Machine Learning Model for Predicting Recovery After Critical Illness

Hospital de Clinicas de Porto Alegre2,016 enrolled

Overview

This study aims to develop and test an artificial intelligence (AI) model to predict long-term functional status and return to work after critical illness. The main question is: Can we develop and validate a machine learning model to predict long-term functional status and return to work after critical illness?

This is a retrospective observational study using secondary data from two primary studies conducted between 2017-2019 and 2020-2022. No new participants will be recruited. Data were combined and harmonized for the present secondary analysis.

Study Type

OBSERVATIONAL

Enrollment

2,016

Conditions

Funcional Status Return to Work Predictive Learning Models

Interventions

No interventions were performed.OTHER

No interventions were performed.

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

Inclusion Criteria: * Age ≥18 years * ICU stay ≥ 72 hours * ICU stay ≥ 120 hours if the participant was admitted for elective surgery Exclusion Criteria: * No telephone contact available * Failure to establish contact * Transfer to another ICU

Locations (1)

Hospital de Clínicas de Porto Alegre, Intensive Care Nursing Department

Porto Alegre, Rio Grande do Sul, Brazil

Outcomes

Primary Outcomes

Functional Status

The primary outcome will be functional status at 6 months after ICU discharge, assessed using the Barthel Index (BI). The BI is a validated measure of functional status in activities of daily living, with total scores ranging from 0 to 100. For prediction modeling, the outcome will be treated as a binary variable, with functional impairment defined as a BI score \<91 points.

Time frame: 3rd, 6th and 12th months after ICU discharge

Secondary Outcomes

Return to Work

Being employed

Time frame: 3rd, 6th and 12th months after ICU discharge

Data from ClinicalTrials.gov

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