Cargando…
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
INTRODUCTION: Real-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 metr...
Autores principales: | , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Healthcare
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6822856/ https://www.ncbi.nlm.nih.gov/pubmed/31140124 http://dx.doi.org/10.1007/s12325-019-00970-1 |
_version_ | 1783464423337230336 |
---|---|
author | Griffith, Sandra D. Tucker, Melisa Bowser, Bryan Calkins, Geoffrey Chang, Che-hsu (Joe) Guardino, Ellie Khozin, Sean Kraut, Josh You, Paul Schrag, Deb Miksad, Rebecca A. |
author_facet | Griffith, Sandra D. Tucker, Melisa Bowser, Bryan Calkins, Geoffrey Chang, Che-hsu (Joe) Guardino, Ellie Khozin, Sean Kraut, Josh You, Paul Schrag, Deb Miksad, Rebecca A. |
author_sort | Griffith, Sandra D. |
collection | PubMed |
description | INTRODUCTION: Real-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-based approaches. METHODS: In this retrospective study, cohorts were randomly selected from Flatiron Health’s database of de-identified patient-level EHR data in advanced non-small cell lung cancer. A RECIST-based approach tested for feasibility (N = 26). Three non-RECIST approaches were tested for feasibility, reliability, and validity (N = 200): (1) radiology-anchored, (2) clinician-anchored, and (3) combined. Qualitative and quantitative methods were used. RESULTS: A RECIST-based approach was not feasible: cancer progression could be ascertained for 23% (6/26 patients). Radiology- and clinician-anchored approaches identified at least one rwP event for 87% (173/200 patients). rwP dates matched 90% of the time. In 72% of patients (124/173), the first clinician-anchored rwP event was accompanied by a downstream event (e.g., treatment change); the association was slightly lower for the radiology-anchored approach (67%; 121/180). Median overall survival (OS) was 17 months [95% confidence interval (CI) 14, 19]. Median real-world progression-free survival (rwPFS) was 5.5 months (95% CI 4.6, 6.3) and 4.9 months (95% CI 4.2, 5.6) for clinician-anchored and radiology-anchored approaches, respectively. Correlations between rwPFS and OS were similar across approaches (Spearman’s rho 0.65–0.66). Abstractors preferred the clinician-anchored approach as it provided more comprehensive context. CONCLUSIONS: RECIST cannot adequately assess cancer progression in EHR-derived data because of missing data and lack of clarity in radiology reports. We found a clinician-anchored approach supported by radiology report data to be the optimal, and most practical, method for characterizing tumor-based endpoints from EHR-sourced data. FUNDING: Flatiron Health Inc., which is an independent subsidiary of the Roche group. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s12325-019-00970-1) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6822856 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Springer Healthcare |
record_format | MEDLINE/PubMed |
spelling | pubmed-68228562019-11-06 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 Griffith, Sandra D. Tucker, Melisa Bowser, Bryan Calkins, Geoffrey Chang, Che-hsu (Joe) Guardino, Ellie Khozin, Sean Kraut, Josh You, Paul Schrag, Deb Miksad, Rebecca A. Adv Ther Original Research INTRODUCTION: Real-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-based approaches. METHODS: In this retrospective study, cohorts were randomly selected from Flatiron Health’s database of de-identified patient-level EHR data in advanced non-small cell lung cancer. A RECIST-based approach tested for feasibility (N = 26). Three non-RECIST approaches were tested for feasibility, reliability, and validity (N = 200): (1) radiology-anchored, (2) clinician-anchored, and (3) combined. Qualitative and quantitative methods were used. RESULTS: A RECIST-based approach was not feasible: cancer progression could be ascertained for 23% (6/26 patients). Radiology- and clinician-anchored approaches identified at least one rwP event for 87% (173/200 patients). rwP dates matched 90% of the time. In 72% of patients (124/173), the first clinician-anchored rwP event was accompanied by a downstream event (e.g., treatment change); the association was slightly lower for the radiology-anchored approach (67%; 121/180). Median