Shen Yongshi, Lin Kangni, Yang Liuxin, Zheng Peng, Zhang Wei, Weng Jinsen, Ye Yong
Department of Intensive Care Unit, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China.
Department of Service Center, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China.
BMC Cancer. 2025 Apr 8;25(1):637. doi: 10.1186/s12885-025-14013-2.
Sepsis remains a leading cause of mortality in critically ill patients, particularly those with malignancies who face heightened risks due to immunosuppression and metabolic dysregulation. This study aimed to evaluate the prognostic value of the lactate dehydrogenase-to-albumin ratio (LDAR) for predicting 28-day ICU mortality in septic patients with malignancies.
A retrospective cohort analysis was conducted using data from 1,635 septic patients with malignancies in the MIMIC-IV (3.1) database. Participants were stratified into quartiles based on LDAR values. The primary outcome was 28-day ICU mortality, with secondary outcomes including in-hospital and ICU mortality. Multivariable logistic regression, restricted cubic spline (RCS) analysis, and machine learning models were employed to assess associations between LDAR and outcomes. Subgroup analyses and feature importance evaluations were performed to validate robustness. The Shapley additive explanations method was used to enhance model interpretability and assess individual predictor contributions.
Higher LDAR is independently associated with increased 28-day ICU mortality (OR: 3.441, 95% CI: 2.497-4.741), ICU mortality (OR: 3.478, 95% CI: 2.396-5.049), and in-hospital mortality (OR: 3.747, 95% CI: 2.688-5.222), even after adjustment, highlighting its potential as a prognostic marker in ICU patients. RCS analysis revealed a nonlinear relationship, with mortality risk escalating sharply beyond log₂(LDAR) = 6.940. Metastatic cancer patients had higher median LDAR (135.0 vs. 118.5, P = 0.004) and mortality rates (52.0% vs. 36.4%, P < 0.001). Boruta feature selection showed that LDAR as the top predictor of mortality. Nine machine learning model with 20 variables were built, with random forest model performing best, achieving an AUC of 0.751 (0.708-0.794) in validation and 0.727 (0.682- 0.772) in text cohort.
LDAR is a robust, independent prognostic biomarker for 28-day ICU mortality in septic patients with malignancies, outperforming traditional scoring systems. The identified threshold (log₂(LDAR) ≥ 6.940) may aid early risk stratification and clinical decision-making. Prospective studies are warranted to validate these findings and explore dynamic LDAR monitoring in diverse populations.
脓毒症仍然是重症患者死亡的主要原因,尤其是那些患有恶性肿瘤的患者,由于免疫抑制和代谢失调,他们面临更高的风险。本研究旨在评估乳酸脱氢酶与白蛋白比值(LDAR)对预测恶性肿瘤脓毒症患者28天ICU死亡率的预后价值。
使用MIMIC-IV(3.1)数据库中1635例恶性肿瘤脓毒症患者的数据进行回顾性队列分析。参与者根据LDAR值分为四分位数。主要结局是28天ICU死亡率,次要结局包括住院和ICU死亡率。采用多变量逻辑回归、受限立方样条(RCS)分析和机器学习模型来评估LDAR与结局之间的关联。进行亚组分析和特征重要性评估以验证稳健性。使用Shapley加法解释方法来增强模型的可解释性并评估个体预测因子的贡献。
即使在调整后,较高的LDAR也与28天ICU死亡率增加(OR:3.441,95%CI:2.497 - 4.741)、ICU死亡率增加(OR:3.478,95%CI:2.396 - 5.049)和住院死亡率增加(OR:3.747,95%CI:2.688 - 5.222)独立相关,突出了其作为ICU患者预后标志物的潜力。RCS分析显示存在非线性关系,当log₂(LDAR) > 6.940时,死亡风险急剧上升。转移性癌症患者的LDAR中位数较高(135.0对118.5,P = 0.004),死亡率也较高(52.0%对36.4%,P < 0.001)。Boruta特征选择表明LDAR是死亡率的最佳预测因子。构建了9个包含20个变量的机器学习模型,随机森林模型表现最佳,在验证中AUC为0.751(0.708 - 0.794),在文本队列中为0.727(0.682 - 0.772)。
LDAR是恶性肿瘤脓毒症患者28天ICU死亡率的强大独立预后生物标志物,优于传统评分系统。确定的阈值(log₂(LDAR)≥6.940)可能有助于早期风险分层和临床决策。有必要进行前瞻性研究来验证这些发现,并探索在不同人群中动态监测LDAR。