Patent Application: US-89175204-A

Abstract:
a method of data management for assessing a patient &# 39 ; s autonomic balance , risk of death , and the patient &# 39 ; s response to therapy in terms of these assessments is described . this method describes a process by which a set of “ raw variables ” are translated into one or more of a new variable , defined as an mortality prediction index , that quantifies the patient &# 39 ; s cardiovascular reflex control and risk of death . the translated variables are representative of both central and peripheral chemo receptivity , baroreflexes , and peripheral ergo receptors , which , in turn , provide the measurement of sympathovagal , or autonomic , balance . the process of selection and measurement of the mpi , and thus the sympathetic and parasympathetic components of autonomic balance at rest and during dynamic , isotonic exercise and recovery is described . the invention will further define risk of death using a kaplan - meier plot for certain translated variables . the method will enable physicians to collect , view , track and manage complicated data from multiple sources using simple , well - understood visualization techniques to better understand the consequences of their therapeutic actions .

Description:
the following detailed description with respect to patient data is intended to be exemplary of a preferred method of utilizing the concepts of the present invention and is not intended to be exhaustive or limiting in any manner with respect to similar methods and additional or other steps which might occur to those skilled in the art . the following description further utilizes illustrative examples which are believed sufficient to convey an adequate understanding of the broader concepts of processing data from a plurality of classes of related variables to those skilled in the art and exhaustive examples are believed unnecessary . as indicated above , one class of data , dynamic - cardiopulmonary ( dcp ), is obtained using physical exercise testing performed in accordance with a standardized workload protocol as the forcing function to elicit physiologic changes resulting from increasing amounts of workload . such data can be viewed as a description of the primary “ endpoint ” for a wide variety of medical therapies — data describing how an individual is able to function in the physical world in terms of the physiologic changes that the individual experiences when engaged in the performance of physical work . the physiologic changes are measured using a cardiopulmonary exercise testing system ( cpx ), and these measurements , or “ raw variables ” ( rv = vo 2 , vco 2 , ve , hr ), are then translated in successive stages to : ( 1 ) breakpoints , defined in terms of anaerobic threshold , onset of respiratory compensation , peak vo 2 , and peak o 2 pulse ; ( 2 ) “ cardiopulmonary slope variable ” ( csv ) ( 3 ) visual display using a “ virtual barometer ” of the measured breakpoint and csv in relation to the mean value and standard deviation for the breakpoint and csv , ( 4 ) a computation of a mortality prediction index for the individual breakpoint and csv ( 5 ) a summation of all such csv &# 39 ; s and breakpoints into a cumulative mpi using a “ virtual balance beam scale ”, and ( 6 ) a quantified risk of death using a kaplan - meier plot . in doing so , the “ raw variables ” are translated from a form from which nothing ( other than a simple value with a unit of measurement ) can be implied to a form from which meaningful information ( diagnostic and prognostic ) can be derived ( this individual &# 39 ; s capacity for physical work is less than it should be for a normal person ) and expressed in statistical terms derived from scientific studies that define the meaning of the term “ normal ”. by analogy , traffic safety laws are based upon the measurement of the speed of an automobile , not it &# 39 ; s position at any point in time . it then follows that the “ safety ” of an individual from death from chronic disease should not be judged by the heart rate at any point in time , but rather , for example , the rate of change of the heart rate ( speed ) when measured against the work performed over time . as a convenience to the physician to improve and centralize pertinent data to more completely assess patient condition , additional classes of patient information are made available . as an example , static - biochemical / neurohumoral variables ( sbnv ), can be collected from available laboratory blood chemistry instrumentation . for each sbnv , steps similar to 4 and 5 are taken to derive an mpi for this class . when breakpoints , csv &# 39 ; s and sbnv &# 39 ; s are accrued and analyzed together , their power of patient risk prediction becomes even more pronounced . in doing so , a physician is relieved from performing the data translation and integration necessary to derive a true , physiologic assessment of the patient &# 39 ; s condition at any point in time . by also providing trend plots of the translated data over time , the physician can better understand the consequence of any given