Prognostic Value of Left Ventricular Lead Impedance in Cardiac Resynchronization Therapy

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Lucie Kohoutková, Martin Augustynek, Mgr. Oldřich Motyka This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7203153/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 Background: Cardiac resynchronization therapy (CRT) improves outcomes in heart failure patients, but response remains variable. New predictive tools are needed. Methods: A retrospective analysis of CRT patients was conducted using a multivariate logistic regression model. Predictors included left ventricular impedance change, BMI, age, and sex. EF improvement ≥ 10% was the outcome of interest. Results: Male sex (OR = 3.22), early impedance increase (OR = 2.82), and late impedance increase (OR = 2.01) were significantly associated with EF improvement. The model achieved an AUC of 0.77. Conclusion: LV lead impedance trends, combined with clinical variables, may support early prediction of CRT response and aid in patient stratification. Cardiac resynchronization therapy ejection fraction lead impedance logistic regression heart failure CRT response remote monitoring Figures Figure 1 Figure 2 Introduction Cardiac resynchronization therapy (CRT) is an established intervention for selected heart failure patients, particularly those with reduced left ventricular ejection fraction (LVEF) and conduction delays. Despite its overall clinical benefit, approximately 20–30% of patients fail to experience meaningful improvement in EF or symptoms after CRT implantation [1,2]. A major challenge in heart failure management is the early identification of likely responders and non-responders. While various biomarkers and imaging techniques have been explored [3,4], readily available, device parameters such as lead impedance remain underutilized for prognostic purposes. Changes in lead impedance may reflect myocardial remodeling or lead-tissue interface changes, this providing indirect insight into cardiac structural dynamics. Impedance is known to be influenced by tissue contact and dielectric properties of surrounding myocardium, as described in foundational bioimpedance studies [5,6]. This study investigates whether trends in left ventricular lead impedance, combined with basic demographic and anthropometric parameters, can help predict EF improvement following CRT. We hypothesize that distinct impedance trajectories are associated with functional myocardial recovery. Results Predictive Performance of the Multivariate Model To evaluate the discriminative power of impedance changes and clinical parameters in predicting left ventricular ejection fraction (EF) improvement, a multivariate logistic regression model was developed. The model included the following predictors: impedance changes, BMI at baseline and follow-up, patient age, and sex. The receiver operating characteristic (ROC) curve demonstrated a moderate to good predictive performance, with an area under the curve (AUC) of 0.77 (Figure 1). This indicates that the model is able to distinguish between patients with and without EF improvement with 77% accuracy. (Figure 1 the ROC curve for the multivariate logistic regression model including impedance trend, BMI, age, and sex). The full set of regression coefficients, including odds ratios, 95% confidence intervals, and p-values, is available in Figure 2 Regression Model Coefficients. The most influential predictors were: Male sex (OR = 3.22) Early impedance increase (Δ Impedance ₁₋₂ ) (OR = 2.82) Late impedance increase (Δ Impedance ₄₋₅ ) (OR = 2.01) BMI and age showed smaller yet measurable effects on the probability of EF recovery. These findings support the utility of impedance trajectory analysis combined with basic demographic and anthropometric data to stratify patients post-CRT implantation in terms of potential EF response. Discussion Our analysis confirms the prognostic value of impedance trajectory in CRT patients, especially when interpreted alongside basic demographic data. While impedance has traditionally been viewed as a technical parameter, its association with functional cardiac recovery highlights its potential role in clinical monitoring. Notably, the observed associations between impedance changes and EF improvement may reflect physiological processes such as fibrosis resolution, improved myocardial compliance, or optimized ventricular pacing [7,8]. These findings are consistent with previously reported associations between fibrosis resolution and impedance behavior [9].The predictive contribution of BMI and age was modest but aligns with previous evidence linking metabolic and