Architecture and Implementation of a Clinical Research Data Warehouse for Prostate
            Cancer

Seneviratne, Martin; Seto, Tina; Blayney, Douglas W.; Brooks, James D.; Hernandez‐Boussard, Tina

doi:10.5334/egems.234

Cited by 29 publications

(25 citation statements)

References 23 publications

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“…The clinical data warehouse is described elsewhere. 27 OptumLabs, co-founded by Mayo Clinic and Optum in late 2012, is a commercial data, infrastructure services, and care organization that is part of UnitedHealth Group. OptumLabs now has 30 partners and a HIPAA-compliant deidentified database of .200 million people.…”

Section: Data Sourcesmentioning

confidence: 99%

PSA Testing Use and Prostate Cancer Diagnostic Stage After the 2012 U.S. Preventive Services Task Force Guideline Changes

Magnani

Seto³

et al. 2019

Journal of the National Comprehensive Cancer Network

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Background: Most patients with prostate cancer are diagnosed with low-grade, localized disease and may not require definitive treatment. In 2012, the U.S. Preventive Services Task Force (USPSTF) recommended against prostate cancer screening to address overdetection and overtreatment. This study sought to determine the effect of guideline changes on prostate-specific antigen (PSA) screening and initial diagnostic stage for prostate cancer. Patients and Methods: A difference-in-differences analysis was conducted to compare changes in PSA screening (exposure) relative to cholesterol testing (control) after the 2012 USPSTF guideline changes, and chi-square test was used to determine whether there was a subsequent decrease in early-stage, low-risk prostate cancer diagnoses. Data were derived from a tertiary academic medical center’s electronic health records, a national commercial insurance database (OptumLabs), and the SEER database for men aged ≥35 years before (2008–2011) and after (2013–2016) the guideline changes. Results: In both the academic center and insurance databases, PSA testing significantly decreased for all men compared with the control. The greatest decrease was among men aged 55 to 74 years at the academic center and among those aged ≥75 years in the commercial database. The proportion of early-stage prostate cancer diagnoses (<T2) decreased across age groups at the academic center and in the SEER database. Conclusions: In primary care, PSA testing decreased significantly and fewer prostate cancers were diagnosed at an early stage, suggesting provider adherence to the 2012 USPSTF guideline changes. Long-term follow-up is needed to understand the effect of decreased screening on prostate cancer survival.

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Section: Data Sourcesmentioning

confidence: 99%

PSA Testing Use and Prostate Cancer Diagnostic Stage After the 2012 U.S. Preventive Services Task Force Guideline Changes

Magnani

Seto³

et al. 2019

Journal of the National Comprehensive Cancer Network

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“…We assumed an EMR to be defined as "observational data from clinical practice", including laboratory and diagnostic results and prescribed medication [45], while an EHR summarizes the ongoing health issues of a single person, linking the EMR with information from other databases, such as claims data with diagnostic and procedure codes as well as cost information. In recent years, some studies from the US have explored using EHR data to measure outcomes (most often in combination with claims data), including a Stanford University research group in California, testing the use of EHRs specifically for prostate cancer [45][46][47]. However, poor data quality (including accuracy of clinical coding, which is prone to subjectivity, variability and error), issues regarding privacy, ownership and access, the use of different software systems across health care settings, and the difficulty and expense of mining clinical notes may limit their application [3,45,46,48].…”

Section: Limitationsmentioning

confidence: 99%

A scoping review of core outcome sets and their ‘mapping’ onto real-world data using prostate cancer as a case study

