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Background <p>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.</p> Results <p>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&#xa0;min) was applied to APCI and esCCI. The polar concordance rate at 30° exceeded 92% for moving average windows between 20 and 30&#xa0;min, with APCI peaking between 21 and 27&#xa0;min. These improvements reflected both time-shifting and filtering effects of the moving average process.</p> Conclusions <p>Moving average processing over 20 to 30&#xa0;min 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月6日

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  • Yoshihiro Sugo,
  • Ryoichi Ochiai

摘要

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 min) was applied to APCI and esCCI. The polar concordance rate at 30° exceeded 92% for moving average windows between 20 and 30 min, with APCI peaking between 21 and 27 min. These improvements reflected both time-shifting and filtering effects of the moving average process.

Conclusions

Moving average processing over 20 to 30 min 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.