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糖尿病足患者合并皮肤软组织感染危险因素及其预测模型 被引量:4

Risk factors for skin and soft tissue infection in patients with diabetic foot and their prediction model
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摘要 目的探讨糖尿病足患者合并皮肤软组织感染危险因素及预测模型评价.方法回顾性纳入2016年5月-2021年5月甘肃省第三人民医院收治的80例糖尿病足合并皮肤软组织感染患者(研究组),对软组织分泌物进行培养及药敏试验,分析病原菌及主要病原菌耐药情况,另选取同期住院治疗的糖尿病未感染者130例纳入对照组,回顾性分析两组一般临床资料,归纳皮肤软组织感染的危险因素,并构建皮肤软组织感染预测模型,应用受试者工作特征(ROC)曲线分析其预测价值.结果80例患者中共分离出91株病原菌,其中革兰阳性菌56株占61.54%,以金黄色葡萄球菌、表皮葡萄球菌、溶血葡萄球菌为主;革兰阴性菌28株占30.77%,以铜绿假单胞菌、肺炎克雷伯菌为主;真菌7株占7.69%;金黄色葡萄球菌对米诺环素、呋喃妥因、左氧氟沙星的耐药率均>70%;溶血葡萄球菌对左氧氟沙星、米诺环素、呋喃妥因、苯唑西林的耐药率均>70%;表皮葡萄球菌对米诺环素、呋喃妥因、苯唑西林、左氧氟沙星的耐药率均>60%;铜绿假单胞菌对红霉素、头孢曲松的耐药率均>77%;肺炎克雷伯菌对红霉素、环丙沙星、氧氟沙星的耐药率均>60%;年龄、病程、空腹血糖、侵入性操作、抗菌药物使用是糖尿病足患者皮肤软组织感染的危险因素(P<0.05);以年龄、病程、空腹血糖、侵入性操作、抗菌药物使用因素建立预测模型发现以上指标在糖尿病足合并皮肤软组织感染中预测敏感度为85.64%,特异度为62.54%,曲线下面积(AUC)为0.774.结论糖尿病足患者合并皮肤软组织感染主要病原菌为革兰阳性菌,结合年龄、病程、空腹血糖、侵入性操作、抗菌药物使用建立的风险预测模型,可为糖尿病足合并皮肤软组织感染早期筛查、干预提供客观依据. OBJECTIVE To investigate the risk factors for skin and soft tissue infection in patients with diabetic foot and to evaluate the predictive model.METHODS Eighty patients with diabetic foot complicated with skin and soft tissue infections(study group)admitted to the Third People's Hospital of Gansu Province between May 2016 and May 2021 were retrospectively included(study group),soft tissue secretions were cultured and drug susceptibility test were performed to analyze the pathogenic bacteria and drug resistance of major pathogens.Meanwhile,130 uninfected patients with diabetes hospitalized during the same period were selected as the control group.General clinical data of the two groups were retrospectively analyzed,and the risk factors for skin and soft tissue infection were summarized.A prediction model for skin and soft tissue infection was constructed,and its predictive valuewas analyzed using the receiver operating characteristic(ROC)curve.RESULTS A total of 91 strains of pathogenicbacteria were isolated from the 80 infected patients,of which 56 strains of gram-positive bacteria accounted for61.54%,dominated by Staphylococcus aureus,Staphylococcus epidermidis and Staphylococcus haemolyticus.28 strains of gram-negative bacteria accounted for 30.77%,dominated by Pseudomonas aeruginosa and Klebsiellapneumoniae and 7 strains of fungi accounted for 7.69%.The resistance rates of Staphylococcus aureus to minocycline,nitrofurantoin and levofloxacin were higher than 70%.The resistance rates of Staphylococcus haemolyticusto levofloxacin,minocycline,nitrofurantoin and oxaillin were higher than 70%.The resistance rates of Staphylococcus epidermidis to minocycline,nitrofurantoin,oxacillin and levofloxacin were higher than 60%.The resistancerates of Pseudomonas aeruginosa to erythromycin and ceftriaxone were higher than 77%.The resistancerates of Klebsiella pneumoniae to erythromycin,ciprofloxacin and ofloxacin were higher than 60%.Logistic regression analysis found that age,course of disease,fasting blood glucose,invasive procedures,and use of antibacterialdrugs were risk factors for skin and soft tissue infection in patients with diabetic foot(P<0.05).A predictionmodel using age,course of disease,fasting blood glucose,invasive procedures,and use of antibacterial drugsfound that the above indicators had a predictive sensitivity of 85.64% and a specificity of 62.54%,with an areaunder the curve(AUC)of 0.744 in diabetic foot co-morbid skin soft tissue infections.CONCLUSION Gram-positivebacteria were main pathogenic bacteria of skin and soft tissue infection in patients with diabetic foot.The predictionmodel combining age,course of disease,fasting blood glucose,invasive procedures,and use of antibacterialdrugs could provide an objective basis for early screening and intervention in diabetic foot patients with co-morbidskin and soft tissue infection.
作者 祁琴 杨青平 郑捷 靳媛媛 张菲菲 QI Qin;YANG Qing-ping;ZHENG Jie;JIN Yuan-yuan;ZHANG Fei-fei(The Third People's Hospital of Gansu Province,Lanzhou,Gansu 730020,China)
出处 《中华医院感染学杂志》 CAS CSCD 北大核心 2023年第19期2931-2935,共5页 Chinese Journal of Nosocomiology
基金 甘肃省自然科学基金资助项目(23JRRA1246)。
关键词 糖尿病足 皮肤软组织感染 病原菌 危险因素 预测模型 Diabetic foot Skin and soft tissue infection Pathogen Risk factor Prediction model
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