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

项目总结/摘要 这个职业发展奖将支持马文兰斯顿博士在他的过渡到独立， 翻译研究员他的长期研究目标包括了解 泌尿系统疾病，重点是前列腺疾病（前列腺癌、前列腺炎和下尿路 继发于良性前列腺增生（BPH/LUTS）的症状）;开发和验证临床工具， 改进和个性化治疗选择;以及在"真实的世界"临床环境中实施这些工具。 BPH/LUTS是老年男性中非常常见的一组疾病，可以被认为是 进行性疾病，主要特征是随着时间的推移，LUTS恶化，造成相当大的困扰 生活质量下降，尿潴留，在极少数情况下死亡。常规药理学 在约30%的治疗患者中，治疗策略未能预防长期临床结果。尽管 尽管有大量的药理学研究，但在BPH/LUTS的治疗方面进展非常有限， 近年因此，需要创新和精心设计的研究来了解长期的 当代BPH/LUTS药物治疗的比较有效性，同时利用临床 可以在"大数据"源（如电子健康记录）中捕获的协变量。 为了解决这些局限性，在目标1中，兰斯顿博士将评估长期比较 一项来自Kaiser的观察性研究中BPH/LUTS药物治疗的有效性 Permanente会员人口，完全集成的医疗保健网络。如果设计良好的研究 证明当代药理学方法在预防长期 BPH/LUTS进展比单独使用传统方法，那么个性化治疗选择可能 最终提供减少长期BPH/LUTS进展的最佳策略。因此，在目标2博士。 兰斯顿将利用现有的人口统计学、临床和实验室数据开发和验证预测模型 在BPH/LUTS诊断时，以确定长期BPH/LUTS临床风险增加的患者 结果（急性尿潴留和BPH手术）使用基于机器学习的预测分析。 随着电子数据的数量、可用性和可访问性的增加， 内容和方法学专业知识，以正确使用和翻译这些数据，以改善泌尿外科护理。 该K01为兰斯顿博士提供培训，以满足作为翻译研究人员的这一挑战， 成功争取未来的R01资金。

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.