Nanjing Lishui District Hospital of Traditional Chinese Medicine, Department of Laboratory Medicine - Nanjing, China.
GanZhou Women and Children's Health Care Hospital, Department of Laboratory Medicine - Ganzhou, China.
Rev Assoc Med Bras (1992). 2024 Aug 16;70(7):e20231561. doi: 10.1590/1806-9282.20231561. eCollection 2024.
Neonatal sepsis is a serious disease that needs timely and immediate medical attention. So far, there is no specific prognostic biomarkers or model for dependable predict outcomes in neonatal sepsis. The aim of this study was to establish a predictive model based on readily available laboratory data to assess 30-day mortality in neonatal sepsis.
Neonates with sepsis were recruited between January 2019 and December 2022. The admission information was obtained from the medical record retrospectively. Univariate or multivariate analysis was utilized to identify independent risk factors. The receiver operating characteristic curve was drawn to check the performance of the predictive model.
A total of 195 patients were recruited. There was a big difference between the two groups in the levels of hemoglobin and prothrombin time. Multivariate analysis confirmed that hemoglobin>133 g/L (hazard ratio: 0.351, p=0.042) and prothrombin time >16.6 s (hazard ratio: 4.140, p=0.005) were independent risk markers of 30-day mortality. Based on these results, a predictive model with the highest area under the curve (0.756) was built.
We established a predictive model that can objectively and accurately predict individualized risk of 30-day mortality. The predictive model should help clinicians to improve individual treatment, make clinical decisions, and guide follow-up management strategies.
新生儿败血症是一种严重的疾病，需要及时和立即的医疗关注。到目前为止，还没有特定的预后生物标志物或模型可以可靠地预测新生儿败血症的结局。本研究旨在建立一个基于易于获得的实验室数据的预测模型，以评估新生儿败血症的 30 天死亡率。
2019 年 1 月至 2022 年 12 月期间，招募了患有败血症的新生儿。入院信息从病历中回顾性获得。利用单因素或多因素分析来确定独立的危险因素。绘制受试者工作特征曲线以检查预测模型的性能。
共招募了 195 名患者。两组在血红蛋白和凝血酶原时间水平上存在显著差异。多因素分析证实血红蛋白>133g/L（危险比：0.351，p=0.042）和凝血酶原时间>16.6s（危险比：4.140，p=0.005）是 30 天死亡率的独立危险因素。基于这些结果，建立了一个具有最高曲线下面积（0.756）的预测模型。
我们建立了一个可以客观准确地预测 30 天死亡率的个体化风险的预测模型。该预测模型应有助于临床医生改善个体治疗、做出临床决策并指导后续管理策略。
