Accuracy of methods to estimate central aortic SBP via upper arm cuff: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Central aortic BP may predict cardiovascular outcomes better than upper arm brachial BP. In recent years, technology has enabled central BP estimation by recording a peripheral BP waveform from a standard upper arm cuff. The accuracy of these devices is not well documented, and this study aimed to address this issue. METHODS: This study was a systematic review, conducted according to PRISMA guidelines, of observational studies published between 2008 and 2023 that reported accuracy testing of cuff-based central BP devices, compared with reference invasive aortic BP. The primary analysis was stratified according to each commercially available device. Pooled estimates were calculated using random-effects models based on mean differences and standard errors. RESULTS: Six thousand four hundred and fifteen studies were screened, and 27 studies met inclusion criteria (plus one unpublished study). This generated data for seven devices that are commercially available, which were tested among 2125 adult participants. There was very high heterogeneity when all devices were pooled ( I2 = 97.5%), and, when stratified by device, the accuracy of estimated central BP was highly device-dependent (range of accuracy across different devices -12.4 mmHg (-16.3 to -8.5) to 3.2 mmHg (0.2-6.1). Two of the seven commercially available devices had not undergone external validation testing. CONCLUSION: The accuracy of commercially available cuff-based central BP devices is highly device-specific and not all are accurate for the estimation of central SBP. These findings have major implications for the appropriate interpretation of studies that use cuff-based estimated central BP.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.049 | 0.125 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.048 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".