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中华疝和腹壁外科杂志(电子版) ›› 2024, Vol. 18 ›› Issue (01) : 75 -82. doi: 10.3877/cma.j.issn.1674-392X.2024.01.015

论著

基于影像学指标构建永久性乙状结肠造口旁疝发病风险预测模型
刘化胜1, 郑龙波2, 秦琛3, 王伟芹4, 郑学风2, 马金龙5, 马正2, 王洪霞2, 刘磊2, 胡继霖2,†()   
  1. 1. 266003　山东，青岛大学附属医院胃肠外科；262550　山东，潍坊市益都中心医院乳腺甲状腺外科
    2. 266003　山东，青岛大学附属医院胃肠外科
    3. 266042　山东，青岛大学附属青岛市中心医院肿瘤科
    4. 266300　山东，青岛市市立医院呼吸与危重症医学科
    5. 266071　山东，青岛大学
  • 收稿日期:2023-02-10 出版日期:2024-02-18
  • 通信作者: 胡继霖

Development of a nomogram prediction model for the risk of permanent sigmoid parastomal hernia based on radiologic predictors

Huasheng Liu1, Longbo Zheng2, Chen Qin3, Weiqin Wang4, Xuefeng Zheng2, Jinlong Ma5, Zheng Ma2, Hongxia Wang2, Lei Liu2, Jilin Hu2,†()   

  1. 1. Department of Gastroenterology, the Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China; Department of Breast and Thyroid Surgery, Yidu Central Hospital, Weifang, Weifang 262550, Shandong, China
    2. Department of Gastroenterology, the Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China
    3. Department of Oncology, the Affiliated Qingdao Central Hospital of Qingdao University, Qingdao, 266042, Shandong, China
    4. Department of Pulmonology and Critical Care Medicine, Qingdao Municipal Hospital, Qingdao, 266300, Shandong, China
    5. Qingdao University, Qingdao, 266071, Shandong, China
  • Received:2023-02-10 Published:2024-02-18
  • Corresponding author: Jilin Hu
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引用本文：

刘化胜, 郑龙波, 秦琛, 王伟芹, 郑学风, 马金龙, 马正, 王洪霞, 刘磊, 胡继霖. 基于影像学指标构建永久性乙状结肠造口旁疝发病风险预测模型[J]. 中华疝和腹壁外科杂志(电子版), 2024, 18(01): 75-82.

Huasheng Liu, Longbo Zheng, Chen Qin, Weiqin Wang, Xuefeng Zheng, Jinlong Ma, Zheng Ma, Hongxia Wang, Lei Liu, Jilin Hu. Development of a nomogram prediction model for the risk of permanent sigmoid parastomal hernia based on radiologic predictors[J]. Chinese Journal of Hernia and Abdominal Wall Surgery(Electronic Edition), 2024, 18(01): 75-82.

目的

对永久性乙状结肠造口旁疝的危险因素进行分析，并基于这些风险因素，构建并验证永久性乙状结肠造口旁疝发病风险的列线图预测模型，以期为造口患者提供精准的个体化治疗方案提供依据。

方法

回顾分析2010年1月至2018年1月在青岛大学附属医院接受永久性乙状结肠造口术的291例患者的病历资料，构建列线图预测模型；同时，回顾性收集2010年1月至2018年1月在青岛市立医院接受永久性乙状结肠造口患者63例用于预测模型的外部验证。采用单因素分析和多因素logistic回归分析筛选永久性乙状结肠造口术后发生造口旁疝的独立危险因素，基于独立危险因素建立列线图预测模型。使用Bootstrap方法进行内部验证并在验证组进行外部验证。

结果

2010年1月至2018年1月，291例患者接受了永久性乙状结肠造口术，2年内61例患者发生造口旁疝，230例患者未发生造口旁疝。经多因素Logistic回归分析发现，年龄（OR 1.063，95% CI 1.014～1.114，P=0.011）、性别（OR 3.927，95% CI 1.327～11.621，P=0.013）、体重指数（OR 1.553，95% CI 1.228～1.963，P=0.001）、白蛋白（OR 0.876，95% CI 0.783～0.981，P=0.022）、造口直径（OR 4.426，95% CI 1.976～9.915，P=0.001）、皮下脂肪指数（OR 1.028，95% CI 1.006～1.050，P=0.011）、腹直肌指数（OR 0.591，95% CI 0.352～0.993，P=0.047）是永久性乙状结肠造口患者发生造口旁疝的独立危险因素。基于独立危险因素建立的列线图预测模型，训练组和验证组的受试者工作特征曲线下面积分别为0.941和0.887，Hosmer-Lemeshow检验（P>0.05）和校准曲线显示实际概率与预测概率具有良好的一致性。

