脳卒中患者における自律神経系の包括的評価：回復予測の礎としての心拍変動

Autonomic nervous system (ANS) dysfunction significantly impacts stroke outcomes, yet standardized autonomic monitoring remains underutilized in clinical practice. Recent evidence highlights heart rate variability (HRV) as a robust prognostic marker, while complementary modalities like blood pressure variability (BPV) and pupillometry enhance risk stratification.

To synthesize current evidence on multimodal ANS assessment in stroke patients, with HRV as the cornerstone biomarker, and provide practical recommendations for clinical implementation.

We conducted a narrative review of peer-reviewed studies (2018-2024) from major databases, focusing on HRV and complementary ANS modalities in stroke patients. The analysis included 53 studies from diverse global regions, with a specific focus on technology-enabled investigations and prospective cohorts that emerged from 2022 to 2024.

Time-domain HRV metrics such as SDNN (standard deviation of all normal-to-normal RR intervals) and RMSSD (root mean square of successive differences) consistently predicted functional outcomes and cardiovascular complications post-stroke. Beat-to-beat blood pressure variability (BPV) within the first 24 h post-ischemic stroke outperforms HRV alone for short-term prognosis, with an AUC improvement of 10-15%. Nocturnal HRV combined with BPV and endothelial function enhanced prediction of recurrent cerebrovascular events. Automated pupillometry, with Neurological Pupil index (NPi < 3), predicts post-stroke delirium; integration with HRV metrics improves prognostic accuracy. Consumer-grade wearables demonstrated high agreement with ECG standards, enabling scalable remote monitoring.

HRV-centered multimodal ANS assessment offers robust, non-invasive prognostic tools for stroke management. Integration with BPV, pupillometry, and wearable technology enhances risk stratification across acute and rehabilitation phases. Standardized protocols and technology adoption could transform stroke outcomes globally, particularly in resource-limited settings.