BACKGROUND AND OBJECTIVES: Systemic inflammation has an important role in the initiation of atherosclerosis, which is associated with arterial stiffness (AS). Aortic flow propagation velocity (APV) is a new echocardiographic parameter of aortic stiffness. The relationship between systemic inflammation and AS has not yet been described in patients with familial Mediterranean fever (FMF). We aimed to investigate the early markers of AS in patients with FMF by measuring APV and carotid intima-media thickness (CIMT). SUBJECTS AND METHODS: Sixty-one FMF patients (43 women; mean age 27.3±6.7 years) in an attack-free period and 57 healthy individuals (36 women; mean age 28.8±7.1 years) were included in this study. The individuals with atherosclerotic risk factors were excluded from the study. The flow propagation velocity of the descending aorta and CIMT were measured to assess AS. RESULTS: APV was significantly lower (60.2±16.5 vs. 89.5±11.6 cm/sec, p<0.001) and CIMT was significantly higher (0.49±0.09 vs. 0.40±0.10 mm, p<0.001) in the FMF group compared to the control group. There were significant correlations between APV and mean CIMT (r=-0.424, p<0.001), erythrocyte sedimentation rate (ESR) (r=-0.198, p=0.032), and left ventricle ejection fraction (r=0.201, p=0.029). APV and the ESR were independent predictors of FMF in logistic regression analysis (OR=-0.900, 95% CI=0.865-0.936, p<0.001 and OR=-1.078, 95% CI=1.024-1.135, p=0.004, respectively). Mean CIMT and LVEF were independent factors associated with APV in linear regression analysis (β=-0.423, p<0.001 and β=0.199, p=0.017, respectively). CONCLUSION: We demonstrated that APV was lower in FMF patients and is related to CIMT. According to our results, APV may be an independent predictor of FMF.
Aorta, Thoracic ; Atherosclerosis ; Blood Sedimentation ; Carotid Intima-Media Thickness ; Echocardiography ; Familial Mediterranean Fever* ; Female ; Heart Ventricles ; Humans ; Inflammation ; Linear Models ; Logistic Models ; Observational Study* ; Risk Factors ; Vascular Stiffness

PURPOSE: In clinical practice, atypical small acinar proliferation (ASAP) and high-grade prostatic intraepithelial neoplasia (HGPIN) are two common findings on prostate biopsies. Knowing the frequency of a prostate cancer diagnosis on repeat biopsies would aid primary treating physicians regarding their decisions in suspicious cases. MATERIALS AND METHODS: One hundred forty-three patients in whom biopsies revealed ASAP or HGPIN or both were enrolled in the present study; prostate cancer was not reported in the biopsy specimens and at least one repeat biopsy was performed. Age, digital rectal examination findings, prostate volumes, and free and total prostate-specific antigen (PSA) levels and the biopsy results of the patients were recorded. RESULTS: Of the 97 patients with ASAP on the first set of biopsies, prostate cancer was diagnosed in the second and third biopsies of 32 and 6 patients, respectively. Prostate cancer was not detected in the second or third biopsies of the 40 patients with HGPIN in the first biopsy. Of the 6 patients with ASAP+HGPIN in the first biopsy, prostate cancer was detected in 3 patients in the second biopsy and in 1 patient in the third biopsy. CONCLUSIONS: The diagnosis of ASAP is a strong risk factor for prostate cancer. A repeat biopsy should be performed for the entire prostate subsequent to the diagnosis of ASAP. In patients with HGPIN according to the biopsy result, the clinical decision should be based on other parameters, such as PSA values and rectal examination, and a repeat biopsy should be avoided if the initial biopsy was performed with multiple sampling.
Biopsy ; Biopsy, Needle ; Digital Rectal Examination ; Humans ; Prostate ; Prostate-Specific Antigen ; Prostatic Intraepithelial Neoplasia ; Prostatic Neoplasms ; Risk Factors

Purpose: To assess the predictive capability of hematological inflammatory markers for urethral stricture recurrence after primary urethroplasty and to compare traditional statistical methods with a machine-learning-based artificial intelligence algorithm. Materials and Methods: Two hundred eighty-seven patients who underwent primary urethroplasty were scanned. Ages, smoking status, comorbidities, hematological inflammatory parameters (neutrophil-lymphocyte ratios, platelet-lymphocyte ratios [PLR], systemic immune-inflammation indexes [SII], and pan-immune-inflammation values [PIV]), stricture characteristics, history of previous direct-visual internal urethrotomy, urethroplasty techniques, and grafts/flaps placements were collected. Patients were followed up for one year for recurrence and grouped accordingly. Univariate and multivariate logistic regression analyses were conducted to create a predictive model. Additionally, a machine-learning-based logistic regression analysis was implemented to compare predictive performances. p<0.05 was considered statistically significant. Results: Comparative analysis between the groups revealed statistically significant differences in stricture length (p=0.003), localization (p=0.027), lymphocyte counts (p=0.008), PLR (p=0.003), SII (p=0.003), and PIV (p=0.001). In multivariate analysis, stricture length (odds ratio [OR] 1.230, 95% confidence interval [CI] 1.142–1.539, p<0.0001) and PIV (OR 1.002, 95% CI 1.000–1.003, p=0.039) were identified as significant predictors of recurrence. Classical logistic regression model exhibited a sensitivity of 0.76, specificity of 0.43 with an area under curve (AUC) of 0.65. However, the machine-learning algorithm outperformed traditional methods achieving a sensitivity of 0.80, specificity of 0.76 with a higher AUC of 0.82. Conclusions PIV and machine-learning algorithms shows promise on predicting urethroplasty outcomes, potentially leading to develop possible nomograms. Evolving machine-learning algorithms will contribute to more personalized and accurate approaches in managing urethral stricture.

