# Potentially MOdifiable factors To ImproVe outcomes of mechanically Ventilated patients in a low-income country Intensive Care Units (MOTIVATE-ICU): rationale and protocol for a registry-embedded prospective observational study

## TL;DR

This study aims to find factors in intensive care units in Uganda that can be changed to improve outcomes for patients on ventilators.

## Contribution

This is Uganda’s first multicenter ICU study to systematically assess modifiable factors affecting ventilated patient outcomes.

## Key findings

- The study will identify potentially modifiable ICU factors linked to patient outcomes in low-income settings.
- It will use high-quality registry data to explore associations between care processes and mortality or length of stay.
- A sub-study will examine tracheostomy-related outcomes among ventilated patients.

## Abstract

To identify modifiable intensive care unit factors associated with outcomes among patients receiving invasive mechanical ventilation in a low-income setting.

This prospective, multicenter, registry-embedded observational study has two components: a prospective registry-based cohort assessing patient- and care-process-related factors and a cross-sectional intensive care unit survey evaluating organizational structure. Functional intensive care units in Uganda will be included. Patients aged ≥ 15 years old requiring invasive mechanical ventilation will be enrolled. Patients extubated within 48 hours, transferred after > 24 hours, and imminent early death will be excluded. Primary outcomes will include 28-day intensive care unit mortality, intensive care unit length of stay, and mechanical ventilation duration. Tracheostomy-related outcomes will be explored in a pre-planned sub-study. Factors potentially associated with outcomes will be categorized into non-modifiable and potentially modifiable factors. Non-modifiable factors will include patient-related factors like age, comorbidities, and illness severity; potentially modifiable factors include processes of care (e.g., sedation levels) and intensive care unit organizational structure (e.g., staffing patterns). Multilevel multivariable logistic regression models will assess association outcomes. Survival analysis (Kaplan-Meier curves) will explore mortality trends. Confounders will be identified using directed acyclic graphs.

This study will generate high-quality data on modifiable intensive care unit factors associated with ventilated patient outcomes in low-resource settings.

This is Uganda’s first registry-embedded, multicenter intensive care unit study to systematically potentially modifiable factors associated with ventilated patient outcomes. This study will provide evidence-based insights to optimize critical care management in low- and middle-income countries by leveraging real-time intensive care unit registry data.