Patent Publication Number: US-10332621-B2

Title: Method and apparatus for calibration and testing of scientific measurement equipment

Description:
PRIORITY APPLICATION(S) 
     This patent application is a continuation of and claims the benefit of priority to U.S. patent application Ser. No. 12/841,055, filed Jul. 21, 2010, which claims the benefit of priority, under 35 U.S.C. § 119(e), to U.S. Provisional Patent Application Ser. No. 61/227,348, filed Jul. 21, 2009, the benefit of priority of each of which is claimed hereby, and each of which are incorporated herein by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     The inventive subject matter relates generally to scientific measurement equipment and devices, and more particularly to method and apparatus for testing, comparing and calibrating scientific measurement equipment and devices. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  shows a representative 24 hour precision profile graph for quality control data. 
         FIG. 2  shows a representative quality control precision profile graph. 
         FIG. 3  shows a patient precision profile graph for sodium analyzed in an intensive care unit. 
         FIGS. 4 through 8  illustrate various methods and computer programs according to the inventive subject matter. 
         FIG. 9  illustrates a laboratory analyzer. 
         FIG. 10  illustrates a computer system. 
     
    
    
     DETAILED DESCRIPTION 
     Clinical laboratory analyzers are used extensively in the medical and forensics profession and in research to perform tests on biological and other substances. The proper calibration and operation of these analyzers, and other laboratory equipment, is critical to producing accurate test results for patients and accurate measurements for researchers. As a result, proper use of such equipment requires regular recalibration. Such recalibration may be performed on a periodic basis based on the passage of time, or may be based on the number of uses of equipment between calibration, or based on a test of the equipment to determine its accuracy and recalibration only as necessary to maintain the desired accuracy level. 
     According to one example embodiment, there is described method, and apparatus, including programmed computers, to produce precision profiles for scientific measurement equipment in general, and in particular clinical laboratory analyzers. In this approach, either the analyzers&#39; quality control data or serial patient data are numerically reduced to generate graphical precision profiles. Precision profiles for serial patient data show increased (im)precision vs time implying increased patient variation over increased time. Precision profiles for quality control data, according to one implementation, can demonstrate three different zones: 1) increased imprecision for quality control determinations that are close spaced (implies the discovery of an error condition and rapid reanalysis, 2) the usual imprecision and 3) a zone of increased imprecision which indicates either a need for a quality control analysis or re-calibration. 
     These precision profiles are used to summarize and compare the performance of the different analyzers that have produced these quality control or patient data. These precision profiles are also used to improve the quality control practices that are used with the analyzers. 
     To generate the quality control precision profile, on approach is to statistically summarize all of the quality control data that are generated for a particular test and quality control level (all of the qc data generated from the analysis of a one or more lots of quality control material of a single level over a period of several weeks to several years). To generate the patient data precision profile, one approach is to statistically reduce large volumes (at least 3 months) of patient data that are produced by hospital clinical laboratory analyzers, including point of care analytic systems. 
       FIG. 1  is a graph showing a representative 24 hour precision profile for quality control data for the troponin (used to diagnose myocardial infarction) test and  FIG. 2  is a graph showing a representative quality control precision profile for the PTH (parathyroid hormone) test.  FIG. 3  is a graph showing a patient precision profile for sodium analyzed in an intensive care unit. These error vs. time profiles can be used to summarize and compare the analytic precision and indirectly the accuracy of different analytic technologies in the clinical laboratory. 
     According to one example embodiment, a precision profile may be used for quality control as follows:
         Determine the optimal quality control limits for the application of statistical quality control of the laboratory analyzer   Demonstrate outlying quality control data   Demonstrate the general times that analytic errors may be more prevalent   Demonstrate when quality control specimens should be analyzed   Compare analytic precision of similar systems in similar or different laboratory environments [referral laboratory, university hospital laboratory, near patient testing (point of care)].   Use to educate laboratorian on appropriate quality control practices   Use all the above to classify analyzers as in or out of compliance with quality control requirements and to determine how and when to calibrate the analyzers (measurement equipment in general)       

     According to another example embodiment, a precision profile may be used to provide a patient data precision profile as follows:
         Evaluate total analytic imprecision starting from blood drawing, to specimen processing to analysis and reporting   Recommend how to use clusters of analyzers in the most appropriate manner (for example, if a laboratory has two systems, should it be using one system for a week or a month and not run the other or should there be alternation between the two systems, and what would be the most favorable period that each would be run before alternating to the other system)   Help determine whether change of calibration frequency will improve quality of analytic results   Compare analytic precision of different systems in different laboratory environments (referral laboratory, university hospital laboratory, near patient testing (point of care).       

     The process and compute programs for the data analysis which provides these profiles for both patient and quality control data is described in more detail in the attached paper:  The Use of Serial Patient Blood Gas Electrolyte and Glucose Results to Derive Biologic Variation , the entirety of which is hereby incorporated herein by reference. This paper describes the analysis of patient data. Quality control data can be reduced in the same manner with one level of quality control representing one patient who is measured over the time of viability of the quality control product. Also attached and incorporated by reference are four studies:  Use of Patient Result - Derived Imprecisions to Assess the Analytic Quality of Electrolyte and Creatinine Measurements  by Vitros and Beckman  Methodologies, The Use of Serial Patient Blood Gas, Electrolyte and Glucose Results to Derive Biologic Variation: a New Tool to Gauge the Acceptability of ICU Testing, Use of Serial Patient Differences of HPLC HbA 1 c to Determine Long Term Instrument Performance , and Tandem Roche Hitachi 917, and Tandem Beckman LX-20 Operated in Two Tertiary Care Hospitals Exhibit Comparable Total Patient-Based Imprecisions. 
     Thus, according to one example embodiment illustrated in  FIG. 4 , there is provided a method and corresponding computer program for generating and using a precision profile for a laboratory analyzer in particular, and scientific measurement equipment in general, including:
         1. Analyze a series of biological (or non-biological) samples for one or more patients using an analyzer to produce corresponding test results ( 410 ).   2. Use the test results to generate a precision profile for serial patient data from the analyzer ( 420 ). The precision profile may comprise, for example, a graph that may demonstrate three different zones: 1) increased imprecision for quality control determinations that are close spaced (implies the discovery of an error condition and rapid reanalysis), such as shown in  FIGS. 1-2  as the ellipse labeled “higher error prevalence”; 2) the usual imprecision, such as shown in  FIGS. 1-2  as the ellipse labeled “standard SD”; and 3) a zone of increased imprecision which indicates either a need for a quality control analysis or re-calibration, such as shown in  FIGS. 1-2  as the elliupse labeled “requires QC monitoring after 12 h” and “requires QC monitoring after 16 h,” respectively.   3. Perform any of the actions set forth above for quality control or patient data precision in response to information obtained from the precision profile ( 430 ).   4. Optionally use the analyzer to analyze further biological samples to produce further corresponding test results ( 440 ).       

     Thus, according to one example embodiment illustrated in  FIG. 5 , there is provided a method and corresponding computer program for calibrating a laboratory analyzer in particular, and scientific measurement equipment in general, including:
         1. Analyze a series of biological (or non-biological) samples for one or more patients using an analyzer to produce corresponding test results ( 510 ).   2. Use the test results to generate a precision profile for serial patient data from the analyzer ( 520 ). The precision profile may comprise, for example, a graph that may demonstrate three different zones: 1) increased imprecision for quality control determinations that are close spaced (implies the discovery of an error condition and rapid reanalysis), 2) the usual imprecision and 3) a zone of increased imprecision which indicates either a need for a quality control analysis or re-calibration.   3. Re-calibrate the analyzer in response to information obtained from the precision profile ( 530 ).   4. Use the recalibrated analyzer to analyze further biological samples to produce further corresponding test results ( 540 ).       

