Supporting translational research on inherited cardiomyopathies through information technology.

OBJECTIVES: The INHERITANCE project, funded by the European Commission, is aimed at studying genetic or inherited Dilated cardiomyopathies (DCM) and at understanding the impact and management of the disease within families that suffer from heart conditions that are caused by DCMs. The biomedical informatics research activity of the project aims at implementing information technology solutions to support the project team in the different phases of their research, in particular in genes screening prioritization and new gene-disease association discovery. METHODS: In order to manage the huge quantity of scientific, clinical and patient data generated by the project several advanced biomedical informatics tools have been developed. The paper describes a layer of software instruments to support translation of the results of the project in clinical practice as well as to support the scientific discovery process. This layer includes data warehousing, intelligent querying of the phenotype data, integrated search of biological data and knowledge repositories, text mining of the relevant literature, and case based reasoning. RESULTS: At the moment, a set of 1,394 patients and 9,784 observations has been stored into the INHERITANCE data warehouse. The literature database contains more than 1,100,000 articles retrieved from the Pubmed and generically related to cardiac diseases, already analyzed for extracting medical concepts and genes. CONCLUSIONS: After two years of project the data warehouse has been completely set up and the text mining tools for automatic literature analysis have been implemented and tested. A first prototype of the decision support tool for knowledge discovery and gene prioritization is available, but a more complete release is still under development.

Automatic pedigree reconstruction for genetic studies in isolated populations.

This paper describes a tool implemented to automatically reconstruct the pedigree of an isolated population of Northern Italy with the aim of supporting genetic studies. The goal of such studies is to analyze genealogic, clinical and genetic data for genetic dissection of complex diseases. In this context the reconstruction of the population pedigree is fundamental to verify that such population is a genetic isolate and obtain the parental relationships among the individuals participating to the study. The algorithm presented in the paper, from heterogeneous data sources (demographic municipal and parish archives and other data sources), derives the pedigree applying several heuristic rules in a predefined order. One of the main difficulties in performing such task stands in the "record linkage" process that requires the definition of a sufficiently general strategy for managing the ambiguities caused by missing or imprecise/erroneous input data. The paper, finally, presents and discusses the preliminary results obtained by reconstructing the pedigree of four villages from the data collected during the first eighteen months of project.

The M2DM Project--the experience of two Italian clinical sites with clinical evaluation of a multi-access service for the management of diabetes mellitus patients.

OBJECTIVES: This paper presents a multi-access service for the management of diabetes mellitus patients and the results of its assessment in two Italian clinical sites. METHODS: The service was evaluated for one year in order to prove the advantages of these kind of systems from different points of view. In this paper the clinical, usability and technical outcomes are presented. RESULTS: The evaluation results show that, thanks to the high flexibility of the implemented service, the telemedicine management of diabetes patients is feasible, well accepted by patients and clinically effective. However, in Italy the problem of quantifying the reimbursement rate of telematic services and the impact they have on the organization are factors that may hamper their introduction in routine clinical practice. CONCLUSIONS: The evaluation study showed that the telemedicine intervention has been satisfactory both for physicians because it allows to constantly monitor the patients' blood glucose level and for patients because it strengthens their motivation to self-monitor the metabolic situation.

Clustering gene expression data with temporal abstractions.

This paper describes a new technique for clustering short time series coming from gene expression data. The technique is based on the labelling of the time series through temporal trend abstractions and a consequent clustering of the series on the basis of their labels. Clustering is performed at three different levels of aggregation of the original time series, so that the results are organized and visualized as a three-levels hierarchical tree. Results on simulated and on yeast data are shown. The technique appears robust and efficient and the results obtained are easy to be interpreted.

A telemedicine support for diabetes management: the T-IDDM project.

