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.

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 CO2 (etco2), respiratory rate, pulse rate, and SpO2 were electronically recorded in 1-second intervals. Procedure times, drug delivery, and interventions were electronically annotated. Kaplan-Meier curves were used to describe the onset of clinical interventions as a function of sedation time. The onset of apnea (15 consecutive seconds with carbon dioxide ≤10 mm Hg) and clinical interventions were estimated with a series of Cox proportional hazards survival models, with time to first apnea or clinical intervention as the dependent variable. Finally, we tested the association between apnea and clinical intervention. RESULTS: Three hundred twelve patients were analyzed (53% male patients). Apnea was preceded by etco2 less than 30 mm Hg or greater than 50 mm Hg at 30, 60, and 90 seconds before its onset. Clinical interventions were predicted by apnea, SpO2, and propofol use. Increasing age predicted both apnea and interventions. Apnea was not predicted by respiratory rate or SpO2. Apnea occurred in half of the patients and clinical interventions in a quarter of them. Clinical intervention was not predicted by abnormal respiratory rate or abnormal etco2 level. The majority of clinical interventions (85%) were minor, with no cases of assisted ventilation, intubation, or complications. CONCLUSION: Alterations in etco2 predicted apnea along a specific time course. Alterations in SpO2, apnea, and propofol use predicted clinical interventions. Increasing age predicted both apnea and clinical intervention.

MRI log file analysis for workflow improvement.

Magnetic Resonance Imaging (MRI) is an important diagnostic scanning tool for the detection and monitoring of specific diseases and conditions. However, the equipment cost, maintenance and specialty training of the technologists make the examination expensive. Consequently, unnecessary scanner time caused by poor scheduling, repeated sequences, aborted sequences, scanner idleness, or capture of non-diagnostic or low-value sequences is an opportunity to reduce costs and increase efficiency. This paper analyzes data collected from log files on 29 scanners over several years. 'Wasted' time is defined and key performance indicators (KPIs) are identified. A decrease in exam duration results when actively modifying and monitoring the number of sequences that comprise the exam card for a protocol.

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. A final population of 757 individual scans and 212 high-risk patients had at least one LI-RADS 3 observation. Differences in observation time to progression between modalities were determined using uni- and multivariable analysis. RESULTS: Of the 212 patients with a LI-RADS 3 observation, 52 (25%) had progression to LI-RADS 5. Tp ranged from 64 - 818 days (median: 196 days). One hundred and three patients (49%) had MR and 109 patients (51%) had CT as their index study. Twenty-four patients with an MR index exam progressed to LI-RADS 5 during the follow-up interval, with progression rates of 22% (CI:13%-30%) at 1 year and 29% (CI:17%-40%) at 2 years. Twenty-eight patients with a CT index exam progressed to LI-RADS 5 during follow-up, with progression rates of 26% (CI:16%-35%) at 1 year and 31% (CI:19%-41%) at 2 years. Progression rates were not significantly different between patients whose LI-RADS 3 observation was initially diagnosed on MR vs CT (HR: 0.81, P = 0.44). DISCUSSION: MR and CT modalities are comparable for demonstrating progression from LI-RADS 3 to 5 for high risk patients.

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. A multivariate logistic regression model was built to examine relationships between NSV rate and median income, commute distance, maximum daily temperature, and daily snowfall. RESULTS: There were 270,574 (8.0%) cancellations and 87,407 (2.6%) NSVs among 3,379,947 scheduled outpatient radiology appointments and 575,206 unique patients from 2000 to 2015. Overall cancellation rates decreased from 14% to 8%, and NSV rates decreased from 6% to 1% as median income increased from $20,000 to $120,000 per year. In a multivariate model, the odds of NSV decreased 10.7% per $10,000 increase in median income (95% confidence interval [CI]: 10.3%-11.1%) and 2.0% per 10°F increase in maximum daily temperature (95% CI: 1.3%-1.6%). The odds of NSV increased 1.4% per 10-mile increase in commute distance (95% CI: 1.3%-1.6%) and 4.5% per 1-inch increase in daily snowfall (95% CI: 3.6%-5.3%). Commute distance was more strongly associated with NSV for those in the two lower tertiles of income than the highest tertile (P < .001). CONCLUSION: Environmental factors are strongly associated with patients' attendance at scheduled outpatient radiology examinations. Modeling of appointment failure risk based on environmental features can help increase the attendance of outpatient radiology appointments.

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

Capnography records CO2 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). Effective discrimination between normal respiration and obstructive lung disease can be performed using capnogram-derived estimates of respiratory parameters in a simple mechanistic model of CO2 exhalation. We propose an enhanced mechanistic model that can capture specific capnogram characteristics in congestive heart failure (CHF) by incorporating a representation of the inertance associated with fluid in the lungs. The 4 associated parameters are estimated on a breath-by-breath basis by fitting the model output to the exhalations in the measured capnogram. Estimated parameters from 40 exhalations of 7 CHF and 7 COPD patients were used as a training set to design a quadratic discriminator in the parameter space, aimed at distinguishing between CHF and COPD patients. The area under the ROC curve for the training set was 0.94, and the corresponding equal-error-rate value of approximately 0.1 suggests classification accuracies of the order of 90% are attainable. Applying this discriminator without modification to 40 exhalations from each CHF and COPD patient in a fresh test set, and deciding on a simple majority basis whether the patient has CHF or COPD, results in correctly labeling all 8 out of the 8 CHF patients and 6 out of the 8 COPD patients in the test set, corresponding to a classification accuracy of 87.5%.

