Algorithmic processing of pressure waveforms to facilitate estimation of cardiac elastance.

BACKGROUND: Cardiac elastances are highly invasive to measure directly, but are clinically useful due to the amount of information embedded in them. Information about the cardiac elastance, which can be used to estimate it, can be found in the downstream pressure waveforms of the aortic pressure (P(ao)) and the pulmonary artery (P(pa)). However these pressure waveforms are typically noisy and biased, and require processing in order to locate the specific information required for cardiac elastance estimations. This paper presents the method to algorithmically process the pressure waveforms. METHODS: A shear transform is developed in order to help locate information in the pressure waveforms. This transform turns difficult to locate corners into easy to locate maximum or minimum points as well as providing error correction. RESULTS: The method located all points on 87 out of 88 waveforms for Ppa, to within the sampling frequency. For Pao, out of 616 total points, 605 were found within 1%, 5 within 5%, 4 within 10% and 2 within 20%. CONCLUSIONS: The presented method provides a robust, accurate and dysfunction-independent way to locate points on the aortic and pulmonary artery pressure waveforms, allowing the non-invasive estimation of the left and right cardiac elastance.

Beat-to-beat estimation of the continuous left and right cardiac elastance from metrics commonly available in clinical settings.

INTRODUCTION: Functional time-varying cardiac elastances (FTVE) contain a rich amount of information about the specific cardiac state of a patient. However, a FTVE waveform is very invasive to directly measure, and is thus currently not used in clinical practice. This paper presents a method for the estimation of a patient specific FTVE, using only metrics that are currently available in a clinical setting. METHOD: Correlations are defined between invasively measured FTVE waveforms and the aortic and pulmonary artery pressures from 2 cohorts of porcine subjects, 1 induced with pulmonary embolism, the other with septic shock. These correlations are then used to estimate the FTVE waveform based on the individual aortic and pulmonary artery pressure waveforms, using the "other" dysfunction's correlations as a cross validation. RESULTS: The cross validation resulted in 1.26% and 2.51% median errors for the left and right FTVE respectively on pulmonary embolism, while the septic shock cohort had 2.54% and 2.90% median errors. CONCLUSIONS: The presented method accurately and reliably estimated a patient specific FTVE, with no added risk to the patient. The cross validation shows that the method is not dependent on dysfunction and thus has the potential for generalisation beyond pulmonary embolism and septic shock.

Nasal high flow improves ventilation during propofol sedation: A randomized cross-over study in healthy volunteers.

OBJECTIVE: Hypoventilation and carbon dioxide (CO2) retention are common during sedation. The current study investigated the ventilation responses to nasal high flow (NHF) during sedation with propofol. METHODS: NHF of 30 L/min and 60 L/min with room air was applied during wakefulness and sedation in 10 male volunteers. Ventilation was monitored by respiratory inductance plethysmography, transcutaneous partial pressure of CO2 (TcCO2), and SpO2. RESULTS: During sedation, NHF of 30 L/min and 60 L/min reduced the TcCO2 by 2.9 ± 2.7 mmHg (p = 0.025) and by 3.6 ± 3.4 mmHg (p = 0.024) without affecting SpO2 and reduced the mean respiratory rate by 3 ± 3 breaths/min (p = 0.011) and by 4 ± 3 breaths/min (p = 0.003), respectively. CONCLUSION: During sedation with propofol, NHF without supplemental oxygen attenuated CO2 retention and reduced the respiratory rate. The findings show that NHF can improve ventilation during sedation, which may reduce the risk of complications related to hypoventilation.

Structural identifiability analysis of a cardiovascular system model.

The six-chamber cardiovascular system model of Burkhoff and Tyberg has been used in several theoretical and experimental studies. However, this cardiovascular system model (and others derived from it) are not identifiable from any output set. In this work, two such cases of structural non-identifiability are first presented. These cases occur when the model output set only contains a single type of information (pressure or volume). A specific output set is thus chosen, mixing pressure and volume information and containing only a limited number of clinically available measurements. Then, by manipulating the model equations involving these outputs, it is demonstrated that the six-chamber cardiovascular system model is structurally globally identifiable. A further simplification is made, assuming known cardiac valve resistances. Because of the poor practical identifiability of these four parameters, this assumption is usual. Under this hypothesis, the six-chamber cardiovascular system model is structurally identifiable from an even smaller dataset. As a consequence, parameter values computed from limited but well-chosen datasets are theoretically unique. This means that the parameter identification procedure can safely be performed on the model from such a well-chosen dataset. Thus, the model may be considered suitable for use in diagnosis.

Continuous stroke volume estimation from aortic pressure using zero dimensional cardiovascular model: proof of concept study from porcine experiments.

