Novel Use of Regex for Echocardiogram Data Transformation

Novel Use of Regex for Echocardiogram Data Transformation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Novel Use of Regex for Echocardiogram Data Transformation Michael Brockman, John Aparece This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6275989/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Echocardiogram data provides a rich dataset about a patient's cardiac function and, more broadly, an individual's health status. Given this, these data have been subject to significant research and analysis; however, data analysis requires manual data transformation from the medical report to analytically favorable formats such as spreadsheet file types. There exists a need for a tool for automated data transformation. Results Ready-made, Easy Echocardiogram Data for Research (REEDR) is a software script that utilizes regular expressions for rapid data transformation from echocardiogram reports to spreadsheet format. REEDR provides a software solution emphasizing ease of use and reliability. Its goal is to instantaneously iterate over unlimited echocardiogram reports and transform the values into analytically friendly spreadsheet format. Discussion Prior to REEDR, manual data entry and curation required significant human labor and were susceptible to data entry errors. The novel use of regular expressions through a Pythonic program script provides the flexibility to iterate over many differing types of echocardiogram medical reports to instantaneously generate an analysis-friendly format, a spreadsheet file type. Source code and documentation can be obtained from https://github.com/mbrockman1/REEDR . Echocardiogram Python Regular expressions Figures Figure 1 Introduction An echocardiogram, or cardiac ultrasound, is an imaging modality of the heart that yields critical information[ 1 ]. Ejection fraction, cardiac output, and structural integrity are critical aspects that can be measured in these studies[ 2 , 3 ]. More importantly, the echocardiogram provides vital information on an individual's health status[ 4 , 5 ]. Thus, significant research has been dedicated to studying this imaging modality[ 6 ]. The results of these data analytics have often been the foundation of many critical aspects of guideline medical management[ 7 ]. Results are only as good as the data's integrity, which often degrades with large sample sizes due to entry errors. Many data points can be drawn from a single echocardiogram, and beyond this, hundreds or thousands of echocardiograms may be required in scientific studies[ 8 ]. Adding to the vast data point burden is that many electronic medical record echocardiogram reporting systems often produce the reports in a non-analytical friendly text format. When this occurs, manual data curation and data transformation must be done to transform text data into an easy-to-use file format, such as a spreadsheet. It takes significant time and dedication to perform this task appropriately. There is a current need for an automated software solution that addresses these concerns. The Python programming language has allowed for significant strides in customized software for specific tasks and automation[ 9 ]. The scripting language is also highly modular, which allows for simple functionality expansion with pre-written code. One module, for example, is a regular expression iteration tool suite. Regular expressions are text patterns that can be algorithmically searched and manipulated[ 10 , 11 ]. The tool suite allows text pattern recognition and subsequent value storage in more meaningful, analytical ways than a simple text file. Ready-made, Easy Echocardiogram Data for Research (REEDR) is a software solution for transforming echocardiogram text file data to spreadsheet format, using regular expression iteration as its backbone. Methods Aim The aim of Ready-made, Easy Echocardiogram Data for Research (REEDR) is to provide an automated software solution for mass manual echocardiogram data entry into a spreadsheet format. Given the significant use of echocardiogram data for research and the pervasive use of spreadsheet file types for analysis, such software is needed to accelerate medical research. Design The Python programming language was selected as the foundation language due to its broad adoption among software engineers and its ease of use. This parallels REEDR's project goals of being portable, easy to use, and modifiable to suit the user's intended purpose. The project is open-source and free under the GNU General Public License Version 3 to incentivize continued development and usage[ 12 ]. Echocardiogram Reports Echocardiograms are reported in a pseudo-standardized reporting method such that the impressions can be quickly understood between physicians. Findings such as ejection fraction reported in percent and left ventricular wall thickness reported in centimeters are found in nearly all echocardiogram reports. The reports are commonly stored in rich text format in a healthcare system's electronic medical record. Figure 1 is an example of an echocardiogram report Software Structure and Method Given the repeated reporting terminology and units of measure, the Pythonic regular expression (regex) software package was leveraged to find and store the reported data quickly. This was accomplished by writing a regex-based line