Clinical Trial Details
— Status: Not yet recruiting
Administrative data
NCT number |
NCT06289075 |
Other study ID # |
23-1096-retro |
Secondary ID |
|
Status |
Not yet recruiting |
Phase |
|
First received |
|
Last updated |
|
Start date |
March 1, 2024 |
Est. completion date |
December 1, 2024 |
Study information
Verified date |
February 2024 |
Source |
University Hospital of Cologne |
Contact |
Sandra Emily Stoll, DR. AP |
Phone |
+491735697566 |
Email |
sandraemilystoll[@]googlemail.com |
Is FDA regulated |
No |
Health authority |
|
Study type |
Observational
|
Clinical Trial Summary
The Objective of our retrospective multicenter- study is to forecast ICU length of stay
(ICULOS) and length of mechanical ventilation (LOMV) in ICU patients of different groups
(regarding gender, age group, medical vs surgical admission) worldwide for the next years up
to the year of 2040 using statistical forecasting models and historical, national and
international ICU databases and population databases.
Description:
Adequate resource allocation in Intensive Care Medicine is especially challenging due to
limited resources and increasing demands for ICU capacities due to an aging population and
medical advances. Several studies in the past were trying to predict ICULOS using different
models. The Objective and aim of our retrospective multicenter study are to forecast ICU
length of stay (ICULOS) and length of mechanical ventilation (LOMV) in ICU patients of
different groups (regarding gender, age group, medical vs surgical admission) worldwide for
the next years up to the year of 2040 using statistical forecasting models.
To achieve this objective, we collect historical ICU data spanning from 2005 to 2023 from
international ICU databases worldwide as well as population data from national and
international databases and employ different statistical forecasting models (ARIMA-Model
(Auto-Regressive Integrated Moving Average), logistic regression, Poisson Regression and ETS
(Exponential smoothing)) to make these predictions. The Validity of the 4 different models is
assessed with out-of-time-cross validity by splitting the data in 2 subsets for generation
and testing of the model in a ratio of approximately 75:25 of the dataset. The most valid
model of the 4 different models will be chosen. The statistical analysis follows he
guidelines for Accurate and Transparent Health Estimates Reporting (GATHER Statement) von
Stevens et al. from the year 2016.
The ultimate goal of this project is to provide valuable insights to healthcare system
decision-makers worldwide regarding future requirements of ICU beds and ventilator
capacities. With this insight we want to enable healthcare- system decision makers worldwide
to proactively anticipate and allocate appropriate ICU resources for the future.