Adopting a Framework for Rapid Real-World Data Analyses in Safety Signal Assessment.

The expanding availability of real-world data (RWD) has led to an increase in both the interest and possibilities for using this information in postmarketing safety analyses and signal management. While there is enormous potential value from the safety insights generated through RWD, the analysis preparation, execution, and communication required to reliably deliver the evidence can be time consuming. Since the safety signal assessment process is a regulated and timebound process, any supporting RWD analyses require a rapid turnaround of well-designed and informative results. To address this challenge, a TransCelerate BioPharma working group was formed and developed a framework to help teams responsible for safety signal assessment overcome the challenges of working with RWD rapidly to deliver analyses within regulatory timelines. Here, a previously performed safety assessment was evaluated within the context of the developed framework to illustrate how the framework may be adopted in practice.

Knowledge Base Prototype Creating with Using Interdisciplinary Metathesaurus.

This article presents our experience in development an ontological model can be used in clinical decision support systems (CDSS) creating. We have used the largest international biomedical terminological metathesaurus the Unified Medical Language System (UMLS) as the basis of our model. This metathesaurus has been adapted into Russian using an automated hybrid translation system with expert control. The product we have created was named as the National Unified Terminological System (NUTS). We have added more than 33 million scientific and clinical relationships between NUTS terms, extracted from the texts of scientific articles and electronic health records. We have also computed weights for each relationship, standardized their values and created symptom checker in preliminary diagnostics based on this. We expect, that the NUTS allow solving task of named entity recognition (NER) and increasing terms interoperability in different CDSS.

Excellence in the management of Advanced Hybrid Closed-Loop Systems: Lessons from the Polish cohort.

BACKGROUND: The aim of the study was to analyze the real-world performance of MiniMed 780G (MM780G) Advanced Hybrid Closed Loop (AHCL) system users from Poland (PL) and compare it to the European region excluding Poland (EU-PL) in order to identify factors contributing to potential differences. The former achieved some of the best Time in Range (TIR) results globally using this technology. METHODS: CareLink Personal data uploaded by MM780G system users from August 2020 to December 2022 were analyzed. RESULTS: The Polish users (N=1304) on average reached to TIR of 79.1 ± 8.7 % (vs 73.0 ± 10.0 % for EU-PL, N=55659), a TBR<54 mg/dL of 0.6 ± 0.7 % (vs 0.4 ± 0.6 %) and a TBR<70 mg/dL of 2.9 ± 2.1 % (vs 2.1 ± 1.8 %). The adoption rate of optimal settings (i.e, GT=100 mg/dL, AIT=2hr) in PL was high (19.7 % vs 6.3 %), and filtering on optimal setting users led to less pronounced differences in glycemic control between PL and EU-PL. A univariable analysis with post-AHCL TIR showed that geography itself (PL vs EU-PL) is not a significant contributor to a high post-AHCL TIR (p = 0.15), and that much of the Polish post-AHCL TIR can be explained by the high pre-AHCL TIR. CONCLUSION: The Polish MM780G users achieved better glycemic control than the general European population (excluding Poland). This is largely attributable to the adoption of optimal settings in Poland and the already high glycemic outcomes at system start. As these characteristics can be implemented elsewhere, we believe this outstanding result can be obtained in other countries as well.

Data quality and timeliness analysis for post-vaccination adverse event cases reported through healthcare data exchange to FDA BEST pilot platform.

