126 research outputs found
Potential of joint modelling of longitudinal observations and time-to-event data to improve prognosis in chronic heart failure studies
Background:
Heart failure is a clinical syndrome with inter-relationships between numerous biochemical, physical, and imaging characteristics, and adverse outcomes. Patients with heart failure typically have poor prognosis, and previous prognostic models are limited to using only baseline measurements of these characteristics. In recent years there has been a rise in interest and usage of joint modelling. Joint modelling seeks to combine two or more models, typically containing longitudinal observations and time-to-event (survival) data. The purpose of which is to reduce bias and increase efficiency, allowing for these repeat measurements of longitudinal observations whilst accounting for correlation and measurement error. The inter-relationships within heart failure and properties of joint modelling make heart failure an excellent candidate for joint modelling. It is therefore the aim of this thesis to explore the use of joint modelling in heart failure and to see whether it can be used to improve prognosis.
Methods:
This research comprised of a systematic review paired with an exemplar to introduce joint modelling and how joint models are currently being applied to heart failure; whilst also illustrating how joint modelling can be applied to clinical trial data. Following this, seven joint models were fit under a Bayesian framework, using data from a randomised control trial, and validated with data from different randomised control trials. These joint models were then compared to models fitted using current standards of prognostic model methodology to evaluate and assess how joint modelling can potentially improve model performance. Finally, a web application was developed to illustrate how these joint models can translate into real world applications.
Results:
On average the joint models performed better, in a statistical sense, than the traditional models (considered the current standard for prognosis) and performed adequately when validated with data from another randomised control trial. The web application effectively shows how these joint models can be used in practice and highlights the potential of the dynamic nature of joint models when used in a prognostic setting.
Conclusion:
This thesis illustrates how joint modelling can improve on the current standard of prognostic models, adding repeated measurements and allowing for dynamic predictions over time, whilst outperforming the traditional models. However, with limitations around the use of latent parameters such as random effects, and the novel nature of these models with their limited use, it may be prudent to wait until these types of models mature, are evaluated further, and the statistical packages used to fit these models mature before implementing them in clinical practice
Anwendungen maschinellen Lernens für datengetriebene Prävention auf Populationsebene
Healthcare costs are systematically rising, and current therapy-focused healthcare systems are not sustainable in the long run. While disease prevention is a viable instrument for reducing costs and suffering, it requires risk modeling to stratify populations, identify high- risk individuals and enable personalized interventions. In current clinical practice, however, systematic risk stratification is limited: on the one hand, for the vast majority of endpoints, no risk models exist. On the other hand, available models focus on predicting a single disease at a time, rendering predictor collection burdensome. At the same time, the den- sity of individual patient data is constantly increasing. Especially complex data modalities, such as -omics measurements or images, may contain systemic information on future health trajectories relevant for multiple endpoints simultaneously. However, to date, this data is inaccessible for risk modeling as no dedicated methods exist to extract clinically relevant information. This study built on recent advances in machine learning to investigate the ap- plicability of four distinct data modalities not yet leveraged for risk modeling in primary prevention. For each data modality, a neural network-based survival model was developed to extract predictive information, scrutinize performance gains over commonly collected covariates, and pinpoint potential clinical utility. Notably, the developed methodology was able to integrate polygenic risk scores for cardiovascular prevention, outperforming existing approaches and identifying benefiting subpopulations. Investigating NMR metabolomics, the developed methodology allowed the prediction of future disease