A simple nomogram for predicting early complications in patients after primary knee arthroplasty

Abstract

BackgroundThis study sought to construct a nomogram for patients based on preoperative and intraoperative variables to individually predict the likelihood of complications within 30 days after primary knee arthroplasty. MethodsData were obtained from the medical record of patients who underwent primary knee arthroplasty at our institution from 2015 to 2018. Preoperative and intraoperative factors were collected critically. Predictor variables include 15 common complications occurring within 30 days. The predictive model was developed using multivariable logistic regression and least absolute shrinkage and selection operator regression. Clinical usefulness and calibration of the predicting model were assessed using C-index, calibration plot, receiver operating curve, and decision curve analysis. Internal validation was assessed using the bootstrapping validation. ResultsThe prediction nomogram identified six variables associated with complications, including hemoglobin, tourniquet time, operative time, estimated intraoperative blood loss, American Society of Anesthesiologists Classification (ASA class) and type of anesthesia. The model displayed good discrimination with a C-index of 0.822 (95% confidence interval: 0.760–0.884), an area under the curve of 0.822 and good calibration. High C-index value of 0.810 could still be reached in the interval validation. Decision curve analysis showed that the nomogram was clinically useful when intervention was decided at the complications possibility threshold in the three percent to 100% range. ConclusionWe constructed and validated a nomogram for predicting the probability of postoperative complications within 30 days after primary knee arthroplasty. Our nomogram may prove to be a useful tool for guiding physicians in terms of their decisions.