Veronica Brady1, Meagan Whisenant1, Xueying Wang2, Vi K Ly2, Gen Zhu2, David Aguilar3, Hulin Wu2. 1. Cizik School of Nursing, The University of Texas Health Science Center at Houston, Houston, TX. 2. School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX. 3. McGovern School of Medicine, The University of Texas Health Science Center at Houston, Houston, TX.
Abstract
OBJECTIVE: A variety of symptoms may be associated with type 2 diabetes and its complications. Symptoms in chronic diseases may be described in terms of prevalence, severity, and trajectory and often co-occur in groups, known as symptom clusters, which may be representative of a common etiology. The purpose of this study was to characterize type 2 diabetes-related symptoms using a large nationwide electronic health record (EHR) database. Methods: We acquired the Cerner Health Facts, a nationwide EHR database. The type 2 diabetes cohort (n = 1,136,301 patients) was identified using a rule-based phenotype method. A multistep procedure was then used to identify type 2 diabetes-related symptoms based on International Classification of Diseases, 9th and 10th revisions, diagnosis codes. Type 2 diabetes-related symptoms and co-occurring symptom clusters, including their temporal patterns, were characterized based the longitudinal EHR data. Results: Patients had a mean age of 61.4 years, 51.2% were female, and 70.0% were White. Among 1,136,301 patients, there were 8,008,276 occurrences of 59 symptoms. The most frequently reported symptoms included pain, heartburn, shortness of breath, fatigue, and swelling, which occurred in 21-60% of the patients. We also observed over-represented type 2 diabetes symptoms, including difficulty speaking, feeling confused, trouble remembering, weakness, and drowsiness/sleepiness. Some of these are rare and difficult to detect by traditional patient-reported outcomes studies. Conclusion: To the best of our knowledge, this is the first study to use a nationwide EHR database to characterize type 2 diabetes-related symptoms and their temporal patterns. Fifty-nine symptoms, including both over-represented and rare diabetes-related symptoms, were identified.
OBJECTIVE: A variety of symptoms may be associated with type 2 diabetes and its complications. Symptoms in chronic diseases may be described in terms of prevalence, severity, and trajectory and often co-occur in groups, known as symptom clusters, which may be representative of a common etiology. The purpose of this study was to characterize type 2 diabetes-related symptoms using a large nationwide electronic health record (EHR) database. Methods: We acquired the Cerner Health Facts, a nationwide EHR database. The type 2 diabetes cohort (n = 1,136,301 patients) was identified using a rule-based phenotype method. A multistep procedure was then used to identify type 2 diabetes-related symptoms based on International Classification of Diseases, 9th and 10th revisions, diagnosis codes. Type 2 diabetes-related symptoms and co-occurring symptom clusters, including their temporal patterns, were characterized based the longitudinal EHR data. Results: Patients had a mean age of 61.4 years, 51.2% were female, and 70.0% were White. Among 1,136,301 patients, there were 8,008,276 occurrences of 59 symptoms. The most frequently reported symptoms included pain, heartburn, shortness of breath, fatigue, and swelling, which occurred in 21-60% of the patients. We also observed over-represented type 2 diabetes symptoms, including difficulty speaking, feeling confused, trouble remembering, weakness, and drowsiness/sleepiness. Some of these are rare and difficult to detect by traditional patient-reported outcomes studies. Conclusion: To the best of our knowledge, this is the first study to use a nationwide EHR database to characterize type 2 diabetes-related symptoms and their temporal patterns. Fifty-nine symptoms, including both over-represented and rare diabetes-related symptoms, were identified.
Authors: Melissa Mazor; Janine K Cataldo; Kathryn Lee; Anand Dhruva; Bruce Cooper; Steven M Paul; Kimberly Topp; Betty J Smoot; Laura B Dunn; Jon D Levine; Yvette P Conley; Christine Miaskowski Journal: Eur J Oncol Nurs Date: 2017-12-19 Impact factor: 2.398
Authors: Nikolaos Papachristou; Payam Barnaghi; Bruce Cooper; Kord M Kober; Roma Maguire; Steven M Paul; Marilyn Hammer; Fay Wright; Jo Armes; Eileen P Furlong; Lisa McCann; Yvette P Conley; Elisabeth Patiraki; Stylianos Katsaragakis; Jon D Levine; Christine Miaskowski Journal: Sci Rep Date: 2019-02-19 Impact factor: 4.379
Authors: Sudhi G Upadhyaya; Dennis H Murphree; Che G Ngufor; Alison M Knight; Daniel J Cronk; Robert R Cima; Timothy B Curry; Jyotishman Pathak; Rickey E Carter; Daryl J Kor Journal: Mayo Clin Proc Innov Qual Outcomes Date: 2017-04-28