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) ›› 2022, Vol. 16 ›› Issue (01) : 53 -59. doi: 10.3877/cma.j.issn.1674-3253.2022.01.012

临床研究

在尿路结石患者中预测草酸钙结石列线图的建立与应用
谢茂春1, 曹明德2, 戴英波1, 延敏博1,(), 王晋华1, 张豪1, 吴振杰1   
  1. 1. 519000 珠海,中山大学附属第五医院泌尿外科
    2. 519000 珠海,中山大学附属第五医院创伤与关节外科
  • 收稿日期:2020-04-23 出版日期:2022-02-01
  • 通信作者: 延敏博

Establishment and application of the nomogram for predicting calcium oxalate stones in patients with urinary calculus

Maochun Xie1, Mingde Cao2, Yingbo Dai1, Minbo Yan1,(), Jinhua Wang1, hao Zhang1, Zhenjie Wu1   

  1. 1. Department of Urology, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai 519000, China
    2. Department of Orthopaedics, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai 519000, China
  • Received:2020-04-23 Published:2022-02-01
  • Corresponding author: Minbo Yan
引用本文:

谢茂春, 曹明德, 戴英波, 延敏博, 王晋华, 张豪, 吴振杰. 在尿路结石患者中预测草酸钙结石列线图的建立与应用[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2022, 16(01): 53-59.

Maochun Xie, Mingde Cao, Yingbo Dai, Minbo Yan, Jinhua Wang, hao Zhang, Zhenjie Wu. Establishment and application of the nomogram for predicting calcium oxalate stones in patients with urinary calculus[J/OL]. Chinese Journal of Endourology(Electronic Edition), 2022, 16(01): 53-59.

目的

建立和应用个性化的列线图模型,探讨列线图预测尿路结石患者中草酸钙结石的准确性及可行性。

方法

回顾性分析2017年1月1日至2018年12月31日在中山大学附属第五医院接受手术治疗的298例泌尿系结石患者资料,以7∶3的比例随机分为建模组和验证组,基于建模组采用最小绝对值收敛和选择算子回归(Lasso)模型及多变量Logistic回归分析选择草酸钙结石的最佳预测特征,根据最佳预测特征以列线图的形式构建预测模型。通过C指数、校准曲线和决策曲线分别评估列线图的辨别力、校准和临床实用性,并基于验证组对外部验证进行评估。

结果

在LASSO模型中选择的最佳预测特征包括结石位置、甘油三酯(TG)和尿比重(SG)。将以上最佳预测特征和性别、年龄一起建立列线图模型后,建模组和验证组的C指数分别为0.706、0.603,表明模型具有良好的辨别能力。校准曲线中标准曲线与预测校准曲线贴合良好,提示校正效果良好。决策曲线分析表明,在草酸钙结石可能性阈值为31%时使用该列线图可以在临床上获益。

结论

本研究建立的列线图预测模型可有效预测草酸钙结石,有助于筛选和早期识别草酸钙尿路结石的高危患者,对泌尿科医师进行临床治疗决策可能有一定的指导意义。

Objective

To develop an individualized nomogram model and explore the feasibility and veracity of nomogram to predict calcium oxalate stones in patients with urinary calculus.

Methods

Clinical data of 298 patients with urinary calculus who underwent surgery in the Fifth Affiliated Hospital of Sun Yat-sen University from January 1, 2017 to December 31, 2018 were retrospectively analyzed. The patients were randomly divided into a development group and a validation group by 7∶3 ratio. The least absolute shrinkage and selection operator regression (LASSO) model and multivariable logistic regression analysis were used to select the best prediction characteristics of calcium oxalate stones based on the development group, and a prediction model was constructed in the form of a nomogram according to the best prediction characteristics. Discrimination, calibration, and clinical usefulness of the nomogram were assessed respectively using the C-index, calibration plot, and decision curve analysis, and external validation was assessed based on the validation group.