Purpose: To assess the predictive capability of hematological inflammatory markers for urethral stricture recurrence after primary urethroplasty and to compare traditional statistical methods with a machine-learning-based artificial intelligence algorithm. Materials and Methods: Two hundred eighty-seven patients who underwent primary urethroplasty were scanned. Ages, smoking status, comorbidities, hematological inflammatory parameters (neutrophil-lymphocyte ratios, platelet-lymphocyte ratios [PLR], systemic immune-inflammation indexes [SII], and pan-immune-inflammation values [PIV]), stricture characteristics, history of previous direct-visual internal urethrotomy, urethroplasty techniques, and grafts/flaps placements were collected. Patients were followed up for one year for recurrence and grouped accordingly. Univariate and multivariate logistic regression analyses were conducted to create a predictive model. Additionally, a machine-learning-based logistic regression analysis was implemented to compare predictive performances. p<0.05 was considered statistically significant. Results: Comparative analysis between the groups revealed statistically significant differences in stricture length (p=0.003), localization (p=0.027), lymphocyte counts (p=0.008), PLR (p=0.003), SII (p=0.003), and PIV (p=0.001). In multivariate analysis, stricture length (odds ratio [OR] 1.230, 95% confidence interval [CI] 1.142–1.539, p<0.0001) and PIV (OR 1.002, 95% CI 1.000–1.003, p=0.039) were identified as significant predictors of recurrence. Classical logistic regression model exhibited a sensitivity of 0.76, specificity of 0.43 with an area under curve (AUC) of 0.65. However, the machine-learning algorithm outperformed traditional methods achieving a sensitivity of 0.80, specificity of 0.76 with a higher AUC of 0.82. Conclusions PIV and machine-learning algorithms shows promise on predicting urethroplasty outcomes, potentially leading to develop possible nomograms. Evolving machine-learning algorithms will contribute to more personalized and accurate approaches in managing urethral stricture.

Purpose: To assess the predictive capability of hematological inflammatory markers for urethral stricture recurrence after primary urethroplasty and to compare traditional statistical methods with a machine-learning-based artificial intelligence algorithm. Materials and Methods: Two hundred eighty-seven patients who underwent primary urethroplasty were scanned. Ages, smoking status, comorbidities, hematological inflammatory parameters (neutrophil-lymphocyte ratios, platelet-lymphocyte ratios [PLR], systemic immune-inflammation indexes [SII], and pan-immune-inflammation values [PIV]), stricture characteristics, history of previous direct-visual internal urethrotomy, urethroplasty techniques, and grafts/flaps placements were collected. Patients were followed up for one year for recurrence and grouped accordingly. Univariate and multivariate logistic regression analyses were conducted to create a predictive model. Additionally, a machine-learning-based logistic regression analysis was implemented to compare predictive performances. p<0.05 was considered statistically significant. Results: Comparative analysis between the groups revealed statistically significant differences in stricture length (p=0.003), localization (p=0.027), lymphocyte counts (p=0.008), PLR (p=0.003), SII (p=0.003), and PIV (p=0.001). In multivariate analysis, stricture length (odds ratio [OR] 1.230, 95% confidence interval [CI] 1.142–1.539, p<0.0001) and PIV (OR 1.002, 95% CI 1.000–1.003, p=0.039) were identified as significant predictors of recurrence. Classical logistic regression model exhibited a sensitivity of 0.76, specificity of 0.43 with an area under curve (AUC) of 0.65. However, the machine-learning algorithm outperformed traditional methods achieving a sensitivity of 0.80, specificity of 0.76 with a higher AUC of 0.82. Conclusions PIV and machine-learning algorithms shows promise on predicting urethroplasty outcomes, potentially leading to develop possible nomograms. Evolving machine-learning algorithms will contribute to more personalized and accurate approaches in managing urethral stricture.

Purpose: To assess the predictive capability of hematological inflammatory markers for urethral stricture recurrence after primary urethroplasty and to compare traditional statistical methods with a machine-learning-based artificial intelligence algorithm. Materials and Methods: Two hundred eighty-seven patients who underwent primary urethroplasty were scanned. Ages, smoking status, comorbidities, hematological inflammatory parameters (neutrophil-lymphocyte ratios, platelet-lymphocyte ratios [PLR], systemic immune-inflammation indexes [SII], and pan-immune-inflammation values [PIV]), stricture characteristics, history of previous direct-visual internal urethrotomy, urethroplasty techniques, and grafts/flaps placements were collected. Patients were followed up for one year for recurrence and grouped accordingly. Univariate and multivariate logistic regression analyses were conducted to create a predictive model. Additionally, a machine-learning-based logistic regression analysis was implemented to compare predictive performances. p<0.05 was considered statistically significant. Results: Comparative analysis between the groups revealed statistically significant differences in stricture length (p=0.003), localization (p=0.027), lymphocyte counts (p=0.008), PLR (p=0.003), SII (p=0.003), and PIV (p=0.001). In multivariate analysis, stricture length (odds ratio [OR] 1.230, 95% confidence interval [CI] 1.142–1.539, p<0.0001) and PIV (OR 1.002, 95% CI 1.000–1.003, p=0.039) were identified as significant predictors of recurrence. Classical logistic regression model exhibited a sensitivity of 0.76, specificity of 0.43 with an area under curve (AUC) of 0.65. However, the machine-learning algorithm outperformed traditional methods achieving a sensitivity of 0.80, specificity of 0.76 with a higher AUC of 0.82. Conclusions PIV and machine-learning algorithms shows promise on predicting urethroplasty outcomes, potentially leading to develop possible nomograms. Evolving machine-learning algorithms will contribute to more personalized and accurate approaches in managing urethral stricture.