A novel vital sign pattern predicts sepsis-related myocardial injury mortality

Non-invasive, real-time monitorable indicators for early assessment of sepsis-associated myocardial injury (SMI) are still lacking. We aimed to develop non-invasive, real-time indicators for early assessment of SMI using bedside heart rate (HR) and diastolic arterial pressure (DAP) monitors. In this multi-center cohort study, piece-wise exponential additive mixed models were used to estimate the exposure window and time fraction of the hazardous exposure proportion, and secondarily to analyze the exposure characterization on this basis to identify high-risk exposure pattern. In total, 20,043 patients were finally included; we found that SMI patients had the highest survival rate when HR was <90 bpm or DAP was between 50 and 70 mmHg. Further investigation revealed that the SMI high-risk exposure pattern was the H<sub>1</sub>D<sub>-1</sub> (HR ≥ 90 bpm and DAP ≤ 50 mmHg, exposure proportion > 0.3 and 0.2, respectively, and exposure window on admission day 1). H<sub>1</sub>D<sub>-1</sub> exposure pattern using glucocorticoids significantly increased the risk of mortality in H<sub>1</sub>D<sub>-1</sub>. Validation against various methodologies and data sources demonstrated acceptable consistency.

Authors

Institutions

Publication Details

Journal
iScience
Published
2024-08-23
DOI
https://doi.org/10.1016/j.isci.2024.110787
Citations
5
Primary Topic
Sepsis Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
1.93

Funders

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

A novel vital sign pattern predicts sepsis-related myocardial injury mortality

Wanjun Liu, Pengyue Zhang, Haoshu Fang, Zhenhua Zhang et al.
5 citations
iScience
Sepsis Diagnosis and Treatment
1.93
article

A novel vital sign pattern predicts sepsis-related myocardial injury mortality

Wanjun Liu, Pengyue Zhang, Haoshu Fang, Zhenhua Zhang, Yafei Zhang, Jinjin Dai, menglin Ni
article en
5 citations

Abstract

Non-invasive, real-time monitorable indicators for early assessment of sepsis-associated myocardial injury (SMI) are still lacking. We aimed to develop non-invasive, real-time indicators for early assessment of SMI using bedside heart rate (HR) and diastolic arterial pressure (DAP) monitors. In this multi-center cohort study, piece-wise exponential additive mixed models were used to estimate the exposure window and time fraction of the hazardous exposure proportion, and secondarily to analyze the exposure characterization on this basis to identify high-risk exposure pattern. In total, 20,043 patients were finally included; we found that SMI patients had the highest survival rate when HR was <90 bpm or DAP was between 50 and 70 mmHg. Further investigation revealed that the SMI high-risk exposure pattern was the H<sub>1</sub>D<sub>-1</sub> (HR ≥ 90 bpm and DAP ≤ 50 mmHg, exposure proportion > 0.3 and 0.2, respectively, and exposure window on admission day 1). H<sub>1</sub>D<sub>-1</sub> exposure pattern using glucocorticoids significantly increased the risk of mortality in H<sub>1</sub>D<sub>-1</sub>. Validation against various methodologies and data sources demonstrated acceptable consistency.

iScienceVol. 27(9)
Anhui Medical University (CN), Second Affiliated Hospital of Anhui Medical University (CN)
Natural Science Foundation of Anhui Province
Good health and well-being
Openalex Percentile: Top 13%
Sepsis Diagnosis and Treatment
1.93
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.

A novel vital sign pattern predicts sepsis-related myocardial injury mortality — Wanjun Liu, Pengyue Zhang, et al. · iScience (2024) | TGRS Research Map | TGRS