Common Data Model Research Articles

IncidencePrevalence: An R package to calculate population-level incidence rates and prevalence using the OMOP common data model.

Real-world data (RWD) offers a valuable resource for generating population-level disease epidemiology metrics. We aimed to develop a well-tested and user-friendly R package to compute incidence rates and prevalence in data mapped to the observational medical outcomes partnership (OMOP) common data model (CDM). We created IncidencePrevalence, an R package to support the analysis of population-level incidence rates and point- and period-prevalence in OMOP-formatted data. On top of unit testing, we assessed the face validity of the package. To do so, we calculated incidence rates of COVID-19 using RWD from Spain (SIDIAP) and the United Kingdom (CPRD Aurum), and replicated two previously published studies using data from the Netherlands (IPCI) and the United Kingdom (CPRD Gold). We compared the obtained results to those previously published, and measured execution times by running a benchmark analysis across databases. IncidencePrevalence achieved high agreement to previously published data in CPRD Gold and IPCI, and showed good performance across databases. For COVID-19, incidence calculated by the package was similar to public data after the first-wave of the pandemic. For data mapped to the OMOP CDM, the IncidencePrevalence R package can support descriptive epidemiological research. It enables reliable estimation of incidence and prevalence from large real-world data sets. It represents a simple, but extendable, analytical framework to generate estimates in a reproducible and timely manner.

Design of infrastructure for visualising and benchmarking patient data from general practice (CARA)

Abstract Background Patient Management Software (PMS) is commonly used for patient consultations. PMS supports individual patient data storage but accessing and using aggregated information is challenging. CARA set out to provide an infrastructure to enable Irish general practitioners (GPs) to understand their patient population, disease management and prescribing through dashboards. This paper explains the design and development of the CARA infrastructure. Methods The first exemplar dashboard focused on antibiotic prescribing to develop and showcase the proposed infrastructure. The data integration process involved extracting, loading and transforming de-identified patient data into data models. These, in turn, feed the interactive dashboards for GPs to visualise, compare and audit their data. Results The architecture of the CARA infrastructure includes two main sections: the ELT process (extract, load and transform anonymous patient data into data models) and a REST API (which provides the security barrier between the data models and their visualisation on the CARA dashboard). CARAconnect was created to facilitate data extraction and de-identification of patient data from the practice database. Conclusions CARA includes two dashboards, a practice overview and an exemplar dashboard focused on antibiotic prescribing which includes an audit tool as well as filters (within practice) and between-practice comparisons. Next steps will include the development of additional dashboards for topics such as chronic disease and mental health. Key messages • CARA developed a sustainable infrastructure to enable Irish GPs to better understand their patient population and management through dashboards. • CARA infrastructure consists of CARAconnect to extract and de-identify data, a common data model (to combine data from different PMS systems), and CARA dashboards for use in Irish general practice.

Preterm birth and stillbirth rates during COVID-19 pandemic: differences by socioeconomic status

Abstract Background At the onset of the COVID-19 pandemic, there were concerns about maternal and newborn health because of increased stress, economic difficulties and restrictions to healthcare. However, preterm birth (PTB) rates declined in some countries in 2020, while stillbirth (SB) rates remained stable or were slightly elevated. Hypotheses for the PTB result include positive consequences of lockdowns (more rest, less pollution, less exposure to infection) or restricted healthcare, leading to fewer indicated preterm births. These effects have not been examined on a population-level by socioeconomic (SES) group. Methods The Euro-Peristat network developed a Common Data Model and R scripts to produce aggregated data tables from vital statistics and birth registers. This analysis uses data from 2015 to 2020 on singleton PTB rates and SB rates as well as on mother's educational level (preferred) or area level deprivation/occupation of mother (if education unavailable). SES measures were harmonised into 3 groups (low, medium, high). We computed observed versus expected risks of the outcomes for the year 2020, given linear trends from 2015 to 2019, overall and by SES group; these estimates were pooled using random effects meta-analysis. Results 19 countries provided data on perinatal outcomes by SES group. The overall pooled mixed effect measure for PTB was -0.04 (95%CI of -0.06 to -0.02), corresponding to a 4% decline in 2020 over expected levels. This estimate was the same in low and high SES groups (-0.04 (-0.07 to 0.02) versus -0.04 (-0.06 to -0.02)). For stillbirth, the overall pooled measure was 0.02 (-0.03 to 0.07), corresponding to an increase of 2%, with higher estimates in the low SES group 0.05 (-0.02 to 0.11) versus 0.2 (-0.3 to 0.7) in the high group, although confidence limits overlapped. Conclusions A small decrease in PTB was observed in all SES groups, while results are suggestive of increases in SB rates among women in lower SES groups. Key messages • Previous results in some countries of decreased preterm birth rates in the general population in 2020 were confirmed in this European study, with similar associations in all socioeconomic groups. • Stillbirth rates were not increased in the overall population, but a 5% increase was observed in lower SES groups.

