预测患有脓毒症和 2 型糖尿病的重症监护室老年患者 28 天死亡率的风险因素分析和提名图开发

Pub Date : 2024-09-10 DOI:10.1177/1721727x241282483
Haopeng Li, Yaru Zu, Qinghua Wang, Tong Zi, Xin Qin, Yan Zhao, Wei Jiang, Xin’an Wang, Chengdang Xu, Xi Chen, Gang Wu
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摘要

背景:2 型糖尿病(T2DM)是败血症的重要诱因,与单纯败血症患者相比,同时患有这两种疾病的患者病情更严重,死亡率更高。重症监护室(ICU)中的老年人尤其容易出现这些合并症。为了准确评估脓毒症合并 T2DM 老年患者的预后并指导治疗,我们开发了一个提名图预测模型。研究方法分析了重症监护医学信息市场IV(MIMIC-IV)数据库中1489名脓毒症合并T2DM患者的数据,并将其分为28天生存组(1156人)和28天死亡组(333人)。利用数据集的临床特征创建了一个预测患有脓毒症和 T2DM 的老年 ICU 患者 28 天死亡率的提名图。最小绝对收缩和选择算子(LASSO)回归确定了候选预测因子,随后进行了多变量逻辑回归分析,将 p < .05 的变量纳入最终模型。然后利用这些重要的风险预测因子构建了一个提名图。通过接收者操作曲线(ROC)和曲线下面积(AUC)对模型的判别能力进行评估。此外,还通过校准图和 Hosmer-Lemeshow 拟合度检验(HL 检验)评估了模型的性能,并通过决策曲线分析(DCA)检验了临床实用性。结果纳入提名图的风险因素包括年龄、重症监护室住院时间、平均血压(MBP)、转移性实体瘤、序贯器官功能衰竭评估(SOFA)评分、逻辑器官功能障碍系统(LODS)评分、血尿素氮(BUN)和血管加压素的使用。该预测模型具有很强的识别能力,训练数据集的AUC为0.802(95% CI 0.768-0.835),验证集的AUC为0.753(95% CI 0.697-0.809)。HL 检验证实了校准结果(p > .05),DCA 显示了临床实用性。结论这一新的提名图是预测患有脓毒症和 T2DM 的 ICU 老年患者 28 天死亡率的实用工具。根据该模型优化治疗策略可提高这些患者的 28 天生存率。
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Risk factor analysis and nomogram development for predicting 28-day mortality in elderly ICU patients with sepsis and type 2 diabetes mellitus
Background: Type 2 diabetes mellitus (T2DM) significantly contributes to sepsis, with patients suffering from both conditions exhibiting greater severity and higher mortality rates compared to those with sepsis alone. Elderly individuals in the intensive care unit (ICU) are particularly prone to these comorbidities. A nomogram prediction model was developed to accurately assess prognosis and guide treatment for elderly patients with sepsis and T2DM. Methods: Data from 1489 patients with sepsis and T2DM in the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were analyzed and categorized into 28-days survival ( n = 1156) and 28-days death groups ( n = 333). The dataset’s clinical characteristics were employed to create a nomogram predicting 28-days mortality in elderly ICU patients with sepsis and T2DM. The least absolute shrinkage and selection operator (LASSO) regression identified candidate predictors, followed by a multivariate logistic regression analysis incorporating variables with p < .05 into the final model. A nomogram was then constructed using these significant risk predictors. The model’s discriminatory power was evaluated through a receiver operating curve (ROC) and the area under the curve (AUC). Additionally, model performance was assessed using a calibration plot and the Hosmer-Lemeshow goodness-of-fit test (HL test), and clinical utility was examined via decision curve analysis (DCA). Results: Risk factors incorporated into the nomogram included age, ICU length of stay, mean blood pressure (MBP), metastatic solid tumor, Sequential Organ Failure Assessment (SOFA) score, Logistic Organ Dysfunction System (LODS) score, blood urea nitrogen (BUN), and vasopressor use. The predictive model demonstrated robust discrimination, with an AUC of 0.802 (95% CI 0.768–0.835) in the training dataset and 0.753 (95% CI 0.697–0.809) in the validation set. Calibration was confirmed with the HL test ( p > .05), and DCA indicated clinical usefulness. Conclusion: This new nomogram serves as a practical tool for predicting 28-days mortality among elderly ICU patients with sepsis and T2DM. Optimizing treatment strategies based on this model could enhance 28-days survival rates for these patients.
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