A dynamic time-to-event model for prediction of acute graft-versus-host disease in patients after allogeneic hematopoietic stem cell transplantation.

BACKGROUND: Acute graft-versus-host disease (aGvHD) is a major cause of death for patients following allogeneic hematopoietic stem cell transplantation (HSCT). Effective management of moderate to severe aGvHD remains challenging despite recent advances in HSCT, emphasizing the importance of prophylaxis and risk factor identification. METHODS: In this study, we analyzed data from 1479 adults who underwent HSCT between 2005 and 2017 to investigate the effects of aGvHD prophylaxis and time-dependent risk factors on the development of grades II-IV aGvHD within 100 days post-HSCT. RESULTS: Using a dynamic longitudinal time-to-event model, we observed a non-monotonic baseline hazard overtime with a low hazard during the first few days and a maximum hazard at day 17, described by Bateman function with a mean transit time of approximately 11 days. Multivariable analysis revealed significant time-dependent effects of white blood cell counts and cyclosporine A exposure as well as static effects of female donors for male recipients, patients with matched related donors, conditioning regimen consisting of fludarabine plus total body irradiation, and patient age in recipients of grafts from related donors on the risk to develop grades II-IV aGvHD. Additionally, we found that higher cumulative hazard on day 7 after allo-HSCT are associated with an increased incidence of grades II-IV aGvHD within 100 days indicating that an individual assessment of the cumulative hazard on day 7 could potentially serve as valuable predictor for later grades II-IV aGvHD development. Using the final model, stochastic simulations were performed to explore covariate effects on the cumulative incidence over time and to estimate risk ratios. CONCLUSION: Overall, the presented model showed good descriptive and predictive performance and provides valuable insights into the interplay of multiple static and time-dependent risk factors for the prediction of aGvHD.

XplOit: An Ontology-Based Data Integration Platform Supporting the Development of Predictive Models for Personalized Medicine.

Predictive models can support physicians to tailor interventions and treatments to their individual patients based on their predicted response and risk of disease and help in this way to put personalized medicine into practice. In allogeneic stem cell transplantation risk assessment is to be enhanced in order to respond to emerging viral infections and transplantation reactions. However, to develop predictive models it is necessary to harmonize and integrate high amounts of heterogeneous medical data that is stored in different health information systems. Driven by the demand for predictive instruments in allogeneic stem cell transplantation we present in this paper an ontology-based platform that supports data owners and model developers to share and harmonize their data for model development respecting data privacy.

A novel tool for the identification of correlations in medical data by faceted search.

This work focuses on the integration of multifaceted extensive data sets (e.g. laboratory values, vital data, medications) and partly unstructured medical data such as discharge letters, diagnostic reports, clinical notes etc. in a research database. Our main application is an integrated faceted search in nephrology based on information extraction results. We describe the details of the application of transplant medicine and the resulting technical architecture of the faceted search application.

Semi-automated De-identification of German Content Sensitive Reports for Big Data Analytics.

Purpose Projects involving collaborations between different institutions require data security via selective de-identification of words or phrases. A semi-automated de-identification tool was developed and evaluated on different types of medical reports natively and after adapting the algorithm to the text structure. Materials and Methods A semi-automated de-identification tool was developed and evaluated for its sensitivity and specificity in detecting sensitive content in written reports. Data from 4671 pathology reports (4105 + 566 in two different formats), 2804 medical reports, 1008 operation reports, and 6223 radiology reports of 1167 patients suffering from breast cancer were de-identified. The content was itemized into four categories: direct identifiers (name, address), indirect identifiers (date of birth/operation, medical ID, etc.), medical terms, and filler words. The software was tested natively (without training) in order to establish a baseline. The reports were manually edited and the model re-trained for the next test set. After manually editing 25, 50, 100, 250, 500 and if applicable 1000 reports of each type re-training was applied. Results In the native test, 61.3 % of direct and 80.8 % of the indirect identifiers were detected. The performance (P) increased to 91.4 % (P25), 96.7 % (P50), 99.5 % (P100), 99.6 % (P250), 99.7 % (P500) and 100 % (P1000) for direct identifiers and to 93.2 % (P25), 97.9 % (P50), 97.2 % (P100), 98.9 % (P250), 99.0 % (P500) and 99.3 % (P1000) for indirect identifiers. Without training, 5.3 % of medical terms were falsely flagged as critical data. The performance increased, after training, to 4.0 % (P25), 3.6 % (P50), 4.0 % (P100), 3.7 % (P250), 4.3 % (P500), and 3.1 % (P1000). Roughly 0.1 % of filler words were falsely flagged. Conclusion Training of the developed de-identification tool continuously improved its performance. Training with roughly 100 edited reports enables reliable detection and labeling of sensitive data in different types of medical reports. Key Points: · Collaborations between different institutions require de-identification of patients' data. · Software-based de-identification of content-sensitive reports grows in importance as a result of 'Big data'. · A de-identification software was developed and tested natively and after training. · The proposed de-identification software worked quite reliably, following training with roughly 100 edited reports. · A final check of the texts by an authorized person remains necessary. Citation Format · Seuss H, Dankerl P, Ihle M et al. Semi-automated De-identification of German Content Sensitive Reports for Big Data Analytics. Fortschr Röntgenstr 2017; 189: 661 - 671.