Trends in caesarean section rates in Europe from 2015-2019 using Robson's Ten-Group Classification System: A Euro-Peristat registry study

Abstract Objective To assess changes in caesarean section (CS) in Europe from 2015-2019 and utilise the Robson Ten Group Classification System (TGCS) to evaluate the contribution of different obstetric populations to overall CS rates and trends Methods This was an observational study utilising routine population birth data from 28 European countries participating in the Euro-Peristat network. We included all births ≥22 weeks gestational age, from 2015 to 2019. A federated model used individual-level data from routine sources in each country formatted to common data model to generate anonymous, aggregate data tables on yearly CS rates and the Robson TGCS classification. We computed the Robson group-specific relative size, CS rate, relative and absolute contribution to overall CS rate Results Among the 28 European countries, CS rates (2015: 16.0-55.9%; 2019: 16.0-52.2%) and trends varied (-3.7-+4.7%; 9 countries decreasing, 7 stable [ ≤ ±0.2], 12 increasing). Using the Robson Classification (17 countries), in most countries, labour induction increased (Groups 2a, 4a), while multiple pregnancies (Group 8) decreased. In countries with decreasing overall CS rates, CS tended to decrease in most Robson groups, while in countries with increasing overall CS rates, CS tended to increase in most Robson groups. In countries with the greatest increase in CS rates (>1%), absolute contributions of Groups 1 (nulliparous term cephalic singletons, spontaneous labour), 2a and 4a, 2b and 4b (prelabour CS), and 10 (preterm cephalic singletons) to overall CS tended to increase. Conclusions Analysis of the Robson Classification shows varying CS trends and rates across Europe. Comparisons between European countries, particularly those with divergent trends, could provide insight into strategies to reduce CS without indication.

Federated causal inference based on real-world observational data sources: application to a SARS-CoV-2 vaccine effectiveness assessment

IntroductionCausal inference helps researchers and policy-makers to evaluate public health interventions. When comparing interventions or public health programs by leveraging observational sensitive individual-level data from populations crossing jurisdictional borders, a federated approach (as opposed to a pooling data approach) can be used. Approaching causal inference by re-using routinely collected observational data across different regions in a federated manner, is challenging and guidance is currently lacking. With the aim of filling this gap and allowing a rapid response in the case of a next pandemic, a methodological framework to develop studies attempting causal inference using federated cross-national sensitive observational data, is described and showcased within the European BeYond-COVID project.MethodsA framework for approaching federated causal inference by re-using routinely collected observational data across different regions, based on principles of legal, organizational, semantic and technical interoperability, is proposed. The framework includes step-by-step guidance, from defining a research question, to establishing a causal model, identifying and specifying data requirements in a common data model, generating synthetic data, and developing an interoperable and reproducible analytical pipeline for distributed deployment. The conceptual and instrumental phase of the framework was demonstrated and an analytical pipeline implementing federated causal inference was prototyped using open-source software in preparation for the assessment of real-world effectiveness of SARS-CoV-2 primary vaccination in preventing infection in populations spanning different countries, integrating a data quality assessment, imputation of missing values, matching of exposed to unexposed individuals based on confounders identified in the causal model and a survival analysis within the matched population.ResultsThe conceptual and instrumental phase of the proposed methodological framework was successfully demonstrated within the BY-COVID project. Different Findable, Accessible, Interoperable and Reusable (FAIR) research objects were produced, such as a study protocol, a data management plan, a common data model, a synthetic dataset and an interoperable analytical pipeline.ConclusionsThe framework provides a systematic approach to address federated cross-national policy-relevant causal research questions based on sensitive population, health and care data in a privacy-preserving and interoperable way. The methodology and derived research objects can be re-used and contribute to pandemic preparedness.

Application of OMOP Common Data Model for Data Integration: The Bulgarian Diabetes Register.

