scholarly journals Creation of a ustekinumab external control arm for Crohn's disease using electronic health records data: a pilot study

2021 ◽  
Author(s):  
Vivek Ashok Rudrapatna ◽  
Yao-Wen Cheng ◽  
Colin Feuille ◽  
Arman Mosenia ◽  
Jonathan Shih ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Electronic Health Records ◽  
Disease Activity ◽  
Real World ◽  
External Control ◽  
Health Records ◽  
Electronic Health ◽  
Trial Efficiency ◽  
Manual Review

Objectives: The use of external control arms to support claims of efficacy and safety is growing in interest among drug sponsors and regulators. However, experience with performing these kinds of studies for complex, immune-mediated diseases is limited. We sought to establish a method for creating an external control arm for Crohn's disease. Methods: We queried electronic health records databases and screened records at the University of California, San Francisco to identify patients meeting the major eligibility criteria of TRIDENT, a concurrent trial involving ustekinumab as a reference arm. Timepoints were defined to balance the tradeoff between missing disease activity and bias. We compared two imputation models by their impacts on cohort membership and outcomes. We compared the results of ascertaining disease activity using structured data algorithms against manual review. We used these data to estimate ustekinumab's real-world effectiveness. Results: Screening identified 183 patients. 30% of the cohort had missing baseline data. Two imputation models were tested and had similar effects on cohort definition and outcomes. Algorithms for ascertaining non-symptom-based elements of disease activity were similar in accuracy to manual review. The final cohort consisted of 56 patients. 34% of the cohort was in steroid-free clinical remission by week 24. Conclusions: Differences in the timing and goals of real-world encounters as compared to controlled studies directly translate into significant missing data and lost sample size. Efforts to improve real-world data capture and better align trial design with clinical practice may enable robust external control arm studies and improve trial efficiency.

Mo1342 A Concept Recognition Tool to Identify the Surgical Complications of Crohn's Disease in Electronic Health Records

2013 ◽  
Vol 144 (5) ◽  
pp. S-641-S-642
Author(s):  
Shyam Visweswaran ◽  
Melissa I. Saul ◽  
Jeremy U. Espino ◽  
John Levander ◽  
Jason M. Swoger ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Electronic Health Records ◽  
Surgical Complications ◽  
Health Records ◽  
Concept Recognition ◽  
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

Assessing function of electronic health records for real-world data generation

2018 ◽  
Vol 24 (3) ◽  
pp. 95-98 ◽  
Author(s):  
Daphne Guinn ◽  
Erin E Wilhelm ◽  
Grazyna Lieberman ◽  
Sean Khozin
Keyword(s):  
Electronic Health Records ◽  
Real World ◽  
Data Generation ◽  
Real World Data ◽  
Health Records ◽  
World Data ◽  
Electronic Health

scholarly journals Secondary Use of Electronic Health Records for Building Large, Real-World ILD Cohorts

2021 ◽  
Author(s):  
E.D. Farrand ◽  
O. Gologorskaya ◽  
H. Mills ◽  
L. Radhakrishnan ◽  
H.R. Collard ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Real World ◽  
Health Records ◽  
Secondary Use ◽  
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 A Distribution-based Method for Assessing The Differences between Clinical Trial Target Populations and Patient Populations in Electronic Health Records

2014 ◽  
Vol 05 (02) ◽  
pp. 463-479 ◽  
Author(s):  
P. Ryan ◽  
Y. Zhang ◽  
F. Liu ◽  
J. Gao ◽  
J.T. Bigger ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Electronic Health Records ◽  
Real World ◽  
World Population ◽  
Health Records ◽  
Target Populations ◽  
Electronic Health

SummaryObjective: To improve the transparency of clinical trial generalizability and to illustrate the method using Type 2 diabetes as an example.Methods: Our data included 1,761 diabetes clinical trials and the electronic health records (EHR) of 26,120 patients with Type 2 diabetes who visited Columbia University Medical Center of New-York Presbyterian Hospital. The two populations were compared using the Generalizability Index for Study Traits (GIST) on the earliest diagnosis age and the mean hemoglobin A1c (HbA1c) values.Results: Greater than 70% of Type 2 diabetes studies allow patients with HbA1c measures between 7 and 10.5, but less than 40% of studies allow HbA1c<7 and fewer than 45% of studies allow HbA1c>10.5. In the real-world population, only 38% of patients had HbA1c between 7 and 10.5, with 12% having values above the range and 52% having HbA1c<7. The GIST for HbA1c was 0.51. Most studies adopted broad age value ranges, with the most common restrictions excluding patients >80 or <18 years. Most of the real-world population fell within this range, but 2% of patients were <18 at time of first diagnosis and 8% were >80. The GIST for age was 0.75. Conclusions: We contribute a scalable method to profile and compare aggregated clinical trial target populations with EHR patient populations. We demonstrate that Type 2 diabetes studies are more generalizable with regard to age than they are with regard to HbA1c. We found that the generalizability of age increased from Phase 1 to Phase 3 while the generalizability of HbA1c decreased during those same phases. This method can generalize to other medical conditions and other continuous or binary variables. We envision the potential use of EHR data for examining the generaliz-ability of clinical trials and for defining population-representative clinical trial eligibility criteria.Citation: Weng C, Li Y, Ryan P, Zhang Y, Liu F, Gao J, Bigger JT, Hripcsak G. A distribution-based method for assessing the differences between clinical trial target populations and patient populations in electronic health records. Appl Clin Inf 2014; 5: 463–479 http://dx.doi.org/10.4338/ACI-2013-12-RA-0105

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