The EPILEPSIAE database: an extensive electroencephalography database of epilepsy patients.

From the very beginning the seizure prediction community faced problems concerning evaluation, standardization, and reproducibility of its studies. One of the main reasons for these shortcomings was the lack of access to high-quality long-term electroencephalography (EEG) data. In this article we present the EPILEPSIAE database, which was made publicly available in 2012. We illustrate its content and scope. The EPILEPSIAE database provides long-term EEG recordings of 275 patients as well as extensive metadata and standardized annotation of the data sets. It will adhere to the current standards in the field of prediction and facilitate reproducibility and comparison of those studies. Beyond seizure prediction, it may also be of considerable benefit for studies focusing on seizure detection, basic neurophysiology, and other fields.

EPILEPSIAE - a European epilepsy database.

With a worldwide prevalence of about 1%, epilepsy is one of the most common serious brain diseases with profound physical, psychological and, social consequences. Characteristic symptoms are seizures caused by abnormally synchronized neuronal activity that can lead to temporary impairments of motor functions, perception, speech, memory or, consciousness. The possibility to predict the occurrence of epileptic seizures by monitoring the electroencephalographic activity (EEG) is considered one of the most promising options to establish new therapeutic strategies for the considerable fraction of patients with currently insufficiently controlled seizures. Here, a database is presented which is part of an EU-funded project "EPILEPSIAE" aiming at the development of seizure prediction algorithms which can monitor the EEG for seizure precursors. High-quality, long-term continuous EEG data, enriched with clinical metadata, which so far have not been available, are managed in this database as a joint effort of epilepsy centers in Portugal (Coimbra), France (Paris) and Germany (Freiburg). The architecture and the underlying schema are here reported for this database. It was designed for an efficient organization, access and search of the data of 300 epilepsy patients, including high quality long-term EEG recordings, obtained with scalp and intracranial electrodes, as well as derived features and supplementary clinical and imaging data. The organization of this European database will allow for accessibility by a wide spectrum of research groups and may serve as a model for similar databases planned for the future.

Anticipating the unobserved: prediction of subclinical seizures.

Subclinical seizures (SCS) have rarely been considered in the diagnosis and therapy of epilepsy and have not been systematically analyzed in studies on seizure prediction. Here, we investigate whether predictions of subclinical seizures are feasible and how their occurrence may affect the performance of prediction algorithms. Using the European database of long-term recordings of surface and invasive electroencephalography data, we analyzed the data from 21 patients with SCS, including in total 413 clinically manifest seizures (CS) and 3341 SCS. Based on the mean phase coherence we investigated the predictive performance of CS and SCS. The two types of seizures had similar prediction sensitivities. Significant performance was found considerably more often for SCS than for CS, especially for patients with invasive recordings. When analyzing false alarms triggered by predicting CS, a significant number of these false predictions were followed by SCS for 9 of 21 patients. Although currently observed prediction performance may not be deemed sufficient for clinical applications for the majority of the patients, it can be concluded that the prediction of SCS is feasible on a similar level as for CS and allows a prediction of more of the seizures impairing patients, possibly also reducing the number of false alarms that were in fact correct predictions of CS. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.

The role of high-quality EEG databases in the improvement and assessment of seizure prediction methods.

Initially, seizure prediction was based on the analysis of brief EEG segments preceding clinically manifest seizures. Whereas such approaches suggested that the sensitivities of various EEG-derived features in predicting seizures were high, the inclusion of longer interictal periods and the combined assessment of sensitivity and specificity and the application of statistical validation methods have put into question the validity of such claims. We here show that the duration of EEG on which analyses are based and the number of seizures assessed negatively correlate with the reported sensitivities of prediction studies. Methodological aspects of seizure prediction are discussed in the framework of currently existing databases and of the newly established European Union database. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.

Seizure prediction in epilepsy: from circadian concepts via probabilistic forecasting to statistical evaluation.

Seizure prediction performance is hampered by high numbers of false predictions. Here we present an approach to reduce the number of false predictions based on circadian concepts. Based on eight representative patients we demonstrate that this approach increases the performance considerably. The fraction of patients for whom we found a significant seizure prediction performance was increased from 25% to 38% by accounting for circadian dependencies.

Views of patients with epilepsy on seizure prediction devices.

Patients views on the relevance, performance requirements, and implementation of seizure prediction devices have so far not been evaluated in a standardized form. We here report views of outpatients with uncontrolled epilepsy from the epilepsy centers at Freiburg, Germany, and Coimbra, Portugal, based on a questionnaire. Interest in the development of methods for seizure prediction both for warning and for closed-loop interventions is high. High sensitivity of prediction is regarded as more important than specificity. Short prediction time windows are preferred, but the indication of seizure-prone periods is also considered worthwhile. Only a few patients are, however, willing to wear EEG electrodes for signal acquisition on a long-term basis. These data support the view that seizure prediction is of high interest to patients with uncontrolled epilepsy. Improvements in the performance of presently available prediction algorithms and technical improvements in EEG recording will, however, be necessary to meet patients requirements.