This paper considers mapping of the Bulgarian Diabetes Register(BDR) onto OMOP Common Data Model (CDM). Research results are referenced and plans for analysis of drug shortages from federated data sources are outlined.

Effect of cholesterol variability on the incidence of cataract, dementia, and osteoporosis: A study using a common data model.

The effects of cholesterol variability on cataracts, dementia, and osteoporosis remain controversial. Using a common data model, we investigated the effects of variations in cholesterol levels on the development of cataracts, dementia, and osteoporosis. Patients who received statin therapy between 2011 and 2020 and those with 3 or more tests for total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) levels were included. The patients were divided into those with a coefficient of variation (CV) of TC higher than the mean (high-CV group) and those with a lower CV of TC (low-CV group). Moreover, 1:1 propensity score matching was conducted based on demographic variables. Cataract, dementia, or osteoporosis was defined as having a diagnostic, drug, or surgical code based on the cohort definition. Of the 12,882 patients, cataracts, dementia, and osteoporosis were developed in 525 (4.1%), 198 (1.5%), and 438 (3.4%) patients, respectively. The stratified Cox proportional hazards model showed that the incidences of cataracts and osteoporosis were 1.38 and 1.45 times greater in the high-CV group than in the low-CV group, respectively. Our study revealed that TC variability is associated with developing cataracts and osteoporosis.

Long-Term Clinical Implications of Atrial Fibrillation on Mortality in Patients Hospitalized with COVID-19: A Nationwide Cohort Study.

Atrial fibrillation (AF) increases the risk of long-term mortality in patients hospitalized with Coronavirus Disease 2019 (COVID-19), but the evidence is limited. This study used data from the Common Data Model of the Health Insurance Review and Assessment Service of Korea collected between 1 January 2020 and 30 April 2022. A total of 107,247 patients hospitalized with COVID-19 were included in this study. They were divided into two groups according to a history of AF. The primary outcome was all-cause mortality. After propensity score stratification, 1919 patients with a history of AF and 105,328 patients without a history of AF who were hospitalized with COVID-19 were analyzed to determine long-term mortality. The primary outcome occurred in 99 of 1919 patients (5.2%) with a history of AF and in 1397 of 105,328 patients (1.3%) without a history of AF (hazard ratio, 1.49; 95% confidence interval 1.20-1.82; p < 0.01). A history of AF was also associated with an increased risk of within 30-day mortality. A history of AF was associated with an increased risk of long-term mortality in patients hospitalized with COVID-19. Our findings indicate the necessity for physicians to reevaluate the optimal management of patients with AF following discharge.

Increased risk of pancreatic cancer among 141,387 diabetic patients treated with DPP-4 inhibitors analysed with common data model

Increased risk of pancreatic cancer among 141,387 diabetic patients treated with DPP-4 inhibitors analysed with common data model

FRI633 Financial Benefits Of Renal Dose-adjusted Dipeptidyl Peptidase-4 Inhibitors For Patients With Type 2 Diabetes And Chronic Kidney Disease: A Multicenter Study Utilizing Common Data Model

Abstract Disclosure: H. Choe: None. Y. Ko: None. S. Moon: None. C.H. Ahn: None. K. Ha: None. H. Lee: None. J. Bae: None. H. Joo: None. H. Lee: None. J. Sohn: None. D. Kim: None. S. Kim: None. K. Kim: None. Y. Cho: None. Objective: To estimate the economic benefit of replacing non-renal dose adjusted (NRDA) dipeptidyl peptidase-4 (DPP4) inhibitors with renal dose adjusted (RDA) DPP4 inhibitors in patients with impaired kidney function and type 2 diabetes. Patients and Methods: This multicenter study analyzed a total of 67,964,996 prescription records of five teaching hospitals in Korea from January 1, 2012 to December 31, 2018 using a common data model. Prescription patterns of NRDA and RDA DPP4 inhibitors were subgrouped based on estimated glomerular filtration rate (eGFR) determined by Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Results: NRDA DPP4 inhibitors were more frequently prescribed to patients with impaired kidney function than those with preserved kidney function (25.7%, 51.3%, 64.3%, and 71.6% in patients with eGFR of ≥60, &amp;lt;60, &amp;lt;45, and &amp;lt;30 mL/min/1.73m2, respectively). When applying the prescription pattern of DPP4 inhibitors for patients with preserved kidney function to those with impaired kidney function, cost savings per year were 7.6% for eGFR &amp;lt;60 mL/min/1.73m2 and 30.4% for eGFR &amp;lt;30 mL/min/1.73m2. Assuming patients with impaired kidney function are solely prescribed with RDA DPP4 inhibitors with appropriate dose reduction, 15.4% to 51.2% per year could be saved depending on kidney impairment severity. Conclusion: NRDA DPP4 inhibitors are frequently prescribed in patients with type 2 diabetes and CKD. Prescribing RDA DPP4 inhibitors with doses adjusted to individual kidney function could alleviate the economic burden associated with medical expenditure. Presentation: Friday, June 16, 2023

