A Correlation Study on the Prediction of Coronary Artery Lesion Degree by Pulse Wave Harmonics Based on SYNTAX Score

A Correlation Study on the Prediction of Coronary Artery Lesion Degree by Pulse Wave Harmonics Based on SYNTAX Score | 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 Article A Correlation Study on the Prediction of Coronary Artery Lesion Degree by Pulse Wave Harmonics Based on SYNTAX Score Haitian Li, buxing Chen, GinChung Wang, Yunxiao Wang, Le Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6201108/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective This study aimed to investigate the correlation between the differences in pulse wave harmonic indexes between the left and right hands and the SYNTAX score, and to explore the potential of pulse wave harmonics in predicting the degree of coronary artery lesions. Methods The arterial pressure wave signals of the left and right hands of patients scheduled for coronary angiography were collected by photoplethysmography. According to the "visceral resonance theory", taking integer multiples of the heartbeat from 0 to 11 as the resonance frequencies, the collected arterial pressure waves were decomposed into the 0th to 11th harmonics by the Fourier transform method. The harmonic characteristics were quantified by amplitude (Cn), phase (Pn), and energy (Dn) (n is the harmonic serial number), and the coefficient of variation of the indexes was calculated and suffixed as CV. The absolute value of the difference in the corresponding harmonic indexes between the left and right hands of the patients was calculated. The SYNTAX score of the included cases was calculated based on the imaging data of coronary angiography. The included cases were divided into a male group and a female group according to gender. For each group, a logistic regression equation was established with the difference value of the harmonic index as the independent variable and SYNTAX score ≥ 22 as the dependent variable. The equation corresponding to the minimum value of the Akaike information criterion (AIC) was taken as the optimal prediction model. Finally, the discriminant ability of the prediction model was evaluated by the ROC curve method and the Bootstrap internal validation method. Results A total of 348 patients were included, including 249 males and 99 females. The discriminant model for predicting SYNTAX score ≥ 22 based on C10, D6, D9, D10, P8, P10, P1CV, and C9CV in the male group was statistically significant (P < 0.05), with the minimum AIC value of 105.47, the area under the ROC curve (AUC) of 0.89, and the average AUC of 0.85 in the Bootstrap internal validation. The discriminant model based on D2, D3, D5, D6, D9, C2CV, C4CV, C5CV, C6CV, and C9CV in the female group was statistically significant (P < 0.05), with the minimum AIC value of 59.34. The AUC of the ROC curve of this prediction model was 0.92, and the average AUC in the Bootstrap internal validation was 0.84. Conclusion The difference characteristics of pulse wave harmonics between the left and right hands can effectively reflect the degree of coronary artery lesions. Through the analysis of pulse wave harmonics, a diagnostic model with good discriminant ability for predicting the degree of coronary artery lesions can be constructed, which may offer a valuable non-invasive tool for clinical assessment of CAD. Health sciences/Cardiology Physical sciences/Physics arterial pressure wave harmonics SYNTAX score degree of coronary artery lesions Figures Figure 1 Figure 2 Figure 3 Introduction Coronary Heart Disease (CHD), one of the leading causes of death in humans, poses a serious threat to the health of the global population [ 1 ] . Accurate assessment of the degree of coronary artery lesions plays a crucial role in guiding the formulation of treatment strategies for patients and predicting the prognosis [ 2 ] . Currently, the clinical judgment of the degree of coronary artery lesions mainly relies on methods such as coronary enhanced computed tomography (CT) and coronary angiography. However, these methods not only require specialized and technically complex equipment, but also take a relatively long time for the examination process and carry certain risks, such as potential hazards like radiation exposure and contrast agent allergy. To some extent, these factors limit their widespread application. Therefore, there is an urgent clinical need for a convenient, rapid, and safe new method to accurately evaluate the degree of coronary artery lesions in patients. The human body, as a complex and sophisticated system, contains rich biological information, such as electrocardiogram information, electroencephalogram information, and arterial pressure wave information. These pieces of information, like hidden codes, can reflect the physiological and pathological states of the human system or organs [ 3 , 4 ] . Among them, arterial pressure wave information can be obtained by photoplethysmography. Compared with other detection methods, its detection equipment is small in size and easy to operate, and it is more convenient to collect in clinical practice, which provides great convenience for clinical application. Professor Wang Weigong proposed that the blood conduction between the heart and the arterial system is achieved by resonance. Based on this, the resonance characteristics of the pulse wave are measured by photoplethysmography, and the Fourier transform is used to convert them into harmonic indexes for quantitative description. This is the arterial pressure wave harmonic analysis method. This method has been proven to effectively reflect the pathophysiological states of major organ systems in the human body, including the heart [ 5 , 6 ] . Previous studies have also found significant differences in the relevant indexes between the left and right hands of patients. In view of this, this study intends to deeply analyze the correlation between the arterial pressure waves of the left and right hands of patients and the coronary SYNTAX score respectively, aiming to explore the feasibility of the arterial pressure wave harmonic analysis method in evaluating the degree of coronary artery lesions. It is expected to open up a new and efficient path for the clinical evaluation of the degree of coronary artery lesions, making up for the deficiencies of existing methods, and has high clinical significance and application value. Data and Methods 1.1 Research Subjects The subjects of this study were all patients hospitalized in the Department of Cardiology, the Third Affiliated Hospital of Beijing University of Chinese Medicine from February 2023 to May 2023. 1.2 Research Methods 1.2.1 Inclusion Criteria Included patients must meet the following conditions simultaneously: (1) Patients who have been diagnosed or are suspected to be diagnosed with coronary heart disease. (2) Patients scheduled to undergo coronary angiography. (3) Patients aged 18 years or older. (4) Patients who voluntarily participate in the study. 1.2.2 Exclusion Criteria Patients meeting any of the following conditions should be excluded: (1) Patients with concomitant severe diseases of the respiratory, urinary, digestive, hematopoietic, endocrine and metabolic systems, as well as other mental and neurological disorders. (2) Pregnant or lactating patients. (3) Patients in whom pulse diagnosis instruments are unable to measure. (4) Patients with peripheral vascular diseases or arteriosclerosis of the upper extremities. 1.2.3 Arterial Pressure Wave Measurement Before all patients undergo coronary angiography, arterial pressure wave measurement should be carried out to ensure that the obtained data can accurately reflect the patients' physiological state at that time. During the measurement, a high-precision intelligent electronic pulse acquisition instrument (Model HMYH1300, Jinmu Health Technology) is selected. When measuring, the patients are required to lie in a supine position, ensuring that their bodies are in a relaxed state, with their hands naturally placed on both sides of their bodies. A quiet environment should be strictly maintained to reduce the impact of external interference on the measurement results. The operator measures the index fingers of the patients' left and right hands simultaneously to ensure the consistency and synchronism of data collection, so as to obtain comprehensive and accurate arterial pressure wave data.All methods comply with the Declaration of Helsinki.The experimental protocol has been approved by Ethics Committee of the Third Affiliated Hospital of Beijing University of Traditional Chinese Medicine(BZYSY-2022KYKTPJ-09).All subjects provided informed consent. 