Moving-average processing enables accurate quantification of time delay and compares the trending ability of cardiac output monitors with different response times

Moving-average processing enables accurate quantification of time delay and compares the trending ability of cardiac output monitors with different response times | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Moving-average processing enables accurate quantification of time delay and compares the trending ability of cardiac output monitors with different response times Yoshihiro Sugo, Ryoichi Ochiai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7116969/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Oct, 2025 Read the published version in BMC Biomedical Engineering → Version 1 posted 12 You are reading this latest preprint version Abstract Background Continuous cardiac output (CCO) monitoring using pulmonary artery (PA) thermodilution and newly introduced beat-to-beat cardiac output (CO) monitoring technologies exhibits different response time delays. These differences can hinder accurate comparisons of their trending abilities. To address this, we applied moving average processing to the beat-to-beat CO monitor data to evaluate its effect on trending assessment accuracy. This study aimed to confirm the effectiveness of moving average processing for such comparisons. Results This was a single-center, retrospective, observational study conducted at a 916-bed university hospital. A total of 20 patients undergoing kidney transplantation were included. We analyzed the trending ability of arterial pressure cardiac index (APCI) and estimated continuous cardiac index (esCCI) relative to continuous cardiac index (CCI) derived from PA thermodilution. Trending ability was assessed using a Polar plot and Bland-Altman analyses. A wide range of moving average windows (0–60 minutes) was applied to APCI and esCCI. The polar concordance rate at 30° exceeded 92% for moving average windows between 20 and 30 minutes, with APCI peaking between 21 and 27 minutes. These improvements reflected both time-shifting and filtering effects of the moving average process. Conclusions Moving average processing over 20 to 30 minutes significantly enhanced concordance between esCCI and reference CCI, with APCI demonstrating similarly high concordance in the same time window. This approach effectively compensates for differences in response time delays between CO monitoring modalities, enabling more accurate assessment of trending ability. Cardiac Output Pulse Wave Transit Time Response Time Trends Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Continuous cardiac output (CCO) monitoring using pulmonary artery (PA) thermodilution is known to provide delayed information in clinical settings due to its reliance on historical data1,2. Moreover, several reports have suggested that hemodynamic management using the Swan-Ganz catheter does not improve patient outcomes, 3–5, although it continues to be used. 6 This highlights the need for caution in its application. 7–9 In contrast, various continuous cardiac output (CO) monitoring technologies that operate on a beat-to-beat basis—such as those using blood pressure waveforms10 or bioimpedance11—have been introduced over the past two decades. One such example is the estimated continuous cardiac output (esCCO) monitor, which calculates CO based on the time interval from the R-wave of the electrocardiogram to the arrival of the peripheral pulse oximetry waveform12,13. Recent studies have compared beat-to-beat monitors, such as arterial pressure-based cardiac output (APCO) and esCCO, to traditional cardiac output (CCO) monitors. 14,15 However, the validity of these comparisons remains uncertain. As noted by Saugel et al.16, when evaluating the trending ability of two CO monitors, it is crucial to account for their respective response delays. While the exact cause of delays in CCO monitoring remains unclear, previous research17 suggests that moving average signal processing may be a contributing factor. Moving averages can introduce two key effects on a signal: time shift and filtering. Therefore, to understand the nature of such delays, it is important to examine these two effects separately in addition to evaluating the moving average as a whole. The moving average is a well-known low-pass filter and has been previously used to estimate central blood pressure waveforms from peripheral ones18 and to remove motion artifacts from distorted pulse waveforms19. The amplitude and phase characteristics of a signal processed by a moving average can be described analytically. If the original signal is a sinusoidal waveform: $$\:\text{y}=\text{x}\left(t\right)=\text{sin}\left(2\pi\:ft\right)\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(1\right)$$ And the moving average is calculated over a time window t, then the filtered signal becomes: $$\:{y}^{\tau\:}=\frac{1}{\tau\:}\underset{t-\tau\:}{\overset{t}{\int\:}}\text{sin}\left(2\pi\:ft\right)dt=\frac{1}{2\pi\:f\tau\:}\sqrt{2\left(1-\text{cos}\left(2\pi\:f\tau\:\right)\right)}\times\:\text{sin}\left(2\pi\:f\left(t-\frac{\tau\:}{2}\right)\right)\:\:\:\left(2\right)$$ From Eq. (2), the amplitude of the filtered signal is: $$\:\frac{1}{2\pi\:f\tau\:}\sqrt{2\left(1-\text{cos}\left(2\pi\:f\tau\:\right)\right)}$$ and the phase delay corresponds to half the averaging time: $$\:\frac{\tau\:}{2}$$ If two signals have different moving average times t0 and t, then their amplitude ratio r and phase difference f are given by: $$\:\text{r}=\raisebox{1ex}{$\tau\:$}\!\left/\:\!\raisebox{-1ex}{${\tau\:}_{0}$}\right.\times\:\raisebox{1ex}{$\text{sin}\left(\pi\:f{\tau\:}_{0}\right)$}\!\left/\:\!\raisebox{-1ex}{$\text{sin}\left(\pi\:f\tau\:\right)$}\right.$$ 3 $$\:{\phi\:}=\raisebox{1ex}{${\tau\:}_{0}-\tau\:$}\!\left/\:\!\raisebox{-1ex}{$2$}\right.$$ 4 According to Equations ( 3 ) and ( 4 ), when t = t0, the amplitude ratio is 1 and the phase difference is 0. Under these conditions, Polar plot analysis, as described by Critchley et al.2, yields optimal trending agreement, characterized by a mean polar angle of 0° and a minimal polar angle standard deviation (SD). If the delay in CCO monitoring arises primarily from moving average processing, then applying appropriate moving average adjustments to CO monitor data should improve their concordance, particularly when evaluated using polar plot metrics. Accordingly, this study applied moving average processing to compare the trending ability of two CO monitors with different response delays. In addition to standard moving averages, we independently assessed the individual effects of time shifts and signal filtering. To address the potential for information loss inherent in averaging20, we applied a wide range of moving average windows, exceeding those previously examined14,15, in search of the optimal range. Besides Polar plot analysis—which evaluates trending ability—we also conducted Bland-Altman analysis to assess the standard deviation (SD) of differences and to investigate the presence of proportional bias. The rationale for including Bland-Altman analysis is that the total variance observed in the differences includes both between-subject and within-subject components. Since trending ability is inherently a within-subject property, a prominent within-subject variance would be reflected in the SD of differences. The degree of proportional bias is likewise indicative of trending performance21. In summary, this study investigates the necessity and optimal conditions of moving average processing when comparing CCO monitors with other CO monitoring methods. The relationship between moving average time and trending performance, as revealed by Polar plot and Bland-Altman analyses, forms the basis for this approach using clinical data. Methods We conducted simulations of continuous cardiac index (CCI) data measured by pulmonary artery (PA) thermodilution, as well as arterial pressure cardiac index (APCI) and estimated continuous cardiac index (esCCI) data. These datasets were originally obtained in the study by Terada et al. [ 13 ]. CCI was measured using balloon-tipped, flow-directed thermodilution pulmonary artery catheters (Edwards Lifesciences, Irvine, CA, USA) and the Hemodynamic Monitor Vigilance II (Edwards Lifesciences, Irvine, CA, USA). APCI was measured using the FloTrac/Vigileo™ system, Version 3 (Edwards Lifesciences, Irvine, CA, USA). Pulse wave transit time for esCCI was obtained using a BSM-9101 bedside monitor (Nihon Kohden, Tokyo, Japan), and transmitted to a personal computer, where esCCI was calculated using a custom C-compiled program. APCI values were averaged over 20-second intervals, while esCCI was computed using a dynamic average of 64 consecutive heartbeats. The dataset comprised 20 patients undergoing kidney transplantation. Data were collected at 1-minute intervals, yielding a total of 5,027 data points over 83.8 hours. The study was approved by the ethics committee of Toho University Omori Medical Center, and all procedures conformed to the ethical standards of the 1964 Declaration of Helsinki and its later amendments. Written informed consent was obtained from all participating patients. Three types of signal processing were applied to the esCCI and APCI data, as illustrated in Fig. 1 : 1. Moving Average: The signal was averaged over a past window of duration τ (minutes) up to the current time: $$\:{a}_{mov\_avg}=\raisebox{1ex}{$\sum\:_{j=0}^{m}{a}_{i-j}$}\!\left/\:\!\raisebox{-1ex}{$m+1$}\right.$$ 5 2. Time Shift: The signal was shifted in time by τ/2, using the past value as the \(\:{a}_{shift}={a}_{i-n}\) (6) 3. Filtering: A symmetric moving average was applied, centered on the current time, using values from τ/2 before to τ/2 after: $$\:{a}_{filt}=\raisebox{1ex}{$\sum\:_{j=-n}^{n}{a}_{i-j}$}\!\left/\:\!\raisebox{-1ex}{$2n+1$}\right.