2019
DOI: 10.1007/s12325-019-00970-1
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Generating Real-World Tumor Burden Endpoints from Electronic Health Record Data: Comparison of RECIST, Radiology-Anchored, and Clinician-Anchored Approaches for Abstracting Real-World Progression in Non-Small Cell Lung Cancer

Abstract: IntroductionReal-world evidence derived from electronic health records (EHRs) is increasingly recognized as a supplement to evidence generated from traditional clinical trials. In oncology, tumor-based Response Evaluation Criteria in Solid Tumors (RECIST) endpoints are standard clinical trial metrics. The best approach for collecting similar endpoints from EHRs remains unknown. We evaluated the feasibility of a RECIST-based methodology to assess EHR-derived real-world progression (rwP) and explored non-RECIST-… Show more

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Cited by 127 publications
(95 citation statements)
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“…Real-world progression was determined by physician assessment as described previously [37]; the date and type of progression (actual progression [based on radiographic evidence, pathologic evidence, or clinical assessment], pseudo-progression, or mixed progression) were recorded in the Flatiron database and comprised variables within the dataset. PFS was defined as the time until the earliest record of actual disease progression or death from any cause from initiation of line of therapy (first-or second-line).…”
Section: Study Outcomesmentioning
confidence: 99%
“…Real-world progression was determined by physician assessment as described previously [37]; the date and type of progression (actual progression [based on radiographic evidence, pathologic evidence, or clinical assessment], pseudo-progression, or mixed progression) were recorded in the Flatiron database and comprised variables within the dataset. PFS was defined as the time until the earliest record of actual disease progression or death from any cause from initiation of line of therapy (first-or second-line).…”
Section: Study Outcomesmentioning
confidence: 99%
“…The patient-level data in the EHRs include structured data in addition to unstructured data collected via technology-enabled medical record abstraction from physician's notes and other unstructured documents (eg, biomarker reports). 18,19 Data provided for this study were deidentified, and provisions were in place to prevent reidentification to protect patients' confidentiality. Rigorous quality control for structured and unstructured data was conducted for this data set.…”
Section: Data Sourcementioning
confidence: 99%
“…Our prior work compared multiple methods for curating real-world progression (rwP) from unstructured EHR documents. 7 Here, we evaluate one of those methods in a large, EHR-based cohort of patients with advanced non–small-cell lung cancer (aNSCLC), the leading cause of cancer mortality in the United States. 8 On the basis of a proposed development framework, 9 we examined three questions: Is capturing rwP retrospectively and with sufficient completeness feasible in large cohorts?…”
Section: Introductionmentioning
confidence: 99%