PAT: An intelligent Authoring Tool for facilitating Clinical Trial - - PDF document
Institute of Communication and Computer Systems (ICCS), National Technical University of Athens, (NTUA), Greece PAT: An intelligent Authoring Tool for facilitating Clinical Trial Design Anastasios TAGARIS, Vassiliki ANDRONIKOU, Efstathios
Anastasios TAGARIS, Vassiliki ANDRONIKOU, Efstathios KARANASTASIS, Efthymios CHONDROGIANNIS, Charalambos TSIRMPAS, Theodora VARVARIGOU and Dimitris KOUTSOURIS
– Efficient Patient Recruitment for Innovative Clinical Trials of Existing Drugs to
– Facilitating Clinical trial design
data bases
– Drug Repositioning
Switching and Drug re-tasking) is the application of known drugs and compounds to new indications, i.e. new diseases
7 countries 1 company 2 research institutes 6 universities TU D ICCS/NTUA NKU A CNR/IFC IOPR CU H LU H SM I
Institute of Psychophysiology & Rehabilitation
National Research Council Institute of Clinical Physiology (CNR-IFC)
in being approved by the regulatory bodies.
commercially upon introduction in the marketplace because of side-effects.
incompleteness, ambiguity and inconsistency with most errors being identified within the protocol writing process.
Criteria (quite often poorly justified, vague, ambiguous or overly rigid).
– Increase patient risks and weaken the ability to utilize the inclusion/exclusion criteria to establish appropriate labeling
– Failure to represent the real patient population and mimic clinical practice – Limited generalizability – Efficacy remaining hidden – Increased study complexity & costs – Limited patient accrual affecting study feasibility, duration and costs
mechanisms aiming at improving several important processes (and their corresponding parameters) in clinical research:
1. CTP Management & Design 2. Specification of study parameters, (duration, no. of patients, …) 3. Description of the eligible patient population (I/E Criteria Specification) 4. Links to Intelligent search through predefined queries and background knowledge 5. Suggestion of I/E criteria based on investigational drug, study disease and published research findings 6. Patient safety and Study efficacy-oriented validation checks on I/E criteria 7. Estimation of number of patients available at cooperating hospitals allowing for checking on study feasibility and for evaluating the recruitment sites 8. Translation of the I/E criteria into the PONTE EHR model parameters
– Scope: sketch out the CTP structure, parameter dependencies and semantic relations – Materials used: Existing CTProtocols ! static & dynamic Mockups – Outcome:
– CTP Structure, – specification of parameter dependencies – identification of the informational elements which could benefit from decision support – predefined queries per protocol section
clinicaltrials.gov
– Scope: Understand the way that PIs use to express the EC, discover the common criteria categories and corresponding structures to build user friendly GUIs. – Materials used: XML description of studies => Downloaded in a local DB
– Analysed types of study parameters (study type, blinding methods, control types, end points, etc) – Analysed the EC structure, categorization, detail levels; Analysis was based on ~1000 criteria selected across all studies of the portal, where a wide spectrum
– Outcome:
– Eligibility Criteria categorisation and corresponding structures – In parallel this procedure helped us capture the semantics of EC and develop the Eligibility Criteria Ontology (EC-O) that allows for semantic inference.
Specs to be followed for implementation
– “access from anywhere”, – “collaboration among experts” – “diversity in the structure of data to handle”
Controller (MVC) approach
– MVC separates the representation of information from the user's interaction with it ! code reusability and separation of concerns.
