Thomas Ganslandt

Evaluation of data completeness in the electronic health record for the purpose of patient recruitment into clinical trials: a retrospective analysis of element presence

BackgroundComputerized clinical trial recruitment support is one promising field for the application of routine care data for clinical research. The primary task here is to compare the eligibility criteria defined in trial protocols with patient data contained in the electronic health record (EHR). To avoid the implementation of different patient definitions in multi-site trials, all participating research sites should use similar patient data from the EHR. Knowledge of the EHR data elements which are commonly available from most EHRs is required to be able to define a common set of criteria. The objective of this research is to determine for five tertiary care providers the extent of available data compared with the eligibility criteria of randomly selected clinical trials.MethodsEach participating study site selected three clinical trials at random. All eligibility criteria sentences were broken up into independent patient characteristics, which were then assigned to one of the 27 semantic categories for eligibility criteria developed by Luo et al. We report on the fraction of patient characteristics with corresponding structured data elements in the EHR and on the fraction of patients with available data for these elements. The completeness of EHR data for the purpose of patient recruitment is calculated for each semantic group.Results351 eligibility criteria from 15 clinical trials contained 706 patient characteristics. In average, 55% of these characteristics could be documented in the EHR. Clinical data was available for 64% of all patients, if corresponding data elements were available. The total completeness of EHR data for recruitment purposes is 35%. The best performing semantic groups were ‘age’ (89%), ‘gender’ (89%), ‘addictive behaviour’ (74%), ‘disease, symptom and sign’ (64%) and ‘organ or tissue status’ (61%). No data was available for 6 semantic groups.ConclusionsThere exists a significant gap in structure and content between data documented during patient care and data required for patient eligibility assessment. Nevertheless, EHR data on age and gender of the patient, as well as selected information on his disease can be complete enough to allow for an effective support of the manual screening process with an intelligent preselection of patients and patient data.

Disease burden and risk profile in referred patients with moderate chronic kidney disease: composition of the German Chronic Kidney Disease (GCKD) cohort

BACKGROUND A main challenge for targeting chronic kidney disease (CKD) is the heterogeneity of its causes, co-morbidities and outcomes. Patients under nephrological care represent an important reference population, but knowledge about their characteristics is limited. METHODS We enrolled 5217 carefully phenotyped patients with moderate CKD [estimated glomerular filtration rate (eGFR) 30-60 mL/min per 1.73 m(2) or overt proteinuria at higher eGFR] under routine care of nephrologists into the German Chronic Kidney Disease (GCKD) study, thereby establishing the currently worldwide largest CKD cohort. RESULTS The cohort has 60% men, a mean age (±SD) of 60 ± 12 years, a mean eGFR of 47 ± 17 mL/min per 1.73 m(2) and a median (IQR) urinary albumin/creatinine ratio of 51 (9-392) mg/g. Assessment of causes of CKD revealed a high degree of uncertainty, with the leading cause unknown in 20% and frequent suspicion of multifactorial pathogenesis. Thirty-five per cent of patients had diabetes, but only 15% were considered to have diabetic nephropathy. Cardiovascular disease prevalence was high (32%, excluding hypertension); prevalent risk factors included smoking (59% current or former smokers) and obesity (43% with BMI >30). Despite widespread use of anti-hypertensive medication, only 52% of the cohort had an office blood pressure <140/90 mmHg. Family histories for cardiovascular events (39%) and renal disease (28%) suggest familial aggregation. CONCLUSIONS Patients with moderate CKD under specialist care have a high disease burden. Improved diagnostic accuracy, rigorous management of risk factors and unravelling of the genetic predisposition may represent strategies for improving prognosis.

Unlocking Data for Clinical Research - The German i2b2 Experience

OBJECTIVE Data from clinical care is increasingly being used for research purposes. The i2b2 platform has been introduced in some US research communities as a tool for data integration and querying by clinical users. The purpose of this project was to assess the applicability of i2b2 in Germany regarding use cases, functionality and integration with privacy enhancing tools. METHODS A set of four research usage scenarios was chosen, including the transformation and import of ontology and fact data from existing clinical data collections into i2b2 v1.4 instances. Query performance was measured in comparison to native SQL queries. A setup and administration tool for i2b2 was developed. An extraction tool for CDISC ODM data was programmed. Interfaces for the TMF privacy enhancing tools (PID Generator, Pseudonymization Service) were implemented. RESULTS Data could be imported in all tested scenarios from various source systems, including the generation of i2b2 ontology definitions. The integration of TMF privacy enhancing tools was possible without modification of the platform. Limitations were found regarding query performance in comparison to native SQL and certain temporal queries. CONCLUSIONS i2b2 is a viable platform for data query tasks in use cases typical for networked medical research in Germany. The integration of privacy enhancing tools facilitates the use of i2b2 within established data protection concepts. Entry barriers should be lowered by providing tools for simplified setup and import of medical standard formats like CDISC ODM.