parser to obtain each reported value in the report. The parse stored each value in a Pandas data frame. The Python software library Pandas provides a data frame structure that can dynamically store, manipulate, and export data in various file types[ 13 ]. Once the parser completes a single report, it closes the file and applies the same functionality to the following file in numerical order. After iterating over every file in the target folder, the parser closes, and the Pandas data frame, containing all the report data, is exported as a spreadsheet file type. Error Reporting One of REEDR's critical functions is an error reporting system. Suppose a file is corrupted, unable to be read, or not in the appropriate format. In that case, REEDR prints the file location that it could not complete its task and does not addend the Pandas data frame with potentially partially collected data. Results REEDR's performance was evaluated by placing the software under varying amounts of, for example, echocardiogram-rich text format reports. To accurately assess the performance under each example load, REEDR was tested 100,000 times per example load and was timed from the start of code execution to completion. The timing was assessed using the Pythonic function, 'timeit,' which expressed that the development purpose was to accurately assess function execution time. REEDR required 1.29 ms ± 11.1 µs to iterate over a single echocardiogram report and export the results to a spreadsheet filetype. When performing the same function on 100 echocardiogram reports, it required 1.3 ms ± 52.2 µs. For 500 echocardiogram reports, it required 1.29 ms ± 37.8 µs. For 1,000 echocardiogram reports, REEDR completed 1.29 ms ± 44.5 µs. Finally, REEDR was able to complete the iteration of 100,000,000 echocardiograms in. The computer platform that the performance tests were completed on was an Apple MacBook Pro with an Apple M1 processor running MacOS 14.5 (23F79). Results can be found in Table 1 . Table 1 REEDR ‘timeit’ performance evaluation under varying loads. The script was executed 100,000 times and revealed mean completion time (ms) and standard deviation (µs). Ready-made, Easy Echocardiogram Data for Research Iteration Performance Number of echocardiogram reports analyzed by REEDR Mean completion time after 100,000 code executions (ms) Standard deviation of time after 100,000 code executions (µs) 1 1.29 11.1 100 1.3 52.2 500 1.29 37.8 1,000 1.29 44.5 1,000,000 1.3 117 The data reported from REEDR in the exported spreadsheet accurately reflected all the reported findings in echocardiogram reports, such as medical record number, age, ejection fraction in percent, pulmonic systolic velocity in centimeters per second, left ventricular wall thickness in centimeters, presence of pericardial effusion, etc. Discussion Echocardiogram data provides significant information on an individual's cardiac function and, more broadly, an individual's health status[ 4 , 5 ]. Given the value of echocardiogram data, the data has been used for landmark clinical trials that change the course of medical therapy[ 14 – 16 ]. These important data, however, can be subject to limitations when manual data transformation from echocardiogram reports to more traditional analytics formats such as spreadsheet filetype is required. As healthcare analytics moves into the realm of Big Data, data collection speed and accuracy are critical[ 17 , 18 ]. Regardless of the data type, manual collection, and curation are subject to human error, which can result in inaccurate conclusions. Companies have responded by creating software suites that offer automated solutions, often at a price[ 19 , 20 ]. Moreover, tools to address these problems have been lacking in the healthcare data space. Ready-made, Easy Echocardiogram Data for Research (REEDR) was initiated in response to the significant human labor required to transform echocardiogram reports into a more traditional analytical file type. There lacked an existence of a free-to-use, fast, and automated solution that can replace manual data transformation. This project demonstrates the novel use of regular expression parsing of echocardiogram reports to transform the report data into a spreadsheet file type. REEDR has already been successfully used in three published project evaluating echocardiogram data in certain disease states[ 21 – 23 ]. Over these projects, REEDR allowed for expedited analytics of over 500 echocardiograms reports. Not only was this project successful in demonstrating feasibility and scalability, but the project also shows the potential for replicating these methods towards other healthcare data stored as non-traditional analytical file types, such as pulmonary function test data. Continued creation of open-source, free-to-use healthcare analytics tools holds the potential to dramatically improve the quality and quantity of healthcare research, with the end goal of improving individual lives. Conclusion Ready-made, Easy Echocardiogram Data for Research (REEDR) is a software solution to manual echocardiogram data entry. With its novel use of regular expression iteration, REEDR can rapidly transform echocardiogram text reports to spreadsheet format for further analysis. Source code and documentation can be obtained from https://github.com/mbrockman1/REEDR . Declarations Data Availability Statement No applicable data was used for the presented research. The Ready-made, Easy Echocardiogram Data for Research (REEDR) software script has been made freely available for download and continued development at https://github.com/mbrockman1/REEDR. REEDR performance evaluation has been provided within the manuscript. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials No data was utilized concerning this presented research. The Ready-made, Easy Echocardiogram Data for Research (REEDR) software script has been made freely available for download and continued development at https://github.com/mbrockman1/REEDR. Competing interests Not applicable. Funding Not applicable. Authors' contributions M.B. and J.A. wrote the main manuscript text and prepared Table 1 and Figure 1 and performed the software performance evaluation. M.B. and L.B. are the main developers of Ready-made, Easy Echocardiogram Data for Research (REEDR). M.B. and L.B. are maintainers of the code currently. All authors reviewed the manuscript. References Safe IE. What is Echocardiography? 1986. Tam JW, et al. What is the real clinical utility of echocardiography? A prospective observational study. J Am Soc Echocardiogr. 1999;12(9):689–97. Ashley EA, Niebauer J. Understanding the echocardiogram , in Cardiology explained . Remedica; 2004. Gardin JM, et al. Echocardiographic design of a multicenter investigation of free-living elderly subjects: the Cardiovascular Health Study. J Am Soc Echocardiogr. 1992;5(1):63–72. Fralick M, et al. Health Services: Value of routine echocardiography in the management of stroke. CMAJ. 2019;191(31):E853–9. Patterson OV, et al. Unlocking echocardiogram measurements for heart disease research through natural language processing. BMC Cardiovasc Disord. 2017;17:1–11. Porter TR, et al. Guidelines for the use of echocardiography as a monitor for therapeutic intervention in adults: a report from the American Society of Echocardiography. J Am Soc Echocardiogr. 2015;28(1):40–56. Watson LE, et al. Baseline echocardiographic values for adult male rats. J Am Soc Echocardiogr. 2004;17(2):161–7. Van Rossum G. Python Programming Language . in USENIX annual technical conference . 2007. Santa Clara, CA. Chapman C, Stolee KT. Exploring regular expression usage and context in Python . in Proceedings of the 25th International Symposium on Software Testing and Analysis . 2016. Chapman C, Wang P, Stolee KT. Exploring regular expression comprehension . in 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) . 2017. IEEE. License GGP. Gnu general public license. Retrieved December, 1989. 25: p. 2014. McKinney W. pandas: a foundational Python library for data analysis and statistics. Python high Perform Sci Comput. 2011;14(9):1–9. van der Meer P, Gaggin HK, Dec GW. ACC/AHA versus ESC guidelines on heart failure: JACC guideline comparison. J Am Coll Cardiol. 2019;73(21):2756–68. Mentz RJ, et al. PROVIDE-HF primary results: patient-reported outcomes investigation following initiation of drug therapy with entresto (sacubitril/valsartan) in heart failure. Am Heart J. 2020;230:35–43. Fraser AG, et al. A concise history of echocardiography: timeline, pioneers, and landmark publications. Eur Heart Journal-Cardiovascular Imaging. 2022;23(9):1130–43. Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health Inform Sci Syst. 2014;2:1–10. Belle A, et al. Big data analytics in healthcare. Biomed Res Int. 2015;2015(1):370194. Ajit-Kumar R. Impact of Big Data Analytics on Healthcare and Society’. J Biom Biostat. 2016;7(300):2. Milenkovic MJ, Vukmirovic A, Milenkovic D. Big data analytics in the health sector: challenges and potentials. Management: Journal of Sustainable Business and Management Solutions in Emerging Economies, 2019. 24(1): pp. 23–33. Murguia AR et al. Evaluation of Four Validated Risk Scores to Predict Outcomes in Hispanic Patients With Acute Pulmonary Embolism. Angiology. 0(0): p. 00033197241230716. Fadah K, et al. Insights Into Differences in Pulmonary Hemodynamics in Hispanic Patients With Pulmonary Arterial Hypertension. Cardiol Res. 2024;15(2):117. Murguia AR et al. Evaluation of Four Validated Risk Scores to Predict Outcomes in Hispanic Patients With Acute Pulmonary Embolism. Angiology, 2024: p. 00033197241230716. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 29 Apr, 2025 Reviews received at journal 28 Apr, 2025 Reviews received at journal 23 Apr, 2025 Reviewers agreed at journal 22 Apr, 2025 Reviewers agreed at journal 09 Apr, 2025 Reviewers invited by journal 28 Mar, 2025 Editor assigned by journal 24 Mar, 2025 Submission checks completed at journal 24 Mar, 2025 First submitted to journal 21 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6275989","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":440902978,"identity":"63b76bd7-813e-4388-b60f-8d1a3bcafe10","order_by":0,"name":"Michael Brockman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYDACCRBxAIiZmUGkhAwJWtjZEkBcHhK08PMYgJiEtejO7n344MeZbfLmzDyfX92oseBhYD98dAM+LWZ3jhsb9ty4bbizmXebdc4xoMN40tJu4NVyI41NmuHDbcYNh3m3GeewAbVI8JgRpcV+w2GeZ8Y5/4jWcuN2IlAL8+PcNmK03DnGbNhz5nbyhsNsZsy5fRI8bAT9cruN8cGPY7dtN5w//Phzzrc6OX72w8fwakEGbOA4YiNWOQgwfyBF9SgYBaNgFIwcAAD17Er8QD2uywAAAABJRU5ErkJggg==","orcid":"","institution":"Texas Tech University Health Sciences Center El Paso","correspondingAuthor":true,"prefix":"","firstName":"Michael","middleName":"","lastName":"Brockman","suffix":""},{"id":440902979,"identity":"f675d6ae-5495-4e33-875e-8b9756604c15","order_by":1,"name":"John Aparece","email":"","orcid":"","institution":"Texas Tech University Health Sciences Center El Paso","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"Aparece","suffix":""}],"badges":[],"createdAt":"2025-03-21 09:08:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6275989/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6275989/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81012627,"identity":"dcf31fe7-229c-4c49-8db1-7c6d485e6c86","added_by":"auto","created_at":"2025-04-21 08:31:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":80926,"visible":true,"origin":"","legend":"\u003cp\u003eExample echocardiogram report.