An experimental system for detection and localization of hemorrhage using ultra-wideband microwaves with deep learning.

Stroke is a leading cause of mortality and disability. Emergent diagnosis and intervention are critical, and predicated upon initial brain imaging; however, existing clinical imaging modalities are generally costly, immobile, and demand highly specialized operation and interpretation. Low-energy microwaves have been explored as a low-cost, small form factor, fast, and safe probe for tissue dielectric properties measurements, with both imaging and diagnostic potential. Nevertheless, challenges inherent to microwave reconstruction have impeded progress, hence conduction of microwave imaging remains an elusive scientific aim. Herein, we introduce a dedicated experimental framework comprising a robotic navigation system to translate blood-mimicking phantoms within a human head model. An 8-element ultra-wideband array of modified antipodal Vivaldi antennas was developed and driven by a two-port vector network analyzer spanning 0.6-9.0 GHz at an operating power of 1 mW. Complex scattering parameters were measured, and dielectric signatures of hemorrhage were learned using a dedicated deep neural network for prediction of hemorrhage classes and localization. An overall sensitivity and specificity for detection >0.99 was observed, with Rayleigh mean localization error of 1.65 mm. The study establishes the feasibility of a robust experimental model and deep learning solution for ultra-wideband microwave stroke detection.

Autonomous animal heating and cooling system for temperature-regulated magnetic resonance experiments.

Temperature is a hallmark parameter influencing almost all magnetic resonance properties (e.g., T1 , T2 , proton density, and diffusion). In the preclinical setting, temperature has a large influence on animal physiology (e.g., respiration rate, heart rate, metabolism, and oxidative stress) and needs to be carefully regulated, especially when the animal is under anesthesia and thermoregulation is disrupted. We present an open-source heating and cooling system capable of regulating the temperature of the animal. The system was designed using Peltier modules capable of heating or cooling a circulating water bath with active temperature feedback. Feedback was obtained using a commercial thermistor, placed in the animal rectum, and a proportional-integral-derivative controller was used to modulate the temperature. Its operation was demonstrated in a phantom as well as in mouse and rat animal models, where the standard deviation of the temperature of the animal upon convergence was less than a 10th of a degree. An application where brain temperature of a mouse was modulated was demonstrated using an invasive optical probe and noninvasive magnetic resonance spectroscopic thermometry measurements.

An experimental system for detection and localization of hemorrhage using ultra-wideband microwaves with deep learning.

Stroke is a leading cause of mortality and disability. Emergent diagnosis and intervention are critical, and predicated upon initial brain imaging; however, existing clinical imaging modalities are generally costly, immobile, and demand highly specialized operation and interpretation. Low-energy microwaves have been explored as low-cost, small form factor, fast, and safe probes of tissue dielectric properties, with both imaging and diagnostic potential. Nevertheless, challenges inherent to microwave reconstruction have impeded progress, hence microwave imaging (MWI) remains an elusive scientific aim. Herein, we introduce a dedicated experimental framework comprising a robotic navigation system to translate blood-mimicking phantoms within an anatomically realistic human head model. An 8-element ultra-wideband (UWB) array of modified antipodal Vivaldi antennas was developed and driven by a two-port vector network analyzer spanning 0.6-9.0 GHz at an operating power of 1 mw. Complex scattering parameters were measured, and dielectric signatures of hemorrhage were learned using a dedicated deep neural network for prediction of hemorrhage classes and localization. An overall sensitivity and specificity for detection >0.99 was observed, with Rayliegh mean localization error of 1.65 mm. The study establishes the feasibility of a robust experimental model and deep learning solution for UWB microwave stroke detection.

Autonomous animal heating and cooling system for temperature-regulated MR experiments.

