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Real-World Evidence — What Is It and What Can It Tell Us?
Abstract
The FDA is developing guidance on the use of “real-world evidence” — health care information from atypical sources, including electronic health records, billing databases, and product and disease registries — to assess the safety and effectiveness of drugs and devices.
... The FDA defines RWE as "clinical evidence about the usage and potential benefits or risks of a medical product derived from analysis of RWD." 1 Simply put, RWD is the collective term generally used to describe data collected outside of a traditional randomized clinical trial (RCT) research setting, often in patientclinician encounters and/or from information generated by patients in terms of their perception of pain, function, quality of life, and so on, and/or from biosensors worn by study participants. 2 Randomization may be introduced at baseline to facilitate balanced comparison groups for pragmatic clinical trials (PCT), which then assume many of the characteristics of RWE after randomization, such as studying treatments as used rather than according to intent-to-treat analyses and including more diverse populations. 3,4 PCT generally focus on health outcomes that inform a clinical or policy decision, 5,6 in contrast to intermediate and surrogate outcomes often studied in traditional RCT prepared for regulatory approval of new medical products and supplementary new indications, which may be less reflective of true clinical outcomes. ...
... 24 Here, we describe four approaches used to generate RWE and provide pointers for clinicians interested in study design and execution. We offer tactical guidance about (1) opportunistic study designs, (2) considerations about representativeness of patients selected for study, (3) the need for transparency of data provenance, data handling, and data quality assessments, and (4) considerations for strengthening studies using record linkage and randomization in pragmatic clinical trials. ...
... To address this challenge, there is increasing interest in the use of non-interventional real-world data [1,2]. However, concerns remain regarding the reliability and validity of estimates derived from real-world evidence (RWE) studies [3,4]. To assess whether RWE studies using similar methodologies can provide supportive evidence for RCTs, it is necessary to calibrate RWE studies against the treatment effect of RCTs [5]. ...
Background
Emulating randomized controlled trials (RCTs) by real-world evidence (RWE) studies would benefit future clinical and regulatory decision-making by balancing the limitations of RCT. We aimed to evaluate whether the findings from RWE studies can support regulatory decisions derived from RCTs of non-vitamin K antagonist oral anticoagulants (NOACs) in patients with venous thromboembolism (VTE).
Methods
Five landmark trials (AMPLIFY, RE-COVER II, Hokusai-VTE, EINSTEIN-DVT, and EINSTEIN-PE) of NOACs were emulated using the South Korean nationwide claims database (January 2012 to August 2020). We applied an active comparator and new-user design to include patients who initiated oral anticoagulants within 28 days from their VTE diagnoses. The prespecified eligibility criteria, exposure (each NOAC, such as apixaban, rivaroxaban, dabigatran, and edoxaban), comparator (conventional therapy, defined as subcutaneous heparin followed by warfarin), and the definition of outcomes from RCTs were emulated as closely as possible in each separate emulation cohort. The primary outcome was identical to each trial, which was defined as recurrent VTE or VTE-related death. The safety outcome was major bleeding. Propensity score matching was conducted to balance 69 covariates between the exposure groups. Effect estimates for outcomes were estimated using the Mantel–Haenszel method and Cox proportional hazards model and subsequently compared with the corresponding RCT estimates.
Results
Compared to trial populations, real-world study populations were older (range: 63–69 years [RWE] vs. 54–59 years [RCT]), with more females (55–60.5% vs. 39–48.3%) and had a higher prevalence of active cancer (4.2–15.4% vs. 2.5–9.5%). The emulated estimates for effectiveness outcomes showed superior effectiveness of NOAC (AMPLIFY: relative risk 0.81, 95% confidence interval 0.70–0.94; RE-COVER II: hazard ratio [HR] 0.60, 0.37–0.96; Hokusai-VTE: 0.49, 0.31–0.78; EINSTEIN-DVT: 0.54, 0.33–0.89; EINSTEIN-PE: 0.50, 0.34–0.74), when contrasted with trials that showed non-inferiority. For safety outcomes, all emulations except for AMPLIFY and EINSTEIN-DVT yielded results consistent with their corresponding RCTs.
Conclusions
This study revealed the feasibility of complementing RCTs with RWE studies by using claims data in patients with VTE. Future studies to consider the different demographic characteristics between RCT and RWE populations are needed.
... Additionally, without blindness, whether the patient receives the test treatment is subjective, and patients may be more likely to drop off if the treatment is not beneficial. Due to these unfavorable characteristics of RWD, it is essential to pay attention to how the data were collected and the methodology used to conduct the research [21]. In this section, challenging issues regarding the use of RWD in support of clinical investigation of the test treatment are demonstrated. ...
For the development of a test treatment or drug product, it is necessary to conduct composite hypothesis testing to test for effectiveness and safety simultaneously, since some approved drug products have been recalled due to safety concerns. One of the major issues in conducting a composite hypothesis testing for effectiveness and safety is the requirement of a huge sample size to achieve the desired power for detecting clinically meaningful differences in both safety and effectiveness. Situation can be much difficult in orphan drug development. In this article, a generalized two-stage innovative approach to test for effectiveness and safety simultaneously is proposed. Additionally, to alleviate the requirement of a large randomized clinical trial (RCT) and revealing effectiveness, real-world data is suggested to use in conjunction with RCT data for orphan drug development. The proposed approach can help investigators test for effectiveness and safety at the same time without worrying about the sample size. It also helps reduce the probability of approving a drug product with safety concerns.
... The present research has a significant limitation, as the majority of the evidence supporting the predictive significance of HRQL in esophagogastric cancer comes from RCTs and population-based studies, with the former exceeding the latter [112]. These trials have rigorous inclusion criteria, so the typical trial patient only represents a fraction of the target group, and systematic bias is quite likely given that the real-world cancer population is not abundantly represented in RCTs [113]. ...
Upper gastrointestinal (GI) conditions vastly affect each individual’s physical, social, and emotional status. The decision-making process by the medical personnel about these patients is currently based on a patient’s life quality evaluation scale, HRQL scales. By utilizing HRQL scales, a better understanding of the various surgical and non-surgical treatment options, as well as their long-term consequences, can be achieved. In our study, an organ-based approach is used in an attempt to examine and characterized the effect of upper GI surgery on HRQL. Therefore, HRQL scales’ function as a prognostic tool is useful, and the need for future research, the creation of valid training programs, and modern guidelines is highlighted.
... 37,38 This registry will provide a unique opportunity to investigate the global impact of risk assessment, the current screening methods, and the effectiveness of therapeutic interventions derived from clinical trials as they are implemented in real-world practice. 39 ...
BACKGROUND
Global collaboration in cardio-oncology is needed to understand the prevalence of cancer therapy-related cardiovascular toxicity in different risk groups, practice settings, and geographic locations. There are limited data on the socioeconomic and racial/ethnic disparities that may impact access to care and outcomes. To address these gaps, we established the Global Cardio-Oncology Registry, a multinational, multicenter prospective registry.
METHODS
We assembled cardiologists and oncologists from academic and community settings to collaborate in the first Global Cardio-Oncology Registry. Subsequently, a survey for site resources, demographics, and intention to participate was conducted. We designed an online data platform to facilitate this global initiative.
RESULTS
A total of 119 sites responded to an online questionnaire on their practices and main goals of the registry: 49 US sites from 23 states and 70 international sites from 5 continents indicated a willingness to participate in the Global Cardio-Oncology Registry. Sites were more commonly led by cardiologists (85/119; 72%) and were more often university/teaching (81/119; 68%) than community based (38/119; 32%). The average number of cardio-oncology patients treated per month was 80 per site. The top 3 Global Cardio-Oncology Registry priorities in cardio-oncology care were breast cancer, hematologic malignancies, and patients treated with immune checkpoint inhibitors. Executive and scientific committees and specific committees were established. A pilot phase for breast cancer using Research Electronic Data Capture Cloud platform recently started patient enrollment.
CONCLUSIONS
We present the structure for a global collaboration. Information derived from the Global Cardio-Oncology Registry will help understand the risk factors impacting cancer therapy-related cardiovascular toxicity in different geographic locations and therefore contribute to reduce access gaps in cardio-oncology care. Risk calculators will be prospectively derived and validated.
... Thus, results from registrational trials cannot be easily extrapolated to patient populations in community settings treated under routine clinical practice [14,15]. Hence, the generation of real-world evidence (RWE) from large and unselected patient populations is highly relevant to inform on patient population characteristics and outcomes, and to complement phase III trial results [16]. Here, we report effectiveness, safety and quality-of-life data from COMBI-r, a large, multi-center, prospective, single-cohort, non-interventional study conducted to generate real-world data from skin cancer centers in Germany. ...
