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Background 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. Objectives 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. Methods 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‑regres‑ sion of the different parameters used across algorithms. Results 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 2 2 0.79–0.92] with significant heterogeneity (I = 91.9, p < 0.001 and I = 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 predic‑ tor did not improve performance of these algorithms with similar specificity estimates (0.92, 95%CI 0.88–0.96). Gaud Catho and Niccolò Buetti have contributed equally to this work. *Correspondence: Jean‑Marie Januel jean‑email@example.com Full list of author information is available at the end of the article © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Januel et al. Antimicrobial Resistance & Infection Control (2023) 12:87 Page 2 of 10 Conclusions 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/ prosp ero/ displ ay_ record. php? ID= CRD42 02229 9641 Keywords CLABSI, CRBSI, Automated monitoring, Algorithm, Accuracy, Surveillance, Healthcare associated infections Background Methods Intravascular catheters (IVC) are indispensable and Design commonly used medical devices in hospitalized We performed a systematic review and meta-analysis on patients, and substantially predispose patients to the predictive performance of automated surveillance develop healthcare-associated infections (HAIs) [1–9]. algorithms for the detection of CLABSI/CRBSI among In 2016, the mean prevalence of HAIs in European hospitalized patients. This study was registered within countries was estimated to be 6.5% . Hospital- the PROSPERO international prospective register of acquired bloodstream infections (HA-BSI) account for systematic reviews (CRD42022299641) on January 21, 14.2% of HAI  and a large proportion of HA-BSI is 2022, and was reported in accordance with the Preferred attributable to IVC. In Europe, both central line-asso- Reporting Items for Systematic reviews and Meta-Analy- ciated bloodstream infection (CLABSI) and catheter- ses (PRISMA) statement [13, 14]. related bloodstream infection (CRBSI) represent 36.5% of intensive care unit (ICU)-acquired bloodstream Search strategy infections (BSIs) and the incidence has been found to We conducted a systematic search using two electronic vary between 1.7 and 4.8 episodes per 1000 catheter databases, PubMed and EMBASE, for relevant articles days . CLABSI/CRBSI are associated with excess published between 1 January 2000 and 31 December mortality rates, extended duration of hospitalization 2021. We searched for original studies using the keyword and greater healthcare expenditure [5–9]. More than algorithms described in the supplementary material. 50% of CLABSI/CRBSI can be considered as prevent- The search was limited to articles published in English. able . We searched for studies that reported on the predictive Surveillance of CLABSI/CRBSI among patients with performance of automated algorithms for the detection IVC allows for the burden of disease to be quantified and of HAI (to increase the sensitivity of the search strategy) for the effectiveness of interventions to prevent CLABSI/ and of intravascular catheter infections (to increase the CRBSI to be assessed. Automated algorithms may offer specificity of the search strategy). The records from the approaches to improve the efficiency of CLABSI/CRBSI two databases search were merged and duplicates were surveillance. Compared to manual surveillance, auto- removed using the EndNote program (Thomson Reuters, mated surveillance has been demonstrated to reduce New-York, NY, USA). time and workload for healthcare workers and infection control practitioners, and to provide data in real time Study selection that may allow for more timely clinical interventions Two investigators (J.M.J. and N.L.) screened titles and [10–12]. However, little is known as to the predictive per- abstracts and examined the full text of original articles formance of automated algorithms for CLABSI/CRBSI selected for study inclusion independently and in dupli- surveillance, as well as the relative performance of dif- cate; disagreements were resolved by consensus. ferent parameters that could be used within automated algorithms for the timely identification of CLABSI/ Inclusion criteria CRBSI among patients with IVC. Original studies were included if they assessed accuracy The main objective of this study was to evaluate the of automated algorithms for the surveillance of CLABSI predictive performance of automated