A simple nomogram for predicting osteoarthritis severity in patients with knee osteoarthritis

Abstract Objective: To explore the influencing factors of knee osteoarthritis (KOA) severity and establish a KOA nomogram model.Methods: Inpatient data from our hospital’s Department of Joint Surgery from January 2020-January 2022 were collected, and the least absolute shrinkage and selection operator (LASSO) methods were used to screen the factors for KOA severity to determine the best predictive index. Then, after combining the significant factors from the LASSO and multivariate logistic regressions, a prediction model was established. All potential prediction factors were included in the KOA severity prediction model, and the corresponding nomogram was drawn. The consistency index (C-index), area under the receiver operating characteristic (ROC) curve (AUC), GiViTi calibration band, net classification improvement (NRI) index, and integrated discrimination improvement (IDI) index evaluation of a model predicted KOA severity. Decision curve analysis (DCA) and clinical influence curves were used to study the model’s potential clinical value.Results: Four hundred KOA patients were included. The nomogram’s predictive factors were age, pulse, absolute value of lymphocytes, mean corpuscular haemoglobin concentration (MCHC)and blood urea nitrogen (BUN). The C-index and AUC of the model were 0.802. Giviti calibration band (P = 0.065), NRI (0.091) and IDI (0.033) showed that the modified model can distinguish between severe KOA and nonsevere KOA. DCA showed that the KOA severity nomogram has clinical application value with threshold probabilities between 0.01-0.78.Conclusions: A nomogram model for predicting KOA severity was established for the first time, can visually identify patients with severe KOA, and is novel for indirectly evaluating KOA severity by nonimaging means.