Zhang Lei, Li Xiaojun, Cai Donghao, Mei Chuangchuang, Lu Lu
Department of Quality Control, Guangdong Provincial Second Hospital of Traditional Chinese Medicine, Guangzhou, Guangdong, 510095, People's Republic of China.
Guangdong Provincial Key Laboratory of Research and Development in Traditional Chinese Medicine Guangzhou, Guangdong, 510095, People's Republic of China.
Infect Drug Resist. 2025 Jul 25;18:3677-3687. doi: 10.2147/IDR.S529528. eCollection 2025.
To evaluate the impact of secondary bloodstream infections (BSI) on healthcare quality indicators in patients with cerebral infarction, and to develop a validated predictive model.
This study conducted a retrospective analysis of 7,698 distinct patients with cerebral infarction (2023) from a tertiary hospital in Guangzhou. Patients were categorized into two groups: BSI-negative (n=7,573) and BSI-positive (n=125). Healthcare quality indicators were compared using Mann-Whitney -test. A predictive model was created using Least Absolute Shrinkage and Selection Operator (LASSO) regression, based on a 7:3 training-validation split. The model's performance was validated through the area under the Receiver Operating Characteristic curve (AUC), calibration curves, and decision curve analysis (DCA).
Patients with BSI had significantly prolonged hospital stays (median of 17 days versus 11 days, p<0.001), higher costs (median of 34,859 yuan compared to 16,921 yuan, p<0.001), and increased adverse outcomes (34.4% versus 1.6%, p<0.001). The LASSO analysis identified four predictors: The following variables were found to have a statistically significant relationship to the occurrence of the primary complication: peripherally inserted central venous catheters (PICC) (odds ratio [OR] = 2.791, 95% confidence interval [CI] =1.514-5.148), use of ventilators(VA) (OR = 2.771, 95% CI=1.410-5.443), Indwelling urinary catheters(CAU) (OR = 1.800, 95% CI= 0.990-3.276), and hypoalbuminemia (OR = 3.643, 95% CI=2.195-6.046).The nomogram demonstrated an AUC of 0.789 in the training set and 0.778 in the test set, indicating a satisfactory model fit across data sets. Good model fit based on Hosmer-Lemeshow-values(Hosmer-Lemeshow=0.338/0.170).DCA indicated a net clinical benefit at risk thresholds of 0-15%.
Secondary BSI in patients with cerebral infarction can seriously affect the quality of medical care.The developed nomogram functions as a pragmatic instrument for the preliminary identification of patients at high risk. It facilitates the implementation of targeted interventions, thereby reducing the incidence of BSI and enhancing patient outcomes.
评估继发性血流感染（BSI）对脑梗死患者医疗质量指标的影响，并建立一个经过验证的预测模型。
本研究对广州一家三级医院7698例不同的脑梗死患者（2023年）进行了回顾性分析。患者分为两组：BSI阴性组（n = 7573）和BSI阳性组（n = 125）。使用曼-惠特尼检验比较医疗质量指标。基于7:3的训练-验证分割，使用最小绝对收缩和选择算子（LASSO）回归创建预测模型。通过受试者操作特征曲线（AUC）下的面积、校准曲线和决策曲线分析（DCA）验证模型的性能。
BSI患者的住院时间显著延长（中位数为17天对11天，p < 0.001），费用更高（中位数为34859元，而16921元，p < 0.001），不良结局增加（34.4%对1.6%，p < 0.001）。LASSO分析确定了四个预测因素：发现以下变量与原发性并发症的发生有统计学显著关系：经外周静脉穿刺中心静脉置管（PICC）（比值比[OR] = 2.791，95%置信区间[CI] = 1.514 - 5.148）、使用呼吸机（VA）（OR = 2.771，95% CI = 1.410 - 5.443）、留置导尿管（CAU）（OR = 1.800，95% CI = 0.990 - 3.276）和低蛋白血症（OR = 3.643，95% CI = 2.195 - 6.046）。列线图在训练集中的AUC为0.789，在测试集中为0.778，表明跨数据集的模型拟合良好。基于Hosmer-Lemeshow值的模型拟合良好（Hosmer-Lemeshow = 0.338/0.170）。DCA表明在0 - 15%的风险阈值下有净临床益处。
脑梗死患者的继发性BSI会严重影响医疗质量。所开发的列线图是一种实用工具，可用于初步识别高危患者。它有助于实施有针对性的干预措施，从而降低BSI的发生率并改善患者结局。
