Sources of Error in Office Blood Pressure Measurement

Morcos, Roy N.; Carter, Kimbroe J.; Castro, Frank; Koirala, Sumira; Sharma, Deepshikha; Syed, Haroon

doi:10.3122/jabfm.2019.05.190085

Cited by 14 publications

(17 citation statements)

References 32 publications

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“…Following the extensive analysis of the validation protocols [7,8,9,10,11] and the ISO81060-2:2018 guideline, seven uncertainty components were defined to compute the expanded uncertainty (U) for of the systolic BP measurement. Based on the analysis of the process flow and experimental design recommended by the ISO81060-2:2018 guideline, the following components were implemented into the Monte Carlo simulations (Fig.…”

Section: Resultsmentioning

confidence: 99%

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The Effect of Expanded Measurement Uncertainty on the Outcome of Blood Pressure Measurement Validation Protocol Based on the ISO81060-2:2018 Guideline - A Monte Carlo Simulation Approach

Pálhalmi¹

2020

2020 Computing in Cardiology Conference (CinC)

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The most common hemodynamic measurement technologies are the classical auscultatoric sphygmomanometric method and numerous solutions for automated sphygmomanometry. The classical method is the reference for testing the automated solutions according to the ISO81060-2:2018 guideline.Both of the above mentioned blood pressure estimations are indirect measurement methods according to the approach of metrology.According to the ISO81060-2:2018 guideline, two indirect measurement and estimation methods are compared with each other without the theoretical and technical consideration of the possible effect of the measurement uncertainty on the results.Within this simulation study several components of the measurement uncertainty were implemented to test the reliability and confidence limit of the ISO guideline recommended process flow, experimental design and statistical analysis.

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Section: Resultsmentioning

confidence: 99%

“…Components of the standard measurement uncertainty were determined based on the relevant OIML standards [7,8,9], ISO guidelines [4] and data available in the literature [10,11]. Components of standard measurement uncertainty related to random error, systematic error and maximum permissible error were combined according to formula 1.…”

Section: Methodsmentioning

confidence: 99%

The Effect of Expanded Measurement Uncertainty on the Outcome of Blood Pressure Measurement Validation Protocol Based on the ISO81060-2:2018 Guideline - A Monte Carlo Simulation Approach

Pálhalmi¹

2020

2020 Computing in Cardiology Conference (CinC)

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“…There is widespread evidence for end digit preference in BP measured by clinicians in the surgery or by patients at home using manual telemonitoring systems [10][11][12][13]. It is therefore recognised as an important source of potential bias in BP records.…”

Section: End Digit Preferencementioning

confidence: 99%

Examining the effectiveness of telemonitoring with routinely acquired blood pressure data in primary care: challenges in the statistical analysis

Parker

Padfield

Hanley

et al. 2021

Preprint

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Background Scale-up BP was a quasi-experimental implementation study, following a successful randomised controlled trial of the roll-out of telemonitoring in primary care across Lothian, Scotland. Our primary objective was to assess the effect of telemonitoring on blood pressure (BP) control using routinely collected data. Telemonitored systolic and diastolic BP were compared with surgery BP measurements from patients not using telemonitoring (comparator patients). The statistical analysis and interpretation of findings was challenging due to the broad range of biases potentially influencing the results, including differences in the frequency of readings, ‘white coat effect’, end digit preference, and missing data. Methods Four different statistical methods were employed in order to minimise the impact of these biases on the comparison between telemonitoring and comparator groups. These methods were “standardisation with stratification”, “standardisation with matching”, “regression adjustment for propensity score” and “random coefficient modelling”. The first three methods standardised the groups so that all participants provided exactly two measurements at baseline and 6-12 months follow-up prior to analysis. . The fourth analysis used linear mixed modelling based on all available data. Results The standardisation with stratification analysis showed a significantly lower systolic BP in telemonitoring patients at 6-12 months follow-up (-3.42, 95% CI -1.72 to -5.11, p<0.001). For the standardisation with matching and regression adjustment for propensity score analyses systolic BP was also significantly lower (-5.96, 95% CI -3.55 to -8.36, p<0.001) and (-3.73, -5.34 to -2.13, p<0.001) respectively, even after assuming that -5 of the difference was due to ‘white coat effect’. For the random coefficient modelling, the improvement in systolic BP was estimated to be -4.68 (95% CI -3.12 to -6.24, p<0.001) after one year.Conclusions The four analyses provide additional evidence for the effectiveness of telemonitoring in controlling BP in routine primary care. The random coefficient analysis is particularly recommended due to its ability to utilise all available data. However, adjusting for the complex array of biases was difficult. Researchers should appreciate the potential for bias in implementation studies and seek to acquire a detailed understanding of the study context in order to design appropriate analytical approaches.

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Section: End Digit Preferencementioning

confidence: 99%

Examining the Effectiveness of Telemonitoring with Routinely Acquired Blood Pressure Data in Primary Care: Challenges in the Statistical Analysis

Parker

Padfield

Hanley

et al. 2020

Preprint

View full text Add to dashboard Cite

BackgroundScale-up BP was a quasi-experimental implementation study, following a successful randomised controlled trial of the roll-out of telemonitoring in primary care across Lothian, Scotland. Our primary objective was to assess the effect of telemonitoring on blood pressure (BP) control using routinely collected data. Telemonitored systolic and diastolic BP were compared with surgery BP measurements from patients not using telemonitoring (comparator patients). The statistical analysis and interpretation of findings was challenging due to the broad range of biases potentially influencing the results, including differences in the frequency of readings, ‘white coat effect’, end digit preference, and missing data.MethodsThree different statistical methods were employed in order to minimise the impact of these biases on the comparison between telemonitoring and comparator groups. These methods were “standardisation with stratification”, “standardisation with matching”, and “random coefficient modelling”. The first two methods standardised the groups so that all participants provided exactly two measurements at baseline and 6-12 months follow-up before using stratification or matched cohort analysis to compare the groups. The third analysis used linear mixed modelling based on all available data. ResultsThe standardisation with stratification analysis showed a significantly lower systolic BP in telemonitoring patients at 6-12 months follow-up (-3.42, 95% CI -1.72 to -5.11, p<0.001). For the standardisation with matching analysis, systolic BP was also significantly lower (-5.96, 95% CI -3.55 to -8.36, p<0.001), even after assuming that -5 of the difference was due to ‘white coat effect’. For the random coefficient modelling, the improvement in systolic BP was estimated to be -4.68 (95% CI -3.12 to -6.24, p<0.001) after one year. ConclusionsThe three analyses provide additional evidence for the effectiveness of telemonitoring in controlling BP in routine primary care. However, adjusting for the complex array of biases was difficult. Researchers should appreciate the potential for bias in implementation studies and seek to acquire a detailed understanding of the study context in order to design appropriate analytical approaches.

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Sources of Error in Office Blood Pressure Measurement

Cited by 14 publications

References 32 publications

The Effect of Expanded Measurement Uncertainty on the Outcome of Blood Pressure Measurement Validation Protocol Based on the ISO81060-2:2018 Guideline - A Monte Carlo Simulation Approach

The Effect of Expanded Measurement Uncertainty on the Outcome of Blood Pressure Measurement Validation Protocol Based on the ISO81060-2:2018 Guideline - A Monte Carlo Simulation Approach

Examining the effectiveness of telemonitoring with routinely acquired blood pressure data in primary care: challenges in the statistical analysis

Examining the Effectiveness of Telemonitoring with Routinely Acquired Blood Pressure Data in Primary Care: Challenges in the Statistical Analysis

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