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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

Capnography records CO 2 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 CO 2 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%.

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.

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 CO 2 and time spent at this value, normalized time spent at end-tidal CO 2 , 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 r 2 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.

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.

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.