onset for many common diseases at once, indicating potential applicability as a drop-in replacement for commonly collected covariates. Extending the methodology to phenome-wide risk modeling, elec- tronic health records were found to be a general source of predictive information with high systemic relevance for thousands of endpoints. Assessing retinal fundus photographs, the developed methodology identified diseases where retinal information most impacted health trajectories. In summary, the results demonstrate the capability of neural survival models to integrate complex data modalities for multi-disease risk modeling in primary prevention and illustrate the tremendous potential of machine learning models to disrupt medical practice toward data-driven prevention at population scale.Die Kosten im Gesundheitswesen steigen systematisch und derzeitige therapieorientierte Gesundheitssysteme sind nicht nachhaltig. Angesichts vieler verhinderbarer Krankheiten stellt die Prävention ein veritables Instrument zur Verringerung von Kosten und Leiden dar. Risikostratifizierung ist die grundlegende Voraussetzung für ein präventionszentri- ertes Gesundheitswesen um Personen mit hohem Risiko zu identifizieren und Maßnah- men einzuleiten. Heute ist eine systematische Risikostratifizierung jedoch nur begrenzt möglich, da für die meisten Krankheiten keine Risikomodelle existieren und sich verfüg- bare Modelle auf einzelne Krankheiten beschränken. Weil für deren Berechnung jeweils spezielle Sets an Prädiktoren zu erheben sind werden in Praxis oft nur wenige Modelle angewandt. Gleichzeitig versprechen komplexe Datenmodalitäten, wie Bilder oder -omics- Messungen, systemische Informationen über zukünftige Gesundheitsverläufe, mit poten- tieller Relevanz für viele Endpunkte gleichzeitig. Da es an dedizierten Methoden zur Ex- traktion klinisch relevanter Informationen fehlt, sind diese Daten jedoch für die Risikomod- ellierung unzugänglich, und ihr Potenzial blieb bislang unbewertet. Diese Studie nutzt ma- chinelles Lernen, um die Anwendbarkeit von vier Datenmodalitäten in der Primärpräven- tion zu untersuchen: polygene Risikoscores für die kardiovaskuläre Prävention, NMR Meta- bolomicsdaten, elektronische Gesundheitsakten und Netzhautfundusfotos. Pro Datenmodal- ität wurde ein neuronales Risikomodell entwickelt, um relevante Informationen zu extra- hieren, additive Information gegenüber üblicherweise erfassten Kovariaten zu quantifizieren und den potenziellen klinischen Nutzen der Datenmodalität zu ermitteln. Die entwickelte Me-thodik konnte polygene Risikoscores für die kardiovaskuläre Prävention integrieren. Im Falle der NMR-Metabolomik erschloss die entwickelte Methodik wertvolle Informa- tionen über den zukünftigen Ausbruch von Krankheiten. Unter Einsatz einer phänomen- weiten Risikomodellierung erwiesen sich elektronische Gesundheitsakten als Quelle prädik- tiver Information mit hoher systemischer Relevanz. Bei der Analyse von Fundusfotografien der Netzhaut wurden Krankheiten identifiziert für deren Vorhersage Netzhautinformationen genutzt werden könnten. Zusammengefasst zeigten die Ergebnisse das Potential neuronaler Risikomodelle die medizinische Praxis in Richtung einer datengesteuerten, präventionsori- entierten Medizin zu verändern
Screening people with diabetes for atrial fibrillation
Thesis by alternative route comprising four independent yet related studies exploring the relationship between diabetes and atrial fibrillation and whether screening people with diabetes for this common heart rhythm disorder, would be valuable. A systematic review critiques the utility, effectiveness and feasibility of the AliveCor screening application, demonstrating this as a valid and effective tool for this purpose. A screening study then demonstrates a higher prevalence of atrial fibrillation in people with diabetes, also showing statistical significance that increasing age, is a predictor for developing atrial fibrillation in this population. Another study then considers the quality of life study in people with atrial fibrillation and then people with atrial fibrillation and diabetes, showing the quality of life to be poorer in the majority of assessed domains, when both conditions coexist. Lastly, a qualitative interview study considers the views and experiences of people screened in the earlier study, demonstrating variable understanding around atrial fibrillation and views around how, when and who to screen
Simple Tests for Assessing Kidney and Cardiovascular Function in Chromic Kidney Disease Patients (Stages 2, 3 and 4)
