Article

An Interpretable EHR-Derived Lactate–Hemodynamic Framework for Mortality Risk Stratification in ICU Sepsis: A MIMIC-IV Derivation and eICU Validation Study

2026-07-10

Abstract excerpt

<title>Abstract</title> <p>Background Early sepsis care relies on routinely available bedside indicators of perfusion, including lactate, mean arterial pressure (MAP), and vasopressor use. Consistent with Sepsis-3, metabolic and hemodynamic abnormalities may carry complementary rather than interchangeable prognostic information. We evaluated whether a simple bedside framework integrating these signals could stra...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: ea9d97be-91c6-5eb7-b829-eed08a11c510
DOI: 10.21203/rs.3.rs-9785501/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

An Interpretable EHR-Derived Lactate–Hemodynamic Framework for Mortality Risk Stratification in ICU Sepsis: A MIMIC-IV Derivation and eICU Validation StudyDOI 10.21203/rs.3.rs-9785501/v1Read

Impact of Clinical Sepsis Phenotypes on Mortality and Fluid Status in Critically Ill Patients.Semantic proximity Read

Phenotype discovery and mortality prediction in sepsis-induced myocardial dysfunction: a deep learning and stratified modeling approach.Semantic proximity Read

<p class="MsoNormal" style="margin-bottom: 12.0pt; text-align: left; mso-line-height-alt: 14.0pt; layout-grid-mode: char; mso-layout-grid-align: none;" align="left">Physiological Phenotypes of Sepsis Reveal a Normotensive High-Risk “Cryptic Shock” State: An Unsupervised Machine Learning Analysis of a Multicentre ICU Cohort and External Validation in Mimic-IVSemantic proximity Read

A novel Vascular Leak Index identifies sepsis patients with a higher risk for in-hospital death and fluid accumulationSemantic proximity Read

Identification of Clinical Phenotypes in Septic Patients Presenting With Hypotension or Elevated LactateSemantic proximity Read

Derivation and Validation of Generalized Sepsis-induced Acute Respiratory Failure Phenotypes Among Critically Ill Patients: A Retrospective StudySemantic proximity Read

[Sepsis phenotypes and individualized fluid therapy in patients with chronic diseases].Semantic proximity Read

Unsupervised Machine Learning in Identification of Septic Shock Phenotypes and Their In-Hospital Outcomes: A Multicenter Cohort StudySemantic proximity Read

A New Proposed Approach to Categorize Sepsis Sub-phenotypes Based on Combined Trajectories of Perfusion Index and Central Venous Oxygen Saturation.Semantic proximity Read

A Machine Learning-Derived “Cryptic Shock” Phenotype Provides Incremental Prognostic Value and Supports Bedside Risk Stratification in Elderly Mechanically Ventilated Patients: A Multicenter Study with External ValidationSemantic proximity Read

Cardiovascular and Autonomic Phenotypes Reveal Distinct Mechanisms of Sepsis Decompensation via Deep LearningSemantic proximity Read

Lactate-to-Albumin Ratio Predicts Mortality in Patients with Sepsis-Associated ARDS: A Dual-Cohort Retrospective StudySemantic proximity Read