[建立和评估用于预测糖尿病足患者败血症风险的提名图模型]。

Q3 Medicine
Lingjun Lin, Junwei Wang, Yongli Wan, Yang Gao
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引用次数: 0

摘要

目的:建立预测糖尿病足患者败血症风险的提名图模型,为临床预防和治疗提供参考:建立糖尿病足患者败血症风险预测提名图模型,为临床防治提供参考:回顾性分析并收集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治疗的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、糖尿病足德州分级以及入院后24小时内的实验室指标。根据住院期间有无败血症将患者分为败血症组和非败血症组。比较两组患者临床数据的差异。采用多元 Logistic 回归分析筛选糖尿病足患者住院期间败血症的影响因素,并建立了一个提名图预测模型。预测模型的性能通过接收器操作特征曲线(ROC 曲线)、校准曲线和决策曲线分析(DCA)进行评估。采用 Bootstrap 方法进行了内部验证:共有 430 名患者入选,其中 90 名患者在住院期间出现败血症,340 名患者未出现败血症。两组患者在糖尿病病程、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)等方面均存在统计学差异。多变量逻辑回归分析显示,糖尿病病程[几率比(OR)= 2.774,95% 置信区间(95%CI)为 1.053-7.308,P = 0.039]、德克萨斯糖尿病足分级(OR = 2.312,95%CI 为 1.014-5.273,P = 0.046)、白细胞(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发生败血症的独立危险因素,而 Alb 则是一个保护因素(OR = 0.885,95%CI 为 0.805-0.972,P = 0.011)。根据上述 6 项指标构建了一个提名图预测模型。ROC 曲线显示,提名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)。经过内部验证后,提名图预测模型的 AUC 为 0.918(95%CI 为 0.887-0.946)。Hosmer-Lemeshow 检验显示 χ 2 = 2.978,P = 0.936,表明预测模型的校准度良好。校正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,提名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等危险因素的提名图预测模型对糖尿病足患者住院期间发生败血症具有较好的预测价值,有助于临床筛查糖尿病足患者发展为败血症的可能性,并针对不同患者及时进行个性化干预。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
[Establishment and evaluation of a nomogram model for predicting the risk of sepsis in diabetic foot patients].

Objective: To establish a nomogram model for predicting the risk of sepsis in diabetic foot patients, and to provide reference for clinical prevention and treatment.

Methods: 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.

Results: 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 = 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.

Conclusions: 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.

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Zhonghua wei zhong bing ji jiu yi xue
Zhonghua wei zhong bing ji jiu yi xue Medicine-Critical Care and Intensive Care Medicine
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