PDF(Pubmed)
Abstract：
UNASSIGNED: Aneurysmal subarachnoid hemorrhage (aSAH) patients typically have poor prognoses. The anion gap (AG) has been proven to correlate with mortality in various critically ill patients. However, hypoalbuminemia can lead to underestimations of the true anion gap levels. This study was conducted to verify the prognostic value of single AG and albumin-corrected anion gap (ACAG) among aSAH patients.
UNASSIGNED: Significant factors in the univariate logistic regression analysis were included in the multivariate logistic regression analysis to explore the risk factors for mortality in aSAH patients and to confirm the independent relationship between ACAG and mortality. The restricted cubic spline (RCS) was used to visually show the relationship between ACAG level and mortality risk of aSAH patients. The predictive model for mortality was developed by incorporating significant factors into the multivariate logistic regression analysis. The prognostic value of ACAG and the developed model was evaluated by calculating the area under the receiver operating characteristics curve (AUC).
UNASSIGNED: Among 710 aSAH patients, a 30-day mortality was observed in 20.3% of the cases. A positive relationship was demonstrated between the ACAG level and mortality in aSAH patients using the RCS curve. The multivariate logistic regression analysis helped discover that only six factors were finally and independently related to mortality of aSAH patients after adjusting for confounding effects, including the Hunt-Hess scale score (p = 0.006), surgical options (p < 0.001), white blood cell count (p < 0.001), serum chloride levels (p = 0.023), ACAG (p = 0.039), and delayed cerebral ischemia (p < 0.001). The AUC values for the AG, albumin, and ACAG in predicting mortality among aSAH patients were 0.606, 0.536, and 0.617, respectively. A logistic regression model, which includes the Hunt-Hess scale score, surgical options, white blood cell count, serum chloride levels, ACAG, and delayed cerebral ischemia, achieved an AUC of 0.911 for predicting mortality.
UNASSIGNED: The ACAG is an effective prognostic marker for aSAH patients. A prognostic model incorporating ACAG could help clinicians evaluate the risk of poor outcomes among aSAH patients, thereby facilitating the development of personalized therapeutic strategies.
UNASSIGNED: Significant factors in the univariate logistic regression analysis were included in the multivariate logistic regression analysis to explore the risk factors for mortality in aSAH patients and to confirm the independent relationship between ACAG and mortality. The restricted cubic spline (RCS) was used to visually show the relationship between ACAG level and mortality risk of aSAH patients. The predictive model for mortality was developed by incorporating significant factors into the multivariate logistic regression analysis. The prognostic value of ACAG and the developed model was evaluated by calculating the area under the receiver operating characteristics curve (AUC).
UNASSIGNED: Among 710 aSAH patients, a 30-day mortality was observed in 20.3% of the cases. A positive relationship was demonstrated between the ACAG level and mortality in aSAH patients using the RCS curve. The multivariate logistic regression analysis helped discover that only six factors were finally and independently related to mortality of aSAH patients after adjusting for confounding effects, including the Hunt-Hess scale score (p = 0.006), surgical options (p < 0.001), white blood cell count (p < 0.001), serum chloride levels (p = 0.023), ACAG (p = 0.039), and delayed cerebral ischemia (p < 0.001). The AUC values for the AG, albumin, and ACAG in predicting mortality among aSAH patients were 0.606, 0.536, and 0.617, respectively. A logistic regression model, which includes the Hunt-Hess scale score, surgical options, white blood cell count, serum chloride levels, ACAG, and delayed cerebral ischemia, achieved an AUC of 0.911 for predicting mortality.
UNASSIGNED: The ACAG is an effective prognostic marker for aSAH patients. A prognostic model incorporating ACAG could help clinicians evaluate the risk of poor outcomes among aSAH patients, thereby facilitating the development of personalized therapeutic strategies.
摘要：
■动脉瘤性蛛网膜下腔出血(aSAH)患者通常预后较差。阴离子间隙（AG）已被证明与各种危重患者的死亡率相关。然而,低蛋白血症会导致对真实阴离子间隙水平的低估。本研究旨在验证单个AG和白蛋白校正阴离子间隙（ACAG）在aSAH患者中的预后价值。
■将单因素logistic回归分析中的重要因素纳入多因素logistic回归分析，以探索aSAH患者死亡的危险因素，并确认ACAG与死亡率之间的独立关系。限制性三次样条（RCS）用于直观显示ACAG水平与aSAH患者死亡风险之间的关系。通过将重要因素纳入多变量逻辑回归分析，建立了死亡率预测模型。通过计算受试者工作特征曲线下面积（AUC）来评估ACAG和开发模型的预后价值。
■在710名aSAH患者中，在20.3%的病例中观察到30天死亡率.使用RCS曲线证明aSAH患者的ACAG水平与死亡率之间存在正相关。多因素logistic回归分析有助于发现，在校正混杂效应后，只有六个因素与aSAH患者的死亡率最终且独立相关。包括亨特-赫斯量表评分(p=0.006)，手术选择（p<0.001），白细胞计数（p<0.001），血清氯化物水平（p=0.023），ACAG(p=0.039),和迟发性脑缺血(p<0.001)。AG的AUC值，白蛋白,和ACAG在预测aSAH患者死亡率方面分别为0.606,0.536和0.617.逻辑回归模型，其中包括亨特-赫斯量表得分，手术选择,白细胞计数,血清氯化物水平，ACAG,和迟发性脑缺血,预测死亡率的AUC为0.911。
■ACAG是aSAH患者的有效预后标志物。纳入ACAG的预后模型可以帮助临床医生评估aSAH患者预后不良的风险。从而促进个性化治疗策略的制定。
■将单因素logistic回归分析中的重要因素纳入多因素logistic回归分析，以探索aSAH患者死亡的危险因素，并确认ACAG与死亡率之间的独立关系。限制性三次样条（RCS）用于直观显示ACAG水平与aSAH患者死亡风险之间的关系。通过将重要因素纳入多变量逻辑回归分析，建立了死亡率预测模型。通过计算受试者工作特征曲线下面积（AUC）来评估ACAG和开发模型的预后价值。
■在710名aSAH患者中，在20.3%的病例中观察到30天死亡率.使用RCS曲线证明aSAH患者的ACAG水平与死亡率之间存在正相关。多因素logistic回归分析有助于发现，在校正混杂效应后，只有六个因素与aSAH患者的死亡率最终且独立相关。包括亨特-赫斯量表评分(p=0.006)，手术选择（p<0.001），白细胞计数（p<0.001），血清氯化物水平（p=0.023），ACAG(p=0.039),和迟发性脑缺血(p<0.001)。AG的AUC值，白蛋白,和ACAG在预测aSAH患者死亡率方面分别为0.606,0.536和0.617.逻辑回归模型，其中包括亨特-赫斯量表得分，手术选择,白细胞计数,血清氯化物水平，ACAG,和迟发性脑缺血,预测死亡率的AUC为0.911。
■ACAG是aSAH患者的有效预后标志物。纳入ACAG的预后模型可以帮助临床医生评估aSAH患者预后不良的风险。从而促进个性化治疗策略的制定。