Combined BRAF/MEK-inhibition constitutes a relevant treatment option for BRAF-mutated advanced melanoma. The prospective, non-interventional COMBI-r study assessed the effectiveness and tolerability of the BRAF-inhibitor dabrafenib combined with the MEK-inhibitor trametinib in patients with advanced melanoma under routine clinical conditions. Progression-free survival (PFS) was the primary objective, and secondary objectives included overall survival (OS), disease control rate, duration of therapy, and the frequency and severity of adverse events. This study enrolled 472 patients at 55 German sites. The median PFS was 8.3 months (95%CI 7.1–9.3) and the median OS was 18.3 months (14.9–21.3), both tending to be longer in pre-treated patients. In the 147 patients with CNS metastases, PFS was similar in those requiring corticosteroids (probably representing symptomatic patients, 5.6 months (3.9–7.2)) compared with those not requiring corticosteroids (5.9 months (4.8–6.9)); however, OS was shorter in patients with brain metastases who received corticosteroids (7.8 (6.3–11.6)) compared to those who did not (11.9 months (9.6–19.5)). The integrated subjective assessment of tumor growth dynamics proved helpful to predict outcome: investigators’ upfront categorization correlated well with time-to-event outcomes. Taken together, COMBI-r mirrored PFS outcomes from other prospective, observational studies and confirmed efficacy and safety findings from the pivotal phase III COMBI-d/-v and COMBI-mb trials.
... Such data can provide valuable insight into the care individuals receive when treated in real-world clinical settings. 1 The U.S. Food and Drug Administration (FDA) defines RWD as "data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources," such as electronic health records (EHRs), administrative claims, and billing data among others. 2 Increasingly, RWD providers have enabled access to population-scale clinical data, with many national and international clinical data research networks curating large collections of RWD from diverse sources. ...
Electronic health records (EHR) data have considerable variability in data completeness across sites and patients. Lack of "EHR data-continuity" or "EHR data-discontinuity", defined as "having medical information recorded outside the reach of an EHR system" can lead to a substantial amount of information bias. The objective of this study was to comprehensively evaluate (1) how EHR data-discontinuity introduces data bias, (2) case finding algorithms affect downstream prediction models, and (3) how algorithmic fairness is associated with racial-ethnic disparities. We leveraged our EHRs linked with Medicaid and Medicare claims data in the OneFlorida+ network and used a validated measure (i.e., Mean Proportions of Encounters Captured [MPEC]) to estimate patients' EHR data continuity. We developed a machine learning model for predicting type 2 diabetes (T2D) diagnosis as the use case for this work. We found that using cohorts selected by different levels of EHR data-continuity affects utilities in disease prediction tasks. The prediction models trained on high continuity data will have a worse fit on low continuity data. We also found variations in racial and ethnic disparities in model performances and model fairness in models developed using different degrees of data continuity. Our results suggest that careful evaluation of data continuity is critical to improving the validity of real-world evidence generated by EHR data and health equity.
... However, RCTs often come with limitations, including strict inclusion criteria, short follow-up periods, and potential lack of representation of realworld patient populations. Recently, there has been an increased interest in understanding the potential benefits of generating real-world evidence [12][13][14], particularly when it is difficult to complete randomized trials. Real-world evidence addresses these shortcomings by providing data and information collected from routine clinical practice, electronic health records, observational studies, and patient registries. ...
Surgery is the standard treatment for stage I non-small cell lung cancer (NSCLC); however, no clear randomized trial demonstrates its superiority to stereotactic body radiotherapy (SBRT) regarding survival. We aimed to retrospectively evaluate the treatment outcomes of SBRT in operable patients with stage I NSCLC using a large Japanese multi-institutional database to show real-world outcome. Exactly 399 patients (median age 75 years; 262 males and 137 females) with stage I (IA 292, IB 107) histologically proven NSCLC (adenocarcinoma 267, squamous cell carcinoma 96, others 36) treated at 20 institutions were reviewed. SBRT was prescribed at a total dose of 48–70 Gy in 4–10 fractions. The median follow-up period was 38 months. Local progression-free survival rates were 84.2% in all patients and 86.1% in the T1, 78.6% in T2, 89.2% in adenocarcinoma, and 70.5% in squamous cell subgroups. Overall 3-year survival rates were 77.0% in all patients: 90.7% in females, 69.6% in males, and 41.2% in patients with pulmonary interstitial changes. Fatal radiation pneumonitis was observed in two patients, all of whom had pulmonary interstitial changes. This real-world evidence will be useful in shared decision-making for optimal treatment, including SBRT for operable stage I NSCLC, particularly in older patients.
... The current research relies on a retrospective single-center analysis of real-life data, implying inherent strengths and weaknesses. Real-world studies have become a meaningful tool in cancer research, as they provide different stakeholders with evidence that can bridge the gap between clinical trials and routine practice [31]. In this regard, the retrospective analysis methodology of this study sought to address the issue of the predictive potential of APAP exposure, which would be unlikely to be the subject of prospective research [32]. ...
(1) Background: Several studies have investigated potential interactions between immune checkpoint inhibitors (ICIs) and commonly prescribed medications. Although acetaminophen (APAP) has not been considered susceptible to interaction with ICIs, recent research has shown that detectable plasma levels of this drug can hinder the efficacy of PD-1/PD-L1 blockade therapies. A reliable assessment of the potential interaction between APAP and ICIs in advanced non-small cell lung cancer (NSCLC) patients would be worthwhile since it is often prescribed in this condition. We sought to evaluate the impact of the concomitant use of APAP in patients with advanced NSCLC on PD-1/PD-L1 blockade using real-world evidence. (2) Methods: This study included consecutive patients with histologically proven stage IV NSCLC who underwent first-line therapy with pembrolizumab as a single agent or in combination with platinum-based chemotherapy, or second-line therapy with pembrolizumab, nivolumab, or atezolizumab. The intensity of APAP exposure was classified as low (therapeutic intake lasting less than 24 h or a cumulative intake lower than 60 doses of 1000 mg) or high (therapeutic intake lasting more than 24 h or a total intake exceeding 60 doses of 1000 mg). The favorable outcome of anti-PD-1/PD-L1 therapies was defined by durable clinical benefit (DCB). Progression-free survival (PFS) and overall survival (OS) were relevant to our efficacy analysis. Propensity score matching (PSM) methods were applied to adjust for differences between the APAP exposure subgroups. (3) Results: Over the course of April 2018 to October 2022, 80 patients were treated with first-line pembrolizumab either as single-agent therapy or in combination with platinum-based chemotherapy. During the period from June 2015 to November 2022, 145 patients were given anti-PD-1/PD-L1 blockade therapy as second-line treatment. Subsequent efficacy analyses relied on adjusted PSM populations in both treatment settings. Multivariate testing revealed that only the level of APAP and corticosteroid intake had an independent effect on DCB in both treatment lines. Multivariate Cox regression analysis confirmed high exposure to APAP and immunosuppressive corticosteroid therapy as independent predictors of shorter PFS and OS in both treatment settings. (4) Conclusions: Our findings would strengthen the available evidence that concomitant intake of APAP blunts the efficacy of ICIs in patients with advanced NSCLC. The detrimental effects appear to depend on the cumulative dose and duration of exposure to APAP. The inherent shortcomings of the current research warrant confirmation in larger independent series.
... Nonetheless, conventional clinical trials involving natural products frequently suffer from limited sample sizes and lack external validity, as the participants' characteristics and behaviors may not accurately represent those of real-world users. Consequently, studies such as this try to reflect the real-world effects of such products and possess distinct value in their capacity to provide evidence for regulatory and clinical decision-making and additional clinical trial design [39]. ...
Inadequate sleep is a global health concern. Sleep is multidimensional and complex; new multi-ingredient agents are needed. This study assessed the comparative effects of two multi-ingredient supplements on sleep relative to placebo. Adults (N = 620) seeking better sleep were randomly assigned to receive one of three study products. Sleep A (contained lower (0.35 mg THC and higher levels of botanicals (75 mg each hops oil and valerian oil), Sleep B (contained higher THC (0.85 mg) and lower botanicals (20 mg each hops oil and valerian oil) or placebo) for 4 weeks. Sleep disturbance was assessed at baseline and weekly using NIH’s Patient-Reported Outcomes Measurement Information System (PROMIS™) Sleep Disturbance SF 8A survey. Anxiety, stress, pain, and well-being were assessed using validated measures at baseline and weekly. A linear mixed-effects regression model was used to assess the change in health outcome score between active product groups and the placebo. There was a significant difference in sleep disturbance, anxiety, stress, and well-being between Sleep A and placebo. There was no significant difference in any health parameter between Sleep B and placebo. Side effects were mild or moderate. There were no significant differences in the frequency of side effects between the study groups. A botanical blend containing a low concentration of THC improved sleep disturbance, anxiety, stress, and well-being in healthy individuals that reported better sleep as a primary health concern.
... Randomized controlled trials (RCTs) are considered as the gold standard for evaluation of drugs. With the advancement of drug development concepts and technologies, limitations of RCTs have become increasingly recognized; for example, the sample size is relatively small, the research environment is idealized, and the cost is relatively high [1]. Realworld data (RWD) and real-world evidence (RWE) come from clinical/medical environments, with a wide range of data sources and a large sample size, which can serve as an important supplement to RCTs. ...
Real-world data (RWD) and real-world evidence (RWE) have garnered great interest for supporting drug research and development (R&D) by medical researchers and regulators in recent years. The application and development of RWD/E in drug regulatory decision-making have been vigorously promoted in China. This study seeks to provide a broad overview of how RWE has been contributing to drug regulatory decisions in China. In this paper, we review the development of RWD and RWE, summarize key elements that promote application of RWE, introduce relevant methods and guidelines, elaborate on the opportunities and challenges of RWE in regulatory decision-making in China, and put forward suggestions to promote the application of RWE in China's regulatory decision-making and to further facilitate innovative drug evaluation and regulation.
