Identification of Physician-Diagnosed Alzheimer’s Disease and Related Dementias in Population-Based Administrative Data: A Validation Study Using Family Physicians’ Electronic Medical Records

2016 ◽  
Vol 54 (1) ◽  
pp. 337-349 ◽  
Author(s):  
R. Liisa Jaakkimainen ◽  
Susan E. Bronskill ◽  
Mary C. Tierney ◽  
Nathan Herrmann ◽  
Diane Green ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Electronic Medical Records ◽  
Administrative Data ◽  
Validation Study ◽  
Medical Records ◽  
Family Physicians ◽  
Population Based

scholarly journals Neighborhood Deprivation and Incident Alzheimer's Disease: A Regional Cohort Study of Electronic Medical Records

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 60-60
Author(s):  
Jarrod Dalton ◽  
Elizabeth Pfoh ◽  
Kristen Berg ◽  
Douglas Gunzler ◽  
Lyla Mourany ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Proportional Hazards ◽  
Outcome Data ◽  
Cox Proportional Hazards ◽  
Area Deprivation ◽  
Composite Outcome ◽  
Neighborhood Deprivation

Abstract The prevalence of Alzheimer’s disease (AD) is anticipated to increase drastically. Neighborhood socioeconomic position (SEP) has been related to multiple processes of health. Understanding whether SEP is related to AD can inform who is at greatest risk of developing this disease. We analyzed electronic medical records of 394892 patients from the two largest health systems in Northeast Ohio to evaluate the relationship between Ohio Area Deprivation Index quintiles (defined at the census tract level) and hazard for a composite outcome of AD diagnosis or primary AD death. We included residents of Cuyahoga and neighboring counties, and used the first outpatient visit beyond age 60 occurring between 2005 and 2015 as study baseline. Outcome data were censored at the earlier of a) the beginning of any 3-year time period without visits or b) non-AD death. We estimated a Cox proportional hazards regression model, adjusting ADI quintile effects for the interaction between age at baseline, sex and race as well as birth year. We used quadratic terms for continuous predictors. After adjusting for these factors, ADI quintile was significantly related (χ2 = 83.0 on 4 d.f.; p < 0.0001) to the composite time-to-event outcome. Compared to the lowest-deprivation quintile, ADI quintiles 4 (adjusted hazard ratio [95% confidence interval]: 1.18 [1.10, 1.26]) and 5 (1.37 [1.28, 1.47]) had significantly higher hazard for the composite outcome. In conclusion, neighborhood deprivation may be a risk factor for AD independent of demographic factors. Preventive efforts should target individuals living in neighborhoods with high levels of deprivation.

scholarly journals Deep clinical phenotyping of Alzheimer’s Disease Patients Leveraging Electronic Medical Records Data Identifies Sex-Specific Clinical Associations

2021 ◽  
Author(s):  
Alice Tang ◽  
Tomiko Oskotsky ◽  
William Mantyh ◽  
Caroline Warly Solsberg ◽  
Billy Zeng ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Drug Repurposing ◽  
Enrichment Analysis ◽  
Test Results ◽  
Clinical Associations ◽  
Lab Test ◽  
Clinical Phenotyping

AbstractAlzheimer’s Disease (AD) is a devastating disorder that is still not fully understood. Sex modifies AD vulnerability, but the reasons for this are largely unknown. There has been efforts to understand select comorbidities, covariates, and biomarkers of AD, with and without sex stratification - but there has not yet been an integrative, big data approach to identify clinical and sex specific associations with AD in an unbiased manner. Electronic Medical Records (EMR) contain extensive information on patients, including diagnoses, medications, and lab test results, providing a unique opportunity to apply phenotyping approaches to derive insights into AD clinical associations. Here, we utilize EMRs to perform deep clinical phenotyping and network analysis of AD patients to provide insight into its clinical characteristics and sex-specific clinical associations. We performed embeddings and network representation of patient diagnoses to visualize patient heterogeneity and comorbidity interactions and observe greater connectivity of diagnosis among AD patients compared to controls. We performed enrichment analysis between cases and controls and identified multiple known and new diagnostic and medication associations, such as positive associations with AD and hypertension, hyperlipidemia, anemia, and urinary tract infection - and negative associations with neoplasms and opioids. Furthermore, we performed sex-specific enrichment analyses to identify novel sex-specific associations with AD, such as osteoporosis, depression, cardiovascular risk factors, and musculoskeletal disorders diagnosed in female AD patients and neurological, behavioral, and sensory disorders enriched in male AD patients. We also analyzed lab test results, resulting in clusters of patient phenotype groups, and we observed greater calcium and lower alanine aminotransferase (ALT) in AD, as well as abnormal hemostasis labs in female AD. With this method of phenotyping, we can represent AD complexity, and identify clinical factors that can be followed-up for further temporal and predictive analysis or integrate with molecular data to aid in diagnosis and generate hypotheses about disease mechanisms. Furthermore, the negative associations can help identify factors that may decrease likelihood of AD and help motivate future drug repurposing or therapeutic approaches.

