Du Chunjing, Zhang Hua, Zhang Yi, Zhang Hanwen, Zheng Jiajia, Liu Chao, Lu Fengmin, Shen Ning
Department of Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, 100191, People's Republic of China.
Department of Critical Care Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, People's Republic of China.
Infect Drug Resist. 2025 Mar 1;18:1237-1251. doi: 10.2147/IDR.S500523. eCollection 2025.
infections pose a significant threat to public health with high morbidity and mortality rates. The early identification of risk factors for mortality and accurate prognostic evaluation are important. Therefore, we aimed to identify the risk factors for mortality in patients with infections and develop a nomogram model for prognosis.
Patients diagnosed with infection were recruited from the intensive care unit of Peking University Third Hospital. The enrolled patients were categorized into survivor and non-survivor groups. Univariate and multivariate regression analyses were performed to identify independent risk factors for 30-day mortality, and a nomogram was constructed and validated.
A total of 408 patients infected with at different sites were included in this study. PO, lactate, respiratory failure, urinary tract infection, heart rate, 24h-urineoutput, neutrophil count, alkaline phosphatase, and vasoactive drug use were significant risk factors and were integrated into a nomogram to predict the risk of 7-day, 14-day, 21-day, and 28-day mortality. The nomogram demonstrated superior prognostic ability, achieving higher area under the receiver operating characteristic curve (AUC) (>0.8) and concordance index (C-index) (>0.8) values than the Pitt bacteremia, sequential organ failure assessment (SOFA), and acute physiology and chronic health evaluation (APACHE) II scores (all AUC and C-index < 0.75). Cross-validation of the nomogram confirmed its consistent performance, with both AUC and C-index values exceeding 0.75. The nomogram demonstrated a strong Hosmer-Leme-show goodness-of-fit and good calibration (p > 0.05). Additionally, decision curve analysis revealed that the nomogram provided significant clinical utility for prognostic prediction.
The 30-day mortality risk factors for infections were identified, and a predictive nomogram model was developed. The nomogram demonstrated good accuracy and predictive efficiency, providing a practical tool for short-term risk assessment and potentially improving clinical outcomes by providing early intervention and personalized patient management.
感染对公众健康构成重大威胁，发病率和死亡率很高。早期识别死亡风险因素并进行准确的预后评估很重要。因此，我们旨在确定感染患者的死亡风险因素，并建立一个用于预后的列线图模型。
从北京大学第三医院重症监护病房招募诊断为感染的患者。将纳入的患者分为存活组和非存活组。进行单因素和多因素回归分析以确定30天死亡率的独立风险因素，并构建和验证列线图。
本研究共纳入408例不同部位感染的患者。血乳酸水平、呼吸衰竭、尿路感染、心率、24小时尿量、中性粒细胞计数、碱性磷酸酶和血管活性药物的使用是显著的风险因素，并被纳入列线图以预测7天、14天、21天和28天死亡率的风险。该列线图显示出卓越的预后能力，与皮特菌血症、序贯器官衰竭评估（SOFA）和急性生理与慢性健康评估（APACHE）II评分相比，其受试者操作特征曲线下面积（AUC）（>0.8）和一致性指数（C指数）（>0.8）更高（所有AUC和C指数均<0.75）。列线图的交叉验证证实了其稳定的性能，AUC和C指数值均超过0.75。该列线图显示出良好的Hosmer-Leme-show拟合优度和校准（p>0.05）。此外，决策曲线分析表明，该列线图在预后预测方面具有显著的临床实用性。
确定了感染的30天死亡风险因素，并建立了预测列线图模型。该列线图显示出良好的准确性和预测效率，为短期风险评估提供了一个实用工具，并可能通过提供早期干预和个性化的患者管理来改善临床结果。