THU287 Comparison Of Renal Protective Effects Between SGLT2 Inhibitors And DPP4 Inhibitors In Real-World Clinical Practice

Abstract Disclosure: H. Park: None. S. Park: None. S. Kwon: None. H. Kim: None. D. Byun: None. Recent prospective randomized studies have shown that sodium-glucose cotransporter 2 (SGLT2) inhibitors had renal protective effects compared to placebo. In this study, we compared the renal composite outcomes between patients with diabetes treated with SGLT2 inhibitors and those treated with dipeptidyl peptidase 4 (DPP4) inhibitors using real-world clinical data. This retrospective observational study used the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) database at four different university hospitals (Soonchunhyang university hospitals in Seoul, Bucheon, Chunan, and Gumi) in Korea. Subjects prescribed SGLT2 inhibitors or DPP4 inhibitors for at least 90 days were included in the SGLT2 inhibitor or DPP4 inhibitor group, respectively. Subjects prescribed GLP-1 receptor agonist or insulin were excluded in both groups. Renal composite outcomes included a 30% decline in estimated glomerular filtration rate (eGFR) compared to baseline or creatinine doubling or dialysis or death from any cause. After propensity score matching, clinical characteristics in each group at each hospital were well balanced at baseline. Our results from hospitals in Seoul, Bucheon, and Gumi have shown that SGLT2 inhibitor decreased renal composite outcomes compared to DPP4 inhibitor (hazard ratio (HR) 0.644, p=0.020; HR 0.560, p&amp;lt;0.001; HR 0.657, p=0.010, respectively). Furthermore, when all the data were combined, renal composite outcomes were significantly lower in the SGLT2 inhibitor group compared to the DPP4 inhibitor group (HR 0.659, p&amp;lt;0.001). In conclusion, SGLT2 inhibitors effectively reduce renal composite outcomes compared to DPP4 inhibitors in real-world clinical practice. Presentation: Thursday, June 15, 2023

Transforming Estonian health data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model: lessons learned.

To describe the reusable transformation process of electronic health records (EHR), claims, and prescriptions data into Observational Medical Outcome Partnership (OMOP) Common Data Model (CDM), together with challenges faced and solutions implemented. We used Estonian national health databases that store almost all residents' claims, prescriptions, and EHR records. To develop and demonstrate the transformation process of Estonian health data to OMOP CDM, we used a 10% random sample of the Estonian population (n = 150824 patients) from 2012 to 2019 (MAITT dataset). For the sample, complete information from all 3 databases was converted to OMOP CDM version 5.3. The validation was performed using open-source tools. In total, we transformed over 100 million entries to standard concepts using standard OMOP vocabularies with the average mapping rate 95%. For conditions, observations, drugs, and measurements, the mapping rate was over 90%. In most cases, SNOMED Clinical Terms were used as the target vocabulary. During the transformation process, we encountered several challenges, which are described in detail with concrete examples and solutions. For a representative 10% random sample, we successfully transferred complete records from 3 national health databases to OMOP CDM and created a reusable transformation process. Our work helps future researchers to transform linked databases into OMOP CDM more efficiently, ultimately leading to better real-world evidence.

Scalable Infrastructure Supporting Reproducible Nationwide Healthcare Data Analysis toward FAIR Stewardship

Transparent and FAIR disclosure of meta-information about healthcare data and infrastructure is essential but has not been well publicized. In this paper, we provide a transparent disclosure of the process of standardizing a common data model and developing a national data infrastructure using national claims data. We established an Observational Medical Outcome Partnership (OMOP) common data model database for national claims data of the Health Insurance Review and Assessment Service of South Korea. To introduce a data openness policy, we built a distributed data analysis environment and released metadata based on the FAIR principle. A total of 10,098,730,241 claims and 56,579,726 patients’ data were converted as OMOP common data model. We also built an analytics environment for distributed research and made the metadata publicly available. Disclosure of this infrastructure to researchers will help to eliminate information inequality and contribute to the generation of high-quality medical evidence.

