Purpose: Tophi can cause several severe complications. However, the predictors of tophi formation are not intensively researched. The aim of the study is to develop and validate a new prediction model for tophi formation amongst patients with gout. Methods: A prediction model was developed using data collected from 158 gout patients treated in the inpatient department of The First Affiliated Hospital of Zhejiang Chinese Medical University from May 2018 to May 2020. For the establishment and validation of the prediction nomogram, the least absolute shrinkage and selection operator regression model and the multivariable logistic regression analysis were conducted to determine the predictors. C-index, calibration plot and decision curve analysis were utilised to evaluate discrimination, calibration and clinical effectiveness of the predicting nomogram. Then, the nomogram was internally validated using a bootstrap procedure. Results: Nine predictors – hospitalisation frequency, disease duration, number of joints involved in gouty arthritis, gout flares frequency, smoking, and whether combined with atherosclerosis, diabetes, hypertension and kidney dysfunction – were determined from the prediction nomogram. The C-index of the nomogram was 0.854 (95% confidence interval: 0.772-0.936), and was confirmed to be 0.810 when tested through a bootstrap validation, suggesting the model’s good discrimination and prediction capability. Conclusion: A new model with nine predictors was developed to predict the risks of tophi formation amongst gout patients. The included predictors were practical and easy to obtain, whilst the nomogram was proved to predict the risks of tophi formation effectively and accurately. Keywords：tophi formation, gout, predictors, nomogram