... Due to stringent inclusion criteria, among other factors, clinical trials have poor external validity and may not be suitable for effectiveness research, highlighting the importance of complementary real-world data [23,24]. A post-marketing study of all adverse events received by Takeda after drug approval in 2014 were held on the Vedolizumab Global Safety Database and later analyzed [25]. ...
Introduction:
Vedolizumab (Entyvio) is a humanized monoclonal antibody that disrupts the interaction between α4β7 integrin on circulating T-lymphocytes and MAdCAM-1 on the vascular endothelium to prevent their egress to sites of gut inflammation. It has proven therapeutic efficacy for the treatment of moderate-to-severe Crohn's disease, ulcerative colitis, and pouchitis.
Areas covered:
This narrative review assesses the safety profile of vedolizumab from the registration trial programs, open-label extension studies, observational real-world data, and pooled safety analyses. This includes an evaluation of the long-term overall safety in special populations typically underrepresented in clinical trials.
Expert opinion:
Vedolizumab is an effective therapy for inflammatory bowel disease with a well-established safety profile. No unexpected long-term safety signals have been identified. Safety data in pregnancy, in pediatric and elderly populations, in patients undergoing surgery, and in patients with a prior history of cancer are reassuring. Due to its safety merits, we propose that vedolizumab is an excellent candidate for advanced combination treatment with an anti-cytokine approach using another biologic or novel small molecule inhibitor. This is important in patients with medically refractory IBD, in patients at high risk of developing disease-related complications, or in patients with concomitant uncontrolled immune-mediated inflammatory diseases.
... Given the lower risk of acute and chronic GVHD in pediatric patients [9], the translatability of the ABA2 results to this population would benefit from a focused analysis. Furthermore, patients treated on clinical trials may benefit from selection bias that can lead to improved outcomes compared to patients treated off study [10,11]. The objective of this analysis was to explore the real-world impact of abatacept + CNI/MTX in pediatric patients not enrolled on ABA2. ...
... Participation of patients in randomized clinical trials is limited (< 5%) due to strict inclusion criteria [13]. Additionally, patients in clinical trials usually have a better functional status and less comorbidities compared to all patients in daily practice, which could lead to inferior outcomes in a real-world context [14,15]. Thus, the impact of systemic therapy on HRQoL could differ for patients in daily practice compared to patients in clinical trials. ...
Purpose
To investigate the effect of systemic therapy on health-related quality of life (HRQoL) in patients with advanced esophagogastric cancer in daily clinical practice. This study assessed the HRQoL of patients with esophagogastric cancer during first-line systemic therapy, at disease progression, and after progression in a real-world context.
Methods
Patients with advanced esophagogastric cancer (2014–2021) receiving first-line systemic therapy registered in the Prospective Observational Cohort Study of Oesophageal-gastric cancer (POCOP) were included (n = 335). HRQoL was measured with the EORTC QLQ-C30 and QLQ-OG25. Outcomes of mixed-effects models were presented as adjusted mean changes.
Results
Results of the mixed-effect models showed the largest significant improvements during systemic therapy for odynophagia (− 18.9, p < 0.001), anxiety (− 18.7, p < 0.001), and dysphagia (− 13.8, p < 0.001) compared to baseline. After progression, global health status (− 6.3, p = 0.002) and cognitive (− 6.2, p = 0.001) and social functioning (− 9.7, p < 0.001) significantly worsened. At and after progression, physical (− 9.0, p < 0.001 and − 8.8, p < 0.001) and role functioning (− 15.2, p = 0.003 and − 14.7, p < 0.001) worsened, respectively. Trouble with taste worsened during systemic therapy (11.5, p < 0.001), at progression (12.0, p = 0.004), and after progression (15.3, p < 0.001).
Conclusion
In general, HRQoL outcomes in patients with advanced esophagogastric cancer improved during first-line therapy. Deterioration in outcomes was mainly observed at and after progression.
Implications for cancer survivors
Identification of HRQoL aspects is important in shared decision-making and to inform patients on the impact of systemic therapy on their HRQoL.
... Real-world data (RWD) refer to information regarding the health status of patients and the provision of health care, which is routinely gathered from diverse sources [25]. A real-world study (RWS) entails the collection of RWD within an actual environment, analyzing them to obtain real-world evidence regarding the practical value and potential advantages or risks associated with medical products [26]. ...
Background:
eSources consist of data that were initially documented in an electronic structure. Typically, an eSource encompasses the direct acquisition, compilation, and retention of electronic information (such as electronic health records [EHRs] or wearable devices), which serves to streamline clinical research. eSources have the potential to enhance the accuracy of data, promote patient safety, and minimize expenses associated with clinical trials. An opinion study published in September 2020 by TransCelerate outlined a road map for the future application of eSource technology and identified 5 key areas of challenges. The background of this study concerns the use of eSource technology in clinical research.
Objective:
The aim of this study was to present challenges and possible solutions for the implementation of eSource technology in real-world studies by summarizing team experiences and lessons learned from an eSource record (ESR) project.
Methods:
After initially developing a simple prototype of the ESR software that can be demonstrated systematically, the researchers conducted in-depth interviews and interacted with different stakeholders to obtain guidance and suggestions. The researchers selected 5 different roles for interviewees: regulatory authorities, pharmaceutical company representatives, hospital information department employees, medical system providers, and clinicians.
Results:
After screening all consultants, the researchers concluded that there were 25 representative consultants. The hospital information department needs to implement many demands from various stakeholders, which makes the existing EHR system unable to meet all the demands of eSources. The emergence of an ESR is intended to divert the burden of the hospital information department from the enormous functional requirements of the outdated EHR system. The entire research process emphasizes multidisciplinary and multibackground expert opinions and considers the complexity of the knowledge backgrounds of personnel involved in the chain of clinical source data collection, processing, quality control, and application in real-world scenarios. To increase the readability of the results, the researchers classified the main results in accordance with the paragraph titles in "Use of Electronic Health Record Data in Clinical Investigations," a guide released by the US Food and Drug Administration.
Conclusions:
This study introduces the requirement dependencies of different stakeholders and the challenges and recommendations for designing ESR software when implementing eSource technology in China. Experiences based on ESR projects will provide new insights into the disciplines that advance the eSource research field. Future studies should engage patients directly to understand their experiences, concerns, and preferences regarding the implementation of eSource technology. Moreover, involving additional stakeholders, including community health care providers and social workers, will provide valuable insights into the challenges and potential solutions across various health care settings.
... The term "real world data" refers to information on health care that is derived from multiple sources outside typical clinical research settings, including electronic health records (EHRs), claims and billing data, disease registries, and data gathered through surveys, personal devices and health applications. 4 Studies based on real world data reveal how COPD is managed, and how effective pharmacological and non-pharmacological therapies are when used in routine practice. They can also examine different patterns of care within populations by geography or over time and be used to make inferences about the impact of those care patterns on patient and disease outcomes. ...
Real world data comprise information on health care that is derived from multiple sources outside typical clinical research settings. This review focuses on what real world evidence tells us about problems with the diagnosis of chronic obstructive pulmonary disease (COPD), problems with the initial and follow-up pharmacological and non-pharmacological management, problems with the management of exacerbations and problems with palliative care. Data from real world studies show errors in the management of COPD with delays to diagnosis, lack of confirmation of the diagnosis with spirometry, lack of holistic assessment, lack of attention to smoking cessation, variable adherence to management guidelines, delayed implementation of appropriate interventions, under-recognition of patients at higher risk of adverse outcomes, high hospitalisation rates for exacerbations and poor implementation of palliative care. Understanding that these problems exist and considering how and why they occur is fundamental to developing solutions to improve the diagnosis and management of patients with COPD.
... [52] The reasons associated with the higher tube failure rate in TVT IRIS, such as reoperation for glaucoma (not seen in TVT RCT), were also analyzed, providing insight into patient groups that may consider one surgical option over the other given the potential benefits and adverse events. By serving as real-world evidence, [54] big data-based emulation may guide glaucoma treatment decision-making by confirming the efficacy and safety of treatment options in a clinic population and clarifying the differences between RCT settings and real-world practice patterns, as well as informing us about how these differences may affect treatment outcomes. ...
Ophthalmology has been at the forefront of the medical application of big data. Often harnessed with a machine learning approach, big data has demonstrated potential to transform ophthalmic care, as evidenced by prior success on clinical tasks such as the screening of ophthalmic diseases and lesions via retinal images. With the recent establishment of various large ophthalmic datasets, there has been greater interest in determining whether the benefits of big data may extend to the downstream process of ophthalmic disease management. An area of substantial investigation has been the use of big data to help guide or streamline management of glaucoma, which remains a leading cause of irreversible blindness worldwide. In this review, we summarize relevant studies utilizing big data and discuss the application of the findings in the risk assessment and treatment of glaucoma.
... Hence, there are gaps in the accuracy, completeness, and quality of Real-World data. 18,19,20 The challenges with RWE can be categorized broadly in legal, regulatory, technical and acceptability of data/inferences. Legal and regulatory issues relate to data quality, safety, ownership, and access to data. ...