surveillance sys- and/or CRBSI. CLABSI was defined by one positive tems for the detection of CLABSI/CRBSI among hospi- blood culture and clinical manifestation of infection in a talized patients, and to identify which parameters have patient with a catheter in place and with no other source a greater influence on the predictive performance, so as of bacteremia except the catheter. CRBSI was defined to inform future automated surveillance algorithms for as one positive blood culture obtained from peripheral CLABSI/CRBSI detection. vein and clinical manifestation of infection, and at least Januel et al. Antimicrobial Resistance & Infection Control (2023) 12:87 Page 3 of 10 one of the following: (1) a positive CVC culture with the plots observed data in ROC plane to assess threshold same micro-organism by qualitative or semi-quantita- effect visually. In addition, we used graphical model tests tive (i.e., ≥ 15 CFU) methods or (2) a differential time to to depict both the residual-based goodness-of-fit and the positivity of more than 120 min between central cath- bivariate normal distribution, to check for how observa- eter blood culture and peripheral blood culture (blood tions influenced analyses and to detect outliers. samples drawn at the same time), or (3) a ratio of micro- Finally, we performed a meta-regression to explore organism quantity from CVC blood sample on microor- how individual and pooled parameters included in the ganism quantity from peripheral blood sample greater algorithms influenced the performance of automated than 3 [15–17]. We selected studies that compared the algorithms for CLABSI/CRBSI detection as compared to predictive performance of automated algorithms with manual surveillance. data from manual surveillance. Selected studies needed We used the “midas” command (meta-analysis integra- to include sensitivity and specificity estimates calculated tion of diagnostic test accuracy studies), a comprehensive using diagnostic test methods. Moreover, studies needed program of statistical and graphical routines for under- to directly or indirectly include all of the following: num- taking meta-analysis of diagnostic test performance in ber of true positives (TP), false positives (FP), true nega- STATA developed by Dwamena . Each individual tives (TN), and false negatives (FN). Studies that did not parameter and combination of parameter were included provide all these data to determine the predictive perfor- in the meta-regression as an independent explanatory mance of the automated algorithm were excluded. variable. We considered results as significant for P-val - ues < 0.05. We used STATA/MP software (version 16.0). Data extraction and quality assessment STATA codes are reported in the Additional file 1. Data were extracted from the selected studies according to predefined rules that were used to identify IVC infec - Results tions. If multiple algorithms (i.e., algorithms with differ - Systematic literature search ent definitions for identifying IVC infections) or multiple We identified 586 non-redundant study records. Eighty study populations were evaluated in a single study, we (13.7%) full text articles were assessed for eligibility after defined each single algorithm as a single observation in abstract and title screening (Additional file 1: Fig. S1). Of the meta-analysis. The total number of algorithms ana - these, 5 (1%) were included in the systematic review and lyzed was therefore higher than the number included meta-analysis [22–26]. Details on the search strategy are studies. For each algorithm, we extracted the data on the given in the Additional file 1. endpoint (CLABSI/CRBSI) and on the predictive perfor- mance of the automated algorithm: TP, FP, FN, and TN. Characteristics of included studies From these different algorithms, we identified individ - Three of the studies included in the systematic review ual and pooled parameters for CLABSI/CRBSI detection. were monocentric [23–25] and the remaining two mul- Using the revised tool for the Quality Assessment of ticentric [22, 26] (Table 1). Four studies analyzed only Diagnostic Accuracy Studies (QUADAS 2) , we eval- central venous catheters (CVC) and used CLABSI as uated the quality of studies based on four items: patient an outcome [22, 24–26]. One study included unspeci- selection, index test, reference standard, and flow and fied IVC and used CRBSI as an outcome . Two stud - timing. We assessed intra-study risk of bias and concern ies (40%) were conducted in the ICU setting [24, 26]. All for applicability using a three-level rating scale (high, low studies were observational and used manually