| 胡爱玲,杜雅丽,衡媛,等.肺炎克雷伯菌尿路感染 167例碳青霉烯耐药风险列线图预测模型的构建[J].安徽医药,2024,28(3):623-627. |
| 肺炎克雷伯菌尿路感染 167例碳青霉烯耐药风险列线图预测模型的构建 |
| Establishment of a nomogram model for carbapenem resistance in 167 cases of Klebsiella pneumoniae urinary tract infection |
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| DOI：10.3969/j.issn.1009-6469.2024.03.043 |
| 中文关键词: 肺炎克雷伯菌 泌尿道感染 耐药性 列线图 预测模型 |
| 英文关键词: Klebsiella pneumoniae Urinary tract infection Drug resistance Nomogram Predictive model |
| 基金项目:秦皇岛市科学技术研究与发展计划( 202101A164)；秦皇岛市科学技术研究与发展计划( 202301A024) |
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| 中文摘要: |
| 目的构建肺炎克雷伯菌尿路感染碳青霉烯耐药风险列线图预测模型。方法回顾性分析 2018年 1月至 2020年 12月在秦皇岛市第一医院确诊为肺炎克雷伯菌尿路感染的成人住院病人 167例临床资料，将尿液标本中检出耐碳青霉烯类肺炎克雷伯菌( CRKP)的 62例病人设为 CRKP组，非 CRKP的 105例病人设为非 CRKP组。采用 logistic回归分析发生 CRKP尿路感染的独立危险因素，并将 167例病人按照分层随机抽样以 7∶3比例分为训练集(118例)和验证集( 49例)然后使用训练集根据独立危险因素建立 CRKP尿路感染列线图风险预测模型。采用校准曲线和受试者操作特征曲线( ROC曲线，)评估列线图预测模型的准确度和区分度。结果 CRKP组肺炎克雷伯菌对头孢呋辛、哌拉西林 /他唑巴坦、美罗培南、阿米卡星耐药性分别为 100%(62/62)、 100%(62/62)、 98.4%(61/62)、 51.6%(32/62)，明显高于非 CRKP组的 42.9%(45/105)、 8.6%(9/105)、1.0%(1/105)、 3.8%(4/105)。多因素 logistic回归分析显示，入住重症监护室、两周内使用碳青霉烯和酶抑制剂为 CRKP尿路感染的独立危险因素( OR=8.95、5.52、6.12，P<0.05)。训练集、验证集一致性指数( C-index)分别为 0.88，0.90。ROC曲线下面积为 0.88。校准曲线和 ROC曲线提示模型准确度、区分度良好。结论通过分析 CRKP尿路感染的危险因素构建风险列线图预测模型，模型具有良好的准确度和区分度，可有效预测肺炎克雷伯菌尿路感染碳青霉烯耐药发生风险，并可依此实施更有针对性的防护措施和制定合理的药物治疗方案。 |
| 英文摘要: |
| Objective To construct a predictive model for the risk of carbapenem resistance in Klebsiella pneumoniae urinary tract in. fection using a nomogram.Methods The clinical data of 167 adult patients diagnosed with Klebsiella pneumoniae urinary tract infec.tion in the First Hospital of Qinhuangdao City from January 2018 to December 2020 were retrospectively analyzed. Sixty-two patients with carbapenem-resistant Klebsiella pneumoniae (CRKP) detected in urine specimens were assigned to the CRKP group. One hundredand five patients without CRKP were assigned to the non-CRKP group. Logistic regression was used to analyze the independent risk fac.tors for CRKP urinary tract infection, and 167 patients were randomly divided into a training set (118 cases) and a validation set (49cases) according to stratified 7:3 ratio, and then the training set was used to establish a CRKP urinary tract infection risk predictionmodel based on the independent risk factors. Calibration curves and receiver operating characteristic (ROC) curves were used to evalu.ate the accuracy and discrimination of nomogram prediction models.Results The resistance of Klebsiella pneumoniae to cefuroxime,piperacillin/tazobactam, meropenem and amicacin in CRKP group was 100% (62/62), 100% (62/62), 98.4% (61/62) and 51.6% (32/62),respectively, which were significantly higher than those of the non-CRKP group 42.9% (45/105), 8.6% (9/105), 1.0% (1/105), 3.8% (4/105). Multivariate logistic regression analysis showed that admission to the intensive care unit, usage of carbapenem and enzyme inhibi.tors within two weeks were independent risk factors for urinary tract infections in CRKP (OR=8.95, 5.52, 6.12, P<0.05). The consisten. cy index (C-index) of the training set and the validation set were 0.88 and 0.90, respectively. The area under the ROC curve is 0.88.The calibration curve and ROC curve indicate that the model has good accuracy and good discrimination.Conclusions By analyzingthe risk factors of CRKP urinary tract infection, a risk nomogram prediction model was constructed. The model has good accuracy anddiscrimination, and can effectively predict the risk of urinary tract infection with carbapenem-resistant Klebsiella pneumoniae. Then more targeted protective measures and formulate reasonable drug treatment plans can be formulated. |
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