1.2.4 Coronary Angiography and SYNTAX Score Calculation Coronary angiography was performed using a Siemens large C-arm angiography machine (Model Artis zee floor), and iodixanol injection was selected as the contrast agent. Based on the imaging results obtained from coronary angiography, two experienced cardiologists strictly followed the SYNTAX score rules [ 7 ] to score the degree of coronary artery lesions in the patients included in the study. If the difference between the scores given by the two doctors was less than 5 points, the SYNTAX score of the patient was taken as the average of the two scores. If the difference between the scores was greater than 5 points, a cardiovascular specialist with a higher professional title (the third doctor) would be involved to re-score, ensuring the accuracy and reliability of the SYNTAX score. 1.2.5 Calculation Method of the Difference Index of Arterial Pressure Wave Harmonics between the Left and Right Hands The collected arterial pressure wave data were subjected to Fourier transformation. Taking integer multiples of the heartbeat as the resonance frequencies, the arterial pressure waves were finely decomposed into the 0th to 11th harmonics. For each harmonic, its amplitude (Cn), phase (Pn), and energy (Dn) were calculated respectively. These parameters can quantitatively describe the characteristics of the harmonics. Meanwhile, to further analyze the degree of data dispersion, the coefficient of variation of each index was calculated, and "CV" was uniformly added after the corresponding index name to indicate it. Subsequently, by taking the absolute value of the difference in the corresponding harmonic indexes between the left and right hands of the same patient, the difference values of the arterial pressure wave harmonic indexes between the left and right hands were accurately obtained. The calculation formulas for the harmonic indexes seeing in Fig. 1. 1.2.6 Statistical Methods The Matlab 2024b software was applied to conduct a comprehensive and systematic data processing. Firstly, all the cases included in the study were accurately divided into a male group and a female group based on gender differences, to ensure that the subsequent analysis could fully consider the potential differences among different gender groups.Based on the scientific relationship of a 1:10 ratio between the logistic regression parameters and the sample size, with the difference values of the arterial pressure wave harmonic indexes between the left and right hands of the patients as the independent variables and a SYNTAX score ≥ 22 as the dependent variable, multivariate linear regression analysis models were established for the two groups respectively. On this basis, a receiver operating characteristic (ROC) curve analysis was further carried out to deeply explore the performance of the models.To construct the most effective prediction model, three classic modeling methods, namely the forward selection method, the backward elimination method, and the stepwise regression method, were adopted during the research process. By comparing the Akaike information criterion (AIC) values of the models constructed by different methods, the model with the smallest AIC value was selected as the optimal model. As a widely used criterion for model selection, the AIC can find the best balance between the goodness of fit of the model and its complexity, ensuring that the selected model can accurately fit the data and has good generalization ability.The constructed prediction model was comprehensively evaluated from two key dimensions: discrimination and calibration. In terms of discrimination evaluation, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used as the evaluation index. The AUC value can intuitively reflect the ability of the model to distinguish between different categories of samples. In this study, an AUC statistic > 0.7 was used as the standard to determine that the model has good discrimination ability, so as to measure the effectiveness of the model in distinguishing between patients with a SYNTAX score ≥ 22 and those with a SYNTAX score < 22.For the calibration evaluation, the Bootstrap method was used to conduct 2000 resamplings, and a calibration curve was drawn through internal validation to accurately evaluate the consistency between the predicted results and the actual results. Meanwhile, the Hosmer-Lemeshow goodness-of-fit test was used to quantitatively evaluate the calibration. This test can determine the degree of fit between the predicted probabilities of the model and the actual observed results from a statistical perspective, ensuring that the model has high accuracy and reliability in the prediction process. Results 2.1 General Information A total of 348 patients were included in this study, among whom 249 were male and 99 were female. In the male patient group, 217 patients had a SYNTAX score of less than 22, and 32 patients had a SYNTAX score of 22 or higher. Among the female patients, 86 patients had a SYNTAX score of less than 22, and 13 patients had a SYNTAX score of 22 or higher. 2.2 Results of Multivariate Regression Analysis Model and ROC Curve Analysis for the Difference Values of Arterial Pressure Wave Harmonic Indices between the Left and Right Hands in the Male Group The analysis results showed that the overall model was statistically significant (P = 0.00), and among various attempts at model construction, the Akaike information criterion (AIC) value of this model was the smallest, being 105.47. This indicates that this model has achieved the best balance between goodness of fit and complexity, and has good reliability and generalization ability. Further analysis revealed that the correlations between the difference values C10, D6, P8, P10, P1CV, C9CV and a SYNTAX score ≥ 22 were statistically significant (the detailed data are shown in Table 1 ).In terms of model performance evaluation, through the receiver operating characteristic (ROC) curve analysis, the area under the ROC curve (AUC) of this model was obtained as 0.89 (see Fig. 2 for details). This result intuitively shows that the model has a strong ability to distinguish between patients with different SYNTAX score levels. The Hosmer-Lemeshow goodness-of-fit test was used to evaluate the calibration of the model, and the calibration value obtained was 4.32 (P = 0.82), indicating a high degree of fit between the predicted probabilities of the model and the actual observed results. In addition, through the Bootstrap internal validation method, after multiple repeated sampling verifications, the results showed that the average area under the ROC curve was 0.85, with a confidence interval of [0.80, 0.87], further confirming the stability and reliability of the model results. Table 1 Results of Logistic Regression Analysis between the Harmonic Difference Indices of Arterial Pressure Waves of the Left and Right Hands and SYNTAX Score ≥ 22 in Male Patients Coefficient SE tStat P Value c10 128.92 58.08 2.22 0.03* d6 4.94 1.96 2.52 0.01* d9 -15.90 5.41 -2.94 0.00* d10 5.69 3.07 1.85 0.06 p8 4.10 1.43 2.86 0.00* p10 -2.73 1.04 -2.62 0.01* p1cv 61.86 24.43 2.53 0.01* c9cv 3.91 1.36 2.88 0.00* Note : Coefficient refers to the fitting coefficient; SE represents the standard deviation; tStat is the statistical coefficient; * indicates p < 0.05. 2.3 Results of Multivariate Regression Analysis Model and ROC Curve Analysis for the Difference Values of Arterial Pressure Wave Harmonic Indices between the Left and Right Hands in the Female Group Logistic regression analysis was rigorously conducted with the difference values of the arterial pressure wave harmonic indexes between the left and right hands of female patients, namely D2, D3, D5, D6, D9, C2CV, C4CV, C5CV, C6CV, and C9CV, as the independent variables, and a SYNTAX score ≥ 22 as the dependent variable. The analysis showed that the overall model was of extremely significant statistical significance (P = 0.00). Among the comparisons of numerous model construction schemes, the Akaike information criterion (AIC) value of this model was the smallest, being 59.34. This result clearly indicates that when fitting the data, this model can accurately conform to the characteristics of the samples and has excellent generalization performance, achieving an ideal balance between the goodness of fit and the complexity of the model.Upon in-depth analysis of the model, it was further determined that there were statistically significant associations between the indexes of the difference values such as D2, D3, D6, D9, C5CV, C6CV, and C9CV and a SYNTAX score ≥ 22 (see Table 2 ).During the comprehensive evaluation of the performance of this model, by using the receiver operating characteristic (ROC) curve analysis method, the area under the ROC curve (AUC) of this model was as high as 0.92 (see Fig. 2 ). This value intuitively and powerfully demonstrates that this model has excellent discrimination ability for female patients with different SYNTAX score levels and can accurately identify the group of patients with a SYNTAX score ≥ 22.In order to further evaluate the consistency between the predicted probabilities of the model and the actual observed results, the Hosmer-Lemeshow goodness-of-fit test method was used to quantitatively evaluate the calibration of the model, and finally, the calibration value obtained was 3.03 (P = 0.93). This result shows that there is a very high degree of fit between the predicted values of the