Zhou Ran, Chen Xiaolong, Jia Hengmin, Duan Wen
Department of Pharmacy, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230001, People's Republic of China.
Department of Pharmacy, The Second People's Hospital of Chizhou, Chizhou, Anhui, 247100, People's Republic of China.
Infect Drug Resist. 2025 Jul 26;18:3723-3734. doi: 10.2147/IDR.S527114. eCollection 2025.
This study aimed to construct and evaluate a nomogram based on data from neurosurgery ward patients to predict the probability of multidrug-resistant (MDR) pneumonia occurrence.
We retrospectively collected clinical data, early laboratory test results, and physician prescriptions for 35 variables from patients. Univariate and stepwise regression analyses were used to screen variables to determine predictive factors, and a nomogram was constructed in the training group based on the results of the logistic regression model. Using the validation group, discrimination, calibration, and clinical applicability were assessed based on the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA).
Among 3397 patients admitted to the neurosurgery ward from January 1, 2021, to September 30, 2024, 438 patients had pulmonary infections, including 208 patients with MDR pneumonia and 230 patients with non-MDR pneumonia. We randomly divided these patients into a training group (70%, N = 307) and a validation group (30%, N = 131). The nomogram consisted of only six predictive factors (creatinine clearance rate (CCR)≥130 mL/min/1.73 m, the Day 1 neutrophil-to-lymphocyte ratio (NLR), albumin≤30 g/L, hemoglobin, combination of antibacterial drugs, and tracheostomy), which demonstrated significantly higher sensitivity and specificity in the early identification of MDR pneumonia (AUC of the training group = 0.816 (95% CI: 0.760-0.862), AUC of the validation group = 0.797 (95% CI: 0.720-0.874)) and good calibration. DCA confirmed the clinical applicability of this nomogram.
We propose for the first time that augmented renal clearance (ARC) is an independent risk factor for the occurrence of MDR pneumonia in neurosurgical patients. Moreover, we successfully established a convenient prediction model that consists of six prediction factors, which can assist neurosurgeons in making early predictions of the incidence of MDR pneumonia.
本研究旨在基于神经外科病房患者的数据构建并评估一种列线图，以预测多重耐药（MDR）肺炎发生的概率。
我们回顾性收集了患者35个变量的临床数据、早期实验室检查结果和医生处方。采用单因素和逐步回归分析筛选变量以确定预测因素，并根据逻辑回归模型的结果在训练组中构建列线图。使用验证组，基于受试者操作特征曲线、校准曲线和决策曲线分析（DCA）评估区分度、校准度和临床适用性。
在2021年1月1日至2024年9月30日入住神经外科病房的3397例患者中，438例发生肺部感染，其中208例为MDR肺炎，230例为非MDR肺炎。我们将这些患者随机分为训练组（70%，N = 307）和验证组（30%，N = 131）。该列线图仅由六个预测因素组成（肌酐清除率（CCR）≥130 mL/min/1.73 m²、第1天中性粒细胞与淋巴细胞比值（NLR）、白蛋白≤30 g/L、血红蛋白、抗菌药物联合使用以及气管切开术），在MDR肺炎的早期识别中显示出显著更高的敏感性和特异性（训练组AUC = 0.816（95% CI：0.760 - 0.862），验证组AUC = 0.797（95% CI：0.720 - 0.874））以及良好的校准度。DCA证实了该列线图的临床适用性。
我们首次提出增强肾清除率（ARC）是神经外科患者发生MDR肺炎的独立危险因素。此外，我们成功建立了一个由六个预测因素组成的便捷预测模型，可协助神经外科医生对MDR肺炎的发生率进行早期预测。