therapeutic action . by providing a closed - loop system of action ( therapy ) and physiologic response ( to therapy ), the quality of treating patient &# 39 ; s with cardiac and cardiovascular disease will be increased and the cost reduced . in order to convey the required detail , it is not believed necessary to explain the translation process for each individual breakpoint , csv , or sbnv or to explain how all are individually used to produce the desired outputs — a “ virtual barometer ”, the translated variable , a “ virtual balance beam scale ” using the cumulative mpi , trend graphs for each individual breakpoint , csv , sbnv , mpi , and a kaplan meier plot . to avoid unnecessary repetition , the method by which a single breakpoint , csv , and sbn is translated to an mpi will be described in detail . the additional methods used to produce the intended outputs from the generated mpi will also be described in detail . the data gathering aspect of the invention involves known techniques and analyses and it is the aspects of processing and combining the data in which the invention enables an observer to gain new and valuable insight into the present condition and condition trends in patents . thus , in accordance with the preferred method , a cardiopulmonary exercise test ( cpx ) is performed for each data set . the performance of such a test is well understood by individuals skilled in the art , and no further explanation of this is believed necessary . in addition , the measurement of the sbnv class of data is obtained by blood analysis using commonly available laboratory blood chemistry instrumentation in a well - known manner , and no further explanation of this procedure is believed required . with this in mind typical hardware is shown in fig1 which illustrates typical equipment whereby a cardiopulmonary exercise test ( cpx ) may be conducted and the results displayed in accordance with the method of the present invention . the system is seen to include a data processing device , here shown as a personal computer of pc 12 which comprises a video display terminal 14 with associated mouse 16 , report printer 17 and a keyboard 18 . the system further has a floppy disc handler 20 with associated floppy disc 22 . as is well known in the art , the floppy - disc handler 20 input / output interfaces comprise read / write devices for reading prerecorded information stored , deleting , adding or changing recorded information , on a machine - readable medium , i . e ., a floppy disc , and for providing signals which can be considered as data or operands to be manipulated in accordance with a software program loaded into the ram or rom memory ( not shown ) included in the computing module 12 . the equipment used in the protocol includes a bicycle ergometer designed for use in a cardiopulmonary stress testing system ( cpx ) as is represented at 28 together with a subject 30 operating a pedal crank input device 32 . a graphic display device 34 interfaces with the subject during operation of the cpx device . data in the form of stress dependent physiological and psychological variables are measured . the physiological variables may be selected from heart rate ( hr ), ventilation ( ve ), rate of oxygen uptake or consumption ( vo 2 ) and carbon dioxide production ( vco 2 ) or other recognized variables . physiological data collected is fed into the computing module 12 via a conductor 31 , or other communication device . the raw dcp variables of vo 2 , vco 2 , ve , hr , and are first measured using cpx testing while the patient exercises on an ergometer as shown in fig1 . this list is not intended to be all - inclusive or limiting , and , over time , additional such variables , such as blood pressure , will be included . as illustrated in fig2 three phases of data collection are used , namely , rest 40 , isotonic exercise 42 , and recovery 44 . it will be recognized that , because the raw dcp variables are translated into cardiopulmonary slope variables ( csv &# 39 ; s ), the patient is not required to exercise to exhaustion during the isotonic exercise phase . instead , the exercise workload is terminated at 46 due to 1 ) patient fatigue , or 2 ) sudden acceleration of ve relative to vo 2 and vco 2 . the raw dcp variables are measured and collected for a predetermined amount of time after the workload has been removed ( recovery period ). the raw dcp variables are then translated into one or more class of csv . initially , csv &# 39 ; s include : ( 1 ) the ventilatory efficiency ( slope of ve / vco 2 ), ( 2 ) chronotropic response index ( ratio of heart rate reserve used to metabolic reserve used ), ( 3 ) aerobic power ( slope of vo2 / work rate ), ( 4 ) oxygen uptake efficiency ( slope of vo2 / log ve ), and ( 5 ) heart rate recovery ( slope of heart rate / time after 1 minute of recovery from exercise ). as previously stated , this list is not intended to be all - inclusive , and it is expected that additional such csv &# 39 ; s will become available from the scientific literature over time . the first step in the preferred translation method is the