Publication bias in clinical trials of electronic health records.
Ontology highlight
ABSTRACT: Objective
To measure the rate of non-publication and assess possible publication bias in clinical trials of electronic health records.Methods
We searched ClinicalTrials.gov to identify registered clinical trials of electronic health records and searched the biomedical literature and contacted trial investigators to determine whether the results of the trials were published. Publications were judged as positive, negative, or neutral according to the primary outcome.Results
Seventy-six percent of trials had publications describing trial results; of these, 74% were positive, 21% were neutral, and 4% were negative (harmful). Of unpublished studies for which the investigator responded, 43% were positive, 57% were neutral, and none were negative; the lower rate of positive results was significant (p<0.001).Conclusion
The rate of non-publication in electronic health record studies is similar to that in other biomedical studies. There appears to be a bias toward publication of positive trials in this domain.
SUBMITTER: Vawdrey DK
PROVIDER: S-EPMC3662474 | biostudies-literature | 2013 Feb
REPOSITORIES: biostudies-literature
Publications
Publication bias in clinical trials of electronic health records.
Vawdrey David K DK Hripcsak George G
Journal of biomedical informatics 20120910 1
<h4>Objective</h4>To measure the rate of non-publication and assess possible publication bias in clinical trials of electronic health records.<h4>Methods</h4>We searched ClinicalTrials.gov to identify registered clinical trials of electronic health records and searched the biomedical literature and contacted trial investigators to determine whether the results of the trials were published. Publications were judged as positive, negative, or neutral according to the primary outcome.<h4>Results</h4 ...[more]
Similar Datasets
Project description:The conduct of clinical trials is increasingly challenging due to greater complexity and governance requirements as well as difficulties with recruitment and retention. Electronic Health Records for Clinical Research (EHR4CR) aims at improving the conduct of trials by using existing routinely collected data, but little is known about stakeholder views on data availability, information governance, and acceptable working practices.Senior figures in healthcare organisations across Europe were provided with a description of the project and structured interviews were subsequently conducted to elicit their views.37 structured interviewees in Germany, UK, Switzerland, and France indicated strong support for the proposed EHR4CR platform. All interviewees reported that using the platform for assessing feasibility would enhance the conduct of clinical trials and the majority also felt it would reduce workloads. Interviewees felt the platform could enhance trial recruitment and adverse event reporting but also felt it could raise either ethical or information governance concerns in their country.There was clear support for EHR4CR and a belief that it could reduce workloads and improve the conduct and quality of trials. However data security, privacy, and information governance issues would need to be carefully managed in the development of the platform.
| S-EPMC4619877 | biostudies-literature
Project description:ObjectiveElectronic health records (EHR) data have become a central data source for clinical research. One concern for using EHR data is that the process through which individuals engage with the health system, and find themselves within EHR data, can be informative. We have termed this process informed presence. In this study we use simulation and real data to assess how the informed presence can impact inference.Materials and methodsWe first simulated a visit process where a series of biomarkers were observed informatively and uninformatively over time. We further compared inference derived from a randomized control trial (ie, uninformative visits) and EHR data (ie, potentially informative visits).ResultsWe find that only when there is both a strong association between the biomarker and the outcome as well as the biomarker and the visit process is there bias. Moreover, once there are some uninformative visits this bias is mitigated. In the data example we find, that when the "true" associations are null, there is no observed bias.DiscussionThese results suggest that an informative visit process can exaggerate an association but cannot induce one. Furthermore, careful study design can, mitigate the potential bias when some noninformative visits are included.ConclusionsWhile there are legitimate concerns regarding biases that "messy" EHR data may induce, the conditions for such biases are extreme and can be accounted for.
