Patent Publication Number: US-2021190740-A1

Title: Automated chromatogram analysis for blood test evaluation

Description:
BACKGROUND 
     1. Technical Field 
     The subject matter described generally relates to analyzing diagnostic testing data, and in particular to computer-aided blood test evaluation. 
     2. Background Information 
     A hemoglobinopathy is a genetic defect that results in an unusual structure of hemoglobin molecules in an individual&#39;s blood. For example, sickle-cell disease is caused by a hemoglobinopathy that can result in the red blood cells forming a rigid sickle shape under certain circumstances. These misshapen red blood cells can obstruct capillaries and restrict blood flow, leading to a range of health problems. In contrast, a thalassemia is a genetic condition that results in reduced hemoglobin production. Some hemoglobinopathies also impact hemoglobin production, and are thus also thalassemias. 
     Various medical conditions are characterized by the presence of certain hemoglobin variants and the proportions of different variants in the blood. Blood tests provide information about the proportions of different hemoglobin variants in a blood sample. However, interpreting this information can be challenging. Different conditions can have similar impacts on the presence of certain variants. The analysis is further complicated because other environmental and health factors can impact the proportions of the variants present. For example, an unusually large amount of hemoglobin F may indicate a genetic disorder or may indicate that an individual was pregnant or an infant at the time the sample was taken. Furthermore, relatively small amounts of a variant (or change in the amount of a variant present) may be clinically significant, but masked by variants that are present in far larger amounts. 
     Computer technology provides novel opportunities to analyze blood test data and more reliably distinguish between different response patterns produced by samples containing variants. This can reduce the reliance on human analysts, who may be subject to making errors and require more time and training to reach diagnoses than may be achieved using technology. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a high-level block diagram illustrating a networked computing environment in which diagnostic data is generated and analyzed, according to one embodiment. 
         FIG. 2  is a high-level block diagram illustrating a laboratory terminal suitable for use in the networked computing environment of  FIG. 1 , according to one embodiment. 
         FIG. 3  is a high-level block diagram illustrating the chromatogram analysis tool of a laboratory terminal, according to one embodiment. 
         FIG. 4  is a high-level block diagram illustrating an example of a computer suitable for use as a laboratory terminal, according to one embodiment. 
         FIG. 5  illustrates an example chromatogram, according to one embodiment. 
         FIG. 6  is a table illustrating an example division of a chromatogram into regions, according to one embodiment. 
         FIG. 7A  shows example visual representations of regions with of chromatogram data overlaid with best-fit match templates. 
         FIG. 7B  shows an example of chromatogram data and a report that might be generated by the chromatogram analysis tool, according to one embodiment. 
         FIG. 8  shows an example of a report of a plurality of results generated by the chromatogram analysis tool, according to one embodiment. 
         FIG. 9  is a flow-chart illustrating a method for generating a report for blood chromatography data, according to one embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     The Figures (FIGS.) and the following description describe certain embodiments by way of illustration only. One skilled in the art will readily recognize from the following description that alternative embodiments of the structures and methods may be employed without departing from the principles described. Reference will now be made to several embodiments, examples of which are illustrated in the accompanying figures. It is noted that wherever practicable similar or like reference numbers are used in the figures to indicate similar or like functionality. 
     OVERVIEW AND BENEFITS 
     A chromatogram analysis tool is used as part of a laboratory blood test system to identify genetic conditions based on the relative proportions of various types of hemoglobin in a sample. The blood test system generates chromatography data from the sample. The chromatogram analysis tool identifies regions of the chromatography data and, for each region, determines a match between the chromatography data in that region and one of a set of possible templates. The regions may have a predetermined size. The templates are of archetypical shapes of the hemoglobin data within the corresponding region, and may be constructed, portions of individual or pooled real exemplary chromatograms, or combinations of real and/or constructed exemplary chromatograms. 
     The chromatogram analysis tool generates a report based on the best-fit matches. The report may indicate one or more possible medical conditions. The report may also include additional comments and notes, such as suggestions for additional testing that should be performed, common diagnosis pitfalls, additional information about the corresponding condition (e.g., demographic factors that correlate with diagnosis), possible reproductive risks, and the like. 
