Key result
Bootstrap methodologies for estimating confidence intervals of oscillometric blood pressure measurements yielded narrower confidence intervals than conventional methods such as Student's t-distribution.
Bootstrap methodologies can provide narrower confidence intervals for oscillometric blood pressure measurements from small sample sets compared to conventional methods.
Bootstrap methods may tighten CIs for oscillometric BP in small samples; hypothesis-generating and requires clinical validation before any practice change.
Although estimation of average blood pressure is commonly done with oscillometric measurements, confidence intervals (CIs) for systolic blood pressure (SBP) and diastolic blood pressure (DBP) are not usually estimated. This paper adopts bootstrap methodologies to build CI from a small sample set of measurements, which is a situation commonly encountered in practice. Three bootstrap methodologies, namely, nonparametric percentile bootstrap, standard bootstrap, and bias-corrected and accelerated bootstrap are investigated. A two-step methodology is proposed based on pseudomeasurements using bootstrap principles to first derive the pseudomaximum amplitudes and then the pseudoenvelopes (PEs). The SBP and DBP are estimated using the new relationships between mean cuff pressure and PE and then the CIs for such estimates are obtained. In order to reduce the amount of processing, a single-step methodology that directly derives PE using bootstrap principles is also presented. Application of the proposed methodology on an experimental data set of 85 patients with five sets of measurements for each patient has yielded a narrower CI than the currently available conventional methods such as Student'st-distribution method.
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Lee et al. (2011) studied this question. Bootstrap methodologies for confidence interval estimation vs. Conventional methods (Student's t-distribution method) was evaluated on Confidence interval width for systolic and diastolic blood pressure estimates. Bootstrap methodologies for estimating confidence intervals of oscillometric blood pressure measurements yielded narrower confidence intervals than conventional methods such as Student's t-distribution.
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