The aim of this study was to evaluate whether machine learning (ML) of noncontrast computed tomographic (CT) and clinical variables improves the prediction of atherosclerotic cardiovascular disease ...(ASCVD) and coronary heart disease (CHD) deaths compared with coronary artery calcium (CAC) Agatston scoring and clinical data.
The CAC score provides a measure of the global burden of coronary atherosclerosis, and its long-term prognostic utility has been consistently shown to have incremental value over clinical risk assessment. However, current approaches fail to integrate all available CT and clinical variables for comprehensive risk assessment.
The study included data from 66,636 asymptomatic subjects (mean age 54 ± 11 years, 67% men) without established ASCVD undergoing CAC scanning and followed for cardiovascular disease (CVD) and CHD deaths at 10 years. Clinical risk assessment incorporated the ASCVD risk score. For ML, an ensemble boosting approach was used to fit a predictive classifier for outcomes, followed by automated feature selection using information gain ratio. The model-building process incorporated all available clinical and CT data, including the CAC score; the number, volume, and density of CAC plaques; and extracoronary scores; comprising a total of 77 variables. The overall proposed model (ML all) was evaluated using a 10-fold cross-validation framework on the population data and area under the curve (AUC) as metrics. The prediction performance was also compared with 2 traditional scores (ASCVD risk and CAC score) and 2 additional models that were trained using all the clinical data (ML clinical) and CT variables (ML CT).
The AUC by ML all (0.845) for predicting CVD death was superior compared with those obtained by ASCVD risk alone (0.821), CAC score alone (0.781), and ML CT alone (0.804) (p < 0.001 for all). Similarly, for predicting CHD death, AUC by ML all (0.860) was superior to the other analyses (0.835 for ASCVD risk, 0.816 for CAC, and 0.827 for ML CT; p < 0.001).
The comprehensive ML model was superior to ASCVD risk, CAC score, and an ML model fitted using CT variables alone in the prediction of both CVD and CHD death.
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Optimal risk stratification with machine learning (ML) from myocardial perfusion imaging (MPI) includes both clinical and imaging data. While most imaging variables can be derived automatically, ...clinical variables require manual collection, which is time-consuming and prone to error. We determined the fewest manually input and imaging variables required to maintain the prognostic accuracy for major adverse cardiac events (MACE) in patients undergoing a single-photon emission computed tomography (SPECT) MPI.
This study included 20 414 patients from the multicentre REFINE SPECT registry and 2984 from the University of Calgary for training and external testing of the ML models, respectively. ML models were trained using all variables (ML-All) and all image-derived variables (including age and sex, ML-Image). Next, ML models were sequentially trained by incrementally adding manually input and imaging variables to baseline ML models based on their importance ranking. The fewest variables were determined as the ML models (ML-Reduced, ML-Minimum, and ML-Image-Reduced) that achieved comparable prognostic performance to ML-All and ML-Image. Prognostic accuracy of the ML models was compared with visual diagnosis, stress total perfusion deficit (TPD), and traditional multivariable models using area under the receiver-operating characteristic curve (AUC). ML-Minimum (AUC 0.798) obtained comparable prognostic accuracy to ML-All (AUC 0.799, P = 0.19) by including 12 of 40 manually input variables and 11 of 58 imaging variables. ML-Reduced achieved comparable accuracy (AUC 0.796) with a reduced set of manually input variables and all imaging variables. In external validation, the ML models also obtained comparable or higher prognostic accuracy than traditional multivariable models.
Reduced ML models, including a minimum set of manually collected or imaging variables, achieved slightly lower accuracy compared to a full ML model but outperformed standard interpretation methods and risk models. ML models with fewer collected variables may be more practical for clinical implementation.
Roughly 789 million people have no access to energy, and around 2.8 billion people lack access to clean cooking solutions according to the World Bank, and so we also find many people that cannot ...afford energy (reliable and clean) at the current prices. In the literature, accessibility, availability, and affordability are underlined as the key drivers of energy poverty. In South America, these aspects have not been studied in depth. This research is relevant because it provides a standardized, cross-country, and comparable analysis of multidimensional energy poverty in the region. The study of energy poverty is critical for the development and well-being of countries, especially in regions such as South America, where this issue can be affected by geographical, cultural, infrastructure, and/or socio-economic differences. In this study, we measured the magnitude of energy poverty in Argentina, Brazil, Uruguay, and Paraguay. This methodology is based on the analysis of energy poverty through a multidimensional approach, considering three parameters as drivers of energy poverty in the countries: accessibility, availability, and affordability. Through a two-step process, first, we calculate the Weighted Average Energy Poverty Index (WAEPI), based on three proposed scenarios (W
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, W
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, and W
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), and finally, through the Composite Energy Poverty Index (CEPI), we measure the existing gaps, based on the selected indicators, between the countries under study and the benchmark country. Additionally, we decided to focus our analysis on the country that has shown the highest level and gaps on multidimensional energy poverty in the region, as a case study to validate the results obtained through the chosen methodology. The results show that during the period of analysis (2000–2016), Paraguay has been the most energy-poor country among the countries under study, while Argentina has been the least energy-poor country. At the local level, we observed that, Paraguay, despite being one of the largest producers and exporters of clean hydroelectric energy in the region, still presents high levels of consumption of biomass or coal for cooking, while electricity only represents 17% of the total final energy consumption in the country (biomass and fossil fuels account for 83%). These results could lead the design of energy policies, projects, and programs to reduce the multidimensional energy poverty, nationally, also at the common platform: MERCOSUR. Finally, this study includes an analysis of policy implications and alternative solutions to eradicate energy poverty in the region.