     The analysis of regions of the chromatogram using templates has several advantages. First, it may help with result interpretation, enabling laboratories to deliver more standardized results without the need for additional training. In fact, it may reduce the amount of training required for laboratory technicians to operate efficiently. Second, it may enable results to be compared substantially in real time with large databases of reference cases that are available on-line, which may result in more accurate preliminary identifications of potential conditions. Third, by applying templates to regions, variations in scaling are inherently incorporated in the templates corresponding to each region. Thus, matching regions can provide greater accuracy over approaches that match templates to the chromatogram as a whole. Fourth, the method does not rely on peaks not found in normal samples to be integrated and assigned to a specific window. Fifth, the report can result in suggestions for next steps in reaching a diagnosis, which can reduce reliance on human-made connections between test results and possible causes. In some cases, the next steps can be triggered automatically or semi-automatically (e.g., if the required data for the next step is already available in a database), reducing the time taken to complete the testing process. 
     Example Systems 
       FIG. 1  shows one embodiment of a networked computing environment  100  in which diagnostic data is generated and analyzed. In the embodiment shown in  FIG. 1 , the networked computing environment includes a laboratory information system (LIS)  110 , laboratory equipment  120 , and laboratory terminals  130 , all connected via a network  170 . Although two items of laboratory equipment  120  and two laboratory terminals  130  are shown, a given deployment may include any amount of equipment and any number of terminals (including just a single terminal). In other embodiments, the networked computing environment  100  contains different or additional elements. In addition, the functions may be distributed among the elements in a different manner than described. For example, each item of laboratory equipment  120  may include a computer system that provides the functionality of a laboratory terminal  130 . 
     The LIS  110  is a computer-based system that supports the operations of the laboratory. In various embodiments, the LIS  110  provides tools that help technicians and other users function in the laboratory efficiently. For example, the LIS  110  might provide data tracking, automated backup, data exchange, work flow management, sample management, data analysis, data mining, instrument management, report generation, data auditing, and the like. In the embodiment shown in  FIG. 1 , the LIS  110  stores medical data  112 . The medical data  112  is stored on one or more computer readable media, such as a hard drive. The medical data  112  can include patient records, test results, medical literature, and the like. One of skill in the art will recognize other functionality that the LIS  110  may provide and other types of data that may be stored as part of the medical data  112 . 
     The laboratory equipment  120  is one or more devices that perform medical tests. In one embodiment, the laboratory equipment  120  includes a chromatography system that produces a chromatogram indicating the relative proportions of different variants of hemoglobin present in a sample. An example of such a system is the D-100™ produced by Bio-Rad™. The laboratory equipment  120  can also include devices that perform other tests, such as DNA testing, urine testing, and the like. By identifying a possible medical condition, a chromatogram analysis tool may trigger a series of tests for aiding in differential diagnosis of the sample, e.g., a sickling test, a stability test (isopropanol test), electrophoresis tests, MS/MS, molecular studies, and the like. 
     The laboratory terminals  130  are computing devices with which users interact with the LIS  110  and lab equipment  120 . In various embodiments, a technician initiates a test on a sample using a terminal  130  that includes a chromatogram analysis tool. The terminal  130  presents a report generated by the chromatogram analysis tool including results analysis and suggestions. In one embodiment, the technician approves the report and it is sent to the LIS  110  for storage. In another embodiment, a laboratory supervisor must also approve the report (e.g., using a second terminal  130 ). The terminal  130  may also send instructions (e.g., to the LIS  110 ) to initiate additional tests or provide the results of previously conducted tests based on the recommendations generated by the chromatogram analysis tool. Embodiments of the terminal  130 , and in particular operation of the chromatogram analysis tool, are described in additional detail below, with reference to  FIGS. 2 and 3 . 
     The network  170  provides the communication channels via which the other elements of the networked computing environment  100  communicate. The network  170  can include any combination of local area or wide area networks, using both wired or wireless communication systems. In one embodiment, the network  170  uses standard communications technologies or protocols. For example, the network  170  can include communication links using technologies such as Ethernet, 802.11, worldwide interoperability for microwave access (WiMAX), 3G, 4G, code division multiple access (CDMA), digital subscriber line (DSL), etc. Examples of networking protocols used for communicating via the network  170  include multiprotocol label switching (MPLS), transmission control protocol/Internet protocol (TCP/IP), hypertext transport protocol (HTTP), simple mail transfer protocol (SMTP), and file transfer protocol (FTP). Data exchanged over the network  170  may be represented using any suitable format, such as hypertext markup language (HTML) or extensible markup language (XML). In one embodiment, some or all of the components are connected using an RS-232 serial connection. In some embodiments, all or some of the communication links of the network  170  may be encrypted using any suitable technique or techniques. 
