eTACTS: A method for dynamically filtering clinical trial search results

Miotto, Riccardo; Jiang, Silis Y.; Weng, Chunhua

doi:10.1016/j.jbi.2013.07.014

Cited by 20 publications

(12 citation statements)

References 30 publications

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“…EHRs are also useful for studying distributions of disease indicators [17,18], such as hemoglobin A 1c (HbA 1c ) and serum glucose, in both inpatient and outpatient populations. Meanwhile, the mandatory public registry for clinical trials, ClinicalTrials.gov [19], provides rich information from more than 160,000 clinical trials investigating thousands of diseases, facilitating systematic analysis of the distributions of the characteristics of clinical trial target populations, as reflected in recruitment eligibility criteria, which can be downloaded, parsed, and aggregated [20][21][22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

A Distribution-based Method for Assessing The Differences between Clinical Trial Target Populations and Patient Populations in Electronic Health Records

Weng

Ryan

et al. 2014

Appl Clin Inform

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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 NewYork Presbyterian Hospital. The two populations were compared using the Generalizability Index for Study Traits (GIST) on the earliest diagnosis age and the mean hemoglobin A 1c (HbA 1c ) values. Results: Greater than 70% of Type 2 diabetes studies allow patients with HbA 1c measures between 7 and 10.5, but less than 40% of studies allow HbA 1c <7 and fewer than 45% of studies allow HbA 1c >10.5. In the real-world population, only 38% of patients had HbA 1c between 7 and 10.5, with 12% having values above the range and 52% having HbA 1c <7. The GIST for HbA 1c 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 HbA 1c . We found that the generalizability of age increased from Phase 1 to Phase 3 while the generalizability of HbA 1c 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 generalizability of clinical trials and for defining population-representative clinical trial eligibility criteria.

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Section: Introductionmentioning

confidence: 99%

A Distribution-based Method for Assessing The Differences between Clinical Trial Target Populations and Patient Populations in Electronic Health Records

Weng

Ryan

et al. 2014

Appl Clin Inform

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“…When there is a specific maximum value range defined in DK, strategy (1) is used to enlarge the maximum value range to [min_value/ threshold1, max_value*threshold1] directly. For example, the enlarged range is [2,24] for HbA1C supposing the defined maximum value range is [4,12]. Once there is no pre-defined value range available, Valx calculates the average value associated with the same variable across statements.…”

Section: Step 7: Heuristic Rule-based Comparison Statement Verificationmentioning

confidence: 99%

“…Both the application of a clinical practice guideline on a patient and the recruitment of a research volunteer into a clinical study need to first assess if the patient or the volunteer meets the clinical care or research eligibility criteria, which exist largely as free text in clinical practice guidelines or clinical trial protocols [1][2][3][4][5][6]. Anecdotally over 40% of free-text eligibility criteria contain numeric comparison statements, e.g., "HbA1c superior or equal to 7.5%" and "age eligibility for study: 18 years and older".…”

Section: Introductionmentioning

confidence: 99%

Valx: A System for Extracting and Structuring Numeric Lab Test Comparison Statements from Text

Hao¹,

Liu²,

Weng³

2016

Methods Inf Med

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Objectives To develop an automated method for extracting and structuring numeric lab test comparison statements from text and evaluate the method using clinical trial eligibility criteria text. Methods Leveraging semantic knowledge from the Unified Medical Language System (UMLS) and domain knowledge acquired from the Internet, Valx takes 7 steps to extract and normalize numeric lab test expressions: 1) text preprocessing, 2) numeric, unit, and comparison operator extraction, 3) variable identification using hybrid knowledge, 4) variable - numeric association, 5) context-based association filtering, 6) measurement unit normalization, and 7) heuristic rule-based comparison statements verification. Our reference standard was the consensus-based annotation among three raters for all comparison statements for two variables, i.e., HbA1c and glucose, identified from all of Type 1 and Type 2 diabetes trials in ClinicalTrials.gov. Results The precision, recall, and F-measure for structuring HbA1c comparison statements were 99.6%, 98.1%, 98.8% for Type 1 diabetes trials, and 98.8%, 96.9%, 97.8% for Type 2 Diabetes trials, respectively. The precision, recall, and F-measure for structuring glucose comparison statements were 97.3%, 94.8%, 96.1% for Type 1 diabetes trials, and 92.3%, 92.3%, 92.3% for Type 2 diabetes trials, respectively. Conclusions Valx is effective at extracting and structuring free-text lab test comparison statements in clinical trial summaries. Future studies are warranted to test its generalizability beyond eligibility criteria text. The open-source Valx enables its further evaluation and continued improvement among the collaborative scientific community.

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“…We have developed methods for parsing eligibility features from free-text eligibility criteria [17, 27–41] and the derived frequent eligibility features across ClinicalTrials.gov study summaries have produced promising results for searching and indexing studies [29], probing disease relatedness [30], and clustering studies with similar eligibility criteria [17]. Enabled by these techniques, we have created a database of discrete clinical trial eligibility features extracted from ClinicalTrials.gov called COMPACT (Commonalities in Target Populations of Clinical Trials) [42], which allows users to flexibly query sets of clinical studies (e.g., Type 2 diabetes studies) on their shared eligibility features (e.g., HbA1c or BMI) and attributes (e.g., allowed value range for HbA1c or BMI).…”

Section: Introductionmentioning

confidence: 99%

Visual aggregate analysis of eligibility features of clinical trials

Carini

Sim

et al. 2015

Journal of Biomedical Informatics

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Objective To develop a method for profiling the collective populations targeted for recruitment by multiple clinical studies addressing the same medical condition using one eligibility feature each time. Methods Using a previously published database COMPACT as the backend, we designed a scalable method for visual aggregate analysis of clinical trial eligibility features. This method consists of four modules for eligibility feature frequency analysis, query builder, distribution analysis, and visualization, respectively. This method is capable of analyzing (1) frequently used qualitative and quantitative features for recruiting subjects for a selected medical condition, (2) distribution of study enrollment on consecutive value points or value intervals of each quantitative feature, and (3) distribution of studies on the boundary values, permissible value ranges, and value range widths of each feature. All analysis results were visualized using Google Charts API. Five recruited potential users assessed the usefulness of this method for identifying common patterns in any selected eligibility feature for clinical trial participant selection. Results We implemented this method as a Web-based analytical system called VITTA (Visual Analysis Tool of Clinical Study Target Populations). We illustrated the functionality of VITTA using two sample queries involving quantitative features BMI and HbA1c for conditions “hypertension” and “Type 2 diabetes”, respectively. The recruited potential users rated the user-perceived usefulness of VITTA with an average score of 86.4/100. Conclusions We contributed a novel aggregate analysis method to enable the interrogation of common patterns in quantitative eligibility criteria and the collective target populations of multiple related clinical studies. A larger-scale study is warranted to formally assess the usefulness of VITTA among clinical investigators and sponsors in various therapeutic areas.

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eTACTS: A method for dynamically filtering clinical trial search results

Cited by 20 publications

References 30 publications

A Distribution-based Method for Assessing The Differences between Clinical Trial Target Populations and Patient Populations in Electronic Health Records

A Distribution-based Method for Assessing The Differences between Clinical Trial Target Populations and Patient Populations in Electronic Health Records

Valx: A System for Extracting and Structuring Numeric Lab Test Comparison Statements from Text

Visual aggregate analysis of eligibility features of clinical trials

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