Development and external validation of a prediction model for digit replantation failure after traumatic amputations based on a prospective multicenter cohort.

BACKGROUND:Failure of digit replantation after traumatic amputation is difficult to predict. We aimed to develop a prognostic model to better identify factors that better predict replantation failure following traumatic digit amputation. MATERIALS AND METHODS:In this multicenter prospective cohort, we identified patients who had received digit replantation between January 1, 2015, and January 1, 2019. Univariable and multivariable analyses were performed successively to identify independently predictive factors for failure of replanted digit. 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. This model was then internally validated with bootstrap resampling and further externally validated in validation cohort. RESULTS:Digit replantation was failed in 101 of 1062 (9.5%) digits and 146 of 1156 digits (12.6%) in the training and validation cohorts, respectively. We found that six independent prognostic variables were associated with digit replantation failure: age, mechanism of injury, ischemia duration, smoking status, amputation pattern (complete or incomplete), and surgeon's experience. The prediction model achieved good discrimination, with concordance indexes of 0.81 (95% CI, 0.76-0.85) and 0.70 (95% CI, 0.65-0.74) in predicting digit failure in the training and validation cohorts, respectively. Calibration curves were well-fitted for both training and validation cohorts. CONCLUSIONS:The proposed prediction model effectively predicted the failure rate of digit replantation for individual digits of all patients. It could assist in selecting the most suitable surgical plan for the patient.