There is a growing need for broader information on Real-World Effectiveness and safety of any new intervention, service or protocol vs data limited by standardized and strictly controlled environment like in a RCT. RWE studies gives the freedom for analysis based on a varied and diverse database. As Real-World Studies gain higher acceptance, it is important to understand the types of RWE studies and design that can be used. Data from real-world patient experience has the potential to improve the quality and delivery of medical care, impact overall costs and outcomes. This review helps understand study designs, issues, and its implications in improving medical services. Though the RWE is challenged by diversity of information, large data sets of uncertain quality, and methodological rigor, however if utilized properly has potential to shape policies, protocols and develop programs to implement best practices.RWE has many issues including Legal, technological, data privacy, transparency, and standardization. These challenges can be and necessarily need to be addressed while planning the RWE which would then vastly enhance the acceptance of the evidence generated by RWE. Several researchers, professional societies, government agencies and multi-stakeholder initiatives worldwide have disseminated guidance, framework, and standards in this aspect which can address gaps in data standardization and improve the quality of RWD
... The limited follow-up duration and newly diagnosed CVD cases in these clinical trials might have resulted in inadequate statistical power to detect significant effects. Moreover, the designated patients and well-controlled circumstances also limited the generalization of the findings of these trials to larger and more inclusive populations [11]. CHD is the most common disease in CVD among the general population and in patients with diabetes [2]. ...
This study aimed to explore the association between habitual intake of fish oil supplementation and the risk of developing CHD in patients with prediabetes and diabetes. Habitual use of fish oil was assessed by repeated questionnaires. Cox proportional hazard models were applied to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). Over a median follow-up of 11.6 years, 4304 and 3294 CHD cases were documented among 47,663 individuals with prediabetes and 22,146 patients with diabetes in the UK Biobank, respectively. After multivariable adjustment, the HRs (95% CI) of CHD were 0.91 (0.85-0.98) and 0.87 (0.80-0.95) for individuals utilizing fish oil supplementation compared with non-users among the participants with prediabetes and diabetes, respectively. Furthermore, we identified an inverse relationship between fish oil use and CHD incidence, which was significantly mediated by serum C-reactive protein (CRP) levels in individuals with prediabetes and by very-low-density lipoprotein cholesterol (VLDL-C) in patients with diabetes at baseline. The inverse associations were consistent in the analyses stratified by potential confounders. In conclusion, the consumption of fish oil supplements was linked to decreased serum CRP and VLDL-C levels and subsequent CHD risk among adults with prediabetes and diabetes. Our findings highlight the important role of the habitual intake of fish oil supplements in preventing CHD in individuals with impaired glucose metabolism.
... 3 There is growing interest in using evidence from non-randomised studies of interventions (NRSIs)-including quasi-randomised controlled trials (quasi-RCTs), nonrandomised controlled trials (non-RCTs), cohort studies and case-control studies-to assess the effectiveness of health interventions. 4 A potential advantage of NRSI is that enrolled patients may be more representative ...
Introduction
Although interest in including non-randomised studies of interventions (NRSIs) in meta-analysis of randomised controlled trials (RCTs) is growing, estimates of effectiveness obtained from NRSIs are vulnerable to greater bias than RCTs. The objectives of this study are to: (1) explore how NRSIs can be integrated into a meta-analysis of RCTs; (2) assess concordance of the evidence from non-randomised and randomised trials and explore factors associated with agreement; and (3) investigate the impact on estimates of pooled bodies of evidence when NRSIs are included.
Methods and analysis
We will conduct a systematic survey of 210 systematic reviews that include both RCTs and NRSIs, published from 2017 to 2022. We will randomly select reviews, stratified in a 1:1 ratio by Core vs non-Core clinical journals, as defined by the National Library of Medicine. Teams of paired reviewers will independently determine eligibility and abstract data using standardised, pilot-tested forms. The concordance of the evidence will be assessed by exploring agreement in the relative effect reported by NRSIs and RCT addressing the same clinical question, defined as similarity of the population, intervention/exposure, control and outcomes. We will conduct univariable and multivariable logistic regression analyses to examine the association of prespecified study characteristics with agreement in the estimates between NRSIs and RCTs. We will calculate the ratio of the relative effect estimate from NRSIs over that from RCTs, along with the corresponding 95% CI. We will use a bias-corrected meta-analysis model to investigate the influence on pooled estimates when NRSIs are included in the evidence synthesis.
Ethics and dissemination
Ethics approval is not required. The findings of this study will be disseminated through peer-reviewed publications, conference presentations and condensed summaries for clinicians, health policymakers and guideline developers regarding the design, conduct, analysis, and interpretation of meta-analysis that integrate RCTs and NRSIs.
... Therefore, it can provide scientific information on how factors such as clinical setting influence the treatment effects and outcomes for decisionmaking in clinical practice. 28 As far as we know, there are only a few studies investigated the mid-term outcomes of AKI in elderly COVID 19 patients. Zhang and colleagues reported that most of the new-set AKI during hospitalization of elderly COVID-19 patients (with a median age of 64 years) recovered 4 months after discharge (130 of 143). ...
Background:
Since little is known about the acute kidney injury (AKI) in aging population infected with SARS-CoV-2 Omicron variant, we investigated the incidence, clinical features, risk factors and mid-term outcomes of AKI in hospitalized geriatric patients with and without COVID-19 and established a prediction model for mortality.
Methods:
A real-time data from the Shanghai Ninth People's Hospital information system of inpatients with COVID-19 from 1 April 2022 to 30 June 2022 were extracted. Clinical spectrum, laboratory results, and clinical prognosis were included for the risk analyses. Moreover, Cox and Lasso regression analyses were applied to predict the 90-day death and a nomogram was established.
Results:
A total of 1607 SARS-CoV-2 infected patients were enrolled; hypertension was the most common comorbidity, followed by chronic cardiovascular disease, diabetes mellitus, and lung disease. Most of the participants were non-vaccinated and the mean age of patients was 82.6 years old (range, 60-103 years). The AKI incidence was higher in relatively older patients (16.29% vs 3.63% in patients older than 80 years and 60 to 80 years, respectively). Linear regression models identified some variables associated with the incidence of AKI, such as older age, clinical spectrum, D-dimer level, number of comorbidities, baseline eGFR, and antibiotic or corticosteroid treatment. In this cohort, 11 patients died in-hospital and 21 patients died at 90-day follow-up. The predictive nomogram of 90-day death achieved a good C-index of 0.823 by using 5 predictor variables: ICU admission, D-dimer, peak of serum creatinine, rate of serum creatinine decline and white blood cell count (WBC).
Conclusion:
Older age, clinical spectrum, D-dimer level, number of comorbidities, baseline eGFR, and antibiotic or corticosteroid treatment are clinical risk factors for the incidence of AKI in geriatric COVID-19 patients. The prediction nomogram achieved an excellent performance at the prediction of 90-day mortality.
Though electronic health record (EHR) systems are a rich repository of clinical information with large potential, the use of EHR-based phenotyping algorithms is often hindered by inaccurate diagnostic records, the presence of many irrelevant features, and the requirement for a human-labeled training set. In this paper, we describe a knowledge-driven online multimodal automated phenotyping (KOMAP) system that i) generates a list of informative features by an online narrative and codified feature search engine (ONCE) and ii) enables the training of a multimodal phenotyping algorithm based on summary data. Powered by composite knowledge from multiple EHR sources, online article corpora, and a large language model, features selected by ONCE show high concordance with the state-of-the-art AI models (GPT4 and ChatGPT) and encourage large-scale phenotyping by providing a smaller but highly relevant feature set. Validation of the KOMAP system across four healthcare centers suggests that it can generate efficient phenotyping algorithms with robust performance. Compared to other methods requiring patient-level inputs and gold-standard labels, the fully online KOMAP provides a significant opportunity to enable multi-center collaboration.
CAD is an emerging field, but most models are not equipped to handle missing and noisy data in real-world medical scenarios, particularly in the case of rare tumors like pancreatic neuroendocrine neoplasms (pNENs). Multi-label models meet the needs of real-world study, but current methods do not consider the issue of missing and noisy labels. This study introduces a multi-label model called Self-feedback Transformer (SFT) that utilizes a transformer to model the relationships between labels and images, and uses a ingenious self-feedback strategy to improve label utilization. We evaluated SFT on 11 clinical tasks using a real-world dataset of pNENs and achieved higher performance than other state-of-the-art multi-label models with mAUCs of 0.68 and 0.76 on internal and external datasets, respectively. Our model has four inference modes that utilize self-feedback and expert assistance to further increase mAUCs to 0.72 and 0.82 on internal and external datasets, respectively, while maintaining good performance even with input label noise ratios up to 40% in expert-assisted mode.
Objective
The primary objective of this review is to examine which disease-modifying antirheumatic drugs (DMARDs) and biologics used to treat pregnant individuals with rheumatic conditions have been reported in observational studies using population-based health administrative data. The secondary objective is to describe which adverse pregnancy outcomes (both maternal and neonatal) have been reported, their definitions, and corresponding diagnostic and/or procedural codes.
Introduction
Pregnant individuals are typically excluded from drug trials due to unknown potential risks to both mother and fetus, leaving most antirheumatic drugs understudied for use in pregnancy. Despite these substantial knowledge gaps, most pregnant individuals continue to be maintained on antirheumatic medications due to benefits generally outweighing risks. In contrast to previous systematic reviews of findings from randomized trials, our scoping review aims to leverage this real-world data to generate real-world evidence on antirheumatic drug safety during pregnancy.
