A model for early failure prediction of blood pressure measurement devices in a stepped validation approach

Abstract

Blood pressure monitoring (BPM) devices have to be validated according to strict international validation protocols. Each protocol requests a specific number of participants to be included. All protocols use vast amounts of resources, as three people have to be present for every measurement, making trials costly, especially when the manufacturer has no intention to execute a validation study, reflected in the low share of validated in the commercially available BPM devices. The aim of our study was to develop criteria, which could detect low accuracy devices that could not pass a validation protocol early in the course of the validation process. The 2010 European Society of Hypertension International Protocol (ESH-IP) and the Universal Standard for Validation of BPM devices (AAMI/ESH/ISO) were scrutinized for criteria which can be used for preclusion of passing. Based on this, we developed a fail model. We found that a BPM device cannot pass the ESH-IP protocol, if there are ≥27, 13, or 4 single measurements differing more than 5, 10, or 15 mmHg, respectively, from the reference. For the AAMI/ESH/ISO protocol, we developed a model, which calculates best-case standard deviations (SDs) to detect SDs which would prevent the passing of the protocol before its completion, making a stepwise validation process possible. In conclusion, we found that our model is able to predict failure of low-accuracy BPM devices early during a validation protocol if used in a stepwise-approach. This can be useful to keep costs of validation studies low and to enable investigator-initiated trials. K E Y W O R D Sblood pressure measurement, fail criteria, home blood pressure measurement, validation protocol INTRODUCTIONArterial hypertension (AHT) is the leading preventable cause of premature death worldwide, with almost a third of the world populationThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Comparability of a Blood-Pressure-Monitoring Smartphone Application with Conventional Measurements—A Pilot Study

“…The ESH-IP 2010 requests the inclusion of 33 participants with 99 measurement pairs [28]. A device will fail the ESH-IP 2010 if more measurement pairs differ by more than 5, 10, or 15 mmHg, respectively, than the number stated in Table 1 [28,30]. For example, if a test device delivers four measurements with a difference of >15 mmHg and 13 measurements with a difference of >10 mmHg to the CuffBP value, it cannot pass the ESH-IP 2010 validation criteria.…”

“…For the "two of" criterion, the test device will certainly fail if the stated number of observed biases is reached in two of the stated categories, and for the "either" criterion, if the stated number of observed biases is reached for any of the categories. Adapted after [30].…”

“…The SD is the square root of the variance, which is the sum of the squared differences of each data point (i.e., each difference AppBP-CuffBP) with the cohort-wise bias (mean of all differences AppBP-CuffBP) [31]. We modelled a best-case scenario SD by calculating the SD over all pairs of measurement from our results (Criterion 1 for individual BP readings [29]) for different assumed biases between −5 and 5 mmHg, assuming that all missing measurements to reach 255 pairs of measurements 6 of 15 would be equal to the assumed mean [30]. Similarly, we proceeded with the mean of all measurement pairs of each participant (Criterion 2 for individual subjects [29]) but used assumed missing values to reach 85 participants [30].…”

“…We modelled a best-case scenario SD by calculating the SD over all pairs of measurement from our results (Criterion 1 for individual BP readings [29]) for different assumed biases between −5 and 5 mmHg, assuming that all missing measurements to reach 255 pairs of measurements 6 of 15 would be equal to the assumed mean [30]. Similarly, we proceeded with the mean of all measurement pairs of each participant (Criterion 2 for individual subjects [29]) but used assumed missing values to reach 85 participants [30]. The results were displayed graphically by drawing the maximally allowed SD and the calculated best-case scenario SD from the AppBP-CuffBP differences for each assumed mean.…”