model and the actual observed values, and the predicted results of the model are highly reliable.In addition, by means of the Bootstrap internal validation method, after 2000 repeated sampling verifications, the final average area under the ROC curve was 0.84(see Fig. 3 ), with a confidence interval of [0.72, 0.91]. This result further validates the stability and reliability of the model results. Even under different sampling situations, the model can still maintain relatively stable and accurate prediction performance. Table 2 Results of Logistic Regression Analysis between the Difference Values of Arterial Pressure Wave Harmonic Indices of the Left and Right Hands and SYNTAX Score ≥ 22 in Female Patients Coefficient SE tStat P Value D2 -42.78 19.77 -2.16 0.03* D3 -32.48 16.66 -1.95 0.05 D5 31.05 14.15 2.19 0.03* D6 -8.96 3.76 -2.38 0.02* D9 12.93 4.93 2.62 0.01* C2CV -55.56 29.18 -1.90 0.06 C4CV -23.18 14.41 -1.61 0.11 C5CV 16.95 8.06 2.10 0.04* C6CV 26.27 10.36 2.54 0.01* C9CV -9.95 4.64 -2.14 0.03* Note Coefficient refers to the fitting coefficient; SE represents the standard deviation; tStat is the statistical coefficient; * indicates p < 0.05. Discussion The SYNTAX score, proposed by the American College of Cardiology/American Heart Association (ACC/AHA), is a comprehensive and accurate tool for assessing the degree of coronary artery stenosis and guiding the selection of coronary revascularization methods, and it has been widely applied in clinical practice [ 8 ] . This scoring system constructs a risk stratification scoring system based on the anatomical characteristics of coronary artery lesions. Compared with other scoring criteria, it can more comprehensively demonstrate the anatomical features of coronary artery lesions, such as location, severity, bifurcation, and calcification. In view of this, the SYNTAX score was selected as an objective indicator for quantitatively evaluating the degree of coronary artery lesions in patients in this study [ 9 ] . In addition, the SYNTAX score of 22 was set as the cut-off point of the prediction model because 22 points is widely recognized by cardiovascular experts as the key threshold for judging the severity of coronary artery lesions [ 10 ] . Studies have shown that some cardiovascular diseases can cause asymmetry in the photoplethysmogram (PPG) waveform, especially between the finger signals of the left and right hands. This asymmetry is associated with various cardiac abnormalities, including coronary atherosclerotic heart disease (CAD). Although the mechanism underlying this asymmetry has not been fully elucidated, factors such as impaired circulatory function, vascular dysfunction, or autonomic nervous imbalance are speculated to be possible causes [ 11 ] . Fourier transform, a commonly used method in physics for analyzing complex waves, is based on the core theory that a complex wave is composed of multiple simple waves. These simple waves can be separated from the complex wave through specific frequencies, and then the characteristics of the simple waves can be studied to analyze the complex wave [ 12 ] . The resonance theory states that the conduction of blood in various organs of the human body occurs at integer multiples of the heart rate as specific frequencies. The arterial pressure wave can be decomposed into multiple simple waves by means of Fourier transform. These simple waves contain the state information of various systems in the human body and can be used to study organ diseases or the functional state of the human body [ 13 ] . Since the harmonics from 0 to 11 already contain more than 98% of the energy of the arterial pressure wave, these 12 harmonics can be used to reflect the characteristics of human blood circulation. From the perspective of cardiac pathophysiology, the degree of coronary artery lesion stenosis is closely related to the degree of myocardial ischemia. Changes in the degree of myocardial ischemia will trigger corresponding abnormal changes in cardiac motion, which in turn lead to changes in the arterial pressure wave. In this study, a logistic regression model was constructed to determine whether the SYNTAX score was ≥ 22 points based on the arterial pressure wave harmonic indexes, and the results were statistically significant. This finding strongly indicates that the degree of coronary artery lesions can be reflected by analyzing the characteristics of arterial pressure wave harmonics. A total of 348 patients were included in this study. Based on the relationship between the logistic regression parameters and the sample size at a ratio of 1:10, the number of parameters used in the logistic regression equations established in this study to determine whether the SYNTAX score was greater than or equal to 22 points was less than 10 times the sample size. Therefore, the sample size was statistically sufficient. The study was divided into a male group and a female group mainly because previous studies have found significant differences between males and females in multiple harmonic indexes. As a non-invasive detection method, photoplethysmography can reflect changes in pulse pressure. The detection instrument is similar to a pulse oximeter and has the characteristics of being portable and requiring a short detection time (only 1 minute) [ 14 ] . The data collected through the Internet can be directly uploaded to the computing system, and the results can be fed back to the doctor's terminal, such as a mobile phone, enabling detection at any time, which is convenient, fast, and safe. In addition, this detection method is easy to operate, has high repeatability, and the equipment is small in size, making it suitable for various environments, and it can even be operated by patients themselves. In contrast, other methods for detecting pulse waves have problems such as difficult operation, difficulty in promotion, and the accuracy being easily interfered with by various factors. Therefore, this study has exploratorily revealed the potential application value of the analysis method of the difference values of arterial pressure wave harmonic indexes in evaluating the degree of coronary artery lesions. In the future, with the further increase in the research sample size and by comparing with the results of the radial artery intravascular pressure wave detection method, it is expected to construct a data diagnostic model for evaluating the degree of coronary artery lesions based on the arterial pressure wave indexes using the harmonic analysis method, opening up a new path for the diagnosis and treatment of coronary heart disease and contributing to the improvement of the diagnosis and treatment level of cardiovascular diseases. Limitations of the study Sample Size and Generalizability: Although 348 patients were included, the sample size may still be relatively small, potentially limiting the generalizability of the results to the entire population. Additionally, the patients were all from a single hospital, which may introduce selection bias, and the findings may not be applicable to other regions or patient populations with different characteristics.Lack of Longitudinal Data: This study was cross - sectional, lacking longitudinal data to observe the dynamic changes of arterial pressure wave harmonics and SYNTAX scores over time. Thus, it is unable to establish a causal relationship between them and cannot predict the long - term progression of coronary artery lesions.Potential Confounding Factors: Despite excluding patients with certain comorbidities, there may still be other unaccounted confounding factors, such as lifestyle factors (diet, exercise), genetic factors, and environmental factors, which could affect the arterial pressure wave harmonics and SYNTAX scores and influence the accuracy of the model. Declarations Resource availability Data: The patient data was collected from the Department of Cardiology, the Third Affiliated Hospital of Beijing University of Chinese Medicine between February 2023 and May 2023. All data are securely stored in the hospital's electronic system. Under compliance with ethical approval and data protection regulations, the corresponding author can provide the anonymized data upon reasonable request.Software: Matlab 2024b software was used for data processing, including Fourier transform, regression analysis, and ROC curve construction. The custom-written code for these analyses is available from the corresponding author upon request.Equipment: The arterial pressure wave measurements were carried out using a high - precision intelligent electronic pulse acquisition instrument (Jinmu Health Technology Model HMYH1300). This instrument was provided by Jinmu Health Technology Co., Ltd. The Siemens large C - arm angiography machine for coronary angiography was from Siemens Healthcare. Both devices were located at the Third Affiliated Hospital of Beijing University of Chinese Medicine. Declaration of interests There were no conflicts of interest involved in this study. Author Contribution L.H.T and Y.Y conceptualized, designed, managed the project, collected