| S-EPMC6857502 | biostudies-literature
Project description:BackgroundThe electronic health record (EHR) contains a wealth of clinical data that may be used to streamline the identification of potential clinical trial participants. However, there is little empirical information on site-level facilitators of and barriers to optimal use of EHR systems with respect to trial recruitment.MethodsWe conducted qualitative focus groups and quantitative surveys as part of the EHR Ancillary Study, which is being conducted alongside the multicenter, global, Harmony Outcomes Trial comparing albiglutide to standard care for the prevention of cardiovascular events in type 2 diabetes. Subject matter experts used findings from focus groups to draft a 20-question survey examining the use of the EHR for participant identification, common site recruitment strategies, and variation in perceived barriers to optimal use of the EHR. The final survey was fielded with 446 site investigators actively enrolling participants in the main trial.ResultsNearly two-thirds of respondents were study coordinators (63.2%), 23.1% were principal investigators, and 13.7% held other research roles. Approximately half of the respondents reported using the EHR to find potential trial participants. Of these, 79.4% reported using EHR searches in conjunction with other recruitment methods, including reviewing of upcoming clinic schedules (75.3%) and contacting past trial participants (71.2%). Important barriers to optimal use of the EHR included the lack of availability of certain research-focused EHR modules and limitations on the ability to contact patients cared for by other providers. Of survey respondents who did not use the EHR to find potential participants, one-quarter reported that the EHR was not accessible in their country; this finding varied from 2.6% of respondents in North America to 50% of respondents in the Asia Pacific.ConclusionsWhile EHR screening was commonly used for recruitment in a cardiovascular outcomes trial, important technical, governance, and regulatory barriers persist. Multifaceted, scalable, and customizable strategies are needed to support the optimal use of the EHR for trial participant identification.Trial registrationClinicalTrials.gov NCT02465515. Registered on 8 June 2015.
| S-EPMC8287813 | biostudies-literature
Project description:Pragmatic randomised clinical trials could use existing electronic health records (EHRs) to identify trial participants, perform randomisation, and to collect follow-up data. Achieving adequate informed consent in routine care and clinician recruitment have been identified as key barriers to this approach to clinical trials. We propose a model where written informed consent for a pragmatic comparative effectiveness trial is obtained in advance by the research team, recorded in the EHR, and then confirmed by the general practitioner (GP) at the time of enrolment. The EHR software then randomly assigns a patient to one of two treatments. Follow-up data is collected in the EHR. Twenty-two of 23 GPs surveyed (96%) were 'definitely' or 'probably' comfortable with confirming consent. Twenty-one out of 23 GPs (91%) were 'definitely' or 'probably' comfortable with a patient being randomised to one of two comparable drugs during a routine consultation. Twenty-two out of 23 GPs (96%) were 'definitely' or 'probably' comfortable with allowing the electronic system to randomise a patient to drug A or drug B and generate a prescription. Ten out of 23 GPs (43%) identified time constraints as the main hurdle to conducting this sort of research in the primary care setting. On average, it was felt that 6.5 min, in addition to a usual consult, would be acceptable to complete enrolment. Our survey found this model of a comparative effectiveness trial to be acceptable to the majority of GPs.
| S-EPMC5956547 | biostudies-literature
Project description:Individualized treatment rules (ITRs) tailor medical treatments according to patient-specific characteristics in order to optimize patient outcomes. Data from randomized controlled trials (RCTs) are used to infer valid ITRs using statistical and machine learning methods. However, RCTs are usually conducted under specific inclusion/exclusion criteria, thus limiting their generalizability to a broader patient population in real-world practice settings. Because electronic health records (EHRs) document treatment prescriptions in the real world, transferring information in EHRs to RCTs, if done appropriately, could potentially improve the performance of ITRs, in terms of precision and generalizability. In this work, we propose a new domain adaptation method to learn ITRs by incorporating information from EHRs. Unless we assume that there is no unmeasured confounding in EHRs, we cannot directly learn the optimal ITR from the combined EHR and RCT data. Instead, we first pretrain "super" features from EHRs that summarize physician treatment decisions and patient observed benefits in the real world, as these are likely to be informative of the optimal ITRs. We then augment the feature space of the RCT and learn the optimal ITRs by stratifying by super features using subjects enrolled in RCT. We adopt Q-learning and a modified matched-learning algorithm for estimation. We present heuristic justification of our method and conduct simulation studies to demonstrate the performance of super features. Finally, we apply our method to transfer information learned from EHRs of patients with type 2 diabetes to learn individualized insulin therapies from RCT data.