Results

The best predictive features selected in the LASSO model include stone location, triglycerides (TG), and urine specific gravity (SG). After the gender, age and the best predictive characteristics were used to establish a nomogram model, the C indexes of the development group and the validation group were 0.706 and 0.603, respectively, indicating that the model had good discrimination ability. The standard curve in the calibration curve fit well with the predicted calibration curve, which indicates good calibration. Decision curve analysis showed that the calcium oxalate stones nomogram was clinically useful when intervention was decided at the calcium oxalate stones possibility threshold of 31%.

Conclusion

A nomogram prediction model for the prediction of calcium oxalate stones was established. This model is helpful in screening and early identifying patients who are at high risk of calcium oxalate urinary stones, and is significant to help urologists make clinical treatment decisions.

表1 草酸钙结石组和其他类型结石组患者分别在建模组和验证组中的差异比较[例(%)]
患者特征 建模组 验证组
草酸钙结石(n=147) 其他类型结石(n=63) 总计(n=210) 草酸钙结石(n=63) 其他类型结石(n=25) 总计(n=88)
性别            
  男性 111(75.51) 43(68.25) 154(73.33) 30(47.62) 15(60.00) 45(51.14)
  女性 36(24.49) 20(31.75) 56(26.67) 33(52.38) 10(40.00) 43(48.86)
年龄(岁)            
  < 40 25(17.01) 13(20.63) 38(18.10) 14(22.22) 4(16.00) 18(20.45)
  ≥40和<65 103(70.07) 35(55.56) 138(65.71) 41(65.08) 18(72.00) 59(67.05)
  ≥65 19(12.93) 15(23.81) 34(16.19) 8(12.70) 3(12.00) 11(12.50)
位置            
  上尿路 132(89.80) 47(74.60) 179(85.24) 61(96.83) 22(88.00) 83(94.32)
  下尿路 15(10.20) 16(25.40) 31(14.76) 2(3.17) 3(12.00) 5(5.68)
血肌酐(μmol/L)            
  > 97 37(25.17) 18(28.57) 55(26.19) 16(25.40) 6(24.00) 22(25.00)
  ≤97 110(74.83) 45(71.43) 155(73.81) 47(74.60) 19(76.00) 66(75.00)
血尿酸(μmol/L)            
  > 450 41(27.89) 17(26.98) 58(27.62) 16(25.40) 6(24.00) 22(25.00)
  ≤450 106(72.11) 46(73.02) 152(72.38) 47(74.60) 19(76.00) 66(75.00)
血钾(mmol/L)            
  > 5.3 2(1.36) 0(0.00) 2(0.95) 1(1.59) 0(0.00) 1(1.14)
  ≤5.3 145(98.64) 63(100.00) 208(99.05) 62(98.41) 25(100.00) 87(98.86)
血钙(mmol/L)            
  > 2.52 1(0.68) 1(1.59) 2(0.95) 3(4.76) 0(0.00) 3(3.41)
  ≤2.52 146(99.32) 62(98.41) 208(99.05) 60(95.24) 25(100.00) 85(96.59)
血磷(mmol/L)            
  > 1.51 10(6.80) 4(6.35) 14(6.67) 5(7.94) 2(8.00) 7(7.95)
  ≤1.51 137(93.20) 59(93.65) 196(93.33) 58(92.06) 23(92.00) 81(92.05)
空腹血糖(mmol/L)            
  > 6.1 20(13.61) 11(17.46) 31(14.76) 17.46 4(16.00) 15(17.05)
