PMU68 Emulating the Main Outcomes of Randomised Controlled Studies through Statistical Analysis of Real World Data Directly Extracted from Electronic Medical Records.

Real-world data (RWD), that is, data from sources other than controlled clinical trials, play an increasingly important role in medical research. The development of quality clinical registers, increasing access to administrative data sources, growing computing power and data linkage capacities have contributed to greater availability of RWD. Evidence derived from RWD increases our understanding of prostate cancer (PCa) aetiology, natural history and effective management. While randomised controlled trials offer the best level of evidence for establishing the efficacy of medical interventions and making causal inferences, studies using RWD offer complementary evidence about the effectiveness, long-term outcomes and safety of interventions in real-world settings. RWD provide the only means of addressing questions about risk factors and exposures that cannot be “controlled”, or when assessing rare outcomes. This review provides examples of the value of RWD for generating evidence about PCa, focusing on studies using data from a quality clinical register, namely the National Prostate Cancer Register (NPCR) Sweden, with longitudinal data on advanced PCa in Patient-overview Prostate Cancer (PPC) and data linkages to other sources in Prostate Cancer data Base Sweden (PCBaSe).

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A study of risk analysis and prognosis models for the mortality of sepsis based on real-world data in China

10.1101/2020.07.19.20151738 ◽

2020 ◽

Author(s):

Yu Lu ◽

Qing Kong ◽

Jing Li ◽

Tao Jiang ◽

Zihui Tang

Keyword(s):

Real World ◽

Medical Records ◽

High Performance ◽

Univariate Analysis ◽

Predictive Performance ◽

General Information ◽

Real World Data ◽

World Data ◽

Factors Associated ◽

Breath Rate

Background: The study aimed to explore the factors associated with the mortality of sepsis and to develop prognosis models for predicting outcomes based on real world data in China. Methods: Data regarding sepsis patients medical records were extracted from the hospital information systems in four hospitals. The data included general information, laboratory tests, score systems, and supportive treatment for sepsis. In total, 507 medical records with complete data were available for data analysis. Multiple variable regression (MR) analysis used to explore associations, and to develop prognosis models Results: The mortality of sepsis was 0.3124 in the total sample. A univariate analysis indicated 23 variables significantly associated with the mortality of sepsis (p <0.05 for all). The MLR analysis showed independent and significant variables of age, GCS, SOFA, shock, breath rate, TBIL, CHE, BUN, LAC, OI, HCO3, IMV, and ALB (P <0.05 for all). Prognosis models have a high predictive performance (AUC = 0.885, 95% CI: 0.854 to 0.917 in model2). Conclusion: The study showed evidence of independent and significant factors associated with the mortality of sepsis, including age, GCS, SOFA, septic shock, breath rate, TBIL, CHE, BUN, LAC, OI, HCO3, IMV, and ALB. Prognosis models with a high performance were developed.

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Outcomes of Hydroxychloroquine Treatment Among Hospitalized COVID-19 Patients in the United States- Real-World Evidence From a Federated Electronic Medical Record Network

10.1101/2020.05.12.20099028 ◽

2020 ◽

Cited By ~ 9

Author(s):

Shailendra Singh ◽

Ahmad Khan ◽

Monica Chowdhry ◽

Arka Chatterjee

Keyword(s):

Electronic Medical Records ◽

Real World ◽

Medical Records ◽

Treatment Strategies ◽

The United States ◽

Matched Cohort ◽

Real World Data ◽

Real World Evidence ◽

Clinical Data Analysis

On March 28, 2020, in response to the rapidly accelerating COVID-19 pandemic, U.S FDA issued emergency use authorization for hydroxychloroquine (HCQ) in hospitalized COVID-19 patients based on limited in-vitro and anecdotal clinical data. Analysis of the accumulated real-world data utilizing electronic medical records (EMR) could indicate HCQ therapy benefits as we await the results of clinical trials. However, any such analysis of retrospective observational data should account for variables such as demographics and comorbidities that could affect treatment strategies or outcomes. Therefore, we report the outcomes of HCQ treatment in a propensity-matched cohort of COVID-19 hospitalized patients. Our analysis of a large retrospective cohort of hospitalized COVID-19 patients treated with HCQ did not show benefits in mortality or the need for mechanical ventilation when compared to a matched cohort of patients who did not receive HCQ.

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Protocol for data extraction: how real-world data have been used in the National Institute for Health and Care Excellence appraisals of cancer therapy

BMJ Open ◽

10.1136/bmjopen-2021-055985 ◽

2022 ◽

Vol 12 (1) ◽

pp. e055985

Author(s):

Jiyeon Kang ◽

John Cairns

Keyword(s):

Real World ◽

Data Extraction ◽

Systematic Research ◽

London School ◽

Real World Data ◽

World Data ◽

Data Extraction Tool ◽

Potential Benefits ◽

Critical Components ◽

Randomised Controlled

IntroductionDue to the limitations of relying on randomised controlled trials, the potential benefits of real-world data (RWD) in enriching evidence for health technology assessment (HTA) are highlighted. Despite increased interest in RWD, there is limited systematic research investigating how RWD have been used in HTA. The main purpose of this protocol is to extract relevant data from National Institute for Health and Care Excellence (NICE) appraisals in a transparent and reproducible manner in order to determine how NICE has incorporated a broader range of evidence in the appraisal of oncology medicines.Methods and analysisThe appraisals issued between January 2011 and May 2021 are included following inclusion criteria. The data extraction tool newly developed for this research includes the critical components of economic evaluation. The information is extracted from identified appraisals in accordance with extraction rules. The data extraction tool will be validated by a second researcher independently. The extracted data will be analysed quantitatively to investigate to what extent RWD have been used in appraisals. This is the first protocol to enable data to be extracted comprehensively and systematically in order to review the use of RWD.Ethics and disseminationThis study is approved by the Ethics Committee of the London School of Hygiene and Tropical Medicine on 14 November 2019 (17315). Results will be published in peer-reviewed journals.