Inclusion criteria
Articles must report on observational studies using population-based health administrative data from pregnant individuals with rheumatic conditions (rheumatoid arthritis, systemic lupus erythematosus, ankylosing spondylitis, and psoriatic arthritis) receiving antirheumatic drug therapy (DMARDs and biologics). Randomized trials, reviews, case studies, opinion pieces, and abstracts will be excluded.
Methods
Electronic databases (MEDLINE (Ovid), Embase (Ovid), CINAHL (EBSCOhost)) and gray literature (OpenGrey, Health Services Research Projects in Progress, World Health Organization Library, and Google Scholar) will be searched for relevant evidence. Search terms will combine 4 concepts: rheumatic diseases, drug therapy, pregnancy, and health care administrative data. Identified articles will be independently screened, selected, and extracted by 2 researchers. Data will be analyzed descriptively and presented in tables.
Details of this review are available at Open Science Framework
https://osf.io/5e6tp
Judicious interpretation of survival analyses is imperative for oncologists to appropriately use RWD to guide clinical care. Attempting to identify the cause of data missingness, calculating survival months using decimals instead of integers, and using statistical methods to minimize guarantee-time bias can guard against common pitfalls when analyzing RWD, allowing for more accurate and clinically meaningful data interpretation.
Objective
Pharmacotherapy is the most common strategies to reduce pain for osteoarthritis (OA) patients. To analyze the trend and pattern of prescription analgesic medication use in American OA patients. Besides, our study also tried to figure out the demographic characteristics of opioid use among OA population which may helpful for managing the use of opioids.
Methods
We included 2214 OA patients from 2007 to 2018. We extracted data from National Health and Nutrition Examination Survey (NHANES) database. We compared analgesics and anti-depression medications use by category between survey participants with OA and without.
Results
For OA patients, NSAIDs, acetaminophen and gabapentinoid were the mostly highly used analgesics (10.2%, 9.0% and 8.9%, respectively). However, we also found that opioids use was very common in OA patients (7.7%) and the duration of opioids use was significantly long. In addition, the opioids use did not decrease from 2007 to 2018, while gabapentinoid increased rapidly from recent decade (From 5.0% to 12.1%). The common analgesic combination used by OA population was opioids with acetaminophen and gabapentinoid with selective serotonin reuptake inhibitors (SSRIs) (2.9% and 2.7%, respectively).
Conclusion
The use of gabapentinoid increased rapidly from recent decade, while opioids use did not decrease. The long-term excessive use of opioids was also a serious problem for OA pain control. More improvements such as focusing more on healthcare education and paying more attention on non-pharmacotherapy and the psychological situation of patients are needed.
Introduction
N‐acetylcysteine (NAC) augmentation of antipsychotic medication has been studied in psychotic disorders but the results are inconsistent. This meta‐analysis aimed to evaluate the efficacy and acceptability of NAC as an augmentation strategy for psychotic disorders.
Methods
PubMed, Web of Science, EMBASE, PsycINFO, Cochrane Library, and ClinicalTrials.gov were searched until the date of November 28, 2022. The inclusion criteria were randomized controlled trials (RCTs) comparing NAC and placebo in patients with psychotic disorders. The outcomes were the psychotic symptoms measured by the Positive and Negative Syndrome Scale (PANSS) and drop‐out rates.
Results
A total of 594 patients from eight trials were included. The results showed that no difference was found in score changes of PANSS total, positive, negative, or general psychopathology scale scores between the NAC group and placebo group in both time points (≤24 weeks and >24 weeks). There was also no statistical difference in drop‐out rates between the two groups.
Conclusion
For the moment, it is not appropriate to recommend NAC as an augmentation of antipsychotic medication to treat psychotic disorders in routine clinical practice.
Ocrelizumab is a B cell-depleting drug widely used in relapsing-remitting multiple sclerosis (RRMS) and primary-progressive MS. In RRMS, it is becoming increasingly apparent that accumulation of disability not only manifests as relapse-associated worsening (RAW) but also as progression independent of relapse activity (PIRA) throughout the disease course. This study's objective was to investigate the role of PIRA in RRMS patients treated with ocrelizumab. We performed a single-center, retrospective, cross-sectional study of clinical data acquired at a German tertiary multiple sclerosis referral center from 2018 to 2022. All patients with RRMS treated with ocrelizumab for at least six months and complete datasets were analyzed. Confirmed disability accumulation (CDA) was defined as a ≥ 12-week confirmed increase from the previous expanded disability status scale (EDSS) score of ≥ 1.0 if the previous EDSS was ≤ 5.5 or a ≥ 0.5-point increase if the previous EDSS was > 5.5. PIRA was defined as CDA without relapse since the last EDSS measurement and at least for the preceding 12 weeks. RAW was defined as CDA in an interval of EDSS measurements with ≥ 1 relapses. Cox proportional hazard models were used to analyze the probability of developing PIRA depending on various factors, including disease duration, previous disease-modifying treatments (DMTs), and optical coherence tomography-assessed retinal degeneration parameters. 97 patients were included in the analysis. Mean follow-up time was 29 months (range 6 to 51 months). 23.5% developed CDA under ocrelizumab therapy (n = 23). Of those, the majority developed PIRA (87.0% of CDA, n = 20) rather than RAW (13.0% of CDA, n = 3). An exploratory investigation using Cox proportional hazards ratios revealed two possible factors associated with an increased probability of developing PIRA: a shorter disease duration prior to ocrelizumab (p = 0.02) and a lower number of previous DMTs prior to ocrelizumab (p = 0.04). Our data show that in ocrelizumab-treated RRMS patients, the main driver of disability accumulation is PIRA rather than RAW. Furthermore, these real-world data show remarkable consistency with data from phase 3 randomized controlled trials of ocrelizumab in RRMS, which may increase confidence in translating results from tightly controlled RCTs into the real-world clinical setting.
The chapter will describe epidemiological indicators adopted to evaluate the burden of disease, to estimate a risk related to an exposure, and to assess health status changes. Moreover, a few elements of epidemiological reasoning (bias, confounding, study designs) and a pragmatic framework of statistical reasoning will be presented. It will be described the role played by an appropriate methodology driven by a specific research question, the role of the study designs to address knowledge gaps and confounders.KeywordsEpidemiologyStatistical analysesEpidemiological indicatorsStudy designResearch study
Background: Racial and ethnic minority groups and individuals facing social disadvantages, which often stem from their social determinants of health (SDoH), bear a disproportionate burden of type 2 diabetes (T2D) and its complications. It is therefore crucial to implement effective social risk management strategies at the point of care. Objective: To develop an EHR-based machine learning (ML) analytical pipeline to identify the unmet social needs associated with hospitalization risk in patients with T2D. Methods: We identified 10,192 T2D patients from the EHR data (from 2012 to 2022) from the University of Florida Health Integrated Data Repository, including contextual SDoH (e.g., neighborhood deprivation) and individual-level SDoH (e.g., housing stability). We developed an electronic health records (EHR)-based machine learning (ML) analytic pipeline, namely individualized polysocial risk score (iPsRS), to identify high social risk associated with hospitalizations in T2D patients, along with explainable AI (XAI) techniques and fairness assessment and optimization. Results: Our iPsRS achieved a C statistic of 0.72 in predicting 1-year hospitalization after fairness optimization across racial-ethnic groups. The iPsRS showed excellent utility for capturing individuals at high hospitalization risk; the actual 1-year hospitalization rate in the top 5% of iPsRS was ~13 times as high as the bottom decile. Conclusion: Our ML pipeline iPsRS can fairly and accurately screen for patients who have increased social risk leading to hospitalization in T2D patients.
Purpose
To evaluate the positive predictive value (PPV) of an endometrial cancer case finding algorithm using International Classification of Disease 10th revision Clinical Modification (ICD‐10‐CM) diagnosis codes from US insurance claims for implementation in a planned post‐marketing safety study. Two algorithm variants were evaluated.
Methods
Provisional incident endometrial cancer cases were identified from 2016 through 2020 among women aged ≥50 years. One algorithm variant used diagnosis codes for malignant neoplasms of uterine sites (C54.x), excluding C54.2 (malignant neoplasm of myometrium); the other used only C54.1 (malignant neoplasm of endometrium). A random sample of medical records of recent incident provisional cases (2018–2020) was requested for adjudication. Confirmed cases showed biopsy evidence of endometrial cancer, documentation of cancer staging, or hysterectomy following diagnosis. We estimated the PPV of the variants with 95% confidence intervals (CI) excluding cases that had insufficient information.
Results
Of 294 provisional cases adjudicated, 85% were from outpatient settings ( n = 249). Mean age at diagnosis was 69.3 years. Among the 294 adjudicated cases (identified with the broader algorithm variant), the same 223 were confirmed endometrial cancer cases by both algorithm variants. The PPV (95% CI) for the broader algorithm variant was 84.2% (79.2% and 88.3%), and for the variant using only C54.1 was 85.8% (80.9% and 89.8%).