结论

根据永久性乙状结肠造口患者发生造口旁疝的独立危险因素建立的列线图预测模型，对预测永久性乙状结肠造口旁疝的发生具有较高的临床意义。为专业外科医生进行造口手术的患者提供了个性化和疾病相关评估。

关键词: 造口旁疝, 列线图, 危险因素, 计算机断层扫描
Objective

To analyze risk factors for permanent sigmoid parastomal hernias(PSH), and to develop and validate a nomogram prediction model for the risk of PSH based on these risk factors, in order to provide precise individualized treatment plan for stoma patients.

Methods

The medical records from January 2010 to January 2018 of 291 patients who underwent permanent sigmoid colostomy surgery at Qingdao University Affiliated Hospital were retrospectively analyzed to construct a nomogram prediction model. Simultaneously, we retrospectively collected data of 63 patients who received permanent sigmoid colostomy at Qingdao Municipal Hospital during the same period for external validation of the model. Independent risk factors for parastomal hernia after permanent sigmoid colostomy were identified using univariate and multivariate logistic regression analysis. The nomogram prediction model was established based on these risk factors. Internal validation was conducted using the Bootstrap method, and external validation was performed in the validation group.

Results

From January 2010 to January 2018, 291 patients underwent permanent sigmoid colostomy, 61 patients developed parastomal hernia within 2 years and 230 patients did not. Results of multivariate logistic regression analysis showed that independent risk factors of developing parastomal hernia in patients with permanent sigmoid colostomy including age (OR 1.063, 95% CI 1.014-1.114, P=0.011), sex (OR 3.927, 95% CI 1.327-11.621, P=0.013), body mass index (OR 1.553, 95% CI 1.228-1.963, P=0.001), serum albumin levels (OR 0.876, 95% CI 0.783-0.981, P=0.022), stoma diameter (OR 4.426, 95% CI 1.976-9.915, P=0.001), subcutaneous fat index (OR 1.028, 95% CI 1.006-1.050, P=0.011), and rectus abdominis index (OR 0.591, 95% CI 0.352-0.993, P=0.047). The nomogram prediction model based on these independent risk factors, exhibited an area under the receiver operating characteristic curve of 0.941 for the training group and 0.887 for the validation group. The Hosmer-Lemeshow test (P>0.05) and calibration curves underscored the commendable alignment between actual and predicted probabilities.

Conclusion

Based on the independent risk factors for the occurrence of parastomal hernia in patients with permanent sigmoid colostomy, the established nomogram prediction model has significant clinical significance in predicting the occurrence of parastomal hernia. This model provides personalized and disease-specific assessments for patients undergoing ostomy surgery by professional surgeons.

