Literature DB >> 31764009

Comparison between blood pressure readings using a mercury versus an aneroid sphygmomanometer.

Kinaan Farhan1, S Tahira Shah Naqvi1, Syed Asad Hasan Rizvi2, Amara Zafar2, Muhammad Shabbir Rawala3.   

Abstract

OBJECTIVE: For more than a century since its introduction, mercury sphygmomanometer (HgS) had been the mainstay for office measurement of blood pressure (BP). In light of the environmental and health hazards associated with mercury, there is a need to replace it with mercury-free alternatives all over the world. We aimed to validate the widely used aneroid sphygmomanometer (AnS) by comparing its BP readings against BP readings taken with an HgS.
METHODS: We compared the BP readings using AnS vs. HgS on a sample of 300 patients of 18 years or older age admitted to a tertiary care hospital in Karachi, Pakistan.
RESULTS: The differences between mean HgS and AnS BP readings were found to be statistically significant (P-value <0.01). The mean systolic blood pressure (SBP) readings of the two devices were still significantly correlated (r = 0.989; P < 0.01). Similarly, the mean diastolic blood pressure (DBP) readings were also significantly correlated (r = 0.988; P < 0.01). The aneroid device identified a higher proportion of hypertensive participants compared to the mercury device.
CONCLUSION: The difference in the two devices used was found to be significant; however, the readings were correlated with each other. The AnS significantly overestimated BP readings, thereby identifying a higher proportion of hypertensives as compared to the HgS. There is a considerable room for improvement in the accuracy of the AnS, only then an accurate and a well-calibrated AnS could provide an acceptable alternative to the use of the HgS.

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Year:  2020        PMID: 31764009     DOI: 10.1097/MBP.0000000000000417

Source DB:  PubMed          Journal:  Blood Press Monit        ISSN: 1359-5237            Impact factor:   1.444


  1 in total

Review 1.  Recent Advances in Non-Invasive Blood Pressure Monitoring and Prediction Using a Machine Learning Approach.

Authors:  Siti Nor Ashikin Ismail; Nazrul Anuar Nayan; Rosmina Jaafar; Zazilah May
Journal:  Sensors (Basel)       Date:  2022-08-18       Impact factor: 3.847

  1 in total

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