Detection and Monitoring of Viral Infections via Wearable Devices and Biometric Data.

Mounting clinical evidence suggests that viral infections can lead to detectable changes in an individual's normal physiologic and behavioral metrics, including heart and respiration rates, heart rate variability, temperature, activity, and sleep prior to symptom onset, potentially even in asymptomatic individuals. While the ability of wearable devices to detect viral infections in a real-world setting has yet to be proven, multiple recent studies have established that individual, continuous data from a range of biometric monitoring technologies can be easily acquired and that through the use of machine learning techniques, physiological signals and warning signs can be identified. In this review, we highlight the existing knowledge base supporting the potential for widespread implementation of biometric data to address existing gaps in the diagnosis and treatment of viral illnesses, with a particular focus on the many important lessons learned from the coronavirus disease 2019 pandemic.

Is There a Difference Between LI-RADS 3 to LI-RADS 5 Progression Assessment Using CT Versus MR? A Retrospective, Single-Center, Longitudinal Study of Patients Who Underwent 5082 Radiologic Examinations for Surveillance of Hepatocellular Carcinoma Over a 43-Month Period.

Objective: The Liver Imaging Reporting and Data System (LI-RADS) has been widely applied to CT and MR liver observations in patients at high-risk for hepatocellular carcinoma (HCC). We investigated the impact of CT vs MR in upgrading LI-RADS 3 to LI-RADS 5 observations using a large cohort of high-risk patients.

Methods: We performed a retrospective, longitudinal study of CT and MR radiographic reports (June 2013 - February 2017) with an assigned LI-RADS category.

Environmental Factors Predictive of No-Show Visits in Radiology: Observations of Three Million Outpatient Imaging Visits Over 16 Years.

Purpose: To evaluate the impact of environmental and socioeconomic factors on outpatient cancellations and "no-show visits" (NSVs) in radiology.

Materials And Methods: We conducted a retrospective analysis by collecting environmental factor data related to outpatient radiology visits occurring between 2000 and 2015 at our multihospital academic institution. Appointment attendance records were joined with daily weather observations from the National Oceanic and Atmospheric Administration and estimated median income from the US Census American Community Survey.

An Enhanced Mechanistic Model For Capnography, With Application To CHF-COPD Discrimination.

Capnography records CO partial pressure in exhaled breath as a function of time or exhaled volume. Time-based capnography, which is our focus, is a point-of-care, noninvasive, effort-independent and widely available clinical monitoring modality. The generated waveform, or capnogram, reflects the ventilation-perfusion dynamics of the lung, and thus has value in the diagnosis of respiratory conditions such as chronic obstructive pulmonary disease (COPD).

Understanding Why Patients No-Show: Observations of 2.9 Million Outpatient Imaging Visits Over 16 Years.

Purpose: To understand why patients "no-show" for imaging appointments, and to provide new insights for improving resource utilization.

Materials And Methods: We conducted a retrospective analysis of nearly 2.9 million outpatient examinations in our radiology information system from 2000 to 2015 at our multihospital academic institution.

Predictive modeling to identify scheduled radiology appointments resulting in non-attendance in a hospital setting.

No-show appointments are a troublesome, but frequent, occurrence in radiology hospital departments and private practice. Prior work in medical appointment no-show prediction has focused on general practice and has not considered features specific to the radiology environment. We collect data from 16 years of outpatient examinations in a multi-site hospital radiology department.

Statistical analysis of the age dependence of the normal capnogram.

The age dependence of the time-based capnogram from normal, healthy subjects has not been quantitatively characterized. The existence of age dependence would impact the development and operation of automated quantitative capnographic tools. Here, we quantitatively assess the relationship between normal capnogram shape and age.

Model-Based Estimation of Respiratory Parameters from Capnography, With Application to Diagnosing Obstructive Lung Disease.

Objective: We use a single-alveolar-compartment model to describe the partial pressure of carbon dioxide in exhaled breath, as recorded in time-based capnography. Respiratory parameters are estimated using this model, and then related to the clinical status of patients with obstructive lung disease.

Methods: Given appropriate assumptions, we derive an analytical solution of the model, describing the exhalation segment of the capnogram.

Characteristics of and Predictors for Apnea and Clinical Interventions During Procedural Sedation.

Study Objective: We describe the characteristics of and predictors for apnea and clinical interventions during emergency department (ED) procedural sedation.

Methods: High-resolution data were collected prospectively, using a convenience sample of ED patients undergoing propofol or ketofol sedation. End tidal CO (etco), respiratory rate, pulse rate, and SpO were electronically recorded in 1-second intervals.

Clustering of capnogram features to track state transitions during procedural sedation.

Procedural sedation has allowed many painful interventions to be conducted outside the operating room. During such procedures, it is important to maintain an appropriate level of sedation to minimize the risk of respiratory depression if patients are over-sedated and added pain or anxiety if under-sedated. However, there is currently no objective way to measure the patient's evolving level of sedation during a procedure.

Model-based estimation of pulmonary compliance and resistance parameters from time-based capnography.

We propose a highly-simplified single-alveolus mechanistic model of lung mechanics and gas mixing that leads to an analytical solution for carbon dioxide partial pressure in exhaled breath, as measured by time-based capnography. Using this solution, we estimate physiological parameters of the lungs on a continuous, breath-by-breath basis. We validate our model with capnograms from 15 subjects responding positively (>20% FEV1 drop from baseline) to methacholine challenge, and subsequently recovering with bronchodilator treatment.

Automated quantitative analysis of capnogram shape for COPD-normal and COPD-CHF classification.

We develop an approach to quantitative analysis of carbon dioxide concentration in exhaled breath, recorded as a function of time by capnography. The generated waveform--or capnogram--is currently used in clinical practice to establish the presence of respiration as well as determine respiratory rate and end-tidal CO 2 concentration. The capnogram shape also has diagnostic value, but is presently assessed qualitatively, by visual inspection.