A Machine Learning Predictive Model for Sepsis

NCT04771429 | Unknown DMC

• 1 other identifier: XHEC-C-2021-104-1
• Study Type: observational
• Target: 4,500
• Locations: 1 country
• Sites: 1

Brief Summary

Timely and accurately predicting the occurrence of sepsis and actively intervening in treatment may effectively improve the survival and cure rate of patients with sepsis. Using machine learning and natural language processing, we want to develop models to 1) identify all children with sepsis admitted to hospital and 2) stratify them to distinguish those who are at high risk of death b) How will you undertake your work? From Shanghai hospitals anf MIMIC III, we will develop a very large dataset of patient admissions for all medical conditions including sepsis from the electronic health record. This data will include both structured data such as age, gender, medications, laboratory values, co-morbidities as well as unstructured data such as discharge summaries and physician notes. Using the dataset, we will train a model through natural language processing and machine learning to be able to identify people admitted with sepsis and identify those patients who will be at high risk of death. We will test the ability of these models to determine our predictive accuracies. We will then test these models at other institutions.

Conditions

Sepsis

Keywords

sepsis prediction, machine learning, mix interpolation

Outcome Measures

Primary Outcomes (1)

• Identification of sepsis

  Sepsis patients were screened based on the Sepsis-III standard

  Baseline

Eligibility Criteria

• Age: 1 Year - 18 Years
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

For data analysis in winter 2021, we have access to sepsis data up to February 2016 to July 2018.

You may qualify if:

• diagnosed with "infection", "septic shock" or "sepsis" or "septicemia"

You may not qualify if:

• Acute upper respiratory tract infection
• Newborns

Sponsors & Collaborators

• Xinhua Hospital, Shanghai Jiao Tong University School of Medicine: lead

Study Sites (1)

Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

Shanghai, Yangpu, 200092, China

RECRUITING

MeSH Terms

Conditions

Sepsis

Condition Hierarchy (Ancestors)

Infections; Systemic Inflammatory Response Syndrome; Inflammation; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Xin Sun, MD

  Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Qin Gao, MD

CONTACT

13761402225; 13761402225@163.com

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 24, 2021
• First Posted: February 25, 2021
• Study Start: April 1, 2019
• Primary Completion: March 1, 2021
• Study Completion: April 1, 2021
• Last Updated: February 25, 2021

Record last verified: 2021-02

Locations

CN (1)