Temperature is a hallmark parameter influencing almost all magnetic resonance properties (e.g., T\textsubscript{1}, T\textsubscript{2}, proton density, diffusion and more). In the pre-clinical setting, temperature has a large influence on animal physiology (e.g., respiration rate, heart rate, metabolism, cellular stress, and more) and needs to be carefully regulated, especially when the animal is under anesthesia and thermoregulation is disrupted. We present an open-source heating and cooling system capable of stabilizing the temperature of the animal. The system was designed using Peltier modules capable of heating or cooling a circulating water bath with active temperature feedback. Feedback was obtained using a commercial thermistor, placed in the animal rectum, and a proportional{\text -}integral{\text -}derivative (PID) controller capable of locking the temperature. Operation was demonstrated in a phantom as well as mouse and rat animal models, where the standard deviation of the temperature of the animal upon convergence was less than a tenth of a degree. An application where brain temperature of a mouse was modulated was demonstrated using an invasive optical probe and non-invasive magnetic resonance spectroscopic thermometry measurements.

Low-Order Mechanistic Models for Volumetric and Temporal Capnography: Development, Validation, and Application.

OBJECTIVE: Develop low-order mechanistic models accounting quantitatively for, and identifiable from, the capnogram - the CO 2 concentration in exhaled breath, recorded over time (Tcap) or exhaled volume (Vcap). METHODS: The airflow model's single "alveolar" compartment has compliance and inertance, and feeds a resistive unperfused airway comprising a laminar-flow region followed by a turbulent-mixing region. The gas-mixing model tracks mixing-region CO 2 concentration, fitted breath-by-breath to the measured capnogram, yielding estimates of model parameters that characterize the capnogram. RESULTS: For the 17 examined records (310 breaths) of airflow, airway pressure and Tcap from ventilated adult patients, the models fit closely (mean rmse 1% of end-tidal CO 2 concentration on Vcap; 1.7% on Tcap). The associated parameters (4 for Vcap, 5 for Tcap) for each exhalation, and airflow parameters for the corresponding forced inhalation, are robustly estimated, and consonant with literature values. The models also allow, using Tcap alone, estimation of the entire exhaled airflow waveform to within a scaling. This suggests new Tcap-based tests, analogous to spirometry but with normal breathing, for discriminating chronic obstructive pulmonary disease (COPD) from congestive heart failure (CHF). A version trained on 15 exhalations from each of 24 COPD/24 CHF Tcap records from one hospital, then tested 100 times with 15 random exhalations from each of 27 COPD/31 CHF Tcap records at another, gave mean accuracy 80.6% (stdev 2.1%). Another version, tested on 29 COPD/32 CHF, yielded AUROC 0.84. CONCLUSION: Our mechanistic models closely fit Tcap and Vcap measurements, and yield subject-specific parameter estimates. SIGNIFICANCE: This can inform cardiorespiratory care.

Implementation of an Automated Sepsis Screening Tool in a Community Hospital Setting.

BACKGROUND: Early identification of sepsis remains the greatest barrier to compliance with recommended evidence-based bundles. PURPOSE: The purpose was to improve the early identification and treatment of sepsis by developing an automated screening tool. METHODS: Six variables associated with sepsis were identified. Logistic regression was used to weigh the variables, and a predictive model was developed to help identify patients at risk. A retrospective review of 10 792 records of hospitalizations was conducted including 339 cases of sepsis to retrieve data for the model. RESULTS: The final model resulted an area under the curve of 0.857 (95% CI, 0.850-0.863), suggesting that the screening tool may assist in the early identification of patients developing sepsis. CONCLUSION: By using artificial intelligence capabilities, we were able to screen 100% of our inpatient population and deliver results directly to the caregiver without any manual intervention by nursing staff.

Physician variation in ordering of transthoracic echocardiography in outpatient pediatric cardiac clinics.

BACKGROUND: The pediatric Appropriate Use Criteria (AUC) for outpatient transthoracic echocardiography (TTE) aim to reduce practice variation. Little is known on variation in TTE use between physicians. Understanding this variation will help identify areas for improvement in standardization of TTE use. METHODS AND RESULTS: This is a retrospective review of initial outpatient visits at 6 pediatric cardiology centers in the United States prior to AUC release. Variation in TTE use was examined using multilevel generalized mixed effects models. Forward selection identified combinations of variables that explained the most variance in TTE use between physicians. Due to collinearity, physician compensation model and center were analyzed separately. Of 2883 encounters, the most common indication was murmur (36%), followed by chest pain (15.2%). Overall TTE use was 41.9%, and varied widely between centers (22.9%-52.6%), and between physicians within centers. Center alone explained 29% of this physician variance. Adding physician characteristics increased the variance explained to 57%, which only minimally improved by adding patient characteristics. The variance explained was driven by subspecialty. The center-based multivariable model explained more variance over compensation model. CONCLUSIONS: Center was the single largest determinant of physician variance in TTE use, followed by physician subspecialty. Efforts to reduce practice variation, such as the AUC, should be employed across centers and all pediatric cardiac providers. Center appears to have a stronger impact on variance than compensation model, though in this dataset the effect of center and compensation are hard to separate from each other and deserve further evaluation.