In the context of the EU funded Telematic Management of Insulin-Dependent Diabetes Mellitus (T-IDDM) project, we have designed, developed and evaluated a telemedicine system for insulin dependent diabetic patients management. The system relies on the integration of two modules, a Patient Unit (PU) and a Medical Unit (MU), able to communicate over the Internet and the Public Switched Telephone Network. Using the PU, patients are allowed to automatically download their monitoring data from the blood glucose monitoring device, and to send them to the hospital data-base; moreover, they are supported in their every day self monitoring activity. The MU provides physicians with a set of tools for data visualization, data analysis and decision support, and allows them to send messages and/or therapeutic advice to the patients. The T-IDDM service has been evaluated through the application of a formal methodology, and has been used by European patients and physicians for about 18 months. The results obtained during the project demonstration, even if obtained on a pilot study of 12 subjects, show the feasibility of the T-IDDM telemedicine service, and seem to substantiate the hypothesis that the use of the system could present an advantage in the management of insulin dependent diabetic patients, by improving communications and, potentially, clinical outcomes.

Intelligent analysis of clinical time series: an application in the diabetes mellitus domain.

This paper describes the application of a method for the intelligent analysis of clinical time series in the diabetes mellitus domain. Such a method is based on temporal abstractions and relies on the following steps: (i) 'pre-processing' of raw data through the application of suitable filtering techniques: (ii) 'extraction' from the pre-processed data of a set of abstract episodes (temporal abstractions); and (iii) 'post-processing' of temporal abstractions; the post-processing phase results in a new set of features that embeds high level information on the patient dynamics. The derived features set is used to obtain new knowledge through the application of machine learning algorithms. The paper describes in detail the application of this methodology and presents some results obtained on simulated data and on a data-set of four diabetic patients monitored for > 1 year.

Protocol-based reasoning in diabetic patient management.

We propose a system for teleconsultation in Insulin Dependent Diabetes Mellitus (IDDM) management, accessible through the use of the net. The system is able to collect monitoring data, to analyze them through a set of tools, and to suggest a therapy adjustment in order to tackle the identified metabolic problems and to fit the patient's needs. The therapy revision has been implemented through the Episodic Skeletal Planning Methodi, it generates an advice and employs it to modify the current therapeutic protocol, presenting to the physician a set of feasible solutions, among which she can choose the new one.

A distributed system for diabetic patient management.

This paper describes a telemedicine system for diabetic patients management, presenting its architecture, the technical solutions adopted and the methodologies on which it is based. The system, designed to provide decision support in a distributed environment, is composed of two modules, a Patient Unit and a Medical Unit, connected by telecommunication services. We outline how the two modules can interact to perform an effective monitoring and a cooperative control of glucose metabolism. In particular, we detail the data analysis tasks performed by the two units and how the results are exploited to assist patients and physicians in revising and adjusting the therapeutic protocol. We will finally describe the current prototypical implementation of the system that uses HTTP as the communication protocol and HTML pages as the graphical user interface.

Mining biomedical time series by combining structural analysis and temporal abstractions.

This paper describes the combination of Structural Time Series analysis and Temporal Abstractions for the interpretation of data coming from home monitoring of diabetic patients. Blood Glucose data are analyzed by a novel Bayesian technique for time series analysis. The results obtained are post-processed using Temporal Abstractions in order to extract knowledge that can be exploited "at the point of use" from physicians. The proposed data analysis procedure can be viewed as a Knowledge Discovery in Data Base process that is applied to time-varying data. The work here described is part of a Web-based telemedicine system for the management of Insulin Dependent Diabetes Mellitus patients, called T-IDDM.

Distributed intelligent data analysis in diabetic patient management.

This paper outlines the methodologies that can be used to perform an intelligent analysis of diabetic patients' data, realized in a distributed management context. We present a decision-support system architecture based on two modules, a Patient Unit and a Medical Unit, connected by telecommunication services. We stress the necessity to resort to temporal abstraction techniques, combined with time series analysis, in order to provide useful advice to patients; finally, we outline how data analysis and interpretation can be cooperatively performed by the two modules.

Predictors of prognosis in patients awaiting heart transplantation.