structural factors to CRT response. Although the number of predictors in our logistic regression model was limited, their selection was guided by clinical relevance and prior evidence, rather than solely statistical significance. This approach aligns with the purposeful selection method described by Hosmer et al., which emphasizes expert judgment and domain knowledge in model construction. Conclusion Impedance trajectory analysis—specifically increases in early and late post-implantation intervals—combined with demographic variables, can provide clinically meaningful prediction of EF improvement. This logistic regression model demonstrated good discriminative power and identified male sex and impedance changes as key predictors. These results support the integration of lead impedance monitoring into routine CRT follow-up and risk stratification workflows. Given the ease of access, non-invasiveness, and automation of impedance data, it represents a promising tool in both in-clinic and remote patient management settings. Future studies should validate these findings in prospective cohorts and explore the incorporation of impedance data into machine learning models for more nuanced outcome prediction. Methods This retrospective analysis utilized CRT device data from annual outpatient evaluations of 95 patients . A multivariate logistic regression model was constructed to assess predictors of EF improvement, defined as an absolute increase in EF of ≥10% from baseline. According to the traditional approach described by Hosmer, the construction of a logistic regression model should not be viewed as a fully automated process, but rather as one guided by sound judgment and the expertise of the analyst, ideally in collaboration with a subject matter expert familiar with the underlying data. This procedure is referred to as purposeful selection of variables. [10]. Patient Population and Device Characteristics The study was conducted between 2018 and 2024 at the Department of Cardiovascular Medicine, University Hospital Ostrava. During this period, a total of 100 patients were monitored retrospectively and in real time during routine clinical follow-up. Five patients were excluded due to lead malfunction, extreme changes in body mass index (BMI), or death during follow-up. The final study population consisted of 95 patients. Baseline demographic and clinical characteristics, including age, sex, BMI categories, heart failure classification, and type of implanted device, are summarized in Table 1 . Left ventricular leads from multiple manufacturers were used to maintain neutrality in device selection. The specific choice of system was determined by the implanting physician and depended on the need for MRI-compatible systems, which require all components to come from the same manufacturer. The following LV leads were included: Abbott (formerly St. Jude Medical) : Quickflex 1258T, Quickflex µ, Quartet 1456Q and 1458Q Boston Scientific : Acuity Steerable, Acuity X4 4671 Straight, and X4 4678 Spiral L Implanted CRT devices included pacemakers (Allure, Quadra Allure, Visionist) and defibrillators (Unify Assura, Quadra Assura, Autogen, Charisma, Consulta). Lead impedance was measured using manufacturer-specific programmers (Abbott or Boston Scientific) during scheduled device interrogations in the cardiology/arrhythmia outpatient clinic at University Hospital Ostrava. Echocardiographic assessments were performed using the GE Vivid E95 ultrasound system (GE Healthcare, Milwaukee, USA) by experienced cardiologists. Parameters relevant to this study included left ventricular ejection fraction (LVEF) and left ventricular size to assess systolic function and remodeling. Patients with significant mitral, aortic, or tricuspid regurgitation were excluded from the analysis. In total, 95 patients were included in impedance monitoring and echocardiographic follow-up over a five-year period. This study was conducted in accordance with the Declaration of Helsinki and approved by the institutional ethics committee (approval number 193/2004, Faculty hospital Ostrava, Czech Republic). All data used in the analysis were retrospectively collected and fully pseudonymized prior to evaluation. Therefore, individual informed consent was not required under applicable national regulations. Predictor variables included: Total impedance change: Defined as the sum of early (Δ Impedance₁₋₂) and late (Δ Impedance₄₋₅) changes BMI at baseline and follow-up Age Sex Receiver operating characteristic (ROC) analysis was used to evaluate model performance. Odds ratios (OR) were calculated to estimate the strength of association between predictors and EF