Surgical management of PJI remains challenging with patients failing treatment despite the best efforts. An important question is whether these later failures reflect reinfection or the persistence of infection. Proponents of reinfection believe hosts are vulnerable to developing infection and new organisms emerge. The alternative hypothesis is that later failure is a result of an organism that was present in the joint but was not picked up by initial culture or was not a pathogen initially but became so under antibiotic pressure. This multicenter study explores the above dilemma. Utilizing next-generation sequencing (NGS), we hypothesize that failures after two stage exchange arthroplasty can be caused by an organism that was present at the time of initial surgery but not isolated by culture. This prospective study involving 15 institutions collected samples from 635 revision total hip (n=310) and knee (n=325) arthroplasties. Synovial fluid, tissue and swabs were obtained intraoperatively for NGS analysis. Patients were classified per 2018 Consensus definition of PJI. Treatment failure was defined as reoperation for infection that yielded positive cultures, during minimum 1-year follow-up. Concordance of the infecting pathogen cultured at failure with NGS analysis at initial revision was determined.Introduction
Methods
Natural Language Processing (NLP) offers an automated method to extract data from unstructured free text fields for arthroplasty registry participation. Our objective was to investigate how accurately NLP can be used to extract structured clinical data from unstructured clinical notes when compared with manual data extraction. A group of 1,000 randomly selected clinical and hospital notes from eight different surgeons were collected for patients undergoing primary arthroplasty between 2012 and 2018. In all, 19 preoperative, 17 operative, and two postoperative variables of interest were manually extracted from these notes. A NLP algorithm was created to automatically extract these variables from a training sample of these notes, and the algorithm was tested on a random test sample of notes. Performance of the NLP algorithm was measured in Statistical Analysis System (SAS) by calculating the accuracy of the variables collected, the ability of the algorithm to collect the correct information when it was indeed in the note (sensitivity), and the ability of the algorithm to not collect a certain data element when it was not in the note (specificity).Aims
Methods
80% of health data is recorded as free text and not easily accessible for use in research and QI. Natural Language Processing (NLP) could be used as a method to abstract data easier than manual methods. Our objectives were to investigate whether NLP can be used to abstract structured clinical data from notes for total joint arthroplasty (TJA). Clinical and hospital notes were collected for every patient undergoing a primary TJA. Human annotators reviewed a random training sample(n=400) and test sample(n=600) of notes from 6 different surgeons and manually abstracted historical, physical exam, operative, and outcomes data to create a gold standard dataset. Historical data collected included pain information and the various treatments tried (medications, injections, physical therapy). Physical exam information collected included ROM and the presence of deformity. Operative information included the angle of tibial slope, angle of tibial and femoral cuts, and patellar tracking for TKAs and approach and repair of external rotators for THAs. In addition, information on implant brand/type/size, sutures, and drains were collected for all TJAs. Finally, the occurrence of complications was collected. We then trained and tested our NLP system to automatically collect the respective variables. Finally, we assessed our automated approach by comparing system-generated findings against the gold standard.Background
Methods
Tranexamic acid (TXA) is proven to reduce blood loss following total knee arthroplasty (TKA), but there are limited data on the impact of similar dosing regimens in revision TKA. The purpose of this multicentre randomized clinical trial was to determine the optimal regimen to maximize the blood-sparing properties of TXA in revision TKA. From six-centres, 233 revision TKAs were randomized to one of four regimens: 1 g of intravenous (IV) TXA given prior to the skin incision, a double-dose regimen of 1 g IV TXA given both prior to skin incision and at time of wound closure, a combination of 1 g IV TXA given prior to skin incision and 1 g of intraoperative topical TXA, or three doses of 1950 mg oral TXA given two hours preoperatively, six hours postoperatively, and on the morning of postoperative day one. Randomization was performed based on the type of revision procedure to ensure equivalent distribution among groups. Power analysis determined that 40 patients per group were necessary to identify a 1 g/dl difference in the reduction of haemoglobin postoperatively between groups with an alpha of 0.05 and power of 0.80. Per-protocol analysis involved regression analysis and two one-sided Aims
Patients and Methods
Tranexamic acid (TXA) is proven to reduce blood loss following total knee arthroplasty (TKA), but there are limited data on the impact of similar dosing regimens in revision TKA that is associated with greater blood loss. The purpose of this multi-center randomized trial was to determine the optimal regimen to maximize the blood-sparing properties of TXA in revision TKA. 233 Septic and aseptic revision TKA from six-centers were randomized to either receive 1g pre-incision intravenous (IV) TXA, 1g pre- and post-incision IV TXA, 1g pre-incision IV and 1g intra-operative topical TXA, or three doses of 1950mg oral TXA given 2 hours pre-operatively, 6 hours post-operatively, and the morning of postoperative day 1. Randomization was performed based on type of revision to ensure equivalent distribution among groups. The primary outcome was reduction in hemoglobin. Power analysis determined 40 patients per group were necessary to identify a 1g/dL difference with an alpha of 0.05 and beta of 0.80. Per-protocol analysis involved regression analysis and two one-sided t-tests for equivalence.Introduction
Methods