  ≤6.1 127(86.39) 52(82.54) 179(85.24) 52(82.54) 21(84.00) 73(82.95)
血甘油三酯(mmol/L)            
  > 1.88 29(19.73) 18(28.57) 47(22.38) 14(22.22) 5(20.00) 19(21.59)
  ≤1.88 118(80.27) 45(71.43) 163(77.62) 49(77.78) 20(80.00) 69(78.41)
血胆固醇(mmol/L)            
  > 6.21 15(10.20) 10(15.87) 25(11.90) 5(7.94) 3(12.00) 8(9.09)
  ≤6.21 132(89.80) 53(84.13) 185(88.10) 58(92.06) 22(88.00) 80(90.91)
尿pH值            
  < 6 57(38.78) 26(41.27) 83(39.52) 22(34.92) 12(48.00) 34(38.64)
  ≥6 90(61.22) 37(58.73) 127(60.48) 41(65.08) 13(52.00) 54(61.36)
尿比重            
  > 1.020 33(22.45) 7(11.11) 40(19.05) 22(34.92) 8(32.00) 30(34.09)
  ≤1.020 114(77.55) 56(88.89) 170(80.95) 41(65.08) 17(68.00) 58(65.91)
尿白细胞(个/μl)            
  > 25 77(52.38) 30(47.62) 107(50.95) 56(88.89) 19(76.00) 75(85.23)
  ≤25 70(47.62) 33(52.38) 103(49.05) 7(11.11) 6(24.00) 13(14.77)
图4 草酸钙结石列线图的决策曲线分析
表2 草酸钙结石的多因素分析结果
[1]
Ye Z, Zeng G, Huan Y, et al. The Status and Characteristics of Urinary Stone Composition in China[J]. BJU Int, 2019.
[2]
Pearle MS, Goldfarb DS, Assimos DG, et al. Medical management of kidney stones: AUA guideline[J]. J Urol, 2014. 192(2): 316-324.
[3]
罗敏, 苟淼, 沈鹏飞, 等. 代谢综合征与泌尿系结石的危险因素及结石成分的关系[J]. 四川医学, 2018, 39(2): 138-141.
[4]
Moreira DM, Friedlander JI, Hartman C, et al. Using 24-hour urinalysis to predict stone type[J]. J Urol, 2013. 190(6): 2106-2111.
[5]
Moreira DM, Friedlander JI, Carons A, et al. Association of serum biochemical metabolic panel with stone composition[J]. Int J Urol, 2015. 22(2): 195-199.
[6]
Otto BJ, Bozorgmehri S, Kuo J, et al. Age, Body mass index, and gender predict 24-hour urine parameters in recurrent idiopathic calcium oxalate stone formers[J]. J Endourol, 2017. 31(12): 1335-1341.
[7]
Kidd AC, McGettrick M, Tsim S, et al. Survival prediction in mesothelioma using a scalable Lasso regression model: instructions for use and initial performance using clinical predictors[J]. BMJ Open Respir Res, 2018, 5(1): e000240.
[8]
Knoll T, Schubert AB, Fahlenkamp D, et al. Urolithiasis through the ages: data on more than 200,000 urinary stone analyses[J]. J Urol, 2011, 185(4): 1304-1311.
[9]
Trinchieri A, Esposito N, Castelnuovo C, Dissolution of radiolucent renal stones by oral alkalinization with potassium citrate/potassium bicarbonate[J]. Arch Ital Urol Androl, 2009, 81(3): 188-191.
[10]
Imamura Y, Kawamura K, Sazuka T, et al. Development of a nomogram for predicting the stone-free rate after transurethral ureterolithotripsy using semi-rigid ureteroscope[J]. Int J Urol, 2013, 20(6): 616-621.
[11]