improvement. Declarations Ethics approval and consent to participate: This study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from Ethics Committee of University Hospital Ostrava (No. 193/2004), and all patients provided written informed consent. Consent for publication: Not applicable. Availability of data and materials: The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. All data were fully pseudonymized in accordance with institutional and national ethical regulations. Competing interests: The authors declare no competing interests. Funding: This research was not supported by any external funding. Authors' contributions: Author designed the study and developed the statistical model, collected and processed the data, wrote the manuscript draft. All authors contributed to revisions and approved the final manuscript. References Cleland JGF, Daubert J-C, Erdmann E, et al. (2005). The effect of cardiac resynchronization on morbidity and mortality in heart failure. New England Journal of Medicine , 352(15), 1539–1549. Moss AJ, Hall WJ, Cannom DS, et al. (2009). Cardiac-resynchronization therapy for the prevention of heart-failure events. New England Journal of Medicine , 361(14), 1329–1338. Delgado V, Bax JJ. (2011). Imaging in cardiac resynchronization therapy: past, present, and future. Circulation , 123(9), 986–999. Ypenburg C, van Bommel RJ, Delgado V, et al. (2008). Optimal left ventricular lead position predicts reverse remodeling and survival after cardiac resynchronization therapy. Journal of the American College of Cardiology , 52(17), 1402–1409. Martinsen ØG, Heiskanen M. (2023). Introduction to Biomedical Impedance Measurements. Academic Press. Korpas D. (2011). Biomedicínská technika. VŠB-TU Ostrava. Kutyifa V, Zareba W, McNitt S, et al. (2013). Left ventricular lead position and the risk of heart failure or death in patients with nonischemic cardiomyopathy. Circulation: Arrhythmia and Electrophysiology , 6(3), 455–462. Lin G, Nishimura RA, Gersh BJ, et al. (2010). Device therapy in heart failure: ICDs and CRT. Nature Reviews Cardiology , 7(9), 520–529. Rosina J, Pokorný J, Rosina M. (2021). Biophysics of the Human Body. Springer. Hosmer DW, Lemeshow S, Sturdivant RX. (2013). Applied Logistic Regression. 3rd ed. Wiley. Table 1 Table 1. Baseline Characteristics of the Study Population Category CRT-P CRT-D HFrEF (EF ≤ 40%) HFpEF (EF ≥ 50%) HFmrEF (EF 40–49%) Sex Female 9.0 21.0 22.0 4.0 4.0 Male 11.0 54.0 56.0 5.0 4.0 Age category ≤ 60 years 0.0 11.0 11.0 0.0 0.0 61–70 years 1.0 11.0 10.0 0.0 1.0 ≥ 70 years 19.0 53.0 58.0 9.0 6.0 BMI category ≤ 25 kg/m² 5.0 10.0 11.0 2.0 2.0 25–30 kg/m² 5.0 35.0 34.0 3.0 3.0 ≥ 30 kg/m² 10.0 30.0 34.0 4.0 2.0 CRT-P: Cardiac Resynchronization Therapy Pacemaker; CRT-D: Cardiac Resynchronization Therapy Defibrillator; HFrEF: Heart Failure with Reduced Ejection Fraction; HFpEF: Heart Failure with Preserved Ejection Fraction; HFmrEF: Heart Failure with Mildly Reduced Ejection Fraction; BMI: Body Mass Index. Additional Declarations No competing interests reported. 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Lucie Kohoutková","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIie3QsQrCMBCA4StCXaKuddFXuFJQBMVXqQQ6dXHrINIiZAq41reoCM4thbj0Ady0CM51c3Cw6qKI0dEhPyTc8pFwACrVH1YvTwxgA5aD5pdXoxLLif5EtOBGmsz+TuCFoPhGDJrHBTitbpV389BL25aoJXuYTCXEwSQE1+rxzAyiLDXXok4RhC4hLqQEvFG0dc0gZ4623vGOoflETi5PZLhi5EYMOQFwH2TJ+qNIvxP8TMgRE46OhZkYL8KsT0NBLLQla2tU6aE4e7SFm1l04p4xmDNi7gvJxh69/eLzGyqVSqX6pSsWP1L+JgHuWwAAAABJRU5ErkJggg==","orcid":"","institution":"University Hospital Ostrava","correspondingAuthor":true,"prefix":"","firstName":"Ing.","middleName":"Lucie","lastName":"Kohoutková","suffix":""},{"id":490720676,"identity":"f104ba29-c111-42ca-954d-202c493ba12b","order_by":1,"name":"Martin Augustynek","email":"","orcid":"","institution":"VŠB – Technical University of Ostrava","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Augustynek","suffix":""},{"id":490720679,"identity":"415b2094-089c-43fe-af02-4be355e05a58","order_by":2,"name":"Mgr. Oldřich Motyka","email":"","orcid":"","institution":"VŠB – Technical University of Ostrava","correspondingAuthor":false,"prefix":"","firstName":"Mgr.","middleName":"Oldřich","lastName":"Motyka","suffix":""}],"badges":[],"createdAt":"2025-07-24 08:26:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7203153/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7203153/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87796583,"identity":"d9b3a48c-6f0b-495e-b936-81598b39a25e","added_by":"auto","created_at":"2025-07-29 07:07:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":196974,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve for the multivariate logistic regression model predicting EF improvement. The model includes impedance trajectory, BMI, age, and sex. AUC = 0.77.