collected or unclear). surveillance data as reference [22–26]. Across the 5 studies, 32 different automated algorithms Statistical analysis or population samples were identified and included in the We first described characteristics of included studies meta-analysis. Among the 32 algorithms, we identified (mono versus multicenter, type of catheter included, set- 7 individual parameters, and 9 combinations of param- ting) and outcomes. We then estimated pooled sensitiv- eters used for automated detection of CLABSI/CRBSI ity and specificity of automated surveillance algorithms (Table 2 and Additional file 1: Table S1). These 16 single for the identification of CLABSI/CRBSI with 95% con - or pooled parameters were then tested in the univariable fidence intervals (95% CI) for algorithms using bivariate meta-regression. random-effects models. We used I statistics to assess potential heterogeneity between algorithms [19, 20], with Quality of studies I > 75% representing considerable heterogeneity. We Using the QUADAS-2 tool, the quality of the five studies subsequently calculated areas under summary receiver included in the systematic review and meta-analysis was operating characteristic curves (SAUROC), and we used assessed to be high (Additional file 1: Table S2). Overall, Januel et al. Antimicrobial Resistance & Infection Control (2023) 12:87 Page 4 of 10 Table 1 Characteristics of included studies Study Setting Type of ward Location Study period Study population Catheter Outcome sample size types included Trick et al.  2 hospitals All wards exclud‑ US, Chicago Sep 1st, 2001 to Feb 99 patients (104 posi‑ CVC CLABSI ing neonatal 28th, 2002 tive blood culture) and pediatric wards in one hospital, and 28 patients (31 positive blood culture) in the other hospital Bellini et al.  1 hospital All types Switzerland, Laus‑ 3‑ years period 669 positive blood Unspeci‑ CRBSI anne culture fied intra‑ vascular catheter Woeltje et al.  1 hospital 6 ICU US, Missouri July 1st, 2005 to Dec 540 patients (694 CVC CLABSI 31, 2006 positive blood culture) Woeltje et al.  1 hospital 4 non‑ICU US, Missouri Jul 1st, 2005 to Dec 331 patients (391 CVC CLABSI 31, 2006 positive blood culture) Snyders et al.  11 hospitals 17 ICU US, Missouri Jan1st to Jun 30, 518 patients (643 CVC CLABSI 2011 positive blood culture) CVC: central venous catheter; CLABSI: central line-associated bloodstream infection; CRBSI: catheter-related bloodstream infection ICU: intensive care unit the risk of bias was low: two studies were rated as low risk site” and differed regarding the time window considered of potential bias, among all assessed categories [22, 24], for the parameter “Cultures from other body sites” (from and three studies were rated as high risk of bias for only admission to the positive blood culture (algorithm 19; one algorithm within each study [23, 25, 26]. The appli - sensitivity 0.94 [95% CI 0.87–0.98] and specificity 0.94 cability was rated as high for four of the five studies [22, [95% CI 0.91–0.96]) or from admission and within 7 days 24–26], and only one study had low applicability in two of before or after the positive blood culture (algorithm 20; three algorithms identified in the study . The evalu - sensitivity 0.92 [95% CI 0.84–0.97] and specificity 0.98 ation of publication bias is illustrated in Additional file [95% CI 0.96–0.99]) or from admission and within (Additional file 1: Figs. S2 and S3). 14 days before to 7 days after the positive blood culture (algorithm 21; sensitivity 0.92 [95% CI 0.84–0.97] and Pooled sensitivity, specificity and SAUROC specificity 0.98 [95% CI 0.96–0.99]). The pooled sensitivity and specificity were 0.89 [95% CI Outliers regarding sensitivity and specificity are illus - 0.85–0.92] and 0.83 [95% CI 0.71–0.91] with significant trated in the Additional file (Additional file 1: Fig. S2). heterogeneity between algorithms included in the meta- 2 2 analysis (I = 91.36, p < 0.001 and I = 99.20, p < 0.001), Meta‑regression respectively (Fig. 1). Given the heterogeneity between algorithms included in The area under the SAUROC curve was 0.93 [95% CI the meta-analysis, the meta-regression sought to iden- 0.91–0.95] and this identified four algorithms with sen - tify which parameters had the greatest influence on the sitivity greater than 0.89 and specificity greater than 0.83 measures of effect. Additional file 1: Fig. S5 shows the (Figure S4A). These 4 best performing algorithms were all frequency of all individual and pooled parameters identi- from the study of Woeltje et al. 2011 . Algorithm 17 fied within the 32 algorithms. The individual and pooled was defined by the