Evaluation of OMOP CDM, i2b2 and ICGC ARGO for supporting data harmonization in a breast cancer use case of a multicentric European AI project

ObjectiveObservational research in cancer poses great challenges regarding adequate data sharing and consolidation based on a homogeneous data semantic base. Common Data Models (CDMs) can help consolidate health data repositories from different institutions minimizing loss of meaning by organizing data into a standard structure. This study aims to evaluate the performance of the Observational Medical Outcomes Partnership (OMOP) CDM, Informatics for Integrating Biology & the Bedside (i2b2) and International Cancer Genome Consortium, Accelerating Research in Genomic Oncology (ICGC ARGO) for representing non-imaging data in a breast cancer use case of EuCanImage. MethodsWe used ontologies to represent metamodels of OMOP, i2b2, and ICGC ARGO and variables used in a cancer use case of a European AI project. We selected four evaluation criteria for the CDMs adapted from previous research: content coverage, simplicity, integration, implementability. Resultsi2b2 and OMOP exhibited higher element completeness (100% each) than ICGC ARGO (58.1%), while the three achieved 100% domain completeness. ICGC ARGO normalizes only one of our variables with a standard terminology, while i2b2 and OMOP use standardized vocabularies for all of them. In terms of simplicity, ICGC ARGO and i2b2 proved to be simpler both in terms of ontological model (276 and 175 elements, respectively) and in the queries (7 and 20 lines of code, respectively), while OMOP required a much more complex ontological model (615 elements) and queries similar to those of i2b2 (20 lines). Regarding implementability, OMOP had the highest number of mentions in articles in PubMed (130) and Google Scholar (1,810), ICGC ARGO had the highest number of updates to the CDM since 2020 (4), and i2b2 is the model with more tools specifically developed for the CDM (26). ConclusionICGC ARGO proved to be rigid and very limited in the representation of oncologic concepts, while i2b2 and OMOP showed a very good performance. i2b2′s lack of a common dictionary hinders its scalability, requiring sites that will share data to explicitly define a conceptual framework, and suggesting that OMOP and its Oncology extension could be the more suitable choice. Future research employing these CDMs with actual datasets is needed.

Towards interoperability in infection control: a standard data model for microbiology

The COVID-19 pandemic has made it clear: sharing and exchanging data among research institutions is crucial in order to efficiently respond to global health threats. This can be facilitated by defining health data models based on interoperability standards. In Germany, a national effort is in progress to create common data models using international healthcare IT standards. In this context, collaborative work on a data set module for microbiology is of particular importance as the WHO has declared antimicrobial resistance one of the top global public health threats that humanity is facing. In this article, we describe how we developed a common model for microbiology data in an interdisciplinary collaborative effort and how we make use of the standard HL7 FHIR and terminologies such as SNOMED CT or LOINC to ensure syntactic and semantic interoperability. The use of international healthcare standards qualifies our data model to be adopted beyond the environment where it was first developed and used at an international level.

A Processing and Analytics System for Microscopy Data Workflows: The Pycroscopy Ecosystem of Packages

AbstractMajor advancements in fields as diverse as biology and quantum computing have relied on a multitude of microscopy techniques. Despite the considerable proliferation of these instruments, significant bottlenecks remain in terms of processing, analysis, storage, and retrieval of the acquired datasets. Aside from lack of file standards, individual domain‐specific analysis packages are often disjoint from the underlying datasets, and thus keeping track of analysis and processing steps remains tedious for the end‐user, hampering reproducibility. Here, the pycroscopy ecosystem of packages is introduced, an open‐source python‐based ecosystem underpinned by a common data model. The data model, termed the N‐dimensional spectral imaging data format, is realized in pycroscopy's sidpy package. This package is built on top of dask arrays, thus leveraging dask array attributes, but expanding them to accelerate microscopy relevant analysis and visualization. Several examples of the use of the pycroscopy ecosystem to create workflows for data ingestion and analysis of scanning transmission electron microscopy (STEM) and scanning probe microscopy data are shown. Adoption of such standardized routines will be critical to usher in the next generation of autonomous instruments where processing, computation, and meta‐data storage will be critical to overall experimental operations.

