Update and, internal and temporal-validation of the FRANCE-2 and ACC-TAVI early-mortality prediction models for Transcatheter Aortic Valve Implantation (TAVI) using data from the Netherlands heart registration (NHR).

BACKGROUND: The predictive performance of the models FRANCE-2 and ACC-TAVI for early-mortality after Transcatheter Aortic Valve Implantation (TAVI) can decline over time and can be enhanced by updating them on new populations. We aim to update and internally and temporally validate these models using a recent TAVI-cohort from the Netherlands Heart Registration (NHR). METHODS: We used data of TAVI-patients treated in 2013-2017. For each original-model, the best update-method (model-intercept, model-recalibration, or model-revision) was selected by a closed-testing procedure. We internally validated both updated models with 1000 bootstrap samples. We also updated the models on the 2013-2016 dataset and temporally validated them on the 2017-dataset. Performance measures were the Area-Under ROC-curve (AU-ROC), Brier-score, and calibration graphs. RESULTS: We included 6177 TAVI-patients, with 4.5% observed early-mortality. The selected update-method for FRANCE-2 was model-intercept-update. Internal validation showed an AU-ROC of 0.63 (95%CI 0.62-0.66) and Brier-score of 0.04 (0.04-0.05). Calibration graphs show that it overestimates early-mortality. In temporal-validation, the AU-ROC was 0.61 (0.53-0.67).The selected update-method for ACC-TAVI was model-revision. In internal-validation, the AU-ROC was 0.63 (0.63-0.66) and Brier-score was 0.04 (0.04-0.05). The updated ACC-TAVI calibrates well up to a probability of 20%, and subsequently underestimates early-mortality. In temporal-validation the AU-ROC was 0.65 (0.58-0.72). CONCLUSION: Internal-validation of the updated models FRANCE-2 and ACC-TAVI with data from the NHR demonstrated improved performance, which was better than in external-validation studies and comparable to the original studies. In temporal-validation, ACC-TAVI outperformed FRANCE-2 because it suffered less from changes over time.

External validation of existing prediction models of 30-day mortality after Transcatheter Aortic Valve Implantation (TAVI) in the Netherlands Heart Registration.

BACKGROUND: Several mortality prediction models (MPM) are used for predicting early (30-day) mortality following transcatheter aortic valve implantation (TAVI). Little is known about their predictive performance in external TAVI populations. We aim to externally validate established MPMs on a large TAVI dataset from the Netherlands Heart Registration (NHR). METHODS: We included data from NHR-patients who underwent TAVI during 2013-2017. We calculated the predicted mortalities per MPM. We assessed the predictive performance by discrimination (Area Under Receiver Operating-characteristic Curve, AU-ROC); the Area Under Precision-Recall Curve, AU-PRC; calibration (using calibration-intercept and calibration-slope); Brier Score and Brier Skill Score. We also assessed the predictive performance among subgroups: tertiles of mortality-risk for non-survivors, gender, and access-route. RESULTS: We included 6177 TAVI-patients with an observed early-mortality rate of 4.5% (n = 280). We applied seven MPMs (STS, EuroSCORE-I, EuroSCORE-II, ACC-TAVI, FRANCE-2, OBSERVANT, and German-AV) on our cohort. The highest AU-ROCs were 0.64 (95%CI 0.61-0.67) for ACC-TAVI and 0.63 (95%CI 0.60-0.67) for FRANCE-2. All MPMs had a very low AU-PRC of ≤0.09. ACC-TAVI had the best calibration-intercept and calibration-slope. Brier Score values ranged between 0.043 and 0.063. Brier Skill Score ranged between -0.47 and 0.004. ACC-TAVI and FRANCE-2 predicted high mortality-risk better than other MPMs. ACC-TAVI outperformed other MPMs in different subgroups. CONCLUSION: The ACC-TAVI model has relatively the best predictive performance. However, all models have poor predictive performance. Because of the poor discrimination, miscalibration and limited accuracy of the models there is a need to update the existing models or develop new TAVI-specific models for local populations.

An international course on strategic information management for medical informatics students: international perspectives and evaluation.

All over the world, countries more and more take part in the international society and economy. To meet the stringent requirements of this globalization asks for internationally oriented and well-educated graduates. A major challenge of academia thus lies in qualifying graduates for international positions in this new world. A crucial element in the training and education of tomorrow's medical informatics specialists is exposure to health care systems across national borders. In this contribution, we report on the international aspects of and experiences with an inter-university course for medical informatics students on hospital information systems, in particular on their strategic information management. From 2001 onwards, this course was offered jointly for students of the University of Amsterdam, the University of Heidelberg/University of Applied Sciences Heilbronn and the University of Health Informatics and Technology, Tyrol (UMIT). Based on our experiences, future establishment of international courses in the medical and health informatics field is recommended.

An international course on strategic information management for medical informatics students: aim, content, structure, and experiences.

We report on a course for medical informatics students on hospital information systems, especially on its strategic information management. Starting as course at the Medical Informatics Program of the University of Heidelberg/University of Applied Sciences Heilbronn, it is now organized as international course in the framework of the International Partnership for Health Informatics Education (http:// www.iphie.org) jointly for medical information science students from the University of Amsterdam, medical informatics students, as well as health information management students from the Universities of Heidelberg/Heilbronn. In 2002, medical informatics students from the Master of Science program of the newly founded University for Health Informatics and Technology Tyrol (UMIT) at Innsbruck, Austria, joined. We report about the aim of this course, its audience, and the educational programs involved, about its content and structure, as well as about our experiences gained so far.

A diagnostic advice system based on pathophysiological models of diseases.

Medical decision-support systems in which uncertainty plays an essential role are increasingly based on the formalism of probabilistic networks. Although this formalism is very powerful, the construction of actual networks is not straightforward, and requires the availability of clearly structured medical domain models as a starting point. In this paper it is argued that medical pathophysiological knowledge constitutes a good start for the development of such models, even though pathophysiological knowledge is semantically different from probabilistic knowledge. Two models concerning anaemia, which are part of a broad system covering the domain of anaemia, are discussed to illustrate the general approach.

A development protocol for a diagnostic DSS.

A diagnostic decision support system (DSS) in medicine is an expert system that aids the physician in the determination of the diagnosis based on findings and test results. The DSS can be divided into 2 different types of components: the knowledge component and the information system component. Methods from software engineering, knowledge engineering and management are combined into a dynamic development cycle that allows stepwise update and refinement. The development of the knowledge component is based on knowledge engineering. In the starting phases, rapid prototyping is convenient to determine and evaluate the specification. The content of the knowledge base is frequently updated during its life time. For this purpose, a knowledge modelling protocol is supplied.