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Related Experiment Video

Updated: Apr 9, 2026

A Reproducible Intensive Care Unit-Oriented Endotoxin Model in Rats
05:56

A Reproducible Intensive Care Unit-Oriented Endotoxin Model in Rats

Published on: February 20, 2021

2.6K

Development and validation of Machine Learning model to predict refractory septic shock.

Vinay Gandhi Mukkelli1, Puneet Khanna1, Amit Mehndiratta2

  • 1Department of Anesthesiology, Pain Medicine and Critical care, All India Institute of Medical Sciences, New Delhi, India.

Shock (Augusta, Ga.)
|April 8, 2026
PubMed
Summary

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A machine learning model accurately predicts refractory septic shock (RSS) in sepsis patients. Early identification of high-risk individuals using this tool can improve patient outcomes.

Area of Science:

  • Critical Care Medicine
  • Medical Informatics
  • Machine Learning in Healthcare

Background:

  • Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection.
  • Refractory septic shock (RSS) is associated with high mortality rates and presents a significant clinical challenge.
  • Accurate and early prediction of RSS is crucial for timely intervention and improved patient outcomes.

Purpose of the Study:

  • To develop and validate a machine learning (ML) model for predicting refractory septic shock (RSS) in adult intensive care unit (ICU) patients.
  • To identify key clinical and laboratory features predictive of RSS.
  • To assess the model's performance in a prospective validation cohort.

Main Methods:

  • An ambispective study design was employed, utilizing data from 1,008 patients for model development and 102 patients for prospective validation.
Keywords:
Artificial IntelligenceIntensive Care UnitMachine LearningPrediction ModelRandom ForestRefractory Septic ShockSepsisSeptic Shock

Related Experiment Videos

Last Updated: Apr 9, 2026

A Reproducible Intensive Care Unit-Oriented Endotoxin Model in Rats
05:56

A Reproducible Intensive Care Unit-Oriented Endotoxin Model in Rats

Published on: February 20, 2021

2.6K
  • A three-tiered feature selection process was used to identify significant variables.
  • Random Forest classifiers were trained and optimized using selected features.

    • Main Results:

    • The best-performing ML model incorporated 27 clinical and laboratory features.
    • The model achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.877 in the training cohort and 0.839 in prospective validation.
    • The model demonstrated high accuracy, precision, and recall in predicting RSS.

    Conclusions:

    • The validated ML model shows strong predictive ability and interpretability for identifying patients at risk of refractory septic shock.
    • Early identification of high-risk patients can facilitate timely interventions and potentially improve outcomes.
    • Further multicenter studies are warranted to confirm the generalizability of the model for widespread clinical implementation.