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Artificial Intelligence Prognostic Model for Sepsis Based on Time Series Analysis

NCT06724120 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 3641

Last updated 2024-12-09

No results posted yet for this study

Summary

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. It is one of the leading causes of death and disability worldwide, with an inpatient mortality rate of 10-20%. Sepsis is a severe complication in critically ill patients and can lead to septic shock and multiple organ dysfunction syndrome (MODS), usually triggered by severe trauma, surgery, and infections. Despite the availability of advanced diagnostic, therapeutic, and monitoring technologies, the incidence and mortality of sepsis remain high, posing a significant global challenge to the medical community. Over 49 million people worldwide develop sepsis annually, with approximately 11 million deaths, resulting in a mortality rate of about 15%-25%.

This study aims to develop a prognosis prediction model for sepsis patients using a neural network architecture (Transformer algorithm), based on time-series data. The primary outcome observed is the mortality outcome of sepsis patients. The goal of the research is to enhance the early identification of high-risk sepsis patients, thereby optimizing the timing of sepsis treatment and intervention and improving the accuracy of prognosis prediction for sepsis patients.

Conditions

  • Sepsis
  • Prognostic Model
  • Artificial Intelligence
  • Individuality

Interventions

OTHER

No interventions

No interventions

Sponsors & Collaborators

  • West China Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2020-01-01
Primary Completion
2023-12-31
Completion
2023-12-31

Countries

  • China

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06724120 on ClinicalTrials.gov