ArticleiScience2024
A novel vital sign pattern predicts sepsis-related myocardial injury mortality.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Machine learning-based prediction of sepsis-induced myocardial injury: external validation and SHAP interpretation.BMC infectious diseases · 2026Article
- Enhancing prognostic accuracy in sepsis-induced cardiomyopathy: a machine learning approach.European journal of medical research · 2025Article
- OPTIMAL PAO 2 IS 130-160 MMHG IN THE FIRST WEEK FOR SEPSIS PATIENTS IN ICU: A RETROSPECTIVE COHORT STUDY BASED ON MIMIC-IV DATABASE.Shock (Augusta, Ga.) · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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
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Registered trials
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.