Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

124
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
124

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Nutritional Interventions for Perimenopausal Anxiety and Depression Targeting Tryptophan and GABA Pathways: A Narrative Review.

Nutrients·2026
Same author

Novel Phenotypes of Acute Respiratory Failure and Differential Response to Awake Prone Positioning: A Multi-Cohort Study.

MedComm·2026
Same author

MWCNT-supported PtRhFeCoMo high-entropy alloy nanocomposite for acetylcholinesterase-mediated turn-on colorimetric detection of trichlorfon.

Mikrochimica acta·2026
Same author

Predicting risk of mental health deterioration using multimodal data from the UK biobank.

Journal of advanced research·2026
Same author

A prototype-augmented graph representation learning framework for identifying brain disorder-associated genes and facilitating drug repurposing.

PLoS computational biology·2026
Same author

Quantitative Wear Models for Microscale Material Removal.

Nanomaterials (Basel, Switzerland)·2026

Related Experiment Video

Updated: Jun 27, 2025

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

167

Identification and validation of sepsis subphenotypes using time-series data.

Chenxiao Hao1, Rui Hao2, Huiying Zhao1

  • 1Department of Critical Care Medicine, Peking University People's Hospital, Beijing, 100044, China.

Heliyon
|May 1, 2024
PubMed
Summary

Sepsis is heterogeneous. Researchers identified three distinct sepsis subphenotypes (Type A, B, C) using time-series data, showing varied inflammatory responses and organ function. These findings aid in targeted sepsis treatment strategies.

Keywords:
ClusteringDynamic time warpingSepsisSubclassesTime-series data

More Related Videos

Murine Oropharyngeal Aspiration Model of Ventilator-associated and Hospital-acquired Bacterial Pneumonia
04:32

Murine Oropharyngeal Aspiration Model of Ventilator-associated and Hospital-acquired Bacterial Pneumonia

Published on: June 28, 2018

11.7K
Design of Cecal Ligation and Puncture and Intranasal Infection Dual Model of Sepsis-Induced Immunosuppression
07:30

Design of Cecal Ligation and Puncture and Intranasal Infection Dual Model of Sepsis-Induced Immunosuppression

Published on: June 15, 2019

10.0K

Related Experiment Videos

Last Updated: Jun 27, 2025

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

167
Murine Oropharyngeal Aspiration Model of Ventilator-associated and Hospital-acquired Bacterial Pneumonia
04:32

Murine Oropharyngeal Aspiration Model of Ventilator-associated and Hospital-acquired Bacterial Pneumonia

Published on: June 28, 2018

11.7K
Design of Cecal Ligation and Puncture and Intranasal Infection Dual Model of Sepsis-Induced Immunosuppression
07:30

Design of Cecal Ligation and Puncture and Intranasal Infection Dual Model of Sepsis-Induced Immunosuppression

Published on: June 15, 2019

10.0K

Area of Science:

  • Critical Care Medicine
  • Computational Biology
  • Translational Medicine

Background:

  • Sepsis is a complex syndrome with significant heterogeneity.
  • Identifying distinct sepsis subphenotypes is crucial for developing targeted and effective treatment strategies.
  • Current understanding of sepsis heterogeneity requires further refinement through advanced data analysis.

Purpose of the Study:

  • To identify and validate distinct sepsis subphenotypes using time-series analysis of vital signs and laboratory indicators.
  • To investigate the heterogeneity in inflammatory biomarkers and treatment responses among different sepsis subphenotypes.
  • To provide a data-driven foundation for personalized treatment approaches in sepsis management.

Main Methods:

  • Utilized time-series k-means clustering and dynamic time warping on 21 vital signs and laboratory indicators from the MIMIC-IV and Peking University People's Hospital ICU databases.
  • Randomly divided MIMIC-IV data into development (80%) and internal validation (20%) cohorts; external validation used Peking University data.
  • Compared inflammatory biomarkers and treatment heterogeneity across identified subphenotypes.

Main Results:

  • Identified three sepsis subphenotypes: Type A (stable vitals, fair organ function), Type B (obvious inflammation, stable organ function), and Type C (severely impaired organ function).
  • Type C exhibited the highest mortality (33%) and inflammatory markers, followed by Type B (24%), and Type A (11%).
  • Subphenotypes were validated across internal and external cohorts, showing consistent features and mortality rates. Survivors in Type C had lower fluid intake; Types B and C survivors had higher central venous catheter use.

Conclusions:

  • Successfully identified and validated three novel sepsis subphenotypes using time-series data analysis.
  • Demonstrated significant heterogeneity in inflammatory biomarkers, treatment responses, and clinical outcomes among sepsis subphenotypes.
  • These validated subphenotypes offer a foundation for more precise and personalized sepsis treatment strategies.