■本研究旨在探讨大血管闭塞后循环急性缺血性卒中(PC-AIS)患者机械取栓(MT)后预后及出血性转化的危险因素。我们试图开发一个列线图来预测PC-AIS患者的不良预后和症状性脑出血(sICH)的风险。
■对81例接受MT治疗的PC-AIS患者进行回顾性分析。我们收集患者的临床信息,根据CT结果和美国国立卫生研究院卒中量表(NIHSS)评分评估sICH和预后。随后,他们被随访了3个月,并使用改良的Rankin量表评估其预后。我们使用最小绝对收缩和选择算子(LASSO)和多变量逻辑回归来确定影响预后的因素以构建列线图。通过受试者工作特性曲线评估列线图的性能,校正曲线,决策曲线分析,和临床影响曲线。
■在81例PC-AIS患者中,33人预后良好,48人预后不良,19与sICH一起提交,和62没有出现sICH。LASSO回归的结果表明,变量,包括HPT,基线NIHSS评分,SBP峰值,SBPCV,SBPSD,SBP峰值,DBPCV,HbA1c,和BGSD,是患者预后的预测因子。变量如AF、SBP峰值,峰值DBP预测sICH的风险。多因素logistic回归分析显示基线NIHSS评分(OR=1.115,95%CI1.002-1.184),峰值收缩压(OR=1.060,95%CI1.012-1.111),SBPCV(OR=1.296,95%CI1.036~1.621)和HbA1c(OR=3.139,95%CI1.491~6.609)是影响预后的独立危险因素。AF(OR=6.823,95%CI1.606-28.993),峰值收缩压(OR=1.058,95%CI1.013-1.105),和峰值DBP(OR=1.160,95%CI1.036-1.298)与sICH的风险相关。在接下来的步骤中,制定了列线图,表现出良好的歧视,校准,和临床适用性。
■我们构建了列线图来预测接受MT的PC-AIS患者的不良预后和sICH风险。该模型表现出良好的鉴别力,校准,和临床适用性。
UNASSIGNED: This study aimed to investigate the risk factors of prognosis and hemorrhagic transformation after mechanical thrombectomy (MT) in patients with posterior circulation acute ischemic stroke (PC-AIS) caused by large vessel occlusion. We sought to develop a nomogram for predicting the risk of poor prognosis and symptomatic intracerebral hemorrhage (sICH) in patients with PC-AIS.
UNASSIGNED: A retrospective analysis was conducted on 81 patients with PC-AIS who underwent MT treatment. We collected clinical information from the patients to assessed sICH and prognosis based on CT results and National Institutes of Health Stroke Scale (NIHSS) scores. Subsequently, they were followed up for 3 months, and their prognosis was assessed using the Modified Rankin Scale. We used the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression to determine the factors affecting prognosis to construct a nomogram. The nomogram\'s performance was assessed through receiver operating characteristic curves, calibration curves, decision curve analysis, and clinical impact curves.
UNASSIGNED: Among the 81 patients with PC-AIS, 33 had a good prognosis, 48 had a poor prognosis, 19 presented with sICH, and 62 did not present with sICH. The results of the LASSO regression indicated that variables, including HPT, baseline NIHSS score, peak SBP, SBP CV, SBP SD, peak SBP, DBP CV, HbA1c, and BG SD, were predictors of patient prognosis. Variables such as AF, peak SBP, and peak DBP predicted the risk of sICH. Multivariate logistic regression revealed that baseline NIHSS score (OR = 1.115, 95% CI 1.002-1.184), peak SBP (OR = 1.060, 95% CI 1.012-1.111), SBP CV (OR = 1.296, 95% CI 1.036-1.621) and HbA1c (OR = 3.139, 95% CI 1.491-6.609) were independent risk factors for prognosis. AF (OR = 6.823, 95% CI 1.606-28.993), peak SBP (OR = 1.058, 95% CI 1.013-1.105), and peak DBP (OR = 1.160, 95% CI 1.036-1.298) were associated with the risk of sICH. In the following step, nomograms were developed, demonstrating good discrimination, calibration, and clinical applicability.
UNASSIGNED: We constructed nomograms to predict poor prognosis and risk of sICH in patients with PC-AIS undergoing MT. The model exhibited good discrimination, calibration, and clinical applicability.