Surgical site infection after wound closure with staples versus sutures in elective knee and hip arthroplasty:a systematic review and meta-analysis

PURPOSE: This systematic review and meta-analysis aimed to study surgical site infection of wound closure using staples versus sutures in elective knee and hip arthroplasties. METHODS: A systematic literature review was performed to search for randomized controlled trials that compared surgical site infection after wound closure using staples versus sutures in elective knee and hip arthroplasties. The primary outcome was surgical site infection. The risk of bias was assessed with the Cochrane risk of bias assessment tool. The relative risk and 95% confidence interval with a random-effects model were assessed. RESULTS: Eight studies were included in this study, including 2 studies with a low risk of bias, 4 studies having ‘some concerns’, and 2 studies with high risk of bias. Significant difference was not found in the risk of SSI for patients with staples (n = 557) versus sutures (n = 573) (RR: 1.70, 95% CI: 0.94–3.08, I(2) = 16%). The results were similar after excluding the studies with a high risk of bias (RR: 1.67, 95% CI: 0.91–3.07, I(2) = 32%). Analysis of studies with low risk of bias revealed a significantly higher risk of surgical site infection in patients with staples (n = 331) compared to sutures (n = 331) (RR: 2.56, 95% CI: 1.20–5.44, I(2) = 0%). There was no difference between continuous and interrupted sutures (P > 0.05). In hip arthroplasty, stapling carried a significantly higher risk of surgical site infection than suturing (RR: 2.51, 95% CI: 1.15–5.50, I(2) = 0%), but there was no significant difference in knee arthroplasty (RR: 0.87, 95% CI: 0.33–2.25, I(2) = 22%; P > 0.05). CONCLUSIONS: Stapling might carry a higher risk of surgical site infection than suturing in elective knee and hip arthroplasties, especially in hip arthroplasty. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s42836-021-00110-7

Postoperative Ultrasound in Kidney Transplant Recipients: Association Between Intrarenal Resistance Index and Cardiovascular Events

Background. Doppler ultrasound, including intrarenal resistance index (RI) measurement, is a widely used modality to assess kidney transplantation (KTx) vascularization. The aim of this study is to gain insight in the associations between early postoperative RI measurements and cardiovascular events (CVEs), all-cause mortality, and death-censored graft survival. Methods. From 2015 to 2017, a prospective cohort study was

Measurement of the tt̄W and tt̄Z production cross sections in pp collisions at √s = 8 TeV with the ATLAS detector

The production cross sections of top-quark pairs in association with massive vector bosons have been measured using data from pp collisions at s√ = 8 TeV. The dataset corresponds to an integrated luminosity of 20.3 fb−¹ collected by the ATLAS detector in 2012 at the LHC. Final states with two, three or four leptons are considered. A fit to the data considering the tt̄W and tt̄Z processes simultaneously yields a significance of 5.0σ (4.2σ) over the background-only hypothesis for tt¯Wtt¯W (tt̄Z) production. The measured cross sections are σtt̄W = 369 + 100−91 fb and σtt̄Z = 176 + 58−52 fb. The background-only hypothesis with neither tt̄W nor tt̄Z production is excluded at 7.1σ. All measurements are consistent with next-to-leading-order calculations for the tt̄W and tt̄Z processes

Systematic review of machine-learning models in orthopaedic trauma:an overview and quality assessment of 45 studies

Aims Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of this study are to 1) provide an overview of current ML prediction models in orthopaedic trauma; 2) evaluate the completeness of reporting following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement; and 3) assess the risk of bias following the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool. Methods A systematic search screening 3,252 studies identified 45 ML-based prediction models in orthopaedic trauma up to January 2023. The TRIPOD statement assessed transparent reporting and the PROBAST tool the risk of bias. Results A total of 40 studies reported on training and internal validation; four studies performed both development and external validation, and one study performed only external validation. The most commonly reported outcomes were mortality (33%, 15/45) and length of hospital stay (9%, 4/45), and the majority of prediction models were developed in the hip fracture population (60%, 27/45). The overall median completeness for the TRIPOD statement was 62% (interquartile range 30 to 81%). The overall risk of bias in the PROBAST tool was low in 24% (11/45), high in 69% (31/45), and unclear in 7% (3/45) of the studies. High risk of bias was mainly due to analysis domain concerns including small datasets with low number of outcomes, complete-case analysis in case of missing data, and no reporting of performance measures. Conclusion The results of this study showed that despite a myriad of potential clinically useful applications, a substantial part of ML studies in orthopaedic trauma lack transparent reporting, and are at high risk of bias. These problems must be resolved by following established guidelines to instil confidence in ML models among patients and clinicians. Otherwise, there will remain a sizeable gap between the development of ML prediction models and their clinical application in our day-to-day orthopaedic trauma practice.</p