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Abstract：
OBJECTIVE: The objective of this study is to develop a nomogram for the personalized prediction of postoperative complication risks in patients with middle and low rectal cancer who are undergoing transanal total mesorectal excision (taTME). This tool aims to assist clinicians in early identification of high-risk patients and in addressing preoperative risk factors to enhance surgical safety.
METHODS: In this case-control study, 207 patients diagnosed with middle and low rectal cancer and undergoing taTME between February 2018 and November 2023 at The First Affiliated Hospital of Xiamen University were included. Independent risk factors for postoperative complications were analyzed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression and multifactorial logistic regression models. A predictive nomogram was constructed using R Studio.
RESULTS: Among the 207 patients, 57 (27.5%) experienced postoperative complications. The LASSO and multifactorial logistic regression analyses identified operation time (OR = 1.010, P = 0.007), smoking history (OR = 9.693, P < 0.001), anastomotic technique (OR = 0.260, P = 0.004), and ASA score (OR = 9.077, P = 0.051) as significant predictors. These factors were integrated into the nomogram. The model\'s accuracy was validated through receiver operating characteristic curves, calibration curves, consistency indices, and decision curve analysis.
CONCLUSIONS: The developed nomogram, incorporating operation time, smoking history, anastomotic technique, and ASA score, effectively forecasts postoperative complication risks in taTME procedures. It is a valuable tool for clinicians to identify patients at heightened risk and initiate timely interventions, ultimately improving patient outcomes.
METHODS: In this case-control study, 207 patients diagnosed with middle and low rectal cancer and undergoing taTME between February 2018 and November 2023 at The First Affiliated Hospital of Xiamen University were included. Independent risk factors for postoperative complications were analyzed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression and multifactorial logistic regression models. A predictive nomogram was constructed using R Studio.
RESULTS: Among the 207 patients, 57 (27.5%) experienced postoperative complications. The LASSO and multifactorial logistic regression analyses identified operation time (OR = 1.010, P = 0.007), smoking history (OR = 9.693, P < 0.001), anastomotic technique (OR = 0.260, P = 0.004), and ASA score (OR = 9.077, P = 0.051) as significant predictors. These factors were integrated into the nomogram. The model\'s accuracy was validated through receiver operating characteristic curves, calibration curves, consistency indices, and decision curve analysis.
CONCLUSIONS: The developed nomogram, incorporating operation time, smoking history, anastomotic technique, and ASA score, effectively forecasts postoperative complication risks in taTME procedures. It is a valuable tool for clinicians to identify patients at heightened risk and initiate timely interventions, ultimately improving patient outcomes.
摘要：
目的：本研究的目的是开发一个列线图，用于个性化预测中低位直肠癌患者经肛门全直肠系膜切除术（taTME）的术后并发症风险。该工具旨在帮助临床医生早期识别高风险患者，并解决术前风险因素，以提高手术安全性。
方法：在本病例对照研究中，纳入2018年2月至2023年11月在厦门大学附属第一医院诊断为中低位直肠癌并接受taTME的207例患者。使用最小绝对收缩和选择算子（LASSO）回归和多因素logistic回归模型分析术后并发症的独立危险因素。使用RStudio构建预测列线图。
结果：在207名患者中，57例(27.5%)出现术后并发症。LASSO和多因素logistic回归分析确定了手术时间（OR=1.010，P=0.007），吸烟史(OR=9.693,P<0.001),吻合技术(OR=0.260,P=0.004),和ASA评分(OR=9.077,P=0.051)为显著预测因子。这些因素被整合到列线图中。通过接收器工作特性曲线验证了模型的准确性，校正曲线,一致性指数，和决策曲线分析。
结论：开发的列线图，合并操作时间，吸烟史,吻合技术,和ASA得分，有效预测taTME手术的术后并发症风险。它是临床医生识别高风险患者并及时采取干预措施的宝贵工具，最终改善患者预后。
方法：在本病例对照研究中，纳入2018年2月至2023年11月在厦门大学附属第一医院诊断为中低位直肠癌并接受taTME的207例患者。使用最小绝对收缩和选择算子（LASSO）回归和多因素logistic回归模型分析术后并发症的独立危险因素。使用RStudio构建预测列线图。
结果：在207名患者中，57例(27.5%)出现术后并发症。LASSO和多因素logistic回归分析确定了手术时间（OR=1.010，P=0.007），吸烟史(OR=9.693,P<0.001),吻合技术(OR=0.260,P=0.004),和ASA评分(OR=9.077,P=0.051)为显著预测因子。这些因素被整合到列线图中。通过接收器工作特性曲线验证了模型的准确性，校正曲线,一致性指数，和决策曲线分析。
结论：开发的列线图，合并操作时间，吸烟史,吻合技术,和ASA得分，有效预测taTME手术的术后并发症风险。它是临床医生识别高风险患者并及时采取干预措施的宝贵工具，最终改善患者预后。
