Objective To construct prostate cancer(PCa)prediction models based on machine learning algorithms, so as to improve the accuracy of early diagnosis of PCa. Methods A retrospective analysis was performed on the clinical data of 504 patients who underwent prostate biopsy at our hospital during Jan. 2020 and Nov. 2024. Patients' age, body mass index(BMI), history of hypertension, diabetes and smoking, total prostate-specific antigen(tPSA), free prostate-specific antigen(fPSA), f/tPSA, prostate volume(PV), neutrophil count, lymphocyte count, neutrophil-to-lymphocyte ratio(NLR), Prostate Imaging Reporting and Data System(PI-RADS)score, digital rectal examination(DRE)results, and pathological findings were collected. The patients were divided into the training and testing sets at a ratio of 7:3. Ten early diagnosis prediction models of PCa were constructed using 10 supervised machine learning algorithms. Model performance was evaluated and validated using metrics including area under the receiver operating characteristic curve(AUC), accuracy, sensitivity, specificity, calibration curves, and decision curve analysis(DCA). SHAP analysis was used to interpret the models, and to clarify the importance of each feature and the basis for model decisions. Results All models showed good predictive value, with the gradient boosting machine(GBM)model performing the best(AUC=0.905, accuracy=84.1%, sensitivity=90.2%, specificity=80.0%). Calibration curves indicated good calibration and fitting of the GBM model, while DCA demonstrated favorable clinical net benefits. SHAP analysis identified the most significant features affecting PCa occurrence in descending order:tPSA, f/tPSA, PIRADS score, PV, age, and DRE. Conclusion The GBM model exhibits the optimal performance among the 10 models. The importance of features for predicting the occurrence of PCa, from the highest to the lowest, is as follows:tPSA, f/tPSA, PIRADS score, PV, age, and DRE.

Objective:To explore the predictive factors for ureteral stone impaction preoperatively and to construct a nomogram prediction model for impacted ureteral stones.Methods:A retrospective analysis was conducted on the clinical data of 209 patients with ureteral stones treated at The Second Affiliated Hospital of Zhengzhou University from July 2023 to June 2024. There were 164 males（78.5%）and 45 females（21.5%）. The age was 49（47，57）years，and the body mass index（BMI）was 25.10（23.55，27.24）kg/m2. Of the patients，85（40.7%）had comorbid hypertension and 85（40.7%）had comorbid diabetes. Stones were located on the left side in 124 patients（59.3%）and on the right side in 85 patients（40.7%）. Hydronephrosis was present in 169 patients（80.9%），and urine culture was positive in 29 patients（13.9%）. Patients were divided into impacted and non-impacted groups based on the presence or absence of ureteral stone impaction. Univariate and multivariate logistic regression analyses were performed to determine independent predictive factors for impacted ureteral stones. A nomogram model was constructed based on these results. The performance of the predictive model was evaluated using receiver operating characteristic（ROC）curves，calibration plots，and decision curve analysis（DCA）.Results:Among the 209 patients in this study，85（40.7%）experienced ureteral stone impaction. The impacted group had a significantly higher neutrophil-to-lymphocyte ratio（NLR）than the non-impacted group（3.91 ± 2.05 vs. 3.25 ± 2.10， P = 0.024），a higher rate of hydronephrosis［81.2%（69/85）vs. 80.6%（100/124）， P = 0.002］，larger stone surface area［（64.96 ± 39.96）mm2 vs.（51.86 ± 39.80）mm2， P = 0.021］，greater ureteral wall thickness（UWT）［（3.96 ± 1.37）mm vs.（3.06 ± 1.33）mm， P < 0.001］，and a higher ratio of the upper ureter diameter（D1）to the lower ureter diameter（D2）（DDR）（2.87 ± 1.58 vs. 2.00 ± 0.99， P < 0.001）. Univariate analysis showed that NLR，hydronephrosis，stone length，stone surface area，UWT，D1，D2，and DDR were statistically significant（ P < 0.05）. After multivariate logistic regression analysis，the following items were identified as independent predictors of impacted ureteral stones：NLR（ OR = 1.205，95% CI 1.026 - 1.415， P = 0.023），hydronephrosis（ OR = 1.840，95% CI 1.236 - 2.740， P = 0.003），stone length（ OR = 1.587，95% CI 1.142 - 2.206， P = 0.006），ureteral wall thickness（UWT）（ OR = 1.643，95% CI 1.263 - 2.136， P < 0.001），and DDR（ OR = 2.907，95% CI 1.040 - 8.130， P = 0.042）.Based on these independent predictive factors，a nomogram prediction model for impacted ureteral stones was constructed. The area under the ROC curve was 0.797（95% CI 0.737 - 0.858），and the calibration curve showed good consistency. The decision curve suggested that the model had good clinical net benefit. Conclusions:NLR，hydronephrosis，stone length，UWT，and DDR are all independent predictors for impacted ureteral stones. The nomogram model constructed based on these factors has good predictive performance.