Conclusion
We developed and validated an algorithm using ICD‐10‐CM diagnosis codes to identify endometrial cancer cases in health insurance claims with a sufficiently high PPV to use in a planned post‐marketing safety study.
Objective or purpose:
Vitreoretinal lymphoma is a malignancy with high mortality. Incidence is rare, and there is a lack of medical evidence to direct management. This work describes presentation, diagnostic testing and first treatment approaches in a recently diagnosed and treated patient cohort.
Design:
Clinical registry-based observational study.
Subjects:
48 women and 32 men (32 to 91 years, median of 64 years) diagnosed with vitreoretinal lymphoma.
Methods:
An international network of ophthalmologists reported clinical features and management of patients presenting with vitreoretinal lymphoma between January 1, 2020 and December 31, 2022 via an electronic platform.
Main outcome measures:
Visual acuity at presentation (LogMAR); basis for diagnosis; first treatment.
Results:
Vitreoretinal lymphoma was bilateral at presentation in 65% of patients (N = 52) and an initial site of lymphoma in 78% (N = 62). In 127 eyes with lymphoma at presentation, vitreous was involved in 89% (N = 113) and the only involved eye tissue in 40% (N = 51), and retina was involved in 46% (N = 58) and the only involved eye tissue in 9% (N = 11). Median LogMAR visual acuity of the worse-seeing eye was 0.50. The lymphoma was diagnosed from ocular specimens in 80% (N = 64), usually vitreous (N = 57, 89% of 64 patients), and on other clinical information in 20% (N = 16). Cellular studies were performed in 92% (59 of 64 patients), most often cytology. Tumor gene analysis was used in 21 patients (33% of 64) and cytokine assays were used in 13 patients (20% of 64). For 76 patients (95%), treatment was initiated within 6 months of diagnosis and included ocular (48%, N = 38 of 76 patients), extraocular (21%, N = 17 of 76), and ocular plus extraocular (26%, N = 21 of 76) approaches. Intravitreal methotrexate was the most common ocular treatment (N = 83 of 87 eyes, 95%).
Conclusions:
Using data collected from 80 patients diagnosed with vitreoretinal lymphoma since 2020, we show that visual impairment is common, and that management commonly involves diagnosis by cellular tests and treatment with intravitreal chemotherapy.
Purpose:
To describe one-year secondary outcomes in the Tube Versus Trabeculectomy IRIS® (Intelligent Registry In Sight) Registry Study (TVTIRIS), and compare to the TVT randomized controlled trial (TVTRCT).
Design:
TVTIRIS was a retrospective cohort study.
Methods:
The 2013-2017 IRIS Registry was used to identify eyes that received a tube shunt (tube) or trabeculectomy after a previous trabeculectomy and/or cataract surgery, and had one-year of follow-up. The TVTRCT compared a Baerveldt 350-mm2 glaucoma implant to trabeculectomy in similar eyes.
Results:
In the TVTIRIS cohort, the tube (N=236, 56.3%) and trabeculectomy (N=183, 43.7%) groups had similar and significant reductions in IOP from baseline to one year. In the tube group, IOP (mean±SD) decreased from 26.6±6.5 mmHg at baseline to 14.3±4.8 mmHg at one year. In the trabeculectomy group, IOP decreased from 25.3±6.4 mmHg at baseline to 13.5±5.2 mmHg at one year. The trabeculectomy groups from both studies had similar one-year IOP reduction (P=0.18), though the TVTRCT cohort used fewer medications at all time points (P<0.01). There were more pronounced differences in the mean IOP and medications between the tube groups in the two studies, presumably due to the inclusion of valved tubes in TVTIRIS. More reoperations occurred in TVTIRIS.
Conclusion:
The TVTIRIS tube and trabeculectomy groups had comparable one-year IOP reduction, though trabeculectomy eyes used fewer glaucoma medications. The trabeculectomy group in TVTIRIS and TVTRCT had similar IOP and medication reduction at one year. RCTs and electronic health record data both provide invaluable insight into surgical outcomes.
Objective:
Prediction of physiological mechanics are important in medical practice because interventions are guided by predicted impacts of interventions. But prediction is difficult in medicine because medicine is complex and difficult to understand from data alone, and the data are sparse relative to the complexity of the generating processes. Computational methods can increase prediction accuracy, but prediction with clinical data is difficult because the data are sparse, noisy and nonstationary. This paper focuses on predicting physiological processes given sparse, non-stationary, electronic health record data in the intensive care unit using data assimilation (DA), a broad collection of methods that pair mechanistic models with inference methods.
Methods:
A methodological pipeline embedding a glucose-insulin model into a new DA framework, the constrained ensemble Kalman filter (CEnKF) to forecast blood glucose was developed. The data include tube-fed patients whose nutrition, blood glucose, administered insulins and medications were extracted by hand due to their complexity and to ensure accuracy. The model was estimated using an individual's data as if they arrived in real-time, and the estimated model was run forward producing a forecast. Both constrained and unconstrained ensemble Kalman filters were estimated to compare the impact of constraints. Constraint boundaries, model parameter sets estimated, and data used to estimate the models were varied to investigate their influence on forecasting accuracy. Forecasting accuracy was evaluated according to mean squared error between the model-forecasted glucose and the measurements and by comparing distributions of measured glucose and forecast ensemble means.
Results:
The novel CEnKF produced substantial gains in robustness and accuracy while minimizing the data requirements compared to the unconstrained ensemble Kalman filters. Administered insulin and tube-nutrition were important for accurate forecasting, but including glucose in IV medication delivery did not increase forecast accuracy. Model flexibility, controlled by constraint boundaries and estimated parameters, did influence forecasting accuracy.
Conclusion:
Accurate and robust physiological forecasting with sparse clinical data is possible with DA. Introducing constrained inference, particularly on unmeasured states and parameters, reduced forecast error and data requirements. The results are not particularly sensitive to model flexibility such as constraint boundaries, but over or under constraining increased forecasting errors.
Real-world evidence (RWE) plays an important role in the management of type 2 diabetes (T2D). It provides data about the effectiveness and safety of an intervention from outside the randomised controlled trial (RCT) setting and allows healthcare professionals (HCPs) to determine if RCT data are applicable to their patients in routine clinical practice. This review provides a discussion of the value of RWE in T2D management in day-to-day clinical practice, with a focus on RWE with sulfonylureas (SUs), and presents two examples of a new generation of international real-world studies in people with T2D managed in routine clinical practice. RWE plays a valuable role in advising HCPs in the day-to-day management of T2D, informing regulatory authorities with regard to pharmacovigilance and post-approval updates, and providing insights with regard to patients' treatment adherence and preference. RWE should be used alongside RCTs to increase HCP awareness and understanding of their patients' perspectives, potentially allowing for improvements in treatment adherence, glycaemic control and health-related quality of life (HRQoL). In addition, real-world studies must be conducted in a way that generates robust RWE by limiting the risks of bias and confounding as much as possible. A growing body of RWE is emerging from Asia. For example, in a preliminary HRQoL analysis of the Joint Asia Diabetes Evaluation (JADE) Register, Asian people with T2D had better HRQoL with gliclazide-based treatment than with other SU agents, despite being older and having more diabetes-related complications.
Background/aims:
There has been growing interest in better understanding the potential of observational research methods in medical product evaluation and regulatory decision-making. Previously, we used linked claims and electronic health record data to emulate two ongoing randomized controlled trials, characterizing the populations and results of each randomized controlled trial prior to publication of its results. Here, our objective was to compare the populations and results from the emulated trials with those of the now-published randomized controlled trials.
Methods:
This study compared participants' demographic and clinical characteristics and study results between the emulated trials, which used structured data from OptumLabs Data Warehouse, and the published PRONOUNCE and GRADE trials. First, we examined the feasibility of implementing the baseline participant characteristics included in the published PRONOUNCE and GRADE trials' using real-world data and classified each variable as ascertainable, partially ascertainable, or not ascertainable. Second, we compared the emulated trials and published randomized controlled trials for baseline patient characteristics (concordance determined using standardized mean differences <0.20) and results of the primary and secondary endpoints (concordance determined by direction of effect estimates and statistical significance).
Results:
The PRONOUNCE trial enrolled 544 participants, and the emulated trial included 2226 propensity score-matched participants. In the PRONOUNCE trial publication, one of the 32 baseline participant characteristics was listed as an exclusion criterion on ClinicalTrials.gov but was ultimately not used. Among the remaining 31 characteristics, 9 (29.0%) were ascertainable, 11 (35.5%) were partially ascertainable, and 10 (32.2%) were not ascertainable using structured data from OptumLabs. For one additional variable, the PRONOUNCE trial did not provide sufficient detail to allow its ascertainment. Of the nine variables that were ascertainable, values in the emulated trial and published randomized controlled trial were discordant for 6 (66.7%). The primary endpoint of time from randomization to the first major adverse cardiovascular event and secondary endpoints of nonfatal myocardial infarction and stroke were concordant between the emulated trial and published randomized controlled trial. The GRADE trial enrolled 5047 participants, and the emulated trial included 7540 participants. In the GRADE trial publication, 8 of 34 (23.5%) baseline participant characteristics were ascertainable, 14 (41.2%) were partially ascertainable, and 11 (32.4%) were not ascertainable using structured data from OptumLabs. For one variable, the GRADE trial did not provide sufficient detail to allow for ascertainment. Of the eight variables that were ascertainable, values in the emulated trial and published randomized controlled trial were discordant for 4 (50.0%). The primary endpoint of time to hemoglobin A1c ≥7.0% was mostly concordant between the emulated trial and the published randomized controlled trial.
