The incidence of surgical site infection (SSI) after cardiac surgery depends on the definition used. A distinction is generally made between mediastinitis, as defined by the US Centers for Disease Control and Prevention (CDC), and superficial SSI. Our objective was to decipher these entities in terms of presentation and risk factors. We performed a 7-year single centre analysis of prospective surveillance of patients with cardiac surgery via median sternotomy. SSI was defined as the need for reoperation due to infection. Among 7170 patients, 292 (4.1%) developed SSI, including 145 CDC-defined mediastinitis (CDC-positive SSI, 2.0%) and 147 superficial SSI without associated bloodstream infection (CDC-negative SSI, 2.1%). Median time to reoperation for CDC-negative SSI was 18 days (interquartile range, 14-26) and 16 (interquartile range, 11-24) for CDC-positive SSI (p 0.02). Microorganisms associated with CDC-negative SSI were mainly skin commensals (62/147, 41%) or originated in the digestive tract (62/147, 42%); only six were due to Staphylococcus aureus (4%), while CDC-positive SSI were mostly due to S. aureus (52/145, 36%) and germs from the digestive tract (52/145, 36%). Risk factors for SSI were older age, obesity, chronic obstructive bronchopneumonia, diabetes mellitus, critical preoperative state, postoperative vasopressive support, transfusion or prolonged ventilation and coronary artery bypass grafting, especially if using both internal thoracic arteries in female patients. The number of internal thoracic arteries used and factors affecting wound healing were primarily associated with CDC-negative SSI, whereas comorbidities and perioperative complications were mainly associated with CDC-positive SSI. These 2 entities differed in time to revision surgery, bacteriology and risk factors, suggesting a differing pathophysiology.

Background: Machine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious diseases (ID). Objectives: We aim to inform clinicians about the use of ML for diagnosis, classification, outcome prediction and antimicrobial management in ID. Sources: References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, ACM Digital Library, arXiV and IEEE Xplore Digital Library up to July 2019. Content: We found 60 unique ML-clinical decision support systems (ML-CDSS) aiming to assist ID clinicians. Overall, 37 (62%) focused on bacterial infections, 10 (17%) on viral infections, nine (15%) on tuberculosis and four (7%) on any kind of infection. Among them, 20 (33%) addressed the diagnosis of infection, 18 (30%) the prediction, early detection or stratification of sepsis, 13 (22%) the prediction of treatment response, four (7%) the prediction of antibiotic resistance, three (5%) the choice of antibiotic regimen and two (3%) the choice of a combination antiretroviral therapy. The ML-CDSS were developed for intensive care units (n ¼ 24, 40%), ID consultation (n ¼ 15, 25%), medical or surgical wards (n ¼ 13, 20%), emergency department (n ¼ 4, 7%), primary care (n ¼ 3, 5%) and antimicrobial stewardship (n ¼ 1, 2%). Fifty-three ML-CDSS (88%) were developed using data from high-income countries and seven (12%) with data from low-and middle-income countries (LMIC). The evaluation of ML-CDSS was limited to measures of performance (e.g. sensitivity, specificity) for 57 ML-CDSS (95%) and included data in clinical practice for three (5%). Implications: Considering comprehensive patient data from socioeconomically diverse healthcare settings, including primary care and LMICs, may improve the ability of ML-CDSS to suggest decisions adapted to various clinical contexts. Currents gaps identified in the evaluation of ML-CDSS must also be addressed in order to know the potential impact of such tools for clinicians and patients.