Introduction: This study is part of the U.S. Food and Drug Administration (FDA)'s Biologics Effectiveness and Safety (BEST) initiative, which aims to improve the FDA's postmarket surveillance capabilities by using real-world data (RWD). In the United States, using RWD for postmarket surveillance has been hindered by the inability to exchange clinical data between healthcare providers and public health organizations in an interoperable format. However, the Office of the National Coordinator for Health Information Technology (ONC) has recently enacted regulation requiring all healthcare providers to support seamless access, exchange, and use of electronic health information through the interoperable HL7 Fast Healthcare Interoperability Resources (FHIR) standard. To leverage the recent ONC changes, BEST designed a pilot platform to query and receive the clinical information necessary to analyze suspected AEs. This study assessed the feasibility of using the RWD received through the data exchange of FHIR resources to study post-vaccination AE cases by evaluating the data volume, query response time, and data quality. Materials and methods: The study used RWD from 283 post-vaccination AE cases, which were received through the platform. We used descriptive statistics to report results and apply 322 data quality tests based on a data quality framework for EHR. Results: The volume analysis indicated the average clinical resources for a post-vaccination AE case was 983.9 for the median partner. The query response time analysis indicated that cases could be received by the platform at a median of 3 min and 30 s. The quality analysis indicated that most of the data elements and conformance requirements useful for postmarket surveillance were met. Discussion: This study describes the platform's data volume, data query response time, and data quality results from the queried postvaccination adverse event cases and identified updates to current standards to close data quality gaps.

Real-World Study of Adjuvant Biosimilar Trastuzumab-dkst for HER2-Positive Breast Cancer Treatment in a Brazilian Population.

INTRODUCTION: Biological monoclonal antibodies play a pivotal role in cancer treatment, with biosimilars significantly enhancing their accessibility. In Brazil's ethnically diverse setting, real-world evidence is crucial for assessing the effectiveness and applicability of these therapies in routine clinical practice. METHODS: We performed a multicentric, observational, prospective real-world study on biosimilar trastuzumab-dkst for adjuvant treatment of early HER2-positive breast cancer in Brazilian patients. Data were collected using a case-report form. RESULTS: Of the 176 recruited, we present data from the first 59 patients (mean age 51.7 ± 12.9 years) who had completed treatment with trastuzumab-dkst. The mean time from diagnosis to the first adjuvant treatment with trastuzumab-dkst was 5.5 ± 2.7 months. Of the patients, 59% of patients achieved at least a 30-month follow-up. The 31.7-month invasive disease-free survival rate (IDFS) was 94.5% (95% CI 83.9-98.2%) and median IDFS was not achieved, since only three patients had invasive disease recurrence. The overall survival rate was 100% until the last assessment. The observed adverse events were similar to those presented by other studies using biosimilar or reference trastuzumab. Four serious adverse events (8.5%) were observed. A reduction in left ventricular ejection fraction of at least 10% was observed in 16.9% of participants. There was no treatment interruption, and three participants (5.1%) had their trastuzumab-dkst dose reduced. CONCLUSION: Our study reinforces the existing pivotal data, underscoring the real-world efficacy and safety of biosimilar trastuzumab-dkst in the adjuvant treatment for early HER2-positive breast cancer. The preliminary long-term effectiveness and safety data we present further validate trastuzumab-dkst's role as a cost-saving alternative in oncological care. These findings have important implications for improving patient access to crucial treatments and for the more efficient use of healthcare resources. GOV REGISTRATION: NCT03892655.

GEN-RWD Sandbox: bridging the gap between hospital data privacy and external research insights with distributed analytics.

BACKGROUND: Artificial intelligence (AI) has become a pivotal tool in advancing contemporary personalised medicine, with the goal of tailoring treatments to individual patient conditions. This has heightened the demand for access to diverse data from clinical practice and daily life for research, posing challenges due to the sensitive nature of medical information, including genetics and health conditions. Regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. and the General Data Protection Regulation (GDPR) in Europe aim to strike a balance between data security, privacy, and the imperative for access. RESULTS: We present the Gemelli Generator - Real World Data (GEN-RWD) Sandbox, a modular multi-agent platform designed for distributed analytics in healthcare. Its primary objective is to empower external researchers to leverage hospital data while upholding privacy and ownership, obviating the need for direct data sharing. Docker compatibility adds an extra layer of flexibility, and scalability is assured through modular design, facilitating combinations of Proxy and Processor modules with various graphical interfaces. Security and reliability are reinforced through components like Identity and Access Management (IAM) agent, and a Blockchain-based notarisation module. Certification processes verify the identities of information senders and receivers. CONCLUSIONS: The GEN-RWD Sandbox architecture achieves a good level of usability while ensuring a blend of flexibility, scalability, and security. Featuring a user-friendly graphical interface catering to diverse technical expertise, its external accessibility enables personnel outside the hospital to use the platform. Overall, the GEN-RWD Sandbox emerges as a comprehensive solution for healthcare distributed analytics, maintaining a delicate equilibrium between accessibility, scalability, and security.