and analyzed data, and wrote and revised the manuscript.W.G.C assisted in patient recruitment and data collection, provided medical insights during data analysis, and drafted the clinical implications section.C.B.X offered theoretical guidance, reviewed and refined the study design, and provided feedback on data analysis methods.Y.Y and L.H.T processed data using Matlab, developed relevant codes, interpreted results computationally, and created data - visualizing figures and tables.Z. L aided in literature review, assisted in data collection and verification, and contributed to the discussion by comparing with previous research.H.Y.Y coordinated access to patient data, assisted in statistical analysis, and participated in manuscript proofreading.W.Y.X helped in patient recruitment, assisted in data collection, and contributed to the description of study limitations. Acknowledgments We would like to express our sincere gratitude to all the patients who participated in this study. Their contributions are essential for the success of this research. We also thank the medical staff in the Department of Cardiology, the Third Affiliated Hospital of Beijing University of Chinese Medicine, for their assistance in patient recruitment and data collection. Special thanks go to Professor Yang yang and Chen buxing for providing valuable suggestions on the research design and data analysis. This work was supported by the National Natural Science Foundation of China (Grant No. 08177020516). Data Availability The datasets generated and analyzed during the current study are available from the corresponding authors (Yang Yang) upon reasonable request. Raw data from individual trials are not publicly accessible due to ethical restrictions and participant confidentiality but can be provided with permission from the original study authors. 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Value of the SYNTAX Score for the Long-Term Prognosis of Patients with Single or Double Coronary Artery Lesions Undergoing Percutaneous Coronary Intervention. Chinese Journal of Interventional Cardiology, 31, 446–451(2023). (2023). Liu, L. Asymmetry in photoplethysmogram signals from left and right wrists as a marker for cardiovascular risk. J. Med. Syst. , 41 ,162 (2017). Sader, L. et al. Single-Photon Level Dispersive Fourier Transform: Ultrasensitive Characterization of Noise-Driven Nonlinear Dynamics. ACS Photonics, 10,3915–3928(2023). (2023). Lin, W. Y. & Wang, W. K. Why the cardiovascular studies should start with the radial oscillation of arterial wall rather than from axial flow motion of blood. Int. J. Cardiol. , 2019,274,303(2019). Karimpour, P., May, J. M. & Kyriacou, P. A. Photoplethysmography for the Assessment of Arterial Stiffness. Sens. (Basel) , 2023,23,9882(2023). Additional Declarations No competing interests reported. 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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-6201108","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":476917368,"identity":"9467ae42-9c14-4219-9289-c985f0bfc3ec","order_by":0,"name":"Haitian Li","email":"","orcid":"","institution":"Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Haitian","middleName":"","lastName":"Li","suffix":""},{"id":476917372,"identity":"df86714b-8de0-4de4-89fd-75e4f7afb24e","order_by":1,"name":"buxing Chen","email":"","orcid":"","institution":"Beijing University of Chinese Medicine Third Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"buxing","middleName":"","lastName":"Chen","suffix":""},{"id":476917375,"identity":"d461c2fe-18d4-4072-bc54-11bc0907b4b3","order_by":2,"name":"GinChung Wang","email":"","orcid":"","institution":"JinMu Health Technology","correspondingAuthor":false,"prefix":"","firstName":"GinChung","middleName":"","lastName":"Wang","suffix":""},{"id":476917378,"identity":"df74d89a-0862-4c99-8c3b-4a39e75c2f57","order_by":3,"name":"Yunxiao Wang","email":"","orcid":"","institution":"Beijing University of Chinese Medicine Third Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yunxiao","middleName":"","lastName":"Wang","suffix":""},{"id":476917380,"identity":"a6d55b71-0855-4bb1-a4b5-192d91c33418","order_by":4,"name":"Le Zhang","email":"","orcid":"","institution":"Yanqi Hospital","correspondingAuthor":false,"prefix":"","firstName":"Le","middleName":"","lastName":"Zhang","suffix":""},{"id":476917382,"identity":"4dc9dc95-9916-487d-9768-2198987ba85b","order_by":5,"name":"Yingying He","email":"","orcid":"","institution":"Yanqi Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yingying","middleName":"","lastName":"He","suffix":""},{"id":476917385,"identity":"c45a6470-8e7a-4065-8f1e-d6f4980e41e1","order_by":6,"name":"Yang Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYBAC+wYGhsM/DCTkGJiJ1cLYwMD4mKHCwpgkLczGDGcqEhuIdhgz/9lj0oVtEunz23kPfmCosYkmqIVNIi9NemabRO6Gw3zJEgzH0nIJWscjwWMmwQvSwsxjIMHYcJiwFgn+M2At6fLNPMY/iNJiwJBjbMxzRiKB4TDQOuK0SOQlPpxRIWG4AajFIoEYv9j3nz1w4INBnbx8/xnjGx9qbAhrAQYAEjuBsHJ0LaNgFIyCUTAKsAEAkG846pn9RowAAAAASUVORK5CYII=","orcid":"","institution":"Beijing University of Chinese Medicine Third Affiliated Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yang","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2025-03-11 08:08:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6201108/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6201108/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85746381,"identity":"481e4be4-d3dc-4e6f-b1c3-5fe8e67c26fa","added_by":"auto","created_at":"2025-07-01 09:30:40","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":21392,"visible":true,"origin":"","legend":"\u003cp\u003eCalculation formulas for Cn, Pn, Dn, CnCV, and PnCV.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote: \u003c/strong\u003eA(n, m) and θ(n, m) are the amplitude and phase of the n-th Fourier series of the m-th arterial pressure wave measured under a certain pressure. A(0, m) is the average value of the m-th arterial pressure wave. fm(x) is the x-th data point in the m-th arterial pressure wave. L is the total number of data points in Pm(x).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6201108/v1/b687cf4f9cc1c875b9664f2f.jpg"},{"id":85748505,"identity":"9e6de8be-ad21-4d91-9480-80e7f3e74e30","added_by":"auto","created_at":"2025-07-01 09:46:40","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":14677,"visible":true,"origin":"","legend":"\u003cp\u003eResults of ROC curve analysis of the logistic regression model in the male group\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6201108/v1/69bbf5cb3eb030c6e4b7fc92.jpg"},{"id":85747492,"identity":"bb725df2-9c4a-4920-b3c9-874c5a71af28","added_by":"auto","created_at":"2025-07-01 09:38:40","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":20162,"visible":true,"origin":"","legend":"\u003cp\u003eResults of ROC curve analysis of the logistic regression model in the female group.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6201108/v1/06b7b060575979e2bb639db0.jpg"},{"id":97250764,"identity":"51adf5b9-2a55-4ed9-9c76-cb5c489597e2","added_by":"auto","created_at":"2025-12-02 13:15:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":851941,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6201108/v1/0be4d3a5-d9a6-4483-b1e6-9e1c04429a86.pdf"},{"id":85747494,"identity":"b43448c7-5d83-4c59-83fb-b086422ba413","added_by":"auto","created_at":"2025-07-01 09:38:40","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":192180,"visible":true,"origin":"","legend":"","description":"","filename":"scienceSupplementarymaterial1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6201108/v1/b170814b851b21fd94873a14.xlsx"},{"id":85746386,"identity":"3adcc761-5cfb-4565-a9f6-2bd40230a77c","added_by":"auto","created_at":"2025-07-01 09:30:40","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":470640,"visible":true,"origin":"","legend":"","description":"","filename":"scienceSupplementarymaterial2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6201108/v1/80c3bf19a4a52c27a2df675e.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Correlation Study on the Prediction of Coronary Artery Lesion Degree by Pulse Wave Harmonics Based on SYNTAX Score","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoronary Heart Disease (CHD), one of the leading causes of death in humans, poses a serious threat to the health of the global population \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Accurate assessment of the degree of coronary artery lesions plays a crucial role in guiding the formulation of treatment strategies for patients and predicting the prognosis \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Currently, the clinical judgment of the degree of coronary artery lesions mainly relies on methods such as coronary enhanced computed tomography (CT) and coronary angiography. However, these methods not only require specialized and technically complex equipment, but also take a relatively long time for the examination process and carry certain risks, such as potential hazards like radiation exposure and contrast agent allergy. To some extent, these factors limit their widespread application. Therefore, there is an urgent clinical need for a convenient, rapid, and safe new method to accurately evaluate the degree of coronary artery lesions in patients.