Machine Learning-Based Detection of Parkinson's Disease From Resting-State EEG: A Multi-Center Study.

Resting-state EEG (rs-EEG) has been demonstrated to aid in Parkinson's disease (PD) diagnosis. In particular, the power spectral density (PSD) of low-frequency bands (δ and θ) and high-frequency bands (α and ß) has been shown to be significantly different in patients with PD as compared to subjects without PD (non-PD). However, rs-EEG feature extraction and the interpretation thereof can be time-intensive and prone to examiner variability. Machine learning (ML) has the potential to automatize the analysis of rs-EEG recordings and provides a supportive tool for clinicians to ease their workload. In this work, we use rs-EEG recordings of 84 PD and 85 non-PD subjects pooled from four datasets obtained at different centers. We propose an end-to-end pipeline consisting of preprocessing, extraction of PSD features from clinically-validated frequency bands, and feature selection. Following, we assess the classification ability of the features via ML algorithms to stratify between PD and non-PD subjects. Further, we evaluate the effect of feature harmonization, given the multi-center nature of the datasets. Our validation results show, on average, an improvement in PD detection ability (69.6% vs. 75.5% accuracy) by logistic regression when harmonizing the features and performing univariate feature selection (k = 202 features). Our final results show an average global accuracy of 72.2% with balanced accuracy results for all the centers included in the study: 60.6%, 68.7%, 77.7%, and 82.2%, respectively.Clinical relevance- We present an end-to-end pipeline to extract clinically relevant features from rs-EEG recordings that can facilitate the analysis and detection of PD. Further, we provide an ML system that shows a good performance in detecting PD, even in the presence of centers with different acquisition protocols. Our results show the relevance of harmonizing features and provide a good starting point for future studies focusing on rs-EEG analysis and multi-center data.

Prostate Age Gap: An MRI Surrogate Marker of Aging for Prostate Cancer Detection.

BACKGROUND: Aging is the most important risk factor for prostate cancer (PC). Imaging techniques can be useful to measure age-related changes associated with the transition to diverse pathological states. However, biomarkers of aging from prostate magnetic resonance imaging (MRI) remain to be explored. PURPOSE: To develop an aging biomarker from prostate MRI and to examine its relationship with clinically significant PC (csPC, Gleason score ≥7) risk occurrence. STUDY TYPE: Retrospective. POPULATION: Four hundred and sixty-eight (65.97 ± 6.91 years) biopsied males, contributing 7243 prostate MRI slices. A deep learning (DL) model was trained on 3223 MRI slices from 81 low-grade PC (Gleason score ≤6) and 131 negative patients, defined as non-csPC. The model was tested on 90 negative, 52 low-grade (142 non-csPC), and 114 csPC patients. FIELD STRENGTH/SEQUENCE: 3-T, axial T2-weighted spin sequence. ASSESSMENT: Chronological age was defined as the age of the participant at the time of the visit. Prostate-specific antigen (PSA), prostate volume, Gleason, and Prostate Imaging-Reporting and Data System (PI-RADS) scores were also obtained. Manually annotated prostate masks were used to crop the MRI slices, and a DL model was trained with those from non-csPC patients to estimate the age of the patients. Following, we obtained the prostate age gap (PAG) on previously unseen csPC and non-csPC cropped MRI exams. PAG was defined as the estimated model age minus the patient's age. Finally, the relationship between PAG and csPC risk occurrence was assessed through an adjusted multivariate logistic regression by PSA levels, age, prostate volume, and PI-RADS ≥ 3 score. STATISTICAL TESTS: T-test, Mann-Whitney U test, permutation test, receiver operating characteristics (ROC), area under the curve (AUC), and odds ratio (OR). A P value <0.05 was considered statistically significant. RESULTS: After adjusting, there was a significant difference in the odds of csPC (OR = 3.78, 95% confidence interval [CI]: 2.32-6.16). Further, PAG showed a significantly larger bootstrapped AUC to discriminate between csPC and non-csPC than that of adjusted PI-RADS ≥ 3 (AUC = 0.981, 95% CI: 0.975-0.987). DATA CONCLUSION: PAG may be associated with the risk of csPC and could outperform other PC risk factors. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 3.

Joint Analysis of Morphological Parameters and In Silico Haemodynamics of the Left Atrial Appendage for Thrombogenic Risk Assessment.

Background: Atrial fibrillation (AF) is considered the most common human arrhythmia. In nonvalvular AF, around 99% of thrombi are formed in the left atrial appendage (LAA). Nevertheless, there is not a consensus in the community about the relevant factors to stratify the AF population according to thrombogenic risk. Objective: To demonstrate the need for combining left atrial morphological and haemodynamics indices to improve the thrombogenic risk assessment in nonvalvular AF patients. Methods: A cohort of 71 nonvalvular AF patients was analysed. Statistical analysis, regression models, and random forests were used to analyse the differences between morphological and haemodynamics parameters, extracted from computational simulations built on 3D rotational angiography images, between patients with and without transient ischemic attack (TIA) or cerebrovascular accident (CVA). Results: The analysis showed that models composed of both morphological and haemodynamic factors were better predictors of TIA/CVA compared with models based on either morphological or haemodynamic factors separately. Maximum ostium diameter, length of the centreline, blood flow velocity within the LAA, oscillatory shear index, and time average wall shear stress parameters were found to be key risk factors for TIA/CVA prediction. In addition, TIA/CVA patients presented more flow stagnation within the LAA. Conclusion: Thrombus formation in the LAA is the result of multiple factors. Analyses based only on morphological or haemodynamic parameters are not precise enough to predict such a phenomenon, as demonstrated in our results; a better patient stratification can be obtained by jointly analysing morphological and haemodynamic features.

Factors associated with functional loss among community-dwelling Mexican older adults. / Factores asociados con el deterioro funcional en adultos mayores mexicanos

INTRODUCTION: Functional status decline is related to many negative outcomes. OBJECTIVE: To explore the relationship of sociodemographic, medical, and psychological factors with the incidence of functional status decline in Mexican older adults. MATERIALS AND METHODS: Data from the 2012 and 2015 waves of the Mexican Health and Aging Study (MHAS) survey were analyzed. Participants with previous functional status decline at baseline were excluded. We assessed functional status decline individually with activities of daily living (ADL) and instrumental ADL (IADLs) in an individual way. RESULTS: Age was associated with functional limitations in ADL. Being male had an association with limitations for IADL. A poor financial situation and lower education related to higher limitations for ADL. Furthermore, pain, comorbidities, and depression were found to be independently associated with limitations in ADL. IADL limitation was associated with age, poor education, comorbidities, and depression, as well as cognitive impairment. CONCLUSIONS: We found that factors such as age, financial status, educational level, pain, and the number of comorbidities were associated with the incidence of functional status decline. Pain had a greater association in the 3-year functional ADL decline incidence when compared with cognitive impairment. Studying functional decline by domains allowed us to find more detailed information to identify factors susceptible to intervention with the aim to reduce the incidence of functional status decline and dependence.

Introducción. El deterioro funcional está relacionado con muchos resultados adversos. Objetivo. Explorar la relación de los factores sociodemográficos, médicos y psicológicos con la incidencia del deterioro funcional en los adultos mayores mexicanos. Materiales y métodos. Se analizaron los datos de las cohortes de 2012 y 2015 de la encuesta del Estudio Mexicano de Salud y Envejecimiento. Se excluyeron los participantes con discapacidad funcional en el período de referencia (2012). Se evaluó de forma individual el deterioro funcional en las actividades básicas de la vida diaria (AVD) y en las instrumentales (AIVD). Resultados. Se encontró que el dolor, las comorbilidades, el nivel educativo, el estatus socioeconómico y la depresión se asociaban independientemente con el deterioro de las AVD. El deterioro de las AIVD se asoció con la edad, la educación deficiente, las comorbilidades, la depresión y el deterioro cognitivo. Conclusiones. La edad, el sexo, el estado financiero, el nivel educativo, el dolor y el número de comorbilidades se asociaron con la incidencia del deterioro funcional. El dolor tuvo una mayor asociación con la incidencia del deterioro funcional en las AVD a los tres años, en comparación con el deterioro cognitivo. El estudio del deterioro funcional por dominios permitió recabar información más detallada para determinar los factores que pueden intervenirse con el objetivo de reducir la incidencia del deterioro funcional y la dependencia.