Khan SR. Reactive oxygen species as the molecular modulators of calcium oxalate kidney stone formation: evidence from clinical and experimental investigations[J]. J Urol, 2013, 189(3): 803-811.
[12]
Jairath A, Parekh N, Otano N, et al. Oxalobacter formigenes: Opening the door to probiotic therapy for the treatment of hyperoxaluria[J]. Scand J Urol, 2015, 49(4): 334-337.
[13]
Kim SC, Burns EK, Lingeman JE, et al. Cystine calculi: correlation of CT-visible structure, CT number, and stone morphology with fragmentation by shock wave lithotripsy[J]. Urol Res, 2007. 35(6): 319-324.
[14]
Alan C, Kocoglu H, Kosar S, et al. Role of twinkling artifact in characterization of urinary calculi[J]. Actas Urol Esp, 2011, 35(7): 396-402.
[15]
Hassani H, Raynal G, Spie R, et al. Imaging-based assessment of the mineral composition of urinary stones: an in vitro study of the combination of hounsfield unit measurement in noncontrast helical computerized tomography and the twinkling artifact in color doppler ultrasound[J]. Ultrasound Med Biol, 2012, 38(5): 803-810.
[16]
Shang M, Sun X, Liu Q, et al. Quantitative evaluation of the effects of urinary stone composition and size on color doppler twinkling artifact: a phantom study[J]. J Ultrasound Med, 2017, 36(4): 733-740.
[17]
Wisenbaugh ES, Paden RG, Silva AC, et al. Dual-energy vs conventional computed tomography in determining stone composition[J]. Urology, 2014, 83(6): 1243-1247.
[18]
Marchini G S, Remer E M, Gebreselassie S, et al. Stone characteristics on noncontrast computed tomography: establishing definitive patterns to discriminate calcium and uric acid compositions[J]. Urology, 2013. 82(3): 539-46.
[19]
Hughes P, The CARI guidelines. Kidney stones epidemiology[J]. Nephrology (Carlton), 2007, 12 Suppl 1: S26-30.
[20]
颜赟坤, 柳建军, 郑土康, 等. 粤西湛江地区285例泌尿系结石成分分析[J]. 中国医药科学, 2018,8(2): 176-179.
[21]
Barbera M, Tsirgiotis A, Barbera M, et al. The importance of citrates in treatment and prophylaxis of calcium oxalate urinary stones[J]. Arch Ital Urol Androl, 2016,88(4): 343-344.
[22]
McCormack M, Dessureault J, Guitard M, The urine specific gravity dipstick: a useful tool to increase fluid intake in stone forming patients[J]. J Urol, 1991, 146(6): 1475-1477.
[23]
Walker V, Stansbridge E M, Griffin D G, Demography and biochemistry of 2800 patients from a renal stones clinic[J]. Ann Clin Biochem, 2013, 50(Pt 2): 127-139.
[24]
Perinpam M, Ware EB, Smith JA, et al. Effect of Demographics on Excretion of Key Urinary Factors Related to Kidney Stone Risk[J]. Urology, 2015, 86(4): 690-696.
[25]
