Impact of an Automated Surveillance to Detect Surgical-Site Infections in Patients Undergoing Total Hip and Knee Arthroplasty in Brazil

Perdiz, Luciana Baria; Yokoe, Deborah S.; Furtado, Guilherme Henrique Campos; Medeiros, Eduardo Alexandrino

doi:10.1017/ice.2016.86

Cited by 13 publications

(13 citation statements)

References 9 publications

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“…From 2010 on, the numbers of publications increased and peaked in 2014. Contributions originating from South America [56] and Asia [3,24,50,67,68,77] showed that by that time EASS of HAI had adopters worldwide.…”

Section: Timeline and Geographical Distributionmentioning

confidence: 99%

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Electronically assisted surveillance systems of healthcare-associated infections: a systematic review

et al. 2020

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Background: Surveillance of healthcare-associated infections (HAI) is the basis of each infection control programme and, in case of acute care hospitals, should ideally include all hospital wards, medical specialties as well as all types of HAI. Traditional surveillance is labour intensive and electronically assisted surveillance systems (EASS) hold the promise to increase efficiency. Objectives: To give insight in the performance characteristics of different approaches to EASS and the quality of the studies designed to evaluate them. Methods: In this systematic review, online databases were searched and studies that compared an EASS with a traditional surveillance method were included. Two different indicators were extracted from each study, one regarding the quality of design (including reporting efficiency) and one based on the performance (e.g. specificity and sensitivity) of the EASS presented. Results: A total of 78 studies were included. The majority of EASS (n = 72) consisted of an algorithm-based selection step followed by confirmatory assessment. The algorithms used different sets of variables. Only a minority (n = 7) of EASS were hospital-wide and designed to detect all types of HAI. Sensitivity of EASS was generally high (> 0.8), but specificity varied (0.37-1). Less than 20% (n = 14) of the studies presented data on the efficiency gains achieved. Conclusions: Electronically assisted surveillance of HAI has yet to reach a mature stage and to be used routinely in healthcare settings. We recommend that future studies on the development and implementation of EASS of HAI focus on thorough validation, reproducibility, standardised datasets and detailed information on efficiency.

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Section: Timeline and Geographical Distributionmentioning

confidence: 99%

“…Following microbiological data, antibiotic prescriptions data are the second most used variable in EASS algorithms (38 of 78 studies) and, when not combined with microbiological data, most often found in combination with ICD coding [12,16,36,56].…”

Section: Antibioticsmentioning

confidence: 99%

Electronically assisted surveillance systems of healthcare-associated infections: a systematic review

et al. 2020

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“…[8][9][10] Such semiautomated electronic surveillance systems have been reported to reduce the workload for chart reviews while maintaining a high sensitivity of SSI detection for specific surgical procedures such as total hip and knee arthroplasty. [11][12][13][14][15] However, previous studies have been limited to certain types of surgery. The goal of this study was to verify the validity of semiautomated SSI surveillance using electronic screening algorithms in 38 categories of surgery.…”

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confidence: 99%

Validation of semiautomated surgical site infection surveillance using electronic screening algorithms in 38 surgery categories

Cho

Chung

Choi

et al. 2018

Infect. Control Hosp. Epidemiol.

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“…This latter observation also held true for the other types of surgery. In previous studies, data on antibiotics were added to enhance case findings, [17][18][19] but this addition was not essential to this study, and the same held true for data on radiological interventions. Although additional information could be used to optimize algorithm performance, it is feasible to develop well-performing algorithms that rely solely on microbiology results, admission and discharges dates, and procedures codes.…”

Section: Discussionmentioning

confidence: 99%

A framework to develop semiautomated surveillance of surgical site infections: An international multicenter study

Rooden

Tacconelli

Pujol

et al. 2019

Infect. Control Hosp. Epidemiol.

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Objective: Automated surveillance of healthcare-associated infections reduces workload and improves standardization, but it has not yet been adopted widely. In this study, we assessed the performance and feasibility of an easy implementable framework to develop algorithms for semiautomated surveillance of deep incisional and organ-space surgical site infections (SSIs) after orthopedic, cardiac, and colon surgeries.Design: Retrospective cohort study in multiple countries.Methods: European hospitals were recruited and selected based on the availability of manual SSI surveillance data from 2012 onward (reference standard) and on the ability to extract relevant data from electronic health records. A questionnaire on local manual surveillance and clinical practices was administered to participating hospitals, and the information collected was used to pre-emptively design semiautomated surveillance algorithms standardized for multiple hospitals and for center-specific application. Algorithm sensitivity, positive predictive value, and reduction of manual charts requiring review were calculated. Reasons for misclassification were explored using discrepancy analyses.Results: The study included 3 hospitals, in the Netherlands, France, and Spain. Classification algorithms were developed to indicate procedures with a high probability of SSI. Components concerned microbiology, prolonged length of stay or readmission, and reinterventions. Antibiotics and radiology ordering were optional. In total, 4,770 orthopedic procedures, 5,047 cardiac procedures, and 3,906 colon procedures were analyzed. Across hospitals, standardized algorithm sensitivity ranged between 82% and 100% for orthopedic surgery, between 67% and 100% for cardiac surgery, and between 84% and 100% for colon surgery, with 72%-98% workload reduction. Center-specific algorithms had lower sensitivity.Conclusions: Using this framework, algorithms for semiautomated surveillance of SSI can be successfully developed. The high performance of standardized algorithms holds promise for large-scale standardization.The burden of healthcare-associated infections (HAIs) is considerable. In European hospitals,~6% of patients hospitalized are affected by an HAI, and surgical site infections (SSIs) are among the most common complications. 1,2 Feedback of infection rates from surveillance data to clinicians and other stakeholders is a cornerstone of HAI prevention programs, and participation in surveillance programs contributes to the reduction of HAI incidence. [3][4][5] Conventional retrospective surveillance relies on manual chart review to identify HAIs; however, this process is error prone and time-consuming. 6,7 With the large-scale adoption of electronic health records (EHRs), automation of the surveillance process is increasingly feasible. Semiautomated surveillance is commonly used: routine care data stored in the EHR serve as input for an algorithm that indicates a high or a low probability that the targeted

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Impact of an Automated Surveillance to Detect Surgical-Site Infections in Patients Undergoing Total Hip and Knee Arthroplasty in Brazil

Cited by 13 publications

References 9 publications

Electronically assisted surveillance systems of healthcare-associated infections: a systematic review

Electronically assisted surveillance systems of healthcare-associated infections: a systematic review

Validation of semiautomated surgical site infection surveillance using electronic screening algorithms in 38 surgery categories

A framework to develop semiautomated surveillance of surgical site infections: An international multicenter study

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