combination of the four following parameters for CLABSI/CRBSI detection derived from parameters: “Hospital-acquired BSI with CVC”, “True univariable meta-regression is illustrated in Fig. 2. BSI for common skin commensal (CSC)”, “Clinical data” Figure 3 summarizes how each parameter affects and “Cultures from any other site” and has a sensitivity of the sensitivity and specificity of the algorithm. For two 0.95 [95% CI 0.88–0.99] and a specificity of 0.98 [95% CI parameters only, “Hospital-acquired BSI with CVC” and 0.95–0.99]. Three algorithms (19, 20 and 21) combined “Administration of antibiotics”, the sensitivity increased, the same three parameters “Hospital-acquired BSI with and the specificity decreased when the parameter was CVC”, “True BSI for CSC” and “Cultures from any other present. When “Hospital-acquired BSI with CVC” was the Januel et al. Antimicrobial Resistance & Infection Control (2023) 12:87 Page 5 of 10 Table 2 Individual and pooled parameters used in the meta‑regression Individual or pooled parameters Definitions Corresponding letter in Fig. 2 Hospital‑acquired (HA) bloodstream infection (BSI) with IVC Hospital‑acquired BSI was defined as a BSI detected ≥ 48 h A after hospital admission and the patient had an intravascular catheter in situ True BSI for common skin commensal True BSI for common skin commensal (CSC) was defined B as at least two positive blood cultures within 3 to 7 days accord‑ ing to studies included. CSC included diphtheroid, Bacillus species, Cutibacterium species, coagulase‑negative staphylococci, and micrococci Clinical data Clinical data (e.g., fever defined by temperature > 38.0 °C, hypo‑ C tension defined by systolic pressure < 90 mmHg) were consid‑ ered in the algorithm Administration of antibiotics Administration of antibiotics was considered in the algorithm D New episode The same microorganism isolated in a separate blood culture E was considered as a new episode only if identified after at least between 3 and 7 days after the first episode, according to studies Cultures from any other sites A bloodstream infection was not considered catheter related F or associated if the same pathogen was identified from a culture in any other body sites Cultures from specific other sites A bloodstream infection was not considered catheter related G or associated if the same pathogen was identified in cultures from one of the following body sites: respiratory track, urinary or wound HA BSI with IVC + true BSI for common skin commensal + clinical Cf. definitions above H data HA BSI with IVC + true BSI for common skin commensal + admin‑ Cf. definitions above I istration of antibiotics HA BSI with IVC + true BSI for common skin commensal + new Cf. definitions above J episode HA BSI with IVC + true BSI for common skin commensal + culture Cf. definitions above K from any other sites HA BSI with IVC + true BSI for common skin commensal + culture Cf. definitions above L from specific another sites HA BSI with IVC + true BSI for common skin commensal + new Cf. definitions above M episode + culture from any other sites HA BSI with IVC + true BSI for common skin commensal + new Cf. definitions above N episode + culture from specific another sites HA BSI with IVC + true BSI for common skin commensal + clinical Cf. definitions above O data + culture from any other sites HA BSI with IVC + true BSI for common skin commensal + clinical Cf. definitions above P data + culture from specific another sites BSI: bloodstream infection; IVC: intravascular catheter; HA: healthcare associated; CSC: common skin commensal only parameter considered, the sensitivity was 0.98 (95% common skin commensal”. In addition, combination CI 0.96–1.00) as compared to 0.83 (95%CI 0.83- 0.90) with highest specificities included either “Culture from when it was not considered (p = 0.25). When “adminis- specific other sites” or “Culture from any other site” and tration of antibiotics” was the only parameter considered, either “New episode” or “Clinical data”. The combina - the sensitivity was 0.92 (95% CI 0.85–0.99) as compared tions that included the parameter “Culture from specific to 0.88 (95% CI 0.84–0.92) when it was not considered. other sites” had higher specificities (0.92; 95% CI 0.88– For all other individual and pooled parameters, the sen- 0.96) than the combinations that include “Culture from sitivity decreased, and the specificity increased when the any other site” (0.88; 95% CI 0.81–0.95). The addition of parameter was present. the parameter “Clinical data” in the combination did The combination of parameters with the highest spe - not increase the specificity of the