Personalizing the empiric treatment of gonorrhea using machine learning models

Despite the emergence of antimicrobial-resistant (AMR) strains of Neisseria gonorrhoeae, the treatment of gonorrhea remains empiric and according to standardized guidelines, which are informed by the national prevalence of resistant strains. Yet, the prevalence of AMR varies substantially across geographic and demographic groups. We investigated whether data from the national surveillance system of AMR gonorrhea in the US could be used to personalize the empiric treatment of gonorrhea. We used data from the Gonococcal Isolate Surveillance Project collected between 2000–2010 to train and validate machine learning models to identify resistance to ciprofloxacin (CIP), one of the recommended first-line antibiotics until 2007. We used these models to personalize empiric treatments based on sexual behavior and geographic location and compared their performance with standardized guidelines, which recommended treatment with CIP, ceftriaxone (CRO), or cefixime (CFX) between 2005–2006, and either CRO or CFX between 2007–2010. Compared with standardized guidelines, the personalized treatments could have replaced 33% of CRO and CFX use with CIP while ensuring that 98% of patients were prescribed effective treatment during 2005–2010. The models maintained their performance over time and across geographic regions. Predictive models trained on data from national surveillance systems of AMR gonorrhea could be used to personalize the empiric treatment of gonorrhea based on patients’ basic characteristics at the point of care. This approach could reduce the unnecessary use of newer antibiotics while maintaining the effectiveness of first-line therapy.Author summary: Treating gonorrhea is complicated by the spread of drug-resistant strains, yet current approaches rely on empiric guidelines. Our study explored using national data on resistance in gonorrhea to personalize treatment decisions. From 2000 to 2010, we analyzed information from the Gonococcal Isolate Surveillance Project to develop predictive models. These models considered factors like where patients live and their behavior to suggest treatments tailored to their likely resistance profile. Compared to standard guidelines during that period, our personalized approach could have reduced unnecessary use of certain antibiotics by 33%, while still effectively treating 98% of patients. These findings highlight the potential of using data-driven models to improve how we treat gonorrhea, ensuring effective care while safeguarding newer antibiotics for the future.