     According to another example embodiment illustrated in  FIG. 6 , there is provided a method and corresponding computer program for establishing a calibration schedule or protocol for a laboratory analyzer in particular, and scientific measurement equipment in general, including:
         1. Analyze a series of biological (or non-biological) samples for one or more patients using an analyzer to produce corresponding test results ( 610 ).   2. Use the test results to generate a precision profile for serial patient data from the analyzer ( 620 ). The precision profile may comprise, for example, a graph that may demonstrate three different zones: 1) increased imprecision for quality control determinations that are close spaced (implies the discovery of an error condition and rapid reanalysis), 2) the usual imprecision and 3) a zone of increased imprecision which indicates either a need for a quality control analysis or re-calibration.   3. Establishing a recalibration schedule or protocol for the analyzer in response to information obtained from the precision profile ( 630 ).   4. Recalibrating the analyzer according to the recalibration schedule or protocol ( 640 ).   5. Using the analyzer to analyze further biological samples to produce further corresponding test results ( 650 ).       

     Thus, according to one example embodiment illustrated in  FIG. 7 , there is provided a method and corresponding computer program for designing or modifying the design of a laboratory analyzer in particular, and scientific measurement equipment in general, including:
         1. Analyze a series of biological (or non-biological) samples for one or more patients using an analyzer to produce corresponding test results ( 710 ).   2. Use the test results to generate a precision profile for serial patient data from the analyzer ( 720 ). The precision profile may comprise, for example, a graph that may demonstrate three different zones: 1) increased imprecision for quality control determinations that are close spaced (implies the discovery of an error condition and rapid reanalysis), 2) the usual imprecision and 3) a zone of increased imprecision which indicates either a need for a quality control analysis or re-calibration.   3. Design or re-design the analyzer in response to information obtained from the precision profile ( 730 ).   4. Manufacture the designed or redesigned analyzer ( 740 ).       

     Thus, according to one example embodiment illustrated in  FIG. 8 , there is provided a method and corresponding computer program for developing a protocol for use of a cluster of laboratory analyzers in particular, and scientific measurement equipment in general, including:
         1. Analyze a series of biological (or non-biological) samples for one or more patients using one or more analyzers to produce corresponding test results ( 810 ).   2. Use the test results to generate a precision profile for serial patient data from the analyzer(s) ( 820 ). The precision profile may comprise, for example, a graph that may demonstrate three different zones: 1) increased imprecision for quality control determinations that are close spaced (implies the discovery of an error condition and rapid reanalysis), 2) the usual imprecision and 3) a zone of increased imprecision which indicates either a need for a quality control analysis or re-calibration.   3. In response to the precision profile or profiles, specifying how to use clusters of analyzers in the most appropriate manner (for example, if a laboratory has two systems, should it be using one system for a week or a month and not run the other or should there be alternation between the two systems, and what would be the most favorable period that each would be run before alternating to the other system) ( 830 ).   4. Using the cluster of analyzers in the specified manner to test samples ( 840 ).       

     According to one example embodiment, the computer programs that are used to implement all or any part of the processes described in  FIGS. 4 through 8  may be executed on the computer system  1000  illustrated in  FIG. 10 , or executed on a computing system in analyzer  900  of  FIG. 9 . 
     Thus, according to one example embodiment illustrated in  FIG. 9 , there is provided a laboratory analyzer  900  that is capable of:
         1. Analyzing a series of biological (or non-biological) samples for one or more patients using one or more analyzers to produce corresponding test results.   2. Analyzing the test results according to a computer process that is capable of producing all or a portion of a precision profile for serial patient data from the analyzer, and in response to the analysis of the test results automatically performing any one or more of the following:   a. Indicating the analyzer is out of compliance with calibration requirements;   b. Indicating the analyzer requires recalibration;   c. Automatically recalibrating the analyzer.       