overall survival (OS) was 17 months [95% confidence interval (CI) 14, 19]. Median real-world progression-free survival (rwPFS) was 5.5 months (95% CI 4.6, 6.3) and 4.9 months (95% CI 4.2, 5.6) for clinician-anchored and radiology-anchored approaches, respectively. Correlations between rwPFS and OS were similar across approaches (Spearman’s rho 0.65–0.66). Abstractors preferred the clinician-anchored approach as it provided more comprehensive context. CONCLUSIONS: RECIST cannot adequately assess cancer progression in EHR-derived data because of missing data and lack of clarity in radiology reports. We found a clinician-anchored approach supported by radiology report data to be the optimal, and most practical, method for characterizing tumor-based endpoints from EHR-sourced data. FUNDING: Flatiron Health Inc., which is an independent subsidiary of the Roche group. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s12325-019-00970-1) contains supplementary material, which is available to authorized users. Springer Healthcare 2019-05-28 2019 /pmc/articles/PMC6822856/ /pubmed/31140124 http://dx.doi.org/10.1007/s12325-019-00970-1 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Research Griffith, Sandra D. Tucker, Melisa Bowser, Bryan Calkins, Geoffrey Chang, Che-hsu (Joe) Guardino, Ellie Khozin, Sean Kraut, Josh You, Paul Schrag, Deb Miksad, Rebecca A. 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 |
title | 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 |
title_full | 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 |
title_fullStr | 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 |
title_full_unstemmed | 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 |
title_short | 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 |
title_sort | 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 |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6822856/ https://www.ncbi.nlm.nih.gov/pubmed/31140124 http://dx.doi.org/10.1007/s12325-019-00970-1 |
work_keys_str_mv | AT griffithsandrad generatingrealworldtumorburdenendpointsfromelectronichealthrecorddatacomparisonofrecistradiologyanchoredandcliniciananchoredapproachesforabstractingrealworldprogressioninnonsmallcelllungcancer AT tuckermelisa generatingrealworldtumorburdenendpointsfromelectronichealthrecorddatacomparisonofrecistradiologyanchoredandcliniciananchoredapproachesforabstractingrealworldprogressioninnonsmallcelllungcancer AT bowserbryan generatingrealworldtumorburdenendpointsfromelectronichealthrecorddatacomparisonofrecistradiologyanchoredandcliniciananchoredapproachesforabstractingrealworldprogressioninnonsmallcelllungcancer AT calkinsgeoffrey generatingrealworldtumorburdenendpointsfromelectronichealthrecorddatacomparisonofrecistradiologyanchoredandcliniciananchoredapproachesforabstractingrealworldprogressioninnonsmallcelllungcancer AT changchehsujoe generatingrealworldtumorburdenendpointsfromelectronichealthrecorddatacomparisonofrecistradiologyanchoredandcliniciananchoredapproachesforabstractingrealworldprogressioninnonsmallcelllungcancer AT guardinoellie generatingrealworldtumorburdenendpointsfromelectronichealthrecorddatacomparisonofrecistradiologyanchoredandcliniciananchoredapproachesforabstractingrealworldprogressioninnonsmallcelllungcancer AT khozinsean generatingrealworldtumorburdenendpointsfromelectronichealthrecorddatacomparisonofrecistradiologyanchoredandcliniciananchoredapproachesforabstractingrealworldprogressioninnonsmallcelllungcancer AT krautjosh generatingrealworldtumorburdenendpointsfromelectronichealthrecorddatacomparisonofrecistradiologyanchoredandcliniciananchoredapproachesforabstractingrealworldprogressioninnonsmallcelllungcancer AT youpaul generatingrealworldtumorburdenendpointsfromelectronichealthrecorddatacomparisonofrecistradiologyanchoredandcliniciananchoredapproachesforabstractingrealworldprogressioninnonsmallcelllungcancer AT schragdeb generatingrealworldtumorburdenendpointsfromelectronichealthrecorddatacomparisonofrecistradiologyanchoredandcliniciananchoredapproachesforabstractingrealworldprogressioninnonsmallcelllungcancer AT miksadrebeccaa generatingrealworldtumorburdenendpointsfromelectronichealthrecorddatacomparisonofrecistradiologyanchoredandcliniciananchoredapproachesforabstractingrealworldprogressioninnonsmallcelllungcancer |