Arthroscopic versus open irrigation and debridement in native knee septic arthritis: A retrospective review.

Introduction: Native knee septic arthritis is a rare condition with a potential for high morbidity if not promptly treated. Treatment involves surgical decompression of the affected joint along with systemic antibiotic therapy. The purpose of this study is to compare arthroscopic versus open irrigation and debridement for treatment of native knee septic arthritis. Methods: A retrospective review was conducted at a single academic institution of all patients treated for native knee septic arthritis from January 2007 until August 2018 utilizing ICD and CPT codes. Patient demographics, type of surgical procedure, need for reoperation, laboratory values, length of stay, and comorbidities were compared. Results: A cohort of sixty-six patients who underwent 85 surgeries were included. Among these surgeries, 52 (61%) were arthroscopic while 33 (39%) were open arthrotomies, and 21% required more than one operation. While not statistically significant, the odds of reoperation was higher for those that underwent arthroscopic compared to open irrigation and debridement on univariable (OR = 4.05, p = .08) and multivariable analysis (OR = 4.39, p = .10). Additionally, patients were more likely to require a longer hospital stay if they initially underwent arthroscopic rather than open debridement (RR = 1.31, p = .02). Conclusion: Native knee septic arthritis can be treated with a single surgery in the majority of cases. In our sample, there was an increased odds of reoperation in those treated arthroscopically compared to open, though this finding was not statistically significant. We found longer length of stay for patients undergoing arthroscopic rather than open irrigation and debridement - even after controlling for multiple operations, culture status, sex, age, and comorbidities.

Artificial intelligence accurately identifies total hip arthroplasty implants: a tool for revision surgery.

BACKGROUND: A critical part in preoperative planning for revision arthroplasty surgery involves the identification of the failed implant. Using a predictive artificial neural network (ANN) model, the objectives of this study were: (1) to develop a machine-learning algorithm using operative big data to identify an implant from a radiograph; and (2) to compare algorithms that optimise accuracy in a timely fashion. METHODS: Using 2116 postoperative anteroposterior (AP) hip radiographs of total hip arthroplasties from 2002 to 2019, 10 artificial neural networks were modeled and trained to classify the radiograph according to the femoral stem implanted. Stem brand and model was confirmed with 1594 operative reports. Model performance was determined by classification accuracy toward a random 706 AP hip radiographs, and again on a consecutive series of 324 radiographs prospectively collected over 2019. RESULTS: The Dense-Net 201 architecture outperformed all others with 100.00% accuracy in training data, 95.15% accuracy on validation data, and 91.16% accuracy in the unique prospective series of patients. This outperformed all other models on the validation (p < 0.0001) and novel series (p < 0.0001). The convolutional neural network also displayed the probability (confidence) of the femoral stem classification for any input radiograph. This neural network averaged a runtime of 0.96 (SD 0.02) seconds for an iPhone 6 to calculate from a given radiograph when converted to an application. CONCLUSIONS: Neural networks offer a useful adjunct to the surgeon in preoperative identification of the prior implant.

Mapping multidimensional electronic structure and ultrafast dynamics with single-element detection and compressive sensing.

Compressive sensing allows signals to be efficiently captured by exploiting their inherent sparsity. Here we implement sparse sampling to capture the electronic structure and ultrafast dynamics of molecular systems using phase-resolved 2D coherent spectroscopy. Until now, 2D spectroscopy has been hampered by its reliance on array detectors that operate in limited spectral regions. Combining spatial encoding of the nonlinear optical response and rapid signal modulation allows retrieval of state-resolved correlation maps in a photosynthetic protein and carbocyanine dye. We report complete Hadamard reconstruction of the signals and compression factors as high as 10, in good agreement with array-detected spectra. Single-point array reconstruction by spatial encoding (SPARSE) Spectroscopy reduces acquisition times by about an order of magnitude, with further speed improvements enabled by fast scanning of a digital micromirror device. We envision unprecedented applications for coherent spectroscopy using frequency combs and super-continua in diverse spectral regions.