Objective:To evaluate the role of whole exome sequencing（WES）in the etiological diagnosis and precision medicine management of patients with urolithiasis.Methods:We conducted a retrospective review of 21 patients with urolithiasis and pathogenic gene mutations identified by WES at The Second Affiliated Hospital of Zhengzhou University between April 2019 and March 2025. The cohort included 13 males and 8 females，with a mean age of（18.9 ± 11.1）years；18 patients were under 25 years old. Clinical presentations included nephrocalcinosis（8 patients）and urinary tract calculi（13 patients），with five patients exhibiting extra-renal manifestations such as renal tubular acidosis and hyperaldosteronism. Stone composition analysis identified calcium oxalate（16 patients），cystine（4 patients），and carbonate apatite（1 patient）. Metabolic abnormalities were prevalent，including hypocitraturia（11 patients），hyperoxaluria（8 patients），and hypercalciuria（7 patients），with eight patients presenting two or more concurrent disorders. All patients underwent WES and comprehensive metabolic evaluation. Sequencing was performed on an Illumina Hiseq4000 platform，achieving a mean depth of > 100× and coverage of > 98% in target regions. Variants were classified according to the American College of Medical Genetics and Genomics（ACMG）guidelines.Results:WES identified 12 distinct genes across autosomal recessive（9 genes： AGXT， GRHPR， ATP6V1B1， SLC12A1， KCNJ1， SLC3A1， SLC7A9， SLC34A3， WFS1），autosomal dominant（2 genes： CASR， ADCY10），and X-linked recessive（1 gene： CLCN5）inheritance patterns. Genotype-phenotype correlations revealed mutations associated with primary hyperoxaluria（8 patients），hypercalciuria（7 patients），and renal malformation due to a WFS1 mutation（1 patient）. A positive genetic diagnosis was achieved in 100% of patients with either urinary oxalate > 1 000 μmol/24 h or cystine stones. 8 patients received a diagnosis of hereditary stone disease at their first presentation（non-delayed group），while 13 experienced a mean diagnostic delay of（9.6 ± 3.9）years. The delayed diagnosis group had a significantly older age at initial stone onset［（10.2 ± 5.3）years vs.（6.8 ± 3.1）years， P = 0.03］and a higher incidence of impaired renal function（6 patients vs. 1 patient， P = 0.04）. Analysis of diagnostic delay by gene subgroup showed delays in 2/4 patients with cystinuria［ SLC3A1/ SLC7A9；（8.2 ± 3.5）years］，5/8 with primary hyperoxaluria［ AGXT/ GRHPR；（10.5 ± 4.1）years］，5/7 with hypercalciuria-related genes［ CASR/ ADCY10/ SLC12A1/ KCNJ1/ SLC34A3；（9.8 ± 3.8）years］，and 1/2 with other genes［ ATP6V1B1/ WFS1/ CLCN5；（7.6 ± 2.2）years］. Among 32 mutation sites detected，21 were classified as pathogenic/likely pathogenic and 11 as variants of uncertain significance. Four novel mutations were identified： ATP6V1B1（presenting with renal tubular acidosis，nephrocalcinosis，and hypocitraturia）， WFS1（presenting with renal malrotation，hydronephrosis，and stones without metabolic abnormalities）， SLC12A1（presenting with Bartter syndrome type 1，chronic renal insufficiency，hypercalciuria，hypocitraturia，alkalosis，and hyperaldosteronism），and SLC3A1（presenting with bilateral renal stones and cystinuria）. Conclusions:WES is crucial in identifying the underlying etiology of urolithiasis and can guide targeted treatment. We recommend early WES for patients with an initial stone presentation before age 25，those with nephrocalcinosis，or those with abnormal metabolic workups to facilitate precise diagnosis and preventive care.

