切换至 "中华医学电子期刊资源库"

中华腔镜泌尿外科杂志(电子版) ›› 2021, Vol. 15 ›› Issue (04) : 354 -357. doi: 10.3877/cma.j.issn.1674-3253.2021.04.020

综述

腔镜时代体外冲击波碎石患者的选择
章璟1, 吕涛1, 张鹤1, 胡传义1, 崔心刚2, 姜宁1,()   
  1. 1. 200135 上海,浦东新区公利医院泌尿外科
    2. 201805 上海,海军军医大学第三附属医院泌尿外科
  • 收稿日期:2020-08-24 出版日期:2021-08-01
  • 通信作者: 姜宁
  • 基金资助:
    上海市医学重点专科建设计划项目(ZK2019A09); 上海市浦东新区临床高峰学科项目(PWYgf2018-03)

Selection of patients for extracorporeal shock wave lithotripsy in the era

Jing Zhang1, Tao Lu1, He Zhang1   

  • Received:2020-08-24 Published:2021-08-01
引用本文:

章璟, 吕涛, 张鹤, 胡传义, 崔心刚, 姜宁. 腔镜时代体外冲击波碎石患者的选择[J]. 中华腔镜泌尿外科杂志(电子版), 2021, 15(04): 354-357.

Jing Zhang, Tao Lu, He Zhang. Selection of patients for extracorporeal shock wave lithotripsy in the era[J]. Chinese Journal of Endourology(Electronic Edition), 2021, 15(04): 354-357.

