Background: Chronic osteomyelitis remains a major challenge for orthopedic surgeons due to its high recurrence rate. Surgeons currently have few tools to estimate the likelihood of individual recurrence. We here aimed to develop a nomogram to better estimate individual recurrence rate after surgical treatment of chronic osteomyelitis in long bones. Methods: We first retrospectively identified patients as training cohort who had received surgical treatment of chronic osteomyelitis in long bones between January 2010 and January 2016 from four hospitals. Patient demographic, microbiological, clinical, and therapeutic variables were collected and analyzed. Univariate and multivariate analyses were performed successively to identify independently predictive factors for recurrence. To reduce overfitting, the Bayesian information criterion was used to reduce variables in the original model. Nomograms were created with the reduced model after model selection. The nomogram was then internally validated with bootstrap resampling. We then further validated the performance of the established nomogram in validation cohort (data from two distinct institutions). Results: Recurrence was found in 136 of 655 (20.8%) and 52 of 201 patients (25.9%) in training and validation cohorts respectively. We included six independent prognostic factors for recurrence in our prediction model: number of previous recurrences, epiphysial involvement, preoperative serum albumin level, axial length of the infectious lesion, lesion-removal method, and application of a muscular flap. After incorporating these six factors, the nomogram achieved good discrimination, with concordance indexes of 0.82 (95% CI, 0.79-0.85) and 0.80 (95% CI, 0.78-0.83) in predicting recurrence in the training and validation cohorts, respectively. Calibration curves were well fitted for both training and validation cohorts. Conclusions: Our nomogram achieved good preoperative prediction of recurrence in chronic osteomyelitis of long bones. Using this nomogram, the recurrence risk can be confidently predicted for each patient and treatment plan. After considering and discussing the functional prognosis with patients, physicians can establish a rational therapeutic plan.