$$ 7 Here, if τ is even, m = τ and n = τ/2. Polar Concordance Rate Analysis (esCCI vs. CCI and APCI vs. CCI) CCI was used as the reference signal, and esCCI and APCI served as test signals after application of a moving average with varying durations τ. τ was varied from 0 minutes (no averaging) to 60 minutes in 1-minute increments, generating 61 different datasets each for esCCI and APCI. For each τ, we computed the differences in measurements over time intervals t1, ranging from 1 to 60 minutes. This resulted in 3,660 (61 × 60) combinations of τ and t1 for both esCCI and APCI, respectively. For each of the 20 patients, we computed the differential data pairs between CCI and the corresponding esCCI/APCI values and performed Polar plot analysis [ 2 ]. Data pairs with an average CCI change and test signal change (esCCI or APCI) of less than 0.6 L/min/m² were excluded. This threshold corresponded to 15% of the mean CCI value, consistent with the previous study [ 13 ]. Polar Plot and Bland-Altman Analyses (esCCI vs. CCI and APCI vs. CCI) Polar plot analyses were conducted with CCI as the reference and esCCI and APCI (moving averaged at τ) as test signals. The time interval t1 was fixed at 10 minutes, based on prior studies [ 22 ], which suggested it as an appropriate duration for assessing fluid responsiveness. For each τ, we calculated the polar concordance rate at 30°, the mean polar angle, and the standard deviation (SD) of polar angles. The exclusion zone was defined as 0.6 L/min/m². For Bland-Altman analysis, we computed the bias, the SD of the differences, and the Pearson correlation coefficient between the differences (Y-axis) and the means (X-axis). The correlation coefficient was interpreted as an index of proportional bias. Comparison of Signal Processing Methods: Polar Plot and Bland-Altman Analyses To evaluate the effects of different processing methods, esCCI signals were processed using three approaches: moving average, time shift, and filtering. The reference remained CCI. For all methods, t1 was fixed at 10 minutes, and τ was varied. Polar plot and Bland-Altman analyses were conducted as described above. The exclusion zone was set to 0.6 L/min/m² in all comparisons. All statistical analyses were conducted using Microsoft Excel (Office 365; Microsoft Corp., Redmond, WA, USA) and MATLAB R2023a (MathWorks, Natick, MA, USA). Results Polar Concordance Rate at 30° for esCCI vs. CCI and APCI vs. CCI Figure 2 shows the results of the polar concordance rate at 30°, applying an exclusion zone of 0.6 L/min/m², across varying values of t and t1. For both esCCI and APCI, the concordance rate fluctuated more noticeably at lower t1 values, indicating a higher dependency on t. As t1 increased, these fluctuations diminished. Across the entire t1 range, the highest concordance rates were consistently observed at mid-range values of t. Polar Plot and Bland-Altman Analyses for esCCI vs. CCI and APCI vs. CCI Figure 3 A illustrates the polar concordance rate at 30° with an exclusion zone of 0.6 L/min/m², analyzed while varying t and keeping t1 = 10 minutes fixed. For esCCI, the concordance rate was below 60% without any moving average processing (t = 0). However, when t increased to between 17 and 30 minutes, the concordance rate exceeded 90%, peaking between 20 and 30 minutes. In comparison, APCI reached approximately 90% concordance within a narrower window between t = 21 and 27 minutes. Figure 3 B shows that the mean polar angle decreased with increasing t for both APCI and esCCI. The mean polar angle crossed zero at t = 19 minutes for APCI and t = 12 minutes for esCCI. Similarly, the polar angle standard deviation (SD) also trended downward with increasing t (Fig. 3 C). In the Bland-Altman analysis (Fig. 3 D), the bias shifted in the negative direction as t increased for both signals. The smallest bias (closest to zero) was observed at t = 0. Figure 3 E presents the correlation coefficient between the difference (Y-axis) and the mean value (X-axis), which initially showed positive values at t = 0 and transitioned toward negative values with increasing t. The zero crossing of this coefficient occurred at t = 8 minutes for APCI and t = 12 minutes for esCCI. As shown in Fig. 3 F, the SD of the differences decreased with increasing t, reaching a minimum at τ = 24 minutes for APCI and τ = 21 minutes for esCCI, before increasing again at higher པ values. Comparison of esCCI vs. CCI Using Three Signal Processing Methods: Moving Average, Time Shift, and Filtering In addition to the moving average method described above, esCCI was also analyzed using time shift and filtering techniques, with t as the varying parameter and t1 = 10 minutes fixed. As shown in Fig. 4 A, the polar concordance rate at 30° peaked between t = 20 and 30 minutes in both the moving average and time shift methods. However, the maximum concordance rate using the time shift method did not exceed 85.5%. In contrast, the filtering method did not exhibit a similar τ-dependent trend in concordance rate. In terms of mean polar angle (Fig. 4 B), both the moving average and filtering methods showed a decreasing trend with increasing t. The time shift method, however, did not display a clear trend. Regarding polar angle SD (Fig. 4 C), the filtering method showed a consistent decrease beyond τ = 10 (equivalent to a 5-minute filtering window). The time shift method exhibited a U-shaped pattern, with SD decreasing to a minimum between t = 20–30 (i.e., a shift of 10–15 minutes), followed by an increase with further t increments. Figure 4 D shows that bias decreased in the negative direction with increasing t for the time shift method, whereas the filtering method demonstrated an increase in bias that approached zero. Figure 4 E presents the correlation coefficients between the difference and mean values. Similar to the moving average method, both the time shift and filtering methods showed a shift from a positive value at t = 0 toward negative values as t increased. Finally, as shown in Fig. 4 F, SD decreased with increasing t in the time shift method, reaching a minimum at t = 20 (a shift of 10 minutes), before increasing again. In contrast, the filtering method showed a continuous decrease in SD with increasing t. Discussion This study evaluated the effectiveness of applying moving average processing when comparing CCI with beat-to-beat cardiac indices exhibiting different response times, namely esCCI and APCI. We confirmed that esCCI achieved an acceptable concordance rate when a moving average of 20–30 minutes was applied, while APCI demonstrated a high concordance rate within the 21–27-minute range (Fig. 3 (A)). We further explored the underlying mechanisms of moving average effects, specifically decomposing them into time shift and filtering components. As shown in Fig. 4 (B), the mean polar angle crossed zero at t = 8 minutes due to filtering, subsequently shifting further into the negative direction. Figure 4 (C) demonstrated that the decrease in polar angle SD up to t = 24 minutes was primarily attributed to time shift effects; beyond this point, filtering effects predominated. These findings suggest that moving averages exert their influence through a combination of time shift and signal smoothing (filtering). Both the mean polar angle and the correlation coefficient between the difference (Y-axis) and mean (X-axis) values in the Bland-Altman analysis shifted toward the negative direction as τ increased. In esCCI, zero crossings occurred at t = 12 minutes for both the mean polar angle and the correlation coefficient. In APCI, the zero crossing of the mean polar angle occurred at t = 19 minutes, while that of the correlation coefficient occurred earlier at t = 8 minutes. The alignment of zero crossings in esCCI suggests a correspondence between systematic (proportional) error in Bland-Altman analysis and the mean polar angle in polar plot analysis, as previously described [ 21 ]. Although polar angle SD decreased with increasing τ beyond 30 minutes in polar plot analysis (Fig. 3 (C)), the SD of the Bland-Altman analysis increased (Fig. 3 (E)). This discrepancy prompted further comparison between polar plot analyses conducted with and without an exclusion zone. Specifically, we compared results using an exclusion zone of 0.6 L/min/m² versus 0.0 L/min/m². As seen in Fig. 5 (C), omitting the exclusion zone increased polar angle SD. These results suggest that, at τ > 30 minutes, the amplitude of esCCI was significantly attenuated, and the time shift was further prolonged, resulting in greater divergence between CCI and esCCI. Consequently, the SD in the Bland-Altman analysis and the polar angle SD without exclusion increased. However, since the magnitude of changes in both CCI and moving-averaged esCCI decreased as τ increased, the number of excluded data points (i.e., exclusion rate) also rose. This increasing exclusion rate contributed to the observed reduction in polar angle SD when an exclusion zone of 0.6 L/min/m² was applied. Appendix 1 further illustrates the relationship between exclusion rate and polar angle SD, confirming that higher exclusion rates consistently yielded lower SD values. Overall, this study applied moving averages to facilitate a more accurate comparison of trending ability between CCI and other cardiac output indices with differing time responses. The optimal moving average duration (t) for achieving a concordance rate above 92% between CCI and esCCI was found to be 20–30 minutes. Critchley et al. [ 2 ] proposed a threshold of 92% concordance when using a 15% exclusion zone based on mean CO; our findings support this criterion within the specified t range. Although APCI did not reach the 92% threshold, it approached 90% within 21–27 minutes (Fig. 3 (A)). The wide variability in effective moving average times suggests that multiple factors may contribute to the delayed response observed in CCI. These include technical aspects such as the thermal filament heating algorithm [ 1 , 23 ], clinical conditions like mitral regurgitation [ 1 , 24 ], and physiological changes associated with bleeding or resuscitation [ 3 , 26 ]. Given the variability and uncertainty in delay times, CCO alone may be insufficient for clinical decision-making. As noted by Mihm et al. [ 26 ], the reliability of CCO is condition-dependent and should be interpreted alongside other continuous hemodynamic parameters. Oh et al. [ 14 ] assessed the trending ability of APCO and CCO using R-squared-based time adjustments for APCO. However, their four-quadrant plot analysis yielded unacceptably low concordance. As shown in Fig. 4 (A), simple time shifting did not achieve satisfactory results in our study either, emphasizing the advantage of moving averages, which incorporate both time shift and filtering. Takakura et al. [ 15 ] compared esCCO and CCO values before and after extubation in ICU patients, with and without