| S-EPMC7786287 | biostudies-literature
Project description:BackgroundRandomized controlled trials (RCTs) are the gold standard for evidence-based practice, but their development and implementation is resource intensive. We aimed to describe modern RCTs in gastrointestinal (GI) cancer and identify predictors of successful accrual and publication.Materials and methodsClinicalTrials.gov was queried for phase III GI cancer RCTs opened between 2010 and 2019 and divided into two cohorts: past and recruiting. Past trials were analyzed for predictors of successful accrual and the subset with ≥3 years follow-up were analyzed for predictors of publication. Univariate and multivariable (MVA) logistic regression were used to identify covariates associated with complete accrual and publication status.ResultsA total of 533 GI RCTs were opened from 2010 to 2019, 244 of which are still recruiting. In the "past" trials cohort (235/533) MVA, Asian continent of enrollment was a predictor for successful accrual, whereas trials with prolonged enrollment (duration longer than median of 960 days) trended to failed accrual. Predictors for publication on MVA included international enrollment and accrual completion. Sponsorship was not associated with accrual or publication. Notably, 33% of past trials remain unpublished, and 60% of trials that were closed early remain unpublished.ConclusionAccrual rate and the primary continent of enrollment drive both trial completion and publication in GI oncology. Accrual must be streamlined to enhance the impact of RCTs on clinical management. A large portion of trials remain unpublished, underscoring the need to encourage dissemination of all trials to, at a minimum, inform future trial design.Implications for practiceTwo-thirds of gastrointestinal (GI) oncology phase III randomized controlled trials successfully accrue; however, one third of these trials are unpublished and more than half of trials that close early are unpublished. The strongest predictors for publication are successful accrual and international collaborations. Initiatives to optimize the trial enrollment process need to be explored to maximize the potential for trials to engender progress in clinical practice. Moreover, this study identified a significant publication bias in the realm of GI oncology, and the field should promote reporting of all trials in order to better inform future trial questions and design.
| S-EPMC8342580 | biostudies-literature
Project description:BackgroundCross-sectional measures of body mass index (BMI) are associated with cardiovascular disease (CVD) incidence, but less is known about whether weight change affects the risk of CVD.MethodsWe estimated the effect of 2-y weight change interventions on 7-y risk of CVD (CVD death, myocardial infarction, stroke, hospitalization from coronary heart disease, and heart failure) by emulating hypothetical interventions using electronic health records. We identified 138,567 individuals with 45-69 years of age without chronic disease in England from 1998 to 2016. We performed pooled logistic regression, using inverse-probability weighting to adjust for baseline and time-varying confounders. We categorized each individual into a weight loss, maintenance, or gain group.ResultsAmong those of normal weight, both weight loss [risk difference (RD) vs. weight maintenance = 1.5% (0.3% to 3.0%)] and gain [RD = 1.3% (0.5% to 2.2%)] were associated with increased risk for CVD compared with weight maintenance. Among overweight individuals, we observed moderately higher risk of CVD in both the weight loss [RD = 0.7% (-0.2% to 1.7%)] and the weight gain group [RD = 0.7% (-0.1% to 1.7%)], compared with maintenance. In the obese, those losing weight showed lower risk of coronary heart disease [RD = -1.4% (-2.4% to -0.6%)] but not of stroke. When we assumed that chronic disease occurred 1-3 years before the recorded date, estimates for weight loss and gain were attenuated among overweight individuals; estimates for loss were lower among obese individuals.ConclusionAmong individuals with obesity, the weight-loss group had a lower risk of coronary heart disease but not of stroke. Weight gain was associated with increased risk of CVD across BMI groups. See video abstract at, http://links.lww.com/EDE/B838.