estimate: 0.92 (95% cificities always included the parameter “True BSI for CI; 0.88–0.96) with the parameter included vs. 0.92 (95% CI; 0.88–0.96) without it. Januel et al. Antimicrobial Resistance & Infection Control (2023) 12:87 Page 6 of 10 Fig. 1 Forest plot of diagnostic performance, including sensitivity and specificity. *Studies with the best performance based on both sensitivity/ specificity (sensitivity ≥ 0.89 and specificity ≥ 0.83) Fig. 2 Univariable meta‑regression for intravascular catheter bloodstream infection criteria. Blue reference line shows the pooled sensitivity and specificity, respectively. Letters A to G refers to Individual and pooled parameters described in Table 2 Januel et al. Antimicrobial Resistance & Infection Control (2023) 12:87 Page 7 of 10 Fig. 3 Decision tree based on different algorithm’s parameters the BSI , as compared to the CLABSI definition. To Discussion our knowledge, only one previous systematic review col- To our knowledge, this is the first meta-analysis that has lated data on automated surveillance algorithms for the reported pooled sensitivity and specificity as predictive detection of IVC infections . However, the authors of performance of automated surveillance algorithms, using this systematic review focused on all HAIs, did not per- bivariate random-effects approach from 5 studies pub - form a meta-analysis and did not assess heterogeneity in lished in the last two decades [27–30]. This meta-analysis the definitions used for automated surveillance of IVC highlighted two main findings: (1) the pooled sensitivity infections. was high but heterogeneous across all algorithms; and In our study the pooled sensitivity was high (i.e., > 85%), (2) the pooled specificity was also heterogeneous, but and no individual or pooled parameter substantially the meta-regression allowed to identify several individual influenced it. This finding is probably explained by the and pooled parameters that had a greater influence on simplicity of detection of BSI compared to other HAIs the measure of effect, and which could therefore inform (e.g. surgical site infections or ventilator-associated pneu- the development of further automated surveillance algo- monias) that require more complicated detection strate- rithms for the detection of CLABSI/CRBSI. gies and inclusion of clinical data or radiological findings. Few studies have investigated the predictive accuracy Several factors influenced specificities and could of both CLABSI and CRBSI using automated surveillance improve the accuracy of automated monitoring algo- systems. Frequently, authors have focused on CVC and rithms. First, the addition of “clinical signs” did not CLABSI, thus disregarding other IVC and the more spe- substantially improve the accuracy of the specificity com - cific definition CRBSI. Indeed, the CRBSI definition fre - pared to similar algorithms that included microbiological quently needs catheter removal and catheter tip culture, cultures from other body sites. Second, higher specifici - which are not commonly performed in most countries ties could be achieved by including only microbiologi- . However, CRBSI allows a higher degree of certainty cal data from respiratory tract, urinary tract or wound in the attribution of the catheter in the occurrence of Januel et al. Antimicrobial Resistance & Infection Control (2023) 12:87 Page 8 of 10 samples instead of microbiological data from any other heterogeneity factors. This makes meta-regressions and body sites. Clinical signs are frequently difficult to col - meta-analysis a more relevant tool for decision making lect in hospital databases because of the lack of structure purposes taking into account the external validation of and standardization of the data, whereas microbiologi- an automated algorithm. cal cultures, which usually rely only on microbiological This study has some limitations. First, the low num - laboratories, are often more harmonized, more easily ber of studies included suggests that the accuracy of extractable and therefore integrated in an automated sur- the estimates of automated monitoring algorithms veillance algorithm. Automated algorithms should firstly for CLABSI/CRBSI detection could be limited by the integrate microbiological data from other selected body potential heterogeneity of data. Moreover, the inclu- sites to exclude non-catheter associated bloodstream sion of Bellini et al.  study, which relied on CRBSI infections. definition only, could have increased the risk of heter - Our results allow us to make some suggestions regard- ogeneity. Second, classification of algorithms could be ing which