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6275989/v1/eb26b6260b3b3cf925e48724.png"},{"id":81015040,"identity":"5577e8af-074c-41cb-a462-f9ee8150f31c","added_by":"auto","created_at":"2025-04-21 08:47:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":461538,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6275989/v1/396d0bfb-a4da-49e5-b40d-b46e596fc566.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Novel Use of Regex for Echocardiogram Data Transformation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAn echocardiogram, or cardiac ultrasound, is an imaging modality of the heart that yields critical information[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Ejection fraction, cardiac output, and structural integrity are critical aspects that can be measured in these studies[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. More importantly, the echocardiogram provides vital information on an individual's health status[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Thus, significant research has been dedicated to studying this imaging modality[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The results of these data analytics have often been the foundation of many critical aspects of guideline medical management[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResults are only as good as the data's integrity, which often degrades with large sample sizes due to entry errors. Many data points can be drawn from a single echocardiogram, and beyond this, hundreds or thousands of echocardiograms may be required in scientific studies[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Adding to the vast data point burden is that many electronic medical record echocardiogram reporting systems often produce the reports in a non-analytical friendly text format. When this occurs, manual data curation and data transformation must be done to transform text data into an easy-to-use file format, such as a spreadsheet. It takes significant time and dedication to perform this task appropriately. There is a current need for an automated software solution that addresses these concerns.\u003c/p\u003e \u003cp\u003eThe Python programming language has allowed for significant strides in customized software for specific tasks and automation[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The scripting language is also highly modular, which allows for simple functionality expansion with pre-written code. One module, for example, is a regular expression iteration tool suite. Regular expressions are text patterns that can be algorithmically searched and manipulated[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The tool suite allows text pattern recognition and subsequent value storage in more meaningful, analytical ways than a simple text file. Ready-made, Easy Echocardiogram Data for Research (REEDR) is a software solution for transforming echocardiogram text file data to spreadsheet format, using regular expression iteration as its backbone.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eAim\u003c/p\u003e \u003cp\u003eThe aim of Ready-made, Easy Echocardiogram Data for Research (REEDR) is to provide an automated software solution for mass manual echocardiogram data entry into a spreadsheet format. Given the significant use of echocardiogram data for research and the pervasive use of spreadsheet file types for analysis, such software is needed to accelerate medical research.\u003c/p\u003e \u003cp\u003eDesign\u003c/p\u003e \u003cp\u003eThe Python programming language was selected as the foundation language due to its broad adoption among software engineers and its ease of use. This parallels REEDR's project goals of being portable, easy to use, and modifiable to suit the user's intended purpose. The project is open-source and free under the GNU General Public License Version 3 to incentivize continued development and usage[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEchocardiogram Reports\u003c/p\u003e \u003cp\u003eEchocardiograms are reported in a pseudo-standardized reporting method such that the impressions can be quickly understood between physicians. Findings such as ejection fraction reported in percent and left ventricular wall thickness reported in centimeters are found in nearly all echocardiogram reports. The reports are commonly stored in rich text format in a healthcare system's electronic medical record. