Background The purpose of this study was to evaluate the effects of percutaneous transluminal renalr stenting (PTRS) on hypertension and renal function in patients with atherosclerotic renovascular disease.Methods A t...Background The purpose of this study was to evaluate the effects of percutaneous transluminal renalr stenting (PTRS) on hypertension and renal function in patients with atherosclerotic renovascular disease.Methods A total of 147 stents were deployed in 147 lesions of 135 consecutive patients for poorly controlled hypertension or preservation of renal function. Clinical follow-up of the effect of the procedure on renal function, blood pressure control, number of antihypertensive medications, and survival was performed in 128 (95%) patients after 22±14 months. Angiographic follow-up were performed in 70% of the patients at 7.24-5.6 months after PTRS. Results The immediate technical success was 100%. At 22±14 months, systolic and diastolic blood pressures significantly decreased (from 172±23 to 159±20 mm Hg and from 93±16 to 85±13 mm Hg, respectively; P<0.05). The number of antihypertensive medications was reduced on average by 0.74 (from 2.6±1.8 to 1.9±1.7, P<0.01). Among the 49 patients whose renal function was impaired initially (Serum creatinine concentration (SCC) >130 μmol/L), SCC was improved in 25%, became stabilized in 48% and continued to deteriorate in 27%. When SCC was <130 μtmol/L, 97% of the patients remained stabilized, while only 2 patients, SCC deteriorated by 22 months. The cumulative probability of survival was 96% (129/135) at 22 months, with 3 deaths related to end-stage renal disease. The in-stentrestenosis rate was 7.4% (7/95) at a mean follow up of 7.2±5.6 months.Conclusions In patients with atherosclerotic renal-artery stenosis, PTRS could beneficially affect blood pressure control and may improve or prevent further deterioration of renal function.展开更多
文摘Background The purpose of this study was to evaluate the effects of percutaneous transluminal renalr stenting (PTRS) on hypertension and renal function in patients with atherosclerotic renovascular disease.Methods A total of 147 stents were deployed in 147 lesions of 135 consecutive patients for poorly controlled hypertension or preservation of renal function. Clinical follow-up of the effect of the procedure on renal function, blood pressure control, number of antihypertensive medications, and survival was performed in 128 (95%) patients after 22±14 months. Angiographic follow-up were performed in 70% of the patients at 7.24-5.6 months after PTRS. Results The immediate technical success was 100%. At 22±14 months, systolic and diastolic blood pressures significantly decreased (from 172±23 to 159±20 mm Hg and from 93±16 to 85±13 mm Hg, respectively; P<0.05). The number of antihypertensive medications was reduced on average by 0.74 (from 2.6±1.8 to 1.9±1.7, P<0.01). Among the 49 patients whose renal function was impaired initially (Serum creatinine concentration (SCC) >130 μmol/L), SCC was improved in 25%, became stabilized in 48% and continued to deteriorate in 27%. When SCC was <130 μtmol/L, 97% of the patients remained stabilized, while only 2 patients, SCC deteriorated by 22 months. The cumulative probability of survival was 96% (129/135) at 22 months, with 3 deaths related to end-stage renal disease. The in-stentrestenosis rate was 7.4% (7/95) at a mean follow up of 7.2±5.6 months.Conclusions In patients with atherosclerotic renal-artery stenosis, PTRS could beneficially affect blood pressure control and may improve or prevent further deterioration of renal function.
文摘目的:探讨多种机器学习模型预测机器人辅助肾部分切除术(robot-assisted partial nephrectomy,RAPN)后肾功能减退的效能,为临床风险分层提供依据。方法:回顾性纳入2019年1月至2023年12月重庆医科大学附属第一医院泌尿外科733例肾细胞癌(renal cell carcinoma,RCC)行RAPN患者的临床数据,整合人口学特征、实验室指标及围手术期参数,构建7种机器学习模型,采用Shapley加性解释(Shapley additive explanations,SHAP)方法解析关键预测因子,并通过受试者工作特征曲线下面积(receiver operating characteristic curve area under the curve,ROC-AUC)评估模型性能。结果:随机森林模型预测效能最优(AUC=0.84)。SHAP分析显示,中性粒细胞/淋巴细胞比值、肿瘤直径、凝血酶原时间国际标准化比值、白细胞计数及术中出血量等因素对术后肾功能减退有明显影响。结论:本研究为临床提供了一种潜在的预测工具,可帮助识别高风险患者并优化术后管理策略。