execution of a computer program ( fig3 ). in step 1 , a linear regression analysis of two raw variables or rv &# 39 ; s from 50 plotted against one another is performed at 52 to derive the slope 54 of the response illustrated in fig4 using as an example , ve / vco 2 . the cardiopulmonary slope variables ( csv ) slope is also determined at 56 using regression analysis . with respect to the regression analysis , it will be noted that the recorded test data contain the channels minute ventilation ve and carbon dioxide output vco 2 as time series with sample points ( moments of time ) t i , so there are two sets of data points ve i and vco 2i with i − l , . . . , n . to find the best straight line fit ve = a vco2 + b to the ensemble of point pairs ( ve i , vc02 i ) one can use the linear regression analysis minimizing the sum of squares of distances of these points to a straight line , see for instance press , w . h ., b . p . flannery , s . a . teukolsky , w . t . vetterling :; numerical recipes , the art of scientific computing . cambridge university press , cambridge etc ., 1986 , chapter 14 . 2 . the main results of such an analysis are the constants a and b describing the regression line and the regression coefficient r as a measure for the regularity of data lying along and around this line . the constant a is the ve to vco 2 slope of the above mentioned data ensemble . not all recorded data are significant for the determination of the ve to vco 2 slope parameter , but only that part of them belonging to the isotonic exercise phases ( fig2 at 42 ) of a cpx test . in step 2 ( fig3 ), the mean value ( mv ) and standard deviation ( sd ) for the test subject is obtained at 58 from a look - up object definition table 60 ( see also fig5 ). all translated variable types have an entry in the object definition table . in fig3 step 3 , the difference between the measured csv and the mv is computed at 62 , and the value thus derived is divided by the standard deviation of the csv at 64 ( obtained from the aforementioned look - up table at 60 ) to yield a new variable defined as the autonomic balance index for the csv ve / vco 2 slope at 66 . after the cpx testing is finished , a computer program is executed to further analyze the raw dcp variables to determine the breakpoints ( bp ) that reflect upon the forcing workload function and the physiologic changes experienced by the patient during the isotonic exercise period . certain bp &# 39 ; s derived from the dcp class can be further translated into abi values similarly to csv &# 39 ; s as described above . similar statistical information exists in the scientific literature , and such bp &# 39 ; s include ( 1 ) peak attained vo 2 , ( 2 ) maximum attained oxygen pulse ( vo 2 / hr ), ( 3 ) anaerobic threshold , ( 4 ) onset of respiratory compensation ( rc ). this list is not intended to be all - inclusive , and it is expected that additional such bp &# 39 ; s will become accepted standards in the scientific literature . in a process similar to that described above for csv &# 39 ; s , a computer program ( fig3 at 50 , 52 and 54 ) is executed at 68 , 70 and 72 . in step 1 , an analysis of 0 2 pulse ( vo 2 / hr ) is made to derive the bp . it uses fig6 as an example , the plot of 0 2 pulse against time is shown at 68 for detecting the peak value at 70 . the peak 0 2 pulse is shown at 72 . in step 2 , the mean value ( mv ) and standard deviation ( sd ) for peak 0 2 pulse is derived at 58 for the test subject 60 as was the case with the csv variables and is obtained from the object definition look - up table ( fig5 ). in fig3 step 3 , the difference between the measured peak 0 2 pulse and the mv is computed at 74 . the value thus derived is divided by the standard deviation of the peak 0 2 pulse at 76 to yield a new variable defined as the autonomic balance index ( abi ) for the bp variable peak 0 2 pulse at 78 . the raw sbnv , shown at 80 in fig3 is measured as indicated previously . initially , sbnv &# 39 ; s include : ( 1 ) bnp , and ( 2 ) c - reactive protein . this list is not intended to be all - inclusive or limiting , and it is expected that additional such sbnv &# 39 ; s will become available from the scientific literature over time . in a process similar to that described above for csv and bp , a computer program ( fig3 steps 1 - 3 ) is executed . in step 2 , the mean value ( mv ) and standard deviation ( sd ) for the sbnv 80 for the test subject is also obtained at 58 from the object definition table at 60 . in step 3 , the difference between the measured sbnv and the mv is computed at 82 , and the value thus derived is divided by the standard deviation of the sbnv at 84 ( obtained from the aforementioned look - up table 60 ) to yield a new variable defined as the autonomic balance index ( abi ) for the sbnv at 86 . the next step in the preferred translation method , a computer program ( fig7 ) is executed to define an mpi whose properties are defined in the object definition table ( fig5 ). the concept of the normalizing value ( nv ) allows us to further translate the abi . the nv links the measured value