Embryonic implantation: cytokines, adhesion molecules, and immune cells in establishing an implantation environment

Successful implantation is an absolute requirement for the reproduction of species, including humans. The process by which a foreign blastocyst is accepted by the maternal endometrium is complex and requires interplay of many systems. Implantation occurs during the putative implantation window, in which the maternal endometrium is ready to accept the blastocyst, which on the other hand, also plays a specific role. It produces cytokines and chemokines and expresses adhesion molecules and certain classes of MHC molecules. We review the most important players in implantation. Concerning the cytokines, the establishment of controlled aggression is key; an excess of pro- or anti-inflammation is detrimental to pregnancy outcome. Chemokines control the orientation of the embryo. The adhesion molecules are necessary to establish the required physical interaction between mother and blastocyst. Finally, immune cells and in particular, uterine NK and regulatory T cells are pivotal in inducing tolerance to the blastocyst. The aim of this review is to discuss mechanisms at play and their relative importance to the establishment of pregnancy

External validation of semi-automated surveillance algorithms for deep surgical site infections after colorectal surgery in an independent country

Abstract Background Automated surveillance methods that re-use electronic health record data are considered an attractive alternative to traditional manual surveillance. However, surveillance algorithms need to be thoroughly validated before being implemented in a clinical setting. With semi-automated surveillance patients are classified as low or high probability of having developed infection, and only high probability patients subsequently undergo manual record review. The aim of this study was to externally validate two existing semi-automated surveillance algorithms for deep SSI after colorectal surgery, developed on Spanish and Dutch data, in a Swedish setting. Methods The algorithms were validated in 225 randomly selected surgeries from Karolinska University Hospital from the period January 1, 2015 until August 31, 2020. Both algorithms were based on (re)admission and discharge data, mortality, reoperations, radiology orders, and antibiotic prescriptions, while one additionally used microbiology cultures. SSI was based on ECDC definitions. Sensitivity, specificity, positive predictive value, negative predictive value, and workload reduction were assessed compared to manual surveillance. Results Both algorithms performed well, yet the algorithm not relying on microbiological culture data had highest sensitivity (97.6, 95%CI: 87.4–99.6), which was comparable to previously published results. The latter algorithm aligned best with clinical practice and would lead to 57% records less to review. Conclusions The results highlight the importance of thorough validation before implementation in other clinical settings than in which algorithms were originally developed: the algorithm excluding microbiology cultures had highest sensitivity in this new setting and has the potential to support large-scale semi-automated surveillance of SSI after colorectal surgery

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

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