糖尿病足感染病原微生物分布及风险预测模型的建立

Distribution of pathogenic microorganisms in diabetic foot infection and construction of risk prediction model

摘要:

目的 分析糖尿病足（DF）感染病原微生物分布特征，并建立DF感染风险预测模型。方法 选取2019年1月—2022年1月文昌市人民医收治的82例DF患者为研究对象，对其足部创面分泌物进行细菌培养，总结DF感染病原菌分布特点，多因素Logistic回归分析确定DF感染独立危险因素并建立风险预测模型，绘制受试者工作特征（ROC）曲线判断各独立指标与风险预警模型诊断的准确性，交叉验证法验证风险预警模型效能。结果 82例DF患者中有50例足部分泌物中分离出病原菌，共检出病原菌79株，其中革兰阳性菌37株（46.84%），革兰阴性菌38株（48.10%），真菌4株（5.06%）。多因素Logistic回归分析结果提示，DF病程［O^R=2.201（95% CI：1.754，2.763）］、周围神经病变［O^R=3.177（95% CI：1.518，6.652）］、白细胞计数（WBC） ［O^R=2.425（95% CI：1.512，3.890）］、低密度脂蛋白胆固醇（LDL-C） ［O^R=1.976（95% CI：1.481，2.636）］是DF感染的独立危险因素（P <0.05）。ROC曲线结果表明，DF病程、周围神经病变、WBC、LDL-C预测DF感染的曲线下面积（AUC）分别为0.665、0.659、0.685和0.645，敏感性分别为68.0%（0.538，0.841）、60.0%（0.563，0.794）、76.0%（0.550，0.882）和62.0%（0.512，0.803，特异性分别为68.7%（0.548，0.853）、71.9%（0.615，0.899）、59.4%（0.440，0.603）和62.5%（0.536，0.815）。将上述进入多因素Logistic回归模型变量的b作为系数，建立风险预警模型，Logit（P）=e^y/（1+e^y），y =0.789×DF病程+1.156×周围神经病变+0.886×WBC+0.681×LDL-C-3.157。ROC曲线分析结果表明，风险预测模型AUC为0.908，截断值为0.513，敏感性及特异性分别为84.10%（0.651，0.917）和75.61%（0.579，0.855）。结论 DF感染率较高，但多为单一病原菌感染，DF病程、周围神经病变、WBC、LDL-C是DF感染的独立危险因素，以此为基础建立风险预测模型，便于临床快速准确筛查DF感染高危人群。

Abstract:

Objective To analyze the distribution of pathogenic microorganisms in diabetic foot (DF) infection, and to construct the risk prediction model for DF infection.Methods Eighty-two patients with DF in Wenchang City People's Hospital from January 2019 to January 2022 were enrolled, and bacterial cultures were performed on their foot wound secretions. The distribution characteristics of the pathogenic microorganisms were summarized. The independent risk factors for DF infection were determined via multivariable Logistic regression analysis, and based on which the risk prediction model was established. The accuracy of each independent indicator and the risk prediction model for predicting DF infection was assessed by receiver operating characteristic (ROC) curve analysis. Besides, predictive efficacy of the risk prediction model was verified by cross-validation.Results Pathogenic microorganisms were isolated from foot wound secretions in 50 out of 82 DF patients. A total of 79 strains were detected, including 37 strains (46.84%) of gram-positive bacteria, 38 strains (48.10%) of gram-negative bacteria and 4 strains (5.06%) of fungi. Multivariable Logistic regression analysis denoted that duration of DF [O^R = 2.201 (95% CI: 1.754, 2.763) ], peripheral neuropathy [O^R = 3.177 (95% CI: 1.518, 6.652) ], white blood cell (WBC) count [O^R =2.425 (95% CI: 1.512, 3.890) ] and low density lipoprotein cholesterol (LDL-C) [O^R = 1.976 (95% CI: 1.481, 2.636) ] were independent factors affecting DF infection (P < 0.05). The ROC curve analysis revealed that the areas under the ROC curves (AUCs) of duration of DF, peripheral neuropathy, WBC count and LDL-C for predicting DF infection were 0.665, 0.659, 0.685 and 0.645, with the sensitivities being 68.0% (0.538, 0.841), 60.0% (0.563, 0.794), 76.0% (0.550, 0.882) and 62.0% (0.512, 0.803), and the specificities being 68.7% (0.548, 0.853), 71.9% (0.615, 0.899), 59.4% (0.440, 0.603) and 62.5% (0.536, 0.815), respectively. With the regression coefficients of the abovementioned variables included in the multivariable Logistic regression model, a risk prediction model was established as y = 0.789 × duration of DF + 1.156 × peripheral neuropathy + 0.886 × WBC count + 0.681 × LDL-C - 3.157. The ROC curve analysis indicated that the AUC of the risk prediction model was 0.908, with a cut-off value of 0.513, a sensitivity of 84.10% (0.651, 0.917) and a specificity of 75.61% (0.579, 0.855), respectively.Conclusions DF infection has a high prevalence rate, but it is mostly caused by a single pathogen. Moreover, the duration of DF, peripheral neuropathy, WBC count and the level of LDL-C are independent factors affecting DF infection, and the risk prediction model based on these factors facilitates the screening of those at a high risk for DF infection.