Applying Data Warehousing to a Phase III Clinical Trial From the Fondazione Italiana Linfomi Ensures Superior Data Quality and Improved Assessment of Clinical Outcomes.

PURPOSE: Data collection in clinical trials is becoming complex, with a huge number of variables that need to be recorded, verified, and analyzed to effectively measure clinical outcomes. In this study, we used data warehouse (DW) concepts to achieve this goal. A DW was developed to accommodate data from a large clinical trial, including all the characteristics collected. We present the results related to baseline variables with the following objectives: developing a data quality (DQ) control strategy and improving outcome analysis according to the clinical trial primary end points. METHODS: Data were retrieved from the electronic case reporting forms (eCRFs) of the phase III, multicenter MCL0208 trial (ClinicalTrials.gov identifier: NCT02354313) of the Fondazione Italiana Linfomi for younger patients with untreated mantle cell lymphoma (MCL). The DW was created with a relational database management system. Recommended DQ dimensions were observed to monitor the activity of each site to handle DQ management during patient follow-up. The DQ management was applied to clinically relevant parameters that predicted progression-free survival to assess its impact. RESULTS: The DW encompassed 16 tables, which included 226 variables for 300 patients and 199,500 items of data. The tool allowed cross-comparison analysis and detected some incongruities in eCRFs, prompting queries to clinical centers. This had an impact on clinical end points, as the DQ control strategy was able to improve the prognostic stratification according to single parameters, such as tumor infiltration by flow cytometry, and even using established prognosticators, such as the MCL International Prognostic Index. CONCLUSION: The DW is a powerful tool to organize results from large phase III clinical trials and to effectively improve DQ through the application of effective engineered tools.

Modelling and analysis of four telemedicine Italian experiences.

In the last 10 years the European population aged 65 years and over grew of 2.3%, with Italy having the highest share of elderly persons in the total population. OPLON (OPportunities for active and healthy LONgevity) is a project supported by the Italian Ministry of Education, Universities, and Research aiming to identify and prevent frailty and to improve the life quality of elderly subjects. The main goal of OPLON is to develop a "Care&Cure" model for the management of subjects with different morbidities and co-morbidities, adaptable to the subject's risk level and to the regional contexts. In this study we analyzed four Italian telemedicine experiences addressed to chronic, geriatric or partially self-sufficient subjects. Each of them was exhaustively described by means of three process modelling tools (synopsis, workflow and swimlane activity diagrams). Starting from this analysis, we defined a general model of tele-monitoring and tele-assistance of frail and pre-frail people with different needs and pathologies. The proposed model was characterized by three macro processes (enrollment, assessment and assistance) and four groups of actors (patient, general practitioner/specialist physician, multidisciplinary team, and healthcare professionals). Combining this model with a detailed analysis of regulations and legislations in force both at local and national level, it will be possible to design the complete and efficient "Care&Cure" model.

Muscle contractions in cyclic movements: Optimization of CIMAP algorithm.

During cyclic movements, the number of muscle activations and their timing are different from cycle to cycle. In a previous study, the CIMAP algorithm was proposed for grouping cycles showing similar EMG activation intervals, using dendrogram clustering. Even if the algorithm demonstrated good performances on a healthy population, the intra-cluster variability decreased when applied to datasets from pathological subjects. In this work we propose an optimized version of the CIMAP, comparing the performances of 8 different combinations of parameters used for the dendrogram construction. The cut-off point is also modified. The new and the original version of the algorithm are compared, in terms of intra-cluster variability, considering a population of 60 subjects, both healthy and pathological. The results show that the new CIMAP allows for obtaining clusters with lower variability with respect to the original version of the algorithm (p <; 0.001).

Data quality improvement of a multicenter clinical trial dataset.

Medical datasets are usually affected by several problems, such as missing values, inconsistencies, redundancies, that can influence the data mining process and the extraction of useful knowledge. For these reasons, a preprocessing phase should be performed for improving the overall quality of data and, consequently, of the information that may be discovered from them. In this study we applied five steps of data preprocessing to improve the quality of a large dataset derived from a multicenter clinical trial. Our dataset included 298 patients enrolled in a prospective, multicenter, clinical trial, characterized by 22 input variables and one class variable (MIPI value). In particular, data coming from different medical centers were firstly integrated to obtain a homogeneous dataset. The latter was normalized to scale all variables into smaller and similar intervals. Then, all missing values were estimated by means of an imputation step. The complete dataset was finally discretized and reduced to remove redundant variables and decrease the amount of data to be managed. The improvement of data quality after each step was evaluated by means of the patients' classification accuracy using the KNN classifier. Our results showed that the proposed pipeline produced an increment of more than 20% of the classification performances. Moreover, the highest growth of accuracy was obtained after missing value imputation, whereas the discretization and feature selection steps allowed for a significant reduction of variables to be managed, without any deterioration of the information contained in data.