Evidence map›Paper›PMID 41331450›Full record

ArticleJournal of intensive care2025

Development and validation of the Hypotensive Exposure Duration Index for mortality risk prediction in critically ill patients.

Xiao-Yan Ding, Hai-Ping Xu, Jing-Ru Zhang, Han Chen

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Article in Journal of intensive care, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Xiao-Yan Ding *Cardiovascular Intensive Care Unit, Shengli Clinical Medical College of Fujian Medical University, Fuzhou University Affiliated Provincial Hospital, Dongjie 134, Gulou District, Fuzhou, Fujian, China.
Hai-Ping Xu *School of Computer and Data Science, Minjiang University, Fuzhou, China.
Jing-Ru ZhangThe School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.
Han ChenCardiovascular Intensive Care Unit, Shengli Clinical Medical College of Fujian Medical University, Fuzhou University Affiliated Provincial Hospital, Dongjie 134, Gulou District, Fuzhou, Fujian, China. hanchen.cn@icloud.com.

Funding

Fuzhou Science and Technology Project 2024-S-003Natural Science Foundation of Xiamen Municipality 2023J05250Startup Fund for Scientific Research, Fujian Medical University 2021QH1290
6 · The paper itself

Abstract

backgroundBlood pressure management is crucial in critical care, but relationships between pressure patterns and outcomes remain incompletely understood. We analyzed minute-by-minute blood pressure data to develop and validate a novel index quantifying hypotensive exposure burden.

methodsIn this retrospective study using the Salzburg Intensive Care Database, 11,059 ICU admissions with continuous invasive arterial monitoring were analyzed. Heatmaps were constructed from high-resolution hemodynamic data to visualize relationships between blood pressure thresholds (52-120 mmHg), exposure durations (5 min-5 h), and mortality. The Hypotensive Exposure Duration Index (HEDI) was developed to quantify cumulative hypotensive burden by integrating exposure across multiple MAP thresholds. HEDI's prognostic value was evaluated through nine machine learning algorithms. External validation using the eICU database assessed HEDI's consistency across different populations.

resultsNon-survivors showed significantly higher HEDI compared to survivors (0.47 [-0.20, 1.43] vs. -0.15 [-0.41, 0.27], p < 0.001). HEDI demonstrated increasing predictive capability, with AUC values rising from 0.624 at 24 h to 0.700 at 72 h post-admission. The Extra Trees classifier achieved exceptional performance (test AUC: 0.843), with HEDI ranking among the top predictive features. Both internal cross-validation and external validation confirmed the model's robustness, demonstrating HEDI's prognostic value across different patient populations, including both patients with and without vasopressor use.

conclusionsHEDI effectively quantifies cumulative hypotensive burden in critically ill patients, demonstrating significant predictive ability for ICU mortality validated across diverse populations.

Indexed as

Blood pressure exposureHigh-temporal resolutionHypotension exposureHypotensive exposure duration indexMachine learning

Identifiers

PMID41331450
PMCPMC12673709

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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.