Objective:To investigate the independent risk factors for the occurrence of early urinary incontinence after Holmium laser enucleation of the prostate(HoLEP), and to construct a clinical risk predictive model for postoperative urinary incontinence.Methods:A retrospective analysis was conducted on the clinical data of 384 patients who underwent HoLEP between February 2019 and July 2024 at the Second Affiliated Hospital of Zhengzhou University. The cohort had a mean age of (68.3 ± 6.5) years, with a body mass index (BMI) of 22.45 (20.11, 24.39) kg/m 2. The median duration of lower urinary tract symptoms was 60 (36, 60) months. Of the patients, 104 (27.1%) had a history of diabetes mellitus, 139 (36.2%) had hypertension, and 54 (14.1%) had a preoperative indwelling urinary catheter. Additionally, 136 patients (35.4%) had a preoperative prostate-specific antigen (PSA) level ≥ 4 ng/ml, and 197 patients (51.3%) had a preoperative residual urine volume ≥ 50 ml. The International Prostate Symptom Score (IPSS) was ≥ 19 in 227 cases (59.1%). Preoperative detrusor instability was observed in 169 cases (44.0%), with a median maximal urinary flow rate of 5.9 (4.5, 9.3) ml/s and a median urinary flow rate of 4.0 (3.4, 7.3) ml/s. Moreover, 148 cases (38.5%) had a preoperative prostate volume ≥ 65 ml, and the preoperative median maximum urethral length (MUL) was 13.99 (12.40, 16.24) mm. Postoperative follow-up allowed for division of the patients into two groups: those with recovery of urinary control function and those with early postoperative urinary incontinence. The general characteristics of both groups were compared. Independent risk factors for early postoperative urinary incontinence were identified through multifactorial logistic regression. Variables with statistically significant differences were included in a column chart model created using R software. Internal validation was performed through repeated sampling with the Bootstrap method to assess the model's discriminative ability. Calibration curves were plotted to examine the consistency between predicted and actual outcomes, and the Hosmer-Lemeshow test was used to evaluate the model's fit. Results:This study included 384 patients, with 313 in the urinary control function recovery group and 71 in the early incontinence group. There were statistically significant difference between the two groups in age [≥70 years old: 91 (29.1%) vs. 33 (46.5%)], prostate volume [≥65 ml: 110 (35.1%) vs. 38 (53.5%)], MUL [14.21 (12.63, 16.24) mm vs. 13.12 (12.21, 13.95) mm], and non-inhibitory contraction of the urethra muscle in both groups [125 (39.9%) vs. 44 cases (62.0%)] ( P < 0.05). No significant differences were observed between the two groups in terms of BMI, disease duration, history of diabetes mellitus, preoperative catheterization, IPSS, preoperative PSA, residual bladder urine volume, maximum urinary flow rate, average urinary flow rate, operative time, or duration of indwelling urinary catheterization ( P > 0.05). Multifactorial logistic regression analysis revealed that age ≥ 70 years ( OR = 0.414, 95% CI 0.230-0.746, P = 0.003), prostate volume ≥ 65 ml ( OR=0.451, 95% CI 0.251-0.812, P=0.008), MUL( OR=0.688, 95% CI 0.590-0.802, P<0.001), and detrusor instability, uninhibited detrusor contraction ( OR=0.526, 95% CI 0.279-0.994, P=0.048) were independent risk factors for early postoperative urinary incontinence following HoLEP. A prediction model was developed based on these findings, and internal validation showed a C-index of 0.753. The calibration curve was close to the ideal curve, indicating that the model has good predictive performance. Conclusions:Age ≥70 years, prostate volume ≥65 ml, MUL, and uninhibited contraction of the urethra muscle were independent influences on early urinary incontinence after HoLEP, and the nomogram constructed in this way had good predictive performance for the risk of developing early urinary incontinence after HoLEP.

