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Abstract：
OBJECTIVE: To develop a machine learning-based risk prediction model for postoperative parastomal hernia (PSH) in colorectal cancer patients undergoing permanent colostomy, assisting nurses in identifying high-risk groups and devising preventive care strategies.
METHODS: A case-control study was conducted on 495 colorectal cancer patients who underwent permanent colostomy at the Second Affiliated Hospital of Anhui Medical University from June 2017 to June 2023, with a 1-year follow-up period. Patients were categorized into PSH and non-PSH groups based on PSH occurrence within 1-year post-operation. Data were split into training (70%) and testing (30%) sets. Variable selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression, and binary classification prediction models were established using Logistic Regression (LR), Support Vector Classification (SVC), K Nearest Neighbor (KNN), Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Extreme Gradient Boosting (XgBoost). The binary classification label denoted 1 for PSH occurrence and 0 for no PSH occurrence. Parameters were optimized via 5-fold cross-validation. Model performance was evaluated using Area Under Curve (AUC), specificity, sensitivity, accuracy, positive predictive value, negative predictive value, and F1-score. Clinical utility was evaluated using decision curve analysis (DCA), model explanation was enhanced using shapley additive explanation (SHAP), and model visualization was achieved using a nomogram.
RESULTS: The incidence of PSH within 1 year was 29.1% (144 patients). Among the models tested, the RF model demonstrated the highest discrimination capability with an AUC of 0.888 (95% CI: 0.881-0.935), along with superior specificity, accuracy, sensitivity, and F1 score. It also showed the highest clinical net benefit on the DCA curve. SHAP analysis identified the top 10 influential variables associated with PSH risk: body mass index (BMI), operation duration, history and status of chronic obstructive pulmonary disease (COPD), prealbumin, tumor node metastasis (TNM) staging, stoma site, thickness of rectus abdominis muscle (TRAM), C-reactive protein CRP, american society of anesthesiologists physical status classification (ASA), and stoma diameter. These insights from SHAP plots illustrated how these factors influence individual PSH outcomes. The nomogram was used for model visualization.
CONCLUSIONS: The Random Forest model demonstrated robust predictive performance and clinical relevance in forecasting colonic PSH. This model aids in early identification of high-risk patients and guides preventive care.
METHODS: A case-control study was conducted on 495 colorectal cancer patients who underwent permanent colostomy at the Second Affiliated Hospital of Anhui Medical University from June 2017 to June 2023, with a 1-year follow-up period. Patients were categorized into PSH and non-PSH groups based on PSH occurrence within 1-year post-operation. Data were split into training (70%) and testing (30%) sets. Variable selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression, and binary classification prediction models were established using Logistic Regression (LR), Support Vector Classification (SVC), K Nearest Neighbor (KNN), Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Extreme Gradient Boosting (XgBoost). The binary classification label denoted 1 for PSH occurrence and 0 for no PSH occurrence. Parameters were optimized via 5-fold cross-validation. Model performance was evaluated using Area Under Curve (AUC), specificity, sensitivity, accuracy, positive predictive value, negative predictive value, and F1-score. Clinical utility was evaluated using decision curve analysis (DCA), model explanation was enhanced using shapley additive explanation (SHAP), and model visualization was achieved using a nomogram.
RESULTS: The incidence of PSH within 1 year was 29.1% (144 patients). Among the models tested, the RF model demonstrated the highest discrimination capability with an AUC of 0.888 (95% CI: 0.881-0.935), along with superior specificity, accuracy, sensitivity, and F1 score. It also showed the highest clinical net benefit on the DCA curve. SHAP analysis identified the top 10 influential variables associated with PSH risk: body mass index (BMI), operation duration, history and status of chronic obstructive pulmonary disease (COPD), prealbumin, tumor node metastasis (TNM) staging, stoma site, thickness of rectus abdominis muscle (TRAM), C-reactive protein CRP, american society of anesthesiologists physical status classification (ASA), and stoma diameter. These insights from SHAP plots illustrated how these factors influence individual PSH outcomes. The nomogram was used for model visualization.