Secondary use of routinely collected patient data in a clinical trial: An evaluation of the effects on patient recruitment and data acquisition

PURPOSE Clinical trials are time-consuming and require constant focus on data quality. Finding sufficient time for a trial is a challenging task for involved physicians, especially when it is conducted in parallel to patient care. From the point of view of medical informatics, the growing amount of electronically available patient data allows to support two key activities: the recruitment of patients into the study and the documentation of trial data. METHODS The project was carried out at one site of a European multicenter study. The study protocol required eligibility assessment for 510 patients in one week and the documentation of 46-186 data elements per patient. A database query based on routine data from patient care was set up to identify eligible patients and its results were compared to those of manual recruitment. Additionally, routine data was used to pre-populate the paper-based case report forms and the time necessary to fill in the remaining data elements was compared to completely manual data collection. RESULTS Even though manual recruitment of 327 patients already achieved high sensitivity (88%) and specificity (87%), the subsequent electronic report helped to include 42 (14%) additional patients and identified 21 (7%) patients, who were incorrectly included. Pre-populating the case report forms decreased the time required for documentation from a median of 255 to 30s. CONCLUSIONS Reuse of routine data can help to improve the quality of patient recruitment and may reduce the time needed for data acquisition. These benefits can exceed the efforts required for development and implementation of the corresponding electronic support systems.

Numerical Parameters and Quality Indicators in a Medical Emergency Department

BACKGROUND Despite calls for improved quality and efficiency in medical emergency departments, there exist hardly any quality indicators, and no methods of calculating efficiency have been published to date. The present study illustrates a means of presenting numerical parameters of a medical emergency department and of identifying potential quality indicators. METHOD Over a period of 12 months, all patient contacts of the medical emergency department in the University hospital of Erlangen were analyzed with respect to patient flow, diagnoses, and treatment units. The diagnostic agreement (DA) parameter was calculated from a systematic comparison of admitting and discharge diagnoses, and diagnostic efficiency (DE) was defined and calculated as the quotient of DA x100 divided by the length of stay in the emergency department. RESULTS Among the 6683 patients treated, 64.6% underwent further in-hospital care. The diagnostic spectrum of the outpatients differed markedly from that of the inpatients. Patients with diseases of the heart, gastrointestinal tract, and lungs were usually admitted to the hospital for further treatment. Patient contacts had a characteristic circadian and weekly rhythmic pattern. For the overall patient collective, the DA was 71%. The mean length of stay in the emergency department was 116 minutes, and the DE was therefore 0.61/min. The DA was highest (92%) among patients with atrial flutter or fibrillation, while the DE was highest (0.85/min) among patients with acute myocardial infarction. 14% of the patients required further treatment in intensive care. CONCLUSION Numerical parameters and quality indicators for a hospital emergency department can be presented in transparent fashion. DA and DE can be used as parameters for diagnosis-related and intradepartmental quality assessment.

Ontology-Based Data Integration between Clinical and Research Systems

Data from the electronic medical record comprise numerous structured but uncoded ele-ments, which are not linked to standard terminologies. Reuse of such data for secondary research purposes has gained in importance recently. However, the identification of rele-vant data elements and the creation of database jobs for extraction, transformation and loading (ETL) are challenging: With current methods such as data warehousing, it is not feasible to efficiently maintain and reuse semantically complex data extraction and trans-formation routines. We present an ontology-supported approach to overcome this challenge by making use of abstraction: Instead of defining ETL procedures at the database level, we use ontologies to organize and describe the medical concepts of both the source system and the target system. Instead of using unique, specifically developed SQL statements or ETL jobs, we define declarative transformation rules within ontologies and illustrate how these constructs can then be used to automatically generate SQL code to perform the desired ETL procedures. This demonstrates how a suitable level of abstraction may not only aid the interpretation of clinical data, but can also foster the reutilization of methods for un-locking it.

