Causal Inference for ICU Patients
Presenter(s)
Jack O'Connor
Abstract
Sepsis is an infection that can lead to vital organ dysfunction, and it is estimated to occur in 30% of ICU patients. This research aims to assess whether receiving a Transthoracic Echocardiogram (TTE) has an effect on the 28-day mortality rate of ICU patients with Sepsis, as TTEs are currently widely used in medical treatment. Our data source containing ICU patients comes from the Electronic Health Records (EHR) database MIMIC-III. This is an observational data source, so we attempted to establish causality by addressing assumptions and using various causal effect estimators. Additionally, we estimated heterogeneity in treatment effects across different groups of sepsis patients. Ultimately, we established a causal effect and identified which covariates are associated with large treatment effects. This research can be used to better inform clinical decisions when providing care for ICU patients with Sepsis.
College
College of Science & Engineering
Department
Mathematics & Statistics
Campus
Winona
First Advisor/Mentor
Silas Bergen
Start Date
4-24-2025 10:00 AM
End Date
4-24-2025 11:00 AM
Presentation Type
Poster Session
Format of Presentation or Performance
In-Person
Session
1b=10am-11am
Poster Number
48
Causal Inference for ICU Patients
Sepsis is an infection that can lead to vital organ dysfunction, and it is estimated to occur in 30% of ICU patients. This research aims to assess whether receiving a Transthoracic Echocardiogram (TTE) has an effect on the 28-day mortality rate of ICU patients with Sepsis, as TTEs are currently widely used in medical treatment. Our data source containing ICU patients comes from the Electronic Health Records (EHR) database MIMIC-III. This is an observational data source, so we attempted to establish causality by addressing assumptions and using various causal effect estimators. Additionally, we estimated heterogeneity in treatment effects across different groups of sepsis patients. Ultimately, we established a causal effect and identified which covariates are associated with large treatment effects. This research can be used to better inform clinical decisions when providing care for ICU patients with Sepsis.
Comments
WSU Review Needed - Check advisors are different in the program
Faculty Mentors: Rahul Ladhania and Snigdha Panigrahi