The goal of this research is to use bioinformatics modeling to determine the clinicopathologic factors associated with the identification of benign and malignant disease in patients with microscopic hematuria (MH). Despite the association of microhematuria with benign and malignant medical conditions, the degree and severity of MH requiring further investigation is unknown. MH is found incidentally in an asymptomatic patients with a prevalence of 2.4 to 31.1%. There are currently no screening recommendations for MH despite the association of malignant diagnoses ranging from 1-25.8%(Davis, Jones et al. 2012). Even the definition of MH, with ?3 RBC/hpf in one urine sample in absence of benign causes is based largely on expert opinion. MH can be seen in both sexes and at all ages with overall prevalence ranging from 2.4 to 31.1%(Davis, Jones et al. 2012). Currently there is no risk-stratification for the evaluation of MH patients; all are recommended to undergo radiographic evaluation and cystoscopy as part of urologic consultation. Much of the literature assessing the significance of microhematuria is weakened by small sample sizes, heterogeneity of work up, and poor methodology. As such, the uncertainty about the true natural history of MH results in broad and repeated utilization of diagnostic testing. Therefore, the diagnostic evaluation of MH comes at a significant cost to the health system and is both invasive and burdensome to patients. Thus, there is a distinct need for a better understanding of the optimal threshold for the work up of microhematuria.
KeywordsQuality Improvement, Clinical Care Innovation (CCI), Clinical Learning Environment, Data Analytics
Joshua J. Meeks, MD, PhD, Northwestern University The Feinberg School of Medicine
Richard Matulewicz, MD, MS, Northwestern University The Feinberg School of Medicine
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