Anxiety and Depression Associated With Increased Cardiovascular Disease Risk Through Accelerated Development of Risk Factors.

Background: Prior studies have incompletely assessed whether the development of cardiometabolic risk factors (CVDRF) (hypertension, hyperlipidemia, and diabetes mellitus) mediates the association between anxiety and depression (anxiety/depression) and cardiovascular disease (CVD). Objectives: The authors aimed to evaluate the following: 1) the association between anxiety/depression and incident CVDRFs and whether this association mediates the increased CVD risk; and 2) whether neuro-immune mechanisms and age and sex effects may be involved. Methods: Using a retrospective cohort design, Mass General Brigham Biobank subjects were followed for 10 years. Presence and timing of anxiety/depression, CVDRFs, and CVD were determined using ICD codes. Stress-related neural activity, chronic inflammation, and autonomic function were measured by the assessment of amygdalar-to-cortical activity ratio, high-sensitivity CRP, and heart rate variability. Multivariable regression and mediation analyses were employed. Results: Among 71,214 subjects (median age 49.6 years; 55.3% female), 27,048 (38.0%) developed CVDRFs during follow-up. Pre-existing anxiety/depression associated with increased risk of incident CVDRF (OR: 1.71 [95% CI: 1.59-1.83], P < 0.001) and with a shorter time to their development (ß = -0.486 [95% CI: -0.62 to -0.35], P < 0.001). The development of CVDRFs mediated the association between anxiety/depression and CVD events (log-odds: 0.044 [95% CI: 0.034-0.055], P < 0.05). Neuro-immune pathways contributed to the development of CVDRFs (P < 0.05 each) and significant age and sex effects were noted: younger women experienced the greatest acceleration in the development of CVDRFs after anxiety/depression. Conclusions: Anxiety/depression accelerate the development of CVDRFs. This association appears to be most notable among younger women and may be mediated by stress-related neuro-immune pathways. Evaluations of tailored preventive measures for individuals with anxiety/depression are needed to reduce CVD risk.

Incidence and risk factors of fellow-eyes wet conversion in unilateral neovascular age-related macular degeneration over 15-year follow-up.

PURPOSE: Incidence and risk factors of fellow eye wet conversion in unilateral neovascular age-related macular degeneration (nAMD) over 15-years follow-up. METHODS: This retrospective study reviewed 593 unilateral nAMD patients with a minimum of five years up to 15 years of follow-up. The demographic data, visual acuity, fellow eye nAMD conversion rate, and the number of anti-vascular endothelial growth factor (anti-VEGF) injections in the primary eye were evaluated. Also, the nAMD-converted fellow eyes were divided into two groups based on the time of conversion (less and more than two years from the first injection in the primary eye). Based on the data types, the T-test, Chi-square, and Mann-Whitney U test were used to analyze. RESULTS: The total cases were 593 patients, and 248 eyes (41.82%) converted to nAMD in the mean interval of 34.92 ± 30.62 months. The males exhibited a predisposition to wet conversion at 2.54 years earlier than their female counterparts (P = 0.025). In all the converted fellow eyes, the mean age was 2.3 years higher at presentation in the group who converted within two years of follow-up in compared to eyes that converted after two years (79.82 ± 8.64 vs 77.51 ± 8.5 years, P = 0.035). Additionally, eyes converting within two years had a mean baseline LogMAR visual acuity of 0.44 ± 0.47, compared to 0.32 ± 0.41 for conversions after two years (P = 0.014). CONCLUSION: This study reported that males showed a predisposition to fellow eye nAMD conversion at an earlier age. Additionally, there was a trend of faster fellow eye nAMD conversion in individuals with higher age and lower baseline visual acuity. KEY MESSAGES: What is known • Certain risk factors may make the fellow eye of neovascular age-related macular degeneration (nAMD) more likely to progress to wet conversion. • Identifying these risk factors for fellow eye wet conversion can help prevent it, potentially preserving the patient's vision quality for a longer duration. • The studies on the incidence of wet conversion in the fellow eye have yielded controversial results. What is new • During the 15-year follow-up period, nearly half (47.58%) of the fellow eyes that underwent wet conversion did so within the initial two years following the wet conversion of the first eye. • Males showed a predisposition to fellow eye nAMD conversion at an earlier age. • There was a trend of faster fellow eye nAMD conversion in individuals with higher age and lower baseline visual acuity.

Radiomics-Based Prediction of Anti-VEGF Treatment Response in Neovascular Age-Related Macular Degeneration With Pigment Epithelial Detachment.

Purpose: Machine learning models based on radiomic feature extraction from clinical imaging data provide effective and interpretable means for clinical decision making. This pilot study evaluated whether radiomics features in baseline optical coherence tomography (OCT) images of eyes with pigment epithelial detachment (PED) associated with neovascular age-related macular degeneration (nAMD) can predict treatment response to as-needed anti-vascular endothelial growth factor (VEGF) therapy. Methods: Thirty-nine eyes of patients with PED undergoing anti-VEGF therapy were included. All eyes underwent a loading dose followed by as-needed therapy. OCT images at baseline, month 3, and month 6 were analyzed. Images were manually separated into non-responding, recurring, and responding eyes based on the presence or absence of subretinal fluid at month 6. PED radiomics features were then extracted from each image and images were classified as responding or recurring using a machine learning classifier applied to the radiomics features. Results: Linear discriminant analysis classification of baseline features as responsive versus recurring resulted in classification performance of 64.0% (95% confidence interval [CI] = 0.63-0.65), area under the curve (AUC = 0.78, 95% CI = 0.72-0.82), sensitivity 0.79 (95% CI = 0.63-0.87), and specificity 0.58 (95% CI = 0.50-0.67). Further analysis of features in recurring eyes identified a significant shift toward non-responding mean feature values over 6 months. Conclusions: Our results demonstrate the use of radiomics features as predictors for treatment response to as-needed anti-VEGF therapy. Our study demonstrates the potential for radiomics feature in clinical decision support for personalizing anti-VEGF therapy. Translational Relevance: The ability to use PED texture features to predict treatment response facilitates personalized clinical decision making.