       FIG. 2  shows one embodiment of a laboratory terminal  130  suitable for use in the networked computing environment  100  of  FIG. 1 . In the embodiment shown in  FIG. 2 , the laboratory terminal  130  includes a results provider  210 , a display subsystem  220 , a user input subsystem  230 , a chromatogram analysis tool  240 , and local storage  260 . In other embodiments, the laboratory terminal  130  contains different or additional elements. In addition, the functions may be distributed among the elements in a different manner than described. 
     The results provider module  210  interfaces with laboratory equipment  120  to obtain medical data. In one embodiment, the medical data is blood chromatography data that the results provider module  210  uses to create a chromatogram. Alternatively, the chromatogram may be generated by the lab equipment  120  (or elsewhere in the networked computing environment  100 ) and provided as input to the results provider module  210 .  FIG. 5  shows an example of a chromatogram  500 , according to one embodiment. The chromatogram  500  includes a visual representation of the data  510  and a data table  520 . The visual representation  510  includes a plot of detector response over time that includes several peaks  512  (of which only two are labelled for clarity). The data table  520  identifies the retention time (i.e., the time at which the strongest detector response was observed for a peak  512 ) in various windows expected to correspond to different variants of hemoglobin (e.g., A1a, A1b, F, etc.). The data table  520  also includes the area of each peak  512  (which corresponds to the total amount of the given variant present in the sample) and result reported for each peak. 
     Referring back to  FIG. 2 , the display subsystem  220  presents information and controls to a user (e.g., a laboratory scientist). In one embodiment, the display subsystem  220  provides controls with which a technician initiates a test by the laboratory equipment  120 . The display subsystem  220  then provides controls to enable the operator to view and analyze the results of the test (e.g., using the chromatogram analysis tool  240 ). The display subsystem  220  may also provide other functionality, such as viewing patient records, configuring the laboratory equipment  120 , viewing status/maintenance data, and the like. 
     The user input subsystem  230  receives input from a user (e.g., a laboratory scientist or supervisor) and provides it to other elements of the terminal  130 . In one embodiment, the user input subsystem  230  includes a touch screen. Controls are presented on the touch screen enabling the user to control the laboratory equipment  120  or interact with the chromatogram analysis tool  240 . Further details of embodiments of the user interface provided by the user input subsystem  230  are provided below, with reference to  FIGS. 7 and 8 . 
     The chromatogram analysis tool  240  analyzes the data provided by the results provider module  210  to generate a report. In various embodiments, the chromatogram analysis tool  240  subdivides the chromatogram into regions and matches each region to a set of templates corresponding to possible morphologies of the region to find a best-fit match. The chromatogram analysis tool  240  then generates a report based on the best-fit matches for each region and includes comments regarding interpretation of the result. The report may additionally include a likelihood that each best-fit match is correct or a recommendation for further testing that will allow a definitive diagnosis. For example, if the results suggest the subject may be a carrier of an inheritable blood disorder, the chromatogram analysis tool  240  might recommend a DNA test for verification if the subject is considering having children. In one embodiment, the chromatogram analysis tool  240  may automatically trigger further analysis if the required data or equipment is available and update the report accordingly. Details of various embodiments of the chromatogram analysis tool  240  are described in greater detail below, with reference to  FIG. 3 . 
       FIG. 3  shows one embodiment of the chromatogram analysis tool  240  of the laboratory terminal  120  shown in  FIG. 2 . In the embodiment shown in  FIG. 3 , the chromatogram analysis tool  240  includes a pre-processing module  310 , a region identification module  320 , a template store  325 , a template matching module  330 , and a result evaluation module  340 . In other embodiments, the chromatogram analysis tool  240  contains different or additional elements. In addition, the functions may be distributed among the elements in a different manner than described. 
     The pre-processing module  310  performs a variety of baseline operations and quality checks prior to further analysis of the data. In some embodiments, the pre-processing module  310  performs a baseline subtraction on the chromatogram prior to any subsequent analyses. In some embodiments, the pre-processing module  310  may perform initial analyses of the chromatogram. For example, the pre-processing module  310  may calculate heights, areas, and generate calibrated and non-calibrated results. The pre-processing module  310  may also calculate special sums from these calibrated and non-calibrated results that combine data from one or more peaks to aid in efficient analysis. 