11
Runs$on$any$web$browser. Contains$a$JavaScript$library component$(PAT$Web$Client Library)$implemen1ng$the presenta-on$logic$of$the applica1on Provides$rich$user interac-on$capabili-es and$communicates$with the$serverFside components implements$the$core func-onality$of$the$PAT.$It encapsulates$the$user$interface rendering$logic$and$the
workflows Provides$persistence$services$for the$needs$of$the$PAT.$All$CTP parameters$are$persisted$on$the local$storage$throughout$the authoring$lifecycle implements$the$core$applica1on$logic and$is$responsible$for$interac-ng$with PONTE$components$(DSS,$OM).$It$also uses$the$Local&Storage&component$to temporarily$store$the$CTP$parameters filled$in$by$the$users$and$other applica1on$state$informa1on. receives$requests$from$the$browser, invokes$the$appropriate$ac1ons$on$the PAT$Business&Logic&component,$creates the$corresponding$UI$elements$from the$UI&Controls&Library&component$and renders$the$response$back$to$the browser is$also$responsible$for$communica-ng with$the$Authen-ca-on$module$in
the$execu1on$of$the$request evaluate$the$user$creden-als provided$through$the$PAT$in
access$to$the$PONTE$plaQorm. CTP$parameter$dependency checks,$pa1ent$popula1on informa1on,$or$subjects$eligible for$recruitment$along$with profiling the$user$wishes$to$perform$a manual$search$in$the$underlying data$sources invoked$to$access$the$CTP$Repository in$order$to$store$and$retrieve$the$CTP instances$edited$by$the$users. is$called$by$the$PAT$to$retrieve$the$CTP structure,$as$well$as$its$sec-on$and seman-c$dependencies
hypothesis evaluation
evidence about benefit
and relevance to clinical practice
parameters
assesed and evaluated against real-world data - which determine the right patient population for the study
representative target patient population
patient accrual in terms of size and time Step II Step I Patient Selection Final Trial Protocol Incl./Excl. Criteria Evaluation Clinical Trial Design Hypothesis Evaluation Step III
– 3-way navigation through the study parameters – Saving different CTP versions with different levels of access – CTP status management
for study parameter and EC specification. It organizes CTPs around 3 different views: the hierarchy of its parameters, dependencies among them and their semantic linking with the CTP three key concepts
by the experts, as they allowed for “easy and fast detection of inconsistencies” and “less time consuming parameter specification” although the dependencies should be further enriched while the 3 “key concepts” could be expanded to also include “clinical trials” and “patient” concepts.
healthcare entities (i.e., hospitals, clinics, etc.) and provides an estimation of the eligible population size. This functionality was considered of “great value” as it provides an indication about the feasibility of the study and offers automatic retrieval of eligible patients. This is expected to significantly reduce the patient recruitment resources, both in terms of time and effort required, but also, will allow for fast and effective site recruitment.
capable to cover frequently used criteria with a modest degree
capture even more complicated EC but also to enrich the underlying vocabularies.
– the CTP model (in order to capture more parameters and more complicated relations and dependencies) – the EC model and – the supported vocabularies, to allow the specification of even more complex criteria.
– include model editing (modify the structure) for allowing researchers to adjust the CTP model to their needs – Compile the resulting CTP in different file formats (ex. Word, PDF, Secure PDF etc.) to enable communication with other non-PONTE systems (ethical committees)
Bridging the gap between healthcare (especially EHRs) and clinical research will improve the design
eligibility criteria is a necessary condition to reduce patients’ recruitment time, improve safety and efficacy, and reduce cost of CT.
clinical trial costs
recruitment issues
and increased costs
constraints (privacy protection and confidentiality)
heterogeneity across EHRs in different departments with and/or across hospitals - Applied only for Consented patients
their patients’ EHRs.
Step II Step I Patient Selection Final Trial Protocol Incl./Excl. Criteria Evaluation Clinical Trial Design Hypothesis Evaluation Step III Clinical Trial Design
structure, dependencies and semantic relations
different views allowing for detecting inconsistencies across parameters and missing values
with literature for answer retrieval
Harriett G. et Al. JAMA 2007
What is the evidence about the use of TH to improve myocardial function after acute myocardial infarction in diabetes?
clinical trial costs
recruitment issues
and increased costs
constraints (privacy protection and confidentiality)
heterogeneity across EHRs in different departments with and/or across hospitals - Applied only for Consented patients
their patients’ EHRs.