\u003c/p\u003e \u003cp\u003eThe human body, as a complex and sophisticated system, contains rich biological information, such as electrocardiogram information, electroencephalogram information, and arterial pressure wave information. These pieces of information, like hidden codes, can reflect the physiological and pathological states of the human system or organs \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Among them, arterial pressure wave information can be obtained by photoplethysmography. Compared with other detection methods, its detection equipment is small in size and easy to operate, and it is more convenient to collect in clinical practice, which provides great convenience for clinical application. Professor Wang Weigong proposed that the blood conduction between the heart and the arterial system is achieved by resonance. Based on this, the resonance characteristics of the pulse wave are measured by photoplethysmography, and the Fourier transform is used to convert them into harmonic indexes for quantitative description. This is the arterial pressure wave harmonic analysis method. This method has been proven to effectively reflect the pathophysiological states of major organ systems in the human body, including the heart \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Previous studies have also found significant differences in the relevant indexes between the left and right hands of patients. In view of this, this study intends to deeply analyze the correlation between the arterial pressure waves of the left and right hands of patients and the coronary SYNTAX score respectively, aiming to explore the feasibility of the arterial pressure wave harmonic analysis method in evaluating the degree of coronary artery lesions. It is expected to open up a new and efficient path for the clinical evaluation of the degree of coronary artery lesions, making up for the deficiencies of existing methods, and has high clinical significance and application value.\u003c/p\u003e"},{"header":"Data and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Research Subjects\u003c/h2\u003e\u003cp\u003eThe subjects of this study were all patients hospitalized in the Department of Cardiology, the Third Affiliated Hospital of Beijing University of Chinese Medicine from February 2023 to May 2023.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e1.2 Research Methods\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.2.1 Inclusion Criteria\u003c/h2\u003e \u003cp\u003eIncluded patients must meet the following conditions simultaneously:\u003c/p\u003e \u003cp\u003e(1) Patients who have been diagnosed or are suspected to be diagnosed with coronary heart disease.\u003c/p\u003e \u003cp\u003e(2) Patients scheduled to undergo coronary angiography.\u003c/p\u003e \u003cp\u003e(3) Patients aged 18 years or older.\u003c/p\u003e \u003cp\u003e(4) Patients who voluntarily participate in the study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1.2.2 Exclusion Criteria\u003c/h3\u003e\n\u003cp\u003ePatients meeting any of the following conditions should be excluded:\u003c/p\u003e \u003cp\u003e(1) Patients with concomitant severe diseases of the respiratory, urinary, digestive, hematopoietic, endocrine and metabolic systems, as well as other mental and neurological disorders.\u003c/p\u003e \u003cp\u003e(2) Pregnant or lactating patients.\u003c/p\u003e \u003cp\u003e(3) Patients in whom pulse diagnosis instruments are unable to measure.\u003c/p\u003e \u003cp\u003e(4) Patients with peripheral vascular diseases or arteriosclerosis of the upper extremities.\u003c/p\u003e\n\u003ch3\u003e1.2.3 Arterial Pressure Wave Measurement\u003c/h3\u003e\n\u003cp\u003eBefore all patients undergo coronary angiography, arterial pressure wave measurement should be carried out to ensure that the obtained data can accurately reflect the patients' physiological state at that time. During the measurement, a high-precision intelligent electronic pulse acquisition instrument (Model HMYH1300, Jinmu Health Technology) is selected. When measuring, the patients are required to lie in a supine position, ensuring that their bodies are in a relaxed state, with their hands naturally placed on both sides of their bodies. A quiet environment should be strictly maintained to reduce the impact of external interference on the measurement results. The operator measures the index fingers of the patients' left and right hands simultaneously to ensure the consistency and synchronism of data collection, so as to obtain comprehensive and accurate arterial pressure wave data.All methods comply with the Declaration of Helsinki.The experimental protocol has been approved by Ethics Committee of the Third Affiliated Hospital of Beijing University of Traditional Chinese Medicine(BZYSY-2022KYKTPJ-09).All subjects provided informed consent.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e1.2.4 Coronary Angiography and SYNTAX Score Calculation\u003c/h2\u003e \u003cp\u003eCoronary angiography was performed using a Siemens large C-arm angiography machine (Model Artis zee floor), and iodixanol injection was selected as the contrast agent. Based on the imaging results obtained from coronary angiography, two experienced cardiologists strictly followed the SYNTAX score rules\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e to score the degree of coronary artery lesions in the patients included in the study. If the difference between the scores given by the two doctors was less than 5 points, the SYNTAX score of the patient was taken as the average of the two scores. If the difference between the scores was greater than 5 points, a cardiovascular specialist with a higher professional title (the third doctor) would be involved to re-score, ensuring the accuracy and reliability of the SYNTAX score.\u003c/p\u003e \u003cp\u003e \u003cb\u003e1.2.5 Calculation Method of the Difference Index of Arterial Pressure Wave Harmonics between the Left and Right Hands\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe collected arterial pressure wave data were subjected to Fourier transformation. Taking integer multiples of the heartbeat as the resonance frequencies, the arterial pressure waves were finely decomposed into the 0th to 11th harmonics. For each harmonic, its amplitude (Cn), phase (Pn), and energy (Dn) were calculated respectively. These parameters can quantitatively describe the characteristics of the harmonics. Meanwhile, to further analyze the degree of data dispersion, the coefficient of variation of each index was calculated, and \"CV\" was uniformly added after the corresponding index name to indicate it. Subsequently, by taking the absolute value of the difference in the corresponding harmonic indexes between the left and right hands of the same patient, the difference values of the arterial pressure wave harmonic indexes between the left and right hands were accurately obtained. The calculation formulas for the harmonic indexes seeing in Fig.\u0026nbsp;1.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1.2.6 Statistical Methods\u003c/h3\u003e\n\u003cp\u003eThe Matlab 2024b software was applied to conduct a comprehensive and systematic data processing. Firstly, all the cases included in the study were accurately divided into a male group and a female group based on gender differences, to ensure that the subsequent analysis could fully consider the potential differences among different gender groups.Based on the scientific relationship of a 1:10 ratio between the logistic regression parameters and the sample size, with the difference values of the arterial pressure wave harmonic indexes between the left and right hands of the patients as the independent variables and a SYNTAX score\u0026thinsp;\u0026ge;\u0026thinsp;22 as the dependent variable, multivariate linear regression analysis models were established for the two groups respectively. On this basis, a receiver operating characteristic (ROC) curve analysis was further carried out to deeply explore the performance of the models.To construct the most effective prediction model, three classic modeling methods, namely the forward selection method, the backward elimination method, and the stepwise regression method, were adopted during the research process. By comparing the Akaike information criterion (AIC) values of the models constructed by different methods, the model with the smallest AIC value was selected as the optimal model. As a widely used criterion for model selection, the AIC can find the best balance between the goodness of fit of the model and its complexity, ensuring that the selected model can accurately fit the data and has good generalization ability.The constructed prediction model was comprehensively evaluated from two key dimensions: discrimination and calibration. In terms of discrimination evaluation, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used as the evaluation index. The AUC value can intuitively reflect the ability of the model to distinguish between different categories of samples. In this study, an AUC statistic\u0026thinsp;\u0026gt;\u0026thinsp;0.7 was used as the standard to determine that the model has good discrimination ability, so as to measure the effectiveness of the model in distinguishing between patients with a SYNTAX score\u0026thinsp;\u0026ge;\u0026thinsp;22 and those with a SYNTAX score\u0026thinsp;\u0026lt;\u0026thinsp;22.For the calibration evaluation, the Bootstrap method was used to conduct 2000 resamplings, and a calibration curve was drawn through internal validation to accurately evaluate the consistency between the predicted results and the actual results. Meanwhile, the Hosmer-Lemeshow goodness-of-fit test was used to quantitatively evaluate the calibration. This test can determine the degree of fit between the predicted probabilities of the model and the actual observed results from a statistical perspective, ensuring that the model has high accuracy and reliability in the prediction process.