Risk stratification for cardiovascular disease based on prior coronary artery disease, cerebrovascular disease and type 2 diabetes mellitus.

AIMS/INTRODUCTION: History of coronary artery disease (CAD), cerebrovascular disease (CeVD), type 2 diabetes and their combined effect on cardiovascular disease are essential for cardiovascular risk management. We investigated the association of prior CAD, prior CeVD, type 2 diabetes and their combination with the risk of cardiovascular disease. MATERIALS AND METHODS: This is a historical cohort study including 342,033 participants (aged 18-72 years) followed up for ≥5 years between 2008 and 2016. Participants were classified into eight groups (with or without prior CAD, prior CeVD and type 2 diabetes). Type 2 Diabetes was defined by fasting plasma glucose and glycated hemoglobin levels, and antidiabetic drug prescription. Prior and subsequent CAD and CeVD were identified according to claims using International Classification of Diseases 10th Revision codes, medical procedures and questionnaires. Cox regression models were used to evaluate the risk of cardiovascular events. RESULTS: The median follow-up period was 6.4 years. The incidence of composite cardiovascular events of CAD and CeVD in the CAD-/CeVD-, CAD+/CeVD-, CAD-/CeVD+ and CAD+/CeVD+ groups were 1.92 and 6.94, 25.14 and 31.98 per 1,000 person-years in non-diabetes participants, and 8.66, 18.04, 39.98 and 60.72 in type 2 diabetes patients, respectively. Hazard ratios of cardiovascular events compared with CAD-/CeVD-/non-diabetes were 1.66 (95% confidence interval 1.55-1.78) in CAD-/CeVD-/type 2 diabetes and 1.84 (1.56-2.18) in CAD+/CeVD-/non-diabetes. CeVD+ was linked to a 4-7-fold increase in the risk of cardiovascular events regardless of CAD+ or type 2 diabetes. CONCLUSIONS: Type 2 diabetes increased the risk of cardiovascular disease as high as a history of CAD, whereas prior CeVD alone increased the risk of future CeVD without additional effects by type 2 diabetes.

Supplementing Systematic Review Findings with Healthcare System Data: Pilot Projects from the Agency for Healthcare Research and Quality Evidence-based Practice Center Program.

OBJECTIVES: The US Agency for Healthcare Research and Quality (AHRQ), through the Evidence-based Practice Center (EPC) Program, aims to provide health system decision makers with the highest-quality evidence to inform clinical decisions. However, limitations in the literature may lead to inconclusive findings in EPC systematic reviews (SRs). The EPC Program conducted pilot projects to understand the feasibility, benefits, and challenges of utilizing health system data to augment SR findings to support confidence in healthcare decision-making based on real-world experiences. STUDY DESIGN AND SETTING: Three contractors (each an EPC located at a different health system) selected a recently completed systematic review conducted by their center and identified an evidence gap that electronic health record (EHR) data might address. All pilot project topics addressed clinical questions as opposed to care delivery, care organization, or care disparities topics that are common in EPC reports. Topic areas addressed by each EPC included infantile epilepsy, migraine, and hip fracture. EPCs also tracked additional resources needed to conduct supplemental analyses. The workgroup met monthly in 2022-2023 to discuss challenges and lessons learned from the pilot projects. RESULTS: Two supplemental data analyses filled an evidence gap identified in the systematic reviews (raised certainty of evidence, improved applicability) and the third filled a health system knowledge gap. Project challenges fell under three themes: regulatory and logistical issues, data collection and analysis, and interpretation and presentation of findings. Limited ability to capture key clinical variables given inconsistent or missing data within the EHR was a major limitation. The workgroup found that conducting supplemental data analysis alongside an SR was feasible but adds considerable time and resources to the review process (estimated total hours to complete pilot projects ranged from 283-595 across EPCs), and that the increased effort and resources added limited incremental value. CONCLUSION: Supplementing existing systematic reviews with analyses of EHR data is resource intensive and requires specialized skillsets throughout the process. While using EHR data for research has immense potential to generate real-world evidence and fill knowledge gaps, these data may not yet be ready for routine use alongside systematic reviews.

Association Between the Decipher Genomic Classifier and Prostate Cancer Outcome in the Real-world Setting.