Objective:To investigate the independent risk factors for the occurrence of early urinary incontinence after Holmium laser enucleation of the prostate(HoLEP), and to construct a clinical risk predictive model for postoperative urinary incontinence.Methods:A retrospective analysis was conducted on the clinical data of 384 patients who underwent HoLEP between February 2019 and July 2024 at the Second Affiliated Hospital of Zhengzhou University. The cohort had a mean age of (68.3 ± 6.5) years, with a body mass index (BMI) of 22.45 (20.11, 24.39) kg/m 2. The median duration of lower urinary tract symptoms was 60 (36, 60) months. Of the patients, 104 (27.1%) had a history of diabetes mellitus, 139 (36.2%) had hypertension, and 54 (14.1%) had a preoperative indwelling urinary catheter. Additionally, 136 patients (35.4%) had a preoperative prostate-specific antigen (PSA) level ≥ 4 ng/ml, and 197 patients (51.3%) had a preoperative residual urine volume ≥ 50 ml. The International Prostate Symptom Score (IPSS) was ≥ 19 in 227 cases (59.1%). Preoperative detrusor instability was observed in 169 cases (44.0%), with a median maximal urinary flow rate of 5.9 (4.5, 9.3) ml/s and a median urinary flow rate of 4.0 (3.4, 7.3) ml/s. Moreover, 148 cases (38.5%) had a preoperative prostate volume ≥ 65 ml, and the preoperative median maximum urethral length (MUL) was 13.99 (12.40, 16.24) mm. Postoperative follow-up allowed for division of the patients into two groups: those with recovery of urinary control function and those with early postoperative urinary incontinence. The general characteristics of both groups were compared. Independent risk factors for early postoperative urinary incontinence were identified through multifactorial logistic regression. Variables with statistically significant differences were included in a column chart model created using R software. Internal validation was performed through repeated sampling with the Bootstrap method to assess the model's discriminative ability. Calibration curves were plotted to examine the consistency between predicted and actual outcomes, and the Hosmer-Lemeshow test was used to evaluate the model's fit. Results:This study included 384 patients, with 313 in the urinary control function recovery group and 71 in the early incontinence group. There were statistically significant difference between the two groups in age [≥70 years old: 91 (29.1%) vs. 33 (46.5%)], prostate volume [≥65 ml: 110 (35.1%) vs. 38 (53.5%)], MUL [14.21 (12.63, 16.24) mm vs. 13.12 (12.21, 13.95) mm], and non-inhibitory contraction of the urethra muscle in both groups [125 (39.9%) vs. 44 cases (62.0%)] ( P < 0.05). No significant differences were observed between the two groups in terms of BMI, disease duration, history of diabetes mellitus, preoperative catheterization, IPSS, preoperative PSA, residual bladder urine volume, maximum urinary flow rate, average urinary flow rate, operative time, or duration of indwelling urinary catheterization ( P > 0.05). Multifactorial logistic regression analysis revealed that age ≥ 70 years ( OR = 0.414, 95% CI 0.230-0.746, P = 0.003), prostate volume ≥ 65 ml ( OR=0.451, 95% CI 0.251-0.812, P=0.008), MUL( OR=0.688, 95% CI 0.590-0.802, P<0.001), and detrusor instability, uninhibited detrusor contraction ( OR=0.526, 95% CI 0.279-0.994, P=0.048) were independent risk factors for early postoperative urinary incontinence following HoLEP. A prediction model was developed based on these findings, and internal validation showed a C-index of 0.753. The calibration curve was close to the ideal curve, indicating that the model has good predictive performance. Conclusions:Age ≥70 years, prostate volume ≥65 ml, MUL, and uninhibited contraction of the urethra muscle were independent influences on early urinary incontinence after HoLEP, and the nomogram constructed in this way had good predictive performance for the risk of developing early urinary incontinence after HoLEP.