scholarly journals Using Machine Learning to Measure Specialist Wait Times from Family Physicians’ Electronic Medical Records Linked to Ontario Health Administrative Data

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Mohamed Abdalla ◽  
Hong Lu ◽  
Bogdan Pinzaru ◽  
Liisa Jaakkimainen
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Electronic Medical Records ◽  
Administrative Data ◽  
Medical Records ◽  
Family Physicians ◽  
Wait Times ◽  
Free Text ◽  
Health Administrative Data ◽  
Specialist Physician

IntroductionReliable information about the time spent waiting for health care services is a critical metric for measuring health system performance. Wait times are a useful measure of access to various health care sectors. Alongside the increased adoption of electronic medical records (EMR) by Canadian family physicians (FP), is the secondary use of FP EMR data for research. However, using FP EMR data can be challenging in its unstructured, free-text format. Objectives and ApproachOur objective was to identify the target specialist physician type from the EMR FP referral note and then calculate wait times from a FP referral to a specialist physician visit. We used FP EMR data and linked to Ontario, Canada health administrative data (called EMRPC). EMRPC collects the entire clinical record from patients including the content of FP referral notes. We used machine learning (ML) methods to identify the type of specialist physician in which the referral was intended. Labels to test the ML methods were created from physicians’ claims data. Wait times were calculated from the FP EMR referral note date to the specialist physician claim date in administrative data. ResultsOur ML models’ ability to classify 2016 FP EMR referral notes to selected medical and surgical specialists achieved sensitivity and positive predictive values ranging from the high 70s to low 80s.Compared to earlier analyses from 2008, we observed a similar relative ordering to see specific specialist physicians. Overall, the median wait times have increased by 14 days on average, with a maximum increase of 28 days to see a gastroenterologist. Conclusion / ImplicationsThe accuracy of ML on unstructured FP EMR data is high, thereby providing a mechanism to “codifying” information in a timely manner. This information can help inform decision makers and providers about which patients or FP practices are experiencing long wait times in seeing specialist physicians.

scholarly journals Cognitive Performance and Cerebrospinal Fluid Markers in Preclinical Alzheimer’s Disease: Results from the Gothenburg H70 Birth Cohort Studies

2021 ◽  
Vol 79 (1) ◽  
pp. 225-235
Author(s):  
Maya Arvidsson Rådestig ◽  
Johan Skoog ◽  
Henrik Zetterberg ◽  
Jürgen Kern ◽  
Anna Zettergren ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cerebrospinal Fluid ◽  
Cohort Studies ◽  
Cognitive Performance ◽  
Birth Cohort ◽  
Population Based ◽  
Tau Pathology ◽  
Preclinical Alzheimer’S Disease ◽  
Preclinical Ad

Background: We have previously shown that older adults with preclinical Alzheimer’s disease (AD) pathology in cerebrospinal fluid (CSF) had slightly worse performance in Mini-Mental State Examination (MMSE) than participants without preclinical AD pathology. Objective: We therefore aimed to compare performance on neurocognitive tests in a population-based sample of 70-year-olds with and without CSF AD pathology. Methods: The sample was derived from the population-based Gothenburg H70 Birth Cohort Studies in Sweden. Participants (n = 316, 70 years old) underwent comprehensive cognitive examinations, and CSF Aβ-42, Aβ-40, T-tau, and P-tau concentrations were measured. Participants were classified according to the ATN system, and according to their Clinical Dementia Rating (CDR) score. Cognitive performance was examined in the CSF amyloid, tau, and neurodegeneration (ATN) categories. Results: Among participants with CDR 0 (n = 259), those with amyloid (A+) and/or tau pathology (T+, N+) showed similar performance on most cognitive tests compared to participants with A-T-N-. Participants with A-T-N+ performed worse in memory (Supra span (p = 0.003), object Delayed (p = 0.042) and Immediate recall (p = 0.033)). Among participants with CDR 0.5 (n = 57), those with amyloid pathology (A+) scored worse in category fluency (p = 0.003). Conclusion: Cognitively normal participants with amyloid and/or tau pathology performed similarly to those without any biomarker evidence of preclinical AD in most cognitive domains, with the exception of slightly poorer memory performance in A-T-N+. Our study suggests that preclinical AD biomarkers are altered before cognitive decline.

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