Outcomes and factors influencing outcomes of critically ill HIV-positive patients in a tertiary care center in South India.

The incidence of (Human immune deficiency) HIV in India has fallen by 58% since the onset of the HIV epidemic. As of 2016 there are 2.1 million people living in India with HIV and only 49% of the adults with HIV are on ART (1). The HIV infected individuals may require intensive care due to various reasons. This study attempts to look at the outcomes of these patients admitted in the intensive care unit and the predictors of these outcomes. AIMS: 1. To assess the outcomes of critically ill HIV infected patients admitted in the medical intensive care unit. 2. Assessment of the factors that are likely to influence the outcome. MATERIALS AND METHODS: it is a retrospective medical review of all the patient records available on our electronic database. The study period was January 2008 - October 2013. RESULTS: in our study cohort the commonest reason for admission into the intensive care unit was sepsis associated with multi organ dysfunction (64%). A low CD 4 count, renal failure acute respiratory distress syndrome, and hypotension and multi organ dysfunction were predictive of a poor outcome in our study. CONCLUSION: The most common cause of admission of PLHIV in ours study cohort was Infections, ART associated side effects and low CD4 counts, presence of multi organ dysfunction, acute respiratory distress syndrome (ARDS), hypotension were associated with adverse outcomes.

Prenatal Diagnosis Influences Preoperative Status in Neonates with Congenital Heart Disease: An Analysis of the Society of Thoracic Surgeons Congenital Heart Surgery Database.

The early postnatal course for a newborn with critical congenital heart disease (CHD) can be negatively impacted if diagnosis is delayed. Despite this, there continues to be inconsistent evidence regarding potential benefits associated with prenatal diagnosis (PND) in neonates who undergo cardiac surgery. The objective of this study was to better define the impact of a PND on pre-operative morbidity by utilizing a large clinical database. Neonates (< 30 days) undergoing heart surgery from 2010 to 2014 and entered in the Society of Thoracic Surgeons Congenital Heart Surgery Database (STS-CHSD) were included. Multivariable logistic regression was used to evaluate the association between PND and a composite measure including nine major pre-operative risk factors. Co-variates were included to adjust for important patient characteristics (e.g., weight-for-age z-score, genetic syndromes, prematurity), case complexity, and center effects. Centers and patients with excess missing data for relevant co-variates were excluded. Included were 12,899 neonates undergoing surgery at 112 centers. Major pre-operative risk factors were present in 34% overall. By univariate analysis, PND was associated with a lower overall prevalence of major pre-operative risk factors. After adjusting for potential confounders, major pre-operative risk factors were less prevalent among neonates with PND compared to neonates without PND (adjusted OR 0.62, 95% CI 0.57-0.68, p < 0.001). A sensitivity analysis excluding neonates with genetic syndromes, non-cardiac anatomic abnormalities, and prematurity demonstrated similar findings (adjusted OR 0.55, 95% CI 0.49-0.61, p < 0.0001). Among neonates with CHD, prenatal diagnosis is associated with significantly lower rates of pre-operative risk factors for cardiac surgery. Further studies are needed to define association of these pre-operative benefits of a PND with longer term clinical outcomes.

A Fiducial Scaffold for ECG Compression in Low-Powered Devices.