Application of artificial intelligence in the diagnosis of sleep apnea.

STUDY OBJECTIVES: Machine learning (ML) models have been employed in the setting of sleep disorders. This review aims to summarize the existing data about the role of ML techniques in the diagnosis, classification, and treatment of sleep-related breathing disorders. METHODS: A systematic search in Medline, EMBASE, and Cochrane databases through January 2022 was performed. RESULTS: Our search strategy revealed 132 studies that were included in the systematic review. Existing data show that ML models have been successfully used for diagnostic purposes. Specifically, ML models showed good performance in diagnosing sleep apnea using easily obtained features from the electrocardiogram, pulse oximetry, and sound signals. Similarly, ML showed good performance for the classification of sleep apnea into obstructive and central categories, as well as predicting apnea severity. Existing data show promising results for the ML-based guided treatment of sleep apnea. Specifically, the prediction of outcomes following surgical treatment and optimization of continuous positive airway pressure therapy can be guided by ML models. CONCLUSIONS: The adoption and implementation of ML in the field of sleep-related breathing disorders is promising. Advancements in wearable sensor technology and ML models can help clinicians predict, diagnose, and classify sleep apnea more accurately and efficiently. CITATION: Bazoukis G, Bollepalli SC, Chung CT, et al. Application of artificial intelligence in the diagnosis of sleep apnea. J Clin Sleep Med. 2023;19(7):1337-1363.

An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit.

Risk stratification at the time of hospital admission is of paramount significance in triaging the patients and providing timely care. In the present study, we aim at predicting multiple clinical outcomes using the data recorded during admission to a cardiac care unit via an optimized machine learning method. This study involves a total of 11,498 patients admitted to a cardiac care unit over two years. Patient demographics, admission type (emergency or outpatient), patient history, lab tests, and comorbidities were used to predict various outcomes. We employed a fully connected neural network architecture and optimized the models for various subsets of input features. Using 10-fold cross-validation, our optimized machine learning model predicted mortality with a mean area under the receiver operating characteristic curve (AUC) of 0.967 (95% confidence interval (CI): 0.963-0.972), heart failure AUC of 0.838 (CI: 0.825-0.851), ST-segment elevation myocardial infarction AUC of 0.832 (CI: 0.821-0.842), pulmonary embolism AUC of 0.802 (CI: 0.764-0.84), and estimated the duration of stay (DOS) with a mean absolute error of 2.543 days (CI: 2.499-2.586) of data with a mean and median DOS of 6.35 and 5.0 days, respectively. Further, we objectively quantified the importance of each feature and its correlation with the clinical assessment of the corresponding outcome. The proposed method accurately predicts various cardiac outcomes and can be used as a clinical decision support system to provide timely care and optimize hospital resources.

Real-Time Arrhythmia Detection Using Hybrid Convolutional Neural Networks.

Background Accurate detection of arrhythmic events in the intensive care units (ICU) is of paramount significance in providing timely care. However, traditional ICU monitors generate a high rate of false alarms causing alarm fatigue. In this work, we develop an algorithm to improve life threatening arrhythmia detection in the ICUs using a deep learning approach. Methods and Results This study involves a total of 953 independent life-threatening arrhythmia alarms generated from the ICU bedside monitors of 410 patients. Specifically, we used the ECG (4 channels), arterial blood pressure, and photoplethysmograph signals to accurately detect the onset and offset of various arrhythmias, without prior knowledge of the alarm type. We used a hybrid convolutional neural network based classifier that fuses traditional handcrafted features with features automatically learned using convolutional neural networks. Further, the proposed architecture remains flexible to be adapted to various arrhythmic conditions as well as multiple physiological signals. Our hybrid- convolutional neural network approach achieved superior performance compared with methods which only used convolutional neural network. We evaluated our algorithm using 5-fold cross-validation for 5 times and obtained an accuracy of 87.5%±0.5%, and a score of 81%±0.9%. Independent evaluation of our algorithm on the publicly available PhysioNet 2015 Challenge database resulted in overall classification accuracy and score of 93.9% and 84.3%, respectively, indicating its efficacy and generalizability. Conclusions Our method accurately detects multiple arrhythmic conditions. Suitable translation of our algorithm may significantly improve the quality of care in ICUs by reducing the burden of false alarms.

Machine learning versus conventional clinical methods in guiding management of heart failure patients-a systematic review.

Machine learning (ML) algorithms "learn" information directly from data, and their performance improves proportionally with the number of high-quality samples. The aim of our systematic review is to present the state of the art regarding the implementation of ML techniques in the management of heart failure (HF) patients. We manually searched MEDLINE and Cochrane databases as well the reference lists of the relevant review studies and included studies. Our search retrieved 122 relevant studies. These studies mainly refer to (a) the role of ML in the classification of HF patients into distinct categories which may require a different treatment strategy, (b) discrimination of HF patients from the healthy population or other diseases, (c) prediction of HF outcomes, (d) identification of HF patients from electronic records and identification of HF patients with similar characteristics who may benefit form a similar treatment strategy, (e) supporting the extraction of important data from clinical notes, and (f) prediction of outcomes in HF populations with implantable devices (left ventricular assist device, cardiac resynchronization therapy). We concluded that ML techniques may play an important role for the efficient construction of methodologies for diagnosis, management, and prediction of outcomes in HF patients.