Lieske J C, Pena de la Vega L S, Slezak J M, et al. Renal stone epidemiology in Rochester, Minnesota: an update[J]. Kidney Int, 2006, 69(4): 760-764.
[26]
米华,邓耀良, 中国尿石症的流行病学特征[J]. 中华泌尿外科杂志, 2003(10): 66-67.
[27]
Krambeck AE, Khan NF, Jackson ME, et al. Inaccurate reporting of mineral composition by commercial stone analysis laboratories: implications for infection and metabolic stones[J]. J Urol, 2010, 184(4): 1543-1549.
[28]
Masterson JH, Woo JR, Chang DC, et al. Dyslipidemia is associated with an increased risk of nephrolithiasis[J]. Urolithiasis, 2015, 43(1): 49-53.
[29]
Torricelli FC, De SK, Gebreselassie S, et al. Dyslipidemia and kidney stone risk[J]. J Urol, 2014, 191(3): 667-672.
[30]
Khorami MH, Hashemi R, Bagherian-Sararoudi R, et al. The assessment of 24 24-h urine volume by measurement of urine specific gravity with dipstick in adults with nephrolithiasis[J]. Adv Biomed Res, 2012,1: 86.
[1] 胡可, 鲁蓉. 基于多参数超声特征的中老年女性压力性尿失禁诊断模型研究[J/OL]. 中华医学超声杂志(电子版), 2024, 21(05): 477-483.
[2] 余晓青, 高欣, 罗文培, 杨露. BI-RADS 4类结节患者的乳腺癌风险预测模型[J/OL]. 中华乳腺病杂志(电子版), 2024, 18(04): 217-223.
[3] 蒲彦婷, 吴翠先, 兰玉梅. 类风湿关节炎患者骨质疏松症风险预测列线图模型构建[J/OL]. 中华关节外科杂志(电子版), 2024, 18(05): 596-603.
[4] 庄燕, 戴林峰, 张海东, 陈秋华, 聂清芳. 脓毒症患者早期生存影响因素及Cox 风险预测模型构建[J/OL]. 中华危重症医学杂志(电子版), 2024, 17(05): 372-378.
[5] 杜佳丽, 鲍睿, 乔春红, 韩伟. 中孕期宫颈功能不全孕妇经阴道紧急宫颈环扎术后不良妊娠结局预测模型构建[J/OL]. 中华妇幼临床医学杂志(电子版), 2024, 20(04): 403-409.
[6] 白香妮, 孙巨军, 谢鹤, 李宏斌. 急性胰腺炎患者血清微小RNA-142-3p和磷脂酰肌醇3-激酶水平变化及对并发腹腔感染风险预测[J/OL]. 中华实验和临床感染病杂志(电子版), 2024, 18(04): 222-228.
[7] 王淑贤, 张良灏, 王利君, 张慧, 郭源, 许传屾, 李志强, 蔡金贞, 解曼, 饶伟. 成人肝移植围手术期严重心血管事件危险因素分析及预测模型研究[J/OL]. 中华移植杂志(电子版), 2024, 18(04): 222-229.
[8] 屈勤芳, 束方莲. 盆腔器官脱垂患者盆底重建手术后压力性尿失禁发生的影响因素及列线图预测模型构建[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(06): 606-612.
[9] 莫淇舟, 苏劲, 黄健, 李健维, 李思宁, 柳建军. 智能控压输尿管软镜碎石吸引取石术在直径10~25 mm上尿路结石中的应用[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(05): 497-502.
[10] 张瑜, 姜梦妮. 基于DWI信号值构建局部进展期胰腺癌放化疗生存获益预测模型[J/OL]. 中华肝脏外科手术学电子杂志, 2024, 13(05): 657-664.
[11] 杨秀君, 崔梦莹, 刘水, 盛基尧, 张丹. 基于SEER数据库胰头部胰腺神经内分泌癌患者预后列线图构建与验证[J/OL]. 中华肝脏外科手术学电子杂志, 2024, 13(04): 520-525.
[12] 单良, 刘怡, 于涛, 徐丽. 老年股骨颈骨折术后患者心理弹性现状及影响因素分析[J/OL]. 中华老年骨科与康复电子杂志, 2024, 10(05): 294-300.
[13] 刘燚隆, 党荣广, 艾蓉, 张凯. 肝硬化合并静脉曲张出血患者内镜治疗后再出血风险的模型建立与验证[J/OL]. 中华消化病与影像杂志(电子版), 2024, 14(04): 336-342.
[14] 韦巧玲, 黄妍, 赵昌, 宋庆峰, 陈祖毅, 黄莹, 蒙嫦, 黄靖. 肝癌微波消融术后中重度疼痛风险预测列线图模型构建及验证[J/OL]. 中华临床医师杂志(电子版), 2024, 18(08): 715-721.
[15] 颜世锐, 熊辉. 感染性心内膜炎合并急性肾损伤患者的危险因素探索及死亡风险预测[J/OL]. 中华临床医师杂志(电子版), 2024, 18(07): 618-624.
阅读次数
全文


摘要


AI


AI小编
你好!我是《中华医学电子期刊资源库》AI小编,有什么可以帮您的吗?