Large volumes of physiological data can now be routinely collected using wearable devices, though a key challenge that remains is the conversion of raw data into clinically relevant and actionable information. While power constraints prevent continuous wireless streaming of large amounts of raw data for offline processing, on-board microprocessors have become sufficiently powerful for data reduction to be performed in real time on the wearable device itself, so that only aggregate, clinically interpretable measures need to be transmitted wirelessly. Here, we use the curve-length transform to extract key beat-by-beat information from the raw ECG waveform, and to identify clinically relevant timing and amplitude information. Each beat is parameterized by 12 morphological features that serve as fiducial markers, sufficient to directly reconstruct a scaffold representation of the ECG waveform. At a nominal heart rate of 70 beats/min and a sampling rate of 250 Hz, typical for wearable monitors, this represents approximately an 18-fold compression. Using difference encoding, the compression ratio improves to 21. Our algorithm computes a running exponentially-weighted average of each identified morphological feature. When any feature deviates significantly from its running average, the algorithm retains the raw waveform for five beats preceding and following the anomaly, enabling future review of the raw data. The algorithm automatically located 93.8% of the 3,615 expert-annotated QRS onsets and offsets in the PhysioNet QT-Database to within 20 ms. Similarly, it located 83.5% of all 3,194 P-wave onset and offset annotations to within 32 ms, and 89.0% of all 3,542 T-wave offset annotations within 72 ms.

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%.

Factors Influencing Pediatric Outpatient Transthoracic Echocardiography Utilization Before Appropriate Use Criteria Release: A Multicenter Study.

BACKGROUND: Although pediatric appropriate use criteria (AUC) for outpatient transthoracic echocardiography (TTE) are available, little is known about TTE utilization patterns before their release. The aims of this study were to determine the relation between AUC and TTE utilization and to identify patient and physician factors associated with discordance between the AUC and clinical practice. METHODS: A retrospective review of 3,000 initial outpatient pediatric cardiology encounters at six centers was performed. Investigator-determined indications were classified using AUC definitions. Concordance between AUC and TTE utilization was determined. Multivariate analysis was performed to identify patient and physician factors associated with TTE's being performed for rarely appropriate and TTE's not being performed for appropriate indications. RESULTS: Concordance between AUC and TTE utilization was 88%. TTE was performed for rarely appropriate indications in 9% and was associated with patient age < 3 months, indications of murmur, noninvasive imaging physician subspecialty, and physician volume. No TTE was ordered for appropriate indications in 3% and was associated with indications including prior test result (primarily abnormal electrocardiographic findings), older patients, and physician subspecialty other than generalist or imaging. There was high variability in TTE utilization among centers. CONCLUSIONS: There was a reasonable degree of concordance between AUC and clinical practice before AUC publication. Several patient and physician factors were associated with discordance with the AUC. These findings should be considered in efforts to disseminate the AUC and in the development of future iterations. The causes for variation among centers deserve further exploration.

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.

Counseling Practices for Fetal Hypoplastic Left Heart Syndrome.

While counseling parents of a fetus diagnosed with hypoplastic left heart syndrome (HLHS), pediatric cardiologists play a critical role in shaping a family's expectations for the months and years to come. However, techniques for the most effective counseling practices have not been studied, and significant variation among physicians is likely present. Web-based survey of pediatric cardiologists that perform fetal echocardiography using snowball sampling. 201 physicians responded (61% male, 81% from academic centers, and 95% from the U.S.), with an average experience of 12 years. The majority of respondents (73%) typically received initial referrals for HLHS between 20 and 24 weeks of gestation. Most physicians counsel families alone (54%), while others counsel with a nurse (35%), social worker (12%), and/or maternal-fetal medicine colleague (15%). Termination of pregnancy was discussed by 79% of respondents, although 15% did not know their state's legal limit for termination. While initial counseling sessions routinely described the typical earlier ramifications of HLHS, many long-term sequelae of the disease were not commonly discussed. Content of counseling was affected by region of the country, but not by practice setting, experience, or fetal volume. Respondents identified multiple barriers that limited their counseling practices. Our data suggest that current counseling practices often fail to cover important information. Perceived barriers to a full discourse on long-term sequelae of HLHS are common and may lead to a disconnect between reality and a family's understanding of the natural history of palliated HLHS. Opportunities to improve counseling practices exist, and there may be benefits to gain from more formal training.

Development of chylous ascites in an infant with ventricular septal defect: a case report.

This case report describes chylous ascites associated with a CHD in a 4-month-old infant. Although atraumatic chylous ascites are a rare clinical finding, the recognition and treatment of chylous ascites influence the timing of cardiac surgery.