Mapping local laboratory interface terms to LOINC at a German university hospital using RELMA V.5: a semi-automated approach

OBJECTIVE Logical Observation Identifiers Names and Codes (LOINC) mapping of laboratory data is often a question of the effort of mapping compared with the benefits of the structure achieved. The new LOINC mapping assistant RELMA (version 2011) has the potential to reduce the effort required for semi-automated mapping. We examined quality, time effort, and sustainability of such mapping. METHODS To verify the mapping quality, two samples of 100 laboratory terms were extracted from the laboratory system of a German university hospital and processed in a semi-automated fashion with RELMA V.5 and LOINC V.2.34 German translation DIMDI to obtain LOINC codes. These codes were reviewed by two experts from each of two laboratories. Then all 2148 terms used in these two laboratories were processed in the same way. RESULTS In the initial samples, 93 terms from one laboratory system and 92 terms from the other were correctly mapped. Of the total 2148 terms, 1660 could be mapped. An average of 500 terms per day or 60 terms per hour could be mapped. Of the laboratory terms used in 2010, 99% could be mapped. DISCUSSION Semi-automated LOINC mapping of non-English laboratory terms has become promising in terms of effort and mapping quality using the new version RELMA V.5. The effort is probably lower than for previous manual mapping. The mapping quality equals that of manual mapping and is far better than that reported with previous automated mapping activities. CONCLUSION RELMA V.5 and LOINC V.2.34 offer the opportunity to start thinking again about LOINC mapping even in non-English languages, since mapping effort is acceptable and mapping results equal those of previous manual mapping reports.

Simplified generation of biomedical 3D surface model data for embedding into 3D portable document format (PDF) files for publication and education.

The usefulness of the 3D Portable Document Format (PDF) for clinical, educational, and research purposes has recently been shown. However, the lack of a simple tool for converting biomedical data into the model data in the necessary Universal 3D (U3D) file format is a drawback for the broad acceptance of this new technology. A new module for the image processing and rapid prototyping framework MeVisLab does not only provide a platform-independent possibility to create surface meshes out of biomedical/DICOM and other data and to export them into U3D – it also lets the user add meta data to these meshes to predefine colors and names that can be processed by a PDF authoring software while generating 3D PDF files. Furthermore, the source code of the respective module is available and well documented so that it can easily be modified for own purposes.

The Clinical Data Intelligence Project

This article is about a new project that combines clinical data intelligence and smart data. It provides an introduction to the “Klinische Datenintelligenz” (KDI) project which is founded by the Federal Ministry for Economic Affairs and Energy (BMWi); we transfer research and development results (R&D) of the analysis of data which are generated in the clinical routine in specific medical domain. We present the project structure and goals, how patient care should be improved, and the joint efforts of data and knowledge engineering, information extraction (from textual and other unstructured data), statistical machine learning, decision support, and their integration into special use cases moving towards individualised medicine. In particular, we describe some details of our medical use cases and cooperation with two major German university hospitals.

High-Dose Subconjunctival Cyclosporine A Implants Do Not Affect Corneal Neovascularization after High-Risk Keratoplasty

PURPOSE To test whether subconjunctival cyclosporine A (CsA) implants affect the incidence and the degree of corneal neovascularization occurring after penetrating keratoplasty. DESIGN Prospective, randomized, multicenter, controlled phase 2/3 clinical trial. The study comprised 43 trial sites in Germany, India, and the United States. PARTICIPANTS Enrolled patients (n = 97) were randomized to 1 of 3 groups: treatment group A (n = 36), treatment group B (n = 40), and the control group (n = 21). METHODS Patients from each group received either of 2 doses of subconjunctival CsA (group A, low-dose CsA; group B, high-dose CsA) or placebo (carrier only) implants at the time of high-risk penetrating keratoplasty. MAIN OUTCOME MEASURES The incidence and degree of corneal neovascularization occurring after penetrating keratoplasty were evaluated in a substudy (LX201-01 study: NCT00447187). A web-based image upload system was developed. Standardized digital slit-lamp pictures were quantitatively and objectively evaluated using CellˆF morphometry software. RESULTS No statistically significant difference in incidence and degree of corneal neovascularization developing after penetrating keratoplasty was found between treatment groups and placebo group. Mean corneal neovascularization area at week 52 (visit 12) was 2.32±1.79% in treatment group A versus placebo (2.79±2.11%; P = 0.45) and 2.74±2.22% in treatment group B versus placebo (2.79±2.11%; P = 0.94). CONCLUSIONS High-dose subconjunctival CsA implants do not significantly affect corneal neovascularization after high-risk penetrating keratoplasty. This suggests that local CsA has negligible antiangiogenic effects in the human cornea, at least in the transplant setting.