Sakusic Amra1, John C O'Horo2, Tarun D Singh3, Gregory A Wilson4, Rahul Kashyap5, Ronald Petersen6, Rosebud O Roberts6, John D Fryer7, Alejandro A Rabinstein3, Ognjen Gajic8. 1. Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN; Department of Internal Medicine, Tuzla University Medical Center, Bosnia and Herzegovina; Department of Pulmonary Medicine, Tuzla University Medical Center, Bosnia and Herzegovina. Electronic address: Sakusic.Amra@mayo.edu. 2. Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN; Department of Medicine, Multidisciplinary Epidemiology and Translational Research in Intensive Care, Emergency and Perioperative Medicine (METRIC), Mayo Clinic, Rochester, MN; Department of Medicine, Division of Infectious Diseases, Mayo Clinic, Rochester, MN. 3. Department of Medicine, Multidisciplinary Epidemiology and Translational Research in Intensive Care, Emergency and Perioperative Medicine (METRIC), Mayo Clinic, Rochester, MN; Department of Neurology, Mayo, Rochester, MN. 4. Department of Medicine, Multidisciplinary Epidemiology and Translational Research in Intensive Care, Emergency and Perioperative Medicine (METRIC), Mayo Clinic, Rochester, MN. 5. Department of Medicine, Multidisciplinary Epidemiology and Translational Research in Intensive Care, Emergency and Perioperative Medicine (METRIC), Mayo Clinic, Rochester, MN; Department of Anesthesia, Mayo Clinic, Rochester, MN. 6. Department of Neurology, Mayo, Rochester, MN; Department of Health Sciences Research, MN; Department of Neurology, MN. 7. Department of Neuroscience, Mayo Clinic, Jacksonville, FL. 8. Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN; Department of Medicine, Multidisciplinary Epidemiology and Translational Research in Intensive Care, Emergency and Perioperative Medicine (METRIC), Mayo Clinic, Rochester, MN.
Abstract
PURPOSE: Long-term cognitive impairment is a common and important problem in survivors of critical illness. We developed electronic search algorithms to identify cognitive impairment and dementia from the electronic medical records (EMRs) that provide opportunity for big data analysis. MATERIALS AND METHODS: Eligible patients met 2 criteria. First, they had a formal cognitive evaluation by The Mayo Clinic Study of Aging. Second, they were hospitalized in intensive care unit at our institution between 2006 and 2014. The "criterion standard" for diagnosis was formal cognitive evaluation supplemented by input from an expert neurologist. Using all available EMR data, we developed and improved our algorithms in the derivation cohort and validated them in the independent validation cohort. RESULTS: Of 993 participants who underwent formal cognitive testing and were hospitalized in intensive care unit, we selected 151 participants at random to form the derivation and validation cohorts. The automated electronic search algorithm for cognitive impairment was 94.3% sensitive and 93.0% specific. The search algorithms for dementia achieved respective sensitivity and specificity of 97% and 99%. EMR search algorithms significantly outperformed International Classification of Diseases codes. CONCLUSIONS: Automated EMR data extractions for cognitive impairment and dementia are reliable and accurate and can serve as acceptable and efficient alternatives to time-consuming manual data review.
PURPOSE: Long-term cognitive impairment is a common and important problem in survivors of critical illness. We developed electronic search algorithms to identify cognitive impairment and dementia from the electronic medical records (EMRs) that provide opportunity for big data analysis. MATERIALS AND METHODS: Eligible patients met 2 criteria. First, they had a formal cognitive evaluation by The Mayo Clinic Study of Aging. Second, they were hospitalized in intensive care unit at our institution between 2006 and 2014. The "criterion standard" for diagnosis was formal cognitive evaluation supplemented by input from an expert neurologist. Using all available EMR data, we developed and improved our algorithms in the derivation cohort and validated them in the independent validation cohort. RESULTS: Of 993 participants who underwent formal cognitive testing and were hospitalized in intensive care unit, we selected 151 participants at random to form the derivation and validation cohorts. The automated electronic search algorithm for cognitive impairment was 94.3% sensitive and 93.0% specific. The search algorithms for dementia achieved respective sensitivity and specificity of 97% and 99%. EMR search algorithms significantly outperformed International Classification of Diseases codes. CONCLUSIONS: Automated EMR data extractions for cognitive impairment and dementia are reliable and accurate and can serve as acceptable and efficient alternatives to time-consuming manual data review.
Authors: Deborah E Barnes; Jing Zhou; Rod L Walker; Eric B Larson; Sei J Lee; W John Boscardin; Zachary A Marcum; Sascha Dublin Journal: J Am Geriatr Soc Date: 2019-10-14 Impact factor: 5.562
Authors: Andrea L Gilmore-Bykovskyi; Laura M Block; Lily Walljasper; Nikki Hill; Carey Gleason; Manish N Shah Journal: J Am Med Inform Assoc Date: 2018-09-01 Impact factor: 4.497
Authors: Sumera R Ahmad; Alex D Tarabochia; Luann Budahn; Allison M Lemahieu; Brenda Anderson; Kirtivardhan Vashistha; Lioudmila Karnatovskaia; Ognjen Gajic Journal: Front Med (Lausanne) Date: 2022-06-06
Authors: Ellen McCreedy; Andrea Gilmore-Bykovskyi; David A Dorr; Julie Lima; Ellen P McCarthy; David J Meyers; Richard Platt; V G Vinod Vydiswaran; Julie P W Bynum Journal: J Am Geriatr Soc Date: 2021-11-02 Impact factor: 5.562
Authors: K Pun; C W Zhu; M T Kinsella; M Sewell; H Grossman; J Neugroschl; C Li; A Ardolino; N Velasco; M Sano Journal: J Prev Alzheimers Dis Date: 2021