BACKGROUND AND OBJECTIVE: Although the prognostic significance of the Decipher prostate cancer genomic classifier (GC) has been established largely from analyses of archival tissue, less is known about the associations between the results of Decipher testing and oncologic outcomes among patients receiving contemporaneous testing and treatment in the real-world practice setting. Our objective was to assess the associations between the Decipher GC and risks of metastasis and biochemical recurrence (BCR) following prostate biopsy and radical prostatectomy (RP) among patients tested and treated in the real-world setting. METHODS: A retrospective cohort study was conducted using a novel longitudinal linkage of transcriptomic data from the Decipher GC and real-world clinical data (RWD) aggregated from insurance claims, pharmacy records, and electronic health record data across payors and sites of care. Kaplan-Meier and Cox proportional hazards regressions were used to examine the associations between the GC and study outcomes, adjusting for clinical and pathologic factors. KEY FINDINGS AND LIMITATIONS: Metastasis from prostate cancer and BCR after radical prostatectomy, Decipher GC continuous score, and risk categories were evaluated. We identified 58 935 participants who underwent Decipher testing, including 33 379 on a biopsy specimen and 25 556 on an RP specimen. The median age was 67 yr (interquartile range [IQR] 62-72) at biopsy testing and 65 yr (IQR 59-69) at RP. The median GC score was 0.43 (IQR 0.27-0.66) among biopsy-tested patients and 0.54 (0.32-0.79) among RP-tested patients. The GC was independently associated with the risk of metastasis among biopsy-tested (hazard ratio [HR] per 0.1 unit increase in GC 1.21 [95% confidence interval {CI} 1.16-1.27], p < 0.001) and RP-tested (HR 1.20 [95% CI 1.17-1.24], p < 0.001) patients after adjusting for baseline clinical and pathologic risk factors. In addition, the GC was associated with the risk of BCR among RP-tested patients (HR 1.12 [95% CI 1.10-1.14], p < 0.001) in models adjusted for age and Cancer of the Prostate Risk Assessment postsurgical score. CONCLUSIONS AND CLINICAL IMPLICATIONS: This real-world study of a novel transcriptomic linkage conducted at a national scale supports the external prognostic validity of the Decipher GC among patients managed in contemporary practice. PATIENT SUMMARY: This study looked at the use of the Decipher genomic classifier, a test used to help understand the aggressiveness of a patient's prostate cancer. Looking at the results of 58 935 participants who underwent testing, we found that the Decipher test helped estimate the risk of cancer recurrence and metastasis.

Predictors of continuation for asenapine from real-world data in patients with schizophrenia.

BACKGROUND: The continuation rates of pharmacotherapy in schizophrenia exhibit variability, a phenomenon influenced by the specific antipsychotic agent prescribed and patient-related factors such as age and duration of illness. In this context, our study aims to elucidate the predictors of medication continuation for asenapine sublingual tablets, characterized by unique formulation properties. METHODS: Our investigation leveraged real-world data collected through post-marketing surveillance in Japan, comprising 3236 cases. Utilizing multivariate logistic regression analysis, we identified patient-related factors associated with medication continuation as the primary outcome measure, subsequently employing survival analysis for further evaluation. Additionally, adverse event occurrence was assessed as a secondary outcome measure. RESULTS: Multivariate logistic regression analysis unveiled significant predictors of asenapine continuation, notably including patient-related factors such as a chlorpromazine equivalent dose exceeding 600 mg/day and an illness duration of 25 years or more. While the overall continuation rate stood at 40.6%, patients exhibiting factors such as a chlorpromazine equivalent dose surpassing 600 mg/day or an illness duration exceeding 25 years demonstrated continuation rates of 46.3% and 47.9%, respectively. Remarkably, patients presenting both factors showcased the highest continuation rate at 52.5%. CONCLUSIONS: Our findings shed light on distinct patient-related predictors of asenapine continuation, deviating from those observed with other antipsychotic medications. This underscores the necessity of recognizing that predictive factors for antipsychotic medication continuation vary across different agents. Moving forward, elucidating these predictive factors for various antipsychotic medications holds paramount importance in schizophrenia treatment, facilitating the delivery of tailored therapeutic interventions for individual patients.