applying a 20-minute moving average to esCCO. They reported that moving averaging reduced the SD of the difference between the two indices, supporting our current findings, although they did not employ polar plot analysis. Study Limitations (Rewritten) A primary limitation of this study was the lack of detailed information regarding the specific averaging algorithm employed by the CCO monitor. We inferred that the observed response delay was attributable to a moving average process. Accordingly, the objective of this report was to evaluate the effectiveness of applying moving average processing when assessing trending ability between two monitors with differing response times. Another limitation concerns the unequal number of data points analyzed across different t (moving average window) values. This discrepancy arose due to two factors: Larger t values resulted in delayed start times and earlier end times for output data, thereby reducing the total number of available data points. The moving average was not computed if the averaging window included even a single missing value. As τ increased, the likelihood of encountering such missing data also rose, further reducing the number of usable data points. A key challenge for future research is to determine how best to mitigate the effects of data loss and delayed signal responsiveness introduced by large time averaging windows (τ), which can obscure physiologically relevant changes and affect method comparisons. Clear, enough? Finally, although Critchley et al. [ 27 ] recommend that the polar mean angle remain within ± 5° and the radial limits of agreement within ± 30° when comparing CO monitors against thermodilution reference measurements, we focused on trending ability using a single index—namely, the polar concordance rate at 30°. Conclusions This study retrospectively analyzed clinical data using polar plots and Bland-Altman methods to investigate how moving average time affects trending ability among cardiac output monitors. Due to the combined effects of time shift and filtering inherent in moving average processing, an acceptable polar concordance rate at 30° was achieved between CCI and esCCI when esCCI was averaged over 20–30 minutes. Similarly, high concordance rates were observed between CCI and APCI with moving average windows in a comparable range. These findings suggest that moving average processing is a practical and effective method for evaluating the trending ability of cardiac output monitors that differ in response time. Declarations Acknowledgments The authors have no acknowledgments. Author contributions YS analyzed the literature, created the model, analyzed the data, post-processed the findings and was a major contributor to writing the manuscript. RO analyzed the literature, designed the study, created the model, and was a major contributor to reviewing and editing the manuscript. Funding Open Access funding enabled and organized by Nihon Kohden Corp. Data availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable requests. Ethics approval and consent to participate This study received approval from the ethics committee of Toho University Omori Medical Center. All procedures adhered to the principles of the 1964 Helsinki Declaration and its subsequent revisions or similar ethical standards. Written consent was obtained from all patients who had undergone kidney transplants. Consent for publication Not applicable. Competing interests Yoshihiro Sugo works for Nihon Kohden Corporation. Ryoichi Ochiai is an advisor to Nihon Kohden Corporation. Author details 1 Development Department, Ogino Memorial Laboratory, Nihon Kohden Corporation, Saitama, Japan. 2 Faculty of Medicine, Toho University, Tokyo, Japan. 3 Senior Counselor, Tokushukai Medical Corporation, Tokyo, Japan References Reuter DA, Huang C, Edrich T, et al. Cardiac output monitoring using indicator-dilution techniques: basics, limits, and perspectives. Anesth Analg. 2010;110(3):799–811. 10.1213/ANE.0b013e3181cc885a . Critchley LA, Lee A, Ho AM. A critical review of the ability of continuous cardiac output monitors to measure trends in cardiac output. 2010; 111(5): 1180–92. 10.1213/ANE.0b013e3181f08a5b . Epub 2010 Aug 24. Harvey S, Harrison DA, Singer M, et al. Assessment of the clinical effectiveness of pulmonary artery catheters in management of patients in intensive care (PAC-Man): a randomized controlled trial. Lancet. 2005;366(9484):472–7. 10.1016/S0140-6736(05)67061-4 . Sandham JD, Hull RD, Brant RF, et al. A randomized, controlled trial of the use of pulmonary-artery catheters in high-risk surgical patients. N Engl J Med. 2003;348(1):5–14. 10.1056/NEJMoa021108 . Xu F, Wang Q, Zhang H, Chen S et al. Use of pulmonary artery catheter in coronary artery bypass graft. Costs and long-term outcomes. PLoS One. 2015 17;10(2):e0117610. 10.1371/journal.pone.0117610 . eCollection 2015. Judge O, Ji F, Fleming N et al. Current use of the pulmonary artery catheter in cardiac surgery: a survey study. J Cardiothorac Vasc Anesth. 2015;29(1):69–75. 10.1053/j.jvca.2014.07.016 . Epub 2014 Oct 30. Bootsma IT, Boerma EC, Scheeren TWL, et al. The contemporary pulmonary artery catheter. Part 2: measurements, limitations, and clinical applications. J Clin Monit Comput. 2022;36(1):17–31. 10.1007/s10877-021-00673-5 . Epub 2021 Mar 1. Squara P, Cecconi M, Rhodes A, et al. Tracking changes in cardiac output: methodological considerations for the validation of monitoring devices. Intensive Care Med. 2009;35(10):1801–8. 10.1007/s00134-009-1570-9 . Epub 2009 Jul 11. Zöllner C, Goetz AE, Weis M, et al. Continuous cardiac output measurements do not agree with conventional bolus thermodilution cardiac output determination. Can J Anaesth. 2001;48(11):1143–7. 10.1007/BF03020382 . Slagt C, Malagon I, Groeneveld ABJ. Systematic review of uncalibrated arterial pressure waveform analysis to determine cardiac output and stroke volume variation. Br J Anaesth. 2014;112(4):626–37. 10.1093/bja/aet429 . Cote CJ, Sui J, Anderson TA, et al. Continuous noninvasive cardiac output in children: is this the next generation of operating room monitors? Initial experience in 402 pediatric patients. Paediatr Anaesth. 2015;25(2):150–9. 10.1111/pan.12441 . Sugo Y, Ukawa T, Takeda S et al. A novel continuous cardiac output monitor based on pulse wave transit time. Annu Int Conf IEEE Eng Med Biol Soc. 2010; 2010: 2853–2856. 10.1109/IEMBS.2010.5626343 Terada T, Oiwa A, Maemura Y, et al. Comparison of the ability of two continuous cardiac output monitors to measure trends in cardiac output: estimated continuous cardiac output measured by modified pulse wave transit time and an arterial pulse contour-based cardiac output device. J Clin Monit Comput. 2016;30(5):621–7. 10.1007/s10877-015-9772-x . Oh C, Lee S, Oh P, et al. Comparison between fourth-generation FloTrac/Vigileo system and continuous thermodilution technique for cardiac output estimation after time adjustment during off-pump coronary artery bypass graft surgery: A retrospective cohort study. J Clin Med. 2022;11(20):6093. 10.3390/jcm11206093 . Takakura M, Fujii T, Taniguchi T, et al. Accuracy of a noninvasive estimated continuous cardiac output measurement under different respiratory conditions: a prospective observational study. J Anesth. 2023;37(3):394–400. 10.1007/s00540-023-03176-6 . Epub 2023 Mar 11. Saugel B, Grothe O, Wagner JY. Tracking changes in cardiac output: Statistical considerations on the 4-quadrant plot and the polar plot methodology. Anesth Analg. 2015;121(2):514–24. 10.1213/ANE.0000000000000725 . Lazor MA, Pierce ET, Stanley GD, et al. Evaluation of the accuracy and response time of STAT-mode continuous cardiac output. J Cardiothorac Vasc Anesth. 1997;11(4):432–6. 10.1016/s1053-0770(97)90050-1 . Williams B, Lacy PS, Yan P, et al. Development and validation of a novel method to derive central aortic systolic pressure from the radial pressure waveform using an n-point moving-average method. J Am Coll Cardiol. 2011;57(8):951–61. 10.1016/j.jacc.2010.09.054 . Lee J. Motion artifacts reduction from PPG using cyclic moving average filter. Technol Health Care. 2014;22(3):409–17. 10.3233/THC-140798 . Squara P, Scheeren TWL, Aya HD, et al. Metrology part 2: Procedures for the validation of major measurement quality criteria and measuring instrument properties. J Clin Monit Comput. 2021;35(1):27–37. 10.1007/s10877-020-00495-x . Epub 2020 Mar 18. Yang XX, Critchley LA, Rowlands DK, et al. Systematic error of cardiac output measured by bolus thermodilution with a pulmonary artery catheter compared with that measured by an aortic flow probe in a pig model. J Cardiothorac Vasc Anesth. 2013;27(6):1133–9. 10.1053/j.jvca.2013.05.020 . Epub 2013 Sep 10. Vincent JL, Well MH. Fluid challenge revisited. Crit Care Med. 2006;34(5):1333–7. 10.1097/01.CCM.0000214677.76535.A5 . Zöllner C, Goetz AE, Weis M, et al. Continuous cardiac output measurements do not agree with conventional bolus thermodilution cardiac output determination. Can J Anaesth. 2001;48(11):1143–7. 10.1007/BF03020382 . Jardin F. PEEP, tricuspid regurgitation, and cardiac output. Intensive Care Med. 1997;23(8):806–7. 10.1007/s001340050415 . Figueiredo LFP, Malbouisson LM, Varicoda EY, et al. Thermal filament continuous thermodilution cardiac output delayed response limits its value during acute hemodynamic instability. J Trauma. 1999;47(2):288–93. 10.1097/00005373-199908000-00011 . Mihm FG, Gettinger A, Hanson CW, et al. A multicenter evaluation of a new continuous cardiac output pulmonary artery catheter system. Crit Care Med. 1998;26(8):1346–50. 10.1097/00003246-199808000-00018 . Critchley LA, Yang XX, Lee A. Assessment of trending ability of cardiac output monitors by polar plot methodology. J Cardiothorac Vasc Anesth. 2011;25(3):536–46. 10.1053/j.jvca.2011.01.003 . Epub 2011 Mar 17. Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Published Journal Publication published 06 Oct, 2025 Read the published version in BMC Biomedical Engineering → Version 1 posted Editorial decision: Revision requested 04 Aug, 2025 Reviews received at journal 27 Jul, 2025 Reviews received at journal 27 Jul, 2025 Reviewers agreed at journal 21 Jul, 2025 Reviewers agreed at journal 21 Jul, 2025 Reviewers agreed at journal 21 Jul, 2025 Reviewers agreed at journal 21 Jul, 2025 Reviewers invited by journal 21 Jul, 2025 Editor invited by journal 18 Jul, 2025 Editor assigned by journal 18 Jul, 2025 Submission checks completed at journal 18 Jul, 2025 First submitted to journal 14 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7116969","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":488455523,"identity":"b153086e-7343-4049-85c6-dbf23f04f99e","order_by":0,"name":"Yoshihiro Sugo","email":"","orcid":"","institution":"Nihon Kohden Corporation","correspondingAuthor":false,"prefix":"","firstName":"Yoshihiro","middleName":"","lastName":"Sugo","suffix":""},{"id":488455524,"identity":"9cf6aca2-3a0d-41a1-a4e0-6d2b2a124a03","order_by":1,"name":"Ryoichi Ochiai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYPCCAzwMzMwHgAwJGVK0sCWAtPAQrQWIeQxALMJazNt7DBg+1NyR4W/n+fzqRo0FDwP74aMb8GmROXPGgHHGsWc8Eod5t1nnHAM6jCct7QY+LRISOQbMvA2HeRiAWoxz2IBaJHjMiNMif5jnmXHOP1K0GBzmYX6c20aMFp5jBQdnHDvMY3iYzYw5t0+Ch42gX9ibNz74UHPYXu784cefc77VyfGzHz6GVwsDA4fBASiLTQJM4lcOAuwPYCzmD4RVj4JRMApGwUgEAG8xQknV2zgqAAAAAElFTkSuQmCC","orcid":"","institution":"Toho University","correspondingAuthor":true,"prefix":"","firstName":"Ryoichi","middleName":"","lastName":"Ochiai","suffix":""}],"badges":[],"createdAt":"2025-07-14 04:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7116969/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7116969/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s42490-025-00101-8","type":"published","date":"2025-10-06T15:58:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87664186,"identity":"0c171ff1-5fe9-4083-86d5-8b45363875f6","added_by":"auto","created_at":"2025-07-27 10:57:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":93113,"visible":true,"origin":"","legend":"\u003cp\u003eExplanation of each processing method\u003c/p\u003e\n\u003cp\u003eExplanation of each processing method when τ=4. From top to bottom: Moving average, Time shift, Filtering.\u003c/p\u003e\n\u003cp\u003ei represents the address of the corresponding signal for the processed output. The formulas for Moving average, Time shift, and Filtering are shown below. mov_ave, shift, and filt respectively refer to the calculation results of Moving average, Time Shift, and Filtering.\u003c/p\u003e\n\u003cp\u003emov_avg = (a(i) + a_(i-1) + a(i-2) + a(i-3) + a(i-4)) / 5\u003c/p\u003e\n\u003cp\u003eshift = a(i-2)\u003c/p\u003e\n\u003cp\u003efilt = (a(i-2) + a(i-1) + a(i) + a(i+1) + a(i+2)) / 5\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7116969/v1/292bbeda2c07e17fc933efd9.png"},{"id":87665531,"identity":"af27c961-d4a6-4a53-b41d-0cf2dd2d2d37","added_by":"auto","created_at":"2025-07-27 11:05:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3099563,"visible":true,"origin":"","legend":"\u003cp\u003ePolar concordance rate at 30° for esCCI and CCI, and APCI and CCI\u003c/p\u003e\n\u003cp\u003eThe polar concordance rate at 30°, with τ as the moving average time and t1 as the time interval for polar plots analysis is shown. τ ranges from 0 to 60 minutes at 1-minute intervals, and t1 ranges from 1 to 60 minutes at 1-minute intervals.\u003c/p\u003e\n\u003cp\u003eThe exclusion zone is set to 0.6 [L/min/m\u003csup\u003e2\u003c/sup\u003e].\u003c/p\u003e\n\u003cp\u003eThe line segments corresponding to each t1 represent the relationship between τ and the polar concordance rate at 30°.\u003c/p\u003e\n\u003cp\u003eThe X-axis represents moving average time (τ [min]), the Y-axis represents time interval (t1 [min]), and the Z-axis represents the polar concordance rate at 30° [%].\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7116969/v1/7bd8138f36135a202606c830.png"},{"id":87664188,"identity":"c5ac2be9-aa47-47c9-8446-c031f7b5e91f","added_by":"auto","created_at":"2025-07-27 10:57:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":471081,"visible":true,"origin":"","legend":"\u003cp\u003ePolar plots analysis and Bland-Altman analysis for esCCI and CCI, and APCI and CCI with t1 fixed at 10 minutes\u003c/p\u003e\n\u003cp\u003eResults of polar plots analysis, Bland-Altman analysis for esCCI and CCI, and APCI and CCI with t1 fixed at 10 minutes are shown.\u003c/p\u003e\n\u003cp\u003eτ \u0026nbsp;ranges from 0 to 60 minutes at 1-minute intervals, and the exclusion zone is set to 0.6 [L/min/m\u003csup\u003e2\u003c/sup\u003e].\u003c/p\u003e\n\u003cp\u003eA, B, and C represent the analysis values for polar plots analysis, and D, E, and F represent the values for Bland-Altman analysis.\u003c/p\u003e\n\u003cp\u003eBlack circles represent CCI and esCCI, and white circles represent CCI and APCI analysis values.\u003c/p\u003e\n\u003cp\u003eThe X-axis represents the moving average time (τ = 0 to 60 [min]).\u003c/p\u003e\n\u003cp\u003eThe Y-axis for A to F is as follows:\u003c/p\u003e\n\u003cp\u003eA: Polar concordance rate at 30° [%]\u003c/p\u003e\n\u003cp\u003eB: Mean polar angle [°]\u003c/p\u003e\n\u003cp\u003eC: Polar angle standard deviation [°]\u003c/p\u003e\n\u003cp\u003eD: Bias [L/min/m\u003csup\u003e2\u003c/sup\u003e]\u003c/p\u003e\n\u003cp\u003eE: Correlation coefficient between difference and mean\u003c/p\u003e\n\u003cp\u003eF: Standard deviation [L/min/m\u003csup\u003e2\u003c/sup\u003e]\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7116969/v1/cb4ee5b11d0c611301eddcaa.png"},{"id":87664197,"identity":"fa7b9596-dd6b-4c95-b1ca-add312041e58","added_by":"auto","created_at":"2025-07-27 10:57:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":437712,"visible":true,"origin":"","legend":"\u003cp\u003ePolar plots analysis and Bland-Altman analysis for esCCI and CCI with three types of processing\u003c/p\u003e\n\u003cp\u003eResults of polar plots analysis and Bland-Altman analysis for esCCI and CCI with t1 fixed at 10 minutes and τ as a variable with three types of processing: Moving average, Time shift, and Filtering.\u003c/p\u003e\n\u003cp\u003eτ \u0026nbsp;ranges from 0 to 60 minutes at 1-minute intervals, and the exclusion zone is set to 0.6 [L/min/m\u003csup\u003e2\u003c/sup\u003e].\u003c/p\u003e\n\u003cp\u003eA, B, and C represent the analysis values for polar plots analysis, and D, E, and F represent the values for Bland-Altman analysis.\u003c/p\u003e\n\u003cp\u003eBlack circles, white circles, and gray triangles represent the results of processing using Moving average, Time shift, and Filtering, respectively.\u003c/p\u003e\n\u003cp\u003eThe X-axis represents moving average time (τ = 0 to 60 [min]).\u003c/p\u003e\n\u003cp\u003eThe Y-axis for A to F is as follows:\u003c/p\u003e\n\u003cp\u003eA: Polar concordance rate at 30° [%]\u003c/p\u003e\n\u003cp\u003eB: Mean polar angle [°]\u003c/p\u003e\n\u003cp\u003eC: Polar angle standard deviation [°]\u003c/p\u003e\n\u003cp\u003eD: Bias [L/min/m\u003csup\u003e2\u003c/sup\u003e]\u003c/p\u003e\n\u003cp\u003eE: Correlation coefficient between difference and mean\u003c/p\u003e\n\u003cp\u003eF: Standard deviation [L/min/m\u003csup\u003e2\u003c/sup\u003e]\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7116969/v1/5caa4f990f021ef6e28f19e5.png"},{"id":87665532,"identity":"f57f5623-a60a-4ad1-a98e-4b59d5d1f1d6","added_by":"auto","created_at":"2025-07-27 11:05:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":231426,"visible":true,"origin":"","legend":"\u003cp\u003ePolar plots analysis for esCCI and CCI with different exclusion zones\u003c/p\u003e\n\u003cp\u003eResults of polar plots analysis for esCCI and CCI with t1 fixed at 10 minutes and varying exclusion zones.\u003c/p\u003e\n\u003cp\u003eτ \u0026nbsp;ranges from 0 to 60 minutes at 1-minute intervals.\u003c/p\u003e\n\u003cp\u003eBlack circles represent the exclusion zone of 0.6 [L/min/m\u003csup\u003e2\u003c/sup\u003e], and white circles represent the exclusion zone of 0 [L/min/m\u003csup\u003e2\u003c/sup\u003e].\u003c/p\u003e\n\u003cp\u003eThe X-axis represents moving average time (τ = 0 to 60 [min]).\u003c/p\u003e\n\u003cp\u003eThe Y-axis for A to C is as follows:\u003c/p\u003e\n\u003cp\u003eA: Polar concordance rate at 30° [%]\u003c/p\u003e\n\u003cp\u003eB: Mean polar angle [°]\u003c/p\u003e\n\u003cp\u003eC: Polar angle standard deviation [°]\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7116969/v1/8c858bfb7a43629bf823bfec.png"},{"id":93420929,"identity":"08e9c8bb-7120-42b0-a031-ee1fb3657a5f","added_by":"auto","created_at":"2025-10-13 16:10:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4632516,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7116969/v1/6e65740c-e70e-487b-8fb4-043a34fc6c9c.pdf"},{"id":87665530,"identity":"64eca822-6759-4791-a300-4eafecb821f8","added_by":"auto","created_at":"2025-07-27 11:05:04","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2574043,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-7116969/v1/27003cf3ed27b226676681f5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Moving-average processing enables accurate quantification of time delay and compares the trending ability of cardiac output monitors with different response times","fulltext":[{"header":"Introduction","content":"\u003cp\u003eContinuous cardiac output (CCO) monitoring using pulmonary artery (PA) thermodilution is known to provide delayed information in clinical settings due to its reliance on historical data1,2. Moreover, several reports have suggested that hemodynamic management using the Swan-Ganz catheter does not improve patient outcomes, 3\u0026ndash;5, although it continues to be used. 6 This highlights the need for caution in its application. 7\u0026ndash;9\u003c/p\u003e\u003cp\u003eIn contrast, various continuous cardiac output (CO) monitoring technologies that operate on a beat-to-beat basis\u0026mdash;such as those using blood pressure waveforms10 or bioimpedance11\u0026mdash;have been introduced over the past two decades. One such example is the estimated continuous cardiac output (esCCO) monitor, which calculates CO based on the time interval from the R-wave of the electrocardiogram to the arrival of the peripheral pulse oximetry waveform12,13.\u003c/p\u003e\u003cp\u003eRecent studies have compared beat-to-beat monitors, such as arterial pressure-based cardiac output (APCO) and esCCO, to traditional cardiac output (CCO) monitors. 14,15 However, the validity of these comparisons remains uncertain. As noted by Saugel et al.16, when evaluating the trending ability of two CO monitors, it is crucial to account for their respective response delays.