parameters are relevant for clinical decision questionable, and the heterogeneity in definitions did making or hypothesis generation for further clinical stud- not allow us to develop accurate algorithms (e.g., details ies investigating this issue, based on the balance between of clinical signs were not specified, time-windows were sensitivity and specificity and on clinical relevance simplified into our meta-analysis by regrouping time- (Fig. 3). We suggest that the following parameters should windows from different studies). Third, the generaliz - be relevant for inclusion in an automated CLABSI detec- ability of our conclusions could be limited, because tion algorithm: (i) a parameter to differentiate a common studies were frequently monocentric and were all skin contaminant from a true causative pathogen; (ii) a performed in high income countries. Fourth, grey lit- parameter to define two distinct infectious episodes; and erature was not screened. Finally, mostly CVC were (iii) a parameter to consider cultures from other specific included; it is conceivable that our results are not appli- body sites to exclude infection not associated with an cable to other intravascular catheter types. IVC. That is, our results allow us to infer that any auto - mated algorithm for IVC bloodstream surveillance needs to be able to: distinguish the causative pathogen from Conclusions common skin commensal; consider the same microor- Our meta-regression examined the accuracy of auto- ganism isolated in a separate blood culture to be a new mated algorithms developed to monitor CLABSI/ infectious episode if identified after at least between 3 CRBSI and provides valuable information while devel- and 7 days after the first episode; and differentiate any oping valid algorithms for automated monitoring of positive culture(s) from other body sites as being non- intravascular catheter infections. Microbiological catheter related. cultures from selected other body sites could help to The use of a meta-regression versus predictive scores exclude BSI not related to IVC, whereas clinical signs (e.g., Infection Probability Score (IPS) , or its modi- did not substantially improve the accuracy of auto- fication for central venous catheter-related blood - mated systems when microbiological cultures from stream infections ) remains debated. While the selected other body sites were included. development and validation of predictive score are usu- ally based one restricted and temporally defined sample Supplementary Information (i.e., study), systematic reviews, meta-regression and The online version contains supplementary material available at https:// doi. org/ 10. 1186/ s13756‑ 023‑ 01286‑0. meta-analysis include several samples (i.e., several clus- ters, representing different studies). Predictive scores Additional file 1. Search strategy, Supplementray figures and tables. can be validated externally in other settings, allow- ing model discrimination and calibration performance Acknowledgements across several settings. However, random-effects meta- We would like to thank Daniel Teixeira and Loïc Fortchantre for their valuable regression or meta-analysis provide the additional inputs. We thank all members of Swissnoso. Collaborators: For Swissnoso, the National Center for Infection Control: Carlo benefit of average performance and heterogeneity in Balmelli, Niccolò Buetti, Delphine Berthod, Stephan Harbarth, Philipp Jent, performance across different studies. Accordingly, sys - Jonas Marschall, Hugo Sax, Matthias Schlegel, Alexander Schweiger, Laurence tematic reviews, meta-regression and meta-analysis Senn, Rami Sommerstein, Sarah Tschudin‑Sutter, Nicolas Troillet, Danielle Vuichard Gysin, Andreas F Widmer, Aline Wolfensberger, Walter Zingg of validation studies provide a summary of predictive performance from different settings and populations Author contributions . In other words, meta-regressions or meta-analysis Study concept and design: JMJ, NL, GG, RG, NB. Acquisition of data: JMJ, NL Statistical analysis: JMJ. Interpretation of data: JMJ, GC, RG, NB. Drafting of the provide a potentially more accurate and relevant meas- manuscript: JMJ; GC, NB. Critical revision of the manuscript: JMJ, GC, RG, STS, ure of performance, taking into account context and PWS, BG, PJ, ELP, AS, SH. Januel et al. 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Antimicrobial Resistance and Infection Control – Springer Journals
Published: Aug 31, 2023
Keywords: CLABSI; CRBSI; Automated monitoring; Algorithm; Accuracy; Surveillance; Healthcare associated infections
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