Clinical Decision Support System to Detect the Occurrence of Ventilator-Associated Pneumonia in Pediatric Intensive Care

Author:

Rambaud Jerome12ORCID,Sajedi Masoumeh3,Al Omar Sally3ORCID,Chomtom Maryline4,Sauthier Michael1ORCID,De Montigny Simon35,Jouvet Philippe1

Affiliation:

1. Pediatric Intensive Care Unit, Sainte-Justine Hospital, Montreal, QC H3T 1C5, Canada

2. Pediatric and Neonatal Intensive Care Unit, Armand-Trousseau Hospital, Sorbonne University, 75012 Paris, France

3. Research Center, Sainte-Justine Hospital, Montreal, QC H3T 1C5, Canada

4. Pediatric Intensive Care Unit, Caen University Hospital, 14000 Caen, France

5. School of Public Health, Montréal University, Montreal, QC H2X 3E4, Canada

Abstract

Objectives: Ventilator-associated pneumonia (VAP) is a severe care-related disease. The Centers for Disease Control defined the diagnosis criteria; however, the pediatric criteria are mainly subjective and retrospective. Clinical decision support systems have recently been developed in healthcare to help the physician to be more accurate for the early detection of severe pathology. We aimed at developing a predictive model to provide early diagnosis of VAP at the bedside in a pediatric intensive care unit (PICU). Methods: We performed a retrospective single-center study at a tertiary-care pediatric teaching hospital. All patients treated by invasive mechanical ventilation between September 2013 and October 2019 were included. Data were collected in the PICU electronic medical record and high-resolution research database. Development of the clinical decision support was then performed using open-access R software (Version 3.6.1®). Measurements and main results: In total, 2077 children were mechanically ventilated. We identified 827 episodes with almost 48 h of mechanical invasive ventilation and 77 patients who suffered from at least one VAP event. We split our database at the patient level in a training set of 461 patients free of VAP and 45 patients with VAP and in a testing set of 199 patients free of VAP and 20 patients with VAP. The Imbalanced Random Forest model was considered as the best fit with an area under the ROC curve from fitting the Imbalanced Random Forest model on the testing set being 0.82 (95% CI: (0.71, 0.93)). An optimal threshold of 0.41 gave a sensitivity of 79.7% and a specificity of 72.7%, with a positive predictive value (PPV) of 9% and a negative predictive value of 99%, and with an accuracy of 79.5% (95% CI: (0.77, 0.82)). Conclusions: Using machine learning, we developed a clinical predictive algorithm based on clinical data stored prospectively in a database. The next step will be to implement the algorithm in PICUs to provide early, automatic detection of ventilator-associated pneumonia.

Funder

Quebec Respiratory Health Research Network

Canadian Foundation for Innovation

Fonds de Recherche Québec

Quebec Ministry of Health and Sainte-Justine Hospital

Publisher

MDPI AG

Subject

Clinical Biochemistry

Reference33 articles.

1. (2023, September 14). Center for Disease Control, Available online: https://www.cdc.gov/nhsn/PDFs/pscManual/6pscVAPcurrent.pdf.

2. Ventilator-associated pneumonia;Chastre;Am. J. Respir. Crit. Care Med.,2002

3. Clinical epidemiology and outcomes of ventilator-associated pneumonia in critically ill adult patients: Protocol for a large-scale systematic review and planned meta-analysis;Borromeo;Syst. Rev.,2019

4. Ventilator-associated pneumonia in adults: A narrative review;Papazian;Intensive Care Med.,2020

5. Ventilator-Associated Pneumonia in Pediatric Intensive Care Unit: Correspondence;Tullu;Indian J. Pediatr.,2015

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