     In some embodiments, the pre-processing module  310  analyzes the quality of the data. In one such embodiment, the quality analysis checks for features in the data that may indicate a high likelihood of inaccurate results. For example, the quality analysis module  310  can compare the total area for a chromatogram to a minimum area threshold and flag the test data as low-quality if the total area is less than the threshold. In this example, the pre-processing module  310  may use the special sums calculated as described above. If the test data is flagged as low-quality data, the pre-processing module  310  may end the analysis and indicate that a new test should be performed. This can prevent time and resources being wasted on further analysis of data that is unreliable. In such cases, the pre-processing module  310  may automatically trigger retesting of the sample. In another example, the pre-processing module  310  might look at the width, exponentially modified Gaussian fit sigma and tau values, or indicators derived from exponentially modified Gaussian fit sigma and tau values of a known peak (e.g., the A1c or A2 peaks) and add a warning comment if thresholds are exceeded. Other examples include the quality analysis module  310  checking for uneven baselines and highly asymmetrical peaks (e.g., peak tailing) using exponentially modified Gaussian tau/sigma ratio or another indicator. 
     The region identification module  320  divides the chromatogram into regions. The region identification module  320  uses features of the chromatogram and/or absolute or normalized times to determine the times at which each region begins and ends. In one embodiment, the region identification module  320  determines the region boundaries by searching for an expected feature in an expected range. For example, the region determination module  320  may determine a start or end boundary of a region by searching for one or more of the following in the expected range: a first peak start or valley, a last peak start or valley, a peak start or valley with the lowest magnitude, a first valley or peak end, a last valley or peak end, a valley or peak end with the lowest magnitude, a first peak start or valley or peak end, a last peak start or valley or peak end, a peak start or valley or peak end with the lowest magnitude, or a last peak end. For example, the region identification module  320  determines the retention time that is the boundary between region  1  (e.g., region  1   610 ) and region  2  (e.g., region  2   620 ) as the local minima in the retention time range corresponding to where the peak F and the peak LA1c elute, such that all of peak F would be comprised within region  1  and all of peak LA1c would be comprised within region  2 , if both are present. If the expected peak feature is found in the expected range, it is used as the corresponding region boundary. If not, a default value (e.g., an absolute or normalized time) may be used for the boundary. This may account for unusual cases where expected peaks do not appear but unusual ones do. Because the region identification module  320  determines regions based on selected features, the regions may each be of a differing size (i.e., length of retention times). 
       FIG. 6  is a table  600  illustrating an example division of a chromatogram into regions, according to one embodiment. The table  600  divides the chromatogram into five regions and, for each region, enumerates a start feature and an end feature. In the example illustrated by  FIG. 6 , region  1   610  begins at the start of the chromatogram (i.e., retention time 0.0) and ends at the retention time corresponding with the end of peak F, if F is present. That is, region  1   610  comprises a retention time range in which peaks A1a, A1b, and F elute if present. Region  2   620  begins at a retention time corresponding to the start of the peak LA1c, if present, and ends with a retention time corresponding to the end of the peak P3, if present, such that it comprises the retention time range in which peaks LA1c, HbA1c and P3 elute if present. Region  3   630 , region  4   640 , and region  5   650  are similarly defined by their corresponding start and stop features or times. The table  600  describes the boundaries of the regions  610 ,  620 ,  630 ,  640 ,  650  in terms of windowed components often seen in a chromatogram. However, in some embodiments, the boundaries are not dependent on the identification of these windowed components. 
     Referring back to  FIG. 3 , the template store  325  stores one or more sets of templates, or the parameters required to generate the templates as needed. Each template corresponds to an archetypical shape of a region of a chromatogram where each archetypical shape is a representation of a particular archetype of the chromatogram region. The archetypical shapes may be constructed, real, or a combination of real and constructed, where each archetypical shape mimics one or more of the peaks and troughs that are found in the particular region of the chromatogram. The real archetypical shapes come from real data sets of chromatography data, either individual or combined chromatograms. The constructed archetypical shapes are created artificially to represent an archetype, for example, by an expert constructing the expected curve. Each region is associated with a set of templates, each template having different archetypical shape. For example, the templates for region  1  may comprise archetypical shapes of the normal A1a, A1b, and F peaks, wherein each template has a different height, width, symmetry, of one or more of the peaks, and in some templates, some of the peaks may be missing entirely. In others, the expected archetypical shape of an abnormal response may be included. Each set of templates associated with a region in the template store  325  may be indexed and searchable by a variety of factors, such as height of particular peaks, absence of particular peaks, or subsets of templates known to be represent chromatogram data that is associated with certain medical conditions. 