Application of multiple validated algorithms for identifying incident breast cancer among individuals with atopic dermatitis.

PURPOSE: Validated algorithms (VAs) in insurance claims databases are often used to estimate the prevalence and incidence of comorbidities and evaluate safety signals. However, although they are then used in different data sources or subpopulations from those in which they were developed the replicability of these VAs are rarely tested, making their application and performance in these settings potentially unknown. This paper describes testing multiple VAs used to identify incident breast cancer cases in a general population and in an indication-specific population, patients with atopic dermatitis (AD). METHODS: Two algorithms were tested in multiple insurance claims databases and four cohorts were created. Modifications were made to account for the US insurance setting. The resulting incidence rates (IRs) were then compared across algorithms and against surveillance, epidemiology, and end results (SEER) estimates to assess reliability. RESULTS: Algorithm 1 produced low IRs compared to Algorithm 2. Algorithm 2 provided similar estimates to those of SEER. Individuals in the AD cohorts experienced lower incident breast cancer cases than those in the general population cohorts. CONCLUSION: Regardless of an algorithm's reported accuracy, the original study setting and targeted population for the VAs may matter when attempting to replicate the algorithm in an indication-specific subpopulation or varying data sources. Investigators should use caution and conduct sensitivity analyses or use multiple algorithms when attempting to calculate incidence or prevalence estimates using VAs.

The real-world treatment characteristic and efficacy of immune checkpoint inhibitors in non-small cell lung cancer: Data from a retrospective cohort study.

BACKGROUND: The efficacy and prognosis of immune checkpoint inhibitors (ICIs) remain unresolved issues. Here, we assessed the treatment characteristics and efficacy of ICIs in non-small cell lung cancer (NSCLC) using real-world data and evaluated the predictive value of factors, including programmed death-ligand 1 (PD-L1) expression, for the clinical outcome of ICIs in NSCLC. METHODS: Analyzed data was collected from hospitalized patients in the West China Hospital of Sichuan University between January 2017 and March 2023. The Kaplan-Meier method was utilized for analyzing real-world progression-free survival (rwPFS), while Cox regression models was employed to access the correlation between the efficacy of immunotherapy and sociodemographic characteristics, disease information, and characteristics of ICI treatment. RESULTS: A total of 545 patients were included in the retrospective study and characteristics of immunotherapy varied significantly among PD-L1 expression groups. The median rwPFS for the entire population was 9.76 months. Subgroup analyses revealed that patients with high PD-L1 expression, early TNM stage, first-line immunotherapy, EGFR wild-type and those who have not received radiotherapy and targeted therapy previously were more likely to have better rwPFS. Furthermore, multivariate Cox regression analyses identified PD-L1 expression, EGFR mutation status and previous radiotherapy as the most influential predictors of the response to ICI treatment. CONCLUSIONS: This study presents the real-world experience of Chinese NSCLC patients undergoing ICI treatment, offering guidance for clinical decision-making based on various patient conditions, preferences, and indications for ICIs, through the evaluation of immunotherapy efficacy and predictors in NSCLC patients.

Real-World Weekly Efficacy Analysis of Faricimab in Patients with Age-Related Macular Degeneration.