IMPORTANCE Controversy remains regarding the transmission routes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).OBJECTIVE To review current evidence on air contamination with SARS-CoV-2 in hospital settings and the factors associated with contamination, including viral load and particle size. EVIDENCE REVIEWThe MEDLINE, Embase, and Web of Science databases were systematically queried for original English-language articles detailing SARS-CoV-2 air contamination in hospital settings between January 1 and October 27, 2020. This study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. The positivity rate of SARS-CoV-2 viral RNA and culture were described and compared according to the setting, clinical context, air ventilation system, and distance from patients. The SARS-CoV-2 RNA concentrations in copies per meter cubed of air were pooled, and their distribution was described by hospital areas. Particle sizes and SARS-CoV-2 RNA concentrations in copies or median tissue culture infectious dose (TCID50) per meter cubed were analyzed after categorization as less than 1 μm, from 1 to 4 μm, and greater than 4 μm. FINDINGS Among 2284 records identified, 24 cross-sectional observational studies were included in the review. Overall, 82 of 471 air samples (17.4%) from close patient environments were positive for SARS-CoV-2 RNA, with a significantly higher positivity rate in intensive care unit settings (intensive care unit, 27 of 107 [25.2%] vs non-intensive care unit, 39 of 364 [10.7%]; P < .001). There was no difference according to the distance from patients (Յ1 m, 3 of 118 [2.5%] vs >1-5 m, 13 of 236 [5.5%]; P = .22). The positivity rate was 5 of 21 air samples (23.8%) in toilets, 20 of 242 (8.3%) in clinical areas, 15 of 122 (12.3%) in staff areas, and 14 of 42 (33.3%) in public areas. A total of 81 viral cultures were performed across 5 studies, and 7 (8.6%) from 2 studies were positive, all from close patient environments. The median (interquartile range) SARS-CoV-2 RNA concentrations varied from 1.0 × 10 3 copies/m 3 (0.4 × 10 3 to 3.1 × 10 3 copies/m 3 ) in clinical areas to 9.7 × 10 3 copies/m 3 (5.1 × 10 3 to 14.3 × 10 3 copies/m 3 ) in the air of toilets or bathrooms. Protective equipment removal and patient rooms had high concentrations per titer of SARS-CoV-2 (varying from 0.9 × 10 3 to 40 × 10 3 copies/m 3 and 3.8 × 10 3 to 7.2 × 10 3 TCID50/m 3 ), with aerosol size distributions that showed peaks in the region of particle size less than 1 μm; staff offices had peaks in the region of particle size greater than 4 μm. CONCLUSIONS AND RELEVANCEIn this systematic review, the air close to and distant from patients with coronavirus disease 2019 was frequently contaminated with SARS-CoV-2 RNA; however, few of these samples contained viable viruses. High viral loads found in toilets and bathrooms, staff areas, and public hallways suggest that these areas should be carefully considered.

BackgroundMost of the evidence on antimicrobial stewardship programmes (ASP) to help sustain the effectiveness of antimicrobials is generated in high income countries. We report a study investigating implementation of ASP in secondary care across low-, middle- and high-income countries. The objective of this study was to map the key contextual, including cultural, drivers of the development and implementation of ASP across different resource settings.Materials and methodsHealthcare professionals responsible for implementing ASP in hospitals in England, France, Norway, India, and Burkina Faso were invited to participate in face-to face interviews. Field notes from observations, documentary evidence, and interview transcripts were analysed using grounded theory approach. The key emerging categories were analysed iteratively using constant comparison, initial coding, going back the field for further data collection, and focused coding. Theoretical sampling was applied until the categories were saturated. Cross-validation and triangulation of the findings were achieved through the multiple data sources.Results54 participants from 24 hospitals (England 9 participants/4 hospitals; Norway 13 participants/4 hospitals; France 9 participants/7 hospitals; India 13 participants/ 7 hospitals; Burkina Faso 8 participants/2 hospitals) were interviewed. Across Norway, France and England there was consistency in ASP structures. In India and Burkina Faso there were country level heterogeneity in ASP. State support for ASP was perceived as essential in countries where it is lacking (India, Burkina Faso), and where it was present, it was perceived as a barrier (England, France). Professional boundaries are one of the key cultural determinants dictating involvement in initiatives with doctors recognised as leaders in ASP. Nurse and pharmacist involvement was limited to England. The surgical specialty was identified as most difficult to engage with in each country. Despite challenges, one hospital in India provided the best example of interdisciplinary ASP, championed through organisational leadership.ConclusionsASP initiatives in this study were restricted by professional boundaries and hierarchies, with lack of engagement with the wider healthcare workforce. There needs to be promotion of interdisciplinary team work including pharmacists and nurses, depending on the available healthcare workforce in different countries, in ASP. The surgical pathway remains a hard to reach, but critical target for ASP globally. There is a need to develop contextually driven ASP targeting the surgical pathway in different resource settings.