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e is an example of an echocardiogram report\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSoftware Structure and Method\u003c/p\u003e \u003cp\u003eGiven the repeated reporting terminology and units of measure, the Pythonic regular expression (regex) software package was leveraged to find and store the reported data quickly. This was accomplished by writing a regex-based line parser to obtain each reported value in the report. The parse stored each value in a Pandas data frame. The Python software library Pandas provides a data frame structure that can dynamically store, manipulate, and export data in various file types[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOnce the parser completes a single report, it closes the file and applies the same functionality to the following file in numerical order. After iterating over every file in the target folder, the parser closes, and the Pandas data frame, containing all the report data, is exported as a spreadsheet file type.\u003c/p\u003e \u003cp\u003eError Reporting\u003c/p\u003e \u003cp\u003eOne of REEDR's critical functions is an error reporting system. Suppose a file is corrupted, unable to be read, or not in the appropriate format. In that case, REEDR prints the file location that it could not complete its task and does not addend the Pandas data frame with potentially partially collected data.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eREEDR's performance was evaluated by placing the software under varying amounts of, for example, echocardiogram-rich text format reports. To accurately assess the performance under each example load, REEDR was tested 100,000 times per example load and was timed from the start of code execution to completion. The timing was assessed using the Pythonic function, 'timeit,' which expressed that the development purpose was to accurately assess function execution time.\u003c/p\u003e \u003cp\u003eREEDR required 1.29 ms\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1 \u0026micro;s to iterate over a single echocardiogram report and export the results to a spreadsheet filetype. When performing the same function on 100 echocardiogram reports, it required 1.3 ms\u0026thinsp;\u0026plusmn;\u0026thinsp;52.2 \u0026micro;s. For 500 echocardiogram reports, it required 1.29 ms\u0026thinsp;\u0026plusmn;\u0026thinsp;37.8 \u0026micro;s. For 1,000 echocardiogram reports, REEDR completed 1.29 ms\u0026thinsp;\u0026plusmn;\u0026thinsp;44.5 \u0026micro;s. Finally, REEDR was able to complete the iteration of 100,000,000 echocardiograms in. The computer platform that the performance tests were completed on was an Apple MacBook Pro with an Apple M1 processor running MacOS 14.5 (23F79). Results can be found in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eREEDR \u0026lsquo;timeit\u0026rsquo; performance evaluation under varying loads. The script was executed 100,000 times and revealed mean completion time (ms) and standard deviation (\u0026micro;s).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eReady-made, Easy Echocardiogram Data for Research Iteration Performance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of echocardiogram reports analyzed by REEDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean completion time after 100,000 code executions (ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard deviation of time after 100,000 code executions (\u0026micro;s)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe data reported from REEDR in the exported spreadsheet accurately reflected all the reported findings in echocardiogram reports, such as medical record number, age, ejection fraction in percent, pulmonic systolic velocity in centimeters per second, left ventricular wall thickness in centimeters, presence of pericardial effusion, etc.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eEchocardiogram data provides significant information on an individual's cardiac function and, more broadly, an individual's health status[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Given the value of echocardiogram data, the data has been used for landmark clinical trials that change the course of medical therapy[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These important data, however, can be subject to limitations when manual data transformation from echocardiogram reports to more traditional analytics formats such as spreadsheet filetype is required. As healthcare analytics moves into the realm of Big Data, data collection speed and accuracy are critical[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRegardless of the data type, manual collection, and curation are subject to human error, which can result in inaccurate conclusions. Companies have responded by creating software suites that offer automated solutions, often at a price[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Moreover, tools to address these problems have been lacking in the healthcare data space.