Lin Lingjun, Wang Junwei, Wan Yongli, Gao Yang
National Health Commission (NHC) Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Tianjin Institute of Endocrinology, Department of Infection Management, Chu Hsien-I Memorial Hospital, Tianjin Medical University, Tianjin 300134, China.
NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Tianjin Institute of Endocrinology, Department of Intensive Care Unit, Chu Hsien-I Memorial Hospital, Tianjin Medical University, Tianjin 300134, China.
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2024 Jul;36(7):693-698. doi: 10.3760/cma.j.cn121430-20240327-00294.
To establish a nomogram model for predicting the risk of sepsis in diabetic foot patients, and to provide reference for clinical prevention and treatment.
The clinical data of 430 patients with diabetic foot who were hospitalized in Chu Hsien-I Memorial Hospital of Tianjin Medical University from January 2022 to March 2023 were reviewed and collected, including age, gender, past medical history, smoking and drinking history, family history, diabetes course, Texas grade of diabetic foot and laboratory indicators within 24 hours after admission. Patients were divided into sepsis group and non-sepsis group according to the presence or absence of sepsis during hospitalization. The differences in clinical data between the two groups were compared. Multivariate Logistic regression analysis was used to screen the influencing factors of sepsis in patients with diabetic foot during hospitalization, and a nomogram predictive model was established. The performance of the prediction model was evaluated by receiver operator characteristic curve (ROC curve), calibration curve and decision curve analysis (DCA). Internal validation was performed by using Bootstrap method.
A total of 430 patients were enrolled, among which 90 patients developed sepsis during hospitalization and 340 patients did not. There were statistically significant differences in diabetes course, Texas grade of diabetic foot, white blood cell count (WBC), neutrophil count (NEU), lymphocyte count (LYM), neutrophil to lymphocyte ratio (NLR), hemoglobin (Hb), albumin (Alb), glycosylated hemoglobin (HbA1c), C-reactive protein (CRP), and blood urea nitrogen (BUN) between the two groups. Multivariate Logistic regression analysis showed that diabetes course [odds ratio (OR) = 2.774, 95% confidence interval (95%CI) was 1.053-7.308, P = 0.039], Texas grade of diabetic foot (OR = 2.312, 95%CI was 1.014-5.273, P = 0.046), WBC (OR = 1.160, 95%CI was 1.042-1.291, P = 0.007), HbA1c (OR = 1.510, 95%CI was 1.278-1.784, P < 0.001), CRP (OR = 1.007, 95%CI was 1.000-1.014, P = 0.036) were independent risk factors for sepsis in patients with diabetic foot during hospitalization, while Alb was a protective factor (OR = 0.885, 95%CI was 0.805-0.972, P = 0.011). A nomogram predictive model was constructed based on the above 6 indicators. The ROC curve showed that the area under ROC curve (AUC) of the nomogram predictive model for identifying the sepsis patients was 0.919 (95%CI was 0.889-0.948). The AUC of the nomogram predictive model after internal verification was 0.918 (95%CI was 0.887-0.946). Hosmer-Lemeshow test showed χ = 2.978, P = 0.936, indicating that the calibration degree of the predictive model was good. Calibration curve showed that the predicted probability of sepsis was in good agreement with the actual probability. DCA curve showed that the nomogram predictive model had good clinical usefulness.
The nomogram predictive model based on the risk factors of diabetes course, Texas grade of diabetic foot, WBC, HbA1c, CRP and Alb has good predictive value for the occurrence of sepsis in patients with diabetic foot during hospitalization, which is helpful for clinical screening of the possibility of diabetic foot patients progressing to sepsis, and timely personalized intervention for different patients.
建立预测糖尿病足患者脓毒症风险的列线图模型，为临床防治提供参考。
回顾性收集2022年1月至2023年3月在天津医科大学朱宪彝纪念医院住院的430例糖尿病足患者的临床资料，包括年龄、性别、既往病史、吸烟饮酒史、家族史、糖尿病病程、糖尿病足德州分级及入院后24小时内的实验室指标。根据住院期间是否发生脓毒症将患者分为脓毒症组和非脓毒症组。比较两组临床资料的差异。采用多因素Logistic回归分析筛选糖尿病足患者住院期间脓毒症的影响因素，并建立列线图预测模型。通过受试者操作特征曲线（ROC曲线）、校准曲线和决策曲线分析（DCA）对预测模型的性能进行评估。采用Bootstrap法进行内部验证。
共纳入430例患者，其中90例住院期间发生脓毒症，340例未发生。两组患者的糖尿病病程、糖尿病足德州分级、白细胞计数（WBC）、中性粒细胞计数（NEU）、淋巴细胞计数（LYM）、中性粒细胞与淋巴细胞比值（NLR）、血红蛋白（Hb）、白蛋白（Alb）、糖化血红蛋白（HbA1c）、C反应蛋白（CRP）和血尿素氮（BUN）差异有统计学意义。多因素Logistic回归分析显示，糖尿病病程[比值比（OR）=2.774，95%置信区间（95%CI）为1.053 - 7.308，P = 0.039]、糖尿病足德州分级（OR = 2.312，95%CI为1.014 - 5.273，P = 0.046）、WBC（OR = 1.160，95%CI为1.042 - 1.291，P = 0.007）、HbA1c（OR = 1.510，95%CI为1.278 - 1.784，P < 0.001）、CRP（OR = 1.007，95%CI为1.000 - 1.014，P = 0.036）是糖尿病足患者住院期间脓毒症的独立危险因素，而Alb是保护因素（OR = 0.885，95%CI为0.805 - 0.972，P = 0.011）。基于上述6项指标构建列线图预测模型。ROC曲线显示，列线图预测模型识别脓毒症患者的ROC曲线下面积（AUC）为0.919（95%CI为0.889 - 0.948）。内部验证后列线图预测模型的AUC为0.918（95%CI为0.887 - 0.946）。Hosmer - Lemeshow检验显示χ² = 2.978，P = 0.936，表明预测模型的校准度良好。校准曲线显示脓毒症的预测概率与实际概率吻合良好。DCA曲线显示列线图预测模型具有良好的临床实用性。
基于糖尿病病程、糖尿病足德州分级、WBC、HbA1c、CRP和Alb危险因素构建的列线图预测模型对糖尿病足患者住院期间脓毒症的发生具有良好的预测价值，有助于临床筛查糖尿病足患者进展为脓毒症的可能性，并对不同患者进行及时的个性化干预。