Objectives: This study entailed a weekly analysis of real-world data (RWD) on the safety and efficacy of intravitreal (IVT) faricimab in neovascular age-related macular degeneration (nAMD). Methods: A retrospective, single-centre clinical trial was conducted at the Department of Ophthalmology, University Hospital Zurich, University of Zurich, Switzerland, approved by the Cantonal Ethics Committee of Zurich, Switzerland. Patients with nAMD were included. Data from patient charts and imaging were analysed. The safety and efficacy of the first faricimab injection were evaluated weekly until 4 weeks after injection. Results: Sixty-three eyes with a complete 4-week follow-up were enrolled. Six eyes were treatment-naïve; fifty-seven eyes were switched to faricimab from another treatment. Neither group showed signs of retinal vasculitis during the 4 weeks after injection. Central subfield thickness (CST) and volume (CSV) showed a statistically significant decrease compared to the baseline in the switched group (CST: p = 0.00383; CSV: p = 0.00702) after 4 weeks. The corrected visual acuity returned to the baseline level in both groups. The macular neovascularization area decreased in both groups, but this was not statistically significant. A complete resolution of sub- and intraretinal fluid after 4 weeks was found in 40% (switched) and 75% (naïve) of the treated patients. Conclusions: The weekly follow-ups reflect the structure-function relationship beginning with a fast functional improvement within two weeks after injection followed by a return to near-baseline levels after week 3. The first faricimab injection in our cohort showed a high safety profile and a statistically significant reduction in macular oedema in switched nAMD patients.

Data Science Methods for Real-World Evidence Generation in Real-World Data.

In the healthcare landscape, data science (DS) methods have emerged as indispensable tools to harness real-world data (RWD) from various data sources such as electronic health records, claim and registry data, and data gathered from digital health technologies. Real-world evidence (RWE) generated from RWD empowers researchers, clinicians, and policymakers with a more comprehensive understanding of real-world patient outcomes. Nevertheless, persistent challenges in RWD (e.g., messiness, voluminousness, heterogeneity, multimodality) and a growing awareness of the need for trustworthy and reliable RWE demand innovative, robust, and valid DS methods for analyzing RWD. In this article, I review some common current DS methods for extracting RWE and valuable insights from complex and diverse RWD. This article encompasses the entire RWE-generation pipeline, from study design with RWD to data preprocessing, exploratory analysis, methods for analyzing RWD, and trustworthiness and reliability guarantees, along with data ethics considerations and open-source tools. This review, tailored for an audience that may not be experts in DS, aspires to offer a systematic review of DS methods and assists readers in selecting suitable DS methods and enhancing the process of RWE generation for addressing their specific challenges.

A national evaluation analysis and expert interview study of real-world data sources for research and healthcare decision-making.

Real-world data (RWD) can provide intel (real-world evidence, RWE) for research and development, as well as policy and regulatory decision-making along the full spectrum of health care. Despite calls from global regulators for international collaborations to integrate RWE into regulatory decision-making and to bridge knowledge gaps, some challenges remain. In this work, we performed an evaluation of Austrian RWD sources using a multilateral query approach, crosschecked against previously published RWD criteria and conducted direct interviews with representative RWD source samples. This article provides an overview of 73 out of 104 RWD sources in a national legislative setting where major attempts are made to enable secondary use of RWD (e.g. law on the organisation of research, "Forschungsorganisationsgesetz"). We were able to detect omnipresent challenges associated with data silos, variable standardisation efforts and governance issues. Our findings suggest a strong need for a national health data strategy and data governance framework, which should inform researchers, as well as policy- and decision-makers, to improve RWD-based research in the healthcare sector to ultimately support actual regulatory decision-making and provide strategic information for governmental health data policies.

Quantitative evaluation of the impact of relaxing eligibility criteria on the risk-benefit profile of drugs for lung cancer based on real-world data.

INTRODUCTION: Restrictive eligibility criteria in cancer drug trials result in low enrollment rates and limited population diversity. Relaxed eligibility criteria (REC) based on solid evidence is becoming necessary for stakeholders worldwide. However, the absence of high-quality, favorable evidence remains a major challenge. This study presents a protocol to quantitatively evaluate the impact of relaxing eligibility criteria in common non-small cell lung cancer (NSCLC) protocols in China, on the risk-benefit profile. This involves a detailed explanation of the rationale, framework, and design of REC. METHODS: To evaluate our REC in NSCLC drug trials, we will first construct a structured, cross-dimensional real-world NSCLC database using deep learning methods. We will then establish randomized virtual cohorts and perform benefit-risk assessment using Monte Carlo simulation and propensity matching. Shapley value will be utilized to quantitatively measure the effect of the change of each eligibility criterion on patient volume, clinical efficacy and safety. DISCUSSION: This study is one of the few that focuses on the problem of overly stringent eligibility criteria cancer drug clinical trials, providing quantitative evaluation of the effect of relaxing each NSCLC eligibility criterion. This study will not only provide scientific evidence for the rational design of population inclusion in lung cancer clinical trials, but also establish a data governance system, as well as a REC evaluation framework that can be generalized to other cancer studies.