INTRODUCTION: Accurate, continuous, left ventricular stroke volume (SV) measurements can convey large amounts of information about patient hemodynamic status and response to therapy. However, direct measurements are highly invasive in clinical practice, and current procedures for estimating SV require specialized devices and significant approximation. METHOD: This study investigates the accuracy of a three element Windkessel model combined with an aortic pressure waveform to estimate SV. Aortic pressure is separated into two components capturing; 1) resistance and compliance, 2) characteristic impedance. This separation provides model-element relationships enabling SV to be estimated while requiring only one of the three element values to be known or estimated. Beat-to-beat SV estimation was performed using population-representative optimal values for each model element. This method was validated using measured SV data from porcine experiments (N = 3 female Pietrain pigs, 29-37 kg) in which both ventricular volume and aortic pressure waveforms were measured simultaneously. RESULTS: The median difference between measured SV from left ventricle (LV) output and estimated SV was 0.6 ml with a 90% range (5th-95th percentile) -12.4 ml-14.3 ml. During periods when changes in SV were induced, cross correlations in between estimated and measured SV were above R = 0.65 for all cases. CONCLUSION: The method presented demonstrates that the magnitude and trends of SV can be accurately estimated from pressure waveforms alone, without the need for identification of complex physiological metrics where strength of correlations may vary significantly from patient to patient.

Evaluation of a model-based hemodynamic monitoring method in a porcine study of septic shock.

INTRODUCTION: The accuracy and clinical applicability of an improved model-based system for tracking hemodynamic changes is assessed in an animal study on septic shock. METHODS: This study used cardiovascular measurements recorded during a porcine trial studying the efficacy of large-pore hemofiltration for treating septic shock. Four Pietrain pigs were instrumented and induced with septic shock. A subset of the measured data, representing clinically available measurements, was used to identify subject-specific cardiovascular models. These models were then validated against the remaining measurements. RESULTS: The system accurately matched independent measures of left and right ventricle end diastolic volumes and maximum left and right ventricular pressures to percentage errors less than 20% (except for the 95th percentile error in maximum right ventricular pressure) and all R(2) > 0.76. An average decrease of 42% in systemic resistance, a main cardiovascular consequence of septic shock, was observed 120 minutes after the infusion of the endotoxin, consistent with experimentally measured trends. Moreover, modelled temporal trends in right ventricular end systolic elastance and afterload tracked changes in corresponding experimentally derived metrics. CONCLUSIONS: These results demonstrate that this model-based method can monitor disease-dependent changes in preload, afterload, and contractility in porcine study of septic shock.

Validation of subject-specific cardiovascular system models from porcine measurements.

A previously validated mathematical model of the cardiovascular system (CVS) is made subject-specific using an iterative, proportional gain-based identification method. Prior works utilised a complete set of experimentally measured data that is not clinically typical or applicable. In this paper, parameters are identified using proportional gain-based control and a minimal, clinically available set of measurements. The new method makes use of several intermediary steps through identification of smaller compartmental models of CVS to reduce the number of parameters identified simultaneously and increase the convergence stability of the method. This new, clinically relevant, minimal measurement approach is validated using a porcine model of acute pulmonary embolism (APE). Trials were performed on five pigs, each inserted with three autologous blood clots of decreasing size over a period of four to five hours. All experiments were reviewed and approved by the Ethics Committee of the Medical Faculty at the University of Liege, Belgium. Continuous aortic and pulmonary artery pressures (P(ao), P(pa)) were measured along with left and right ventricle pressure and volume waveforms. Subject-specific CVS models were identified from global end diastolic volume (GEDV), stroke volume (SV), P(ao), and P(pa) measurements, with the mean volumes and maximum pressures of the left and right ventricles used to verify the accuracy of the fitted models. The inputs (GEDV, SV, P(ao), P(pa)) used in the identification process were matched by the CVS model to errors <0.5%. Prediction of the mean ventricular volumes and maximum ventricular pressures not used to fit the model compared experimental measurements to median absolute errors of 4.3% and 4.4%, which are equivalent to the measurement errors of currently used monitoring devices in the ICU (∼5-10%). These results validate the potential for implementing this approach in the intensive care unit.

Clinical detection and monitoring of acute pulmonary embolism: proof of concept of a computer-based method.