CONCLUSIONS: The Random Forest model demonstrated robust predictive performance and clinical relevance in forecasting colonic PSH. This model aids in early identification of high-risk patients and guides preventive care.
摘要：
目的：建立基于机器学习的永久性结肠造口术患者术后造口旁疝（PSH）风险预测模型，协助护士识别高危人群并制定预防性护理策略。
方法：对2017年6月至2023年6月在安徽医科大学第二附属医院行永久性结肠造口的495例结直肠癌患者进行病例对照研究，随访1年。根据术后1年内的PSH发生率将患者分为PSH和非PSH组。数据分为训练（70％）和测试（30％）集。使用最小绝对收缩和选择算子(LASSO)回归进行变量选择，并利用Logistic回归(LR)建立了二元分类预测模型，支持向量分类(SVC)K近邻(KNN),随机森林(RF),轻型梯度增压机（LGBM），和极端梯度提升（XgBoost）。二进制分类标签对于PSH发生表示为1,对于没有PSH发生表示为0。通过5倍交叉验证优化参数。使用曲线下面积(AUC)评估模型性能，特异性，灵敏度,准确度,正预测值，负预测值，和F1得分。使用决策曲线分析(DCA)评估临床效用，使用Shapley加法解释(SHAP)增强了模型解释，并使用列线图实现了模型可视化。
结果：1年内PSH的发生率为29.1%（144例患者）。在测试的模型中，RF模型显示出最高的辨别能力，AUC为0.888(95%CI:0.881-0.935)，连同优越的特异性，准确度,灵敏度,F1得分。它还在DCA曲线上显示出最高的临床净效益。SHAP分析确定了与PSH风险相关的前10个影响变量：体重指数(BMI)，操作持续时间,慢性阻塞性肺疾病（COPD）的病史和状态，前白蛋白,肿瘤淋巴结转移(TNM)分期,造口部位,腹直肌厚度（TRAM），C反应蛋白CRP,美国麻醉师学会身体状况分类(ASA)，和气孔直径。来自SHAP图的这些见解说明了这些因素如何影响个体PSH结果。列线图用于模型可视化。
结论：随机森林模型在预测结肠PSH方面表现出稳健的预测性能和临床相关性。该模型有助于早期识别高危患者并指导预防护理。
方法：对2017年6月至2023年6月在安徽医科大学第二附属医院行永久性结肠造口的495例结直肠癌患者进行病例对照研究，随访1年。根据术后1年内的PSH发生率将患者分为PSH和非PSH组。数据分为训练（70％）和测试（30％）集。使用最小绝对收缩和选择算子(LASSO)回归进行变量选择，并利用Logistic回归(LR)建立了二元分类预测模型，支持向量分类(SVC)K近邻(KNN),随机森林(RF),轻型梯度增压机（LGBM），和极端梯度提升（XgBoost）。二进制分类标签对于PSH发生表示为1,对于没有PSH发生表示为0。通过5倍交叉验证优化参数。使用曲线下面积(AUC)评估模型性能，特异性，灵敏度,准确度,正预测值，负预测值，和F1得分。使用决策曲线分析(DCA)评估临床效用，使用Shapley加法解释(SHAP)增强了模型解释，并使用列线图实现了模型可视化。
结果：1年内PSH的发生率为29.1%（144例患者）。在测试的模型中，RF模型显示出最高的辨别能力，AUC为0.888(95%CI:0.881-0.935)，连同优越的特异性，准确度,灵敏度,F1得分。它还在DCA曲线上显示出最高的临床净效益。SHAP分析确定了与PSH风险相关的前10个影响变量：体重指数(BMI)，操作持续时间,慢性阻塞性肺疾病（COPD）的病史和状态，前白蛋白,肿瘤淋巴结转移(TNM)分期,造口部位,腹直肌厚度（TRAM），C反应蛋白CRP,美国麻醉师学会身体状况分类(ASA)，和气孔直径。来自SHAP图的这些见解说明了这些因素如何影响个体PSH结果。列线图用于模型可视化。
结论：随机森林模型在预测结肠PSH方面表现出稳健的预测性能和临床相关性。该模型有助于早期识别高危患者并指导预防护理。