Celebrating the registration of 9.000 patients treated with CAR T cells in the EBMT registry: Collection of real-world data in the context of hematopoietic cellular therapies.

The European society for Blood and Marrow Transplantation (EBMT) has a long-standing interest in the evaluation of hematopoietic cell transplantation. More than three decades ago, its members established a continental registry. Today, more than 700,000 patients have been registered, and information has been gathered on more than 800,000 transplants. This huge amount of information has allowed conducting multiple retrospective studies, evaluating changes in practices over time and for different categories of diseases, benchmarking outcome across EBMT affiliated centers, and increasingly serves to build synthetic comparators to evaluate the introduction of therapeutic innovations in the field of hematology. CAR-T cells therapies draw on human and technical resources that are also used to deliver HCT; they elicit side effects that require the implementation of risk mitigation plans; they are living drugs that persist in the body of the recipient and thus deserve prolonged follow-up; the introduction of CAR-T cells in the pharmacopeia is likely to significantly impact on the practice of BMT; for all these reasons and even before the first approvals of CAR-T Cells in Europe, EBMT engaged in a project aiming at complementing the EBMT Registry with a Cellular Therapy Form, with the objective to register CAR-T cells treated patients and collect information on their short-, middle- and long-term outcome. The goal is to provide EBMT investigators with a tool for primary analyses of the collected information and to support secondary use of data transferred at the individual level to Marketing Authorization Holders and other interested parties, to fulfill their obligations to health authorities and further evaluate the actual medical values of CAR-T Cells in different contexts and indications. The EBMT Registry received a positive opinion from the European Medicines agency in 2019, and five years later contains information on more than 9.000 treated patients. This article describes the journey to start this new activity, lessons to be drawn in view of improving the collection of real-world data, and what existing information tells us in terms of patient access.

Leveraging Real-World Data in Safety Signal Assessment.

PURPOSE: TransCelerate BioPharma surveyed its member biopharmaceutical companies to understand current practices and identify opportunities to complement safety signal assessment with rapid real-world data (RWD) analysis. METHODS: A voluntary 30-question questionnaire regarding the use of RWD in safety signal assessment was disseminated to subject matter experts at all TransCelerate member companies in July 2022. Responses were blinded, aggregated, summarized, and presented. RESULTS: Eighteen of 20 member companies provided responses to the questionnaire. Sixteen (89%) companies reported actively leveraging RWD in their signal assessment processes. Of 18 respondent companies, 8 (44%) routinely use rapid approaches to RWD analysis, 7 (39%) utilize rapid RWD analysis non-routinely or in a pilot setting, 2 (11%) are considering using rapid RWD analysis, and 1 (6%) has no plans to use rapid RWD analysis for their signal assessment. Most companies reported that RWD adds context to and improves quality of signal assessments. To conduct RWD analysis for signal assessment, 16 of 17 (94%) respondent companies utilize or plan to utilize internally available data, 8 (47%) utilize both internal and external data, and 3 (18%) utilize data networks. Respondents identified key challenges to rapidly performing RWD analyses, including data access/availability, time for analysis execution, and uncertainties regarding acceptance of minimal or non-protocolized approaches by health authorities. CONCLUSION: Biopharmaceutical companies reported that they see value in the use of rapid RWD analyses for complementing signal assessments. Future work is recommended to offer a framework and process for use of rapid use of RWD analyses in signal assessment.

Unlocking the Power of Real-World Data: A Framework for Sustainable Healthcare.

Real-world data (RWD) has the potential to revolutionize healthcare by offering valuable insights into patient outcomes and treatment efficacy. However, leveraging RWD effectively presents challenges, including its inherent limitations, diverse stakeholders, and insufficient data management pipelines. A proposed framework advocates three essential elements: adherence to FAIR principles (Findable, Accessible, Interoperable, and Reusable), stakeholder engagement and education, and highlighting the need for inclusive, pragmatic federated hybrid pipelines. By employing these strategies, healthcare organizations can overcome obstacles to RWD utilization and foster sustainable progress in patient care.

Germany's Approach for the Secondary Use of Federated Real-World Data in the German Portal for Medical Research Data.