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7203153/v1/cfb9e13a4d04f156eaf9c92d.png"},{"id":87796580,"identity":"83fc1976-30b0-48e1-a9b3-9061015427e9","added_by":"auto","created_at":"2025-07-29 07:07:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24757,"visible":true,"origin":"","legend":"\u003cp\u003eRegression model coefficients showing odds ratios, 95% confidence intervals, and p-values for each predictor variable.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7203153/v1/0aeae4b260c309d37df90c49.png"},{"id":88351833,"identity":"aeb5dc79-a8c0-491b-98bc-5961e16cdd2a","added_by":"auto","created_at":"2025-08-05 14:24:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":809115,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7203153/v1/c343d17f-fb03-4e1f-b006-3069898b8a9c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Value of Left Ventricular Lead Impedance in Cardiac Resynchronization Therapy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiac resynchronization therapy (CRT) is an established intervention for selected heart failure patients, particularly those with reduced left ventricular ejection fraction (LVEF) and conduction delays. Despite its overall clinical benefit, approximately 20\u0026ndash;30% of patients fail to experience meaningful improvement in EF or symptoms after CRT implantation [1,2].\u003cbr\u003e A major challenge in heart failure management is the early identification of likely responders and non-responders. While various biomarkers and imaging techniques have been explored [3,4], readily available, device parameters such as lead impedance remain underutilized for prognostic purposes. Changes in lead impedance may reflect myocardial remodeling or lead-tissue interface changes, this providing indirect insight into cardiac structural dynamics. Impedance is known to be influenced by tissue contact and dielectric properties of surrounding myocardium, as described in foundational bioimpedance studies [5,6].\u003c/p\u003e\n\u003cp\u003eThis study investigates whether trends in left ventricular lead impedance, combined with basic demographic and anthropometric parameters, can help predict EF improvement following CRT. We hypothesize that distinct impedance trajectories are associated with functional myocardial recovery.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePredictive Performance of the Multivariate Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the discriminative power of impedance changes and clinical parameters in predicting left ventricular ejection fraction (EF) improvement, a multivariate logistic regression model was developed. The model included the following predictors: impedance changes, BMI at baseline and follow-up, patient age, and sex.\u003c/p\u003e\n\u003cp\u003eThe receiver operating characteristic (ROC) curve demonstrated a moderate to good predictive performance, with an area under the curve (AUC) of \u003cstrong\u003e0.77\u003c/strong\u003e (Figure 1). This indicates that the model is able to distinguish between patients with and without EF improvement with 77% accuracy. (Figure 1 the ROC curve for the multivariate logistic regression model including impedance trend, BMI, age, and sex). The full set of regression coefficients, including odds ratios, 95% confidence intervals, and p-values, is available in \u003cstrong\u003eFigure 2\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eRegression Model Coefficients.\u003c/p\u003e\n\u003cp\u003eThe most influential predictors were:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eMale sex\u003c/strong\u003e (OR = 3.22)\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEarly impedance increase (\u0026Delta; Impedance\u003c/strong\u003e\u003cstrong\u003e₁₋₂\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e (OR = 2.82)\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eLate impedance increase (\u0026Delta; Impedance\u003c/strong\u003e\u003cstrong\u003e₄₋₅\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e (OR = 2.01)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBMI and age showed smaller yet measurable effects on the probability of EF recovery.\u003c/p\u003e\n\u003cp\u003eThese findings support the utility of impedance trajectory analysis combined with basic demographic and anthropometric data to stratify patients post-CRT implantation in terms of potential EF response.