\u003c/p\u003e\u003cp\u003eWhile the exact cause of delays in CCO monitoring remains unclear, previous research17 suggests that moving average signal processing may be a contributing factor. Moving averages can introduce two key effects on a signal: time shift and filtering. Therefore, to understand the nature of such delays, it is important to examine these two effects separately in addition to evaluating the moving average as a whole.\u003c/p\u003e\u003cp\u003eThe moving average is a well-known low-pass filter and has been previously used to estimate central blood pressure waveforms from peripheral ones18 and to remove motion artifacts from distorted pulse waveforms19. The amplitude and phase characteristics of a signal processed by a moving average can be described analytically. If the original signal is a sinusoidal waveform:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{y}=\\text{x}\\left(t\\right)=\\text{sin}\\left(2\\pi\\:ft\\right)\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAnd the moving average is calculated over a time window t, then the filtered signal becomes:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{y}^{\\tau\\:}=\\frac{1}{\\tau\\:}\\underset{t-\\tau\\:}{\\overset{t}{\\int\\:}}\\text{sin}\\left(2\\pi\\:ft\\right)dt=\\frac{1}{2\\pi\\:f\\tau\\:}\\sqrt{2\\left(1-\\text{cos}\\left(2\\pi\\:f\\tau\\:\\right)\\right)}\\times\\:\\text{sin}\\left(2\\pi\\:f\\left(t-\\frac{\\tau\\:}{2}\\right)\\right)\\:\\:\\:\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFrom Eq.\u0026nbsp;(2), the amplitude of the filtered signal is:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\frac{1}{2\\pi\\:f\\tau\\:}\\sqrt{2\\left(1-\\text{cos}\\left(2\\pi\\:f\\tau\\:\\right)\\right)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eand the phase delay corresponds to half the averaging time:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:\\frac{\\tau\\:}{2}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIf two signals have different moving average times t0 and t, then their amplitude ratio r and phase difference f are given by:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\text{r}=\\raisebox{1ex}{$\\tau\\:$}\\!\\left/\\:\\!\\raisebox{-1ex}{${\\tau\\:}_{0}$}\\right.\\times\\:\\raisebox{1ex}{$\\text{sin}\\left(\\pi\\:f{\\tau\\:}_{0}\\right)$}\\!\\left/\\:\\!\\raisebox{-1ex}{$\\text{sin}\\left(\\pi\\:f\\tau\\:\\right)$}\\right.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{\\phi\\:}=\\raisebox{1ex}{${\\tau\\:}_{0}-\\tau\\:$}\\!\\left/\\:\\!\\raisebox{-1ex}{$2$}\\right.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAccording to Equations (\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and (\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e4\u003c/span\u003e), when t\u0026thinsp;=\u0026thinsp;t0, the amplitude ratio is 1 and the phase difference is 0. Under these conditions, Polar plot analysis, as described by Critchley et al.2, yields optimal trending agreement, characterized by a mean polar angle of 0\u0026deg; and a minimal polar angle standard deviation (SD).\u003c/p\u003e\u003cp\u003eIf the delay in CCO monitoring arises primarily from moving average processing, then applying appropriate moving average adjustments to CO monitor data should improve their concordance, particularly when evaluated using polar plot metrics.\u003c/p\u003e\u003cp\u003eAccordingly, this study applied moving average processing to compare the trending ability of two CO monitors with different response delays. In addition to standard moving averages, we independently assessed the individual effects of time shifts and signal filtering. To address the potential for information loss inherent in averaging20, we applied a wide range of moving average windows, exceeding those previously examined14,15, in search of the optimal range.\u003c/p\u003e\u003cp\u003eBesides Polar plot analysis\u0026mdash;which evaluates trending ability\u0026mdash;we also conducted Bland-Altman analysis to assess the standard deviation (SD) of differences and to investigate the presence of proportional bias. The rationale for including Bland-Altman analysis is that the total variance observed in the differences includes both between-subject and within-subject components. Since trending ability is inherently a within-subject property, a prominent within-subject variance would be reflected in the SD of differences. The degree of proportional bias is likewise indicative of trending performance21.\u003c/p\u003e\u003cp\u003eIn summary, this study investigates the necessity and optimal conditions of moving average processing when comparing CCO monitors with other CO monitoring methods. The relationship between moving average time and trending performance, as revealed by Polar plot and Bland-Altman analyses, forms the basis for this approach using clinical data.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe conducted simulations of continuous cardiac index (CCI) data measured by pulmonary artery (PA) thermodilution, as well as arterial pressure cardiac index (APCI) and estimated continuous cardiac index (esCCI) data. These datasets were originally obtained in the study by Terada et al. [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. CCI was measured using balloon-tipped, flow-directed thermodilution pulmonary artery catheters (Edwards Lifesciences, Irvine, CA, USA) and the Hemodynamic Monitor Vigilance II (Edwards Lifesciences, Irvine, CA, USA). APCI was measured using the FloTrac/Vigileo\u0026trade; system, Version 3 (Edwards Lifesciences, Irvine, CA, USA). Pulse wave transit time for esCCI was obtained using a BSM-9101 bedside monitor (Nihon Kohden, Tokyo, Japan), and transmitted to a personal computer, where esCCI was calculated using a custom C-compiled program. APCI values were averaged over 20-second intervals, while esCCI was computed using a dynamic average of 64 consecutive heartbeats.\u003c/p\u003e\n\u003cp\u003eThe dataset comprised 20 patients undergoing kidney transplantation. Data were collected at 1-minute intervals, yielding a total of 5,027 data points over 83.8 hours. The study was approved by the ethics committee of Toho University Omori Medical Center, and all procedures conformed to the ethical standards of the 1964 Declaration of Helsinki and its later amendments. Written informed consent was obtained from all participating patients.\u003c/p\u003e\n\u003cp\u003eThree types of signal processing were applied to the esCCI and APCI data, as illustrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1. Moving Average: The signal was averaged over a past window of duration \u0026tau; (minutes) up to the current time:\u003c/p\u003e\n\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ3\" class=\"mathdisplay\"\u003e$$\\:{a}_{mov\\_avg}=\\raisebox{1ex}{$\\sum\\:_{j=0}^{m}{a}_{i-j}$}\\!\\left/\\:\\!\\raisebox{-1ex}{$m+1$}\\right.$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\n\u003cp class=\"EquationNumber\"\u003e2. Time Shift: The signal was shifted in time by \u0026tau;/2, using the past value as the\u003c/p\u003e\n\u003cp class=\"EquationNumber\"\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{a}_{shift}={a}_{i-n}\\)\u003c/span\u003e\u003c/span\u003e (6)\u003c/p\u003e\n\u003cp class=\"EquationNumber\"\u003e3. Filtering: A symmetric moving average was applied, centered on the current time, using values from \u0026tau;/2 before to \u0026tau;/2 after:\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ4\" class=\"mathdisplay\"\u003e$$\\:{a}_{filt}=\\raisebox{1ex}{$\\sum\\:_{j=-n}^{n}{a}_{i-j}$}\\!\\left/\\:\\!\\raisebox{-1ex}{$2n+1$}\\right.$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eHere, if \u0026tau; is even, m\u0026thinsp;=\u0026thinsp;\u0026tau; and n\u0026thinsp;=\u0026thinsp;\u0026tau;/2.\u003c/p\u003e\n\u003cp\u003ePolar Concordance Rate Analysis (esCCI vs. CCI and APCI vs. CCI)\u003c/p\u003e\n\u003cp\u003eCCI was used as the reference signal, and esCCI and APCI served as test signals after application of a moving average with varying durations \u0026tau;. \u0026tau; was varied from 0 minutes (no averaging) to 60 minutes in 1-minute increments, generating 61 different datasets each for esCCI and APCI. For each \u0026tau;, we computed the differences in measurements over time intervals t1, ranging from 1 to 60 minutes. This resulted in 3,660 (61 \u0026times; 60) combinations of \u0026tau; and t1 for both esCCI and APCI, respectively. For each of the 20 patients, we computed the differential data pairs between CCI and the corresponding esCCI/APCI values and performed Polar plot analysis [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eData pairs with an average CCI change and test signal change (esCCI or APCI) of less than 0.6 L/min/m\u0026sup2; were excluded. This threshold corresponded to 15% of the mean CCI value, consistent with the previous study [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003ePolar Plot and Bland-Altman Analyses (esCCI vs. CCI and APCI vs. CCI)\u003c/p\u003e\n\u003cp\u003ePolar plot analyses were conducted with CCI as the reference and esCCI and APCI (moving averaged at \u0026tau;) as test signals. The time interval t1 was fixed at 10 minutes, based on prior studies [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e], which suggested it as an appropriate duration for assessing fluid responsiveness. For each \u0026tau;, we calculated the polar concordance rate at 30\u0026deg;, the mean polar angle, and the standard deviation (SD) of polar angles. The exclusion zone was defined as 0.6 L/min/m\u0026sup2;.\u003c/p\u003e\n\u003cp\u003eFor Bland-Altman analysis, we computed the bias, the SD of the differences, and the Pearson correlation coefficient between the differences (Y-axis) and the means (X-axis). The correlation coefficient was interpreted as an index of proportional bias.\u003c/p\u003e\n\u003cp\u003eComparison of Signal Processing Methods: Polar Plot and Bland-Altman Analyses\u003c/p\u003e\n\u003cp\u003eTo evaluate the effects of different processing methods, esCCI signals were processed using three approaches: moving average, time shift, and filtering. The reference remained CCI. For all methods, t1 was fixed at 10 minutes, and \u0026tau; was varied. Polar plot and Bland-Altman analyses were conducted as described above. The exclusion zone was set to 0.6 L/min/m\u0026sup2; in all comparisons.\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were conducted using Microsoft Excel (Office 365; Microsoft Corp., Redmond, WA, USA) and MATLAB R2023a (MathWorks, Natick, MA, USA).