Evidence map›Paper›PMID 41047412›Full record

ArticleBMC biomedical engineering2025

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

Abstract read
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Article in BMC biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. The time course of fluid responsiveness.Annals of intensive care · 2026
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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Yoshihiro SugoDevelopment Department, Ogino Memorial Laboratory, Nihon Kohden Corporation, Saitama, Japan.
Ryoichi OchiaiFaculty of Medicine, Toho University, 6-11-6 Omori-Nishi, Oota-city, Tokyo, 143-8541, Japan. roy.ochiai@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundContinuous 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.

resultsThis 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.

conclusionsMoving 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.

Indexed as

Cardiac outputPulse wave transit timeResponse timeTrends

Identifiers

PMID41047412
PMCPMC12498450

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.