Conclusion:
Despite challenges, observational methods and real-world data can be leveraged in certain important situations for a more timely evaluation of drug effectiveness and safety in more diverse and representative patient populations.
Objective:
In-hospital stroke cases occur during hospitalization for another diagnosis and reflect a clinically distinct cohort from community-onset stroke. The objective was to validate the diagnostic accuracy of in-hospital stroke identification in stroke audit data at a large teaching hospital.
Methods:
A retrospective clinical validation of in-hospital stroke diagnoses from two linked data sources was completed for a 2-year period from January 1st 2020 to December 31st 2021. The linked data sources include the Hospital Inpatient Enquiry system which assigns coded stroke diagnoses at discharge and/or the local stroke audit coordinators who work clinically in stroke teams and input additional specific clinical data. Diagnostic sensitivity, specificity and the level of agreement using an unweighted Cohen's Kappa were calculated.
Results:
There were 597 strokes admitted during the 2-year period. The median age was 72 years and 55% occurred in males. In total, 88 cases of in-hospital stroke were clinically validated yielding an in-hospital stroke rate of 15%. The clinical audit coordinator identified in-hospital stroke with higher sensitivity (86%; 95% CI 77%-93%) whereas the coding process was more specific at 96% (95% CI 85% to 99%). Levels of agreement with the clinically validated gold standard sample were moderate for the audit coordinator and coding process with κ = 0.57 and K = 0.42 respectively. When both data sources were combined the level of agreement was substantial (κ = 0.65; p < .000).
Conclusions:
Clinical validation studies are required to reinforce data quality within stroke registers. Combining clinical and administrative data sources improves diagnostic accuracy for in-hospital stroke identification.
With the Covid-19 pandemic, the world has been faced with the pragmatic search for effective treatment and highlighted the importance of real-world evidence and data as valuable for decision-making by different stakeholders. This evidence has brought new insights into efficacy, safety, and quality of drugs with patient-centered clinical outcomes. This paper describes some important elements of real-world evidence and data: 1) they are related to the patient's health status and/or the provision of health care routinely collected from various sources; 2) although, controlled clinical trial results are the basis for clinical decision-making, they can currently incorporate real-world evidence and data; 3) there is increasing use to support regulatory decision-making; 4) are underutilized sources to assess the impact on public health in risk minimization, health technology assessment, costs, and clinical decisions.
While kidney transplantation (KTx) has traditionally required lifelong immunosuppression, an investigational stem cell therapy, FCR001, has been demonstrated to induce tolerance and eliminate the need for immunosuppression through the establishment of persistent mixed chimerism in a phase 2 clinical study. Real-world evidence (RWE) methods were employed to compare the safety and efficacy of non-myeloablative conditioning with FCR001 with standard of care [SOC] immunosuppression in a retrospective single-center analysis of outcomes among propensity score matched living-donor KTx receiving SOC (n = 144) or FCR001 (n = 36). Among the FCR001 recipients, 26 (72%) developed persistent chimerism allowing durable elimination of all immunosuppression. There was no significant difference in the composite primary endpoint (biopsy-proven acute rejection [BPAR], graft loss, or death) at 60 months (FCR001 27.8%, n = 10 and SOC 28.5%, n = 41; p = .9). FCR001 recipients demonstrated superior kidney function at 5 years (estimated glomerular filtration rate [eGFR] [mean ± standard deviation]: 64.1 ± 15.3) compared to SOC (51.7 ± 18.8; p = .02). At 5 years, FCR001 recipients experienced fewer complications including new-onset diabetes post-transplant, although two patients developed graft versus host disease. In conclusion, RWE demonstrated that KTx combined with non-myeloablative conditioning and FCR001 resulting in superior kidney function without increasing the risk of rejection, graft loss, or death among patients off immunosuppression.
Aim: The presence of two or more publications that report on overlapping patient cohorts poses a challenge for quantitatively synthesizing real-world evidence (RWE) studies. Thus, we evaluated eight approaches for handling such related publications in network meta-analyses (NMA) of RWE studies. Methods: Bayesian NMAs were conducted to estimate the annualized relapse rate (ARR) of disease-modifying therapies in multiple sclerosis. The NMA explored the impact of hierarchically selecting one pivotal study from related publications versus including all of them while adjusting for correlations. Results: When selecting one pivotal study from related publications, the ARR ratios were mostly similar regardless of the pivotal study selected. When including all related publications, there were shifts in the point estimates and the statistical significance. Conclusion: An a priori hierarchy should guide the selection among related publications in NMAs of RWE. Sensitivity analyses modifying the hierarchy should be considered for networks with few or small studies.
The electronic health record (EHR) represents a rich source of patient information, increasingly being leveraged for cardiovascular research. Although its primary use remains the seamless delivery of health care, the various longitudinally aggregated structured and unstructured data elements for each patient within the EHR can define the computational phenotypes of disease and care signatures and their association with outcomes. Although structured data elements, such as demographic characteristics, laboratory measurements, problem lists, and medications, are easily extracted, unstructured data are underused. The latter include free text in clinical narratives, documentation of procedures, and reports of imaging and pathology. Rapid scaling up of data storage and rapid innovation in natural language processing and computer vision can power insights from unstructured data streams. However, despite an array of opportunities for research using the EHR, specific expertise is necessary to adequately address confidentiality, accuracy, completeness, and heterogeneity challenges in EHR-based research. These often require methodological innovation and best practices to design and conduct successful research studies. Our review discusses these challenges and their proposed solutions. In addition, we highlight the ongoing innovations in federated learning in the EHR through a greater focus on common data models and discuss ongoing work that defines such an approach to large-scale, multicenter, federated studies. Such parallel improvements in technology and research methods enable innovative care and optimization of patient outcomes.
The gold standard for investigating the efficacy of a new therapy is a (pragmatic) randomized controlled trial (RCT). This approach is costly, time-consuming, and not always practicable. At the same time, huge quantities of available patient-level control condition data in analyzable format of (former) RCTs or real-world data (RWD) are neglected. Therefore, alternative study designs are desirable. The design presented here consists of setting up a prediction model for determining treatment effects under the control condition for future patients. When a new treatment is intended to be tested against a control treatment, a single-arm trial for the new therapy is conducted. The treatment effect is then evaluated by comparing the outcomes of the single-arm trial against the predicted outcomes under the control condition. While there are obvious advantages of this design compared to classical RCTs (increased efficiency, lower cost, alleviating participants' fear of being on control treatment), there are several sources of bias. Our aim is to investigate whether and how such a design-the prediction design-may be used to provide information on treatment effects by leveraging external data sources. For this purpose, we investigated under what assumptions linear prediction models could be used to predict the counterfactual of patients precisely enough to construct a test and an appropriate sample size formula for evaluating the average treatment effect in the population of a new study. A user-friendly R Shiny application (available at: https://web.imbi.uni-heidelberg.de/PredictionDesignR/) facilitates the application of the proposed methods, while a real-world application example illustrates them.
The accuracy and precision of the Instant Blood Pressure app are evaluated amid concerns that individuals may use these apps to assess their blood pressure and titrate therapy.Mobile health (mHealth) technologies include unregulated consumer smartphone apps.1 The Instant Blood Pressure app (IBP; AuraLife) estimates blood pressure (BP) using a technique in which the top edge of the smartphone is placed on the left side of the chest while the individual places his or her right index finger over the smartphone’s camera. Between its release on June 5, 2014, and removal on July 30, 2015 (421 days), the IBP app spent 156 days as one of the top 50 best-selling iPhone apps; at least 950 copies of this $4.99 app were sold on each of those days.2 Validation of this popular app or any of the similar iPhone apps still available (eg, Blood Pressure Pocket, Quick Blood Pressure Measure and Monitor), have not been performed. Using a protocol based on national guidelines,3 we investigated the accuracy and precision of IBP.
Cluster randomized trials randomly assign groups of individuals to examine research questions or test interventions and measure their effects on individuals. Recent emphasis on quality improvement, comparative effectiveness, and learning health systems has prompted expanded use of pragmatic cluster randomized trials in routine health-care settings, which in turn poses practical and ethical challenges that current oversight frameworks may not adequately address. The 2012 Ottawa Statement provides a basis for considering many issues related to pragmatic cluster randomized trials but challenges remain, including some arising from the current US research and health-care regulations. In order to examine the ethical, regulatory, and practical questions facing pragmatic cluster randomized trials in health-care settings, the National Institutes of Health Health Care Systems Research Collaboratory convened a workshop in Bethesda, Maryland, in July 2013. Attendees included experts in clinical trials, patient advocacy, research ethics, and research regulations from academia, industry, the National Institutes of Health Collaboratory, and other federal agencies. Workshop participants identified substantial barriers to implementing these types of cluster randomized trials, including issues related to research design, gatekeepers and governance in health systems, consent, institutional review boards, data monitoring, privacy, and special populations. We describe these barriers and suggest means for understanding and overcoming them to facilitate pragmatic cluster randomized trials in health-care settings.
