{"title":"脓毒症恶性肿瘤患者乳酸脱氢酶与白蛋白比值与28天死亡率之间的关系:MIMIC-IV数据库的分析","authors":"Yongshi Shen, Kangni Lin, Liuxin Yang, Peng Zheng, Wei Zhang, Jinsen Weng, Yong Ye","doi":"10.1186/s12885-025-14013-2","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":9131,"journal":{"name":"BMC Cancer","volume":"25 1","pages":"637"},"PeriodicalIF":3.4000,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Association between the lactate dehydrogenase-to-albumin ratio and 28-day mortality in septic patients with malignancies: analysis of the MIMIC-IV database.\",\"authors\":\"Yongshi Shen, Kangni Lin, Liuxin Yang, Peng Zheng, Wei Zhang, Jinsen Weng, Yong Ye\",\"doi\":\"10.1186/s12885-025-14013-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>\",\"PeriodicalId\":9131,\"journal\":{\"name\":\"BMC Cancer\",\"volume\":\"25 1\",\"pages\":\"637\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2025-04-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMC Cancer\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s12885-025-14013-2\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Cancer","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12885-025-14013-2","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
背景:脓毒症仍然是危重患者死亡的主要原因,特别是那些因免疫抑制和代谢失调而面临高风险的恶性肿瘤患者。本研究旨在评估乳酸脱氢酶与白蛋白比值(LDAR)对脓毒症合并恶性肿瘤患者28天ICU死亡率的预测价值。方法:对MIMIC-IV(3.1)数据库中1635例脓毒性恶性肿瘤患者的数据进行回顾性队列分析。根据LDAR值将参与者分为四分位数。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、受限三次样条(RCS)分析和机器学习模型来评估LDAR与预后之间的关系。进行亚组分析和特征重要性评估以验证稳健性。Shapley加性解释法用于提高模型的可解释性和评估个体预测因子的贡献。结果:即使经过调整,较高的LDAR与28天ICU死亡率(OR: 3.441, 95% CI: 2.497-4.741)、ICU死亡率(OR: 3.478, 95% CI: 2.396-5.049)和住院死亡率(OR: 3.747, 95% CI: 2.688-5.222)的增加独立相关,突出了其作为ICU患者预后指标的潜力。RCS分析显示出非线性关系,死亡率风险急剧上升,超过log2 (LDAR) = 6.940。转移性癌症患者的中位LDAR (135.0 vs. 118.5, P = 0.004)和死亡率更高(52.0% vs. 36.4%, P)。结论:LDAR是评估脓毒症合并恶性肿瘤患者28天ICU死亡率的一个强大、独立的预后生物标志物,优于传统评分系统。确定的阈值(log 2 (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,并在不同人群中探索动态LDAR监测。
Association between the lactate dehydrogenase-to-albumin ratio and 28-day mortality in septic patients with malignancies: analysis of the MIMIC-IV database.
Background: 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.
Methods: 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.
Results: 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.
Conclusions: 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.
期刊介绍:
BMC Cancer is an open access, peer-reviewed journal that considers articles on all aspects of cancer research, including the pathophysiology, prevention, diagnosis and treatment of cancers. The journal welcomes submissions concerning molecular and cellular biology, genetics, epidemiology, and clinical trials.