     Referring now to  FIG. 10 , there is illustrated a computer system  1000  that may be used to execute the computer programs described above with respect to  FIG. 4 through 8 , and can be used to generate the visual graphs illustrated in  FIGS. 1 to 3 . In addition, in one example embodiment, the analyzer  900  includes computer system  100  and one or more computer programs stored in its memories or storage unit to perform laboratory analytics, or execute the computer programs described herein to provide calibration. 
     More particularly,  FIG. 10  is a block diagram of a machine in the form of a computing device within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed. In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server or a client machine in server-client network environments, or as a peer machine in peer-to-peer (or distributed) network environments. The machine may be a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a mobile telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. 
     The example computer system  1000  includes a processor  1002  (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both), a main memory  1001  and a static memory  1006 , which communicate with each other via a bus  1508 . The computer system  1500  may further include a display unit  1010 , an alphanumeric input device  1017  (e.g., a keyboard), and a user interface (UI) navigation device  1011  (e.g., a mouse). In one embodiment, the display, input device and cursor control device are a touch screen display. The computer system  1000  may additionally include a storage device (e.g., drive unit  1016 ), a signal generation device  1018  (e.g., a speaker), a network interface device  1020 , and one or more sensors  1021 , such as a global positioning system sensor, compass, accelerometer, or other sensor. 
     The drive unit  1016  includes a machine-readable medium  1022  on which is stored one or more sets of instructions and data structures (e.g., software  1023 ) embodying or utilized by any one or more of the methodologies or functions described herein. The software  1023  may also reside, completely or at least partially, within the main memory  1001  and/or within the processor  1002  during execution thereof by the computer system  1000 , the main memory  1001  and the processor  1002  also constituting machine-readable media. 
     While the machine-readable medium  1022  is illustrated in an example embodiment to be a single medium, the term “machine-readable medium” may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more instructions. The term “machine-readable medium” shall also be taken to include any tangible medium that is capable of storing, encoding or carrying instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present invention, or that is capable of storing, encoding or carrying data structures utilized by or associated with such instructions. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media. Specific examples of machine-readable media include non-volatile memory, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. 
     Recently, the Royal Alexandra Hospital, a large tertiary and quaternary level Edmonton, Alberta hospital replaced its 250 and 950 Vitros chemistry analyzers with two Beckman DxC 800 systems. During the conversion to the new analyzers, laboratory staff observed that the number of outliers increased relative to that previously. We have devised a data-mining statistic (within-patient imprecision regressed to zerotime between specimens) that summarizes the average short term analytic imprecision (sa) and minimized biologic patient variation (sb). This statistic can summarize the analytic imprecision over many reagent lots and calibrations. 
     We analyzed two 12 month periods of Vitros data and a single 10 month period of Beckman data. For chloride, CO2, creatinine, potassium and sodium, we tabulated the measurements of paired intra-patient samples drawn within 24 hours of each other. After outlier removal, we calculated the standard deviations of duplicates (SDD) of the intra-patient pairs grouped in two-hour intervals: 0-2 hours, 2-4 hours, 4-6 hours, ¼ 20-22 hours and 22-24 hours. The SDDs were then regressed against the time intervals of 2 to 14 hours; extrapolation to zero time (y-intercept) represents the average variation (sa2+sb2)1/2. For each test, sa was calculated from the product of the short term within run experimental coefficient of variation (CV) and the control concentration. sb was calculated from sa and y0. CVb was determined by dividing sb by the average patient concentration. The uncertainty of CVb was derived from the standard error of the y-intercept; the relative error was obtained by dividing by the y-intercept. 
     sVitros was calculated using sb from Westgard.com. The increased imprecision due to using the Beckmans was derived from the square root of the differences of the squares of the SDD intercepts. The data mining tool, the within-patient imprecision regressed to zero-time between specimens, appears to be a powerful tool for evaluating imprecision. 
     Method stability and analytical imprecision are two of the most important criteria for instrument selection. We have devised a data-mining statistic (within-patient imprecision regressed to zero-time between specimens [WPI]) that summarizes the average short term analytic imprecision (sa) and minimizes biologic patient variation (sb). Unlike the short term analytic imprecision that is derived from quality control data, this statistic can summarize the analytic imprecision over many reagent lots and calibrations. Acute care hospitals and intensive care units provide adequate data to generate this imprecision statistic. This statistic can be used to compare the analytic performance of different analyzers operating in similar patient care environments. 