[1]
陆袁强,杜丹. 复杂性肾结石的微创治疗新进展[J/CD].中华腔镜泌尿外科杂志(电子版), 2018, 12(2): 136-138.
[2]
Rassweiler J, Rieker P, Rassweiler-Seyfried MC. Extracorporeal shockwave lithotripsy: is it still valid in the era of robotic endourology? Can it be more efficient? [J]. Curr Opin Urol, 2020, 30(2): 120-129.
[3]
Chung KJ, Kim JH, Min GE, et al. Changing trends in the treatment of nephrolithiasis in the real world[J]. J Endourol, 2019, 33(3): 248-253.
[4]
Cone EB, Pareek G, Ursiny M, et al. Cost-effectiveness comparison of ureteral calculi treated with ureteroscopic laser lithotripsy versus shock wave lithotripsy[J]. World J Urol, 2017, 35(1): 161-166.
[5]
Scotland KB, Safaee Ardekani G, Chan JYH, et al. Total surface area influences stone free outcomes in shock wave lithotripsy for distal ureteral calculi[J]. J Endourol, 2019, 33(8): 661-666.
[6]
Pradère B, Doizi S, Proietti S, et al. Evaluation of guidelines for surgical management of urolithiasis[J]. J Urol, 2018, 199(5): 1267-1271.
[7]
Chaussy CG, Tiselius HG. How can and should we optimize extracorporeal shockwave lithotripsy[J]? Urolithiasis, 2018, 46(1): 3-17.
[8]
Turk C, Petrik A, Sarica K, et al. EAU guidelines on diagnosis and conservative management of urolithiasis[J]. Eur Urol, 2016, 69(3): 468-474.
[9]
Torricelli FCM, Monga M, Yamauchi FI, et al. Renal stone features are more important than renal anatomy to predict shock wave lithotripsy outcomes: results from a prospective study with CT follow-up[J]. J Endourol, 2020, 34(1): 63-67.
[10]
Singh NP, Boyd CJ, Poore W, et al. Obesity and kidney stone procedures[J]. Rev Urol, 2020,22(1):24-29.
[11]
Hammad Ft, Balakrishnan A. The effect of fat and nonfat components of the skin-to-stone distance on shockwave lithotripsy outcome[J]. J Endourol, 2010, 24(11): 1825-1829.
[12]
Chaussy CG, Tiselius HG. How can and should we optimize extracorporeal shockwave lithotripsy[J]? Urolithiasis, 2018, 46(1): 3-17.
[13]
Mains EAA, Blackmur JP, Sharma AD, et al. Shock wave lithotripsy is an efficacious treatment modality for obese patients with upper ureteric calculi: logistic regression and matched-pair analyses from a dedicated centre comparing treatment outcomes by skin-stone distance[J]. J Endourol, 2020, 34(4): 487-494.
[14]
Pricop C, Radavoi GD, Puia D, et al. Obesity: a delicate issue choosing the ESWL treatment for patients with kidney and ureteral stone? [J]. Acta Endocrinol (Buchar), 2019, 15(1): 133-138.
[15]
Kaya C, Kaynak Y, Karabag A, et al. The predictive role of abdominal fat parameters and stone density on SWL outcomes[J]. Curr Med Imaging Rev, 2020, 16(1): 80-87.
[16]
李聪,王少刚. 体外冲击波碎石治疗后清石率的影响因素[J]. 中华泌尿外科杂志, 2018, 39(9): 718-720.
[17]
Mazzon G, Pavan N, Chiapparrone G, et al. Factors predictive of SWL failure for ureteral stones: why we need to hurry[J]. Minerva Urol Nefrol, 2019, 71(6): 644-650.
[18]
Hevia M, Garci A, Ancizu FJ, et al. Predicting the effectiveness of extracorporeal shock wave lithotripsy on urinary tract stones. Risk groups for accurate retreatment[J]. Actas Urol Esp, 2017, 41(7): 451-457.
[19]
Yamashita S, Kohjimoto Y, Iguchi T, et al. Variation coefficient of stone density: a novel predictor of the outcome of extracorporeal shockwave lithotripsy[J]. J Endourol, 2017, 31(4): 384-390.
[20]
Waqas M, Saqib I, Jamil MI, et al. Evaluating the importance of different computed tomography scan-based factors in predicting the outcome of extracorporeal shock wave lithotripsy for renal stones[J]. Investig Clin Urol, 2018, 59(1): 25-31.
[21]
Sugino Y, Kato T, Furuya S, et al. The usefulness of the maximum Hounsfield units (HU) in predicting the shockwave lithotripsy outcome for ureteral stones and the proposal of novel indicators using the maximum HU[J]. Urolithiasis, 2020, 48(1): 85-91.
[22]
Tran TY, Mcgillen K, Cone EB, et al. Triple D score is a reportable predictor of shockwave lithotripsy stone-free rates[J]. J Endourol, 2015, 29(2):226-230.
[23]
Ozgor F, Tosun M, Kayali Y, et al. External validation and evaluation of reliability and validity of the triple d score to predict stone-free status after extracorporeal shockwave lithotripsy[J]. J Endourol, 2017, 31(2): 169-173.
