scholarly journals Real-world treatment persistence of golimumab in the management of immune-mediated rheumatic diseases in Europe: a systematic literature review

BMJ Open ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. e027456 ◽  
Author(s):  
Karin Luttropp ◽  
Mary Dozier ◽  
Nahila Justo ◽  
Freddy Cornillie ◽  
Sumesh Kachroo ◽  
...  
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Real World ◽  
Data Extraction ◽  
Relevant Literature ◽  
Real World Data ◽  
Eligibility Criteria ◽  
Treatment Line ◽  
American College Of Rheumatology ◽  
Immune Mediated

ObjectivesTo summarise real-world data from studies reporting golimumab persistence in European immune-mediated rheumatic disease (IMRD) populations and to report pooled estimates.DesignSystematic literature review.Data sourcesRelevant literature was identified through searching Medline and Embase via Ovid as well as the conference databases of European League Against Rheumatism and American College of Rheumatology—Association of Rheumatology Health Professionals.Eligibility criteriaWe screened records using predefined patients, interventions, comparators, outcomes and study design criteria. Eligible studies included reports of persistence among adult IMRD patients in Europe receiving treatment with subcutaneous golimumab. Clinical trials, randomised controlled trials, literature reviews, editorials, guidelines and studies with <20 patients receiving golimumab were excluded.Data extraction and synthesisFollowing double screening by two independent reviewers, 27 studies out of 578 identified records were selected for inclusion and subsequent data extraction. Persistence was most commonly reported at 12and 24 months; hence, pooled persistence estimates were calculated for these two time points and reported according to indication.ResultsPersistence ranged between 58.1% (psoriatic arthritis (PsA) patients regardless of treatment line) and 75.7% (biological-naïve rheumatoid arthritis patients) at 12 months; at 24 months, the range was 43% (axial spondyloarthritis (AxSpA) patients regardless of treatment line) and 69.6% (biological-naïve PsA patients). On the basis of data from 12 studies, persistence with golimumab treatment was either significantly higher or not significantly different from other tumour necrosis factor inhibitors (TNFi).ConclusionsGolimumab persistence at 24 months approximates 50%, with a lower persistence among AxSpA (43%) patients. However, as the number of studies in these populations was low, they warrant further research. In 12 studies comparing various TNFi treatments, golimumab was shown to have significantly better or equal persistence to its comparators.

scholarly journals Real-World Data for Planning Eligibility Criteria and Enhancing Recruitment: Recommendations from the Clinical Trials Transformation Initiative

2021 ◽  
Author(s):  
Scott R. Evans ◽  
Dianne Paraoan ◽  
Jane Perlmutter ◽  
Sudha R. Raman ◽  
John J. Sheehan ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Real World ◽  
Real World Data ◽  
Eligibility Criteria ◽  
World Data ◽  
Study Planning ◽  
Efficiency And Effectiveness ◽  
Multi Stakeholder ◽  
Functional Teams ◽  
Data Driven Decisions

AbstractThe growing availability of real-world data (RWD) creates opportunities for new evidence generation and improved efficiency across the research enterprise. To varying degrees, sponsors now regularly use RWD to make data-driven decisions about trial feasibility, based on assessment of eligibility criteria for planned clinical trials. Increasingly, RWD are being used to support targeted, timely, and personalized outreach to potential trial participants that may improve the efficiency and effectiveness of the recruitment process. This paper highlights recommendations and resources, including specific case studies, developed by the Clinical Trials Transformation Initiative (CTTI) for applying RWD to planning eligibility criteria and recruiting for clinical trials. Developed through a multi-stakeholder, consensus- and evidence-driven process, these actionable tools support researchers in (1) determining whether RWD are fit for purpose with respect to study planning and recruitment, (2) engaging cross-functional teams in the use of RWD for study planning and recruitment, and (3) understanding patient and site needs to develop successful and patient-centric approaches to RWD-supported recruitment. Future considerations for the use of RWD are explored, including ensuring full patient understanding of data use and developing global datasets.

scholarly journals Is there a sex difference in mortality rates for deaths occurring in paediatric intensive care units? Systematic literature review protocol

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e046794
Author(s):  
Ofran Almossawi ◽  
Amanda Friend ◽  
Luigi Palla ◽  
Richard Feltbower ◽  
Bianca De Stavola
Keyword(s):  
Intensive Care ◽  
Literature Review ◽  
Intensive Care Units ◽  
Systematic Literature Review ◽  
Data Extraction ◽  
Linkage Study ◽  
Paediatric Intensive Care ◽  
Admission Rates ◽  
Ethical Approval ◽  
Paediatric Intensive Care Units

