National Key Clinical Specialty Construction Project/Department of Neonatology, Guangdong Women and Children Hospital, Guangzhou, China.
Guangdong Neonatal ICU Medical Quality Control Center, Guangzhou, China.
Epidemiol Infect. 2023 Jul 31;151:e137. doi: 10.1017/S0950268823001231.
Routine blood examination is an easy way to examine infectious diseases. This study is aimed to develop a model to diagnose serious bacterial infections (SBI) in ICU neonates based on routine blood parameters. This was a cross-sectional study, and data were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III). SBI was defined as suffering from one of the following: pyelonephritis, bacteraemia, bacterial meningitis, sepsis, pneumonia, cellulitis, and osteomyelitis. Variables with statistical significance in the univariate logistic regression analysis and log systemic immune-inflammatory index (SII) were used to develop the model. The area under the curve (AUC) was calculated to assess the performance of the model. A total of 1,880 participants were finally included for analysis. Weight, haemoglobin, mean corpuscular volume, white blood cell, monocyte, premature delivery, and log SII were selected to develop the model. The developed model showed a good performance to diagnose SBI for ICU neonates, with an AUC of 0.812 (95% confidence interval (CI): 0.737-0.888). A nomogram was developed to make this model visualise. In conclusion, our model based on routine blood parameters performed well in the diagnosis of neonatal SBI, which may be helpful for clinicians to improve treatment recommendations.
常规血液检查是检查传染病的一种简便方法。本研究旨在基于常规血液参数开发一种用于诊断 ICU 新生儿严重细菌感染（SBI）的模型。这是一项横断面研究，数据从医疗信息共享知识库 III（MIMIC-III）中提取。SBI 的定义为患有以下疾病之一：肾盂肾炎、菌血症、细菌性脑膜炎、败血症、肺炎、蜂窝织炎和骨髓炎。单变量逻辑回归分析和对数系统免疫炎症指数（SII）中具有统计学意义的变量用于开发模型。计算曲线下面积（AUC）以评估模型的性能。最终共纳入 1880 名参与者进行分析。体重、血红蛋白、平均红细胞体积、白细胞、单核细胞、早产和 log SII 被选入模型。开发的模型显示出良好的性能，用于诊断 ICU 新生儿 SBI，AUC 为 0.812（95%置信区间（CI）：0.737-0.888）。开发了一个列线图使该模型可视化。总之，我们基于常规血液参数的模型在诊断新生儿 SBI 方面表现良好，这可能有助于临床医生改善治疗建议。