Objective:To explore the predictive factors for ureteral stone impaction preoperatively and to construct a nomogram prediction model for impacted ureteral stones.Methods:A retrospective analysis was conducted on the clinical data of 209 patients with ureteral stones treated at The Second Affiliated Hospital of Zhengzhou University from July 2023 to June 2024. There were 164 males（78.5%）and 45 females（21.5%）. The age was 49（47，57）years，and the body mass index（BMI）was 25.10（23.55，27.24）kg/m2. Of the patients，85（40.7%）had comorbid hypertension and 85（40.7%）had comorbid diabetes. Stones were located on the left side in 124 patients（59.3%）and on the right side in 85 patients（40.7%）. Hydronephrosis was present in 169 patients（80.9%），and urine culture was positive in 29 patients（13.9%）. Patients were divided into impacted and non-impacted groups based on the presence or absence of ureteral stone impaction. Univariate and multivariate logistic regression analyses were performed to determine independent predictive factors for impacted ureteral stones. A nomogram model was constructed based on these results. The performance of the predictive model was evaluated using receiver operating characteristic（ROC）curves，calibration plots，and decision curve analysis（DCA）.Results:Among the 209 patients in this study，85（40.7%）experienced ureteral stone impaction. The impacted group had a significantly higher neutrophil-to-lymphocyte ratio（NLR）than the non-impacted group（3.91 ± 2.05 vs. 3.25 ± 2.10， P = 0.024），a higher rate of hydronephrosis［81.2%（69/85）vs. 80.6%（100/124）， P = 0.002］，larger stone surface area［（64.96 ± 39.96）mm2 vs.（51.86 ± 39.80）mm2， P = 0.021］，greater ureteral wall thickness（UWT）［（3.96 ± 1.37）mm vs.（3.06 ± 1.33）mm， P < 0.001］，and a higher ratio of the upper ureter diameter（D1）to the lower ureter diameter（D2）（DDR）（2.87 ± 1.58 vs. 2.00 ± 0.99， P < 0.001）. Univariate analysis showed that NLR，hydronephrosis，stone length，stone surface area，UWT，D1，D2，and DDR were statistically significant（ P < 0.05）. After multivariate logistic regression analysis，the following items were identified as independent predictors of impacted ureteral stones：NLR（ OR = 1.205，95% CI 1.026 - 1.415， P = 0.023），hydronephrosis（ OR = 1.840，95% CI 1.236 - 2.740， P = 0.003），stone length（ OR = 1.587，95% CI 1.142 - 2.206， P = 0.006），ureteral wall thickness（UWT）（ OR = 1.643，95% CI 1.263 - 2.136， P < 0.001），and DDR（ OR = 2.907，95% CI 1.040 - 8.130， P = 0.042）.Based on these independent predictive factors，a nomogram prediction model for impacted ureteral stones was constructed. The area under the ROC curve was 0.797（95% CI 0.737 - 0.858），and the calibration curve showed good consistency. The decision curve suggested that the model had good clinical net benefit. Conclusions:NLR，hydronephrosis，stone length，UWT，and DDR are all independent predictors for impacted ureteral stones. The nomogram model constructed based on these factors has good predictive performance.

