Temporal Trends of Multiple Sclerosis Disease Activity: Electronic Health Records Indicators

2021 ◽  
pp. 103333
Author(s):  
Liang Liang ◽  
Nicole Kim ◽  
Jue Hou ◽  
Tianrun Cai ◽  
Kumar Dahal ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Electronic Health Records ◽  
Disease Activity ◽  
Temporal Trends ◽  
Health Records ◽  
Multiple Sclerosis Disease ◽  
Electronic Health

scholarly journals Leveraging electronic health records data to predict multiple sclerosis disease activity

2021 ◽  
Vol 8 (4) ◽  
pp. 800-810
Author(s):  
Yuri Ahuja ◽  
Nicole Kim ◽  
Liang Liang ◽  
Tianrun Cai ◽  
Kumar Dahal ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Electronic Health Records ◽  
Disease Activity ◽  
Health Records ◽  
Multiple Sclerosis Disease ◽  
Electronic Health

scholarly journals Learning longitudinal patterns and identifying subtypes of pediatric Crohn disease treated with infliximab via trajectory cluster analysis of electronic health records

2021 ◽  
Author(s):  
Andrew Chen ◽  
Ronen Stein ◽  
Robert N. Baldassano ◽  
Jing Huang
Keyword(s):  
Electronic Health Records ◽  
Disease Activity ◽  
Crohn Disease ◽  
Cross Sectional ◽  
Health Records ◽  
Disease Evolution ◽  
Current Classification ◽  
Electronic Health ◽  
Over Time

ABSTRACTBackgroundThe current classification of pediatric CD is mainly based on cross-sectional data. The objective of this study is to identify subgroups of pediatric CD through trajectory cluster analysis of disease activity using data from electronic health records.MethodsWe conducted a retrospective study of pediatric CD patients who had been treated with infliximab. The evolution of disease over time was described using trajectory analysis of longitudinal data of C-Reactive Protein (CRP). Patterns of disease evolution were extracted through functional principal components analysis and subgroups were identified based on those patterns using the Gaussian mixture model. We compared patient characteristics, a biomarker for disease activity, received treatments, and long-term surgical outcomes across subgroups.ResultsWe identified four subgroups of pediatric CD patients with differential relapse-and-remission risk profiles. They had significantly different disease phenotype (p < 0.001), CRP (p < 0.001) and calprotectin (p = 0.037) at diagnosis, with increasing percentage of inflammatory phenotype and declining CRP and fecal calprotectin levels from Subgroup 1 through 4. The risk of colorectal surgery within 10 years after diagnosis was significantly different between groups (p < 0.001). We did not find statistical significance in gender or age at diagnosis across subgroups, but the BMI z-score was slightly smaller in subgroup 1 (p =0.055).ConclusionsReadily available longitudinal data from electronic health records can be leveraged to provide a deeper characterization of pediatric Crohn disease. The identified subgroups captured novel forms of variation in pediatric Crohn disease that were not explained by baseline measurements and treatment information.SummaryThe current classification of pediatric Crohn disease mainly relies on cross-sectional data, e.g., the Paris classification. However, the phenotypic classification may evolve over time after diagnosis. Our study utilized longitudinal measures from the electronic health records and stratified pediatric Crohn disease patients with differential relapse-and-remission risk profiles based on patterns of disease evolution. We found trajectories of well-maintained low disease activity were associated with less severe disease at baseline, early initiation of infliximab treatment, and lower risk of surgery within 10 years of diagnosis, but the difference was not fully explained by phenotype at diagnosis.

scholarly journals Seeking out SARI: an automated search of electronic health records

2018 ◽  
Vol 146 (8) ◽  
pp. 1065-1069
Author(s):  
John C. O'Horo ◽  
Mikhail Dziadzko ◽  
Amra Sakusic ◽  
Rashid Ali ◽  
M. Rizwan Sohail ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Electronic Health Records ◽  
Search Algorithm ◽  
Temporal Trends ◽  
Epidemiologic Study ◽  
Health Records ◽  
Severe Acute Respiratory Infection ◽  
Electronic Health ◽  
Automated Search

