Application of Urinary Volatile Organic Compounds (VOCs) for the Diagnosis of Prostate Cancer.

BACKGROUND: Prostate cancer (PCa) screening using serum prostate-specific antigen (PSA) testing has caused unnecessary biopsies and overdiagnosis owing to its low accuracy and reliability. Therefore, there is an increased interest in identifying better PCa biomarkers. Studies showed that trained dogs can discriminate patients with PCa from unaffected men by sniffing urine. We hypothesized that urinary volatile organic compounds (VOCs) may be the source of that odor and could be used to develop urinary VOC PCa diagnosis models. PATIENTS AND METHODS: Urine samples from 55 and 53 biopsy proven PCa-positive and -negative patients respectively were initially obtained for diagnostic model development. Urinary metabolites were analyzed by gas chromatography-mass spectrometry. A PCa diagnosis model was developed and validated using innovative statistical machine-learning techniques. A second set of samples (53 PCa-positive and 22 PCa-negative patients) were used to evaluate the previously developed PCa diagnosis model. RESULTS: The analysis resulted in 254 and 282 VOCs for their significant association (P < .05) with either PCa-positive or -negative samples respectively. Regularized logistic regression analysis and the Firth method were then applied to predict PCa prevalence, resulting in a final model that contains 11 VOCs. Under cross-validation, the area under the receiver operating characteristic curve (AUC) for the final model was 0.92 (sensitivity, 0.96; specificity, 0.80). Further evaluation of the developed model using a testing cohort yielded an AUC of 0.86. As a comparison, the PSA-based diagnosis model only rendered an AUC of 0.54. CONCLUSION: The study describes the development of a urinary VOC-based model for PCa detection.

Risk Factors for Postoperative Fever and Systemic Inflammatory Response Syndrome After Ureteroscopy for Stone Disease.

Introduction: Infectious complications after ureteroscopy (URS) for stone disease lead to emergency department visits, hospitalizations, and other costly health care utilization. The objective of our study was to identify risk factors for postoperative fever (POF) and systemic inflammatory response syndrome (SIRS) after URS for stone disease. Materials and Methods: We performed a retrospective cohort study on 2746 patients who underwent 3298 URS for stone disease at Geisinger from 2008 to 2016. A univariate analysis tested the associations between candidate demographic, preoperative, and intraoperative predictors and the primary outcome of POF (temperature >100.4°F) or SIRS. Variables with a p-value of <0.05 on univariate comparisons were entered into a random-effects logistic regression model. The final model used backward elimination random-effects logistic regression to identify predictors most predictive of POF/SIRS. Results: Overall, 229 (6.9%) of 3298 URS had POF/SIRS. On univariate analysis, individuals with POF/SIRS were older, had higher mean body mass index, higher Charlson Comorbidity Index (CCI), bilateral and larger stones, stone location in the kidney, positive preoperative urine culture, pre-stented, and longer surgical times. In the final model, female gender (adjusted odds ratio [OR] 1.6, 95% confidence interval [CI] 1.19-2.15), surgical time (adjusted OR 1.01, 95% CI 1.0-1.01), CCI ≥2 (adjusted OR 1.86, 95% CI 1.29-2.67), and positive preoperative urine culture (adjusted OR 1.53, 95% CI 1.06-2.22) were the most significant predictors of POF/SIRS. Conclusions: Female gender, longer surgical time, medical complexity, and positive preoperative urine culture are associated with POF/SIRS after URS. These data may be used to identify and counsel high-risk individuals.