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003ePolar Concordance Rate at 30\u0026deg; for esCCI vs. CCI and APCI vs. CCI\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the results of the polar concordance rate at 30\u0026deg;, applying an exclusion zone of 0.6 L/min/m\u0026sup2;, across varying values of t and t1. For both esCCI and APCI, the concordance rate fluctuated more noticeably at lower t1 values, indicating a higher dependency on t. As t1 increased, these fluctuations diminished. Across the entire t1 range, the highest concordance rates were consistently observed at mid-range values of t.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePolar Plot and Bland-Altman Analyses for esCCI vs. CCI and APCI vs. CCI\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA illustrates the polar concordance rate at 30\u0026deg; with an exclusion zone of 0.6 L/min/m\u0026sup2;, analyzed while varying t and keeping t1\u0026thinsp;=\u0026thinsp;10 minutes fixed. For esCCI, the concordance rate was below 60% without any moving average processing (t\u0026thinsp;=\u0026thinsp;0). However, when t increased to between 17 and 30 minutes, the concordance rate exceeded 90%, peaking between 20 and 30 minutes. In comparison, APCI reached approximately 90% concordance within a narrower window between t\u0026thinsp;=\u0026thinsp;21 and 27 minutes.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB shows that the mean polar angle decreased with increasing t for both APCI and esCCI. The mean polar angle crossed zero at t\u0026thinsp;=\u0026thinsp;19 minutes for APCI and t\u0026thinsp;=\u0026thinsp;12 minutes for esCCI. Similarly, the polar angle standard deviation (SD) also trended downward with increasing t (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e\n\u003cp\u003eIn the Bland-Altman analysis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD), the bias shifted in the negative direction as t increased for both signals. The smallest bias (closest to zero) was observed at t\u0026thinsp;=\u0026thinsp;0. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eE presents the correlation coefficient between the difference (Y-axis) and the mean value (X-axis), which initially showed positive values at t\u0026thinsp;=\u0026thinsp;0 and transitioned toward negative values with increasing t. The zero crossing of this coefficient occurred at t\u0026thinsp;=\u0026thinsp;8 minutes for APCI and t\u0026thinsp;=\u0026thinsp;12 minutes for esCCI.\u003c/p\u003e\n\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eF, the SD of the differences decreased with increasing t, reaching a minimum at \u0026tau;\u0026thinsp;=\u0026thinsp;24 minutes for APCI and \u0026tau;\u0026thinsp;=\u0026thinsp;21 minutes for esCCI, before increasing again at higher པ values.\u003c/p\u003e\n\u003cp\u003eComparison of esCCI vs. CCI Using Three Signal Processing Methods: Moving Average, Time Shift, and Filtering\u003c/p\u003e\n\u003cp\u003eIn addition to the moving average method described above, esCCI was also analyzed using time shift and filtering techniques, with t as the varying parameter and t1\u0026thinsp;=\u0026thinsp;10 minutes fixed.\u003c/p\u003e\n\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA, the polar concordance rate at 30\u0026deg; peaked between t\u0026thinsp;=\u0026thinsp;20 and 30 minutes in both the moving average and time shift methods. However, the maximum concordance rate using the time shift method did not exceed 85.5%. In contrast, the filtering method did not exhibit a similar \u0026tau;-dependent trend in concordance rate.\u003c/p\u003e\n\u003cp\u003eIn terms of mean polar angle (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB), both the moving average and filtering methods showed a decreasing trend with increasing t. The time shift method, however, did not display a clear trend. Regarding polar angle SD (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC), the filtering method showed a consistent decrease beyond \u0026tau;\u0026thinsp;=\u0026thinsp;10 (equivalent to a 5-minute filtering window). The time shift method exhibited a U-shaped pattern, with SD decreasing to a minimum between t\u0026thinsp;=\u0026thinsp;20\u0026ndash;30 (i.e., a shift of 10\u0026ndash;15 minutes), followed by an increase with further t increments.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD shows that bias decreased in the negative direction with increasing t for the time shift method, whereas the filtering method demonstrated an increase in bias that approached zero.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE presents the correlation coefficients between the difference and mean values. Similar to the moving average method, both the time shift and filtering methods showed a shift from a positive value at t\u0026thinsp;=\u0026thinsp;0 toward negative values as t increased.\u003c/p\u003e\n\u003cp\u003eFinally, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eF, SD decreased with increasing t in the time shift method, reaching a minimum at t\u0026thinsp;=\u0026thinsp;20 (a shift of 10 minutes), before increasing again. In contrast, the filtering method showed a continuous decrease in SD with increasing t.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study evaluated the effectiveness of applying moving average processing when comparing CCI with beat-to-beat cardiac indices exhibiting different response times, namely esCCI and APCI. We confirmed that esCCI achieved an acceptable concordance rate when a moving average of 20\u0026ndash;30 minutes was applied, while APCI demonstrated a high concordance rate within the 21\u0026ndash;27-minute range (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e(A)).\u003c/p\u003e\n\u003cp\u003eWe further explored the underlying mechanisms of moving average effects, specifically decomposing them into time shift and filtering components. As shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e(B), the mean polar angle crossed zero at t\u0026thinsp;=\u0026thinsp;8 minutes due to filtering, subsequently shifting further into the negative direction. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e(C) demonstrated that the decrease in polar angle SD up to t\u0026thinsp;=\u0026thinsp;24 minutes was primarily attributed to time shift effects; beyond this point, filtering effects predominated. These findings suggest that moving averages exert their influence through a combination of time shift and signal smoothing (filtering).\u003c/p\u003e\n\u003cp\u003eBoth the mean polar angle and the correlation coefficient between the difference (Y-axis) and mean (X-axis) values in the Bland-Altman analysis shifted toward the negative direction as \u0026tau; increased. In esCCI, zero crossings occurred at t\u0026thinsp;=\u0026thinsp;12 minutes for both the mean polar angle and the correlation coefficient. In APCI, the zero crossing of the mean polar angle occurred at t\u0026thinsp;=\u0026thinsp;19 minutes, while that of the correlation coefficient occurred earlier at t\u0026thinsp;=\u0026thinsp;8 minutes. The alignment of zero crossings in esCCI suggests a correspondence between systematic (proportional) error in Bland-Altman analysis and the mean polar angle in polar plot analysis, as previously described [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eAlthough polar angle SD decreased with increasing \u0026tau; beyond 30 minutes in polar plot analysis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e(C)), the SD of the Bland-Altman analysis increased (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e(E)). This discrepancy prompted further comparison between polar plot analyses conducted with and without an exclusion zone. Specifically, we compared results using an exclusion zone of 0.6 L/min/m\u0026sup2; versus 0.0 L/min/m\u0026sup2;. As seen in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e(C), omitting the exclusion zone increased polar angle SD.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese results suggest that, at \u0026tau;\u0026thinsp;\u0026gt;\u0026thinsp;30 minutes, the amplitude of esCCI was significantly attenuated, and the time shift was further prolonged, resulting in greater divergence between CCI and esCCI. Consequently, the SD in the Bland-Altman analysis and the polar angle SD without exclusion increased. However, since the magnitude of changes in both CCI and moving-averaged esCCI decreased as \u0026tau; increased, the number of excluded data points (i.e., exclusion rate) also rose. This increasing exclusion rate contributed to the observed reduction in polar angle SD when an exclusion zone of 0.6 L/min/m\u0026sup2; was applied. Appendix 1 further illustrates the relationship between exclusion rate and polar angle SD, confirming that higher exclusion rates consistently yielded lower SD values.\u003c/p\u003e\n\u003cp\u003eOverall, this study applied moving averages to facilitate a more accurate comparison of trending ability between CCI and other cardiac output indices with differing time responses. The optimal moving average duration (t) for achieving a concordance rate above 92% between CCI and esCCI was found to be 20\u0026ndash;30 minutes. Critchley et al. [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e] proposed a threshold of 92% concordance when using a 15% exclusion zone based on mean CO; our findings support this criterion within the specified t range. Although APCI did not reach the 92% threshold, it approached 90% within 21\u0026ndash;27 minutes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e(A)).\u003c/p\u003e\n\u003cp\u003eThe wide variability in effective moving average times suggests that multiple factors may contribute to the delayed response observed in CCI. These include technical aspects such as the thermal filament heating algorithm [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e], clinical conditions like mitral regurgitation [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e], and physiological changes associated with bleeding or resuscitation [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eGiven the variability and uncertainty in delay times, CCO alone may be insufficient for clinical decision-making. As noted by Mihm et al. [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e], the reliability of CCO is condition-dependent and should be interpreted alongside other continuous hemodynamic parameters.