Development Process of a Clinical Decision Support System for Empiric Antibiotic Therapies in Sepsis Patients

BackgroundThe principal treatment against bacterial infections are antibiotic therapies. However, increasing antibiotic resistances pose a major threat to global health care systems by which sepsis patients are particularly affected. Those patients urgently need to be treated with the most effective antibiotic therapy to maximize their chances of survival while simultaneously preventing the development of both individual and global resistances. Consequently, in order to select a proper empiric antibiotic therapy, the treating physicians need to account for many different factors. A clinical decision support system (CDSS) aims to support physicians in deciding on a fast and targeted antibiotic therapy. ObjectiveThe purpose of this work is to explore the extent to which the realization of a CDSS is possible based on the data available to us, and to document our insights gained during the development of a foundational model designed to assist physicians in determining empiric treatment options for sepsis patients. In this regard, we aim to highlight the importance of close interprofessional collaboration between scientists from various disciplines and to analyze the effects of data quality and quantity on the performance of our statistical models. MethodsEmpirical scientists regularly conducted interviews with medical practitioners in order to acquire medical knowledge required to develop sound statistical models. We developed and applied two-step cross-sectional as well as time series classification models to carefully preprocessed data of sepsis patients admitted to the intensive care unit of a German hospital. ResultsWe identified several factors as crucial information for valid decisions on empiric therapy for treating sepsis patients. These include the patients core data, especially the infection focus. To prevent further resistances, individual risk factors such as travel history and professional background should be considered. The evaluation of a therapys effectiveness is mainly based on the patients general condition and blood values such as procalcitonin and interleukin 6. One key factor in the acceptance of CDSS is the explainability of the results produced by the applied methods. Our models come along with mainly moderate but comprehensive predictive ability for all considered empiric antibiotic therapies. ConclusionThis work highlights the importance of interprofessional collaboration between medical experts and model developers, ensuring that data quality and clinical relevance are central to the process. It emphasizes the urgent need for high-quality, comprehensive data to overcome challenges such as data discontinuity and improve model performance, particularly through enhanced digitization in healthcare. This foundational work will facilitate future efforts to develop a CDSS for treating sepsis patients and to translate it to clinical use.