In the light of big data driven clinical research, fair access to real world clinical health data enables evidence to improve patient care. Germany's healthcare system provides an abundant data resource but unique challenges due to its federated nature, heterogeneity and high data-protection standards. The Medical Informatics Initiative (MII) developed concepts that are being implemented in the German Portal for Medical Research Data (FDPG) to grant access to distributed data-sources across state borders. The portal currently provides access to more than 10 million patient resources containing hundreds of millions of laboratory parameters, diagnostic reports, administered medications, procedures and specimens. Upcoming datasets include among others oncological data, molecular analysis results and microbiological findings. Here, we describe the philosophy, implementation and experience behind the framework: standardized access processes, interoperable fair data, software for in depth feasibility requests, tools to support researchers and hospital stakeholders alike as well as transparency measures to provide data use information for patients. Challenges remain to improve data quality and automatization of technical and organizational processes.

Cohort Builder: A Software Pipeline for Generating Patient Cohorts with Predetermined Baseline Characteristics from Medical Records and Raw Ophthalmic Imaging Data.

In clinical research, the analysis of patient cohorts is a widely employed method for investigating relevant healthcare questions. The ability to automatically extract large-scale patient cohorts from hospital systems is vital in order to unlock the potential of real-world clinical data, and answer pivotal medical questions through retrospective research studies. However, existing medical data is often dispersed across various systems and databases, preventing a systematic approach to access and interoperability. Even when the data are readily accessible, clinical researchers need to sift through Electronic Medical Records, confirm ethical approval, verify status of patient consent, check the availability of imaging data, and filter the data based on disease-specific image biomarkers. We present Cohort Builder, a software pipeline designed to facilitate the creation of patient cohorts with predefined baseline characteristics from real-world ophthalmic imaging data and electronic medical records. The applicability of our approach extends beyond ophthalmology to other medical domains with similar requirements such as neurology, cardiology and orthopedics.

Real-World Data in Greece: Mapping the Papageorgiou General Hospital Data to the OMOP Common Data Model.

This paper showcases the results of the Extract-Transform-Load process mapping the Electronic Health Record of Papageorgiou General Hospital in Thessaloniki, Greece, to the Observational Medical Outcomes Partnership Common Data Model. We describe the staged process utilized to account for the intricate structure of the database, along with some general findings from the mapping. Finally, we investigate potential directions for future research.

Mapping OMOP-CDM to RDF: Bringing Real-World-Data to the Semantic Web Realm.

Real-world data (RWD) (i.e., data from Electronic Healthcare Records - EHRs, ePrescription systems, patient registries, etc.) gain increasing attention as they could support observational studies on a large scale. OHDSI is one of the most prominent initiatives regarding the harmonization of RWD and the development of relevant tools via the use of a common data model, OMOP-CDM. OMOP-CDM is a crucial step towards syntactic and semantic data interoperability. Still, OMOP-CDM is based on a typical relational database format, and thus, the vision of a fully connected semantically enriched model is not fully realized. This work presents an open-source effort to map the OMOP-CDM model and the data it hosts, to an ontological model using RDF to support the FAIRness of RWD and their interlinking with Linked Open Data (LOD) towards the vision of the Semantic Web.

HCE2RNFC: An Efficient Methodology for Reusing the EHR in the Spanish National Hip Fracture Registry.

Hip fracture is a condition associated with ageing and frailty, with an associated prevalence of 7 per 10000 population in Spain. Evidence suggests that factors in the healthcare process can influence clinical outcomes, so the creation of a national registry is an opportunity to monitor and improve this process. In this regards, Electronic Health Record (EHR) can provide a large amount of data, that can be used to populate the Spanish National Hip Fracture Registry (RNFC, by its acronym in Spanish). However, this reuse of the EHR requires a prior effort in modelling and standardization to build the extraction, transformation, and loading (ETL) processes in a flexible, transparent, and scalable manner. In this work, a robust EHR reuse methodology is implemented to obtain EHR-derived data for the RNFC. The main result of this work was the design and implementation of an EHR data reuse methodology, which was able to load 1279 hip fracture cases and almost 68% of the required concepts from the RNFC.

Data Quality in Medical Real-World Data - An Oncological Use Case.

