Abstract: Systems and methods for evaluating patients are provided. An example includes receiving first cardiopulmonary exercise test data for the patient and plotting a first vector based on the first cardiopulmonary exercise test data, the first vector including a first point based on a rest value and a second point based on an exercise value. A first coordinate value of the first point and a first coordinate value of the second point may be based on mixed expired CO2 (PECO2) and a second coordinate value of the first point and a second coordinate value of the second point may be based on end tidal CO2 (PetCO2). The example may also include plotting a midpoint of the first vector and triggering display of the plotted vector and plotted midpoint. The plotted vector and plotted midpoint may be displayed over a coordinate grid having multiple physiological condition zones.

Abstract: Systems and methods for estimating a ventilatory threshold for a patient and estimating likelihood of pulmonary arterial hypertension are provided. An example method includes determining ratios of minute ventilation to rate of oxygen uptake based on breath-by-breath data for a patient. The example method also includes determining a ventilatory threshold based upon the determined ratios and determining a score based upon an end-tidal partial pressure of carbon dioxide (PetCO2) value at the determined ventilatory threshold and a minute ventilation/carbon dioxide production relationship (VE/VCO2) value at the determined ventilatory threshold. The method also includes using the score to estimate a likelihood that the patient has pulmonary arterial hypertension and causing the estimated likelihood to be displayed on a user interface.

Abstract: Systems and methods for quantifying the likelihood of the contribution of multiple possible forms of chronic disease to patient reported dyspnea can include the testing protocol having a flow/volume loop, performed at rest, flowed by the measurement of cardiopulmonary exercise gas exchange variables during rest, exercise and recovery as unique data sets. The data sets are analyzed using feature extraction steps to produce a pictorial image consisting of disease silos displaying the likelihood of the contribution of various chronic diseases to patient reported dyspnea. In some embodiments, the silos are split into subclass silos. In some embodiments, multiple chronic disease indexes are used to differentiate between sub-types of a particular chronic disease (e.g., differentiating WHO 1 PH from WHO 2 or WHO 3 PH). Test results are plotted serially to asses to provide feedback to the physician on the efficacy of therapy provided to the patient.

Abstract: A method employing pattern recognition techniques for identifying the functional status of patients with Pulmonary Hypertension is described. This method describes a process by which sets of cardiopulmonary exercise gas exchange variables are measured during rest, exercise and recovery and stored as unique data sets. The data sets are then analyzed by a series of feature extraction steps, yielding a multi-parametric index (MPIPH) which reflects the current functional status of a patient. The method also employs a description scheme that provides a graphical image that juxtaposes the measured value of MPI to a reference classification system. An additional description scheme provides a trend plot of MPI values measured on a patient over time to provide feedback to the physician on the efficacy of therapy provided to the patient.

Abstract: A method of data management for optimizing the patient outcome from the provision of cardiac resynchronization therapy (CRT) is described. A regression equation is constructed using 3 data points on a plot of AV delay vs. HR. The x-axis consist of the three points consist of resting HR, HR at the optimal AV delay value during light exercise, and the upper tracking or paced HR. The y-values associated with the three points consist of the AV delay values computed using an equation for ventricular filling time and the optimally determined AV delay value. Also described is a process for determining the sensed to paced AV delay offset. The combined processes yield 4 (the three constant values in the polynomial regression equation Y=b2X2+b1X+a and the sensed to paced AV delay offset) which can be stored on the patient's pacemaker for determining dynamically the AV delay value which is physiologically fine-tuned for each patient from resting HR to the upper tracking or paced HR.

Abstract: A method of pattern recognition for classifying the functional status of patients with chronic disease comprising characterizing the functional status based on a multivariable index (MVI) scoring system wherein the MVI is computed by summing a plurality of individual variable values as individual variable indexes (IVI) and dividing the sum by the number of variables and wherein the plurality of IVI includes rest PetCO2, ?PetCO2, SaO2, QUES, Ve/VCO2 slope and Pcap and wherein each IVI is given an equivalent value of <1.00 to >=4.00, the number increasing with increasing severity yielding an MVI value ranging from <1.00 to >=4.00, normal to severe-very severe.

Abstract: A method of data management for optimizing the patient outcome from the provision of cardiac resynchronization therapy (CRT) is described. A regression equation is constructed using 3 data points on a plot of AV delay vs. HR. The x-axis consist of the three points consist of resting HR, HR at the optimal AV delay value during light exercise, and the upper tracking or paced HR. The y-values associated with the three points consist of the AV delay values computed using an equation for ventricular filling time and the optimally determined AV delay value. Also described is a process for determining the sensed to paced AV delay offset. The combined processes yield 4 (the three constant values in the polynomial regression equation Y=b2X2+b1X+a and the sensed to paced AV delay offset) which can be stored on the patient's pacemaker for determining dynamically the AV delay value which is physiologically fine-tuned for each patient from resting HR to the upper tracking or paced HR.

Abstract: A method of data management for optimizing the patient outcome from the provision of external counterpulsation (ECP) therapy is described. This method describes a process by which sets of dynamic cardiopulmonary dependent variables are measured during steady-state conditions, displayed, and translated into quantitative and qualitative measurements while the independent variables of ECP, cuff inflation duration and cuff inflation pressure settings of ECP systems, are altered by a physician. In combination with visual observation and computer-assisted ranking of the dependent variables, a physician can utilize the resulting information to render decisions on the optimal choice of the independent variables. The method will enable physicians to collect, view, track and manage complicated data using well-understood visualization techniques to better understand the consequences, acutely and chronically, of their therapeutic actions in general, and of their provision of ECP therapy in particular.

Abstract: A method employing pattern recognition techniques for identifying the functional status of patients with Pulmonary Hypertension is described. This method describes a process by which sets of cardiopulmonary exercise gas exchange variables are measured during rest, exercise and recovery and stored as unique data sets. The data sets are then analyzed by a series of feature extraction steps, yielding a multi-parametric index (MPIPH) which reflects the current functional status of a patient. The method also employs a description scheme that provides a graphical image that juxtaposes the measured value of MPI to a reference classification system. An additional description scheme provides a trend plot of MPI values measured on a patient over time to provide feedback to the physician on the efficacy of therapy provided to the patient.

Abstract: A method employing pattern recognition techniques for identifying the functional status of patients with chronic disease is described. This method describes a process by which sets of cardiopulmonary exercise gas exchange variables are measured during rest, exercise and recovery and stored as unique data sets. The data sets are then analyzed by a series of feature extraction steps, yielding a multi-parametric index (MPI) which reflects the current functional status of a patient. The method also employs a description scheme that provides a graphical image that juxtaposes the measured value of MPI to a reference classification system. An additional description scheme provides a trend plot of MPI values measured on a patient over time to provide feedback to the physician on the efficacy of therapy provided to the patient. The method will enable physicians to gather, view, and track complicated data using well-understood visualization techniques to better understand the consequences of their therapeutic actions.