Real-World Comparative Effectiveness Assessment Study of a Native Type I Collagen Matrix Plus Polyhexamethylene Biguanide Antimicrobial and a Cryopreserved Cadaveric Skin Allograft for Use in Diabetic Foot Ulcers - A Non-inferiority Analysis.

Objective: To determine the effectiveness of a native type I collagen matrix plus polyhexamethylene biguanide antimicrobial (PCMP) and a cryopreserved cadaveric skin allograft (CCSA) for use in diabetic foot ulcers (DFUs). Methods: A real-world data study was conducted on 989 DFUs analyzed digitally. Of these, 325 and 664 DFUs were treated with PCMP and CCSA, respectively. Non-inferiority testing for equivalence of PCMP and CCSA was performed at a level of significance of P < .05. Results: Cox proportional hazards regression analysis for healing for PCMP and CCSA at weeks 4, 8, 12, and 24 was 12% vs 10%, 27% vs 24%, 39 % vs 37%, and 60% vs. 64%, respectively. No statistically significant differences were shown; P = .95. The median time to healing was 18 and 17 weeks for PCMP and CCSA, respectively; P = .95. The probability of healing was statistically equivalent between PCMP and CCSA; hazard ratio = 0.99; 95% CI (0.85, 1.17). Non-inferiority statistical testing results showed P = .01. Conclusions: Using non-inferiority hypothesis testing at a level of significance of P <.05, we showed that PCMP was equivalent to CCSA; P = .01. PCMP vs CCSA demonstrated no statistically significant differences in median time, percentage, and probability of healing. Data from real-world data comparative effectiveness assessment studies can help guide clinicians to limit overuse of ineffective therapies and underuse of effective therapies.

Incorporating external real-world data (RWD) in confirmatory adaptive design.

Adaptive designs, such as group sequential designs (and the ones with additional adaptive features) or adaptive platform trials, have been quintessential efficient design strategies in trials of unmet medical needs, especially for generating evidence from global regions. Such designs allow interim decision making and making adjustment to study design when necessary, meanwhile maintaining study integrity and operating characteristics. However, driven by the heightened competitive landscape and the desire to bring effective treatment to patients faster, innovation in the already functional designs is still germane to further propel drug development to a more efficient path. One way to achieve this is by leveraging external real-world data (RWD) in the adaptive designs to support interim or final decision making. In this paper, we propose a novel framework of incorporating external RWD in adaptive design to improve interim and/or final analysis decision making. Within this framework, researchers can prespecify the decision process and choose the timing and amount of borrowing while maintaining objectivity and controlling of type I error. Simulation studies in various scenarios are provided to describe power, type I error, and other performance metrics for interim/final decision making. A case study in non-small cell lung cancer is used for illustration on proposed design framework.

Emerging Role of GCN1 in Disease and Homeostasis.

GCN1 is recognized as a factor that is essential for the activation of GCN2, which is a sensor of amino acid starvation. This function is evolutionarily conserved from yeast to higher eukaryotes. However, recent studies have revealed non-canonical functions of GCN1 that are independent of GCN2, such as its participation in cell proliferation, apoptosis, and the immune response, beyond the borders of species. Although it is known that GCN1 and GCN2 interact with ribosomes to accomplish amino acid starvation sensing, recent studies have reported that GCN1 binds to disomes (i.e., ribosomes that collide each other), thereby regulating both the co-translational quality control and stress response. We propose that GCN1 regulates ribosome-mediated signaling by dynamically changing its partners among RWD domain-possessing proteins via unknown mechanisms. We recently demonstrated that GCN1 is essential for cell proliferation and whole-body energy regulation in mice. However, the manner in which ribosome-initiated signaling via GCN1 is related to various physiological functions warrants clarification. GCN1-mediated mechanisms and its interaction with other quality control and stress response signals should be important for proteostasis during aging and neurodegenerative diseases, and may be targeted for drug development.

