Michael F Gensheimer1, Balasubramanian Narasimhan2,3, A Solomon Henry3, Douglas J Wood3, Daniel L Rubin3. 1. Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA. 2. Department of Statistics, Stanford University, Stanford, CA. 3. Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA.
Abstract
PURPOSE: For real-world evidence, it is convenient to use routinely collected data from the electronic medical record (EMR) to measure survival outcomes. However, patients can become lost to follow-up, causing incomplete data and biased survival time estimates. We quantified this issue for patients with metastatic cancer seen in an academic health system by comparing survival estimates from EMR data only and from EMR data combined with high-quality cancer registry data. MATERIALS AND METHODS: Patients diagnosed with metastatic cancer from 2008 to 2014 were included in this retrospective study. Patients who were diagnosed with cancer or received their initial treatment within our system were included in the institutional cancer registry and this study. Overall survival was calculated using the Kaplan-Meier method. Survival curves were generated in two ways: using EMR follow-up data alone and using EMR data supplemented with data from the Stanford Cancer Registry/California Cancer Registry. RESULTS: Four thousand seventy-seven patients were included. The median follow-up using EMR + Cancer Registry data was 19.9 months, and the median follow-up in surviving patients was 67.6 months. There were 1,301 deaths recorded in the EMR and 3,140 deaths recorded in the Cancer Registry. The median overall survival from the date of cancer diagnosis using EMR data was 58.7 months (95% CI, 54.2 to 63.2); using EMR + Cancer Registry data, it was 20.8 months (95% CI, 19.6 to 22.3). A similar pattern was seen using the date of first systemic therapy or date of first hospital admission as the baseline date. CONCLUSION: Using EMR data alone, survival time was overestimated compared with EMR + Cancer Registry data.
PURPOSE: For real-world evidence, it is convenient to use routinely collected data from the electronic medical record (EMR) to measure survival outcomes. However, patients can become lost to follow-up, causing incomplete data and biased survival time estimates. We quantified this issue for patients with metastatic cancer seen in an academic health system by comparing survival estimates from EMR data only and from EMR data combined with high-quality cancer registry data. MATERIALS AND METHODS: Patients diagnosed with metastatic cancer from 2008 to 2014 were included in this retrospective study. Patients who were diagnosed with cancer or received their initial treatment within our system were included in the institutional cancer registry and this study. Overall survival was calculated using the Kaplan-Meier method. Survival curves were generated in two ways: using EMR follow-up data alone and using EMR data supplemented with data from the Stanford Cancer Registry/California Cancer Registry. RESULTS: Four thousand seventy-seven patients were included. The median follow-up using EMR + Cancer Registry data was 19.9 months, and the median follow-up in surviving patients was 67.6 months. There were 1,301 deaths recorded in the EMR and 3,140 deaths recorded in the Cancer Registry. The median overall survival from the date of cancer diagnosis using EMR data was 58.7 months (95% CI, 54.2 to 63.2); using EMR + Cancer Registry data, it was 20.8 months (95% CI, 19.6 to 22.3). A similar pattern was seen using the date of first systemic therapy or date of first hospital admission as the baseline date. CONCLUSION: Using EMR data alone, survival time was overestimated compared with EMR + Cancer Registry data.
Authors: Christopher J Johnson; Hannah K Weir; Aliza K Fink; Robert R German; Jack L Finch; Randi K Rycroft; Daixin Yin Journal: Cancer Epidemiol Date: 2012-09-07 Impact factor: 2.984
Authors: Oanh Kieu Nguyen; Anil N Makam; Christopher Clark; Song Zhang; Bin Xie; Ferdinand Velasco; Ruben Amarasingham; Ethan A Halm Journal: J Gen Intern Med Date: 2016-08-08 Impact factor: 5.128
Authors: Eric D Adler; Adriaan A Voors; Liviu Klein; Fima Macheret; Oscar O Braun; Marcus A Urey; Wenhong Zhu; Iziah Sama; Matevz Tadel; Claudio Campagnari; Barry Greenberg; Avi Yagil Journal: Eur J Heart Fail Date: 2019-11-12 Impact factor: 15.534
Authors: Matthew A Levin; Hung-Mo Lin; Gautham Prabhakar; Patrick J McCormick; Natalia N Egorova Journal: Health Serv Res Date: 2018-12-05 Impact factor: 3.402
Authors: William R Hersh; Mark G Weiner; Peter J Embi; Judith R Logan; Philip R O Payne; Elmer V Bernstam; Harold P Lehmann; George Hripcsak; Timothy H Hartzog; James J Cimino; Joel H Saltz Journal: Med Care Date: 2013-08 Impact factor: 2.983
Authors: David J Benjamin; Amy Klapheke; Primo N Lara; Rosemary D Cress; Jonathan W Riess Journal: Clin Lung Cancer Date: 2019-06-14 Impact factor: 4.785
Authors: Christina A Clarke; Sally L Glaser; Rita Leung; Kathleen Davidson-Allen; Scarlett L Gomez; Theresa H M Keegan Journal: Cancer Epidemiol Date: 2016-12-03 Impact factor: 2.890
Authors: Melissa D Curtis; Sandra D Griffith; Melisa Tucker; Michael D Taylor; William B Capra; Gillis Carrigan; Ben Holzman; Aracelis Z Torres; Paul You; Brandon Arnieri; Amy P Abernethy Journal: Health Serv Res Date: 2018-05-14 Impact factor: 3.402