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur analysis confirms the prognostic value of impedance trajectory in CRT patients, especially when interpreted alongside basic demographic data. While impedance has traditionally been viewed as a technical parameter, its association with functional cardiac recovery highlights its potential role in clinical monitoring.\u003c/p\u003e\n\u003cp\u003eNotably, the observed associations between impedance changes and EF improvement may reflect physiological processes such as fibrosis resolution, improved myocardial compliance, or optimized ventricular pacing [7,8]. These findings are consistent with previously reported associations between fibrosis resolution and impedance behavior [9].The predictive contribution of BMI and age was modest but aligns with previous evidence linking metabolic and structural factors to CRT response.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough the number of predictors in our logistic regression model was limited, their selection was guided by clinical relevance and prior evidence, rather than solely statistical significance. This approach aligns with the purposeful selection method described by Hosmer et al., which emphasizes expert judgment and domain knowledge in model construction.\u003c/p\u003e\n"},{"header":"Conclusion","content":"\u003cp\u003eImpedance trajectory analysis\u0026mdash;specifically increases in early and late post-implantation intervals\u0026mdash;combined with demographic variables, can provide clinically meaningful prediction of EF improvement. This logistic regression model demonstrated good discriminative power and identified male sex and impedance changes as key predictors.\u003c/p\u003e\n\u003cp\u003eThese results support the integration of lead impedance monitoring into routine CRT follow-up and risk stratification workflows. Given the ease of access, non-invasiveness, and automation of impedance data, it represents a promising tool in both in-clinic and remote patient management settings.\u003c/p\u003e\n\u003cp\u003eFuture studies should validate these findings in prospective cohorts and explore the incorporation of impedance data into machine learning models for more nuanced outcome prediction.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis retrospective analysis utilized CRT device data from annual outpatient evaluations of \u003cstrong\u003e95 patients\u003c/strong\u003e. A multivariate logistic regression model was constructed to assess predictors of EF improvement, defined as an absolute increase in EF of \u0026ge;10% from baseline. According to the traditional approach described by Hosmer, the construction of a logistic regression model should not be viewed as a fully automated process, but rather as one guided by sound judgment and the expertise of the analyst, ideally in collaboration with a subject matter expert familiar with the underlying data. This procedure is referred to as purposeful selection of variables.\u0026nbsp;[10].\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003ePatient Population and Device Characteristics\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe study was conducted between 2018 and 2024 at the Department of Cardiovascular Medicine, University Hospital Ostrava. During this period, a total of 100 patients were monitored \u0026nbsp;retrospectively and in real time during routine clinical follow-up. Five patients were excluded due to lead malfunction, extreme changes in body mass index (BMI), or death during follow-up. The final study population consisted of 95 patients.\u003c/p\u003e\n\u003cp\u003eBaseline demographic and clinical characteristics, including age, sex, BMI categories, heart failure classification, and type of implanted device, are summarized in \u003cstrong\u003eTable 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eLeft ventricular leads from multiple manufacturers were used to maintain neutrality in device selection. The specific choice of system was determined by the implanting physician and depended on the need for MRI-compatible systems, which require all components to come from the same manufacturer. The following LV leads were included:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eAbbott (formerly St. Jude Medical)\u003c/strong\u003e: Quickflex 1258T, Quickflex \u0026micro;, Quartet 1456Q and 1458Q\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBoston Scientific\u003c/strong\u003e: Acuity Steerable, Acuity X4 4671 Straight, and X4 4678 Spiral L\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eImplanted CRT devices included pacemakers (Allure, Quadra Allure, Visionist) and defibrillators (Unify Assura, Quadra Assura, Autogen, Charisma, Consulta).