\u003c/p\u003e \u003cp\u003eReady-made, Easy Echocardiogram Data for Research (REEDR) was initiated in response to the significant human labor required to transform echocardiogram reports into a more traditional analytical file type. There lacked an existence of a free-to-use, fast, and automated solution that can replace manual data transformation. This project demonstrates the novel use of regular expression parsing of echocardiogram reports to transform the report data into a spreadsheet file type. REEDR has already been successfully used in three published project evaluating echocardiogram data in certain disease states[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Over these projects, REEDR allowed for expedited analytics of over 500 echocardiograms reports. Not only was this project successful in demonstrating feasibility and scalability, but the project also shows the potential for replicating these methods towards other healthcare data stored as non-traditional analytical file types, such as pulmonary function test data. Continued creation of open-source, free-to-use healthcare analytics tools holds the potential to dramatically improve the quality and quantity of healthcare research, with the end goal of improving individual lives.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eReady-made, Easy Echocardiogram Data for Research (REEDR) is a software solution to manual echocardiogram data entry. With its novel use of regular expression iteration, REEDR can rapidly transform echocardiogram text reports to spreadsheet format for further analysis. Source code and documentation can be obtained from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/mbrockman1/REEDR\u003c/span\u003e\u003cspan address=\"https://github.com/mbrockman1/REEDR\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo applicable data was used for the presented research. The Ready-made, Easy Echocardiogram Data for Research (REEDR) software script has been made freely available for download and continued development at https://github.com/mbrockman1/REEDR. REEDR performance evaluation has been provided within the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo data was utilized concerning this presented research. The Ready-made, Easy Echocardiogram Data for Research (REEDR) software script has been made freely available for download and continued development at https://github.com/mbrockman1/REEDR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.B. and J.A. wrote the main manuscript text and prepared Table 1 and Figure 1 and performed the software performance evaluation.\u003c/p\u003e\n\u003cp\u003eM.B. and L.B. are the main developers of Ready-made, Easy Echocardiogram Data for Research (REEDR). M.B. and L.B. are maintainers of the code currently.\u003c/p\u003e\n\u003cp\u003eAll authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSafe IE. \u003cem\u003eWhat is Echocardiography?\u003c/em\u003e 1986.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTam JW, et al. What is the real clinical utility of echocardiography? A prospective observational study. J Am Soc Echocardiogr. 1999;12(9):689\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshley EA, Niebauer J. \u003cem\u003eUnderstanding the echocardiogram\u003c/em\u003e, in \u003cem\u003eCardiology explained\u003c/em\u003e. Remedica; 2004.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGardin JM, et al. Echocardiographic design of a multicenter investigation of free-living elderly subjects: the Cardiovascular Health Study. J Am Soc Echocardiogr. 1992;5(1):63\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFralick M, et al. Health Services: Value of routine echocardiography in the management of stroke. CMAJ. 2019;191(31):E853\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatterson OV, et al. Unlocking echocardiogram measurements for heart disease research through natural language processing. BMC Cardiovasc Disord. 2017;17:1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePorter TR, et al. Guidelines for the use of echocardiography as a monitor for therapeutic intervention in adults: a report from the American Society of Echocardiography. J Am Soc Echocardiogr. 2015;28(1):40\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatson LE, et al. Baseline echocardiographic values for adult male rats. J Am Soc Echocardiogr. 2004;17(2):161\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Rossum G. \u003cem\u003ePython Programming Language\u003c/em\u003e. in \u003cem\u003eUSENIX annual technical conference\u003c/em\u003e. 2007. Santa Clara, CA.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChapman C, Stolee KT. \u003cem\u003eExploring regular expression usage and context in Python\u003c/em\u003e. in \u003cem\u003eProceedings of the 25th International Symposium on Software Testing and Analysis\u003c/em\u003e. 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChapman C, Wang P, Stolee KT. \u003cem\u003eExploring regular expression comprehension\u003c/em\u003e. in \u003cem\u003e2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)\u003c/em\u003e. 2017. IEEE.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLicense GGP. \u003cem\u003eGnu general public license.\u003c/em\u003e Retrieved December, 1989. 25: p. 2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKinney W. pandas: a foundational Python library for data analysis and statistics. Python high Perform Sci Comput. 2011;14(9):1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan der Meer P, Gaggin HK, Dec GW. ACC/AHA versus ESC guidelines on heart failure: JACC guideline comparison. J Am Coll Cardiol. 2019;73(21):2756\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMentz RJ, et al. PROVIDE-HF primary results: patient-reported outcomes investigation following initiation of drug therapy with entresto (sacubitril/valsartan) in heart failure. Am Heart J. 2020;230:35\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFraser AG, et al. A concise history of echocardiography: timeline, pioneers, and landmark publications. Eur Heart Journal-Cardiovascular Imaging. 