Long-term effectiveness of BPH/LUTS pharmacological therapies and using machine learning based predictive analytics to tailor treatment.

点击翻译按钮获取中文摘要

PROJECT SUMMARY/ABSTRACT This Career Development Award will support Dr. Marvin Langston in his transition to independence as a translational researcher. His long-term research goals include understanding the etiology and outcomes of urologic conditions with an emphasis on prostate diseases (prostate cancer, prostatitis, and lower urinary tract symptoms secondary to benign prostatic hyperplasia (BPH/LUTS)); developing and validating clinical tools to improve and personalize treatment options; and implementing these tools in “real world” clinical settings. BPH/LUTS is an extremely common set of conditions among older men and can be considered a progressive disease, mainly characterized by deteriorating LUTS over time which causes considerable bother and decreased quality of life, urinary retention, and death in rare cases. Conventional pharmacological treatment strategies fail in preventing long-term clinical outcomes in about 30% of treated patients. Despite a high volume of pharmacological research, there have been very limited advances in therapy for BPH/LUTS in recent years. Therefore, innovative and well-designed studies are needed to understand the long-term comparative effectiveness of contemporary BPH/LUTS pharmacological therapies while leveraging the clinical covariates that can be captured in “big data” sources such as the electronic health record. To address these limitations, In Aim 1, Dr. Langston will evaluate the long-term comparative effectiveness of BPH/LUTS pharmacological therapies in an observational study drawn from the Kaiser Permanente membership population, a fully integrated healthcare network. If well designed studies demonstrate that contemporary pharmacological approaches are not more effective at preventing long-term BPH/LUTS progression than conventional approaches alone, then personalized treatment options may ultimately provide the best strategy for reducing long-term BPH/LUTS progression. Therefore, in Aim 2 Dr. Langston will develop and validate predictive models using demographic, clinical, and laboratory data available at the time of BPH/LUTS diagnosis to identify patients at increased risk of long-term BPH/LUTS clinical outcomes (acute urinary retention and BPH surgery) using machine learning based predictive analytics. As the volume, availability, and access to electronic data increases, so does the need for scientists with the content and methodologic expertise to correctly use and translate these data into improved urology care. This K01 provides the training for Dr. Langston to meet this challenge as a translational researcher and compete successfully for future R01 funding.