\u003c/p\u003e\n\u003cp\u003eLead impedance was measured using manufacturer-specific programmers (Abbott or Boston Scientific) during scheduled device interrogations in the cardiology/arrhythmia outpatient clinic at University Hospital Ostrava.\u003c/p\u003e\n\u003cp\u003eEchocardiographic assessments were performed using the GE Vivid E95 ultrasound system (GE Healthcare, Milwaukee, USA) by experienced cardiologists. Parameters relevant to this study included left ventricular ejection fraction (LVEF) and left ventricular size to assess systolic function and remodeling. Patients with significant mitral, aortic, or tricuspid regurgitation were excluded from the analysis.\u003c/p\u003e\n\u003cp\u003eIn total, 95 patients were included in impedance monitoring and echocardiographic follow-up over a five-year period.\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and approved by the institutional ethics committee (approval number 193/2004, Faculty hospital Ostrava, Czech Republic). All data used in the analysis were retrospectively collected and fully pseudonymized prior to evaluation. Therefore, individual informed consent was not required under applicable national regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictor variables included:\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eTotal impedance change:\u003c/strong\u003e Defined as the sum of early (\u0026Delta; Impedance₁₋₂) and late (\u0026Delta; Impedance₄₋₅) changes\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBMI at baseline and follow-up\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eReceiver operating characteristic (ROC) analysis was used to evaluate model performance. Odds ratios (OR) were calculated to estimate the strength of association between predictors and EF improvement.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate:\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from Ethics Committee of University Hospital Ostrava (No. 193/2004), and all patients provided written informed consent.\u003c/p\u003e\n\u003cp\u003eConsent for publication:\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials:\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\u003cp\u003eAll data were fully pseudonymized in accordance with institutional and national ethical regulations.\u003c/p\u003e\n\u003cp\u003eCompeting interests:\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding:\u003c/p\u003e\n\u003cp\u003eThis research was not supported by any external funding.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Authors\u0026apos; contributions: \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthor designed the study and developed the statistical model, collected and processed the data, wrote the manuscript draft. All authors contributed to revisions and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u003cstrong\u003eCleland JGF, Daubert J-C, Erdmann E, et al.\u003c/strong\u003e (2005). The effect of cardiac resynchronization on morbidity and mortality in heart failure. \u003cem\u003eNew England Journal of Medicine\u003c/em\u003e, 352(15), 1539\u0026ndash;1549.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eMoss AJ, Hall WJ, Cannom DS, et al.\u003c/strong\u003e (2009). Cardiac-resynchronization therapy for the prevention of heart-failure events. \u003cem\u003eNew England Journal of Medicine\u003c/em\u003e, 361(14), 1329\u0026ndash;1338.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eDelgado V, Bax JJ.\u003c/strong\u003e (2011). Imaging in cardiac resynchronization therapy: past, present, and future. \u003cem\u003eCirculation\u003c/em\u003e, 123(9), 986\u0026ndash;999.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eYpenburg C, van Bommel RJ, Delgado V, et al.\u003c/strong\u003e (2008). Optimal left ventricular lead position predicts reverse remodeling and survival after cardiac resynchronization therapy. \u003cem\u003eJournal of the American College of Cardiology\u003c/em\u003e, 52(17), 1402\u0026ndash;1409.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eMartinsen \u0026Oslash;G, Heiskanen M.\u003c/strong\u003e (2023). \u003cem\u003eIntroduction to Biomedical Impedance Measurements.\u003c/em\u003e Academic Press.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eKorpas D.\u003c/strong\u003e (2011). \u003cem\u003eBiomedic\u0026iacute;nsk\u0026aacute; technika.\u003c/em\u003e V\u0026Scaron;B-TU Ostrava.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eKutyifa V, Zareba W, McNitt S, et al.\u003c/strong\u003e (2013). Left ventricular lead position and the risk of heart failure or death in patients with nonischemic cardiomyopathy. \u003cem\u003eCirculation: Arrhythmia and Electrophysiology\u003c/em\u003e, 6(3), 455\u0026ndash;462.