Prediction of Recurrence in Pyogenic Vertebral Osteomyelitis by Artificial Neural Network Using Time-series Data of C-Reactive Protein

Abstract

Study Design. A retrospective cohort study. Objective. The aim of this study was to develop recurrence-prediction models of pyogenic vertebral osteomyelitis (PVO). Summary of Background Data. Prediction of recurrence in PVO is crucial to avoid additional prolonged antibiotic therapy and aggressive spinal surgery and to reduce mortality. However, prediction of PVO recurrence by previously identified, initial risk factors is limited in PVO patients who exceptionally require prolonged antibiotic therapy and experience various clinical events during the treatment. We hypothesized that time-series analysis of sequential C-reactive protein (CRP) routinely measured to estimate the response to the antibiotics in PVO patients could reflect such long treatment process and increase the power of the recurrence-prediction model. Methods. A retrospective study was performed to develop a PVO recurrence-prediction model, including initial risk factors and time-series data of CRP. Of 704 PVO patients, 493 and 211 were divided into training and test cohorts, respectively. Conventional stepwise logistic regression and artificial neural network (ANN) models were created from the training cohort, and the predictions of recurrence in the test cohort were compared. Results. Prediction models using initial risk factors showed poor sensitivity (4.7%) in both conventional logistic model and ANN models. However, baseline ANN models using time-series CRP data showed remarkably increased sensitivity (55.8%–60.5%). Ensemble ANN model using both initial risk factors and time-series CRP data showed additional benefit in prediction power. Conclusion. The recurrence-prediction models for PVO created only using the initial risk factors showed low sensitivity, regardless of statistical method. However, ANN models using time-series data of CRP values and their ensemble model showed considerably increased prediction power. Therefore, clinicians treating PVO patients should pay attention to the treatment response including changes of CRP levels to identify high-risk patients for recurrence, and further studies to develop recurrence-prediction model for PVO should focus on the treatment response rather than initial risk factors. Level of Evidence: 4