Age-related hearing loss is not linked to cerebrospinal fluid levels of ß-amyloid or p-tau181.

INTRODUCTION: As Hearing loss and dementia affect people with the same profile, several epidemiological studies have evaluated their relationship. However, the link between age-related hearing loss and Alzheimer's disease is still unclear. METHODS: We selected subjects with no history of exposure to loud noises, blasts, head trauma with hearing loss, or sudden sensorineural hearing loss from a cohort intended to study preclinical phases of Alzheimer's disease. Participants are volunteers over 55 years without cognitive impairment. We correlated the results of an objective auditory evaluation with brain amyloid and p-tau181 levels and with the outcomes of a comprehensive neuropsychological assessment. RESULTS: Fifty-five subjects at different stages of the Alzheimer's disease continuum were evaluated. There were no statistically significant correlations between amyloid-ß and p-tau levels and any of the objective auditory measures. A weak but significant correlation was found between amyloid-ß values and the Hearing Handicap Inventory for the Elderly. The neuropsychological domains more correlated to hearing loss were executive function and processing speed. DISCUSSION: Age-related hearing loss is not linked to any pathological markers of Alzheimer's disease nor to neuropsychological domains typically affected in this disease. The Hearing Handicap Inventory for the Elderly has an important component of subjectivity and further studies are needed to explore its relationship with amyloid-ß levels.

Analysing disease trajectories in a cohort of 71,849 Patients: A visual analytics and statistical approach.

BACKGROUND: Disease trajectories have become increasingly relevant within the context of an aging population and the rising prevalence of chronic illnesses. Understanding the temporal progression of diseases is crucial for enhancing patient care, preventive measures, and effective management. OBJECTIVE: The objective of this study is to propose and validate a novel methodology for trajectory impact analysis and interactive visualization of disease trajectories over a cohort of 71,849 patients. METHODS: This article introduces an innovative comprehensive approach for analysis and interactive visualization of disease trajectories. First, Risk Increase (RI) index is defined that assesses the impact of the initial disease diagnosis on the development of subsequent illnesses. Secondly, visual graphics methods are used to represent cohort trajectories, ensuring a clear and semantically rich presentation that facilitates easy data interpretation. RESULTS: The proposed approach is demonstrated over the disease trajectories of a cohort comprising 71,849 patients from Tolosaldea, Spain. The study finds several clinically relevant trajectories in this cohort, such as that after suffering a cerebral ischemic stroke, the probability of suffering dementia increases 10.77 times. The clinical relevance of the study outcomes have been assessed by an in-depth analysis conducted by expert clinicians. The identified disease trajectories are in agreement with the latest advancements in the field. CONCLUSION: The proposed approach for trajectory impact analysis and interactive visualization offers valuable graphs for the comprehensive study of disease trajectories for improved clinical decision-making. The simplicity and interpretability of our methods make them valuable approach for healthcare professionals.

K-Means Clustering for Shock Classification in Pediatric Intensive Care Units.

Shock is described as an inadequate oxygen supply to the tissues and can be classified in multiple ways. In clinical practice still, old methods are used to discriminate these shock types. This article proposes the application of unsupervised classification methods for the stratification of these patients in order to treat them more appropriately. With a cohort of 90 patients admitted in pediatric intensive care units (PICU), the k-means algorithm was applied in the first 24 h data since admission (physiological and analytical variables and the need for devices), obtaining three main groups. Significant differences were found in variables used (e.g., mean diastolic arterial pressure p < 0.001, age p < 0.001) and not used for training (e.g., EtCO2 min p < 0.001, Troponin max p < 0.01), discharge diagnosis (p < 0.001) and outcomes (p < 0.05). Clustering classification equaled classical classification in its association with LOS (p = 0.01) and surpassed it in its association with mortality (p < 0.04 vs. p = 0.16). We have been able to classify shocked pediatric patients with higher outcome correlation than the clinical traditional method. These results support the utility of unsupervised learning algorithms for patient classification in PICU.