Background: Cisplatin treatment is used for many cancers, including testicular, ovarian, and head and neck malignancies. Cancer survivors with multiple cisplatin-related toxicities can have poor health-related quality of life (HRQOL). Identification of clinical and genetic factors that predict the risk of these neurotoxicities is critical. Methods: Testicular cancer survivors (TCS) enrolled in the Platinum Study completed surveys, underwent physical examination, extensive audiometric testing, and phlebotomy for genotyping and serum platinum analysis. Cases included TCS with two or more severe toxicities (hearing loss [HL], tinnitus, and peripheral sensory neuropathy [PSN]), defined as follows: hearing threshold > 40dB based on geometric mean of 4-12kHz, responding yes to “Do you have ringing or buzzing in the ears?” and/or EORTC-CIPN20 scores in the severe range for items related to sensory neuropathy. Controls were restricted to TCS without any toxicities. TCS with a single toxicity were excluded from analyses. Penalized logistic regression lasso method was used to create the model to predict the binary outcome. Creatinine clearance and residual serum platinum levels were calculated. Polygenic risk scores (PRS) for traits commonly associated with pharmacokinetics and HL, tinnitus, and PSN were calculated for TCS in the training (n = 284) and validation (n = 157) data sets using PRS publicly available in The Polygenic Score Catalog using PRSice 2.3.3. Models were trained and tested in R 4.1.2. Results: A model to assess the risk of developing multiple severe neurotoxicities that could be used without blood work and additional analysis was developed. Clinical predictors incorporated into the model were age at testicular cancer diagnosis, age at phlebotomy, weight and height. PRS incorporated were age-related sensorineural hearing loss (PGS000762), body fat percentage (PGS002133), creatinine in urine (PGS001944), and peripheral nervous system disease (PGS002039). The accuracy of this model was 77.71%, which was significantly greater than the no information rate (NIR) of 65.61% (p = .00067). The positive and negative predictive values (PPV and NPV) were 72.09% and 79.82%, respectively. The AUC-ROC was 0.804. Adding residual platinum levels and creatinine clearance increased the accuracy of the model to 78.34%, which was significantly greater than the NIR (p = .00035). The PPV was 75.00% and the NPV was 79.49%. The area under the receiver operating characteristic curve (AUC-ROC) was 0.832. Conclusions: TCS are often faced with multiple severe neurotoxicities such as HL, tinnitus, and PSN, which impact HRQOL for many decades. If confirmed, a penalized regression model using clinical and genetic characteristics can predict the risk of developing these phenotypes to guide clinicians in treatment and post-treatment management plans.