Dataset Information

Cutibacteriumacnes Septic Arthritis of the Nonoperated Knee: A Case Report.

ABSTRACT: Cutibacterium (Propionibacterium) acnes , a gram-positive bacillus with low pathogenicity, is an uncommon but known cause of prosthetic joint infections, particularly related to shoulder surgery. C. acnes , however, is an extremely rare pathogen in the nonoperated knee joint. This report details an uncommon case of C. acnes septic knee arthritis after multiple intra-articular steroid injections in a 56-year-old male patient. After an indolent presentation and late diagnosis, the patient underwent surgical debridement with IV antibiotic management. This case illustrates that intra-articular corticosteroid injections for the management of osteoarthritis are not without risk. Literature supporting their use remains limited and clinicians should use proficient clinical judgment for appropriate patient selection for these injections. Vigilance following injections or aspirations of the knee should be maintained to identify the indolent clinical presentation of C. acnes septic arthritis.

SUBMITTER: Okoroha KR

PROVIDER: S-EPMC5553506 | biostudies-other | 2017 Jul

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:PurposePrompt diagnosis and treatment of septic arthritis of the knee is crucial. Nevertheless, the quality of evidence for the diagnosis of septic arthritis is low. In this study, the authors developed a machine learning-based diagnostic algorithm for septic arthritis of the native knee using clinical data in an emergency department and validated its diagnostic accuracy.MethodsPatients (n = 326) who underwent synovial fluid analysis at the emergency department for suspected septic arthritis of the knee were enrolled. Septic arthritis was diagnosed in 164 of the patients (50.3%) using modified Newman criteria. Clinical characteristics of septic and inflammatory arthritis were compared. Area under the receiver-operating characteristic (ROC) curve (AUC) statistics was applied to evaluate the efficacy of each variable for the diagnosis of septic arthritis. The dataset was divided into independent training and test sets (comprising 80% and 20%, respectively, of the data). Supervised machine-learning techniques (random forest and eXtreme Gradient Boosting: XGBoost) were applied to develop a diagnostic model using the training dataset. The test dataset was subsequently used to validate the developed model. The ROC curves of the machine-learning model and each variable were compared.ResultsSynovial white blood cell (WBC) count was significantly higher in septic arthritis than in inflammatory arthritis in the multivariate analysis (P = 0.001). In the ROC comparison analysis, synovial WBC count yielded a significantly higher AUC than all other single variables (P = 0.002). The diagnostic model using the XGBoost algorithm yielded a higher AUC (0.831, 95% confidence interval 0.751-0.923) than synovial WBC count (0.740, 95% confidence interval 0.684-0.791; P = 0.033). The developed algorithm was deployed as a free access web-based application ( www.septicknee.com ).ConclusionThe diagnosis of septic arthritis of the knee might be improved using a machine learning-based prediction model.Level of evidenceDiagnostic study Level III (Case-control study).

Dataset Information

Cutibacteriumacnes Septic Arthritis of the Nonoperated Knee: A Case Report.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure