Estimating the Timeframe of Lactate Reduction in Acute Cardiovascular Disease Using Functional Data Analysis Based on Nonlinear Mixed Effects Models

In patients with acute cardiovascular disease, treatment aimed at reducing lactate levels is crucial for improving prognosis. Trends in blood lactate levels for each specific cardiovascular disease can provide an accurate evaluation of the patient's condition. We used functional data analysis with nonlinear mixed effects models to estimate the timeframe of lactate reduction in 3 cardiovascular diseases (acute heart failure, aortic dissection, and ischemic heart disease) by analyzing lactate trends.Among 1,816 patients admitted to the intensive care unit (ICU) or intensive cardiovascular care unit (ICCU) of St. Luke's International Hospital for cardiology or cardiovascular surgery from December 31, 2010, to June 31, 2020, 1,249 adults with a diagnosis of acute heart failure (39%), aortic dissection (24%), or ischemic heart disease (37%) were included in the present study. Using functional data analysis with nonlinear mixed effects models, our study estimated the timeframe of lactate reduction based on blood lactate level trends. Lactate reduction took 30 hours (95% CI, 23-37 hours) in patients with acute heart failure, 40 hours (95% CI, 33-47 hours) in patients with aortic dissection, and 95 hours (95% CI, 49-failed estimate) in patients with ischemic heart disease.We were able to estimate the timeframe of lactate reduction with different cardiovascular diseases. Recognizing the differences in lactate reduction may be useful in developing treatment plans tailored to each disease.

Authors

Institutions

Publication Details

Journal
International Heart Journal
Published
2024-11-29
DOI
https://doi.org/10.1536/ihj.23-659
Citations
1
Primary Topic
Cardiovascular Function and Risk Factors
Type
article
Field-Weighted Citation Impact
0.40

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Estimating the Timeframe of Lactate Reduction in Acute Cardiovascular Disease Using Functional Data Analysis Based on Nonlinear Mixed Effects Models

Shuntaro Sato, Atsushi Mizuno, Takaya Nakashima, Hidetoshi Matsui
1 citations
International Heart Journal
Cardiovascular Function and Risk Factors
0.40
article

Estimating the Timeframe of Lactate Reduction in Acute Cardiovascular Disease Using Functional Data Analysis Based on Nonlinear Mixed Effects Models

Shuntaro Sato, Atsushi Mizuno, Takaya Nakashima, Hidetoshi Matsui
article en
1 citations

Abstract

In patients with acute cardiovascular disease, treatment aimed at reducing lactate levels is crucial for improving prognosis. Trends in blood lactate levels for each specific cardiovascular disease can provide an accurate evaluation of the patient's condition. We used functional data analysis with nonlinear mixed effects models to estimate the timeframe of lactate reduction in 3 cardiovascular diseases (acute heart failure, aortic dissection, and ischemic heart disease) by analyzing lactate trends.Among 1,816 patients admitted to the intensive care unit (ICU) or intensive cardiovascular care unit (ICCU) of St. Luke's International Hospital for cardiology or cardiovascular surgery from December 31, 2010, to June 31, 2020, 1,249 adults with a diagnosis of acute heart failure (39%), aortic dissection (24%), or ischemic heart disease (37%) were included in the present study. Using functional data analysis with nonlinear mixed effects models, our study estimated the timeframe of lactate reduction based on blood lactate level trends. Lactate reduction took 30 hours (95% CI, 23-37 hours) in patients with acute heart failure, 40 hours (95% CI, 33-47 hours) in patients with aortic dissection, and 95 hours (95% CI, 49-failed estimate) in patients with ischemic heart disease.We were able to estimate the timeframe of lactate reduction with different cardiovascular diseases. Recognizing the differences in lactate reduction may be useful in developing treatment plans tailored to each disease.

International Heart JournalVol. 65(6)
Shiga University (JP), Nagasaki University Hospital (JP), Nagasaki University (JP)
Japan Society for the Promotion of Science
Good health and well-being
Openalex Percentile: Top 30%
Cardiovascular Function and Risk Factors
0.40
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.