Reliable measurement of glomerular filtration rate (GFR) is an important aspect of clinical decision making and forms the basis of classification for kidney function. Moreover the interpretation of risk factors for progression of CKD linked to cardiovascular disease (CVD) remains difficult, particularly in older subjects. To address this GFR and the cardiovascular parameters arterial stiffness, heart rate variability (HRV) and natriuretic peptides (NT- proBNP and NT-proCNP) were measured in a cohort of CKD patients of stages 2,3 and 4. The primary aim however of the thesis was to measure the Kidney Functional Reserve (KFR) of CKD3 and CKD4 patients as it has been thought of as analogous to cardiac functional reserve. CysC, creatinine clearance (CrCl) and with simultaneous radionuclide 99technetium diethylenetriaminepentaacetatic acid (Tc-99m) measured GFR (mGFR) of KFR in 19 CKD Stage 3 and 21 CKD Stage 4 patients yielded good agreement. KFR was not correlated with baseline kidney function. Eight CKD Stage 3 (42%) and 11 CKD Stage 4 (52%) subjects reached their lowest serum CysC concentration 4 hours after OPL. CysC KFR and baseline serum creatinine (sCr) predicted Major Adverse Kidney Event, death or dialysis (MAKE-T) and MAKE-F (fast progression with GFR decrease > 5ml/min/year) with a respective area under the curve (AUC) of 0.73 (95% confidence interval [CI] 0.48 – 0.890) and 0.71 (95% CI: 0.51–0.84). Including CysC KFR, age, baseline sCr and nadir CysC predicted a decrease in sCr-estimated GFR >1.2 ml/min/year (MAKE-S) with an AUC of 0.89. In the latter case the inclusion of cardiovascular variables; “Recovery” (1-4 minutes post exercise) RMSSD, urinary and serum NT-proCNP concentration and whether CysC GFR reserve was early or late in nadir improved the AUC to 1.00 (AICc =15.41). In conclusion serial CysC may facilitate monitoring of KFR in clinical practice along with the use of a portable exercise stress test to increase c-f-PWV (carotid-femoral Pulse Wave Velocity) and identify CKD patients with serious/subliminal vascular stiffening. Future research may confirm this studies other preliminary finding that urinary NT-proCNP concentration may relate to renal haemodynamic function in CKD stages, as this correlated with stimulated mGFR (R2 = 0.41). In short this thesis has furthered the premise that the heart and kidney are interlinked via cardiovascular physiology which likely can be used to predict CKD progression
Pacing with restoration of respiratory sinus arrhythmia improved cardiac contractility and the left ventricular output: a translational study
Introduction: Respiratory sinus arrhythmia (RSA) is a prognostic value for patients with heart failure and is defined as a beat-to-beat variation of the timing between the heart beats. Patients with heart failure or patients with permanent cardiac pacing might benefit from restoration of RSA. The aim of this translational, proof-of-principle study was to evaluate the effect of pacing with or without restored RSAon parameters of LV cardiac contractility and the cardiac output
Women in Artificial intelligence (AI)
This Special Issue, entitled "Women in Artificial Intelligence" includes 17 papers from leading women scientists. The papers cover a broad scope of research areas within Artificial Intelligence, including machine learning, perception, reasoning or planning, among others. The papers have applications to relevant fields, such as human health, finance, or education. It is worth noting that the Issue includes three papers that deal with different aspects of gender bias in Artificial Intelligence. All the papers have a woman as the first author. We can proudly say that these women are from countries worldwide, such as France, Czech Republic, United Kingdom, Australia, Bangladesh, Yemen, Romania, India, Cuba, Bangladesh and Spain. In conclusion, apart from its intrinsic scientific value as a Special Issue, combining interesting research works, this Special Issue intends to increase the invisibility of women in AI, showing where they are, what they do, and how they contribute to developments in Artificial Intelligence from their different places, positions, research branches and application fields. We planned to issue this book on the on Ada Lovelace Day (11/10/2022), a date internationally dedicated to the first computer programmer, a woman who had to fight the gender difficulties of her times, in the XIX century. We also thank the publisher for making this possible, thus allowing for this book to become a part of the international activities dedicated to celebrating the value of women in ICT all over the world. With this book, we want to pay homage to all the women that contributed over the years to the field of AI
Energy Data Analytics for Smart Meter Data
The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal
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