Data quality deficiencies significantly limit the applicability of real-world data in data-driven medical research. In this study, using an oncological use case, we report and discuss common quality deficiencies in real-world medical datasets, such as missing data, class imbalances, and timeliness issues. We compiled a multi-departmental real-world dataset comprising 13861 cancer cases diagnosed at University Hospital Cologne and examined data quality throughout the data integration process.

Smart FOX - Enabling Citizen-Based Donation of EHR-Standardised Data for Clinical Research in Austria.

Access to healthcare data for secondary use in clinical research is often restricted due to privacy concerns or business interests, hindering comprehensive analysis across patient pathways. The Smart FOX project seeks to address this challenge by developing concepts, methods, and tools to facilitate citizen/patient-driven donations of health data for clinical research. Leveraging the groundwork, laid by the national Electronic Health Record implementation in Austria (called ELGA), Smart FOX aims to harness structured datasets from ELGA for research purposes through an opt-in approach. With funding secured from the Austrian Research Promotion Agency, the project embarks on innovative solutions encompassing governance frameworks, community engagement, and technical infrastructure. The Smart FOX consortium, comprising key stakeholders across various healthcare-associated domains, will evaluate these efforts through demonstrators focusing on clinical registries, patient-generated data, and recruitment services. The project targets to accompany the development of future data donation infrastructure while ultimately advancing clinical research efficiency and bolstering Austria's preparedness for the European Health Data Space. This paper presents the first systematic evaluation of the technical concept and proposal for the federated system architecture of the Austrian Health Data Donation Space, which is the socio-technical goal of Smart FOX.

A Framework to Support the Standardized Management of Real-World Data.

Relying on our experience on the development of data registration and management systems for clinical and biological data coming from patients with hematological malignancies, as well as on the design of strategies for data collection and analysis to support multi-center, clinical association studies, we designed a framework for the standardized collection and transformation of clinically relevant real-world data into evidence, to meet the challenges of gathering biomedical data collected during daily clinical practice in order to promote basic and clinical research.

Exploring Hospital Overcrowding with an Explainable Time-to-Event Machine Learning Approach.

Emergency department (ED) overcrowding is a complex problem that is intricately linked with the operations of other hospital departments. Leveraging ED real-world production data provides a unique opportunity to comprehend this multifaceted problem holistically. This paper introduces a novel approach to analyse healthcare production data, treating the length of stay of patients, and the follow up decision regarding discharge or admission to the hospital as a time-to-event analysis problem. Our methodology employs traditional survival estimators and machine learning models, and Shapley additive explanations values to interpret the model outcomes. The most relevant features influencing length of stay were whether the patient received a scan at the ED, emergency room urgent visit, age, triage level, and the medical alarm unit category. The clinical insights derived from the explanation of the models holds promise for increase understanding of the overcrowding from the data. Our work demonstrates that a time-to-event approach to the over- crowding serves as a valuable initial to uncover crucial insights for further investigation and policy design.

Assessment of the implementation of risk minimization measures for bosentan: a retrospective study.

BACKGROUND: Bosentan is associated with adverse hepatic effects. To minimize such risk, regulators implemented risk minimization measures (RMMs), including testing for liver injury biomarkers (alanine and aspartate transaminase and bilirubin) prior to therapy initiation and monthly throughout therapy. This study aimed to examine the adherence to hepatic monitoring requirements. RESEARCH DESIGN AND METHODS: This retrospective cohort study collected data about bosentan new-users from the Real-world Evidence Research Network from 2016 to 2022. We ascertained hepatic tests from laboratory files. Adherence to RMM definition was performing the required tests within 90 days before initiation and categorized adherence to monthly testing requirement based on the expected number of tests throughout therapy as low (<50%), moderate (50-74%), and high (≥75%). RESULTS: One hundred patients entered the study cohort and 71% were females, with a median age of 25 years. Adherence to testing prior to bosentan initiation was 60%. Adherence to monthly testing was low in the majority of patients (58.2%). CONCLUSIONS: Adherence to bosentan RMMs relevant to minimizing risk of hepatotoxicity either before starting or throughout therapy was low. Our findings could be used as a baseline for monitoring trends in implementation of RMMs over time or to compare performance of various minimization strategies.

Radiotherapy plus pembrolizumab for advanced urothelial carcinoma: results from the ARON-2 real-world study.

