Correlation analysis between cerebrovascular intervention therapy and perioperative blood pressure variability and cerebral blood flow reserve in elderly patients with intracranial artery stenosis

To explore the correlation between neurological function prognosis, perioperative blood pressure variability (BPV), and the change rate of cerebral blood flow velocity (CBFV) in elderly patients with intracranial arterial stenosis (ICAS) undergoing cerebrovascular intervention therapy, and to analyze the interaction between BPV and the change rate of CBFV in influencing prognosis. This study retrospectively included 116 elderly ICAS patients (aged ≥ 60 years) who received cerebrovascular intervention in our hospital from January 2022 to December 2024. Three months after surgery, the prognosis was evaluated using the modified Rankin Scale (mRS). These patients were divided into a good prognosis group (mRS score ≤ 2 points, 96 cases) and a poor prognosis group (mRS score > 2 points, 20 cases). Perioperative dynamic blood pressure monitoring data were analyzed, including systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP), and mean arterial pressure (MAP). CBFV was measured by transcranial Doppler ultrasound. Multivariate Logistic regression analysis was used to screen out the independent influencing factors for poor prognosis, and sensitivity analysis was conducted using CBFV as a continuous variable. At the same time, all BPV parameters and CBFV were included in a single combined model to evaluate their independent prognostic value. The combined effect of high BPV and decreased CBFV on prognosis was evaluated through stratified interaction analysis. Patients in the poor prognosis group had significantly higher NIHSS scores upon admission, and higher proportion of responsible vessel stenosis ≥ 90% than the good prognosis group (both P < 0.05). In terms of BPV, the SBPV, DBPV, PPV, and MAPV of patients with poor prognosis were much higher than those with good prognosis (all P < 0.05). The decrease in the change rate of CBFV in the poor prognosis group was 80.00%, which was higher than 36.46% in the good prognosis group ( P < 0.05). Perioperative increased SBPV (adjusted OR = 2.712, 95% CI: 1.251–5.369), increased DBPV (adjusted OR = 2.543, 95% CI: 1.198–5.397), increased PPV (adjusted OR = 2.351, 95% CI: 1.183–4.264), increased MAPV (adjusted OR = 2.287, 95% CI: 1.096–4.772), and decreased change rate of CBFV (adjusted OR = 3.126, 95% CI: 1.589–6.149) were all independent risk factors for poor prognosis. The internal validation of Bootstrap showed that the optimistic corrected C-statistic of each model ranged from 0.815 to 0.849, and the calibration slope was 0.91 to 0.95, indicating that the model had good discrimination and calibration. The calibration of the model was good (Hosmer-Lemeshow test P > 0.05), and the internal validation of Bootstrap suggested that the model’s discrimination and calibration were stable. The sensitivity analysis using CBFV as a continuous variable showed that for every 1% point decrease in CBFV, the risk of poor prognosis increased by 9.2% (corrected OR = 1.092, 95% CI: 1.041–1.145, P < 0.001). After including all BPV parameters and CBFV simultaneously in a single combined model, only SBPV (corrected OR = 1.982, 95% CI: 1.087–3.614, P = 0.025) and CBFV (corrected OR = 2.874, 95% CI: 1.412–5.848, P = 0.003) remained independently associated with poor prognosis. DBPV, PPV, and MAPV did not reach statistical significance due to strong collinearity with other BPV parameters (VIF 3.8–5.2). Further interaction analysis revealed a synergistic efficacy between BPV and the change rate of CBFV in influencing the prognosis. Compared with patients with normal change rate of CBFV and low BPV, patients with both decreased change rate of CBFV and high BPV had a significantly increased risk of poor prognosis by 8.77 times (95% CI: 3.25–23.67). BPV and CBFV function had significant synergistic negative effects. However, the results merely suggested that BPV and cerebral blood flow reserve function might be potential biomarkers for predicting poor prognosis and could serve as targets for future intervention studies. In clinical practice, the combined monitoring of BPV and cerebral blood flow reserve helped identify high-risk patients. However, whether implementing interventions based on these factors could improve prognosis still needs to be verified through prospective randomized controlled trials.