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Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Diagnostic and prognostic prediction models in ventilator-associated pneumonia: Systematic review and meta-analysis of prediction modelling studies
Machine learning; Mechanical ventilation; Prognostic model ; Aprenentatge automàtic; Ventilació mecànica; Model pronòstic ; Aprendizaje automático; Ventilacion mecanica; Modelo pronóstico ; 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. ; The project is ...
Diagnostic and prognostic prediction models in ventilator-associated pneumonia: Systematic review and meta-analysis of prediction modelling studies
Machine learning; Mechanical ventilation; Prognostic model ; Aprenentatge automàtic; Ventilació mecànica; Model pronòstic ; Aprendizaje automático; Ventilacion mecanica; Modelo pronóstico ; 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. ; The project is ...
Diagnostic and prognostic prediction models in ventilator-associated pneumonia: Systematic review and meta-analysis of prediction modelling studies
Frondelius, Tuomas (Autor:in) / Atkova, Irina (Autor:in) / Miettunen, Jouko (Autor:in) / Rello Condomines, Jordi (Autor:in) / Jansson, Miia (Autor:in) / Institut Català de la Salut / Frondelius T, Jansson MM Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland. Atkova I University of Oulu, Oulu, Finland. Miettunen J Center for Life Course Health Research, University of Oulu, Oulu, Finland. Medical Research Center Oulu, Oulu University Hospital, University of Oulu, Oulu, Finland. Rello J CIBER de Enfermedades Respiratorias, CIBERES, Instituto de Salud Carlos III, Barcelona, Spain. Grup de Recerca Clínica / Innovació en la Pneumònia i la Sèpsia (CRIPS), Vall d'Hebron Institut de Recerca (VHIR), Barcelona, Spain. Clinical Research, CHU Caremeau, Nimes, France / Vall d'Hebron Barcelona Hospital Campus
01.01.2021
000712259300001
Scientia
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DISEASES::Bacterial Infections and Mycoses::Infection::Cross Infection::Pneumonia , Ventilator-Associated , Respiració artificial , Otros calificadores::Otros calificadores::/diagnóstico , Pneumònia - Prognosi , Other subheadings::Other subheadings::/diagnosis , ENFERMEDADES::infecciones bacterianas y micosis::infección::infección hospitalaria::neumonía asociada al ventilador
DDC:
690
Ventilator-associated pneumonia
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