Background: Premature ejaculation (PE) is a common sexual dysfunction that can significantly impact the quality of life and psychological well-being of affected individuals. Anxiety disorders, often comorbid with PE, exacerbate symptoms and complicate treatment outcomes. There is a critical need for a reliable prediction model to identify patients at risk of developing anxiety disorders associated with PE.

Objective: The primary aim of this study is to develop and validate a predictive model that accurately identifies premature ejaculation patients who are at high risk of developing anxiety disorders. The model will be designed to facilitate early intervention strategies and improve patient outcomes.

Methods: This prospective observational study will enroll male patients diagnosed with PE from multiple clinical centers. Participants will undergo comprehensive assessments including validated questionnaires, clinical interviews, and medical history reviews to collect baseline data on potential predictors of anxiety disorders. Machine learning algorithms will be utilized to analyze the collected data and derive a predictive model. External validation will be conducted using a separate cohort of patients not involved in the initial model development phase. Outcome Measures: The primary outcome measure will be the accuracy of the predictive model in identifying patients who subsequently develop anxiety disorders within a predefined follow-up period. Secondary outcomes include evaluating the model's sensitivity, specificity, positive predictive value, and negative predictive value. Significance: Successful development and validation of this prediction model could lead to improved patient care through targeted interventions and personalized treatment plans for those at risk of anxiety disorders related to PE. This research may also contribute to a better understanding of the complex interplay between sexual dysfunction and mental health.