Dataset Information

PredicTTE: An accessible and optimal tool for time-to-event prediction in neurological diseases.

ABSTRACT: Time-to-event prediction is a key task for biological discovery, experimental medicine, and clinical care. This is particularly true for neurological diseases where development of reliable biomarkers is often limited by difficulty visualising and sampling relevant cell and molecular pathobiology. To date, much work has relied on Cox regression because of ease-of-use, despite evidence that this model includes incorrect assumptions. We have implemented a set of deep learning and spline models for time-to-event modelling within a fully customizable 'app' and accompanying online portal, both of which can be used for any time-to-event analysis in any disease by a non-expert user. Our online portal includes capacity for end-users including patients, Neurology clinicians, and researchers, to access and perform predictions using a trained model, and to contribute new data for model improvement, all within a data-secure environment. We demonstrate a pipeline for use of our app with three use-cases including imputation of missing data, hyperparameter tuning, model training and independent validation. We show that predictions are optimal for use in downstream applications such as genetic discovery, biomarker interpretation, and personalised choice of medication. We demonstrate the efficiency of an ensemble configuration, including focused training of a deep learning model. We have optimised a pipeline for imputation of missing data in combination with time-to-event prediction models. Overall, we provide a powerful and accessible tool to develop, access and share time-to-event prediction models; all software and tutorials are available at www.predictte.org.

SUBMITTER: Weinreich M

PROVIDER: S-EPMC11291041 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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predicTTE: An accessible and optimal tool for time-to-event prediction in neurological diseases.

Weinreich Marcel M McDonough Harry H Yacovzada Nancy N Magen Iddo I Cohen Yahel Y Harvey Calum C Gornall Sarah S Boddy Sarah S Alix James J Mohseni Nima N Kurz Julian M JM Kenna Kevin P KP Zhang Sai S Iacoangeli Alfredo A Al-Khleifat Ahmad A Snyder Michael P MP Hobson Esther E Al-Chalabi Ammar A Hornstein Eran E Elhaik Eran E Shaw Pamela J PJ McDermott Christopher C Cooper-Knock Johnathan J

bioRxiv : the preprint server for biology 20240723

Time-to-event prediction is a key task for biological discovery, experimental medicine, and clinical care. This is particularly true for neurological diseases where development of reliable biomarkers is often limited by difficulty visualising and sampling relevant cell and molecular pathobiology. To date, much work has relied on Cox regression because of ease-of-use, despite evidence that this model includes incorrect assumptions. We have implemented a set of deep learning and spline models for ...[more]

PMID: 39091819

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Project description:BackgroundIn 2017, the German Academy for Rare Neurological Diseases (Deutsche Akademie für Seltene Neurologische Erkrankungen; DASNE) was founded to pave the way for an optimized personalized management of patients with rare neurological diseases (RND) in all age groups. Since then a dynamic national network for rare neurological disorders has been established comprising renowned experts in neurology, pediatric neurology, (neuro-) genetics and neuroradiology. DASNE has successfully implemented case presentations and multidisciplinary discussions both at yearly symposia and monthly virtual case conferences, as well as further educational activities covering a broad spectrum of interdisciplinary expertise associated with RND. Here, we present recommendation statements for optimized personalized management of patients with RND, which have been developed and reviewed in a structured Delphi process by a group of experts.MethodsAn interdisciplinary group of 37 RND experts comprising DASNE experts, patient representatives, as well as healthcare professionals and managers was involved in the Delphi process. First, an online collection was performed of topics considered relevant for optimal patient care by the expert group. Second, a two-step Delphi process was carried out to rank the importance of the selected topics. Small interdisciplinary working groups then drafted recommendations. In two consensus meetings and one online review round these recommendations were finally consented.Results38 statements were consented and grouped into 11 topics: health care structure, core neurological expertise and core mission, interdisciplinary team composition, diagnostics, continuous care and therapy development, case conferences, exchange / cooperation between Centers for Rare Diseases and other healthcare partners, patient advocacy group, databases, translation and health policy.ConclusionsThis German interdisciplinary Delphi expert panel developed consented recommendations for optimal care of patients with RND in a structured Delphi process. These represent a basis for further developments and adjustments in the health care system to improve care for patients with RND and their families.

Project description:BackgroundPrognostication is an essential tool for risk adjustment and decision making in the intensive care unit (ICU). Research into prognostication in ICU has so far been limited to data from admission or the first 24 hours. Most ICU admissions last longer than this, decisions are made throughout an admission, and some admissions are explicitly intended as time-limited prognostic trials. Despite this, temporal changes in prognostic ability during ICU admission has received little attention to date. Current predictive models, in the form of prognostic clinical tools, are typically derived from linear models and do not explicitly handle incremental information from trends. Machine learning (ML) allows predictive models to be developed which use non-linear predictors and complex interactions between variables, thus allowing incorporation of trends in measured variables over time; this has made it possible to investigate prognosis throughout an admission.Methods and findingsThis study uses ML to assess the predictability of ICU mortality as a function of time. Logistic regression against physiological data alone outperformed APACHE-II and demonstrated several important interactions including between lactate & noradrenaline dose, between lactate & MAP, and between age & MAP consistent with the current sepsis definitions. ML models consistently outperformed logistic regression with Deep Learning giving the best results. Predictive power was maximal on the second day and was further improved by incorporating trend data. Using a limited range of physiological and demographic variables, the best machine learning model on the first day showed an area under the receiver-operator characteristic curve (AUC) of 0.883 (σ = 0.008), compared to 0.846 (σ = 0.010) for a logistic regression from the same predictors and 0.836 (σ = 0.007) for a logistic regression based on the APACHE-II score. Adding information gathered on the second day of admission improved the maximum AUC to 0.895 (σ = 0.008). Beyond the second day, predictive ability declined.ConclusionThis has implications for decision making in intensive care and provides a justification for time-limited trials of ICU therapy; the assessment of prognosis over more than one day may be a valuable strategy as new information on the second day helps to differentiate outcomes. New ML models based on trend data beyond the first day could greatly improve upon current risk stratification tools.

Dataset Information

PredicTTE: An accessible and optimal tool for time-to-event prediction in neurological diseases.

Publications

predicTTE: An accessible and optimal tool for time-to-event prediction in neurological diseases.

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