Frondelius, T., Atkova, I., Miettunen, J., Rello, J., & Jansson, M. M. (2022). Diagnostic and prognostic prediction models in ventilator-associated pneumonia: Systematic review and meta-analysis of prediction modelling studies. Journal of Critical Care, 67, 44–56. https://doi.org/10.1016/j.jcrc.2021.10.001
Diagnostic and prognostic prediction models in ventilator-associated pneumonia : systematic review and meta-analysis of prediction modelling studies
|Author:||Frondelius, Tuomas1; Atkova, Irina2; Miettunen, Jouko3,4;|
1Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
2University of Oulu, Oulu, Finland
3Center for Life Course Health Research, University of Oulu, Oulu, Finland
4Medical Research Center Oulu, Oulu University Hospital, University of Oulu, Oulu, Finland
5CIBER de Enfermedades Respiratorias, CIBERES, Instituto de Salud Carlos III, Barcelona, Spain
6Clinical Research/Epidemiology In Pneumonia & Sepsis (CRIPS), Vall d'Hebron Institut of Research (VHIR), Barcelona, Spain
7Clinical Research, CHU Caremeau, Nimes, France
|Online Access:||PDF Full Text (PDF, 0.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022041329084
|Publish Date:|| 2022-06-17
Purpose: Existing expert systems have not improved the diagnostic accuracy of ventilator-associated pneumonia (VAP). The aim of this systematic literature review was to review and summarize state-of-the-art prediction models detecting or predicting VAP from exhaled breath, patient reports and demographic and clinical characteristics.
Methods: Both diagnostic and prognostic prediction models were searched from a representative list of multidisciplinary databases. An extensive list of validated search terms was added to the search to cover papers failing to mention predictive research in their title or abstract. Two authors independently selected studies, while three authors extracted data using predefined criteria and data extraction forms. The Prediction Model Risk of Bias Assessment Tool was used to assess both the risk of bias and the applicability of the prediction modelling studies. Technology readiness was also assessed.
Results: Out of 2052 identified studies, 20 were included. Fourteen (70%) studies reported the predictive performance of diagnostic models to detect VAP from exhaled human breath with a high degree of sensitivity and a moderate specificity. In addition, the majority of them were validated on a realistic dataset. The rest of the studies reported the predictive performance of diagnostic and prognostic prediction models to detect VAP from unstructured narratives [2 (10%)] as well as baseline demographics and clinical characteristics [4 (20%)]. All studies, however, had either a high or unclear risk of bias without significant improvements in applicability.
Conclusions: The development and deployment of prediction modelling studies are limited in VAP and related outcomes. More computational, translational, and clinical research is needed to bring these tools from the bench to the bedside.
Journal of critical care
|Pages:||44 - 56|
|Type of Publication:||
A2 Review article in a scientific journal
|Field of Science:||
3126 Surgery, anesthesiology, intensive care, radiology
3121 General medicine, internal medicine and other clinical medicine
This research is connected to the DigiHealth-project, a strategic profiling project at the University of Oulu. The project is supported by the Academy of Finland (project number 326291) and the University of Oulu.
|Academy of Finland Grant Number:||
326291 (Academy of Finland Funding decision)
© 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)