Article

The Analysis of Sepsis Vascular Response Index Based on Machine Learning

2026-06-24

Abstract excerpt

<title>Abstract</title> <p>Purpose: This study aims to explore the clinical value of monitoring the vascular response index and other hemodynamic parameters using the Pulse Indicator Continuous Cardiac Output (PICCO) monitoring device in the treatment of septic shock patients. It also aims to examine its impact on treatment plans for patients with different hemodynamic states. Based on the results, individualized...

Topics

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

Identifiers and source

Literature Corpus work: 994e5792-08da-532f-b5e4-2031dcacbb6c
DOI: 10.21203/rs.3.rs-9590078/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

The Analysis of Sepsis Vascular Response Index Based on Machine LearningDOI 10.21203/rs.3.rs-9590078/v1Read

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

Clinical Applications of Machine Learning in the Survival Prediction and Classification of Sepsis：Coagulation and Heparin Usage MatterSemantic 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

Clustering analysis based on PiCCO parameters in patients with septic shockSemantic proximity Read

Development and Validation of the VIOSync Sepsis Prediction Index: A Novel Machine Learning Model for Sepsis Prediction in ICU PatientsSemantic proximity Read

Development and validation of a nomogram for predicting sepsis-induced coagulopathy in septic patients: mixed retrospective and prospective cohort studySemantic proximity Read

Evaluation value of bedside ultrasound monitoring of peak flow velocity of abdominal aorta and its branches in volume status of patients with septic shockSemantic proximity Read

Machine Learning Model for Early Prediction of Sepsis Outcomes Based on Immune ResponseSemantic proximity Read

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

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

Comparison of the Prognostic Accuracy of Sepsis Scoring Systems: A Prospective Study in Patients with Suspected Infection in the Emergency DepartmentSemantic proximity Read

Time-Series Deep Learning and Conformal Prediction for Improved Sepsis Diagnosis in Non-ICU Hospitalized PatientsSemantic proximity Read