Factors associated with functional loss among community-dwelling Mexican older adults / Factores asociados con el deterioro funcional en adultos mayores mexicanos

Introduction: Functional status decline is related to many negative outcomes. Objective: To explore the relationship of sociodemographic, medical, and psychological factors with the incidence of functional status decline in Mexican older adults. Materials and methods: Data from the 2012 and 2015 waves of the Mexican Health and Aging Study (MHAS) survey were analyzed. Participants with previous functional status decline at baseline were excluded. We assessed functional status decline individually with activities of daily living (ADL) and instrumental ADL (IADLs) in an individual way. Results: Age was associated with functional limitations in ADL. Being male had an association with limitations for IADL. A poor financial situation and lower education related to higher limitations for ADL. Furthermore, pain, comorbidities, and depression were found to be independently associated with limitations in ADL. IADL limitation was associated with age, poor education, comorbidities, and depression, as well as cognitive impairment. Conclusions: We found that factors such as age, financial status, educational level, pain, and the number of comorbidities were associated with the incidence of functional status decline. Pain had a greater association in the 3-year functional ADL decline incidence when compared with cognitive impairment. Studying functional decline by domains allowed us to find more detailed information to identify factors susceptible to intervention with the aim to reduce the incidence of functional status decline and dependence.

Introducción. El deterioro funcional está relacionado con muchos resultados adversos. Objetivo. Explorar la relación de los factores sociodemográficos, médicos y psicológicos con la incidencia del deterioro funcional en los adultos mayores mexicanos. Materiales y métodos. Se analizaron los datos de las cohortes de 2012 y 2015 de la encuesta del Estudio Mexicano de Salud y Envejecimiento. Se excluyeron los participantes con discapacidad funcional en el período de referencia (2012). Se evaluó de forma individual el deterioro funcional en las actividades básicas de la vida diaria (AVD) y en las instrumentales (AIVD). Resultados. Se encontró que el dolor, las comorbilidades, el nivel educativo, el estatus socioeconómico y la depresión se asociaban independientemente con el deterioro de las AVD. El deterioro de las AIVD se asoció con la edad, la educación deficiente, las comorbilidades, la depresión y el deterioro cognitivo. Conclusiones. La edad, el sexo, el estado financiero, el nivel educativo, el dolor y el número de comorbilidades se asociaron con la incidencia del deterioro funcional. El dolor tuvo una mayor asociación con la incidencia del deterioro funcional en las AVD a los tres años, en comparación con el deterioro cognitivo. El estudio del deterioro funcional por dominios permitió recabar información más detallada para determinar los factores que pueden intervenirse con el objetivo de reducir la incidencia del deterioro funcional y la dependencia.

In silico Optimization of Left Atrial Appendage Occluder Implantation Using Interactive and Modeling Tools.

According to clinical studies, around one third of patients with atrial fibrillation (AF) will suffer a stroke during their lifetime. Between 70 and 90% of these strokes are caused by thrombus formed in the left atrial appendage. In patients with contraindications to oral anticoagulants, a left atrial appendage occluder (LAAO) is often implanted to prevent blood flow entering in the LAA. A limited range of LAAO devices is available, with different designs and sizes. Together with the heterogeneity of LAA morphology, these factors make LAAO success dependent on clinician's experience. A sub-optimal LAAO implantation can generate thrombi outside the device, eventually leading to stroke if not treated. The aim of this study was to develop clinician-friendly tools based on biophysical models to optimize LAAO device therapies. A web-based 3D interactive virtual implantation platform, so-called VIDAA, was created to select the most appropriate LAAO configurations (type of device, size, landing zone) for a given patient-specific LAA morphology. An initial LAAO configuration is proposed in VIDAA, automatically computed from LAA shape features (centreline, diameters). The most promising LAAO settings and LAA geometries were exported from VIDAA to build volumetric meshes and run Computational Fluid Dynamics (CFD) simulations to assess blood flow patterns after implantation. Risk of thrombus formation was estimated from the simulated hemodynamics with an index combining information from blood flow velocity and complexity. The combination of the VIDAA platform with in silico indices allowed to identify the LAAO configurations associated to a lower risk of thrombus formation; device positioning was key to the creation of regions with turbulent flows after implantation. Our results demonstrate the potential for optimizing LAAO therapy settings during pre-implant planning based on modeling tools and contribute to reduce the risk of thrombus formation after treatment.