2022;23(9):1130\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health Inform Sci Syst. 2014;2:1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelle A, et al. Big data analytics in healthcare. Biomed Res Int. 2015;2015(1):370194.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAjit-Kumar R. Impact of Big Data Analytics on Healthcare and Society\u0026rsquo;. J Biom Biostat. 2016;7(300):2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMilenkovic MJ, Vukmirovic A, Milenkovic D. \u003cem\u003eBig data analytics in the health sector: challenges and potentials.\u003c/em\u003e Management: Journal of Sustainable Business and Management Solutions in Emerging Economies, 2019. 24(1): pp. 23\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurguia AR et al. Evaluation of Four Validated Risk Scores to Predict Outcomes in Hispanic Patients With Acute Pulmonary Embolism. Angiology. 0(0): p. 00033197241230716.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFadah K, et al. Insights Into Differences in Pulmonary Hemodynamics in Hispanic Patients With Pulmonary Arterial Hypertension. Cardiol Res. 2024;15(2):117.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurguia AR et al. \u003cem\u003eEvaluation of Four Validated Risk Scores to Predict Outcomes in Hispanic Patients With Acute Pulmonary Embolism.\u003c/em\u003e Angiology, 2024: p. 00033197241230716.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-ultrasound","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caru","sideBox":"Learn more about [Cardiovascular Ultrasound](http://cardiovascularultrasound.biomedcentral.com/)","snPcode":"12947","submissionUrl":"https://submission.nature.com/new-submission/12947/3","title":"Cardiovascular Ultrasound","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Echocardiogram, Python, Regular expressions","lastPublishedDoi":"10.21203/rs.3.rs-6275989/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6275989/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEchocardiogram data provides a rich dataset about a patient's cardiac function and, more broadly, an individual's health status. Given this, these data have been subject to significant research and analysis; however, data analysis requires manual data transformation from the medical report to analytically favorable formats such as spreadsheet file types. There exists a need for a tool for automated data transformation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eReady-made, Easy Echocardiogram Data for Research (REEDR) is a software script that utilizes regular expressions for rapid data transformation from echocardiogram reports to spreadsheet format. REEDR provides a software solution emphasizing ease of use and reliability. Its goal is to instantaneously iterate over unlimited echocardiogram reports and transform the values into analytically friendly spreadsheet format.\u003c/p\u003e\u003ch2\u003eDiscussion\u003c/h2\u003e \u003cp\u003ePrior to REEDR, manual data entry and curation required significant human labor and were susceptible to data entry errors. The novel use of regular expressions through a Pythonic program script provides the flexibility to iterate over many differing types of echocardiogram medical reports to instantaneously generate an analysis-friendly format, a spreadsheet file type. Source code and documentation can be obtained from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/mbrockman1/REEDR\u003c/span\u003e\u003cspan address=\"https://github.com/mbrockman1/REEDR\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e","manuscriptTitle":"Novel Use of Regex for Echocardiogram Data Transformation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-21 08:31:05","doi":"10.21203/rs.3.rs-6275989/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-29T12:35:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-28T17:31:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-23T17:22:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136426737044534721558785413970036009660","date":"2025-04-22T20:22:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59465278408598152707008646430491769243","date":"2025-04-10T03:24:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-28T17:44:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-24T05:35:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-24T05:31:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cardiovascular Ultrasound","date":"2025-03-21T09:01:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-ultrasound","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caru","sideBox":"Learn more about [Cardiovascular Ultrasound](http://cardiovascularultrasound.biomedcentral.com/)","snPcode":"12947","submissionUrl":"https://submission.nature.com/new-submission/12947/3","title":"Cardiovascular Ultrasound","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6df57de3-3539-4e67-a92e-79d631e20837","owner":[],"postedDate":"April 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-05-27T16:38:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-21 08:31:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6275989","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6275989","identity":"rs-6275989","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}