Antonio Cicione1, Luigi Cormio2, Francesco Cantiello3, Italo M Palumbo3, Cosimo DE Nunzio4, Estevao Lima5, Giuseppe Ucciero3, Giuseppe Carrieri2, Rocco Damiano3. 1. Unit of Urology, Magna Graecia University of Catanzaro, Catanzaro, Italy - acicione@libero.it. 2. Department of Urology and Renal Transplantation, University of Foggia, Foggia, Italy. 3. Unit of Urology, Magna Graecia University of Catanzaro, Catanzaro, Italy. 4. Department of Urology, Sant'Andrea Hospital, Sapienza University of Rome, Rome, Italy. 5. Life and Health Sciences Research Institute, University of Minho, Braga, Portugal.
Abstract
BACKGROUND: The assessment of lower urinary tract symptoms (LUTS) is common part of urological investigation. Furthermore, patients bother of prostate cancer (PCa) when they are affected of LUTS. This study was aimed to determine whether the presence and severity of LUTS, as assessed by the International Prostate Symptoms Score (IPSS), could help to identify patients at higher risk of prostate cancer (PCa) on prostate biopsy (PBx). In this effort, an initial PCa predictive model was calculated and IPSS was subsequently added. The diagnostic accuracy of both models was compared. METHODS: The analysis of prospectively collected data of patients scheduled for PBx at four academic hospitals between January 2012 and June 2015 was performed. Univariate and multivariate analysis assessed the correlation between the IPSS and the risk of being diagnosed with PCa; Receiver operator characteristic curve (ROC) analysis evaluated the predictive models including or not the IPSS. RESULTS: Of the 1366 enrolled patients, 706 (52%) were diagnosed with PCa. Patients with PCa had a significantly lower IPSS (10.6±7.4 vs. 12.7±8.1) than those with benign diagnosis. Multivariate logistic regression analysis showed that age, prostate-specific antigen (PSA), prostate volume and IPSS were the most significant predictors of PBx outcome, (OR 1.61, P=0.001; OR 1.20, P=0.001; OR 0.97, P=0.001; OR 0.74, P=0.004; respectively). ROC curve analysis showed that the addition of IPSS to the predictive model based on age, PSA, DRE and prostate volume significantly improved the model diagnostic accuracy (AUC: 0.776 vs. 0.652; P=0.001). CONCLUSIONS: Presence and severity of LUTS are inversely correlated with the risk of being diagnosed with PCa at PBx. Incorporating the IPSS into predictive models may reduce the risk of unnecessary PBxs.
BACKGROUND: The assessment of lower urinary tract symptoms (LUTS) is common part of urological investigation. Furthermore, patients bother of prostate cancer (PCa) when they are affected of LUTS. This study was aimed to determine whether the presence and severity of LUTS, as assessed by the International Prostate Symptoms Score (IPSS), could help to identify patients at higher risk of prostate cancer (PCa) on prostate biopsy (PBx). In this effort, an initial PCa predictive model was calculated and IPSS was subsequently added. The diagnostic accuracy of both models was compared. METHODS: The analysis of prospectively collected data of patients scheduled for PBx at four academic hospitals between January 2012 and June 2015 was performed. Univariate and multivariate analysis assessed the correlation between the IPSS and the risk of being diagnosed with PCa; Receiver operator characteristic curve (ROC) analysis evaluated the predictive models including or not the IPSS. RESULTS: Of the 1366 enrolled patients, 706 (52%) were diagnosed with PCa. Patients with PCa had a significantly lower IPSS (10.6±7.4 vs. 12.7±8.1) than those with benign diagnosis. Multivariate logistic regression analysis showed that age, prostate-specific antigen (PSA), prostate volume and IPSS were the most significant predictors of PBx outcome, (OR 1.61, P=0.001; OR 1.20, P=0.001; OR 0.97, P=0.001; OR 0.74, P=0.004; respectively). ROC curve analysis showed that the addition of IPSS to the predictive model based on age, PSA, DRE and prostate volume significantly improved the model diagnostic accuracy (AUC: 0.776 vs. 0.652; P=0.001). CONCLUSIONS: Presence and severity of LUTS are inversely correlated with the risk of being diagnosed with PCa at PBx. Incorporating the IPSS into predictive models may reduce the risk of unnecessary PBxs.
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