# BP-METROLOGY: non-invasive continuous blood pressure monitoring to predict haemorrhagic transformation after endovascular thrombectomy

## TL;DR

Continuous blood pressure monitoring in the first 8 hours after stroke treatment can better predict bleeding risks than standard methods.

## Contribution

The study shows continuous BP monitoring captures short-timescale variability linked to bleeding risks, which intermittent methods miss.

## Key findings

- Continuous BP variability features like maximum and wavelet energies were significantly associated with haemorrhagic transformation.
- A model using continuous data had an AUC-ROC of 0.62, outperforming intermittent data's 0.48.
- These associations were not observed in later time windows beyond the first 8 hours.

## Abstract

Haemorrhagic transformation (HT) seriously worsens functional outcome after endovascular therapy (EVT). Blood pressure (BP) variability may influence HT risk, but optimal monitoring strategies remain unclear. We aimed to determine whether continuous BP monitoring better identifies patients at risk of HT than standard intermittent measurements during the first 24 h post-EVT.

We conducted a single-centre prospective cohort study including adults with acute ischaemic stroke due to large-vessel occlusion treated with EVT. Non-invasive finger-cuff continuous BP and arm-cuff intermittent BP were recorded simultaneously for 24 h post-EVT. Haemorrhagic transformation on follow-up brain imaging at 24–36 h was the primary outcome. Blood pressure recordings were partitioned into three 8-h windows. Variability metrics (mean, maximum, range, SD, coefficient of variation and wavelet-based coefficient energies) were processed into logistic regression models adjusted for clinical covariates. Predictive performance was assessed using AUC-ROC.

Among 455 enrolled patients, 199 contributed data to the first 8-h window, in which HT occurred in 58 (29%). Continuous BP variability features, particularly maximum, range and wavelet energies capturing < 32-min fluctuations, were significantly associated with HT, whereas no parameter from intermittent monitoring showed such an association. A multivariable model using continuous data yielded an AUC-ROC of 0.62 (95% CI, 0.54–0.71) vs 0.48 (95% CI, 0.37–0.58) for intermittent data. Associations were not observed in later windows.

Short-timescale BP variability captured by continuous monitoring in the first 8 h post-EVT is associated with increased HT risk, whereas intermittent monitoring fails to detect this signal and may miss opportunities for early risk stratification.

Graphical Abstract