Nomogram to predict prognosis in patients with posterior circulation acute ischemic stroke after mechanical...

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.

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.

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.

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.