BACKGROUND CONTEXT Herniations of lumbar intervertebral discs are one of the most common injuries sustained across the United States, and present challenges in management for the spine surgeon. Though patients often experience symptomatic relief following surgical decompression, reherniation is a common, yet unpredictable occurrence that may lead to further adverse outcomes. PURPOSE The purpose of this study was to create a prognostic tool to identify patients at greatest risk for reherniation after surgery. STUDY DESIGN/SETTING Retrospective cohort study. PATIENT SAMPLE Patients were identified by reviewing magnetic resonance imaging (MRI) studies and clinical documentation to identify all patients receiving surgical decompression of the lumbar spine for a herniated nucleus pulposus (HNP) from 2006 to 2018. OUTCOME MEASURES Reherniation of a lumbar intervertebral disc after decompression surgery. METHODS Following identification of the eligible cohort, patient charts were subsequently queried for type, location and incidence of reherniation events after decompression surgery. Baseline demographic (age, sex, body mass index (BMI), American Society of Anesthesiologist (ASA) Class, smoking history), clinical (duration of symptoms, type of herniation, number of planned operative levels), and radiographic information (pelvic tilt (PT), pelvic incidence (PI), sacral slope (SS), coronal angulation (CA), lumbar lordosis (LL)) was also collected. Following an 80:20 training and testing data split, multiple imputation and LASSO logistic regression were employed to build and validate a predictive model to identify patients at risk for reherniations after surgery. Ten folds of cross-validation were used to identify tuning parameters for the LASSO logistic regression model, allowing for selection of the strongest predictors for reherniation events. This model was subsequently refit to the multiply-imputed data to allow calculation of pooled model estimates. A threshold to distinguish patients as low- or high-risk for reherniations was established using Youden's index. The threshold for statistical significance was set p RESULTS A total of 517 patients (318 male, 199 female) presented for surgical decompression of a lumbar HNP, in which 51 experienced reherniations after surgery. Patients had a mean age of 45.2±13.9 years, BMI of 29.4±6.3 kg/m2, and 22.4±28.0 months of follow-up. Most herniations were paracentral (431/517; 83.4%), followed by central (55/517; 10.6%), and far lateral (29/517; 5.6%). Reherniations were seen at a mean of 46.0±70.0 months after surgery. LASSO logistic regression identified nine significant predictors for reherniations: age, sex, ASA class, operative levels, duration of preoperative symptoms, paracentral disc herniation, preoperative PI, preoperative LL, and VAS-Leg scores. Paracentral disc herniations and duration of preoperative symptoms were the strongest positive and negative predictors for reherniation outcomes, respectively. Validation of this model demonstrated excellent model discrimination (AUC: 0.834), sensitivity (71.4%), and specificity (87.6%). Within the testing cohort, 16.3% and 83.7% of patients were identified as high- and low-risk, respectively. Of these, 29.4% of high-risk patients (5/17) sustained future reherniations, while nearly all low-risk patients (85/87; 98.7%) experienced no recurrence of their HNP. CONCLUSIONS This is the first study to develop and validate a screening tool to identify patients with clear differences in risk for reherniation after lumbar decompression for HNP. With further validation, this tool can be readily implemented in clinical practice, and may accurately identify patients likely to experience lasting surgical benefit for lumbar disc herniations. FDA DEVICE/DRUG STATUS This abstract does not discuss or include any applicable devices or drugs.