     The template matching module  330  compares sets of templates to individual regions of the chromatogram to determine a template that is a best-fit match for each region. The template matching module compares a region of the chromatogram to the templates in a set of templates associated with that region stored in the template store  325 . In one embodiment, the template matching module  330  slides a first, template from the template store  325  across the data of the region. The template matching module  330  determines a position of the first template over the region that has the best-fit between the first template and the data of the region. The template matching module  330  may determine the best-fit position of the first template over the data of the region by determining correlation coefficient R-values between the first template and the data at different alignments of the first template and the data. Alignment may occur in one or two dimensions. For example, the alignment may include an offset in one dimension. 
     In some embodiments, the alignment is parameterized by a jitter range, which may be calibrated for different features based on expected retention times. The alignment of the first template and the data with the highest correlation coefficient R-value is the best-fit position of the first template and becomes the R-value associated with the first template for the data. The template matching module repeats the method of determining a best-fit correlation coefficient R-value for other templates in the template store  325 . The template of a set of templates determined to have the overall highest R-value is determined by the template matching module  330  to be the best-fit match for the region. The template matching module  330  finds a best-fit match for each of the regions in the chromatogram. In other embodiments, other metrics indicating closeness of fit may be used. 
     In some embodiments, the template matching module  330  matches every template in a set stored in the template store  325  associated with the particular region. In other embodiments, the template matching module  330  uses determined closeness of fit metrics to expedite a determination of a best-fit match. For example, the template matching module  330  determines a correlation coefficient R-value above a threshold for a first template, which triggers additional comparison with a subset of templates that are similar to the first template. Similarly, the template matching module  330  determines a correlation coefficient R-value below a threshold for a second template, which triggers the template matching module  330  to skip comparison with a subset of templates that are similar to the second template. 
     The template matching module  330  may also add one or more comments. For example, the comments might identify potential diagnosis pitfalls related to the preliminary pattern, suggest further testing that would help reach a diagnosis, or identify other factors that should be considered (e.g., the ethnicity of the subject). 
     The result evaluation module  340  receives the output from the template matching module  330  for each region and creates a report. The result evaluation module  340  incorporates the best-fit matches for each region in an overall analysis. Calibrated percent area or other pre-processing results may be combined with the region match information to make a determination of possible medical conditions. In some embodiments, the result evaluation module  340  generates an overall best-fit template, combining each best-match template end-to-end. In another embodiment, normalized regions are overlaid individually with the best matching templates and displayed side by side as in  FIG. 7 . The report generated by the result evaluation module  340  identifies one or more possible medical conditions or, otherwise, a determination of normalcy, or returns a result of no assignment—possible variant. For example, a region may have a best-fit match template that is associated with a likely indication of a medical condition and a recommendation for additional testing. In some embodiments, one template may indicate a plurality of possible medical conditions. In another example, a combination of the determined best-fit match templates for two or more regions with pre-processed results may indicate a medical condition, or possibly indicate a higher likelihood of the condition than either template match alone. 
     The report generated by the result evaluation module  340  can also include comments or advice regarding interpretation of the report, based on data associated with the individual templates of combinations of templates. For example, the comments may include an indication of the likelihood the individual has each of the one or more medical conditions, recommendations for further testing, common pitfalls associated with the one or more medical conditions, or additional information about each medical condition. For example, when testing for Beta Thalassemia, the generated report may include an HbA1c and/or A2/E result as well as information about the hemoglobin pattern, along with associated comments and notes. The added comments may alert the laboratory scientist and help the clinician in the interpretation of the result. In another example, the comments added by the result evaluation module  340  may include comments regarding features of the test results, such as the presence of a specific hemoglobin variant, or set a flag indicating that the test results should be suppressed or repeated (e.g., if the analysis suggests the results are unreliable). 
       FIG. 7A  shows example visual representations of regions of chromatogram data overlaid with best-fit match templates.  FIG. 7A  includes a visual representation  710  of region  1 , a visual representation  720  of region  2 , a visual representation  730  of region  3 , a visual representation  740  of region  4 , and a visual representation  750  of region  5 . The region identification module  320  divides chromatogram data  760  (see  FIG. 7B ) into regions, which are represented by the visual representations  710 ,  720 ,  730 ,  740 ,  750 . Each visual representation of  710 ,  720 ,  730 ,  740 ,  750  of each region includes a plot of chromatogram data  712 ,  722 ,  732 ,  742 ,  752  of the region and a best-fit template  714 ,  724 ,  734 ,  744 ,  754  determined by the template matching module  330 , respectively. Each visual representation  710 ,  720 ,  730 ,  740 ,  750  also includes a summary of results  716 ,  726 ,  736 ,  746 ,  756 , which provide an offset value and an R value for the match between the plot of chromatogram data  712 ,  722 ,  732 ,  742 ,  752  and the best-fit template  714 ,  724 ,  734 ,  744 ,  754 , for each respective region, as determined by the template matching module  330 . 