Patients enrolled in a clinical heart transplantation program were evaluated to identify the predictors of prognosis in patients with advanced heart disease and to optimize timing of heart transplantation. Three hundred eighty-eight subjects were consecutively evaluated from 1985 through 1989. One hundred eighty-four patients (47.5%) had dilated cardiomyopathy; 164 patients (42.2%) had ischemic heart disease; 34 patients (8.8%) had valvular heart disease, and six patients (1.5%) had miscellaneous disorders. In each patient, 45 different parameters were considered. During follow-up (mean, 8.4 months) 166 patients underwent heart transplantation; 99 patients died (heart failure, 66 patients; sudden death, 26 patients; thromboembolism, two patients; noncardiac causes, five patients). The actuarial survival was 83% at 3 months, 77% at 6 months, 73% at 9 months, 70% at 1 year, and 59% at 2 years. The median survival time was 28 months. Analysis by Cox proportional hazard regression model revealed seven independent and significant prognostic factors: etiology (p < 0.05), NYHA class (p < 0.05), third heart sound (p < 0.05), diastolic pulmonary artery pressure (p < 0.05), pulmonary wedge pressure (p < 0.01), mean systemic blood pressure (p < 0.05), and cardiac output (p < 0.05). Cox's analysis allows the computation of patient-specific curves for predictions of residual survival time at any moment during follow-up. Moreover it can be used to calculate a simple prognostic index, which enables stratification of the patient population into three risk classes: patients at high (n = 105), intermediate (n = 160) and low (n = 123) risk of early death. Pairwise comparisons of survival between the classes were significant at 1% level.

Right ventricular failure after heart transplantation: relationship with preoperative haemodynamic parameters.

The prevalence of right ventricular failure after orthotopic heart transplantation, evaluated in 196 patients, was 11.7%, as assessed by the presence during the first postoperative month of right atrial pressure > 10 mm Hg. Two deaths, related to refractory right ventricular failure, were observed within the first month, both in subjects with preoperative pulmonary arteriolar resistances > 5 Wood Units. The haemodynamic profile after heart transplantation showed a significant decrease (P < 0.01) and an early normalization of pulmonary arterial pressure, pulmonary wedge pressure and pulmonary arteriolar resistances, while right atrial pressure slowly decreased until the third month. In a long-term analysis of survival (death within 1 year) the probability of death was significantly related to the values of right atrial pressure and cardiac index during the first month after heart transplantation. Otherwise, the presence of elevated values of right atrial pressure did not show a significant correlation with the echocardiographic right ventricular end-diastolic diameter nor with the presence of right bundle branch block. The careful selection of patients referred for the cardiac transplantation (mean value of pulmonary arteriolar resistances in the evaluated subjects was 2.5 +/- 1.5 Wood Units) improves the probability of avoiding the appearance of severe right ventricular failure in the postoperative period in most cases. The best predictor of right ventricular failure remains to be clearly identified.

PRIST: a fourth-generation tool for medical information systems.

PRIST is a fourth-generation software package purposely oriented to development and management of medical applications, running under MS/DOS IBM compatible personal computers. The tool has been developed on the top of DBIII Plus language utilizing the Clipper Compiler networking features for the integration in a LAN environment. Several routines written in C and BASIC Microsoft languages integrated this DBMS-kernel system providing I/O, graphics, statistics, retrieval utilities. To increase the interactivity of the system both menu-driven and windowing interfaces have been implemented. PRIST has been utilized to develop a wide variety of small medical applications ranging from research laboratories to intensive care units. The great majority of reactions from the use of these applications were positive, confirming that PRIST is able to assist in practice management and patient care as well as research purposes.

[A new computerized file of juvenile rheumatoid arthritis]. / Un nuovo archivio computerizzato per l'artrite reumatoide giovanile.

A computerized database for patients with juvenile rheumatoid arthritis (JRA) is presented. The program has been developed using PRIST (Patient Record Information System Tool), a flexible tool specifically oriented to clinical data management. The database consists of three main sections: the fixed record devoted to anamnestic data, the periodic record collecting the clinical, laboratory and instrumental data and the balance record devoted to a periodic balance of the disease course. The major advantages of our database are: time saving data handling, elastic procedures and easy retrospective data collection.