Key words: Parastomal hernia, Nomogram, Risk factors, Computed tomography
图1 63岁男性直肠癌患者术前腹部CT，第三腰椎水平的腹壁肌肉和腰大肌区域示意注：绿色为腰大肌；红色为腹壁肌肉
表1 训练组与外部验证组基线资料比较
组别 例数 造口旁疝[例(%)] 性别[例(%)] 年龄(岁,±s) 高血压[例(%)] 糖尿病[例(%)] 呼吸系统疾病[例(%)] 化疗史[例(%)] 吸烟史[例(%)] 饮酒史[例(%)] 体重指数(kg/m2,±s) 白蛋白(g/L,±s)
男性 女性
训练组 291 61(21.0) 191(65.6) 100(34.4) 61.60±11.18 61(21.0) 37(12.7) 60(20.6) 92(31.6) 124(42.6) 111(38.1) 23.83±3.14 39.20±4.69
外部验证组 63 20(31.7) 38(60.3) 25(39.7) 66.18±10.23 9(14.3) 11(17.5) 11(17.5) 16(25.4) 31(49.2) 25(39.7) 24.10±3.01 38.31±3.21
t/χ2   3.41 0.64 -2.99 1.46 1.00 0.32 0.95 0.92 0.05 -0.61 1.82
P   0.07 0.42 <0.01 0.23 0.32 0.57 0.33 0.34 0.82 0.54 0.07
表2 训练组与外部验证组影像学特征比较
组别 腰围(cm,±s) 造口直径[mm,M(IQR)] 总腹壁肌肉指数[cm2/m2,M(IQR)] 皮下脂肪指数[cm2/m2,M(IQR)] 腰大肌指数[cm2/m2,M(IQR)] 腹直肌指数[cm2/m2,M(IQR)]
训练组 90.65±10.14 27.14(24.29~34.29) 32.40(28.76~36.97) 51.13(36.66~67.66) 6.84(5.70~8.03) 3.65(3.05~4.31)
外部验证组 92.91±10.28 27.86(24.03~34.00) 32.10(28.78~36.74) 54.28(36.97~81.10) 7.14(5.90~8.36) 3.67(2.95~4.37)
t/Z 1.60 -0.30 0.05 1.59 0.83 0.46
P 0.11 0.77 0.96 0.12 0.41 0.64
表3 影响造口旁疝发生的单因素分析
影响因素 造口旁疝(n=61) 非造口旁疝(n=230) t/χ2 P 影响因素 造口旁疝(n=61) 非造口旁疝(n=230) t/χ2/Z P
性别[例(%)]     36.92 <0.01 饮酒史 18(30) 93(40) 2.44 0.12
男性 20(33) 171(74)     体重指数(kg/m2,±s) 26.50±3.04 23.12±2.77 8.31 <0.01
女性 41(67) 59(26)     白蛋白(g/L,±s) 37.96±5.32 39.52±4.46 -2.33 0.02
年龄(岁,±s) 66.26±11.03 60.36±10.91 3.75 <0.01 腰围(cm,±s) 97.22±9.63 88.91±9.56 6.03 <0.01
高血压[例(%)] 16(26) 45(20) 1.29 0.26 造口直径[mm,M(IQR)] 35.02(31.00~37.93) 26.43(23.57~31.43) 8.22 <0.01
糖尿病[例(%)] 12(20) 25(11) 3.37 0.07 腹壁肌肉指数[cm2/m2,M(IQR)] 30.72(27.96~35.25) 32.74(29.03~38.35) -1.90 0.06
呼吸系统疾病[例(%)] 13(21) 47(20) 0.02 0.88 皮下脂肪指数[cm2/m2,M(IQR)] 77.43(57.10~102.18) 46.98(34.28~58.27) 8.74 <0.01
化疗史[例(%)] 19(31) 73(32) <0.01 0.93 腰大肌指数[cm2/m2,M(IQR)] 6.25(4.85~7.82) 6.91(5.92~8.11) -2.91 <0.01
吸烟史[例(%)] 18(30) 106(46) 5.42 0.02 腹直肌指数[cm2/m2,M(IQR)] 3.48(2.77~3.91) 3.72(3.08~4.34) -2.57 0.01
表4 多因素logistic回归分析影响造口旁疝发生的独立危险因素
影响因素 OR 95% CI P
性别 3.927 1.327~11.621 0.013
年龄 1.063 1.014~1.114 0.011
体重指数 1.553 1.228~1.963 0.001
白蛋白 0.876 0.783~0.981 0.022
造口直径 4.426 1.976~9.915 0.001
皮下脂肪指数 1.028 1.006~1.050 0.011
腹直肌指数 0.591 0.352~0.993 0.047
图2 永久性乙状结肠造口旁疝发病风险的列线图预测模型
图3 永久性乙状结肠造口旁疝发病风险的列线图预测模型的内、外部验证及ROC曲线注：3A内部验证校准曲线；3B内部验证ROC曲线；3C外部验证校准曲线；3D外部验证ROC曲线。
图4 使用列线图预测模型预测造口旁疝发病风险的示例注：深蓝色线，皮下脂肪指数；浅蓝色线，腹直肌指数；黄色线，造口直径；紫色线，体重指数；红色线，白蛋白；黑色线，性别；绿线，年龄；总分，粗橙色线；PSH造口旁疝。图4A～C为1例62岁女性造口患者，总分约为21.3分，PSH的发病风险超过90%，术后CT显示该患者术后2年内发生PSH（4B，箭头）。图4A为该患者造口术前CT；图4B为该患者术后CT随访；图4C为该患者造口旁疝发病风险的预测模型示意图。图4D～F为1例62岁男性造口患者，总分约为21分，PSH的发病风险超过90%，术后CT显示该患者术后2年内发生PSH（4E，箭头）。图4D为该患者造口术前CT，图4E为该患者术后CT随访，图4F为该患者造口旁疝发病风险的预测模型示意图。图4G～I为1例54岁男性造口患者，总分约为11.3分，PSH的发病风险低于10%，2年随访CT显示该患者未发生PSH（4H，箭头）。图4G为该患者造口术前CT，图4H为该患者术后CT随访，图4I为该患者PSH发病风险的预测模型示意图。
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