     For example, the visual representation  710  of region  1  includes the plot of chromatogram data  712  of region  1  overlaid with a best-fit template  714  for the chromatogram data  712 . The plot of chromatogram data  712  is similar in shape although not exactly the same as the best-fit template  714 . For example, the peaks are shaped similarly but are slightly different heights. The template matching module  330  determined the best-fit template  714  to be an archetypical shape with the highest correlation coefficient R-value of all templates for region  1  in the template store  325  for the plot of chromatogram data  712 . The correlation coefficient R-value for the best-fit template  714  and the plot of chromatogram data  712  is 0.9324, as indicated by the results  716 . Other templates for region  1  in the template store  325  have lower correlation coefficient R-values than 0.9324 for the plot of chromatogram data  712 . The best-fit template  714  may be associated with one or more medical conditions. 
       FIG. 7B  shows an example of chromatogram data  760  and a report  770  that might be generated by the chromatogram analysis tool  240 , according to one embodiment. The chromatogram data  760  can be divided into regions of the plots of chromatogram data  712 ,  722 ,  732 ,  742 ,  752 . The chromatogram data  760  is displayed along with an overlay indicating each region number associated with the plots of chromatogram data  712 ,  722 ,  732 ,  742 ,  752 . The chromatogram analysis tool  240  produces the report  770  from the example chromatogram analysis data  760 . The best-fit templates  714 ,  724 ,  734 ,  744 ,  754  for the plots of chromatogram data  712 ,  722 ,  732 ,  742 ,  752  of  FIG. 7A  are visualizations of the best-fit match templates determined by the chromatogram analysis tool for each region of the chromatogram data  760 . The report  770  is generated based on the best-fit matches  714 ,  724 ,  734 ,  744 ,  754  for each region. 
     The report  770  provides information about the chromatogram data  760 . In the embodiment shown in  FIG. 7B , the report  770  includes various information including patient information  771 , a list of regions  772  with each associated best-fit match template name  773  and associated correlation coefficient R-value  774 , a comment  775 , and optional notes  776 . In other embodiments, the report  770  may include additional or alternate information about the chromatogram data  760 . 
     The patient information  771  includes relevant information about a patient, such as a patient ID and a rack and position indicating the location of a sample tested to produce the chromatogram data  760 . In other embodiments, the patient information  771  may additionally or alternatively include a name, a blood type, responsible physician, demographic data, the date and time of the test, and other health information associated with the patient. The patient information may be stored in local storage  260  rather than being produced by the chromatogram analysis tool  240 . 
     The list of regions  772  enumerates the regions of the chromatogram data  760 . Each region in the list of regions  772  is associated with an enumerated best-fit match template name  773  and associated correlation coefficient R-value  774  for the best-fit match template. The best-fit match template names  773  enumerated in the report  770  are specific names associated with the best-fit matches  714 ,  724 ,  734 ,  744 ,  754  for each region shown in  FIG. 7A . For example, the best fit match  714  is called “BARTS and H1.” The correlation coefficient R-values  774  enumerated in the report  770  are the same as the R-values enumerated in the summary of results  716 ,  726 ,  736 ,  746 ,  756  of  FIG. 7A . For example, the correlation coefficient R-value for region  1  is 0.934 as indicated in both  FIGS. 7A and 7B . 
     The comment  775  indicates one or more possible medical conditions, or other medical information. The comment  76  in  FIG. 7B  indicates the patient likely has “BARTS with Constant Spring.” In other embodiments, the comment  775  can indicate other possible medical conditions, such as the examples given below in relation to  FIG. 8 , or an indication of normalcy. In one embodiment, any comments  775  generated by the chromatogram analysis tool  240  are presented to a laboratory supervisor (e.g., at a terminal  130 ) and are only included on the report  770  if the laboratory supervisor approves them. 
     The notes  776  indicate a % A2 result along with an expected % A2 range for the medical disorder. The notes  776  may be produced by the pre-processing module  310  of the chromatogram analysis tool  240 . In alternate embodiments, the notes  776  may include additional or alternate information. 