\u003c/p\u003e\n\u003cp\u003eOh et al. [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e] assessed the trending ability of APCO and CCO using R-squared-based time adjustments for APCO. However, their four-quadrant plot analysis yielded unacceptably low concordance. As shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e(A), simple time shifting did not achieve satisfactory results in our study either, emphasizing the advantage of moving averages, which incorporate both time shift and filtering.\u003c/p\u003e\n\u003cp\u003eTakakura et al. [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e] compared esCCO and CCO values before and after extubation in ICU patients, with and without applying a 20-minute moving average to esCCO. They reported that moving averaging reduced the SD of the difference between the two indices, supporting our current findings, although they did not employ polar plot analysis.\u003c/p\u003e\n\u003cp\u003eStudy Limitations (Rewritten)\u003c/p\u003e\n\u003cp\u003eA primary limitation of this study was the lack of detailed information regarding the specific averaging algorithm employed by the CCO monitor. We inferred that the observed response delay was attributable to a moving average process. Accordingly, the objective of this report was to evaluate the effectiveness of applying moving average processing when assessing trending ability between two monitors with differing response times.\u003c/p\u003e\n\u003cp\u003eAnother limitation concerns the unequal number of data points analyzed across different t (moving average window) values. This discrepancy arose due to two factors:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eLarger t values resulted in delayed start times and earlier end times for output data, thereby reducing the total number of available data points.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe moving average was not computed if the averaging window included even a single missing value. As \u0026tau; increased, the likelihood of encountering such missing data also rose, further reducing the number of usable data points.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eA key challenge for future research is to determine how best to mitigate the effects of data loss and delayed signal responsiveness introduced by large time averaging windows (\u0026tau;), which can obscure physiologically relevant changes and affect method comparisons. Clear, enough?\u003c/p\u003e\n\u003cp\u003eFinally, although Critchley et al. [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e] recommend that the polar mean angle remain within \u0026plusmn;\u0026thinsp;5\u0026deg; and the radial limits of agreement within \u0026plusmn;\u0026thinsp;30\u0026deg; when comparing CO monitors against thermodilution reference measurements, we focused on trending ability using a single index\u0026mdash;namely, the polar concordance rate at 30\u0026deg;.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study retrospectively analyzed clinical data using polar plots and Bland-Altman methods to investigate how moving average time affects trending ability among cardiac output monitors. Due to the combined effects of time shift and filtering inherent in moving average processing, an acceptable polar concordance rate at 30\u0026deg; was achieved between CCI and esCCI when esCCI was averaged over 20\u0026ndash;30 minutes. Similarly, high concordance rates were observed between CCI and APCI with moving average windows in a comparable range.\u003c/p\u003e\n\u003cp\u003eThese findings suggest that moving average processing is a practical and effective method for evaluating the trending ability of cardiac output monitors that differ in response time.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no acknowledgments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYS analyzed the literature, created the model, analyzed the data, post-processed the findings and was a major contributor to writing the manuscript. RO analyzed the literature, designed the study, created the model, and was a major contributor to reviewing and editing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOpen Access funding enabled and organized by Nihon Kohden Corp.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable requests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received approval from the ethics committee of Toho University Omori Medical Center. All procedures adhered to the principles of the 1964 Helsinki Declaration and its subsequent revisions or similar ethical standards. Written consent was obtained from all patients who had undergone kidney transplants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYoshihiro Sugo works for Nihon Kohden Corporation.\u003c/p\u003e\n\u003cp\u003eRyoichi Ochiai is an advisor to Nihon Kohden Corporation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1 \u003c/sup\u003eDevelopment Department, Ogino Memorial Laboratory, Nihon Kohden Corporation, Saitama, Japan.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003csup\u003e2 \u003c/sup\u003eFaculty of Medicine, Toho University, Tokyo, Japan.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3 \u003c/sup\u003eSenior Counselor, Tokushukai Medical Corporation, Tokyo, Japan\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eReuter DA, Huang C, Edrich T, et al. Cardiac output monitoring using indicator-dilution techniques: basics, limits, and perspectives. Anesth Analg. 2010;110(3):799\u0026ndash;811. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1213/ANE.0b013e3181cc885a\u003c/span\u003e\u003cspan address=\"10.1213/ANE.0b013e3181cc885a\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCritchley LA, Lee A, Ho AM. A critical review of the ability of continuous cardiac output monitors to measure trends in cardiac output. 2010; 111(5): 1180\u0026ndash;92. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1213/ANE.0b013e3181f08a5b\u003c/span\u003e\u003cspan address=\"10.1213/ANE.0b013e3181f08a5b\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2010 Aug 24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarvey S, Harrison DA, Singer M, et al. Assessment of the clinical effectiveness of pulmonary artery catheters in management of patients in intensive care (PAC-Man): a randomized controlled trial. Lancet. 2005;366(9484):472\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(05)67061-4\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(05)67061-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSandham JD, Hull RD, Brant RF, et al. A randomized, controlled trial of the use of pulmonary-artery catheters in high-risk surgical patients. N Engl J Med. 2003;348(1):5\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa021108\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa021108\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu F, Wang Q, Zhang H, Chen S et al. Use of pulmonary artery catheter in coronary artery bypass graft. Costs and long-term outcomes. PLoS One. 2015 17;10(2):e0117610. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0117610\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0117610\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. eCollection 2015.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJudge O, Ji F, Fleming N et al. Current use of the pulmonary artery catheter in cardiac surgery: a survey study. J Cardiothorac Vasc Anesth. 2015;29(1):69\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1053/j.jvca.2014.07.016\u003c/span\u003e\u003cspan address=\"10.1053/j.jvca.2014.07.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2014 Oct 30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBootsma IT, Boerma EC, Scheeren TWL, et al. The contemporary pulmonary artery catheter. Part 2: measurements, limitations, and clinical applications. J Clin Monit Comput. 2022;36(1):17\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10877-021-00673-5\u003c/span\u003e\u003cspan address=\"10.1007/s10877-021-00673-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2021 Mar 1.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSquara P, Cecconi M, Rhodes A, et al. Tracking changes in cardiac output: methodological considerations for the validation of monitoring devices. Intensive Care Med. 2009;35(10):1801\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00134-009-1570-9\u003c/span\u003e\u003cspan address=\"10.1007/s00134-009-1570-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2009 Jul 11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZ\u0026ouml;llner C, Goetz AE, Weis M, et al. Continuous cardiac output measurements do not agree with conventional bolus thermodilution cardiac output determination. Can J Anaesth. 2001;48(11):1143\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/BF03020382\u003c/span\u003e\u003cspan address=\"10.1007/BF03020382\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSlagt C, Malagon I, Groeneveld ABJ. Systematic review of uncalibrated arterial pressure waveform analysis to determine cardiac output and stroke volume variation. Br J Anaesth. 2014;112(4):626\u0026ndash;37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bja/aet429\u003c/span\u003e\u003cspan address=\"10.1093/bja/aet429\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCote CJ, Sui J, Anderson TA, et al. Continuous noninvasive cardiac output in children: is this the next generation of operating room monitors? Initial experience in 402 pediatric patients. Paediatr Anaesth. 2015;25(2):150\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/pan.12441\u003c/span\u003e\u003cspan address=\"10.1111/pan.12441\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSugo Y, Ukawa T, Takeda S et al. A novel continuous cardiac output monitor based on pulse wave transit time. Annu Int Conf IEEE Eng Med Biol Soc. 2010; 2010: 2853\u0026ndash;2856. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/IEMBS.2010.5626343\u003c/span\u003e\u003cspan address=\"10.1109/IEMBS.2010.5626343\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTerada T, Oiwa A, Maemura Y, et al. Comparison of the ability of two continuous cardiac output monitors to measure trends in cardiac output: estimated continuous cardiac output measured by modified pulse wave transit time and an arterial pulse contour-based cardiac output device. J Clin Monit Comput. 