for the csv , bp , or sbn to the research data defining patient risk of death . the nv is a number that , when the abi is subtracted from it , yields a value this indicative of elevated risk . the value of mpi =( nv − abi )/ nv at 98 , and , by definition , a negative value indicates elevated risk . a mitigating factor is that some variables ( ventilatory efficiency slope ) have high values indicating high risk . some ( chronotropic response index ) have low values indicating high risk . for this reason , the sign of the abi must be adjusted accordingly , at 96 . the more negative the mpi value is , the greater the risk of death . a positive mpi simply indicates that the translated value of the measured variable is outside the range of elevated risk as defined by the cutoff point . the calculated mpi values for csv , breakpoint , and sbnv are then computed at 90 , 92 , 94 for a particular corresponding abi 66 , 78 , at 86 . as depicted in fig8 when a user “ right - clicks ” the system mouse at 100 , the mpi properties are displayed in a drop - down list 102 . the next step in the illustrative translation method is the execution of a computer program to display a “ virtual balance beam scale ” loaded with the mpi whose values have been computed as above . each previously defined mpi is processed in fig9 . if the sign of the mpi at 110 is negative ( indicating sympathetic overdrive ), the mpi is “ loaded ” onto the left side of the scale at 112 . if the sign at 110 of the mpi is positive ( indicating autonomic balance ), the mpi is “ loaded ” onto the right side of the scale and becomes part of a cumulative total at 114 . upon completion of this process , all of the mpi that are “ left loaded ” will appear on the left scale pan , and all of the mpi that are “ right loaded ” will appear on the right scale pan . an example of a loaded balance beam scale will appear as in fig1 . the “ virtual pointer ” 120 will then indicate a value on the scale 122 and whether the patient exhibits autonomic balance or is unbalanced toward sympathetic overdrive and elevated risk of death and is shown relatively at fig1 . in fig1 , the translated measurements as shown at 130 , 132 and the statistical mean value and standard deviation are then displayed at 134 , 136 on a “ virtual barometer ”, thereby providing a graphical depiction of the patient &# 39 ; s status in relationship to a “ normal ” individual . the barometer is represented as a bar 138 whose height equals the measured variable . subsequent test values can be displayed at 140 for comparison purposes . in addition , the areas below and above one standard deviation can be color coded to indicate whether the measured variable represents an improvement in the patient &# 39 ; s status ( green shading at 142 ) or a deterioration in the patient &# 39 ; s status ( red shading at 144 ). in this manner trend information can be derived as well the patient risk of death is displayed using a kaplan - meier plot as illustrated in fig1 . the value of the translated variable and the source publication are printed on a reproduced plot , as depicted in fig1 . the next step in the preferred translation method is to provide trend graphs of the measured variables , individual mpi , and cumulative mpi for successive testing dates ( fig1 ). in fig1 , the measurements as shown at 150 , 152 and the statistical mean value and standard deviation for each are then displayed at 154 , 156 , thereby providing a graphical depiction of the patient &# 39 ; s status in relationship to a “ normal ” individual . the cutoff point is displayed at 158 . thus , separate zones are defined : below the mean less one standard deviation 160 , the mean value plus and minus one standard deviation 162 , and the area beyond the cutoff point 164 . in fig1 , another zone can be shown at 166 which is the area above one standard deviation and the cutoff point ( this also illustrates the difference between the terms “ cutoff point ” and “ standard deviation ”). in addition , the areas below one standard deviation 160 above the cutoff point 164 can be color coded to indicate whether the measured variable represents an improvement in the patient &# 39 ; s status ( green shading at 160 ) or a deterioration in the patient &# 39 ; s status ( red shading at 164 ). the invention has been described in considerable detail in order to comply with the patent statutes and to provide those skilled in the art with the information needed to apply the novel principles and to construct and use such specialized components as are required . however , it is to be understood that the invention can be carried out by specifically different equipment and devices , and that various modifications , both as the equipment details and operating procedures can be accomplished without departing from the scope of the invention itself . 1 . j . laukkanen , j . t . t . salonen , et al ., association of maximum oxygen pulse during exercise stress test with the risk of cardiovascular and overall mortality . j . amer . college cardiol . 2002 ; 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