Clinical characteristics and outcomes among hospitalised COVID-19 patients across epidemic waves in Spain: An unCoVer analysis.

BACKGROUND AND OBJECTIVES: The COVID-19 pandemic had a significant impact in population health worldwide, and particularly in people with pre-existing chronic diseases. Early risk identification and stratification is essential to reduce the impact of future outbreaks of pandemic potential. This study aimed to comprehensively examine factors associated with COVID-19 mortality across the pandemic waves in Spain. METHODS: A retrospective study analyzed the characteristics of 13,974 patients admitted to Spanish hospitals due to SARS-CoV-2 infection from 2020-01-28 to 2022-12-31. The demographic and clinical features of patients during hospitalization on each pandemic waves were analyzed. MAIN FINDINGS: The findings highlight the heterogeneity of patient characteristics, comorbidities and outcomes, across the waves. The high prevalence of cardiometabolic diseases (53.9%) among COVID-19 patients emphasizes the importance of controlling these risk factors to prevent severe COVID-19 outcomes. CONCLUSIONS: In summary, the study associate hospital mortality with factors such as advanced age and comorbidities. The decline in mortality after the 4th wave indicates potential influences like vaccination, viral adaptation, or improved treatments. Notably, dementia and cancer metastases emerge as critical factors linked to higher mortality, highlighting the importance of addressing these conditions in COVID-19 management and preparing for future challenges.

The Amager Project IV: suicidal behavior and aftercare: real world data (RWD) from a prospective observational project.

BACKGROUND: In an RCT study, OPAC (outreach, problem solving, adherence, continuity) approach to aftercare after suicide attempts had an effect. The present study used the OPAC method in a clinical setting on Amager Copenhagen to patients after suicide attempt (Group 1) and patients with suicide ideation (Group 2) in a real-world data (RWD) study. AIM: To study whether the OPAC method could provide real world evidence (RWE) for results from the RCT study and long-time prospects. METHOD: This RWD study included 506 patients and followed them for 5 years. Kaplan-Meyer showed 5 years results. Risk factors for 5 years were calculated. RESULTS: 206 males (mean age 37.9) and 300 females (mean age 35.2) participated. A decline in survival accelerated after 3 years. After a 2-year follow-up, Group 1 had an attempted suicide rate of 12,2% and Group 2 5,4%. After 5 years the numbers were 18% and 10%. There were 3 completed suicides. Risk factors were: earlier suicide attempts, one or both parents or they themselves were alcohol/drug abusers, and a poor social network. Group 1 showed the same result as the intervention group in our earlier RCT study. Group 2 did better. Both groups did better than the control group from our RCT study. CONCLUSION: The OPAC effect was translated into the daily clinic. Risk factors were previous suicide attempts, alcohol and drug abuse and poor social networks. More specific therapy is needed for some patients to prevent relapse. Focus on enhancing a sense of belongingness and/or treating substance abuse.

[The impact of primary data quality and interoperability on the analysis of real-world data]. / Die Auswirkungen von Primärdatenqualität und -interoperabilität auf Analysen von Real-World-Daten.

Real-world data is increasingly becoming the focus of healthcare research in the context of digitization. The timely availability of large amounts of data gives hope that research questions can be answered quickly without additional data collection and that a direct benefit for the care of people can be achieved. Especially in acute care situations, such as heat waves or a pandemic, this can be crucial. But real-world data depend quite significantly on the quality and intent of data collection. It is also influenced by determinations on semantic and syntactic standards that are made for primary data - often considering different use cases. In the context of different initiatives on national and international levels, a holistic view on data collection and evaluation and a regular feedback mechanism between data evaluation and specifications for the collection should be established. By including requirements for secondary data evaluation in the definition processes for data collection, the informative value of the data for research can be increased in the long term.In this discussion paper, the activities for standardized data collection in the context of the digitization initiatives and the corresponding European approaches are first presented. After outlining the effects of these activities on the possibilities and difficulties of data consolidation for the analysis of real-world data, the article calls for an ongoing discourse between the different areas.