Wang Zhibo, Sun Baoying, Yu Yimiao, Liu Jingnong, Li Duo, Lu Yun, Liu Ruiqing
Department of Gastroenterological Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China.
Neurology Department, Central Hospital Affiliated to Shandong First Medical University, Jinan, China.
Front Nutr. 2024 Apr 19;11:1345570. doi: 10.3389/fnut.2024.1345570. eCollection 2024.
Postoperative complications in adhesive small bowel obstruction (ASBO) significantly escalate healthcare costs and prolong hospital stays. This study endeavors to construct a nomogram that synergizes computed tomography (CT) body composition data with inflammatory-nutritional markers to forecast postoperative complications in ASBO.
The study's internal cohort consisted of 190 ASBO patients recruited from October 2017 to November 2021, subsequently partitioned into training ( = 133) and internal validation ( = 57) groups at a 7:3 ratio. An additional external cohort comprised 52 patients. Body composition assessments were conducted at the third lumbar vertebral level utilizing CT images. Baseline characteristics alongside systemic inflammatory responses were meticulously documented. Through univariable and multivariable regression analyses, risk factors pertinent to postoperative complications were identified, culminating in the creation of a predictive nomogram. The nomogram's precision was appraised using the concordance index (C-index) and the area under the receiver operating characteristic (ROC) curve.
Postoperative complications were observed in 65 (48.87%), 26 (45.61%), and 22 (42.31%) patients across the three cohorts, respectively. Multivariate analysis revealed that nutrition risk score (NRS), intestinal strangulation, skeletal muscle index (SMI), subcutaneous fat index (SFI), neutrophil-lymphocyte ratio (NLR), and lymphocyte-monocyte ratio (LMR) were independently predictive of postoperative complications. These preoperative indicators were integral to the nomogram's formulation. The model, amalgamating body composition and inflammatory-nutritional indices, demonstrated superior performance: the internal training set exhibited a 0.878 AUC (95% CI, 0.802-0.954), 0.755 accuracy, and 0.625 sensitivity; the internal validation set displayed a 0.831 AUC (95% CI, 0.675-0.986), 0.818 accuracy, and 0.812 sensitivity. In the external cohort, the model yielded an AUC of 0.886 (95% CI, 0.799-0.974), 0.808 accuracy, and 0.909 sensitivity. Calibration curves affirmed a strong concordance between predicted outcomes and actual events. Decision curve analysis substantiated that the model could confer benefits on patients with ASBO.
A rigorously developed and validated nomogram that incorporates body composition and inflammatory-nutritional indices proves to be a valuable tool for anticipating postoperative complications in ASBO patients, thus facilitating enhanced clinical decision-making.
粘连性小肠梗阻（ASBO）术后并发症显著增加医疗成本并延长住院时间。本研究旨在构建一种列线图，将计算机断层扫描（CT）身体成分数据与炎症营养标志物相结合，以预测ASBO患者的术后并发症。
该研究的内部队列由2017年10月至2021年11月招募的190例ASBO患者组成，随后按7:3的比例分为训练组（n = 133）和内部验证组（n = 57）。另一个外部队列包括52例患者。利用CT图像在第三腰椎水平进行身体成分评估。详细记录基线特征以及全身炎症反应。通过单变量和多变量回归分析，确定与术后并发症相关的危险因素，最终创建预测列线图。使用一致性指数（C指数）和受试者操作特征（ROC）曲线下面积评估列线图的准确性。
三个队列中分别有65例（48.87%）、26例（45.61%）和22例（42.31%）患者发生术后并发症。多变量分析显示，营养风险评分（NRS）、肠绞窄、骨骼肌指数（SMI）、皮下脂肪指数（SFI）、中性粒细胞与淋巴细胞比值（NLR）和淋巴细胞与单核细胞比值（LMR）是术后并发症的独立预测因素。这些术前指标是列线图制定的重要组成部分。该模型融合了身体成分和炎症营养指标，表现出卓越性能：内部训练集的AUC为0.878（95%CI，0.802 - 0.954），准确率为0.755，灵敏度为0.625；内部验证集的AUC为0.831（95%CI，0.675 - 0.986），准确率为0.818，灵敏度为0.812。在外部队列中，该模型的AUC为0.886（95%CI，0.799 - 0.974），准确率为0.808，灵敏度为0.909。校准曲线证实预测结果与实际事件之间具有高度一致性。决策曲线分析证实该模型可为ASBO患者带来益处。
一个经过严格开发和验证的列线图，纳入身体成分和炎症营养指标，被证明是预测ASBO患者术后并发症的有价值工具，从而有助于改善临床决策。