BACKGROUND: The diagnostic ability of computer-based methods for cardiovascular system (CVS) monitoring offers significant clinical potential. This research tests the clinical applicability of a newly improved computer-based method for the proof of concept case of tracking changes in important hemodynamic indices due to the influence acute pulmonary embolism (APE). METHODS: Hemodynamic measurements from a porcine model of APE were used to validate the method. Of these measurements, only those that are clinically available or inferable were used in to identify pig-specific computer models of the CVS, including the aortic and pulmonary artery pressure, stroke volume, heart rate, global end diastolic volume, and mitral and tricuspid valve closure times. Changes in the computer-derived parameters were analyzed and compared with experimental metrics and clinical indices to assess the clinical applicability of the technique and its ability to track the disease state. RESULTS: The subject-specific computer models accurately captured the increase in pulmonary resistance (Rpul), the main cardiovascular consequence of APE, in all five pigs trials, which related well (R2 = 0.81) with the experimentally derived pulmonary vascular resistance. An increase in right ventricular contractility was identified, as expected, consistent with known reflex responses to APE. Furthermore, the modeled right ventricular expansion index (the ratio of right to left ventricular end diastolic volumes) closely followed the trends seen in the measured data (R2 = 0.92) used for validation, with sharp increases seen in the metric for the two pigs in a near-death state. These results show that the pig-specific models are capable of tracking disease-dependent changes in pulmonary resistance (afterload), right ventricular contractility (inotropy), and ventricular loading (preload) during induced APE. Continuous, accurate estimation of these fundamental metrics of cardiovascular status can help to assist clinicians with diagnosis, monitoring, and therapy-based decisions in an intensive care environment. Furthermore, because the method only uses measurements already available in the ICU, it can be implemented with no added risk to the patient and little extra cost. CONCLUSIONS: This computer-based monitoring method shows potential for real-time, continuous diagnosis and monitoring of acute CVS dysfunction in critically ill patients.

Model-based prediction of the patient-specific response to adrenaline.

A model for the cardiovascular and circulatory systems has previously been validated in simulated cardiac and circulatory disease states. It has also been shown to accurately capture the main hemodynamic trends in porcine models of pulmonary embolism and PEEP (positive end-expiratory pressure) titrations at different volemic levels. In this research, the existing model and parameter identification process are used to study the effect of different adrenaline doses in healthy and critically ill patient populations, and to develop a means of predicting the hemodynamic response to adrenaline. The hemodynamic effects on arterial blood pressures and stroke volume (cardiac index) are simulated in the model and adrenaline-specific parameters are identified. The dose dependent changes in these parameters are then related to adrenaline dose using data from studies published in the literature. These relationships are then used to predict the future, patient-specific response to a change in dose or over time periods from 1-12 hours. The results are compared to data from 3 published adrenaline dosing studies comprising a total of 37 data sets. Absolute percentage errors for the identified model are within 10% when re-simulated and compared to clinical data for all cases. All identified parameter trends match clinically expected changes. Absolute percentage errors for the predicted hemodynamic responses (N=15) are also within 10% when re-simulated and compared to clinical data. Clinically accurate prediction of the effect of inotropic circulatory support drugs, such as adrenaline, offers significant potential for this type of model-based application. Overall, this work represents a further clinical, proof of concept, of the underlying fundamental mathematical model, methods and approach, as well as providing a template for using the model in clinical titration of adrenaline in a decision support role in critical care. They are thus a further justification in support of upcoming human clinical trials to validate this model.

Screening for diabetic retinopathy using computer vision and physiological markers.

BACKGROUND: Hyperglycemia and diabetes result in vascular complications, most notably diabetic retinopathy (DR). The prevalence of DR is growing and is a leading cause of blindness and/or visual impairment in developed countries. Current methods of detecting, screening, and monitoring DR are based on subjective human evaluation, which is also slow and time-consuming. As a result, initiation and progress monitoring of DR is clinically hard. METHODS: Computer vision methods are developed to isolate and detect two of the most common DR dysfunctions-dot hemorrhages (DH) and exudates. The algorithms use specific color channels and segmentation methods to separate these DR manifestations from physiological features in digital fundus images. The algorithms are tested on the first 100 images from a published database. The diagnostic outcome and the resulting positive and negative prediction values (PPV and NPV) are reported. The first 50 images are marked with specialist determined ground truth for each individual exudate and/or DH, which are also compared to algorithm identification. RESULTS: Exudate identification had 96.7% sensitivity and 94.9% specificity for diagnosis (PPV = 97%, NPV = 95%). Dot hemorrhage identification had 98.7% sensitivity and 100% specificity (PPV = 100%, NPV = 96%). Greater than 95% of ground truth identified exudates, and DHs were found by the algorithm in the marked first 50 images, with less than 0.5% false positives. CONCLUSIONS: A direct computer vision approach enabled high-quality identification of exudates and DHs in an independent data set of fundus images. The methods are readily generalizable to other clinical manifestations of DR. The results justify a blinded clinical trial of the system to prove its capability to detect, diagnose, and, over the long term, monitor the state of DR in individuals with diabetes.