Detection of transthyretin amyloid cardiomyopathy by automated data extraction from electronic health records.

Transthyretin amyloid cardiomyopathy (ATTR-CM), a progressive and fatal cardiomyopathy, is frequently misdiagnosed or entails diagnostic delays, hindering patients from timely treatment. This study aimed to generate a systematic framework based on data from electronic health records (EHRs) to assess patients with ATTR-CM in a real-world population of heart failure (HF) patients. Predictive factors or combinations of predictive factors related to ATTR-CM in a European population were also assessed. Retrospective unstructured and semi-structured data from EHRs of patients from OLV Hospital Aalst, Belgium (2012-20), were processed using natural language processing (NLP) to generate an Observational Medical Outcomes Partnership Common Data Model database. NLP model performance was assessed on a random subset of EHRs by comparing algorithm outputs to a physician-generated standard (using precision, recall, and their harmonic mean, or F1-score). Of the 3127 HF patients, 103 potentially had ATTR-CM (age 78±9years; male 55%; ejection fraction of 48%±16). The mean diagnostic delay between HF and ATTR-CM diagnosis was 1.8years. Besides HF and cardiomyopathy-related phenotypes, the strongest cardiac predictor was atrial fibrillation (AF; 72% in ATTR-CM vs. 60% in non-ATTR-CM, P=0.02), whereas the strongest non-cardiac predictor was carpal tunnel syndrome (21% in ATTR-CM vs. 3% in non-ATTR-CM, P<0.001). The strongest combination predictor was AF, joint disorders, and HF with preserved ejection fraction (29% in ATTR-CM vs. 18% in non-ATTR-CM: odds ratio=2.03, 95% confidence interval=1.28-3.22). Not only well-known variables associated with ATTR-CM but also unique combinations of cardiac and non-cardiac phenotypes are able to predict ATTR-CM in a real-world HF population, aiding in early identification of ATTR-CM patients.

Ranitidine Use and Incident Cancer in a Multinational Cohort

Ranitidine, the most widely used histamine-2 receptor antagonist (H2RA), was withdrawn because of N-nitrosodimethylamine impurity in 2020. Given the worldwide exposure to this drug, the potential risk of cancer development associated with the intake of known carcinogens is an important epidemiological concern. To examine the comparative risk of cancer associated with the use of ranitidine vs other H2RAs. This new-user active comparator international network cohort study was conducted using 3 health claims and 9 electronic health record databases from the US, the United Kingdom, Germany, Spain, France, South Korea, and Taiwan. Large-scale propensity score (PS) matching was used to minimize confounding of the observed covariates with negative control outcomes. Empirical calibration was performed to account for unobserved confounding. All databases were mapped to a common data model. Database-specific estimates were combined using random-effects meta-analysis. Participants included individuals aged at least 20 years with no history of cancer who used H2RAs for more than 30 days from January 1986 to December 2020, with a 1-year washout period. Data were analyzed from April to September 2021. The main exposure was use of ranitidine vs other H2RAs (famotidine, lafutidine, nizatidine, and roxatidine). The primary outcome was incidence of any cancer, except nonmelanoma skin cancer. Secondary outcomes included all cancer except thyroid cancer, 16 cancer subtypes, and all-cause mortality. Among 1 183 999 individuals in 11 databases, 909 168 individuals (mean age, 56.1 years; 507 316 [55.8%] women) were identified as new users of ranitidine, and 274 831 individuals (mean age, 58.0 years; 145 935 [53.1%] women) were identified as new users of other H2RAs. Crude incidence rates of cancer were 14.30 events per 1000 person-years (PYs) in ranitidine users and 15.03 events per 1000 PYs among other H2RA users. After PS matching, cancer risk was similar in ranitidine compared with other H2RA users (incidence, 15.92 events per 1000 PYs vs 15.65 events per 1000 PYs; calibrated meta-analytic hazard ratio, 1.04; 95% CI, 0.97-1.12). No significant associations were found between ranitidine use and any secondary outcomes after calibration. In this cohort study, ranitidine use was not associated with an increased risk of cancer compared with the use of other H2RAs. Further research is needed on the long-term association of ranitidine with cancer development.

