Department of Endocrinology, Air Force Medical Center, No. 30 Fucheng Road, Haidian District, Beijing 100142, China.
Graduate School of China Medical University, Shenyang 110000, China.
Postgrad Med J. 2024 Nov 22;100(1190):908-916. doi: 10.1093/postmj/qgae087.
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.
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.
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.
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.
糖尿病是世界上增长最快的疾病之一，是一种慢性代谢疾病，现已成为全球范围内的公共卫生问题。本研究旨在开发一个预测列线图模型，以展示糖尿病足患者发生主要截肢的风险。
本回顾性研究共纳入 2018 年 1 月至 2023 年 12 月在中国空军军医大学西京医院住院的 634 例 2 型糖尿病（T2DM）合并糖尿病足溃疡患者。其中男性 468 例（73.82%），女性 166 例（26.18%）；年龄 61.64±11.27 岁，平均体质量指数 24.45±3.56kg/m2。采用单因素 logistic 回归和多因素 logistic 回归对预测因素进行评估，并建立预测列线图。评估该主要截肢列线图的受试者工作特征曲线（ROC 曲线）及其曲线下面积、校准曲线和决策曲线分析。通过内部验证集进行模型验证，并进一步使用 ROC 曲线、校准曲线和决策曲线分析评估该列线图模型的性能和临床实用性。
该预测模型中包含的预测因子包括体质量指数、溃疡部位、血红蛋白、中性粒细胞与淋巴细胞比值、血尿酸（BUA）和射血分数。在训练组中，该预测模型的 C 指数为 0.957（95%CI，0.931-0.983），验证组中 C 指数为 0.987（95%CI，0.969-1.000），具有良好的区分度。校准度良好。决策曲线分析表明，在训练组和验证组中使用该列线图预测模型将分别具有临床获益。
该新的列线图模型纳入了体质量指数、溃疡部位、血红蛋白、中性粒细胞与淋巴细胞比值、BUA 和射血分数，对预测糖尿病足患者发生主要截肢的风险具有较好的准确性和预测价值。
