Clockwise Torque of Sliding Hip Screws: Is There a Right Side?

OBJECTIVES: This study evaluated whether patients with a left-sided femoral neck fracture (FNF) treated with a sliding hip screw (SHS) had a higher implant failure rate than patients treated for a right-sided FNF. This was performed to determine the clinical relevance of the clockwise rotational torque of the femoral neck lag screw in a SHS, in relation to the rotational stability of left and right-sided FNFs after fixation. METHODS: Data were derived from the FAITH trial and Dutch Hip Fracture Audit (DHFA). Patients with a FNF, aged ≥50, treated with a SHS, with at least 3-month follow-up data available, were included. Implant failure was analyzed in a multivariable logistic regression model adjusted for age, sex, fracture displacement, prefracture living setting and functional mobility, and American Society for Anesthesiologists Class. RESULTS: One thousand seven hundred fifty patients were included, of which 944 (53.9%) had a left-sided and 806 (46.1%) a right-sided FNF. Implant failure occurred in 60 cases (3.4%), of which 31 were left-sided and 29 right-sided. No association between fracture side and implant failure was found [odds ratio (OR) for left vs. right 0.89, 95% confidence interval (CI) 0.52-1.52]. Female sex (OR 3.02, CI: 1.62-6.10), using a mobility aid (OR 2.02, CI 1.01-3.96) and a displaced fracture (OR 2.51, CI: 1.44-4.42), were associated with implant failure. CONCLUSIONS: This study could not substantiate the hypothesis that the biomechanics of the clockwise screw rotation of the SHS contributes to an increased risk of implant failure in left-sided FNFs compared with right-sided fractures. LEVEL OF EVIDENCE: Therapeutic Level II.See Instructions for Authors for a complete description of levels of evidence

Clockwise Torque of Sliding Hip Screws: Is There a Right Side?

Contains fulltext : 228691.pdf (Publisher’s version ) (Closed access)OBJECTIVES: This study evaluated whether patients with a left-sided femoral neck fracture (FNF) treated with a sliding hip screw (SHS) had a higher implant failure rate than patients treated for a right-sided FNF. This was performed to determine the clinical relevance of the clockwise rotational torque of the femoral neck lag screw in a SHS, in relation to the rotational stability of left and right-sided FNFs after fixation. METHODS: Data were derived from the FAITH trial and Dutch Hip Fracture Audit (DHFA). Patients with a FNF, aged ≥50, treated with a SHS, with at least 3-month follow-up data available, were included. Implant failure was analyzed in a multivariable logistic regression model adjusted for age, sex, fracture displacement, prefracture living setting and functional mobility, and American Society for Anesthesiologists Class. RESULTS: One thousand seven hundred fifty patients were included, of which 944 (53.9%) had a left-sided and 806 (46.1%) a right-sided FNF. Implant failure occurred in 60 cases (3.4%), of which 31 were left-sided and 29 right-sided. No association between fracture side and implant failure was found [odds ratio (OR) for left vs. right 0.89, 95% confidence interval (CI) 0.52-1.52]. Female sex (OR 3.02, CI: 1.62-6.10), using a mobility aid (OR 2.02, CI 1.01-3.96) and a displaced fracture (OR 2.51, CI: 1.44-4.42), were associated with implant failure. CONCLUSIONS: This study could not substantiate the hypothesis that the biomechanics of the clockwise screw rotation of the SHS contributes to an increased risk of implant failure in left-sided FNFs compared with right-sided fractures. LEVEL OF EVIDENCE: Therapeutic Level II.See Instructions for Authors for a complete description of levels of evidence

Electrochemistry and capacitive charging of porous electrodes in asymmetric multicomponent electrolytes

We present porous electrode theory for the general situation of electrolytes containing mixtures of mobile ions of arbitrary valencies and diffusion coefficients (mobilities). We focus on electrodes composed of primary particles that are porous themselves. The predominantly bimodal distribution of pores in the electrode consists of the interparticle or macroporosity outside the particles through which the ions are transported (transport pathways), and the intraparticle or micropores inside the particles, where electrostatic double layers (EDLs) are formed. Both types of pores are filled with electrolyte (solvent plus ions). For the micropores we make use of a novel modified-Donnan (mD) approach valid for strongly overlapped double layers. The mD-model extends the standard Donnan approach in two ways: (1) by including a Stern layer in between the electrical charge and the ions in the micropores, and (2) by including a chemical attraction energy for the ions to go from the macropores into the micropores. This is the first paper where the mD-model is used to model ion transport and electrochemical reactions in a porous electrode. Furthermore we investigate the influence of the charge transfer kinetics on the chemical charge in the electrode, i.e., a contribution to the electrode charge of an origin different from that stemming from the Faradaic reaction itself, e.g. originating from carboxylic acid surface groups as found in activated carbon electrodes. We show that the chemical charge depends on the current via a shift in local pH, i.e. “current-induced charge regulation.” We present results of an example calculation where a divalent cation is reduced to a monovalent ion which electro-diffuses out of the electrode.National Science Foundation (U.S.) (NSF Contract No. DMS 0948071)Massachusetts Institute of Technology. Energy Initiative (Seed grant

A Machine Learning Algorithm to Identify Patients at Risk of Unplanned Subsequent Surgery After Intramedullary Nailing for Tibial Shaft Fractures

Objectives: In the SPRINT trial, 18% of patients with a tibial shaft fracture (TSF) treated with intramedullary nailing (IMN) had one or more unplanned subsequent surgical procedures. It is clinically relevant for surgeon and patient to anticipate unplanned secondary procedures, other than operations that can be readily expected such as reconstructive procedures for soft tissue defects. Therefore, the objective of this study was to develop a machine learning (ML) prediction model using the SPRINT data that can give individual patients and their care team an estimate of their particular probability of an unplanned second surgery. Methods: Patients from the SPRINT trial with unilateral TSFs were randomly divided into a training set (80%) and test set (20%). Five ML algorithms were trained in recognizing patterns associated with subsequent surgery in the training set based on a subset of variables identified by random forest algorithms. Performance of each ML algorithm was evaluated and compared based on (1) area under the ROC curve, (2) calibration slope and intercept, and (3) the Brier score. Results: Total data set comprised 1198 patients, of whom 214 patients (18%) underwent subsequent surgery. Seven variables were used to train ML algorithms: (1) Gustilo-Anderson classification, (2) Tscherne classification, (3) fracture location, (4) fracture gap, (5) polytrauma, (6) injury mechanism, and (7) OTA/AO classification. The best-performing ML algorithm had an area under the ROC curve, calibration slope, calibration intercept, and the Brier score of 0.766, 0.954, -0.002, and 0.120 in the training set and 0.773, 0.922, 0, and 0.119 in the test set, respectively. Conclusions: An ML algorithm was developed to predict the probability of subsequent surgery after IMN for TSFs. This ML algorithm may assist surgeons to inform patients about the probability of subsequent surgery and might help to identify patients who need a different perioperative plan or a more intensive approach.Orthopaedics, Trauma Surgery and Rehabilitatio