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Multiparameters of heart rate variability predict symptomatic hemorrhagic transformation in patients following mechanical thrombectomy
Multiparameters of heart rate variability predict symptomatic hemorrhagic transformation in patients following mechanical thrombectomy
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Multiparameters of heart rate variability predict symptomatic hemorrhagic transformation in patients following mechanical thrombectomy
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Multiparameters of heart rate variability predict symptomatic hemorrhagic transformation in patients following mechanical thrombectomy
Multiparameters of heart rate variability predict symptomatic hemorrhagic transformation in patients following mechanical thrombectomy

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Multiparameters of heart rate variability predict symptomatic hemorrhagic transformation in patients following mechanical thrombectomy
Multiparameters of heart rate variability predict symptomatic hemorrhagic transformation in patients following mechanical thrombectomy
Journal Article

Multiparameters of heart rate variability predict symptomatic hemorrhagic transformation in patients following mechanical thrombectomy

2026
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Overview
Background Hemorrhagic transformation (HT), especially symptomatic HT (sHT), seriously affects the functional prognosis in patients following mechanical thrombectomy (MT). The present study aimed to investigate the association of HT with heart rate variability (HRV) using multiple parameters in patients undergoing MT. Methods Between November 2019 and April 2024, we enrolled eligible patients according to the inclusion criteria and exclusion criteria. Linear parameters and nonlinear approaches reflecting HRV were calculated and analyzed. Results A total of 254 patients were included in the analysis, including 56 patients with HT and 198 patients without, and 24 patients with sHT and 230 patients without. Nonlinear approaches including SD1/SD2 ratio, Higuchi fractal dimension (HFD), large-scale multiscale entropy index (MSE LS ), multiscale entropy area under the curve (MSE AUC ), large-scale composite multiscale entropy index (CMSE LS ), and composite multiscale entropy area under the curve (CMSE AUC ) were showed to be independently associated with sHT (p < 0.05). Meanwhile, receiver operating characteristic curves suggested that CMSE LS was a better indicator to predict sHT than other HRV parameters, with the area under the curve of 0.708 (95%CI 0.581–0.835). Conclusions Nonlinear parameters of HRV including SD1/SD2 ratio, HFD, MSE LS , MSE AUC , CMSE LS , and CMSE AUC provide an independent indicator for predicting sHT in patients following MT.