       FIG. 8  shows an example of a report  800  of a plurality of results generated by the chromatogram analysis tool  240 , according to one embodiment. The report  800  includes the plurality of results obtained from a plurality of analyzed samples. The summary  800  may be displayed on a terminal  130 . In some embodiments, the summary report information may be exported in a format usable by a spreadsheet application, or may be printed. The report  800  includes, for each sample, a sample name, a text name and correlation coefficient for each region, a comment, and optional notes. 
     The text name for each region in each sample indicates a name for a best-fit match template associated with the particular region for the particular sample, as determined by the chromatogram analysis tool  240 . A column title for the text names of each region is abbreviated as “Region  1  Text” in the report  800  for region  1  and likewise for other regions. For example, sample  5  has a region  3  text name of “A0 Predominate.” 
     The correlation coefficient for each region in each sample is a value indicating the closeness between chromatogram data within the region and the best-fit match template (e.g., R-value). A column title for the correlation coefficient is abbreviated as “Region  1  CC” in the report  800  and likewise for other regions. Each correlation coefficient is associated with the best-fit match template associated with the text name directly to the left of the respective correlation coefficient. For example, sample  5  has a region  3  correlation coefficient of 0.9246 for the best-fit match template called A0 Predominate. 
     The comments for each sample provide an indication of one or more possible medical conditions. The comments are provided by the chromatogram analysis tool  240 . A column title for the comments is “Comments” in the report  800 . As enumerated in  FIG. 8 , possible medical conditions indicated by the comments include but are not limited to: HbH, BARTS, Constant Spring, High F, Beta-thal major, Beta 0/E, SC, 0-Arab, CC, SS, EE, Beta-thal trait. 
     The notes for each sample provide additional information about the sample. The comments may be provided by the chromatogram analysis tool  240  or another module. A column title for the note is “Notes” in the report  800 . For example, in  FIG. 8 , to the first row includes a note that the percentage of the A2 peak/E peak is 0.70. The text names, correlation coefficients, comments, and notes associated with samples in the report  800  facilitate the reading of many samples at once. The report  800  may be provided in addition to or as an alternate to the report  770  of  FIG. 7B . 
     One embodiment of the invention was run on a set of 97 chromatograms with variant responses, and results from the invention compared against manually examined assignments. Twenty-two chromatograms were of variant samples that were not contained in the template library. Twenty of the twenty-two chromatograms returned result “No assignment—possible variant” while two chromatograms returned result BARTS. Both results would trigger increased testing and scrutiny of the sample. Two additional chromatograms were from a transfused sample, which also returned result “No assignment—possible variant”. Of the remaining 73 chromatograms, 67 were assigned in alignment with manual chromatogram assignment. Four of the six differences appear attributable to differing interpretations of the % A2 result, so different % A2 cutoffs for normal were used between this method and manual scrutiny. These four chromatograms were identified as normal while beta thal trait was manually assigned. The remaining two discrepancies returned a result of A2+A2′ while the manual assignment was again beta thal. This result should trigger further investigation. 
     Computing System Architecture 
       FIG. 4  illustrates an example computer  400  suitable for use as a laboratory terminal  120  or LIS  110 , according to one embodiment. The example computer  400  includes at least one processor  402  coupled to a chipset  404 . The chipset  404  includes a memory controller hub  420  and an input/output (I/O) controller hub  422 . A memory  406  and a graphics adapter  412  are coupled to the memory controller hub  420 , and a display  418  is coupled to the graphics adapter  412 . A storage device  408 , keyboard  410 , pointing device  414 , and network adapter  416  are coupled to the I/O controller hub  422 . Other embodiments of the computer  400  have different architectures. 
     In the embodiment shown in  FIG. 4 , the storage device  408  is a non-transitory computer-readable storage medium such as a hard drive, compact disk read-only memory (CD-ROM), DVD, or a solid-state memory device. The memory  406  holds instructions and data used by the processor  402 . The pointing device  414  is a mouse, track ball, touch-screen, or other type of pointing device, and is used in combination with the keyboard  410  (which may be an on-screen keyboard) to input data into the computer system  400 . The graphics adapter  412  displays images and other information on the display  418 . The network adapter  416  couples the computer system  400  to one or more computer networks. 
     The types of computers used by the entities of  FIGS. 1 through 3  can vary depending upon the embodiment and the processing power required by the entity. For example, an LIS  110  might include a distributed database system comprising multiple blade servers working together to provide the functionality described. Furthermore, the computers can lack some of the components described above, such as keyboards  410 , graphics adapters  412 , and displays  418 . 
