Diagnosis and Risk Prediction of Dilated Cardiomyopathy in the Era of Big Data and Genomics

Dilated cardiomyopathy (DCM) is a leading cause of heart failure and life-threatening ventricular arrhythmias (LTVA). Work-up and risk stratification of DCM is clinically challenging, as there is great heterogeneity in phenotype and genotype. Throughout the last decade, improved genetic testing of patients has identified genotype–phenotype associations and enhanced evaluation of at-risk relatives leading to better patient prognosis. The field is now ripe to explore opportunities to improve personalised risk assessments. Multivariable risk models presented as “risk calculators” can incorporate a multitude of clinical variables and predict outcome (such as heart failure hospitalisations or LTVA). In addition, genetic risk scores derived from genome/exome-wide association studies can estimate an individual’s lifetime genetic risk of developing DCM. The use of clinically granular investigations, such as late gadolinium enhancement on cardiac magnetic resonance imaging, is warranted in order to increase predictive performance. To this end, constructing big data infrastructures improves accessibility of data by using electronic health records, existing research databases, and disease registries. By applying methods such as machine and deep learning, we can model complex interactions, identify new phenotype clusters, and perform prognostic modelling. This review aims to provide an overview of the evolution of DCM definitions as well as its clinical work-up and considerations in the era of genomics. In addition, we present exciting examples in the field of big data infrastructures, personalised prognostic assessment, and artificial intelligence

Dispersion in slowly moving fluids.

Thesis (Ph.D.)-University of Natal, Durban, 1970.This work is concerned with the characterization of slowly moving fluids and was carried out on the flow of water through a narrow sedimentation tank. Dispersion in the type of flow structure involved is caused mainly by the presence of large eddies and, due to the fact that shear stresses are small, these eddies persist for a considerable period of time. Two flow models are presented : The first model assumes the X- Y- velocity component pair to form a discrete state Markov process in time and dispersion equations for the mean concentration at a point, the variance as well as concentration cross correlations are generated. In the second model the velocity fluctuation components are assumed to be independent, time-stationary Markov processes with normal probability density functions. The stochastic differential equation describing dispersion of tracer is formulated with and without the effect of molecular diffusion and solutions to both cases are presented. Comparison of the model with experimental data obtained from tracer and anemometer measurements show that the model is capable of describing mean dispersion in a relatively small region of the tank and that the tracer experiments were insensitive to molecular diffusion

Checking the Goldbach conjecture on a vector computer

bibliographical data to be processed -- Number theory and applications (Banff, AB, 1988) Pages: 423--433 Series: NATO Adv. Sci. Inst. Ser. C Math. Phys. Sci. Vol: 265 -- Kluwer Acad. Publ. (Dordrecht) -- 1

Automatic multilabel detection of ICD10 codes in Dutch cardiology discharge letters using neural networks

Standard reference terminology of diagnoses and risk factors is crucial for billing, epidemiological studies, and inter/intranational comparisons of diseases. The International Classification of Disease (ICD) is a standardized and widely used method, but the manual classification is an enormously time-consuming endeavor. Natural language processing together with machine learning allows automated structuring of diagnoses using ICD-10 codes, but the limited performance of machine learning models, the necessity of gigantic datasets, and poor reliability of terminal parts of these codes restricted clinical usability. We aimed to create a high performing pipeline for automated classification of reliable ICD-10 codes in the free medical text in cardiology. We focussed on frequently used and well-defined three- and four-digit ICD-10 codes that still have enough granularity to be clinically relevant such as atrial fibrillation (I48), acute myocardial infarction (I21), or dilated cardiomyopathy (I42.0). Our pipeline uses a deep neural network known as a Bidirectional Gated Recurrent Unit Neural Network and was trained and tested with 5548 discharge letters and validated in 5089 discharge and procedural letters. As in clinical practice discharge letters may be labeled with more than one code, we assessed the single- and multilabel performance of main diagnoses and cardiovascular risk factors. We investigated using both the entire body of text and only the summary paragraph, supplemented by age and sex. Given the privacy-sensitive information included in discharge letters, we added a de-identification step. The performance was high, with F1 scores of 0.76–0.99 for three-character and 0.87–0.98 for four-character ICD-10 codes, and was best when using complete discharge letters. Adding variables age/sex did not affect results. For model interpretability, word coefficients were provided and qualitative assessment of classification was manually performed. Because of its high performance, this pipeline can be useful to decrease the administrative burden of classifying discharge diagnoses and may serve as a scaffold for reimbursement and research applications

Relationship between chest radiographic characteristics, sputum bacterial load, and treatment outcomes in patients with extensively drug-resistant tuberculosis

BACKGROUND: Data about the relationship between chest radiographs and sputum bacillary load, with treatment outcomes, in patients with extensively drug-resistant tuberculosis (XDR-TB) from HIV/TB endemic settings are limited. METHODS: Available chest radiographs from 97 South African XDR-TB patients, at the time of diagnosis, were evaluated by two independent readers using a validated scoring system. Chest radiograph findings were correlated with baseline sputum bacillary load (smear-grade and culture time-to-positive in MGIT), and prospectively ascertained clinical outcomes (culture conversion and all-cause mortality). RESULTS: Radiographic bilateral lung disease was present in 75/97 (77%). In the multivariate analysis only a higher total radiographic score (95% CI) was associated with higher likelihood of death [1.16 (1.05-1.28) p=0.003], and failure to culture convert [0.85 (0.74-0.97) p=0.02]. However, when restricting analyses to HIV-infected patients, disease extent, cavitation, and total radiographic scores were not associated with mortality or culture-conversion. Finally, cavitary, disease extent, and total radiographic scores all positively correlated with bacterial load (culture time-to-positive). CONCLUSIONS: In endemic settings, XDR-TB radiological disease extent scores are associated with adverse clinical outcomes, including mortality, in HIV uninfected persons. These data may have implications for clinical and programmatic decision-making and for evaluation of new regimens in clinical trials

Distance travelled : Outcomes and evidence in flexible learning options

Flexible learning options (FLOs) provide individualised learning pathways for disengaged young people with strong emphasis on inclusivity and wellbeing support. Amidst a rapid expansion of Australia’s flexible learning sector, service providers are under increasing pressure to substantiate participant outcomes. This paper stems from a national study of the value of FLOs to young people and the broader Australian community. The study enumerates the outcomes valued by flexible learning practitioners, as well as the various evidence forms they cite to substantiate participant outcomes. Framing success as ‘distance travelled’ (i.e. an individual’s progress relative to his or her own starting point), practitioners demonstrate critical awareness of the social and structural mechanisms by which young people are marginalised from mainstream schooling. Holistic assessment practices also reveal practitioners’ efforts to expand the terms of reference by which educational outcomes may be validated in alternative education settings