[24]
Ichiyanagi O, Fukuhara H, Kurokawa M, et al. Reinforcement of the Triple D score with simple addition of the intrarenal location for the prediction of the stone-free rate after shockwave lithotripsy for renal stones 10-20 mm in diameter[J]. Int Urol Nephrol, 2019, 51(2): 239-245.
[25]
Park HS, Gong MK, Yoon CY, et al. Computed tomography based novel prediction model for the outcome of shockwave lithotripsy in proximal ureteral stones[J]. J Endourol, 2016, 30(7): 810-816.
[26]
Cui HW, Silva MD, Mills AW, et al. Predicting shockwave lithotripsy outcome for urolithiasis using clinical and stone computed tomography texture analysis variables[J]. Sci Rep, 2019, 9(1): 14674.
[27]
Mannil M, Von Spiczak J, Hermanns T, et al. Three-dimensional texture analysis with machine learning provides incremental predictive information for successful shock wave lithotripsy in patients with kidney stones[J]. J Urol, 2018, 200(4):829-836.
[28]
Kim JK, Ha SB, Jeon CH et al. Clinical nomograms to predict stone-free rates after shock-wave lithotripsy: development and internal-validation[J]. PLoS One, 2016, 11(2): e0149333.
[29]
Niwa N, Matsumoto K, Miyahara M, et al. Simple and practical nomograms for predicting the stone-free rate after shock wave lithotripsy in patients with a solitary upper ureteral stone[J]. World J Urol, 2017, 35(9): 1455-1461.
[30]
Yoshioka T, Ikenoue T, Hashimoto H, et al. Development and validation of a prediction model for failed shockwave lithotripsy of upper urinary tract calculi using computed tomography information:the S3HoCKwave score[J]. World J Urol, 2020, 38(12): 3267-3273.
[31]
Shah M, Naik N, Somani BK, et al. Artificial intelligence (AI) in urology-Current use and future directions: An iTRUE study[J]. Turk J Urol, 2020, 46(Supp.1): S27-S39.
[32]
Aminsharifi A, Irani D, Tayebi S, et al.Predicting the postoperative otcome of percutaneous nephrolithotomy with machine learning system: software validation and comparative analysis with Guy's stone score and the CROES nomogram[J].J Endourol, 2020, 34(6): 692-699.
[33]
Seckiner I, Seckiner S, Sen H, et al. A neural network-based algorithm for predicting stone-free status after ESWL therapy[J]. Int Braz J Urol, 2017, 43(6): 1110-1114.
[34]
Choo MS, Uhmn S, Kim JK, et al. A prediction model using machine learning algorithm for assessing stone-free status after single session shock wave lithotripsy to treat ureteral stones[J].J Urol, 2018, 200(6): 1371-1377.
[1] 张梅芳, 谭莹, 朱巧珍, 温昕, 袁鹰, 秦越, 郭洪波, 侯伶秀, 黄文兰, 彭桂艳, 李胜利. 早孕期胎儿头臀长正中矢状切面超声图像的人工智能质控研究[J]. 中华医学超声杂志(电子版), 2023, 20(09): 945-950.
[2] 唐玮, 何融泉, 黄素宁. 深度学习在乳腺癌影像诊疗和预后预测中的应用[J]. 中华乳腺病杂志(电子版), 2023, 17(06): 323-328.
[3] 李圣鹏, 方爱蓝, 刘诗宁, 王丹, 刘湘奇. 下颌阻生第三磨牙拔除难度的预测因素与评估方法[J]. 中华口腔医学研究杂志(电子版), 2023, 17(06): 441-445.
[4] 张俊, 罗再, 段茗玉, 裘正军, 黄陈. 胃癌预后预测模型的研究进展[J]. 中华普通外科学文献(电子版), 2023, 17(06): 456-461.
[5] 唐旭, 韩冰, 刘威, 陈茹星. 结直肠癌根治术后隐匿性肝转移危险因素分析及预测模型构建[J]. 中华普外科手术学杂志(电子版), 2024, 18(01): 16-20.
[6] 方钟进, 黄华生, 陈早庆, 郁兆存, 郑哲明, 谢永康, 陈仲宁, 邹演辉, 刘乾海, 陈镇宏. 负压组合式输尿管镜联合输尿管软镜与经皮肾镜治疗复杂性肾结石的比较[J]. 中华腔镜泌尿外科杂志(电子版), 2023, 17(06): 601-604.
[7] 王晓丹, 王媛, 崔向宇, 任晓磊. 上尿路结石内镜手术后尿源性脓毒血症病原菌耐药及死亡高危因素分析[J]. 中华腔镜泌尿外科杂志(电子版), 2023, 17(06): 611-615.
[8] 李晓阳, 刘柏隆, 周祥福. 大数据及人工智能对女性盆底功能障碍性疾病的诊断及风险预测[J]. 中华腔镜泌尿外科杂志(电子版), 2023, 17(06): 549-552.
[9] 邢晓伟, 刘雨辰, 赵冰, 王明刚. 基于术前腹部CT的卷积神经网络对腹壁切口疝术后复发预测价值[J]. 中华疝和腹壁外科杂志(电子版), 2023, 17(06): 677-681.
[10] 顾睿祈, 方洪生, 蔡国响. 循环肿瘤DNA检测在结直肠癌诊治中的应用与进展[J]. 中华结直肠疾病电子杂志, 2023, 12(06): 453-459.
[11] 王小娜, 谭微, 李悦, 姜文艳. 预测性护理对结直肠癌根治术患者围手术期生活质量、情绪及并发症的影响[J]. 中华消化病与影像杂志(电子版), 2023, 13(06): 525-529.
[12] 秦维, 王丹, 孙玉, 霍玉玲, 祝素平, 郑艳丽, 薛瑞. 血清层粘连蛋白、Ⅳ型胶原蛋白对代偿期肝硬化食管胃静脉曲张出血的预测价值[J]. 中华消化病与影像杂志(电子版), 2023, 13(06): 447-451.
[13] 张郁妍, 胡滨, 张伟红, 徐楣, 朱慧, 羊馨玥, 刘海玲. 妊娠中期心血管超声参数与肝功能的相关性及对不良妊娠结局的预测价值[J]. 中华消化病与影像杂志(电子版), 2023, 13(06): 499-504.
[14] 张曦才, 曹先德, 高建萍, 沈大庆, 曹现祥, 郭诗杰, 李凤岳, 肖琳. 免人工肾积水在超声引导经皮肾镜取石术中的应用[J]. 中华临床医师杂志(电子版), 2023, 17(07): 798-803.
[15] 王亚丹, 吴静, 黄博洋, 王苗苗, 郭春梅, 宿慧, 王沧海, 王静, 丁鹏鹏, 刘红. 白光内镜下结直肠肿瘤性质预测模型的构建与验证[J]. 中华临床医师杂志(电子版), 2023, 17(06): 655-661.
阅读次数
全文


摘要