Objective:To evaluate the role of whole exome sequencing（WES）in the etiological diagnosis and precision medicine management of patients with urolithiasis.Methods:We conducted a retrospective review of 21 patients with urolithiasis and pathogenic gene mutations identified by WES at The Second Affiliated Hospital of Zhengzhou University between April 2019 and March 2025. The cohort included 13 males and 8 females，with a mean age of（18.9 ± 11.1）years；18 patients were under 25 years old. Clinical presentations included nephrocalcinosis（8 patients）and urinary tract calculi（13 patients），with five patients exhibiting extra-renal manifestations such as renal tubular acidosis and hyperaldosteronism. Stone composition analysis identified calcium oxalate（16 patients），cystine（4 patients），and carbonate apatite（1 patient）. Metabolic abnormalities were prevalent，including hypocitraturia（11 patients），hyperoxaluria（8 patients），and hypercalciuria（7 patients），with eight patients presenting two or more concurrent disorders. All patients underwent WES and comprehensive metabolic evaluation. Sequencing was performed on an Illumina Hiseq4000 platform，achieving a mean depth of > 100× and coverage of > 98% in target regions. Variants were classified according to the American College of Medical Genetics and Genomics（ACMG）guidelines.Results:WES identified 12 distinct genes across autosomal recessive（9 genes： AGXT， GRHPR， ATP6V1B1， SLC12A1， KCNJ1， SLC3A1， SLC7A9， SLC34A3， WFS1），autosomal dominant（2 genes： CASR， ADCY10），and X-linked recessive（1 gene： CLCN5）inheritance patterns. Genotype-phenotype correlations revealed mutations associated with primary hyperoxaluria（8 patients），hypercalciuria（7 patients），and renal malformation due to a WFS1 mutation（1 patient）. A positive genetic diagnosis was achieved in 100% of patients with either urinary oxalate > 1 000 μmol/24 h or cystine stones. 8 patients received a diagnosis of hereditary stone disease at their first presentation（non-delayed group），while 13 experienced a mean diagnostic delay of（9.6 ± 3.9）years. The delayed diagnosis group had a significantly older age at initial stone onset［（10.2 ± 5.3）years vs.（6.8 ± 3.1）years， P = 0.03］and a higher incidence of impaired renal function（6 patients vs. 1 patient， P = 0.04）. Analysis of diagnostic delay by gene subgroup showed delays in 2/4 patients with cystinuria［ SLC3A1/ SLC7A9；（8.2 ± 3.5）years］，5/8 with primary hyperoxaluria［ AGXT/ GRHPR；（10.5 ± 4.1）years］，5/7 with hypercalciuria-related genes［ CASR/ ADCY10/ SLC12A1/ KCNJ1/ SLC34A3；（9.8 ± 3.8）years］，and 1/2 with other genes［ ATP6V1B1/ WFS1/ CLCN5；（7.6 ± 2.2）years］. Among 32 mutation sites detected，21 were classified as pathogenic/likely pathogenic and 11 as variants of uncertain significance. Four novel mutations were identified： ATP6V1B1（presenting with renal tubular acidosis，nephrocalcinosis，and hypocitraturia）， WFS1（presenting with renal malrotation，hydronephrosis，and stones without metabolic abnormalities）， SLC12A1（presenting with Bartter syndrome type 1，chronic renal insufficiency，hypercalciuria，hypocitraturia，alkalosis，and hyperaldosteronism），and SLC3A1（presenting with bilateral renal stones and cystinuria）. Conclusions:WES is crucial in identifying the underlying etiology of urolithiasis and can guide targeted treatment. We recommend early WES for patients with an initial stone presentation before age 25，those with nephrocalcinosis，or those with abnormal metabolic workups to facilitate precise diagnosis and preventive care.

Objective:To explore the independent influencing factors of Gleason group upgrading (GGU)after radical prostatectomy (RP) of localized prostate cancer based on 2014 International Society of Urologic Pathology (ISUP)grouping system, then establish an effective prediction model.Methods:The study included 48567 patients with localized prostate cancer who underwent radical prostatectomy from the surveillance, epidemiology and end results (SEER) database. The age was (61.1±7.0) years, including 39 014 cases (80.3%), 7 063 cases (14.5%), 2 331 cases (4.8%), and 159 cases (0.3%) of White, Black, Asian or Pacific Islander, American Indian/Alaska Native. PSA<4 ng/ml, 4-10 ng/ml, 11-19 ng/ml, ≥20 ng/ml were 6 545 cases (13.5%), 35 007 cases (72.1%), 5 578 cases (11.5%), 1 437 cases (3.0%) respectively. Percent positive cores (PPC)≥33% were 24 743 cases (50.9%). Gleason group of biopsy specimen: G1 18 971cases (39.1%), G2 18 446 cases (38.0%), G3 7 093 cases (14.6%), G4 4 057 cases (8.4%). Simple random sampling was assigned to the cases, divided them into training cohort and internal validation cohort according to the ratio of 7∶3. 