Li Xue-Lin, Mi Te, Liu Cancan, Feng Mingchen
Department of Intensive Care Unit, Jining No. 1 People's Hospital, Shandong First Medical University, Jining, People's Republic of China.
Jining Critical Care Diagnosis and Treatment Center, Jining, People's Republic of China.
Int J Gen Med. 2025 Jun 10;18:3045-3062. doi: 10.2147/IJGM.S520290. eCollection 2025.
Septic shock is a high-mortality syndrome, particularly in patients aged 50 and older. Predicting mortality in this population is challenging due to clinical heterogeneity and limitations of traditional scoring systems like SOFA and APACHE II. This study aimed to develop and validate a predictive model using norepinephrine index (NEI)-a novel biomarker defined as the norepinephrine dose administered within the first 24 hours of ICU admission divided by BMI and 24 hours-and procalcitonin (PCT) to improve risk stratification and clinical decision-making.
A retrospective cohort of 94 patients aged ≥50 years with septic shock was analyzed. Key clinical variables within the first 24 hours were collected, and univariate and stepwise logistic regression identified predictors of 28-day mortality. The model's performance was evaluated with ROC curves, AUC, and confusion matrices, alongside internal validation through stratified analysis, bootstrap resampling, and training-test splits. External validation was conducted in an independent cohort of 57 patients.
The final model incorporating NEI and PCT achieved an AUC of 0.91, outperforming individual biomarkers (NEI: AUC = 0.86; PCT: AUC = 0.69). Nonlinear analysis identified NEI > 4mg· m² / (kg· 24h) and PCT < 50 ng/mL as critical thresholds for high mortality risk.
The NEI and PCT-based prognostic model provides a reliable tool for predicting 28-day mortality in septic shock patients aged 50 and above. However, as a single-center study with a relatively small sample size, the generalizability of these findings may be limited. Future multicenter studies with larger sample sizes are necessary to validate this model's applicability across populations. This model holds potential to optimize clinical management, enabling timely interventions such as more intensive hemodynamic support and infection control.
脓毒性休克是一种高死亡率的综合征，尤其是在50岁及以上的患者中。由于临床异质性以及像序贯器官衰竭评估（SOFA）和急性生理与慢性健康状况评分系统II（APACHE II）等传统评分系统的局限性，预测该人群的死亡率具有挑战性。本研究旨在开发并验证一种使用去甲肾上腺素指数（NEI）——一种新的生物标志物，定义为重症监护病房（ICU）入院后首24小时内给予的去甲肾上腺素剂量除以体重指数（BMI）与24小时的乘积——和降钙素原（PCT）的预测模型，以改善风险分层和临床决策。
对94例年龄≥50岁的脓毒性休克患者的回顾性队列进行分析。收集首24小时内的关键临床变量，单因素和逐步逻辑回归确定28天死亡率的预测因素。通过受试者工作特征（ROC）曲线、曲线下面积（AUC）和混淆矩阵评估模型性能，并通过分层分析、自助重采样和训练 - 测试分割进行内部验证。在一个由57例患者组成的独立队列中进行外部验证。
纳入NEI和PCT的最终模型的AUC为0.91，优于单个生物标志物（NEI：AUC = 0.86；PCT：AUC = 0.69）。非线性分析确定NEI > 4mg·m² /（kg·24h）和PCT < 50 ng/mL为高死亡风险的关键阈值。
基于NEI和PCT的预后模型为预测50岁及以上脓毒性休克患者的28天死亡率提供了一种可靠工具。然而，作为一项样本量相对较小的单中心研究，这些发现的可推广性可能有限。未来需要开展更大样本量的多中心研究来验证该模型在不同人群中的适用性。该模型具有优化临床管理的潜力，能够实现诸如更强化的血流动力学支持和感染控制等及时干预。