The addition of metastasis-directed radiotherapy (MDRT) to immunotherapy in patients with advanced urothelial carcinoma (aUC) has shown promising results. We report the real-world data from the ARON-2 study (NCT05290038) on the impact of conventional (CRT) or stereotactic body radiotherapy (SBRT) on the outcome of aUC patients receiving pembrolizumab after platinum-based-chemotherapy. Medical records of 837 patients were reviewed from 60 institutions in 20 countries. Two hundred and sixty-two patients (31%) received radiotherapy (cohort A), of whom 193 (23%) received CRT and 69 (8%) received SBRT. Patients were assessed for overall survival (OS), progression-free survival (PFS), and overall response rate (ORR). Univariate and multivariate analyses were used to explore the association of variables of interest with OS and PFS. With a median follow-up of 22.7 months, the median OS was 10.2 months, 6.8 months and 16.0 months in no RT, CRT and SBRT subgroups (p = 0.005), with an 1y-OS rates of 47%, 34% and 61%, respectively (p < 0.001). The 1y-OS rate in the SBRT subgroup were significantly higher for both lower (63%) and upper tract UC (68%), for pure urothelial histology (63%) and variant histologies (58%), and for patients with bone (40%) and lymph-node metastases (61%). Median PFS was 4.8 months, 9.6 months and 5.8 months in the CRT, SBRT and no RT subgroups, respectively (p = 0.060). The 1y-PFS rate was significantly higher (48%) in the SBRT population and was confirmed in all patient subsets. The difference in terms of ORR was in favour of SBRT. Our real-world analysis showed that the use of SBRT/pembrolizumab combination may play a role in a subset of aUC patients to increase disease control and possibly overall survival.

Preoperative risk-factors for secondary spontaneous pneumothorax surgery in Japan: a nationwide database study.

PURPOSE: We aimed to identify preoperative risk factors for secondary spontaneous pneumothorax surgery. METHODS: The National Clinical Database of Japan, with six annual datasets from 2014 to 2019, was used. All patients who underwent surgery for secondary spontaneous pneumothorax were included, excluding those < 15 years old and those with incomplete data. The effects of preoperative risk factors were analyzed for operative mortality (mortality during hospitalization or within 30 days, regardless of hospitalization status), 30-day mortality, and postoperative respiratory morbidities. RESULTS: Of the 18,309 patients enrolled in the study, operative mortality, 30-day mortality, and postoperative respiratory morbidities were observed in 654 (3.6%), 343 (1.9%), and 2258 (12.3%) patients, respectively. Increasing age, male sex, body mass index < 18.5 or > 30, performance status > 2, emergent surgery, interstitial pneumonia, and diabetes in preoperative co-morbidity, tumors, and other diseases in underlying lung disease were significant risk factors for operative mortality. Those for 30-day mortality included autoimmune disease instead of male sex and diabetes, while those for postoperative respiratory morbidities included lymphangiomyomatosis instead of a body mass index > 30. CONCLUSION: We identified many preoperative risk factors for operative mortality, 30-day mortality, and postoperative respiratory morbidities in secondary spontaneous pneumothorax surgery. These findings will assist in selecting appropriate surgical candidates.

The Importance of Training and Assessing Quality Control Reviewers in Technology-Enabled Abstraction of Real-World Data: A Case Study.

Accurate cancer registry data is crucial for understanding cancer prevention and treatment strategies. Proper education and training are key for successful quality control (QC) programs and an evaluation process is needed to assess effectiveness. Syapse developed a rigorous QC training program that includes a peer review process to assess data quality and an interrater review (IRR) program to evaluate the consistency of QC reviewers. In reviewing IRR cases, we found high rates of agreement in various cancer types: colon (97.74%), prostate (97.75%), ovarian (96.31%), lung (98.03%), breast (97.86%), and bladder (97.88%). A peer review experience questionnaire was also administered. Results indicated that the program facilitated the acquisition of new skills. Through the implementation of robust QC training and assessment procedures for technology-enabled data curation, our Oncology Data Specialist (ODS)-certified professionals at Syapse ensure data quality in a real-world evidence (RWE) platform. QC reviewers deserve an extensive investment in training and professional development to uphold data quality and support cancer research efforts.