| S-EPMC8318567 | biostudies-literature
Project description:Columbia Open Health Data (COHD) is a publicly accessible database of electronic health record (EHR) prevalence and co-occurrence frequencies between conditions, drugs, procedures, and demographics. COHD was derived from Columbia University Irving Medical Center's Observational Health Data Sciences and Informatics (OHDSI) database. The lifetime dataset, derived from all records, contains 36,578 single concepts (11,952 conditions, 12,334 drugs, and 10,816 procedures) and 32,788,901 concept pairs from 5,364,781 patients. The 5-year dataset, derived from records from 2013-2017, contains 29,964 single concepts (10,159 conditions, 10,264 drugs, and 8,270 procedures) and 15,927,195 concept pairs from 1,790,431 patients. Exclusion of rare concepts (count???10) and Poisson randomization enable data sharing by eliminating risks to patient privacy. EHR prevalences are informative of healthcare consumption rates. Analysis of co-occurrence frequencies via relative frequency analysis and observed-expected frequency ratio are informative of associations between clinical concepts, useful for biomedical research tasks such as drug repurposing and pharmacovigilance. COHD is publicly accessible through a web application-programming interface (API) and downloadable from the Figshare repository. The code is available on GitHub.
| S-EPMC6257042 | biostudies-literature
Project description:BACKGROUND:Unnecessary healthcare utilization, non-adherence to current clinical guidelines, or insufficient personalized care are perpetual challenges and remain potential major cost-drivers for healthcare systems around the world. Implementing decision support systems into clinical care is promised to improve quality of care and thereby yield substantial effects on reducing healthcare expenditure. In this article, we evaluate the economic impact of clinical decision support (CDS) interventions based on electronic health records (EHR). METHODS:We searched for studies published after 2014 using MEDLINE, CENTRAL, WEB OF SCIENCE, EBSCO, and TUFTS CEA registry databases that encompass an economic evaluation or consider cost outcome measures of EHR based CDS interventions. Thereupon, we identified best practice application areas and categorized the investigated interventions according to an existing taxonomy of front-end CDS tools. RESULTS AND DISCUSSION:Twenty-seven studies are investigated in this review. Of those, twenty-two studies indicate a reduction of healthcare expenditure after implementing an EHR based CDS system, especially towards prevalent application areas, such as unnecessary laboratory testing, duplicate order entry, efficient transfusion practice, or reduction of antibiotic prescriptions. On the contrary, order facilitators and undiscovered malfunctions revealed to be threats and could lead to new cost drivers in healthcare. While high upfront and maintenance costs of CDS systems are a worldwide implementation barrier, most studies do not consider implementation cost. Finally, four included economic evaluation studies report mixed monetary outcome results and thus highlight the importance of further high-quality economic evaluations for these CDS systems. CONCLUSION:Current research studies lack consideration of comparative cost-outcome metrics as well as detailed cost components in their analyses. Nonetheless, the positive economic impact of EHR based CDS interventions is highly promising, especially with regard to reducing waste in healthcare.
| S-EPMC7491136 | biostudies-literature
Project description:Integration of detailed phenotype information with genetic data is well established to facilitate accurate diagnosis of hereditary disorders. As a rich source of phenotype information, electronic health records (EHRs) promise to empower diagnostic variant interpretation. However, how to accurately and efficiently extract phenotypes from heterogeneous EHR narratives remains a challenge. Here, we present EHR-Phenolyzer, a high-throughput EHR framework for extracting and analyzing phenotypes. EHR-Phenolyzer extracts and normalizes Human Phenotype Ontology (HPO) concepts from EHR narratives and then prioritizes genes with causal variants on the basis of the HPO-coded phenotype manifestations. We assessed EHR-Phenolyzer on 28 pediatric individuals with confirmed diagnoses of monogenic diseases and found that the genes with causal variants were ranked among the top 100 genes selected by EHR-Phenolyzer for 16/28 individuals (p < 2.2 × 10-16), supporting the value of phenotype-driven gene prioritization in diagnostic sequence interpretation. To assess the generalizability, we replicated this finding on an independent EHR dataset of ten individuals with a positive diagnosis from a different institution. We then assessed the broader utility by examining two additional EHR datasets, including 31 individuals who were suspected of having a Mendelian disease and underwent different types of genetic testing and 20 individuals with positive diagnoses of specific Mendelian etiologies of chronic kidney disease from exome sequencing. Finally, through several retrospective case studies, we demonstrated how combined analyses of genotype data and deep phenotype data from EHRs can expedite genetic diagnoses. In summary, EHR-Phenolyzer leverages EHR narratives to automate phenotype-driven analysis of clinical exomes or genomes, facilitating the broader implementation of genomic medicine.
| S-EPMC6035281 | biostudies-literature