Search

BackgroundFifty percent of people living with dementia are undiagnosed. The electronic health record (EHR) Risk of Alzheimer's and Dementia Assessment Rule (eRADAR) was developed to identify older adults at risk of having undiagnosed dementia using routinely collected clinical data.ObjectiveTo externally validate eRADAR in two real-world healthcare systems, including examining performance over time and by race/ethnicity.DesignRetrospective cohort study PARTICIPANTS: 129,315 members of Kaiser Permanente Washington (KPWA), an integrated health system providing insurance coverage and medical care, and 13,444 primary care patients at University of California San Francisco Health (UCSF), an academic medical system, aged 65 years or older without prior EHR documentation of dementia diagnosis or medication.Main measuresPerformance of eRADAR scores, calculated annually from EHR data (including vital signs, diagnoses, medications, and utilization in the prior 2 years), for predicting EHR documentation of incident dementia diagnosis within 12 months.Key resultsA total of 7631 dementia diagnoses were observed at KPWA (11.1 per 1000 person-years) and 216 at UCSF (4.6 per 1000 person-years). The area under the curve was 0.84 (95% confidence interval: 0.84-0.85) at KPWA and 0.79 (0.76-0.82) at UCSF. Using the 90th percentile as the cut point for identifying high-risk patients, sensitivity was 54% (53-56%) at KPWA and 44% (38-51%) at UCSF. Performance was similar over time, including across the transition from International Classification of Diseases, version 9 (ICD-9) to ICD-10 codes, and across racial/ethnic groups (though small samples limited precision in some groups).ConclusionseRADAR showed strong external validity for detecting undiagnosed dementia in two health systems with different patient populations and differential availability of external healthcare data for risk calculations. In this study, eRADAR demonstrated generalizability from a research sample to real-world clinical populations, transportability across health systems, robustness to temporal changes in healthcare, and similar performance across larger racial/ethnic groups.

BackgroundGuidelines 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.ObjectiveTo develop and internally validate a life expectancy calculator from structured EHR data.DesignRetrospective cohort study using national Veteran's Affairs (VA) EHR databases.PatientsVeterans aged 50+ with a primary care visit during 2005.Main measuresWe 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 resultsIn 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.ConclusionsWe 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.

Older adults comprise an increasing proportion of the prison and homeless populations. While older age is associated with adverse post-release health events and incarceration is a risk factor for homelessness, the health status and homelessness risk of older pre-release prisoners are unknown. Moreover, most post-release services are geared towards veterans; it is unknown whether the needs of non-veterans differ from those of veterans.To assess health status and risk of homelessness of older pre-release prisoners, and to compare veterans with non-veterans.Cross-sectional study of 360 prisoners (≥55 years of age) within 2 years of release from prison using data from the 2004 Survey of Inmates in State and Federal Correctional Facilities.Veteran status, health status (based on self-report), and risk of homelessness (homelessness before arrest).Mean age was 61 years; 93.8% were men and 56.5% were white. Nearly 40% were veterans, of whom 77.2% reported likely VA service eligibility. Veterans were more likely to be white and to have obtained a high school diploma or GED. Overall, 79.1% reported a medical condition and 13.6% reported a serious mental illness. There was little difference in health status between veterans and non-veterans. Although 1 in 12 prisoners reported a risk factor for homelessness, the risk factors did not differ according to veteran status.Older pre-release prisoners had a high burden of medical and mental illness and were at risk for post-release homelessness regardless of veteran status. Reentry programs linking pre-release older prisoners to medical and psychiatric services and to homelessness prevention programs are needed for both veterans and non-veterans.