PDF(Pubmed)
Abstract：
UNASSIGNED: Large Hemispheric Infarction (LHI) poses significant mortality and morbidity risks, necessitating predictive models for in-hospital mortality. Previous studies have explored LHI progression to malignant cerebral edema (MCE) but have not comprehensively addressed in-hospital mortality risk, especially in non-decompressive hemicraniectomy (DHC) patients.
UNASSIGNED: Demographic, clinical, risk factor, and laboratory data were gathered. The population was randomly divided into Development and Validation Groups at a 3:1 ratio, with no statistically significant differences observed. Variable selection utilized the Bonferroni-corrected Boruta technique (p < 0.01). Logistic Regression retained essential variables, leading to the development of a nomogram. ROC and DCA curves were generated, and calibration was conducted based on the Validation Group.
UNASSIGNED: This study included 314 patients with acute anterior-circulating LHI, with 29.6% in the Death group (n = 93). Significant variables, including Glasgow Coma Score, Collateral Score, NLR, Ventilation, Non-MCA territorial involvement, and Midline Shift, were identified through the Boruta algorithm. The final Logistic Regression model led to a nomogram creation, exhibiting excellent discriminative capacity. Calibration curves in the Validation Group showed a high degree of conformity with actual observations. DCA curve analysis indicated substantial clinical net benefit within the 5 to 85% threshold range.
UNASSIGNED: We have utilized NIHSS score, Collateral Score, NLR, mechanical ventilation, non-MCA territorial involvement, and midline shift to develop a highly accurate, user-friendly nomogram for predicting in-hospital mortality in LHI patients. This nomogram serves as valuable reference material for future studies on LHI patient prognosis and mortality prevention, while addressing previous research limitations.
UNASSIGNED: Demographic, clinical, risk factor, and laboratory data were gathered. The population was randomly divided into Development and Validation Groups at a 3:1 ratio, with no statistically significant differences observed. Variable selection utilized the Bonferroni-corrected Boruta technique (p < 0.01). Logistic Regression retained essential variables, leading to the development of a nomogram. ROC and DCA curves were generated, and calibration was conducted based on the Validation Group.
UNASSIGNED: This study included 314 patients with acute anterior-circulating LHI, with 29.6% in the Death group (n = 93). Significant variables, including Glasgow Coma Score, Collateral Score, NLR, Ventilation, Non-MCA territorial involvement, and Midline Shift, were identified through the Boruta algorithm. The final Logistic Regression model led to a nomogram creation, exhibiting excellent discriminative capacity. Calibration curves in the Validation Group showed a high degree of conformity with actual observations. DCA curve analysis indicated substantial clinical net benefit within the 5 to 85% threshold range.
UNASSIGNED: We have utilized NIHSS score, Collateral Score, NLR, mechanical ventilation, non-MCA territorial involvement, and midline shift to develop a highly accurate, user-friendly nomogram for predicting in-hospital mortality in LHI patients. This nomogram serves as valuable reference material for future studies on LHI patient prognosis and mortality prevention, while addressing previous research limitations.
摘要：
■大半球梗塞（LHI）具有显著的死亡率和发病率风险，需要住院死亡率的预测模型。以前的研究已经探索了LHI进展为恶性脑水肿（MCE），但没有全面解决院内死亡风险，尤其是在非减压性半切除术（DHC）患者中。
■人口统计学，临床,危险因素，并收集了实验室数据。人口按3:1的比例随机分为开发和验证组，没有观察到统计学上的显著差异。变量选择利用Bonferroni校正的Boruta技术(p<0.01)。Logistic回归保留了基本变量，导致列线图的发展。产生ROC和DCA曲线,并根据验证组进行校准。
■这项研究包括314例急性前循环LHI患者，死亡组（n=93）为29.6%。重要变量，包括格拉斯哥昏迷评分,附带评分,NLR,通风,非MCA领土参与，和中线移位，是通过Boruta算法识别的。最终的Logistic回归模型导致了列线图的创建，表现出优异的辨别能力。验证组中的校准曲线显示出与实际观察的高度一致性。DCA曲线分析表明，在5％至85％的阈值范围内具有实质性的临床净收益。
■我们利用了NIHSS评分，附带评分,NLR,机械通气,非MCA领土参与，和中线移位以开发高度精确的，用于预测LHI患者住院死亡率的用户友好列线图。此列线图为LHI患者预后和死亡率预防的未来研究提供了有价值的参考材料。同时解决了以前研究的局限性。
■人口统计学，临床,危险因素，并收集了实验室数据。人口按3:1的比例随机分为开发和验证组，没有观察到统计学上的显著差异。变量选择利用Bonferroni校正的Boruta技术(p<0.01)。Logistic回归保留了基本变量，导致列线图的发展。产生ROC和DCA曲线,并根据验证组进行校准。
■这项研究包括314例急性前循环LHI患者，死亡组（n=93）为29.6%。重要变量，包括格拉斯哥昏迷评分,附带评分,NLR,通风,非MCA领土参与，和中线移位，是通过Boruta算法识别的。最终的Logistic回归模型导致了列线图的创建，表现出优异的辨别能力。验证组中的校准曲线显示出与实际观察的高度一致性。DCA曲线分析表明，在5％至85％的阈值范围内具有实质性的临床净收益。
■我们利用了NIHSS评分，附带评分,NLR,机械通气,非MCA领土参与，和中线移位以开发高度精确的，用于预测LHI患者住院死亡率的用户友好列线图。此列线图为LHI患者预后和死亡率预防的未来研究提供了有价值的参考材料。同时解决了以前研究的局限性。
