Clinical trial NCT05993377: Prediction of Duration of Mechanical Ventilation in ARDS
Official title: Predicting Length of Mechanical Ventilation in Moderate-to-severe Acute Respiratory Distress Syndrome Using Machine Learning
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Acute Respiratory Distress Syndrome
Keywords: mechanical ventilation
intensive care unit
duration of ventilation
machine learning.
Lead sponsor: Dr. Negrin University Hospital
Sponsor class: OTHER
Collaborators: name: Unity Health Toronto
name: Cardiff University
name: Leiden University Medical Center
First posted: 2023-08-15
Enrollment: 1303
Sex: ALL
Minimum age (years): 18
Maximum age (years): 100
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: De-identified dataset including 1,303 patients with moderate/severe ARDS admitted consecutively in a network of Spanish ICUs.

Brief summary: The investigators are planning to perform a secondary analysis of an academic dataset of 1,303 patients with moderate-to-severe acute respiratory distress syndrome (ARDS) included in several published cohorts (NCT00736892, NCT022288949, NCT02836444, NCT03145974), aimed to characterize the best early scenario during the first three days of diagnosis to predict duration of mechanical ventilation in the intensive care unit (ICU) using supervised machine learning (ML) approaches.

Detailed description: The acute respiratory distress syndrome (ARDS) is an important cause of morbidity, mortality, and costs in intensive care units (ICUs) worldwide. Most ARDS patients require mechanical ventilation (MV). Few studies have investigated the prediction of MV duration of ARDS.

For model description and testing, the investigators will extract data from he first three ICU days after diagnosis of moderate-to-severe ARDS from patients included in the de-identified database, which includes 1,000 mechanically ventilated patients enrolled in several observational cohorts in Spain, coordinated by the principal investigator (JV), and funded by the Instituto de Salud Carlos III (ISCIII). The investigators will follow the TRIPOD guidelines and machine learning techniques will be implemented \[Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Logistic regression analysis) for the development and accuracy of prediction models. Disease progression will be tracked along those 3 ICU days to assess lung severity according to Berlin criteria. For external validation, the investigators will use 303 patients enrolled in a contemporary observational study (NCT03145974). The investigators will evaluate the accuracy of prediction models by calculation several statistics, such as sensitivity, specificity, positive predictive value, negative value for each model. The investigators will select the best early prediction model with data captured on the 1st, 2nd, or 3rd day.

Eligibility criteria: Inclusion Criteria:

* Berlin criteria for moderate to severe acute respiratory distress syndrome

Exclusion Criteria:

* Postoperative patients ventilated \<24h
* brain death patients

Primary outcomes:
measure: Days on mechanical ventilation
timeFrame: from diagnosis to extubation
Secondary outcomes:
measure: ICU mortality
timeFrame: up to 24 weeks
Other outcomes:
Locations:
city: Las Palmas De Gran Canaria
country: Spain
facility: Hospital Universitario Dr. Negrin
state: Las Palmas
city: Majadahonda
country: Spain
facility: Hospital Universitario Puerta de Hierro (ICU)
state: Madrid
city: Santa Cruz de Tenerife
country: Spain
facility: Hospital Universitario NS de Candelaria
state: Tenerife
city: Talavera de la Reina
country: Spain
facility: Hospital NS del Prado
state: Toledo
city: Albacete
country: Spain
facility: Complejo Hospitalario Universitario de Albacete (ICU)
city: Albacete
country: Spain
facility: Complejo Hospitalario de Albacete
city: Barcelona
country: Spain
facility: Department of Anesthesia, Hospital Clinic
city: Ciudad Real
country: Spain
facility: Hospital General de Ciudad Real (ICU)
city: Cuenca
country: Spain
facility: Hospital Virgen de La Luz
city: La Coruña
country: Spain
facility: Hospital Universitario de A Coruña (ICU)
city: León
country: Spain
facility: Complejo Hospitalario Universitario de León
city: Madrid
country: Spain
facility: Hospital Universitario Ramón y Cajal (Anesthesia)
city: Madrid
country: Spain
facility: Hospital Universitario La Paz (ICU)
city: Madrid
country: Spain
facility: Hospital Fundación Jiménez Díaz
city: Murcia
country: Spain
facility: Hospital Universitario Virgen de Arrixaca (ICU)
city: Málaga
country: Spain
facility: Hospital Universitario Regional de Malaga Carlos Haya (ICU)
city: Málaga
country: Spain
facility: Hospital Universitario Carlos Haya
city: Valladolid
country: Spain
facility: Hospital Universitario Río Hortega (ICU)
city: Zamora
country: Spain
facility: Hospital Virgen de la Concha (ICU)
city: Cardiff
country: United Kingdom
facility: Cardiff University
Study officials:
affiliation: Hospital Universitario D. Negrin
name: Jesús Villar
role: PRINCIPAL_INVESTIGATOR
References:
citation: Villar J, Ambros A, Mosteiro F, Martinez D, Fernandez L, Ferrando C, Carriedo D, Soler JA, Parrilla D, Hernandez M, Andaluz-Ojeda D, Anon JM, Vidal A, Gonzalez-Higueras E, Martin-Rodriguez C, Diaz-Lamas AM, Blanco J, Belda J, Diaz-Dominguez FJ, Rico-Feijoo J, Martin-Delgado C, Romera MA, Gonzalez-Martin JM, Fernandez RL, Kacmarek RM; Spanish Initiative for Epidemiology, Stratification and Therapies of ARDS (SIESTA) Network. A Prognostic Enrichment Strategy for Selection of Patients With Acute Respiratory Distress Syndrome in Clinical Trials. Crit Care Med. 2019 Mar;47(3):377-385. doi: 10.1097/CCM.0000000000003624.
citation: Figueroa-Casas JB, Dwivedi AK, Connery SM, Quansah R, Ellerbrook L, Galvis J. Predictive models of prolonged mechanical ventilation yield moderate accuracy. J Crit Care. 2015 Jun;30(3):502-5. doi: 10.1016/j.jcrc.2015.01.020. Epub 2015 Jan 30.