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Background: Machine learning (ML) allows the analysis of complex and large data sets and has the potential to improve health care. The clinical microbiology laboratory, at the interface of clinical practice and diagnostics, is of special interest for the development of ML systems. Aims: This narrative review aims to explore the current use of ML In clinical microbiology. Sources: References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, arXiV, ACM Digital Library and IEEE Xplore Digital Library up to November 2019. Content: We found 97 ML systems aiming to assist clinical microbiologists. Overall, 82 ML systems (85%) targeted bacterial infections, 11 (11%) parasitic infections, nine (9%) viral infections and three (3%) fungal infections. Forty ML systems (41%) focused on microorganism detection, identification and quantification, 36 (37%) evaluated antimicrobial susceptibility, and 21 (22%) targeted the diagnosis, disease classification and prediction of clinical outcomes. The ML systems used very diverse data sources: 21 (22%) used genomic data of microorganisms, 19 (20%) microbiota data obtained by metagenomic sequencing, 19 (20%) analysed microscopic images, 17 (18%) spectroscopy data, eight (8%) targeted gene sequencing, six (6%) volatile organic compounds, four (4%) photographs of bacterial colonies, four (4%) transcriptome data, three (3%) protein structure, and three (3%) clinical data. Most systems used data from high-income countries (n ¼ 71, 73%) but a significant number used data from low-and middle-income countries (n ¼ 36, 37%). Performance measures were reported for the 97 ML systems, but no article described their use in clinical practice or reported impact on processes or clinical outcomes. Implications: In clinical microbiology, ML has been used with various data sources and diverse practical applications. The evaluation and implementation processes represent the main gap in existing ML systems, requiring a focus on their interpretability and potential integration into real-world settings.

SUMMARY A systematic literature review was performed to assess the impact of surgical-staff behaviors on the risk of surgical site infections. Published data are limited, heterogeneous, and weakened by several methodological flaws, underlying the need for more studies with accurate tools. OBJECTIVE To assess the current literature regarding the impact of surgical-staff behaviors on the risk of surgical-site infection (SSI). DESIGN Systematic literature review. METHODS We searched the Medline, EMBASE, Ovid, Web of Science, and Cochrane databases for original articles about the impact of intraoperative behaviors on the risk of SSI published in English before September 2013. RESULTS We retrieved 27 original articles reporting data on number of people in the operating room (n=14), door openings (n=14; number [n=6], frequency [n=7], reasons [n=4], or duration [n=3]), surgical-team discipline (evidence of distraction; n=4), compliance with traffic measures (n=6), or simulated behaviors (n=3). Most (59%) articles were published in 2009-2013. End points were the 30-day SSI rate (n=8), air-particle count (n=2), or microbiological air counts (n=6); 11 studies were only descriptive. Number of people in the operating room and SSI rate or airborne contaminants (particle/bacteria) were correlated in 2 studies. Door openings and airborne bacteria counts were correlated in 2 observational studies and 1 experimental study. Two cohort studies showed a significant association between surgeon interruptions/distraction or noise and SSI rate. The level of evidence was low in all studies. CONCLUSIONS Published data about the impact of operating-room behaviors on the risk of infection are limited and heterogeneous. All studies exhibit major methodological flaws. More studies with accurate tools should be performed to address the influence of operating room behaviors on the infectious risk.

IntroductionInappropriate staff behaviours can lead to environmental contamination in the operating room (OR) and subsequent surgical site infection (SSI). This study will focus on the continued assessment of OR staff behaviours using a motion tracking system and their impact on the SSI risk during surgical procedures.Methods and analysisThis multicentre prospective cross-sectional study will include 10 ORs of cardiac and orthopaedic surgery in 12 healthcare facilities (HCFs). The staff behaviour will be assessed by an objective, continued and prolonged quantification of movements within the OR. A motion tracking system including eight optical cameras (VICON-Bonita) will record the movements of reflective markers placed on the surgical caps/hoods of each person entering the room. Different configurations of markers positioning will be used to distinguish between the staff category. Doors opening will be observed by means of wireless inertial sensors fixed on the doors and synchronised with the motion tracking system. We will collect information on the OR staff, surgical procedures and surgical environment characteristics. The behavioural data obtained will be compared (1) to the ‘best behaviour rules’ in the OR, pre-established using a Delphi method and (2) to surrogates of the infectious risk represented by microbiological air counts, particle counts, and a bacteriological sample of the wound at closing. Statistics will be performed using univariate and multivariate analysis to adjust on the aerolic and architectural characteristics of the OR. A multilevel model will allow including surgical specialty and HCFs effects. Through this study, we will develop an original approach using high technology tools associated to data processing techniques to evaluate ‘automatically’ the behavioural dynamics of the OR staff and their impact on the SSI risk.Ethics and disseminationApprobation of the Institutional Review Board of Paris North Hospitals, Paris 7 University, AP-HP (no 11-113, 6 April 2012). The findings will be disseminated through peer-reviewed journals, and national and international conference presentations.