67 patients with localized prostate cancer who treated in The Second Affiliated Hospital of Zhengzhou University during January 2017 to December 2021 were set as external validation cohort. All these cases were Asian with Chinese nationality, age(66.4±10.3) years. PSA<4 ng/ml, 4-10 ng/ml, 11-19 ng/ml, ≥20 ng/ml were 13 cases (19.4%), 32 cases (47.8%)16 cases (23.9%), 6 cases (9.0%) respectively. PPC≥33% were 38 cases (56.7%). Gleason group of biopsy specimen: G1 18 cases (26.9%), G2 23cases (34.3%), G3 12 cases (17.9%), G4 14 cases (20.9%). The independent predictors of GGU were analyzed by logistic regression analysis, and the nomogram for predicting GGU were established. Then, the performance of nomograms was internally and externally validated via area under curve (AUC) and the calibration curve.Results:There were 8 716 cases (25.6%), 3 677 cases (25.2%) and 24 cases (35.8%) with GGU in the training group, internal validation group and external validation group, respectively. Logistic regression showed Gleason group of biopsy specimen (G2: OR=0.164, P<0.01; G3: OR=0.073, P<0.01; G4: OR=0.087, P<0.01), PSA (4-10 ng/ml: OR=1.550, P<0.01; 11-19 ng/ml: OR=2.084, P<0.01; ≥20 ng/ml: OR=2.664, P<0.01), age (60-69 years: OR=1.292, P<0.01; ≥70 years: OR=1.550, P<0.01), PPC (≥33%: OR=1.334, P<0.01) and race (Asian or Pacific Islander: OR=1.266, P<0.01) were independent influencing factors for GGU. The predicting model, was established according to Gleason group of biopsy specimen, PSA, age and PPC. The AUC of the model in the training cohort, the internal validation cohort and the external validation cohort were 0.759, 0.757 and 0.747 respectively. The calibration curves of the three cohorts were close to the ideal line and showed good consistency. Conclusions:Gleason group of biopsy specimen, PSA, age, PPC and race are independent influencing factors for GGU after RP of localized prostate cancer. G. P.A.P. model can effectively predict the risk of GGU after RP of localized prostate cancer.

OBJECTIVE: To retrieve the evidence for threshold setting of multi-parameter electrocardiograph (ECG) monitors in intensive care unit (ICU), and summarize the best evidence. METHODS: After literature retrieval, clinical guidelines, expert consensus, evidence summary and systematic review that met the requirements were screened. Guidelines were evaluated by the appraisal of guidelines for research and evaluation II (AGREE II), expert consensus and systematic review were evaluated by the Australian JBI evidence-based health care center authenticity evaluation tool, and evidence summary was evaluated by the CASE checklist. High-quality literature was selected to extract evidence related to the use and setup of multi-parameter ECG monitors in the ICU. RESULTS: A total of 19 literatures were included, including 7 guidelines, 2 expert consensus, 8 systematic reviews, 1 evidence summary, and 1 national industry standard. After evidence extraction, translation, proofreading and summary, a total of 32 pieces of evidence were integrated. The included evidence involved the environmental preparation for the application of the ECG monitor, the electrical requirements of the ECG monitor, ECG monitor use process, ECG monitor alarm setting principles, ECG monitor alarm heart rate or heart rhythm monitoring setting, ECG monitor alarm blood pressure monitoring setting, ECG monitor alarm respiratory and blood oxygen saturation threshold setting, alarm delay warning time setting, adjusting alarm setting method, evaluating alarm setting time, improving the comfort of monitoring patients, reducing nuisance alarm report the occurrence, alarm priority processing, alarm intelligent processing and so on. CONCLUSIONS This summary of evidence involves many aspects of the setting and application of ECG monitor. According to the latest guidelines and expert consensus, it is updated and revised to guide healthcare workers to monitor patients more scientifically and safely, and aims to ensure patient safety.
