Zhang Luming, Zhang Feng, Xu Fengshuo, Wang Zichen, Ren Yinlong, Han Didi, Lyu Jun, Yin Haiyan
Intensive Care Unit, The First Affiliated Hospital of Jinan University, Guangzhou, China.
Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, China.
Front Med (Lausanne). 2021 May 21;8:671184. doi: 10.3389/fmed.2021.671184. eCollection 2021.
Urinary tract infection (UTI) is one of the common causes of sepsis. However, nomograms predicting the sepsis risk in UTI patients have not been comprehensively researched. The goal of this study was to establish and validate a nomogram to predict the probability of sepsis in UTI patients. Patients diagnosed with UTI were extracted from the Medical Information Mart for Intensive Care III database. These patients were randomly divided into training and validation cohorts. Independent prognostic factors for UTI patients were determined using forward stepwise logistic regression. A nomogram containing these factors was established to predict the sepsis incidence in UTI patients. The validity of our nomogram model was determined using multiple indicators, including the area under the receiver operating characteristic curve (AUC), correction curve, Hosmer-Lemeshow test, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision-curve analysis (DCA). This study included 6,551 UTI patients. Stepwise regression analysis revealed that the independent risk factors for sepsis in UTI patients were congestive heart failure, diabetes, liver disease, fluid electrolyte disorders, APSIII, neutrophils, lymphocytes, red blood cell distribution width, urinary protein, urinary blood, and microorganisms. The nomogram was then constructed and validated. The AUC, NRI, IDI and DCA of the nomogram all showed better performance than traditional APSIII score. The calibration curve and Hosmer-Lemeshow test results indicate that the nomogram was well-calibrated. Improved NRI and IDI values indicate that our nomogram scoring system is superior to other commonly used ICU scoring systems. The DCA curve indicates that the DCA map of the nomogram has good clinical application ability. This study identified the independent risk factors of sepsis in UTI patients and used them to construct a prediction model. The present findings may provide clinical reference information for preventing sepsis in UTI patients.
尿路感染(UTI)是脓毒症的常见病因之一。然而,预测UTI患者脓毒症风险的列线图尚未得到全面研究。本研究的目的是建立并验证一个列线图,以预测UTI患者发生脓毒症的概率。从重症监护医学信息数据库III中提取诊断为UTI的患者。这些患者被随机分为训练组和验证组。采用向前逐步逻辑回归确定UTI患者的独立预后因素。建立了包含这些因素的列线图,以预测UTI患者的脓毒症发生率。使用多个指标确定我们列线图模型的有效性,包括受试者操作特征曲线下面积(AUC)、校正曲线、Hosmer-Lemeshow检验、综合判别改善(IDI)、净重新分类改善(NRI)和决策曲线分析(DCA)。本研究纳入了6551例UTI患者。逐步回归分析显示,UTI患者发生脓毒症的独立危险因素为充血性心力衰竭、糖尿病、肝病、液体电解质紊乱、急性生理与慢性健康状况评分系统III(APSIII)、中性粒细胞、淋巴细胞红细胞分布宽度、尿蛋白、血尿和微生物。然后构建并验证了列线图。列线图的AUC、NRI、IDI和DCA均显示出比传统APSIII评分更好的性能。校准曲线和Hosmer-Lemeshow检验结果表明列线图校准良好。NRI和IDI值的改善表明我们的列线图评分系统优于其他常用的重症监护病房评分系统。DCA曲线表明列线图的DCA图具有良好的临床应用能力。本研究确定了UTI患者脓毒症的独立危险因素,并利用它们构建了一个预测模型。目前的研究结果可为预防UTI患者发生脓毒症提供临床参考信息。