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.1 General Information\u003c/h2\u003e \u003cp\u003eA total of 348 patients were included in this study, among whom 249 were male and 99 were female. In the male patient group, 217 patients had a SYNTAX score of less than 22, and 32 patients had a SYNTAX score of 22 or higher. Among the female patients, 86 patients had a SYNTAX score of less than 22, and 13 patients had a SYNTAX score of 22 or higher.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.2 Results of Multivariate Regression Analysis Model and ROC Curve Analysis for the Difference Values of Arterial Pressure Wave Harmonic Indices between the Left and Right Hands in the Male Group\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe analysis results showed that the overall model was statistically significant (P\u0026thinsp;=\u0026thinsp;0.00), and among various attempts at model construction, the Akaike information criterion (AIC) value of this model was the smallest, being 105.47. This indicates that this model has achieved the best balance between goodness of fit and complexity, and has good reliability and generalization ability. Further analysis revealed that the correlations between the difference values C10, D6, P8, P10, P1CV, C9CV and a SYNTAX score\u0026thinsp;\u0026ge;\u0026thinsp;22 were statistically significant (the detailed data are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).In terms of model performance evaluation, through the receiver operating characteristic (ROC) curve analysis, the area under the ROC curve (AUC) of this model was obtained as 0.89 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e for details). This result intuitively shows that the model has a strong ability to distinguish between patients with different SYNTAX score levels. The Hosmer-Lemeshow goodness-of-fit test was used to evaluate the calibration of the model, and the calibration value obtained was 4.32 (P\u0026thinsp;=\u0026thinsp;0.82), indicating a high degree of fit between the predicted probabilities of the model and the actual observed results. In addition, through the Bootstrap internal validation method, after multiple repeated sampling verifications, the results showed that the average area under the ROC curve was 0.85, with a confidence interval of [0.80, 0.87], further confirming the stability and reliability of the model results.\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\u003eResults of Logistic Regression Analysis between the Harmonic Difference Indices of Arterial Pressure Waves of the Left and Right Hands and SYNTAX Score\u0026thinsp;\u0026ge;\u0026thinsp;22 in Male Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\u003cp\u003ec10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e128.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ed6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ed9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-15.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ed10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep1cv\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ec9cv\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00*\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\u003e \u003cb\u003eNote\u003c/b\u003e: Coefficient refers to the fitting coefficient; SE represents the standard deviation; tStat is the statistical coefficient; * indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.3 Results of Multivariate Regression Analysis Model and ROC Curve Analysis for the Difference Values of Arterial Pressure Wave Harmonic Indices between the Left and Right Hands in the Female Group\u003c/b\u003e \u003c/p\u003e\u003cp\u003eLogistic regression analysis was rigorously conducted with the difference values of the arterial pressure wave harmonic indexes between the left and right hands of female patients, namely D2, D3, D5, D6, D9, C2CV, C4CV, C5CV, C6CV, and C9CV, as the independent variables, and a SYNTAX score\u0026thinsp;\u0026ge;\u0026thinsp;22 as the dependent variable. The analysis showed that the overall model was of extremely significant statistical significance (P\u0026thinsp;=\u0026thinsp;0.00). Among the comparisons of numerous model construction schemes, the Akaike information criterion (AIC) value of this model was the smallest, being 59.34. This result clearly indicates that when fitting the data, this model can accurately conform to the characteristics of the samples and has excellent generalization performance, achieving an ideal balance between the goodness of fit and the complexity of the model.Upon in-depth analysis of the model, it was further determined that there were statistically significant associations between the indexes of the difference values such as D2, D3, D6, D9, C5CV, C6CV, and C9CV and a SYNTAX score\u0026thinsp;\u0026ge;\u0026thinsp;22 (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).During the comprehensive evaluation of the performance of this model, by using the receiver operating characteristic (ROC) curve analysis method, the area under the ROC curve (AUC) of this model was as high as 0.92 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This value intuitively and powerfully demonstrates that this model has excellent discrimination ability for female patients with different SYNTAX score levels and can accurately identify the group of patients with a SYNTAX score\u0026thinsp;\u0026ge;\u0026thinsp;22.In order to further evaluate the consistency between the predicted probabilities of the model and the actual observed results, the Hosmer-Lemeshow goodness-of-fit test method was used to quantitatively evaluate the calibration of the model, and finally, the calibration value obtained was 3.03 (P\u0026thinsp;=\u0026thinsp;0.93). This result shows that there is a very high degree of fit between the predicted values of the model and the actual observed values, and the predicted results of the model are highly reliable.In addition, by means of the Bootstrap internal validation method, after 2000 repeated sampling verifications, the final average area under the ROC curve was 0.84(see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e), with a confidence interval of [0.72, 0.91]. This result further validates the stability and reliability of the model results. Even under different sampling situations, the model can still maintain relatively stable and accurate prediction performance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Logistic Regression Analysis between the Difference Values of Arterial Pressure Wave Harmonic Indices of the Left and Right Hands and SYNTAX Score\u0026thinsp;\u0026ge;\u0026thinsp;22 in Female Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003etStat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-42.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-32.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-8.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC2CV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-55.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC4CV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-23.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC5CV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC6CV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC9CV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-9.