Noninvasive Intracranial Pressure Determination in Patients with Subarachnoid Hemorrhage.

Intracranial pressure (ICP) should ideally be measured in many conditions affecting the brain. The invasiveness and associated risks of the measurement modalities in current clinical practice restrict ICP monitoring to a small subset of patients whose diagnosis and treatment could benefit from ICP measurement. To expand validation of a previously proposed model-based approach to continuous, noninvasive, calibration-free, and patient-specific estimation of ICP to patients with subarachnoid hemorrhage (SAH), we made waveform recordings of cerebral blood flow velocity in several major cerebral arteries during routine, clinically indicated transcranial Doppler examinations for vasospasm, along with time-locked waveform recordings of radial artery blood pressure (APB), and ICP was measured via an intraventricular drain catheter. We also recorded the locations to which ICP and ABP were calibrated, to account for a possible hydrostatic pressure difference between measured ABP and the ABP value at a major cerebral vessel. We analyzed 21 data records from five patients and were able to identify 28 data windows from the middle cerebral artery that were of sufficient data quality for the ICP estimation approach. Across these windows, we obtained a mean estimation error of -0.7 mmHg and a standard deviation of the error of 4.0 mmHg. Our estimates show a low bias and reduced variability compared with those we have reported before.

Intraoperative Echocardiography for Congenital Aortic Valve Repair: Predictors of Early Reoperation.

BACKGROUND: We sought to identify transesophageal echocardiography (TEE) predictors of early reoperation for recurrent aortic regurgitation (AR) after cardiopulmonary bypass (CPB) in patients undergoing repair for congenital aortic valve disease. METHODS: We analyzed post-CPB TEEs in patients with congenital aortic valve disease undergoing repair for predominant AR. Case patients underwent reoperation for recurrent AR within 2 years, whereas control patients were free from reoperation for more than 3 years. RESULTS: Case patients (n = 22; median time to reoperation 0.3 years) and control patients (n = 22; median freedom from reoperation ≥4.4 years) were similar for demographic characteristics, aortic dimensions, and preoperative AR grade. Among post-CPB TEE variables, univariate logistic regression analysis identified shorter coaptation height (odds ratio [OR] for 1-mm increase 0.72, 95% confidence interval [CI]: 0.54 to 0.95; p = 0.02), decreased ratio of coaptation height to annulus diameter (OR for a 5% decrease 1.37, 95% CI: 1.06 to 1.77; p = 0.02), and increased percentage difference (%diff) between longest and shortest coaptation lengths in a short-axis view (OR for 10% increase 1.84, 95% CI: 1.15 to 2.92; p = 0.01) as risk factors for early reoperation for recurrent AR. Multivariable analysis identified %diff in short-axis coaptation lengths as the strongest post-CPB TEE predictor (area under receiver operator curve = 0.743). The sensitivity and specificity of a %diff of 50% were 0.45 and 0.91, whereas a %diff of 30% had a sensitivity of 0.75 and specificity of 0.67. CONCLUSIONS: Coaptation asymmetry, measured as increased %diff in short-axis coaptation lengths on post-CPB TEE, is associated with early reoperation for recurrent AR after congenital valve repair.

Predicting hyperlactatemia in the MIMIC II database.

Sepsis, which occurs when an infection leads to a systemic inflammatory response, is believed to contribute to one in two to three hospital deaths in the United States. Using the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC II) database of electronic medical records from Boston's Beth Israel Deaconess Medical Center (BIDMC), we worked to characterize sepsis at BIDMC's intensive care units. Additionally, we developed a real-time algorithm to stratify patients with infectious complaints into different risk categories for progressing to septic shock. From time series of heart rate and arterial blood pressure, as well as estimates of cardiac output and total peripheral resistance, we developed a variety of classifiers to predict high serum lactate levels, a proxy for hypoperfusion and imminent circulatory shock. The records from 146 patients met our selection criteria. In discriminating patients whose measured serum lactate stays below 2.5 mmol/L from those whose value drifts above, the best of our classifiers perform with area under the receiver operating characteristic exceeding 0.8 on test data.

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.