Sun Guangming, Xia Yufei, Wang Haoyang, Xiao Yaqin, Zhang Li, Zhang Yupeng, Gao Xuejin, Wang Xinying
Department of General Surgery, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Department of General Surgery, Jinling Hospital, School of Medicine, Southeast University, Nanjing, China.
Front Med (Lausanne). 2024 Dec 11;11:1422596. doi: 10.3389/fmed.2024.1422596. eCollection 2024.
Metabolic Bone Disease (MBD) is common in patients with short bowel syndrome (SBS). This study was to investigate the incidence and risk factors of osteopenia in adult SBS patients.
Hospital records from January 2010 to December 2019 were used to identify all eligible patients. Logistic regression and a nomogram were used to analyze the data.
A total of 120 patients with SBS were included in this study, and 76 patients (63.3%) developed osteopenia during the 10-year observation period, The multivariate analysis using the logistic regression model demonstrated that age (OR = 1.070; 95%CI: 1.016-1.126, = 0.010), female (OR = 5.098; 95%CI: 1.211-21.456, = 0.026), tumor history (OR = 4.481; 95%CI: 1.125-17.854, = 0.033), duration of SBS (OR = 1.0862; 95%CI: 1.022-1.103, = 0.002) and remnant ileum (OR = 4.260; 95%CI: 1.210-15.002, = 0.024) were independent risk factors for osteopenia in adults with SBS. The area under the curve (AUC) for the joint model (age, female, tumor history, duration of SBS, remnant ileum) was 0.848 and the corresponding sensitivity and specificity were 0.855 and 0.705, respectively. The C-index was 0.849 (95% CI: 0.778-0.917); thus, the predictions made by the model were close to the actual outcomes. In the decision curve analysis, the nomogram performed well and was feasible to make beneficial clinical decisions.
This study reveals the high prevalence of osteopenia in SBS patients and highlights the importance of early identification and treatment of osteopenia. A nomogram may provide personalized prediction and guidance for medical intervention.
代谢性骨病(MBD)在短肠综合征(SBS)患者中很常见。本研究旨在调查成年SBS患者骨质疏松症的发病率及危险因素。
使用2010年1月至2019年12月的医院记录来确定所有符合条件的患者。采用逻辑回归和列线图分析数据。
本研究共纳入120例SBS患者,在10年观察期内,76例(63.3%)发生骨质疏松症。使用逻辑回归模型进行的多因素分析表明,年龄(OR = 1.070;95%CI:1.016 - 1.126,P = 0.010)、女性(OR = 5.098;95%CI:1.211 - 21.456,P = 0.026)、肿瘤病史(OR = 4.481;95%CI:1.125 - 17.854,P = 0.033)、SBS病程(OR = 1.0862;95%CI:1.022 - 1.103,P = 0.002)和残余回肠(OR = 4.260;95%CI:1.210 - 15.002,P = 0.024)是成年SBS患者骨质疏松症的独立危险因素。联合模型(年龄、女性、肿瘤病史、SBS病程、残余回肠)的曲线下面积(AUC)为0.848,相应的敏感性和特异性分别为0.855和0.705。C指数为0.849(95%CI:0.778 - 0.917);因此,该模型的预测结果与实际结果接近。在决策曲线分析中,列线图表现良好,对于做出有益的临床决策是可行的。
本研究揭示了SBS患者中骨质疏松症的高患病率,并强调了早期识别和治疗骨质疏松症的重要性。列线图可为医疗干预提供个性化预测和指导。