Titre
Predictive performance of automated surveillance algorithms for intravascular catheter bloodstream infections: a systematic review and meta-analysis.
Type
article
Institution
UNIL/CHUV/Unisanté + institutions partenaires
Auteur(s)
Januel, J.M.
Auteure/Auteur
Lotfinejad, N.
Auteure/Auteur
Grant, R.
Auteure/Auteur
Tschudin-Sutter, S.
Auteure/Auteur
Schreiber, P.W.
Auteure/Auteur
Grandbastien, B.
Auteure/Auteur
Jent, P.
Auteure/Auteur
Lo Priore, E.
Auteure/Auteur
Scherrer, A.
Auteure/Auteur
Harbarth, S.
Auteure/Auteur
Catho, G.
Auteure/Auteur
Buetti, N.
Auteure/Auteur
Contributrices/contributeurs
Balmelli, C.
Berthod, D.
Marschall, J.
Sax, H.
Schlegel, M.
Schweiger, A.
Senn, L.
Sommerstein, R.
Troillet, N.
Gysin, D.V.
Widmer, A.F.
Wolfensberger, A.
Zingg, W.
Groupes de travail
Swissnoso
Liens vers les personnes
Liens vers les unités
ISSN
2047-2994
Statut éditorial
Publié
Date de publication
2023-08-31
Volume
12
Numéro
1
Première page
87
Peer-reviewed
Oui
Langue
anglais
Notes
Publication types: Meta-Analysis ; Systematic Review ; Journal Article ; Review ; Research Support, Non-U.S. Gov't
Publication Status: epublish
Publication Status: epublish
Résumé
Intravascular catheter infections are associated with adverse clinical outcomes. However, a significant proportion of these infections are preventable. Evaluations of the performance of automated surveillance systems for adequate monitoring of central-line associated bloodstream infection (CLABSI) or catheter-related bloodstream infection (CRBSI) are limited.
We evaluated the predictive performance of automated algorithms for CLABSI/CRBSI detection, and investigated which parameters included in automated algorithms provide the greatest accuracy for CLABSI/CRBSI detection.
We performed a meta-analysis based on a systematic search of published studies in PubMed and EMBASE from 1 January 2000 to 31 December 2021. We included studies that evaluated predictive performance of automated surveillance algorithms for CLABSI/CRBSI detection and used manually collected surveillance data as reference. We estimated the pooled sensitivity and specificity of algorithms for accuracy and performed a univariable meta-regression of the different parameters used across algorithms.
The search identified five full text studies and 32 different algorithms or study populations were included in the meta-analysis. All studies analysed central venous catheters and identified CLABSI or CRBSI as an outcome. Pooled sensitivity and specificity of automated surveillance algorithm were 0.88 [95%CI 0.84-0.91] and 0.86 [95%CI 0.79-0.92] with significant heterogeneity (I <sup>2</sup> = 91.9, p < 0.001 and I <sup>2</sup> = 99.2, p < 0.001, respectively). In meta-regression, algorithms that include results of microbiological cultures from specific specimens (respiratory, urine and wound) to exclude non-CRBSI had higher specificity estimates (0.92, 95%CI 0.88-0.96) than algorithms that include results of microbiological cultures from any other body sites (0.88, 95% CI 0.81-0.95). The addition of clinical signs as a predictor did not improve performance of these algorithms with similar specificity estimates (0.92, 95%CI 0.88-0.96).
Performance of automated algorithms for detection of intravascular catheter infections in comparison to manual surveillance seems encouraging. The development of automated algorithms should consider the inclusion of results of microbiological cultures from specific specimens to exclude non-CRBSI, while the inclusion of clinical data may not have an added-value. Trail Registration Prospectively registered with International prospective register of systematic reviews (PROSPERO ID CRD42022299641; January 21, 2022). https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022299641.
We evaluated the predictive performance of automated algorithms for CLABSI/CRBSI detection, and investigated which parameters included in automated algorithms provide the greatest accuracy for CLABSI/CRBSI detection.
We performed a meta-analysis based on a systematic search of published studies in PubMed and EMBASE from 1 January 2000 to 31 December 2021. We included studies that evaluated predictive performance of automated surveillance algorithms for CLABSI/CRBSI detection and used manually collected surveillance data as reference. We estimated the pooled sensitivity and specificity of algorithms for accuracy and performed a univariable meta-regression of the different parameters used across algorithms.
The search identified five full text studies and 32 different algorithms or study populations were included in the meta-analysis. All studies analysed central venous catheters and identified CLABSI or CRBSI as an outcome. Pooled sensitivity and specificity of automated surveillance algorithm were 0.88 [95%CI 0.84-0.91] and 0.86 [95%CI 0.79-0.92] with significant heterogeneity (I <sup>2</sup> = 91.9, p < 0.001 and I <sup>2</sup> = 99.2, p < 0.001, respectively). In meta-regression, algorithms that include results of microbiological cultures from specific specimens (respiratory, urine and wound) to exclude non-CRBSI had higher specificity estimates (0.92, 95%CI 0.88-0.96) than algorithms that include results of microbiological cultures from any other body sites (0.88, 95% CI 0.81-0.95). The addition of clinical signs as a predictor did not improve performance of these algorithms with similar specificity estimates (0.92, 95%CI 0.88-0.96).
Performance of automated algorithms for detection of intravascular catheter infections in comparison to manual surveillance seems encouraging. The development of automated algorithms should consider the inclusion of results of microbiological cultures from specific specimens to exclude non-CRBSI, while the inclusion of clinical data may not have an added-value. Trail Registration Prospectively registered with International prospective register of systematic reviews (PROSPERO ID CRD42022299641; January 21, 2022). https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022299641.
PID Serval
serval:BIB_FA5A93D7B3B3
PMID
Open Access
Oui
Date de création
2023-09-20T09:55:52.462Z
Date de création dans IRIS
2025-05-21T05:49:55Z
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Nom
37653559_BIB_FA5A93D7B3B3.pdf
Version du manuscrit
published
Licence
https://creativecommons.org/licenses/by/4.0
Taille
1.56 MB
Format
Adobe PDF
PID Serval
serval:BIB_FA5A93D7B3B3.P001
URN
urn:nbn:ch:serval-BIB_FA5A93D7B3B37
Somme de contrôle
(MD5):54a7a66b6e610bcce11d8bc2005a2a72