     This data-mining statistic is derived from the y intercept of the regression line of the standard deviations of intra-patient differences graphed against the time intervals between sampling. This statistic can summarize the analytic imprecision over many reagent lots and calibrations. 
     After outlier removal, we calculated the standard deviations of duplicates (SDD) of the intra-patient pairs grouped in two-hour intervals: 0-2 hours, 2-4 hours, 4-6 hours, ¼ 20-22 hours and 22-24 hours. The WPI were obtained by regressing the SDDs against the time intervals of 2 to 14 hours; extrapolation to zero time (yintercept) represents the WPI (sa2+sb2)1/2. 
     Two groups of data were excluded from analysis: (1) Highly abnormal results which render the WPI calculation inaccurate. We generated frequency histograms of the patient data and in combination with the knowledge of reference intervals, we truncated significantly outlying data. (2) Results repeated within 2 hr. Reasons for serial testing within 2 hr include the investigation of a very morbid physiologic states, confirmation of very abnormal laboratory results and determining the response to an extreme therapy. 
     The software  1023  may further be transmitted or received over a communications network  1026  using a transmission medium via the network interface device  1020  utilizing any one of a number of well-known transfer protocols (e.g., HTTP). Examples of communication networks include a local area network (“LAN”), a wide area network (“WAN”), the Internet, mobile telephone networks, Plain Old Telephone (POTS) networks, and wireless data networks (e.g., Wi-Fi® and WiMax® networks). The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software. 
     Recently, the Royal Alexandra Hospital, a large tertiary and quaternary level Edmonton, Alberta hospital replaced its 250 and 950 Vitros chemistry analyzers with two Beckman DxC 800 systems. During the conversion to the new analyzers, laboratory staff observed that the number of outliers increased relative to that previously. We have devised a data-mining statistic (within-patient imprecision regressed to zerotime between specimens) that summarizes the average short term analytic imprecision (sa) and minimized biologic patient variation (sb). This statistic can summarize the analytic imprecision over many reagent lots and calibrations. 
     We analyzed two 12 month periods of Vitros data and a single 10 month period of Beckman data. For chloride, CO2, creatinine, potassium and sodium, we tabulated the measurements of paired intra-patient samples drawn within 24 hours of each other. After outlier removal, we calculated the standard deviations of duplicates (SDD) of the intra-patient pairs grouped in two-hour intervals: 0-2 hours, 2-4 hours, 4-6 hours, ¼ 20-22 hours and 22-24 hours. The SDDs were then regressed against the time intervals of 2 to 14 hours; extrapolation to zero time (y-intercept) represents the average variation (sa2+sb2)1/2. For each test, sa was calculated from the product of the short term within run experimental coefficient of variation (CV) and the control concentration. sb was calculated from sa and y0. CVb was determined by dividing sb by the average patient concentration. The uncertainity of CVb was derived from the standard error of the y-intercept; the relative error was obtained by dividing by the y-intercept. 
     sVitros was calculated using sb from Westgard.com. The increased imprecision due to using the Beckmans was derived from the square root of the differences of the squares of the SDD intercepts. The data mining tool, the within-patient imprecision regressed to zero-time between specimens, appears to be a powerful tool for evaluating imprecision. 
     Method stability and analytical imprecision are two of the most important criteria for instrument selection. We have devised a data-mining statistic (within-patient imprecision regressed to zero-time between specimens [WPI]) that summarizes the average short term analytic imprecision (sa) and minimizes biologic patient variation (sb). Unlike the short term analytic imprecision that is derived from quality control data, this statistic can summarize the analytic imprecision over many reagent lots and calibrations. Acute care hospitals and intensive care units provide adequate data to generate this imprecision statistic. This statistic can be used to compare the analytic performance of different analyzers operating in similar patient care environments. 
     This data-mining statistic is derived from the y intercept of the regression line of the standard deviations of intra-patient differences graphed against the time intervals between sampling. This statistic can summarize the analytic imprecision over many reagent lots and calibrations. 
     After outlier removal, we calculated the standard deviations of duplicates (SDD) of the intra-patient pairs grouped in two-hour intervals: 0-2 hours, 2-4 hours, 4-6 hours, ¼ 20-22 hours and 22-24 hours. The WPI were obtained by regressing the SDDs against the time intervals of 2 to 14 hours; extrapolation to zero time (yintercept) represents the WPI (sa2+sb2)1/2. 
     Two groups of data were excluded from analysis: (1) Highly abnormal results which render the WPI calculation inaccurate. We generated frequency histograms of the patient data and in combination with the knowledge of reference intervals, we truncated significantly outlying data. (2) Results repeated within 2 hr. Reasons for serial testing within 2 hr include the investigation of a very morbid physiologic states, confirmation of very abnormal laboratory results and determining the response to an extreme therapy.