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03*\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\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eCoefficient refers to the fitting coefficient; SE represents the standard deviation; tStat is the statistical coefficient; * indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e "},{"header":"Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003cp\u003eThe SYNTAX score, proposed by the American College of Cardiology/American Heart Association (ACC/AHA), is a comprehensive and accurate tool for assessing the degree of coronary artery stenosis and guiding the selection of coronary revascularization methods, and it has been widely applied in clinical practice\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. This scoring system constructs a risk stratification scoring system based on the anatomical characteristics of coronary artery lesions. Compared with other scoring criteria, it can more comprehensively demonstrate the anatomical features of coronary artery lesions, such as location, severity, bifurcation, and calcification. In view of this, the SYNTAX score was selected as an objective indicator for quantitatively evaluating the degree of coronary artery lesions in patients in this study \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. In addition, the SYNTAX score of 22 was set as the cut-off point of the prediction model because 22 points is widely recognized by cardiovascular experts as the key threshold for judging the severity of coronary artery lesions \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eStudies have shown that some cardiovascular diseases can cause asymmetry in the photoplethysmogram (PPG) waveform, especially between the finger signals of the left and right hands. This asymmetry is associated with various cardiac abnormalities, including coronary atherosclerotic heart disease (CAD). Although the mechanism underlying this asymmetry has not been fully elucidated, factors such as impaired circulatory function, vascular dysfunction, or autonomic nervous imbalance are speculated to be possible causes \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFourier transform, a commonly used method in physics for analyzing complex waves, is based on the core theory that a complex wave is composed of multiple simple waves. These simple waves can be separated from the complex wave through specific frequencies, and then the characteristics of the simple waves can be studied to analyze the complex wave \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. The resonance theory states that the conduction of blood in various organs of the human body occurs at integer multiples of the heart rate as specific frequencies. The arterial pressure wave can be decomposed into multiple simple waves by means of Fourier transform. These simple waves contain the state information of various systems in the human body and can be used to study organ diseases or the functional state of the human body \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Since the harmonics from 0 to 11 already contain more than 98% of the energy of the arterial pressure wave, these 12 harmonics can be used to reflect the characteristics of human blood circulation. From the perspective of cardiac pathophysiology, the degree of coronary artery lesion stenosis is closely related to the degree of myocardial ischemia. Changes in the degree of myocardial ischemia will trigger corresponding abnormal changes in cardiac motion, which in turn lead to changes in the arterial pressure wave. In this study, a logistic regression model was constructed to determine whether the SYNTAX score was \u0026ge;\u0026thinsp;22 points based on the arterial pressure wave harmonic indexes, and the results were statistically significant. This finding strongly indicates that the degree of coronary artery lesions can be reflected by analyzing the characteristics of arterial pressure wave harmonics.\u003c/p\u003e \u003cp\u003eA total of 348 patients were included in this study. Based on the relationship between the logistic regression parameters and the sample size at a ratio of 1:10, the number of parameters used in the logistic regression equations established in this study to determine whether the SYNTAX score was greater than or equal to 22 points was less than 10 times the sample size. Therefore, the sample size was statistically sufficient. The study was divided into a male group and a female group mainly because previous studies have found significant differences between males and females in multiple harmonic indexes.\u003c/p\u003e \u003cp\u003eAs a non-invasive detection method, photoplethysmography can reflect changes in pulse pressure. The detection instrument is similar to a pulse oximeter and has the characteristics of being portable and requiring a short detection time (only 1 minute)\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. The data collected through the Internet can be directly uploaded to the computing system, and the results can be fed back to the doctor's terminal, such as a mobile phone, enabling detection at any time, which is convenient, fast, and safe. In addition, this detection method is easy to operate, has high repeatability, and the equipment is small in size, making it suitable for various environments, and it can even be operated by patients themselves. In contrast, other methods for detecting pulse waves have problems such as difficult operation, difficulty in promotion, and the accuracy being easily interfered with by various factors. Therefore, this study has exploratorily revealed the potential application value of the analysis method of the difference values of arterial pressure wave harmonic indexes in evaluating the degree of coronary artery lesions. In the future, with the further increase in the research sample size and by comparing with the results of the radial artery intravascular pressure wave detection method, it is expected to construct a data diagnostic model for evaluating the degree of coronary artery lesions based on the arterial pressure wave indexes using the harmonic analysis method, opening up a new path for the diagnosis and treatment of coronary heart disease and contributing to the improvement of the diagnosis and treatment level of cardiovascular diseases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLimitations of the study\u003c/h2\u003e \u003cp\u003eSample Size and Generalizability: Although 348 patients were included, the sample size may still be relatively small, potentially limiting the generalizability of the results to the entire population. Additionally, the patients were all from a single hospital, which may introduce selection bias, and the findings may not be applicable to other regions or patient populations with different characteristics.Lack of Longitudinal Data: This study was cross - sectional, lacking longitudinal data to observe the dynamic changes of arterial pressure wave harmonics and SYNTAX scores over time. Thus, it is unable to establish a causal relationship between them and cannot predict the long - term progression of coronary artery lesions.Potential Confounding Factors: Despite excluding patients with certain comorbidities, there may still be other unaccounted confounding factors, such as lifestyle factors (diet, exercise), genetic factors, and environmental factors, which could affect the arterial pressure wave harmonics and SYNTAX scores and influence the accuracy of the model.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eResource availability\u003c/h2\u003e \u003cp\u003eData: The patient data was collected from the Department of Cardiology, the Third Affiliated Hospital of Beijing University of Chinese Medicine between February 2023 and May 2023. All data are securely stored in the hospital's electronic system. Under compliance with ethical approval and data protection regulations, the corresponding author can provide the anonymized data upon reasonable request.Software: Matlab 2024b software was used for data processing, including Fourier transform, regression analysis, and ROC curve construction. The custom-written code for these analyses is available from the corresponding author upon request.Equipment: The arterial pressure wave measurements were carried out using a high - precision intelligent electronic pulse acquisition instrument (Jinmu Health Technology Model HMYH1300). This instrument was provided by Jinmu Health Technology Co., Ltd. The Siemens large C - arm angiography machine for coronary angiography was from Siemens Healthcare. Both devices were located at the Third Affiliated Hospital of Beijing University of Chinese Medicine.\u003c/p\u003e \u003c/div\u003e\n\u003ch2\u003eDeclaration of interests\u003c/h2\u003e \u003cp\u003eThere were no conflicts of interest involved in this study.\u003c/p\u003e \u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eL.H.T and Y.Y conceptualized, designed, managed the project, collected and analyzed data, and wrote and revised the manuscript.W.G.C assisted in patient recruitment and data collection, provided medical insights during data analysis, and drafted the clinical implications section.C.B.X offered theoretical guidance, reviewed and refined the study design, and provided feedback on data analysis methods.Y.Y and L.H.T processed data using Matlab, developed relevant codes, interpreted results computationally, and created data - visualizing figures and tables.Z. L aided in literature review, assisted in data collection and verification, and contributed to the discussion by comparing with previous research.H.Y.Y coordinated access to patient data, assisted in statistical analysis, and participated in manuscript proofreading.W.Y.X helped in patient recruitment, assisted in data collection, and contributed to the description of study limitations.