The Preserving Kidney Function in Children With CKD (PRESERVE) Study: Rationale, Design, and Methods

PRESERVE seeks to provide new knowledge to inform shared decision-making regarding blood pressure (BP) management for pediatric chronic kidney disease (CKD). PRESERVE will compare the effectiveness of alternative strategies for monitoring and treating hypertension on preserving kidney function; expand the National Patient-Centered Clinical Research Network (PCORnet) common data model by adding pediatric- and kidney-specific variables and linking electronic health record data to other kidney disease databases; and assess the lived experiences of patients related to BP management. Multicenter retrospective cohort study (clinical outcomes) and cross-sectional study (patient-reported outcomes [PROs]). PRESERVE will include approximately 20,000 children between January 2009-December 2022 with mild-moderate CKDfrom 15 health care institutions that participate in6 PCORnet Clinical Research Networks (PEDSnet, STAR, GPC, PaTH, CAPRiCORN, and OneFlorida+). The inclusion criteria were≥1 nephrologist visit and≥2 estimated glomerular filtration rate (eGFR) values in the range of 30 to<90mL/min/1.73m2 separated by≥90 days without an intervening value≥90mL/min/1.73m2 and no prior dialysis or kidney transplant. BP measurements (clinic-based and 24-hour ambulatory BP); urine protein; and antihypertensive treatment by therapeutic class. The primary outcome is a composite event of a 50% reduction in eGFR, eGFR of<15mL/min/1.73m2, long-term dialysis or kidney transplant. Secondary outcomes include change in eGFR, adverse events, and PROs. Longitudinal models for dichotomous (proportional hazards or accelerated failure time) and continuous (generalized linear mixed models) clinical outcomes; multivariable linear regression for PROs. We will evaluate heterogeneity of treatment effect by CKD etiology and degree of proteinuria and will examine variation in hypertension management and outcomes based on socio-demographics. Causal inference limited by observational analyses. PRESERVE will leverage the PCORnet infrastructure to conduct large-scale observational studies that address BP management knowledge gaps for pediatric CKD, focusing on outcomes that are meaningful to patients. Hypertension is a major modifiable contributor to loss of kidney function in chronic kidney disease (CKD). The purpose of PRESERVE is to provide evidence to inform shared decision-making regarding blood pressure management for children with CKD. PRESERVE is a consortium of 16 health care institutions in PCORnet, the National Patient-Centered Clinical Research Network, and includes electronic health record data for>19,000 children with CKD. PRESERVE will (1) expand the PCORnet infrastructure for research in pediatric CKD by adding kidney-specific variables and linking electronic health record data to other kidney disease databases; (2) compare the effectiveness of alternative strategies for monitoring and treating hypertension on preserving kidney function; and (3) assess the lived experiences of patients and caregivers related to blood pressure management.

Association of prior treatment with nitrogen-containing bisphosphonates on outcomes of COVID-19 positive patients.

Widely used in osteoporosis treatment, nitrogen-containing bisphosphonates (N-BP) have been associated with reduced mortality and morbidity among older adults. Based on prior studies, we hypothesized that prior treatment with N-BP might reduce intensive care unit (ICU) admission, ventilator use, and death among older adults diagnosed with COVID-19. This retrospective analysis of the PCORnet Common Data Model across 4 academic medical centers through 1 September 2021 identified individuals age >50 years with a diagnosis of COVID-19. The composite outcome included ICU admission, ventilator use, or death within 15, 30, and 180 days of COVID-19 diagnosis. Use of N-BP was defined as a prescription within 3 years prior. ICU admission and ventilator use were determined using administrative codes. Death included both in-hospital and out-of-hospital events. Patients treated with N-BP were matched 1:1 by propensity score to patients without prior N-BP use. Secondary analysis compared outcomes among those prescribed zoledronic acid (ZOL) to those prescribed oral N-BPs. Of 76,223 COVID-19 patients identified, 1,853 were previously prescribed N-BP, among whom 559 were prescribed ZOL. After propensity score matching, there were no significant differences in the composite outcome at 15 days (HR 1.22, 95% CI: 0.89-1.67), 30 days (HR 1.24, 95% CI: 0.93-1.66), or 180 days (HR 1.17, 95% CI: 0.93-1.48), comparing those prescribed and not prescribed N-BP. Compared to those prescribed oral N-BP, there were no significant differences in outcomes among those prescribed ZOL. Among older COVID-19 patients, prior exposure to N-BP including ZOL was not associated with a reduction in ICU admission, ventilator use, or death.