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLin G, Nishimura RA, Gersh BJ, et al.\u003c/strong\u003e (2010). Device therapy in heart failure: ICDs and CRT. \u003cem\u003eNature Reviews Cardiology\u003c/em\u003e, 7(9), 520\u0026ndash;529.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eRosina J, Pokorn\u0026yacute; J, Rosina M.\u003c/strong\u003e (2021). \u003cem\u003eBiophysics of the Human Body.\u003c/em\u003e Springer.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eHosmer DW, Lemeshow S, Sturdivant RX.\u003c/strong\u003e (2013). \u003cem\u003eApplied Logistic Regression.\u003c/em\u003e 3rd ed. Wiley.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1. Baseline Characteristics of the Study Population\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003eCRT-P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003eCRT-D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003eHFrEF (EF \u0026le; 40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003eHFpEF (EF \u0026ge; 50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003eHFmrEF (EF 40\u0026ndash;49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026emsp;\u0026emsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e21.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026emsp;\u0026emsp;Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e54.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e56.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eAge category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026emsp;\u0026emsp;\u0026le; 60 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026emsp;\u0026emsp;61\u0026ndash;70 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026emsp;\u0026emsp;\u0026ge; 70 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e53.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e58.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eBMI category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026emsp;\u0026emsp;\u0026le; 25 kg/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026emsp;\u0026emsp;25\u0026ndash;30 kg/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e35.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e34.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026emsp;\u0026emsp;\u0026ge; 30 kg/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e30.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e34.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3333%;\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;CRT-P: Cardiac Resynchronization Therapy Pacemaker; CRT-D: Cardiac Resynchronization Therapy Defibrillator; HFrEF: Heart Failure with Reduced Ejection Fraction; HFpEF: Heart Failure with Preserved Ejection Fraction; HFmrEF: Heart Failure with Mildly Reduced Ejection Fraction; BMI: Body Mass Index.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Cardiac resynchronization therapy, ejection fraction, lead impedance, logistic regression, heart failure, CRT response, remote monitoring","lastPublishedDoi":"10.21203/rs.3.rs-7203153/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7203153/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCardiac resynchronization therapy (CRT) improves outcomes in heart failure patients, but response remains variable. New predictive tools are needed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA retrospective analysis of CRT patients was conducted using a multivariate logistic regression model. Predictors included left ventricular impedance change, BMI, age, and sex. EF improvement ≥ 10% was the outcome of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMale sex (OR = 3.22), early impedance increase (OR = 2.82), and late impedance increase (OR = 2.01) were significantly associated with EF improvement. The model achieved an AUC of 0.77.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLV lead impedance trends, combined with clinical variables, may support early prediction of CRT response and aid in patient stratification.\u003c/p\u003e","manuscriptTitle":"Prognostic Value of Left Ventricular Lead Impedance in Cardiac Resynchronization Therapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-29 07:07:16","doi":"10.21203/rs.3.rs-7203153/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":"37a1333c-59be-440b-b326-164a3c513788","owner":[],"postedDate":"July 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-05T14:23:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-29 07:07:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7203153","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7203153","identity":"rs-7203153","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}