IntroductionIn the general population, female children have been reported to have a survival advantage. For children admitted to paediatric intensive care units (PICUs), mortality has been reported to be lower in males despite the higher admission rates for males into intensive care. This apparent sex reversal in PICU mortality is not well studied. To address this, we propose to conduct a systematic literature review to summarise the available evidence. Our review will study the reported differences in mortality between males and females aged 0–17, who died in a PICU, to examine if there is a difference between the two sexes in PICU mortality, and if so, to describe the magnitude and direction of this difference.Methods and analysisStudies that directly or indirectly addressed the association between sex and mortality in children admitted to intensive care will be eligible for inclusion. Studies that directly address the association will be eligible for data extraction. The search strings were based on terms related to the population (children in intensive care), the exposure (sex) and the outcome (mortality). We used the databases MEDLINE (1946–2020), Embase (1980–2020) and Web of Science (1985–2020) as these cover relevant clinical publications. We will assess the reliability of included studies using the risk of bias in observational studies of exposures tool. We will consider a pooled effect if we have at least three studies with similar periods of follow up and adjustment variables.Ethics and disseminationEthical approval is not required for this review as it will synthesise data from existing studies. This manuscript is a part of a larger data linkage study, for which Ethical approval was granted. Dissemination will be via peer-reviewed journals and via public and patient groups.PROSPERO registration numberCRD42020203009.

scholarly journals Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Alan Brnabic ◽  
Lisa M. Hess
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Real World ◽  
Learning Algorithms ◽  
External Validation ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Machine Learning Methods

Abstract Background Machine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision making. Methods This systematic literature review was conducted to identify published observational research of employed machine learning to inform decision making at the patient-provider level. The search strategy was implemented and studies meeting eligibility criteria were evaluated by two independent reviewers. Relevant data related to study design, statistical methods and strengths and limitations were identified; study quality was assessed using a modified version of the Luo checklist. Results A total of 34 publications from January 2014 to September 2020 were identified and evaluated for this review. There were diverse methods, statistical packages and approaches used across identified studies. The most common methods included decision tree and random forest approaches. Most studies applied internal validation but only two conducted external validation. Most studies utilized one algorithm, and only eight studies applied multiple machine learning algorithms to the data. Seven items on the Luo checklist failed to be met by more than 50% of published studies. Conclusions A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of machine learning methods to inform patient-provider decision making. There is a need to ensure that multiple machine learning approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that decisions for patient care are being made with the highest quality evidence. Future work should routinely employ ensemble methods incorporating multiple machine learning algorithms.

scholarly journals Leveraging Real-World Data for the Selection of Relevant Eligibility Criteria for the Implementation of Electronic Recruitment Support in Clinical Trials

2021 ◽  
Vol 12 (01) ◽  
pp. 017-026
Author(s):  
Georg Melzer ◽  
Tim Maiwald ◽  
Hans-Ulrich Prokosch ◽  
Thomas Ganslandt
Keyword(s):  
Data Warehouse ◽  
Real World ◽  
Chart Review ◽  
Patient Recruitment ◽  
Real World Data ◽  
Eligibility Criteria ◽  
Health Records ◽  
World Data ◽  
Paper Chart ◽  
Data Elements

Abstract Background Even though clinical trials are indispensable for medical research, they are frequently impaired by delayed or incomplete patient recruitment, resulting in cost overruns or aborted studies. Study protocols based on real-world data with precisely expressed eligibility criteria and realistic cohort estimations are crucial for successful study execution. The increasing availability of routine clinical data in electronic health records (EHRs) provides the opportunity to also support patient recruitment during the prescreening phase. While solutions for electronic recruitment support have been published, to our knowledge, no method for the prioritization of eligibility criteria in this context has been explored. Methods In the context of the Electronic Health Records for Clinical Research (EHR4CR) project, we examined the eligibility criteria of the KATHERINE trial. Criteria were extracted from the study protocol, deduplicated, and decomposed. A paper chart review and data warehouse query were executed to retrieve clinical data for the resulting set of simplified criteria separately from both sources. Criteria were scored according to disease specificity, data availability, and discriminatory power based on their content and the clinical dataset. Results The study protocol contained 35 eligibility criteria, which after simplification yielded 70 atomic criteria. For a cohort of 106 patients with breast cancer and neoadjuvant treatment, 47.9% of data elements were captured through paper chart review, with the data warehouse query yielding 26.9% of data elements. Score application resulted in a prioritized subset of 17 criteria, which yielded a sensitivity of 1.00 and specificity 0.57 on EHR data (paper charts, 1.00 and 0.80) compared with actual recruitment in the trial. Conclusion It is possible to prioritize clinical trial eligibility criteria based on real-world data to optimize prescreening of patients on a selected subset of relevant and available criteria and reduce implementation efforts for recruitment support. The performance could be further improved by increasing EHR data coverage.

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