     Example Methods 
       FIG. 9  is a flow-chart illustrating a method for generating a report for blood chromatography data, according to one embodiment. The steps of  FIG. 9  are illustrated from the perspective of the chromatogram analysis tool  240  performing the method. However, some or all of the steps may be performed by other entities or components. In addition, some embodiments may perform the steps in parallel, perform the steps in different orders, or perform different steps. 
     In the embodiment shown in  FIG. 9 , the method begins with the chromatogram analysis tool  240  receiving  910  blood test chromatography data for a blood sample of a patient. In some embodiments, the chromatogram analysis tool  240  receives the blood test chromatography data from the laboratory equipment  120 . In other embodiments, the chromatography analysis tool  240  receives the blood test chromatography data from the LIS  110 . The received blood test chromatography data includes several peaks. Each peak corresponds to type of hemoglobin and has a value indicating an amount of the corresponding type of hemoglobin present in the blood sample. After receiving  910  the blood test chromatography data, the pre-processing module  310  may analyze the quality of the blood test chromatography data, perform a sample analysis, calculate special sums, or perform a baseline subtraction. 
     The chromatogram analysis tool  240  identifies  920  a plurality of regions of the chromatography data. The identifying  920  is performed by the region identification module  320 , described in relation to  FIG. 3 . The regions each include chromatography data from a different range of chromatography peaks. The start and end points of each region may be identified  920  based on features of the chromatography data, or by absolute times. 
     The chromatography analysis tool  240 , for each region, retrieves  930  a plurality of region templates corresponding to the region and identifies  940  a best-fit match region template by comparing region templates to the chromatography data included in the region. The plurality of region templates is retrieved  930  by the template matching module  330  from the template store  325 . The identifying  940  of a best-fit match region is done by the template matching module  330 . 
     The chromatography analysis tool  240  generates  950  a report based on the best-fit match region templates for each region. In one embodiment, the chromatograph analysis tool  240  generates  950  the repot additionally based on information from the pre-processing module  310 , such as % A2/E. The generating  950  is performed by the result evaluation module  340 . The generated  950  report may include one or more medical conditions, comments regarding the chromatogram data, and comments regarding the one or more medical conditions, including common pitfall, recommendations for additional testing, or additional information. The report may be generated  950  for display, for example, by the display subsystem, or any other display terminal. 
     ADDITIONAL CONSIDERATIONS 
     Some portions of above description describe the embodiments in terms of algorithmic processes or operations. These algorithmic descriptions and representations are commonly used by those skilled in the data processing arts to convey the substance of their work effectively to others skilled in the art. These operations, while described functionally, computationally, or logically, are understood to be implemented by computer programs comprising instructions for execution by a processor or equivalent electrical circuits, microcode, or the like. Furthermore, it has also proven convenient at times, to refer to these arrangements of functional operations as modules, without loss of generality. 
     As used herein, any reference to “one embodiment” or “an embodiment” means that a particular element, feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment. 
     Some embodiments may be described using the expression “coupled” and “connected” along with their derivatives. It should be understood that these terms are not intended as synonyms for each other. For example, some embodiments may be described using the term “connected” to indicate that two or more elements are in direct physical or electrical contact with each other. In another example, some embodiments may be described using the term “coupled” to indicate that two or more elements are in direct physical or electrical contact. The term “coupled,” however, may also mean that two or more elements are not in direct contact with each other, but yet still co-operate or interact with each other. The embodiments are not limited in this context. 
     As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having” or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Further, unless expressly stated to the contrary, “or” refers to an inclusive or and not to an exclusive or. For example, a condition A or B is satisfied by any one of the following: A is true (or present) and B is false (or not present), A is false (or not present) and B is true (or present), and both A and B are true (or present). 
     In addition, use of the “a” or “an” are employed to describe elements and components of the embodiments. This is done merely for convenience and to give a general sense of the disclosure. This description should be read to include one or at least one and the singular also includes the plural unless it is obvious that it is meant otherwise. 
     Upon reading this disclosure, those of skill in the art will appreciate still additional alternative structural and functional designs for a system and a process for providing a chromatogram analysis tool that aids in hemoglobinopathy evaluation. Thus, while particular embodiments and applications have been illustrated and described, it is to be understood that the described subject matter is not limited to the precise construction and components disclosed herein and that various modifications, changes and variations which will be apparent to those skilled in the art may be made in the arrangement, operation and details of the method and apparatus disclosed. The scope of protection should be limited only by the following claims.