACSCs). In the baseline period, patients in our sample had a mean age of 67 years old (SD=11 years), were 97% male, and 78% had a hemoglobin A1c (HbA1c) < 8%. The increase in HT use was associated with a net increase of ED visits per patient (0.215 95% CI [0.152, 0.279]), all-cause hospitalization (0.053 95% CI [0.032, 0.074]), and ACSC hospitalization (0.032 95% CI [0.019, 0.045]). Expanding HT or remote monitoring services may lead to additional needs being identified for patients, including increased access to in-person care, such as ED or hospital services.

Health care utilization at the end of life in Parkinson’s disease: a population-based register study

BackgroundKnowledge of health care utilization at the end of life in Parkinson’s disease (PD) is sparse. This study aims to investigate end of life health care utilization, characterized by emergency room (ER) visits, receipt of specialized palliative care (SPC), and acute hospital deaths in a Swedish population-based PD cohort.MethodsWe conducted a retrospective cohort study on deceased patients (≥ 18 years) with a PD diagnosis during their last year of life (n = 922), based on health care-provider data from Region Stockholm´s data warehouse, for the study period 2015–2021. Univariable and multivariable logistic regression analyses tested associations and adjusted Odds ratios (aORs) were calculated.ResultsDuring the last month of life, approx. half of the cohort had emergency room (ER) visits and risk of frailty (measured by Hospital Frailty Risk Score) significantly predicted these visits (aOR, 3.90 (2.75–5.55)). In total, 120 people (13%) received SPC during their last three months of life, which positively associated with risk for frailty, (aOR, 2.65 (1.43–4.94, p = 0.002). In total, 284 people (31%) died in acute hospital settings. Among community-dwellers, male gender and frailty were strongly associated with acute hospital deaths (aOR, 1.90 (1.15–3.13, p = 0.01) and 3.70 (1.96–6.98, p < 0.0001)).ConclusionsRates of ER visits at end of life and hospital deaths were relatively high in this population-based cohort. Considering a high disease burden, referral to SPC at end of life was relatively low. Sex-specific disparities in health care utilization are apparent. Identifying people with high risk for frailty could assist the planning of optimal end-of-life care for people with PD.

Application of Business Intelligence in the Brazilian Educational System

In this article, we will discuss an application of Business Intelligence (BI) in analyzing data from the Brazilian educational system, specifically working with INEP (The Instituto Brasileiro de Geografia e Estatística — IBGE). This paper then discusses BI implementation's positive contributions and critical challenges in this domain. From the construction of an integrated BI system, our study aimed to offer a helpful tool that was easy to handle and touched on visuals such as dashboards, tables, and graphs to help us understand and manage the educational scenery throughout Brazil. The methodology used has been applied, with case studies and bibliographical research, based on data analysis techniques such as ETL (Extract, Transform, Load) and Data Warehousing. This is all leveraged to interactive dashboards, which should enable data-driven decisions and forge the way for advanced analytics like data mining.

Administrative data for public health: a regional data warehouse applied to dementia epidemiology

Abstract Administrative data can represent valuable sources of information for researchers seeking to advise policymakers. Several countries have established population-based data warehouses (DWH), integrating multiple data sources in single repositories through data linkage techniques. In Lombardy (Italy), a regional population-based DWH was established in the 1980s. It collects data about citizens from multiple sources in the social and health sectors, anonymising them immediately after their acquisition. Lombardy Region signed a specific agreement with the University of Pavia to investigate the epidemiology and access to care among people living with dementia (PLWD), using the DWH. A retrospective cohort study was conducted on hospitalised PLWD, including people aged ≥ 65 years with diagnoses of dementia at discharge from 2002 to 2020. The aim was to study the trends of hospitalisations among PLWD, computing these by calendar year, age, sex and cause of hospitalisation. A total of 340,144 hospital discharges were registered, of which 211,709 (63.0%) were women and 124,195 (37.0%) were men. The rate of hospitalisation decreased over the study period, ranging from 100.6/10,000 in 2002 to 65.1/10,000 in 2020. The average age at hospitalisation increased for men and women from 78.9 and 81.8 years in 2002 to 82.0 years and 84.2 years, respectively, in 2020. Respiratory diseases caused 10.4% of all hospitalisations in 2002 and grew steadily to 26.8% in 2020, becoming the leading cause of hospital admissions since 2017. Results allowed us to elaborate epidemiological measures and to characterise our population in a wide temporal trend, quantifying access to acute care by leading causes of hospitalisation. However, the analysis of administrative data comes along with several challenges, such as data incompleteness and biases. Researchers should take advantage of these data sources while keeping in mind the limitations and constraints related to data quality.

Premature ventricular contraction burden measured by an insertable cardiac monitor and the incidence of ventricular tachycardia and fibrillation

Abstract Background Insertable cardiac monitors (ICM) have the capability to continuously monitor premature ventricular contraction (PVC) burden over time [1] as well as detect ventricular tachycardia and fibrillation (VT/VF). Objective We investigated the association of PVC burden with incidence of VT/VF in a large real-world cohort of patients implanted with ICMs. Methods Patients implanted with an ICM for various reasons for monitoring and PVC detection turned on were included from the ICM manufacturer's de-identified data warehouse. Patients were included if they had at least 90 days of PVC burden follow-up. Tachycardia episodes that were detected by the ICM were first classified as VT/VF, SVT, or oversensing using an artificial intelligence (AI) model that was pre-trained using over 60,000 manually adjudicated ICM detected tachycardia episodes. If the AI model output probability for VT/VF was greater than 0.2, then those episodes were manually adjudicated for incidence of non-induced spontaneous VT/VF. The PVC burden trends recorded by the device were divided into 4 mutually exclusive patient groups: (1) 0% PVC burden on all days, (2) 1-4% PVC burden on ≥1 day, (3) 5-9% PVC burden on ≥1 day, and (4) &amp;gt;10% PVC on ≥1 day. The incidence rate of spontaneous VT/VF was compared between the PVC burden patient groups using a Generalized Estimating Equations model with negative binomial distribution. Time to first spontaneous VT/VF occurrence after the first day of occurrence of PVC burden for the respective PVC burden groups were estimated using Kaplan-Meier analysis and the groups were compared using the Cox proportional hazards model. Results A total of 5,521 patients were included in the analysis. Patients had an average age of 69±15 years and 49.8% being males. There was a total of 33,393 tachycardia episodes from 2,641 patients that were detected by the ICM. After AI model probability-based adjudications, 691 spontaneous VT/VF episodes were identified from 277 patients. Patients with ≥1 day of PVC burden of 1-4%, 5-9%, and ≥10% were associated with 2.9, 7.3, and 7.8 times increased incidence rate of VT/VF episodes during follow-up period relative to patients with 0% PVC burden on all days of follow-up (Figure A). Kaplan Meier curves for incidence of first spontaneous VT/VF episode for the four groups following occurrence of the first day of qualifying PVC burden for the respective groups are shown in figure B. Patients with a day of PVC burden ≥10% were 3.5 times more likely to develop VT/VF in the future compared to patients with 0% PVC burden on all days. Conclusion Days with high PVC burden, as detected by an ICM with continuous PVC detection capability, were associated with increased risk of VT/VF events in a group of real-world patients implanted with ICMs. PVC burden measured by ICMs may be a risk stratification tool for further investigation to prevent sudden cardiac arrest.