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eWe would like to express our sincere gratitude to all the patients who participated in this study. Their contributions are essential for the success of this research. We also thank the medical staff in the Department of Cardiology, the Third Affiliated Hospital of Beijing University of Chinese Medicine, for their assistance in patient recruitment and data collection. Special thanks go to Professor Yang yang and Chen buxing for providing valuable suggestions on the research design and data analysis. This work was supported by the National Natural Science Foundation of China (Grant No. 08177020516).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding authors (Yang Yang) upon reasonable request. Raw data from individual trials are not publicly accessible due to ethical restrictions and participant confidentiality but can be provided with permission from the original study authors. The code used for statistical analysis is available from the lead contact (Hai-Tian Li) upon request.The original data source has been added in the supplementary file.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMartin, S. S. et al. 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association. \u003cem\u003eCirculation\u003c/em\u003e \u003cb\u003e149\u003c/b\u003e, e347\u0026ndash;e913 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGohbara, M. et al. SYNTAX Score and 1-Year Outcomes in the OPTIVUS-Complex PCI Study Multivessel Cohort. \u003cem\u003eAm. J. Cardiol.\u003c/em\u003e \u003cb\u003e205\u003c/b\u003e, 431\u0026ndash;441 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriedman, G., Turk, K. W. \u0026amp; Budson, A. E. The Current of Consciousness: Neural Correlates and Clinical Aspects. \u003cem\u003eCurr. Neurol. Neurosci. Rep.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e, 345\u0026ndash;352 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, C. Y. et al. Noninvasively measured radial pressure wave analysis provides insight into cardiovascular changes during pregnancy and menopause. \u003cem\u003eTaiwan. J. Obstet. Gynecol.\u003c/em\u003e \u003cb\u003e60\u003c/b\u003e, 888\u0026ndash;893 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, Y. Y. et al. Resonance. The missing phenomenon in hemodynamics. \u003cem\u003eCirc. Res.\u003c/em\u003e \u003cb\u003e69\u003c/b\u003e, 246\u0026ndash;249 (1991).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar, R. et al. Echocardiographic and Angiographic Assessment of Right Ventricular Function and Right Coronary Artery Stenosis in Acute Inferior. \u003cem\u003eWall Myocard. Infarct. Cureus\u003c/em\u003e. \u003cb\u003e15\u003c/b\u003e, e46403 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSerruys, P. W., Revaiah, P. C. \u0026amp; Onuma, Y. Refining and Personalizing Prediction: Anatomical to Functional Prognostic Scores in the Era of State-of-the-Art Revascularization. \u003cem\u003eJACC Cardiovasc. Interv\u003c/em\u003e. \u003cb\u003e16\u003c/b\u003e, 2120\u0026ndash;2124 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNdrepepa, G. SYNTAX Score and Outcomes in Complex Coronary Artery Disease After Percutaneous Coronary Intervention: Is the Use of Intravascular Ultrasound an Equalizer of Disease Severity? Am J Cardiol, 206,345\u0026ndash;346(2023). (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMasuda, S. et al. Treatment recommendation based on SYNTAX score 2020 derived from coronary computed tomography angiography and invasive coronary angiography. Int J Cardiovasc Imaging, 39,1795\u0026ndash;1804(2023). (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Juan, X. et al. Value of the SYNTAX Score for the Long-Term Prognosis of Patients with Single or Double Coronary Artery Lesions Undergoing Percutaneous Coronary Intervention. Chinese Journal of Interventional Cardiology, 31, 446\u0026ndash;451(2023). (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, L. Asymmetry in photoplethysmogram signals from left and right wrists as a marker for cardiovascular risk. \u003cem\u003eJ. Med. Syst.\u003c/em\u003e, \u003cb\u003e41\u003c/b\u003e,162 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSader, L. et al. Single-Photon Level Dispersive Fourier Transform: Ultrasensitive Characterization of Noise-Driven Nonlinear Dynamics. ACS Photonics, 10,3915\u0026ndash;3928(2023). (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin, W. Y. \u0026amp; Wang, W. K. Why the cardiovascular studies should start with the radial oscillation of arterial wall rather than from axial flow motion of blood. \u003cem\u003eInt. J. Cardiol.\u003c/em\u003e, 2019,274,303(2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarimpour, P., May, J. M. \u0026amp; Kyriacou, P. A. Photoplethysmography for the Assessment of Arterial Stiffness. \u003cem\u003eSens. (Basel)\u003c/em\u003e, 2023,23,9882(2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"arterial pressure wave, harmonics, SYNTAX score, degree of coronary artery lesions","lastPublishedDoi":"10.21203/rs.3.rs-6201108/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6201108/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e This study aimed to investigate the correlation between the differences in pulse wave harmonic indexes between the left and right hands and the SYNTAX score, and to explore the potential of pulse wave harmonics in predicting the degree of coronary artery lesions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e The arterial pressure wave signals of the left and right hands of patients scheduled for coronary angiography were collected by photoplethysmography. According to the \"visceral resonance theory\", taking integer multiples of the heartbeat from 0 to 11 as the resonance frequencies, the collected arterial pressure waves were decomposed into the 0th to 11th harmonics by the Fourier transform method. The harmonic characteristics were quantified by amplitude (Cn), phase (Pn), and energy (Dn) (n is the harmonic serial number), and the coefficient of variation of the indexes was calculated and suffixed as CV. The absolute value of the difference in the corresponding harmonic indexes between the left and right hands of the patients was calculated. The SYNTAX score of the included cases was calculated based on the imaging data of coronary angiography. The included cases were divided into a male group and a female group according to gender. For each group, a logistic regression equation was established with the difference value of the harmonic index as the independent variable and SYNTAX score ≥ 22 as the dependent variable. The equation corresponding to the minimum value of the Akaike information criterion (AIC) was taken as the optimal prediction model. Finally, the discriminant ability of the prediction model was evaluated by the ROC curve method and the Bootstrap internal validation method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e A total of 348 patients were included, including 249 males and 99 females. The discriminant model for predicting SYNTAX score ≥ 22 based on C10, D6, D9, D10, P8, P10, P1CV, and C9CV in the male group was statistically significant (P \u0026lt; 0.05), with the minimum AIC value of 105.47, the area under the ROC curve (AUC) of 0.89, and the average AUC of 0.85 in the Bootstrap internal validation. The discriminant model based on D2, D3, D5, D6, D9, C2CV, C4CV, C5CV, C6CV, and C9CV in the female group was statistically significant (P \u0026lt; 0.05), with the minimum AIC value of 59.34. The AUC of the ROC curve of this prediction model was 0.92, and the average AUC in the Bootstrap internal validation was 0.84.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e The difference characteristics of pulse wave harmonics between the left and right hands can effectively reflect the degree of coronary artery lesions. Through the analysis of pulse wave harmonics, a diagnostic model with good discriminant ability for predicting the degree of coronary artery lesions can be constructed, which may offer a valuable non-invasive tool for clinical assessment of CAD.\u003c/p\u003e","manuscriptTitle":"A Correlation Study on the Prediction of Coronary Artery Lesion Degree by Pulse Wave Harmonics Based on SYNTAX Score","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-01 09:30:36","doi":"10.21203/rs.3.rs-6201108/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c4c56d11-f775-4039-9478-ede5e15825bc","owner":[],"postedDate":"July 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50644702,"name":"Health sciences/Cardiology"},{"id":50644703,"name":"Physical sciences/Physics"}],"tags":[],"updatedAt":"2025-12-02T06:39:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-01 09:30:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6201108","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6201108","identity":"rs-6201108","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}