© The Author(s) 2015.
The BJC is owned by Cancer Research UK, a charity dedicated to understanding the causes, prevention and treatment of cancer and to making sure that the best new treatments reach patients in the clinic as quickly as possible. The journal reflects these aims. It was founded more than fifty years ago and, from the start, its far-sighted mission was to encourage communication of the very best cancer research from laboratories and clinics in all countries. The breadth of its coverage, its editorial independence and it consistent high standards, have made BJC one of the world's premier general cancer journals. Its increasing popularity is reflected by a steadily rising impact factor.
We estimate hedonic price indexes for clinical trial research, an important component of biomedical R&D, using a large sample of agreements between trial sponsors and clinical investigators obtained from MediData Solutions Worldwide Inc. Nominal prices measured as total grant cost per patient rose by a factor of 4.5 between 1989 and 2011, while the NIH Biomedical R&D Price Index (BRDPI) focused on input costs rose only 2.2-fold. Most of the disparity appears to be attributable to changes in the nature and organization of clinical trials: during this period the average number of patients per site fell substantially while “site work effort” more than doubled. After controlling for these changes in the characteristics of investigator agreements using a variety of methods based on hedonic regressions, we find that the estimated rate of inflation in clinical trials costs tracks the BDRPI very closely. Results from this study suggest that it should be feasible for statistical agencies to develop a producer price index for this type of R&D activity, contributing to broader efforts to develop a deflator for contracted R&D services.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Mobile, social, real-time: the ongoing revolution in the way people communicate has given rise to a new kind of epidemiology. Digital data sources, when harnessed appropriately, can provide local and timely information about disease and health dynamics in populations around the world. The rapid, unprecedented increase in the availability of relevant data from various digital sources creates considerable technical and computational challenges.
Federal regulatory frameworks governing medical products are designed to (1) provide evidence that a product benefits patients when used as intended and should be available despite accompanying risks and (2) ensure timely access to needed therapies and diagnostics. Historically, policy makers and product developers have viewed these objectives as being in tension. However, ensuring safety, expediting patient access, and enabling innovation can be complementary goals within a regulatory framework for medical devices.
Patients, clinicians, and policymakers alike need access to high-quality scientific evidence in order to make informed choices about health and healthcare, but the current national clinical trials enterprise is not yet optimally configured for the efficient creation and dissemination of such evidence. However, new technologies and methods hold significant potential for accelerating the rate at which we are able to translate raw findings gathered from both patient care and clinical research into actionable knowledge. We are now entering a period in which the quantitative sciences are emerging as the critical disciplines for advancing knowledge about health and healthcare, and statisticians will increasingly serve as critical mediators in transforming data into evidence. In this new, data-centric era, biostatisticians not only need to be expert at analyzing data but should also be involved directly in diverse efforts, including the review and analysis of research portfolios in order to optimize the relevance of research questions, the use of “quality by design” principles to improve reliability and validity of each individual trial, and the mining of aggregate knowledge derived from the clinical research enterprise as a whole. In order to meet these challenges, it is imperative that we (1) nurture and build the biostatistical workforce, (2) develop a deeper understanding of the biological and clinical context among statisticians, (3) facilitate collaboration among biostatisticians and other members of the clinical trials enterprise, (4) focus on communication skills in training and education programs, and (5) enhance the quantitative capacity of the research and clinical practice worlds.
Clinical decision making regarding the appropriate use of aspirin for the primary prevention of atherosclerotic cardiovascular disease (ASCVD) events is a complex process that requires assessment of the benefits and risks for each patient. Critically important elements of the process include evaluation of the patient’s absolute risk of ASCVD (the primary determinant of potential benefit from aspirin), the patient’s absolute risk of bleeding (the primary determinant of potential risk), and the patient’s willingness to undergo long-term therapy.¹ Despite numerous general guidelines on the use of aspirin for primary prevention, there is limited formal guidance in making these parallel assessments of benefit and risk or in using this information to identify appropriate patients for treatment. Inappropriate use of aspirin for primary prevention is common in clinical practice,² highlighting the important need for improving evidence-based decision making about aspirin use and for providing tools to facilitate this benefit/risk assessment.
Elevated postprandial blood glucose levels constitute a global epidemic and a major risk factor for prediabetes and type II diabetes, but existing dietary methods for controlling them have limited efficacy. Here, we continuously monitored week-long glucose levels in an 800-person cohort, measured responses to 46,898 meals, and found high variability in the response to identical meals, suggesting that universal dietary recommendations may have limited utility. We devised a machine-learning algorithm that integrates blood parameters, dietary habits, anthropometrics, physical activity, and gut microbiota measured in this cohort and showed that it accurately predicts personalized postprandial glycemic response to real-life meals. We validated these predictions in an independent 100-person cohort. Finally, a blinded randomized controlled dietary intervention based on this algorithm resulted in significantly lower postprandial responses and consistent alterations to gut microbiota configuration. Together, our results suggest that personalized diets may successfully modify elevated postprandial blood glucose and its metabolic consequences. VIDEO ABSTRACT.
The need for high-quality evidence to support decision making about health and health care by patients, physicians, care providers, and policy-makers is well documented. However, serious shortcomings in evidence persist. Pragmatic clinical trials that use novel techniques including emerging information and communication technologies to explore important research questions rapidly and at a fraction of the cost incurred by more "traditional" research methods promise to help close this gap. Nevertheless, while pragmatic clinical trials can bridge clinical practice and research, they may also raise difficult ethical and regulatory challenges. In this article, the authors briefly survey the current state of evidence that is available to inform clinical care and other health-related decisions and discuss the potential for pragmatic clinical trials to improve this state of affairs. They then propose a new working definition for pragmatic research that centers upon fitness for informing decisions about health and health care. Finally, they introduce a project, jointly undertaken by the National Institutes of Health Health Care Systems Research Collaboratory and the National Patient-Centered Clinical Research Network (PCORnet), which addresses 11 key aspects of current systems for regulatory and ethical oversight of clinical research that pose challenges to conducting pragmatic clinical trials. In the series of articles commissioned on this topic published in this issue of Clinical Trials, each of these aspects is addressed in a dedicated article, with a special focus on the interplay between ethical and regulatory considerations and pragmatic clinical research aimed at informing "real-world" choices about health and health care.
Like many physicians, Suzanne Clough, MD, struggled to meet her patients’ needs regarding their type 2 diabetes in a few 12-minute visits each year. But too often, patients’ concerns about day-to-day condition management weren’t fully addressed. Many were frustrated, and some didn’t follow her guidance because they weren’t seeing results.
The recommendations, she said, “didn’t have value [for them].”
Clough wondered whether real-time, 24/7 diabetes management support would help. That question led her on a 10-year journey to develop the WellDoc BlueStar mobile app for patients with type 2 diabetes. It analyzes trends in patient-entered data on blood glucose level, carbohydrate consumption, medication use, and other information to provide real-time coaching for the patient. Patients can then securely share the data with their physician through a web portal.
Background:
Current guidelines recommend the use of intravenous (IV) vasodilators in addition to IV loop diuretics for the treatment of acute heart failure (AHF) patients without hypotension. The evidence basis for these recommendations is limited.
Methods and results:
Hospital billing records for 82,808 AHF patients in the United States were analyzed. Patients receiving IV loop diuretics alone were paired with patients receiving IV loop diuretics + IV nitrates or IV nesiritide with the use of propensity score matching, excluding those with hypotension and/or evidence of cardiogenic shock, myocardial infarction, or acute coronary syndrome. Compared with paired patients receiving IV loop diuretics alone, in-hospital mortality was similar among IV loop diuretics + IV nitrates patients (n = 4,401; 1.9% vs 2.0%; P = .88) and marginally higher for IV loop diuretics + IV nesiritide patients (n = 2,254; 2.2% vs 3.1%; P = .05). Compared with paired IV loop diuretics patients, IV loop diuretics + IV nitrates or IV nesiritide had longer lengths of stay (+1.6 and +2.1 days; P < .01) and 57% higher costs (P < .01).
Conclusions:
Among hospitalized AHF patients, the addition of IV vasodilators to IV loop diuretics did not lower inpatient mortality or rehospitalization rates compared with loop diuretics alone, and was associated with longer lengths of stay and higher hospitalization costs. Although the lack of complete clinical, socioeconomic, and post-discharge data may have confounded these results, this analysis questions whether currently available IV vasodilators can improve outcomes in hospitalized AHF patients.
The ratio of false-positive to false-negative findings (FP:FN ratio) is an informative metric that warrants further evaluation. The FP:FN ratio varies greatly across different epidemiologic areas. In genetic epidemiology, it has varied from very high values (possibly even >100:1) for associations reported in candidate-gene studies to very low values (1:100 or lower) for associations with genome-wide significance. The substantial reduction over time in the FP:FN ratio in human genome epidemiology has corresponded to the routine adoption of stringent inferential criteria and comprehensive, agnostic reporting of all analyses. Most traditional fields of epidemiologic research more closely follow the practices of past candidate gene epidemiology, and thus have high FP:FN ratios. Further, FP and FN results do not necessarily entail the same consequences, and their relative importance may vary in different settings. This ultimately has implications for what is the acceptable FP:FN ratio and for how the results of published epidemiologic studies should be presented and interpreted.
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