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The unforeseen business and medical consequences of EHR data collection for a real-world data multiple myeloma registry.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.e19528 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. e19528-e19528

Author(s):

Kevin M. Keogh ◽

Andrew J. Belli ◽

Monica M. Matta ◽

Kathryn A. Tanenbaum ◽

Kaeleigh Farrish ◽

...

Keyword(s):

Multiple Myeloma ◽

Pilot Study ◽

Real World ◽

Medical Records ◽

Real World Data ◽

World Data ◽

Return Rates ◽

Research Collaborations ◽

Transfer Of Information ◽

Efficient Transfer

e19528 Background: Record retrieval on behalf of a consenting patient within the context of real-world data is not well understood. As the need for real-world data continues to expand, methods for the efficient transfer of information across consenting parties will be critical to enable research collaborations. This need was highlighted through a partnership between COTA and the Multiple Myeloma Research Foundation (MMRF). As one component of the pilot study, COTA managed the record retrieval of consenting patients before going on to abstract the data for use in a registry. Methods: The pilot study identified 23 patients that consented to the release of medical records at 54 institutions across 20 states. COTA partnered with a retrieval vendor and employed its own outreach efforts for acquisition. Outreach and retrieval techniques were similar across COTA and the vendor, including targeted calls, delivery of IRB-approved consent materials, and on-site requests. The 30-day release parameter for a covered entity under 45 CFR 164.524(b)(2) of HIPAA’s Privacy Rule was used to evaluate the observed return rates. Results: A total of 56 medical records were requested, and 48 records were retrieved. The mean (±SD) retrieval time across all sites was 33 (±58) days. We found that 54% of records were released in < = 30 days, and 32% of records > 30 days. 14% of requested records were never released, despite a median of 19 outreach attempts (range 10 to 43). Cited issues for delay or non-release consisted of 22 institutions questioning the validity of the certified electronic patient signature, 6 requiring a physical signature, and 5 requiring their own authorization forms. In no cases did the records contain structured metadata, such as LOINC, MedDRA, and RxNorm. Conclusions: This pilot showed an unpredictable variance associated with the release of records in the context of real-world data. This variance contributed to barriers and delays in broader research efforts. The lack of accompanying metadata with released records resulted in additional required data processing. Future studies should be conducted to establish best practices in the release and retrieval of medical records used to support real-world data research.

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Privacy-Preserving Statistical Analysis Method by Splitting Server Roles for Distributed Real-World Data

IEICE Transactions on Communications ◽

10.1587/transcom.e97.b.1779 ◽

2014 ◽

Vol E97.B (9) ◽

pp. 1779-1789

Author(s):

Jun ISHII ◽

Hiroyuki MAEOMICHI ◽

Akihiro TSUTSUI ◽

Ikuo YODA

Keyword(s):

Statistical Analysis ◽

Real World ◽

Privacy Preserving ◽

Analysis Method ◽

Real World Data ◽

World Data ◽

Statistical Analysis Method

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A systematic review of ICD complications in randomised controlled trials versus registries: is our ‘real-world’ data an underestimation?

Open Heart ◽

10.1136/openhrt-2014-000198 ◽

2015 ◽

Vol 2 (1) ◽

pp. e000198 ◽

Cited By ~ 64

Author(s):

Vivienne A Ezzat ◽

Victor Lee ◽

Syed Ahsan ◽

Anthony W Chow ◽

Oliver Segal ◽

...

Keyword(s):

Systematic Review ◽

Real World ◽

Randomised Controlled Trials ◽

Controlled Trials ◽

Real World Data ◽

World Data ◽

Randomised Controlled

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Intelligent data analysis: issues and challenges

The Knowledge Engineering Review ◽

10.1017/s0269888900008055 ◽

1996 ◽

Vol 11 (4) ◽

pp. 365-371 ◽

Cited By ~ 7

Author(s):

Xiaohui Liu

Keyword(s):

Data Analysis ◽

Statistical Analysis ◽

Computational Methods ◽

Real World ◽

Intelligent Systems ◽

Data Sets ◽

Intelligent Data Analysis ◽

Real World Data ◽

World Data ◽

Analysis Tools

Two phenomena have probably affected modern data analysts' lives more than anything else. First, the size of real-world data sets is getting increasingly large, especially during the last decade or so. Second, modern computational methods and tools are being developed which add further capability to traditional statistical analysis tools. These two developments have created a new range of problems and challenges for analysts, as well as new opportunities for intelligent systems in data analysis.

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