Humans ; Clinical Alarms ; Australia ; Intensive Care Units ; Arrhythmias, Cardiac ; Electrocardiography ; Monitoring, Physiologic

Objective:To investigate the pathogenic bacteria profiles in preoperative urine bacterial cultures of patients with infected kidney stones and use antibacterial drugs to prevent recurrence.Methods:The data of 79 cases with infected kidney stones admitted to the Second Affiliated Hospital of Zhengzhou University from January 2017 to July 2021 were retrospectively analyzed, among whom 29 (36.7%) were male and 50 (63.3%) were female. The age ranged from 17-75 years, with a median age of 49.0 (40, 55) years. Fifteen cases (19.0%) combined hypertension, 13 cases (16.5%) combined diabetes mellitus, and 3 cases (3.8%) combined with cardiovascular disease. Fifty-one cases (64.6%) were diagnosed with cast infectious stones. All patients underwent surgical lithotripsy, and postoperative review of the urological computerized tomography (CT) revealed no residual stones defined as complete lithotripsy, and postoperative oral medication was continued to control infection and prevent stone recurrence. According to post-hospitalization compliance, patients were divided into high and low compliance groups. The high compliance group consisted of patients who returned to the hospital regularly for routine urinalysis and urine bacterial culture after discharge, followed the doctor's prescription for standardized antibacterial drug therapy, and complied with urease inhibitor therapy for ≥6 months. The low compliance group included patients who did not take sensitive antimicrobial drugs regularly and/or were unable to adhere to the medication even after the reduction of vinblastine due to adverse events such as tremor, palpitations, headache, anemia, or gastrointestinal discomfort. The recurrence of stones at 3, 6 and 12 months of follow-up was compared between the two groups.Results:Of the 79 cases in this group, 56(70.9%) were completely clear of stone after surgery. Thirty-three cases (41.8%) presented positive in preoperative urine bacterial culture, and the most common causative organism was Aspergillus oddus in 17 cases (21.5%), followed by Escherichia coli in 8 cases (10.1%) and Klebsiella pneumoniae in 3 cases (3.8%). Among the 17 positive cases of A. oddis, six were positive for ultra broad spectrum β-lactamases (ESBLs), 6/6 were resistant to ampicillin, cefazolin, and cotrimoxazole, 1/6 were resistant to amikacin, cefoxitin, and ticarcillin/stick acid, and none were resistant to imipenem, polymyxin, or aminotrans (0/6 cases). Of the cases, 11 were negative for ESBLs. Ten out of eleven cases were resistant to ampicillin. Furthermore, 8/11 cases were resistant to cefazolin, levofloxacin, ciprofloxacin, and cotrimoxazole and 1/11 were resistant to cefoxitin, cefaclor, furantoin, amikacin, and minocycline, and 0/11 were resistant to imipenem, ticarcillin/stick acid, aminotrans. ESBLs positive strains were resistant to 78.6% of the tested drugs (cefaclor, cefazolin, ceftazidime, furantoin, norfloxacin, levofloxacin, ciprofloxacin, cefoxitin, amoxicillin/rod acid, ticarcillin/rod acid, ampicillin, ceftriaxone, cefotaxime, cefuroxime, cefepime, gentamicin, cotrimoxazole, tobramycin, amikacin, tetracycline, chloramphenicol, and minocycline) at a lower rate of resistance than ESBLs positive strains. Of the eight positive cases of E. coli, seven were ESBLs positive, 7/7 were resistant to ampicillin, cefazolin, cefotaxime, cefuroxime, and cefepime, 1/7 were resistant to cefoxitin and minocycline, and 0/7 were resistant to imipenem, furantoin, or amikacin. One case was ESBLs negative and was resistant to all antimicrobial drugs except for ampicillin. Stone recurrence rates at 3, 6, and 12 months after discharge were 9.1%(4/44) and 31.4%(11/35), 13.6%(6/44), respectively, in the high compliance group, and 60.0%(21/35), 36.4%(16/44), and 71.4% (25/35), respectively, in the low compliance group. All differences were statistically significant.Conclusion:The most common pathogenic bacteria isolated from urine bacterial cultures of patients with infectious stones were A. chimaera, E. coli, and K. pneumoniae. The resistance rate of ESBLs-positive strains to antimicrobial drugs was significantly higher than that of ESBL-negative strains, and the resistance rate of antimicrobial drugs such as β-lactamase inhibitors, cefoxitin, amikacin, and imipenem was low. Combination therapy with standardized sensitive antimicrobial drugs and urease inhibitors significantly reduced the recurrence rate of stones among patients.