2016;30(5):621\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10877-015-9772-x\u003c/span\u003e\u003cspan address=\"10.1007/s10877-015-9772-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOh C, Lee S, Oh P, et al. Comparison between fourth-generation FloTrac/Vigileo system and continuous thermodilution technique for cardiac output estimation after time adjustment during off-pump coronary artery bypass graft surgery: A retrospective cohort study. J Clin Med. 2022;11(20):6093. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jcm11206093\u003c/span\u003e\u003cspan address=\"10.3390/jcm11206093\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTakakura M, Fujii T, Taniguchi T, et al. Accuracy of a noninvasive estimated continuous cardiac output measurement under different respiratory conditions: a prospective observational study. J Anesth. 2023;37(3):394\u0026ndash;400. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00540-023-03176-6\u003c/span\u003e\u003cspan address=\"10.1007/s00540-023-03176-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2023 Mar 11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaugel B, Grothe O, Wagner JY. Tracking changes in cardiac output: Statistical considerations on the 4-quadrant plot and the polar plot methodology. Anesth Analg. 2015;121(2):514\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1213/ANE.0000000000000725\u003c/span\u003e\u003cspan address=\"10.1213/ANE.0000000000000725\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLazor MA, Pierce ET, Stanley GD, et al. Evaluation of the accuracy and response time of STAT-mode continuous cardiac output. J Cardiothorac Vasc Anesth. 1997;11(4):432\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s1053-0770(97)90050-1\u003c/span\u003e\u003cspan address=\"10.1016/s1053-0770(97)90050-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWilliams B, Lacy PS, Yan P, et al. Development and validation of a novel method to derive central aortic systolic pressure from the radial pressure waveform using an n-point moving-average method. J Am Coll Cardiol. 2011;57(8):951\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jacc.2010.09.054\u003c/span\u003e\u003cspan address=\"10.1016/j.jacc.2010.09.054\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee J. Motion artifacts reduction from PPG using cyclic moving average filter. Technol Health Care. 2014;22(3):409\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3233/THC-140798\u003c/span\u003e\u003cspan address=\"10.3233/THC-140798\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSquara P, Scheeren TWL, Aya HD, et al. Metrology part 2: Procedures for the validation of major measurement quality criteria and measuring instrument properties. J Clin Monit Comput. 2021;35(1):27\u0026ndash;37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10877-020-00495-x\u003c/span\u003e\u003cspan address=\"10.1007/s10877-020-00495-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2020 Mar 18.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang XX, Critchley LA, Rowlands DK, et al. Systematic error of cardiac output measured by bolus thermodilution with a pulmonary artery catheter compared with that measured by an aortic flow probe in a pig model. J Cardiothorac Vasc Anesth. 2013;27(6):1133\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1053/j.jvca.2013.05.020\u003c/span\u003e\u003cspan address=\"10.1053/j.jvca.2013.05.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2013 Sep 10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVincent JL, Well MH. Fluid challenge revisited. Crit Care Med. 2006;34(5):1333\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/01.CCM.0000214677.76535.A5\u003c/span\u003e\u003cspan address=\"10.1097/01.CCM.0000214677.76535.A5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZ\u0026ouml;llner C, Goetz AE, Weis M, et al. Continuous cardiac output measurements do not agree with conventional bolus thermodilution cardiac output determination. Can J Anaesth. 2001;48(11):1143\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/BF03020382\u003c/span\u003e\u003cspan address=\"10.1007/BF03020382\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJardin F. PEEP, tricuspid regurgitation, and cardiac output. Intensive Care Med. 1997;23(8):806\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s001340050415\u003c/span\u003e\u003cspan address=\"10.1007/s001340050415\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFigueiredo LFP, Malbouisson LM, Varicoda EY, et al. Thermal filament continuous thermodilution cardiac output delayed response limits its value during acute hemodynamic instability. J Trauma. 1999;47(2):288\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/00005373-199908000-00011\u003c/span\u003e\u003cspan address=\"10.1097/00005373-199908000-00011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMihm FG, Gettinger A, Hanson CW, et al. A multicenter evaluation of a new continuous cardiac output pulmonary artery catheter system. Crit Care Med. 1998;26(8):1346\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/00003246-199808000-00018\u003c/span\u003e\u003cspan address=\"10.1097/00003246-199808000-00018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCritchley LA, Yang XX, Lee A. Assessment of trending ability of cardiac output monitors by polar plot methodology. J Cardiothorac Vasc Anesth. 2011;25(3):536\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1053/j.jvca.2011.01.003\u003c/span\u003e\u003cspan address=\"10.1053/j.jvca.2011.01.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2011 Mar 17.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-biomedical-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bbme","sideBox":"Learn more about [BMC Biomedical Engineering](http://bmcbiomedeng.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bbme/default.aspx","title":"BMC Biomedical Engineering","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cardiac Output, Pulse Wave Transit Time, Response Time, Trends","lastPublishedDoi":"10.21203/rs.3.rs-7116969/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7116969/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eContinuous cardiac output (CCO) monitoring using pulmonary artery (PA) thermodilution and newly introduced beat-to-beat cardiac output (CO) monitoring technologies exhibits different response time delays. These differences can hinder accurate comparisons of their trending abilities. To address this, we applied moving average processing to the beat-to-beat CO monitor data to evaluate its effect on trending assessment accuracy. This study aimed to confirm the effectiveness of moving average processing for such comparisons.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThis was a single-center, retrospective, observational study conducted at a 916-bed university hospital. A total of 20 patients undergoing kidney transplantation were included. We analyzed the trending ability of arterial pressure cardiac index (APCI) and estimated continuous cardiac index (esCCI) relative to continuous cardiac index (CCI) derived from PA thermodilution. Trending ability was assessed using a Polar plot and Bland-Altman analyses. A wide range of moving average windows (0\u0026ndash;60 minutes) was applied to APCI and esCCI. The polar concordance rate at 30\u0026deg; exceeded 92% for moving average windows between 20 and 30 minutes, with APCI peaking between 21 and 27 minutes. These improvements reflected both time-shifting and filtering effects of the moving average process.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eMoving average processing over 20 to 30 minutes significantly enhanced concordance between esCCI and reference CCI, with APCI demonstrating similarly high concordance in the same time window. This approach effectively compensates for differences in response time delays between CO monitoring modalities, enabling more accurate assessment of trending ability.\u003c/p\u003e","manuscriptTitle":"Moving-average processing enables accurate quantification of time delay and compares the trending ability of cardiac output monitors with different response times","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-27 10:56:59","doi":"10.21203/rs.3.rs-7116969/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-04T08:46:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-27T14:25:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-27T05:38:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"171412827220440155415902073558096965738","date":"2025-07-21T09:39:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210614220969852006137584040950126623310","date":"2025-07-21T09:23:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103628586648949859100534292685924594683","date":"2025-07-21T06:37:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"273957978211378996739091714014948229639","date":"2025-07-21T04:57:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-21T04:53:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-18T15:48:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-18T12:43:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-18T12:41:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Biomedical Engineering","date":"2025-07-14T04:44:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-biomedical-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bbme","sideBox":"Learn more about [BMC Biomedical Engineering](http://bmcbiomedeng.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bbme/default.aspx","title":"BMC Biomedical Engineering","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ebd51037-a96b-458f-a38e-7e5628cc9be2","owner":[],"postedDate":"July 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T16:09:24+00:00","versionOfRecord":{"articleIdentity":"rs-7116969","link":"https://doi.org/10.1186/s42490-025-00101-8","journal":{"identity":"bmc-biomedical-engineering","isVorOnly":false,"title":"BMC Biomedical Engineering"},"publishedOn":"2025-10-06 15:58:20","publishedOnDateReadable":"October 6th, 2025"},"versionCreatedAt":"2025-07-27 10:56:59","video":"","vorDoi":"10.1186/s42490-025-00101-8","vorDoiUrl":"https://doi.org/10.1186/s42490-025-00101-8","workflowStages":[]},"version":"v1","identity":"rs-7116969","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7116969","identity":"rs-7116969","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}