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. No-show visits were identified by the "reason code" entry "NOSHOW" in our radiology information system. We restricted data to radiography, CT, mammography, MRI, ultrasound, and nuclear medicine examinations that included all studied variables. These variables included modality, patient age, appointment time, day of week, and scheduling lead time. Multivariate logistic regression was used to identify factors associated with no-show visits. RESULTS: Out of 2,893,626 patient visits that met our inclusion criteria, there were 94,096 no-shows during the 16-year period. Rates of no-show visits varied from 3.36% in 2000 to 2.26% in 2015. The effect size for no-shows was strongest for modality and scheduling lead time. Mammography had the highest modality no-show visit rate of 6.99% (odds ratio [OR] 5.38, P < .001) compared with the lowest modality rate of 1.25% in radiography. Scheduling lead time greater than 6 months was associated with more no-show visits than scheduling within 1 week (OR 3.18, P < .001). Patients 60 years and older were less likely to miss imaging appointments than patients under 40 (OR 0.70, P < .001). Mondays and Saturdays had significantly higher rates of no-show than Sundays (OR 1.52 and 1.51, P < .001). CONCLUSION: Modality type and scheduling lead time were the most predictive factors of no-show. This may be used to guide new interventions such as targeted reminders and flexible scheduling.

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. Data from the radiology information system (RIS) are fused with patient income estimated from U.S. Census data. Features were categorized into three groups: Patient, Exam, and Scheduling. Models based on the total feature set and separately on each feature group were developed using logistic regression to assess the per-appointment likelihood of no-show. After five-fold cross-validation, no-show prediction using the total feature set from 554,611 appointments yielded an area under the curve (AUC) of 0.770 ± 0.003. Feature groups that were most informative in the prediction of no-show appointments were those based on the type of exam and on scheduling attributes such as the lead time of scheduling the appointment. A data-driven no-show prediction model like the one presented here could be useful to schedulers in the implementation of an automated scheduling policy or the assignment of examinations with a high risk of no-show to lower impact appointment slots.

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. Capnograms were collected from healthy subjects, and physiologically-based features (exhalation duration, end-tidal CO2 and time spent at this value, normalized time spent at end-tidal CO2, end-exhalation slope, and instantaneous respiratory rate) were computationally extracted. The mean values of the individual features over 30 exhalations were linearly regressed against subject age, accounting for inter-feature correlation. After data collection, 154 of 178 subjects were eligible for analysis, with an age range of 3-78 years (mean age 39, std. dev. 20 years). The Bonferroni-corrected joint 95% confidence intervals (CIs) of the regression line slopes contained the origin for five of six features (the remaining CI was only slightly offset from the origin). The associated individual r2 values for the regressions were all below 0.07. We conclude that age is not a significant explanatory factor in describing variations in the shape of the normal capnogram. This finding could be exploited in the design of automated methods for quantitative capnogram analysis across a range of ages.

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. This solution is parametrized by alveolar CO2 concentration, dead-space fraction, and the time constant associated with exhalation. These quantities are estimated from individual capnogram data on a breath-by-breath basis. The model is applied to analyzing datasets from normal (n = 24) and chronic obstructive pulmonary disease (COPD) (n = 22) subjects, as well as from patients undergoing methacholine challenge testing for asthma (n = 22). RESULTS: A classifier based on linear discriminant analysis in logarithmic coordinates, using estimated dead-space fraction and exhalation time constant as features, and trained on data from five normal and five COPD subjects, yielded an area under the receiver operating characteristic curve (AUC) of 0.99 in classifying the remaining 36 subjects as normal or COPD. Bootstrapping with 50 replicas yielded a 95% confidence interval of AUCs from 0.96 to 1.00. For patients undergoing methacholine challenge testing, qualitatively meaningful trends were observed in the parameter variations over the course of the test. SIGNIFICANCE: A simple mechanistic model allows estimation of underlying respiratory parameters from the capnogram, and may be applied to diagnosis and monitoring of chronic and reversible obstructive lung disease.

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. Our results suggest that parameter estimates from capnography may provide discriminatory value for lung function comparable to spirometry, thus warranting more detailed study.

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. We investigated the use of capnography-derived features as an objective measure of sedation level. Time-based capnograms were recorded from 30 patients during sedation for cardioversion. Through causal k-means clustering of selected features, we sequentially assigned each exhalation to one of three distinct clusters, or states. Transitions between these states correlated to events during sedation (drug administration, procedure start and end, and clinical interventions). Similar clustering of capnogram recordings from 26 healthy, non-sedated subjects did not reveal distinctly separated states.

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. Prior approaches to quantitatively characterizing the capnogram shape have explored the correlation of various geometric parameters with pulmonary function tests. These studies attempted to characterize the capnogram in normal subjects and patients with cardiopulmonary disease, but no consistent progress was made, and no translation into clinical practice was achieved. We apply automated quantitative analysis to discriminate between chronic obstructive pulmonary disease (COPD) and congestive heart failure (CHF), and between COPD and normal. Capnograms were collected from 30 normal subjects, 56 COPD patients, and 53 CHF patients. We computationally extract four physiologically based capnogram features. Classification on a hold-out test set was performed by an ensemble of classifiers employing quadratic discriminant analysis, designed through cross validation on a labeled training set. Using 80 exhalations of each capnogram record in the test set, performance analysis with bootstrapping yields areas under the receiver operating characteristic (ROC) curve of 0.89 (95% CI: 0.72-0.96) for COPD/CHF classification, and 0.98 (95% CI: 0.82-1.0) for COPD/normal classification. This classification performance is obtained with a run time sufficiently fast for real-time monitoring.