AbstractThe definition of severe acute respiratory infection (SARI) – a respiratory illness with fever and cough, occurring within the past 10 days and requiring hospital admission – has not been evaluated for critically ill patients. Using integrated electronic health records data, we developed an automated search algorithm to identify SARI cases in a large cohort of critical care patients and evaluate patient outcomes. We conducted a retrospective cohort study of all admissions to a medical intensive care unit from August 2009 through March 2016. Subsets were randomly selected for deriving and validating a search algorithm, which was compared with temporal trends in laboratory-confirmed influenza to ensure that SARI was correlated with influenza. The algorithm was applied to the cohort to identify clinical differences for patients with and without SARI. For identifying SARI, the algorithm (sensitivity, 86.9%; specificity, 95.6%) outperformed billing-based searching (sensitivity, 73.8%; specificity, 78.8%). Automated searching correlated with peaks in laboratory-confirmed influenza. Adjusted for severity of illness, SARI was associated with more hospital, intensive care unit and ventilator days but not with death or dismissal to home. The search algorithm accurately identified SARI for epidemiologic study and surveillance.

scholarly journals Viral And Antibody Testing For Coronavirus Disease 2019 (Covid-19): Factors Associated With Positivity In Electronic Health Records From The United States

2021 ◽  
Author(s):  
Lisa Lindsay ◽  
Matthew H. Secrest ◽  
Shemra Rizzo ◽  
Dan Keebler ◽  
Fei Yang ◽  
...  
Keyword(s):  
United States ◽  
Clinical Practice ◽  
Electronic Health Records ◽  
Temporal Trends ◽  
Antibody Test ◽  
The United States ◽  
Health Records ◽  
Factors Associated ◽  
Electronic Health ◽  
Antibody Tests

AbstractBackgroundInsufficient information on SARS-CoV-2 testing results exists in clinical practice from the United States.MethodsWe conducted an observational retrospective cohort study using Optum® de-identified COVID-19 electronic health records from the United States to characterize patients who received a SARS-CoV-2 viral or antibody test between February 20, 2020 and July 10, 2020. We assessed temporal trends in testing and positivity by demographic and clinical characteristics; evaluated concordance between viral and antibody tests; and identified factors associated with positivity via multivariable logistic regression.ResultsOur study population included 891,754 patients. Overall positivity rate for SARS-CoV-2 was 9% and 12% for viral and antibody tests, respectively. Positivity rate was inversely associated with the number of individuals tested and decreased over time across regions and race/ethnicities. Among patients who received a viral test followed by an antibody test, concordance ranged from 90%-93% depending on the duration between the two tests which is notable given uncertainties related to specific viral and antibody test characteristics. The following factors increased the odds of viral and antibody positivity in multivariable models: male, Hispanic or non-Hispanic Black and Asian, uninsured or Medicaid insurance, Northeast residence, dementia, diabetes, and obesity. Charlson Comorbidity Index was negatively associated with test positivity. We identified symptoms that were positively associated with test positivity, as well as, commonly co-occurring symptoms / conditions. Pediatric patients had reduced odds of a positive viral test, but conversely had increased odds of a positive antibody test.ConclusionsThis study identified sociodemographic and clinical factors associated with SARS-CoV-2 testing and positivity within routine clinical practice from the United States.

Estimating the prevalence of multiple sclerosis using 56.6 million electronic health records from the United States

2019 ◽  
Vol 26 (14) ◽  
pp. 1948-1952 ◽  
Author(s):  
Farren BS Briggs ◽  
Eddie Hill
Keyword(s):  
Multiple Sclerosis ◽  
Electronic Health Records ◽  
Census Data ◽  
Black Americans ◽  
The United States ◽  
Health Claims ◽  
Health Records ◽  
Black Race ◽  
Electronic Health ◽  
Age And Sex

Background/objective: In 2019, the 2010 U.S. multiple sclerosis (MS) prevalence was robustly estimated (265.1–309.2/100,000) based on large administrative health-claims datasets. Using 56.6 million electronic health records (EHRs), we sought to generate complementary age, sex, and race standardized estimates. Methods/results: Using de-identified EHRs and 2018 U.S. Census data, we estimated an age- and sex-standardized MS prevalence of 219.5/100,000 which increased to 274.5/100,000 when accounting for White and Black race alone. Women aged 50 to 69 years had the highest prevalence (>600/100,000). Among White and Black Americans, the age- and sex-standardized prevalence was 283.7 and 226.1 per 100,000, respectively. Conclusion: Using 56.6 million EHRs and standardizing for age, sex, and race (White and Black Americans only), we estimated at least 810,504 Americans were living with MS in 2018.

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