Alexandra K Lee1,2, Bocheng Jing3,4, Sun Y Jeon5,3, W John Boscardin5,3,6, Sei J Lee5,3. 1. Division of Geriatrics, University of California, 4150 Clement St, VA181G, San Francisco, CA, 94121, USA. alexandra.lee@ucsf.edu. 2. San Francisco Veterans Affairs Medical Center, San Francisco, USA. alexandra.lee@ucsf.edu. 3. San Francisco Veterans Affairs Medical Center, San Francisco, USA. 4. Northern California Institute for Research and Education, San Francisco, USA. 5. Division of Geriatrics, University of California, 4150 Clement St, VA181G, San Francisco, CA, 94121, USA. 6. Division of Biostatistics, University of California, San Francisco, San Francisco, USA.
Abstract
BACKGROUND: Guidelines recommend breast and colorectal cancer screening for older adults with a life expectancy >10 years. Most mortality indexes require clinician data entry, presenting a barrier for routine use in care. Electronic health records (EHR) are a rich clinical data source that could be used to create individualized life expectancy predictions to identify patients for cancer screening without data entry. OBJECTIVE: To develop and internally validate a life expectancy calculator from structured EHR data. DESIGN: Retrospective cohort study using national Veteran's Affairs (VA) EHR databases. PATIENTS: Veterans aged 50+ with a primary care visit during 2005. MAIN MEASURES: We assessed demographics, diseases, medications, laboratory results, healthcare utilization, and vital signs 1 year prior to the index visit. Mortality follow-up was complete through 2017. Using the development cohort (80% sample), we used LASSO Cox regression to select ~100 predictors from 913 EHR data elements. In the validation cohort (remaining 20% sample), we calculated the integrated area under the curve (iAUC) and evaluated calibration. KEY RESULTS: In 3,705,122 patients, the mean age was 68 years and the majority were male (97%) and white (85%); nearly half (49%) died. The life expectancy calculator included 93 predictors; age and gender most strongly contributed to discrimination; diseases also contributed significantly while vital signs were negligible. The iAUC was 0.816 (95% confidence interval, 0.815, 0.817) with good calibration. CONCLUSIONS: We developed a life expectancy calculator using VA EHR data with excellent discrimination and calibration. Automated life expectancy prediction using EHR data may improve guideline-concordant breast and colorectal cancer screening by identifying patients with a life expectancy >10 years.
BACKGROUND: Guidelines recommend breast and colorectal cancer screening for older adults with a life expectancy >10 years. Most mortality indexes require clinician data entry, presenting a barrier for routine use in care. Electronic health records (EHR) are a rich clinical data source that could be used to create individualized life expectancy predictions to identify patients for cancer screening without data entry. OBJECTIVE: To develop and internally validate a life expectancy calculator from structured EHR data. DESIGN: Retrospective cohort study using national Veteran's Affairs (VA) EHR databases. PATIENTS: Veterans aged 50+ with a primary care visit during 2005. MAIN MEASURES: We assessed demographics, diseases, medications, laboratory results, healthcare utilization, and vital signs 1 year prior to the index visit. Mortality follow-up was complete through 2017. Using the development cohort (80% sample), we used LASSO Cox regression to select ~100 predictors from 913 EHR data elements. In the validation cohort (remaining 20% sample), we calculated the integrated area under the curve (iAUC) and evaluated calibration. KEY RESULTS: In 3,705,122 patients, the mean age was 68 years and the majority were male (97%) and white (85%); nearly half (49%) died. The life expectancy calculator included 93 predictors; age and gender most strongly contributed to discrimination; diseases also contributed significantly while vital signs were negligible. The iAUC was 0.816 (95% confidence interval, 0.815, 0.817) with good calibration. CONCLUSIONS: We developed a life expectancy calculator using VA EHR data with excellent discrimination and calibration. Automated life expectancy prediction using EHR data may improve guideline-concordant breast and colorectal cancer screening by identifying patients with a life expectancy >10 years.
Authors: Scott M Grundy; Neil J Stone; Alison L Bailey; Craig Beam; Kim K Birtcher; Roger S Blumenthal; Lynne T Braun; Sarah de Ferranti; Joseph Faiella-Tommasino; Daniel E Forman; Ronald Goldberg; Paul A Heidenreich; Mark A Hlatky; Daniel W Jones; Donald Lloyd-Jones; Nuria Lopez-Pajares; Chiadi E Ndumele; Carl E Orringer; Carmen A Peralta; Joseph J Saseen; Sidney C Smith; Laurence Sperling; Salim S Virani; Joseph Yeboah Journal: J Am Coll Cardiol Date: 2018-11-10 Impact factor: 24.094
Authors: Robert A Smith; Kimberly S Andrews; Durado Brooks; Stacey A Fedewa; Deana Manassaram-Baptiste; Debbie Saslow; Otis W Brawley; Richard C Wender Journal: CA Cancer J Clin Date: 2018-05-30 Impact factor: 508.702
Authors: Sei J Lee; Alan S Go; Karla Lindquist; Daniel Bertenthal; Kenneth E Covinsky Journal: Am J Public Health Date: 2008-05-29 Impact factor: 9.308
Authors: William James Deardorff; Bocheng Jing; Sun Y Jeon; W John Boscardin; Alexandra K Lee; Kathy Z Fung; Sei J Lee Journal: BMC Geriatr Date: 2022-05-18 Impact factor: 4.070