Fusion of imaging and non-imaging data for disease trajectory prediction for coronavirus disease 2019 patients.

Purpose: Our study investigates whether graph-based fusion of imaging data with non-imaging electronic health records (EHR) data can improve the prediction of the disease trajectories for patients with coronavirus disease 2019 (COVID-19) beyond the prediction performance of only imaging or non-imaging EHR data. Approach: We present a fusion framework for fine-grained clinical outcome prediction [discharge, intensive care unit (ICU) admission, or death] that fuses imaging and non-imaging information using a similarity-based graph structure. Node features are represented by image embedding, and edges are encoded with clinical or demographic similarity. Results: Experiments on data collected from the Emory Healthcare Network indicate that our fusion modeling scheme performs consistently better than predictive models developed using only imaging or non-imaging features, with area under the receiver operating characteristics curve of 0.76, 0.90, and 0.75 for discharge from hospital, mortality, and ICU admission, respectively. External validation was performed on data collected from the Mayo Clinic. Our scheme highlights known biases in the model prediction, such as bias against patients with alcohol abuse history and bias based on insurance status. Conclusions: Our study signifies the importance of the fusion of multiple data modalities for the accurate prediction of clinical trajectories. The proposed graph structure can model relationships between patients based on non-imaging EHR data, and graph convolutional networks can fuse this relationship information with imaging data to effectively predict future disease trajectory more effectively than models employing only imaging or non-imaging data. Our graph-based fusion modeling frameworks can be easily extended to other prediction tasks to efficiently combine imaging data with non-imaging clinical data.

Association of Electronic Cigarette Exposure on Cardiovascular Health: A Systematic Review and Meta-Analysis.

Despite the growing use of electronic cigarettes (EC) in the Unites States, particularly among young people, and their perceived safety, current evidence suggests that EC usage may cause adverse clinical cardiovascular effects. Therefore, we aim to pool all studies evaluating the association of EC exposure with cardiovascular health. Medline, Cochrane CENTRAL, and Scopus were searched for studies from January 1, 2006 until December 31, 2022. Randomized and observational studies reporting cardiovascular outcomes, hemodynamic parameters, and biomarkers of platelet physiology, before and after acute or chronic EC exposure were pooled using a random-effects model. Overall, 27 studies (n = 863) were included. Heart rate increased significantly after acute EC exposure (weighted mean difference [WMD]: 0.76 bpm; 95% confidence interval [CI], 0.48, 1.03; P < 0.00001; I2 = 92%). Significant increases in systolic blood pressure (WMD: 0.28 mmHg; 95% CI, 0.06, 0.51; P = 0.01; I2 = 94%), diastolic blood pressure (WMD: 0.38 mmHg; 95% CI, 0.16, 0.60; P = 0.0006; I2 = 90%), and PWV (WMD: 0.38; 95% CI, 0.13, 0.63; P = 0.003; I2 = 100%) were also observed. Augmentation index increased significantly (SMD: 0.39; 95% CI, 0.11, 0.67; P = 0.007; I2 = 90%), whereas reduction in flow-mediated dilation (WMD: -1.48; 95% CI, -2.49, -0.47; P = 0.004; I2 = 45%) was observed. Moreover, significant rise in both soluble P-selectin (WMD: 4.73; 95% CI, 0.80, 8.66; P = 0.02; I2 = 98%) and CD40L (WMD: 1.14; 95% CI, 0.41, 1.87; P = 0.002; I2 = 79%) was observed. Our results demonstrate that smoking EC is associated with a significant increase in cardiovascular hemodynamic measures and biomarkers. Our findings can aid policymakers in making informed decisions regarding the regulation of EC to ensure public safety.

Graph-based Fusion Modeling and Explanation for Disease Trajectory Prediction.

We propose a relational graph to incorporate clinical similarity between patients while building personalized clinical event predictors with a focus on hospitalized COVID-19 patients. Our graph formation process fuses heterogeneous data, i.e., chest X-rays as node features and non-imaging EHR for edge formation. While node represents a snap-shot in time for a single patient, weighted edge structure encodes complex clinical patterns among patients. While age and gender have been used in the past for patient graph formation, our method incorporates complex clinical history while avoiding manual feature selection. The model learns from the patient's own data as well as patterns among clinically-similar patients. Our visualization study investigates the effects of 'neighborhood' of a node on its predictiveness and showcases the model's tendency to focus on edge-connected patients with highly suggestive clinical features common with the node. The proposed model generalizes well by allowing edge formation process to adapt to an external cohort.

Graph-based Fusion Modeling and Explanation forDisease Trajectory Prediction.

We propose a relational graph to incorporate clinical similarity between patients while building personalized clinical event predictors with a focus on hospitalized COVID-19 patients. Our graph formation process fuses heterogeneous data, i.e., chest X-rays as node features and non-imaging EHR for edge formation. While node represents a snap-shot in time for a single patient, weighted edge structure encodes complex clinical patterns among patients. While age and gender have been used in the past for patient graph formation, our method incorporates complex clinical history while avoiding manual feature selection. The model learns from the patient's own data as well as patterns among clinically-similar patients. Our visualization study investigates the effects of 'neighborhood' of a node on its predictiveness and showcases the model's tendency to focus on edge-connected patients with highly suggestive clinical features common with the node. The proposed model generalizes well by allowing edge formation process to adapt to an external cohort.