背景:糖尿病,作为世界上增长最快的疾病之一,是一种慢性代谢性疾病,现已成为全球范围内的公共卫生问题。这项研究的目的是建立一个预测的列线图模型,以证明糖尿病足患者严重截肢的风险。
方法:我们的回顾性研究包括2018年1月至2023年12月在空军医学中心住院的634例2型糖尿病(T2DM)合并糖尿病足溃疡患者。男性468例(73.82%),女性166例(26.18%),平均年龄为61.64±11.27岁,平均体重指数为24.45±3.56kg/m2。通过单因素logistic回归和多元logistic回归评估预测因素,并建立具有这些特征的预测列线图。接收机工作特性(受试者工作特性曲线)及其曲线下面积,校正曲线,并对该主要截肢列线图进行了决策曲线分析。模型验证由内部验证集执行,和接收器工作特性曲线,校正曲线,和决策曲线分析用于进一步评估列线图模型性能和临床有用性。
结果:该预测模型中包含的预测因子包括体重指数,溃疡部位,血红蛋白,中性粒细胞与淋巴细胞的比率,血尿酸(BUA),和射血分数。该预测模型显示,训练组的C指数为0.957(95%CI,0.931-0.983),验证队列的C指数为0.987(95%CI,0.969-1.000)。显示良好的校准。决策曲线分析表明,在训练队列和验证队列中使用列线图预测模型将分别具有临床益处。
结论:这个新的列线图包含体重指数,溃疡部位,血红蛋白,中性粒细胞与淋巴细胞的比率,BUA,射血分数对预测糖尿病足患者大截肢风险具有较好的准确性和较好的预测价值。
BACKGROUND: Diabetes mellitus, as one of the world\'s fastest-growing diseases, is a chronic metabolic disease that has now become a public health problem worldwide. The purpose of this research was to develop a predictive nomogram model to demonstrate the risk of major amputation in patients with diabetic foot.
METHODS: A total of 634 Type 2 Diabetes Mellitus (T2DM) patients with diabetic foot ulcer hospitalized at the Air Force Medical Center between January 2018 and December 2023 were included in our retrospective study. There were 468 males (73.82%) and 166 females (26.18%) with an average age of 61.64 ± 11.27 years and average body mass index of 24.45 ± 3.56 kg/m2. The predictive factors were evaluated by single factor logistic regression and multiple logistic regression and the predictive nomogram was established with these features. Receiver operating characteristic (subject working characteristic curve) and their area under the curve, calibration curve, and decision curve analysis of this major amputation nomogram were assessed. Model validation was performed by the internal validation set, and the receiver operating characteristic curve, calibration curve, and decision curve analysis were used to further evaluate the nomogram model performance and clinical usefulness.
RESULTS: Predictors contained in this predictive model included body mass index, ulcer sites, hemoglobin, neutrophil-to-lymphocyte ratio, blood uric acid (BUA), and ejection fraction. Good discrimination with a C-index of 0.957 (95% CI, 0.931-0.983) in the training group and a C-index of 0.987 (95% CI, 0.969-1.000) in the validation cohort were showed with this predictive model. Good calibration were displayed. The decision curve analysis showed that using the nomogram prediction model in the training cohort and validation cohort would respectively have clinical benefits.
CONCLUSIONS: This new nomogram incorporating body mass index, ulcer sites, hemoglobin, neutrophil-to-lymphocyte ratio, BUA, and ejection fraction has good accuracy and good predictive value for predicting the risk of major amputation in patients with diabetic foot.