Este estudio tuvo como objetivo comprender la forma como el profesional de Educación Física moviliza estrategias metodológicas para desarrollar su accionar docente con niños con síndrome de Down en ...la ciudad de Paysandú-Uruguay. Tratase de una investigación descriptiva con un abordaje teórico-metodológico cualitativo. Las informaciones producidas fueron analizadas mediante un proceso interpretativo de recurrencias en las respuestas de los colaboradores estableciendo relaciones entre el material empírico, lo teórico-conceptual y la construcción del análisis. El resultado fue condensado en tres categorías: el “perfil”, la “planificación”, y finalmente, las “herramientas metodológicas”; aspectos fundamentales para que el docente pueda accionar su labor pedagógica con el correspondiente grupo poblacional. Las herramientas metodológicas a nuestro parecer son las más relevantes para trabajar con niños con Síndrome de Down ya que son recursos, formas de trabajo y métodos puntuales que se utilizan para llevar a cabo la planificación en el grupo de sujetos a ser integrados.
•We propose a population model to estimate the probability of the bladder presence on a given region during treatment using only the planning CT scan as input information.•We train a ...motion/deformation model, based on longitudinal data, to predict bladder motion and deformation between fractions.•We propose a longitudinal analysis using mixed-effect models to separate intra- and inter-patient variability in order to control confounding.•We reduce, in a factor of 10, the number of variables required to represent bladder surface using spherical harmonics (SPHARM).
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In radiotherapy for prostate cancer irradiation of neighboring organs at risk may lead to undesirable side-effects. Given this setting, the bladder presents the largest inter-fraction shape variations hampering the computation of the actual delivered dose vs. planned dose. This paper proposes a population model, based on longitudinal data, able to estimate the probability of bladder presence during treatment, using only the planning computed tomography (CT) scan as input information. As in previously-proposed principal component analysis (PCA) population-based models, we have used the data to obtain the dominant eigenmodes that describe bladder geometric variations between fractions. However, we have used a longitudinal analysis along each mode in order to properly characterize patient’s variance from the total population variance. We have proposed is a mixed-effects (ME) model in order to separate intra- and inter-patient variability, in an effort to control confounding cohort effects. Other than using PCA, bladder shapes are represented by using spherical harmonics (SPHARM) that additionally enables data compression without information lost. Based on training data from repeated CT scans, the ME model was thus implemented following dimensionality reduction by means of SPHARM and PCA. We have evaluated the model in a leave-one-out cross validation framework on the training data but also using independent data. Probability maps (PMs) were thus generated with several draws from the learnt model as predicted regions where the bladder will likely move and deform. These PMs were compared with the actual regions using metrics based on mutual information distance and misestimated voxels. The prediction was also compared with two previous population PCA-based models. The proposed model was able to reduce the uncertainties in the estimation of the probable region of bladder motion and deformation. This model can thus be used for tailoring radiotherapy treatments.
We investigated a multistate outbreak of Escherichia coli O157:H7 infections. Isolates from 13 case patients from California, Nevada, and Arizona were matched by pulsed-field gel electrophoresis ...subtyping. Five case patients (38%) were hospitalized, and 3 (23%) developed hemolytic uremic syndrome; none died. The median age was 12 years (range, 2–75 years), and 10 (77%) were female. Case-control studies found an association between illness and eating beef tacos at a national Mexican-style fast-food restaurant chain (88% of cases versus 38% of controls; matched OR, undefined; 95% confidence interval, 1.49 to infinity; P = .009). A traceback investigation implicated an upstream supplier of beef, but a farm investigation was not possible. This outbreak illustrates the value of employing hospital laboratory-based surveillance to detect local clusters of infections and the effectiveness of using molecular subtyping to identify geographically dispersed outbreaks. The outbreak investigation also highlights the need for a more efficient tracking system for food products.
Abstract
Aims
Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) stress-only protocols reduce radiation exposure and cost but require clinicians to make immediate ...decisions regarding rest scan cancellation. We developed a machine learning (ML) approach for automatic rest scan cancellation and evaluated its prognostic safety.
Methods and results
In total, 20 414 patients from a solid-state SPECT MPI international multicentre registry with clinical data and follow-up for major adverse cardiac events (MACE) were used to train ML for MACE prediction as a continuous probability (ML score), using 10-fold repeated hold-out testing to separate test from training data. Three ML score thresholds (ML1, ML2, and ML3) were derived by matching the cancellation rates achieved by physician interpretation and two clinical selection rules. Annual MACE rates were compared in patients selected for rest scan cancellation between approaches. Patients selected for rest scan cancellation with ML had lower annualized MACE rates than those selected by physician interpretation or clinical selection rules (ML1 vs. physician interpretation: 1.4 ± 0.1% vs. 2.1 ± 0.1%; ML2 vs. clinical selection: 1.5 ± 0.1% vs. 2.0 ± 0.1%; ML3 vs. stringent clinical selection: 0.6 ± 0.1% vs. 1.7 ± 0.1%, all P < 0.0001) at matched cancellation rates (60 ± 0.7, 64 ± 0.7, and 30 ± 0.6%). Annualized all-cause mortality rates in populations recommended for rest cancellation by physician interpretation, clinical selection approaches were higher (1.3%, 1.2%, and 1.0%, respectively) compared with corresponding ML thresholds (0.6%, 0.6%, and 0.2%).
Conclusion
ML, using clinical and stress imaging data, can be used to automatically recommend cancellation of rest SPECT MPI scans, while ensuring higher prognostic safety than current clinical approaches.
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