Combination of low ankle-brachial index and high ankle-brachial index difference for mortality prediction

Low ankle-brachial index (ABI) and high ABI difference (ABID) are each associated with poor prognosis. No study has assessed the ability of the combination of low ABI and high ABID to predict survival. We created an ABI score by assigning 1 point for ABI < 0.9 and 1 point for ABID ≥ 0.17 and exam...

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Published in:Hypertension research Vol. 44; no. 7; pp. 850 - 857
Main Authors: Tsai, Wei-Chung, Lee, Wen-Hsien, Chen, Ying-Chih, Liu, Yi-Hsueh, Chang, Ching-Tang, Hsu, Po-Chao, Chu, Chun-Yuan, Lin, Tsung-Hsien, Lee, Chee-Siong, Lee, Chien-Hung, Voon, Wen-Chol, Lai, Wen-Ter, Sheu, Sheng-Hsiung, Su, Ho-Ming
Format: Journal Article
Language:English
Published: England Nature Publishing Group 01-07-2021
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Summary:Low ankle-brachial index (ABI) and high ABI difference (ABID) are each associated with poor prognosis. No study has assessed the ability of the combination of low ABI and high ABID to predict survival. We created an ABI score by assigning 1 point for ABI < 0.9 and 1 point for ABID ≥ 0.17 and examine the ability of this ABI score to predict mortality. We included 941 patients scheduled for echocardiographic examination. The ABI was measured using an ABI-form device. ABID was calculated as |right ABI-left ABI|. Among the 941 subjects, the prevalence of ABI < 0.9 and ABID ≥ 0.17 was 6.1% and 6.8%, respectively. Median follow-up to mortality was 93 months. There were 87 cardiovascular and 228 overall deaths. All ABI-related parameters, including ABI, ABID, ABI < 0.9, ABID ≥ 0.17, and ABI score, were significantly associated with overall and cardiovascular mortality in the multivariable analysis (P ≤ 0.009). Further, in the direct comparison of multivariable models, the basic model + ABI score was the best at predicting overall and cardiovascular mortality among the five ABI-related multivariable models (P ≤ 0.049). Hence, the ABI score, a combination of ABI < 0.9 and ABID ≥ 0.17, should be calculated for better mortality prediction.
ISSN:0916-9636
1348-4214
DOI:10.1038/s41440-021-00636-y