Meregaglia

Ciani

Banks

et al. 2020

BMC Med Res Methodol

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Background: A Core Outcomes Set (COS) is an agreed minimum set of outcomes that should be reported in all clinical studies related to a specific condition. Using prostate cancer as a case study, we identified, summarized, and critically appraised published COS development studies and assessed the degree of overlap between them and selected real-world data (RWD) sources. Methods: We conducted a scoping review of the Core Outcome Measures in Effectiveness Trials (COMET) Initiative database to identify all COS studies developed for prostate cancer. Several characteristics (i.e., study type, methods for consensus, type of participants, outcomes included in COS and corresponding measurement instruments, timing, and sources) were extracted from the studies; outcomes were classified according to a predefined 38-item taxonomy. The study methodology was assessed based on the recent COS-STAndards for Development (COS-STAD) recommendations. A 'mapping' exercise was conducted between the COS identified and RWD routinely collected in selected European countries. Results: Eleven COS development studies published between 1995 and 2017 were retrieved, of which 8 were classified as 'COS for clinical trials and clinical research', 2 as 'COS for practice' and 1 as 'COS patient reported outcomes'. Recommended outcomes were mainly categorized into 'mortality and survival' (17%), 'outcomes related to neoplasm' (18%), and 'renal and urinary outcomes' (13%) with no relevant differences among COS study types. The studies generally fulfilled the criteria for the COS-STAD 'scope specification' domain but not the 'stakeholders involved' and 'consensus process' domains. About 72% overlap existed between COS and linked administrative data sources, with important gaps. Linking with patient registries improved coverage (85%), but was sometimes limited to smaller follow-up patient groups. Conclusions: This scoping review identified few COS development studies in prostate cancer, some quite dated and with a growing level of methodological quality over time. This study revealed promising overlap between COS and RWD sources, though with important limitations; linking established, national patient registries to administrative data provide the best means to additionally capture patient-reported and some clinical outcomes over time. Thus, increasing the combination of different data sources and the interoperability of systems to follow larger patient groups in RWD is required.

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“…Data were extracted from a prostate cancer clinical data warehouse described elsewhere. 12 In brief, prostate cancer patients were identified using diagnosis codes (ICD-9: 185, 233.4; and ICD-10: C61) from EPIC Clarity, which was installed in 2008. Patients were linked to the California Cancer Registry (CCR), a state-wide population-based cancer surveillance program that provides tumor characteristics and additional information on treatments outside of our cancer center.…”

Section: Data Sourcesmentioning

confidence: 99%

<p>Clinical Documentation to Predict Factors Associated with Urinary Incontinence Following Prostatectomy for Prostate Cancer</p>

Banerjee

Magnani

et al. 2020

RRU

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Background: Advances in data collection provide opportunities to use population samples in identifying risk factors for urinary incontinence (UI), which occurs in up to 71% of men with prostate cancer following prostatectomy. Most studies on patient-centered outcomes use surveys or manual chart abstraction for data collection, which can be costly and difficult to scale. We sought to evaluate rates of and risk factors for UI following prostatectomy using natural language processing on electronic health record (EHR) data. Methods: We conducted a retrospective analysis of patients undergoing prostatectomy for prostate cancer between January 2008 and August 2018 using EHR data from an academic medical center. UI incidence for each patient in the cohort was assessed using natural language processing from clinical notes generated pre-and postoperatively. Multivariable logistic regression was used to evaluate potential risk factors for postoperative UI at various time points within 2 years following surgery. Results: We identified 3792 patients who underwent prostatectomy for prostate cancer. We found a significant association between preoperative UI and UI in the first (odds ratio [OR], 2.30; 95% confidence interval [CI], 1.24-4.28) and second (OR 2.24, 95% CI 1.04-4.83) years following surgery. Preoperative body mass index was also associated with UI in the second postoperative year (OR 1.11, 95% CI 1.02-1.21). Conclusion: We show that a natural language processing approach using clinical narratives can be used to assess risk for UI in prostate cancer patients. Unstructured clinical narrative text can help advance future population-level research in patient-centered outcomes and quality of care.

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Architecture and Implementation of a Clinical Research Data Warehouse for Prostate Cancer

Cited by 29 publications

References 23 publications

PSA Testing Use and Prostate Cancer Diagnostic Stage After the 2012 U.S. Preventive Services Task Force Guideline Changes

PSA Testing Use and Prostate Cancer Diagnostic Stage After the 2012 U.S. Preventive Services Task Force Guideline Changes

A scoping review of core outcome sets and their ‘mapping’ onto real-world data using prostate cancer as a case study

<p>Clinical Documentation to Predict Factors Associated with Urinary Incontinence Following Prostatectomy for Prostate Cancer</p>

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