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Spectrum effect and spectrum bias in the oscillometric ankle brachial index to diagnose peripheral arterial disease: Clinical implications
dc.contributor.author | Herráiz-Adillo Á. | |
dc.contributor.author | Cavero-Redondo I. | |
dc.contributor.author | Álvarez-Bueno C. | |
dc.contributor.author | Bidner J. | |
dc.contributor.author | Martínez-Vizcaíno V. | |
dc.contributor.author | Notario-Pacheco B. | |
dc.date.accessioned | 2020-09-02T22:20:28Z | |
dc.date.available | 2020-09-02T22:20:28Z | |
dc.date.issued | 2018 | |
dc.identifier | 10.1016/j.atherosclerosis.2018.03.003 | |
dc.identifier.citation | 272, , 8-13 | |
dc.identifier.issn | 00219150 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12728/4886 | |
dc.description | Background and aims: The diagnostic performance of the oscillometric ankle brachial index (ABI) to detect peripheral arterial disease (PAD) varies among populations, suggesting a spectrum effect. When this heterogeneity modifies post-test probabilities, a spectrum bias arises. This study evaluates the presence and influence of spectrum effect and spectrum bias on test performance and clinical decisions. Methods: Oscillometric and Doppler ABI were compared in two settings: Primary-Care (333 legs) and Vascular-Service (41 legs). Spectrum effect was assessed using stratification and logistic regression, while spectrum bias was assessed through graphical and statistical tests based on predictive values and likelihood ratios, respectively. Results: Across subgroups, sensitivity ranged from 61.5% to 90.9%, and specificity from 81.8% to 99.1%. Logistic regression confirmed a spectrum effect in setting, diabetes, smoking status and age (univariate), and setting and diabetes (multivariate model). The positive likelihood ratio ranged from 5.0 to 89.1 in subgroups, leading to a spectrum bias in diabetic, smoking (both subgroups) and age (both subgroups). Therefore, a positive test ruled in differently the disease across subgroups, with a high rate of false positives in diabetic, smoking and >75-year-old patients. The negative likelihood ratio ranged from 0.09 to 0.39 in subgroups, with significant spectrum bias in Primary-Care patients, non-diabetics and smokers. Thus, in these subgroups, a negative test ruled out the disease with less certainty. Conclusions: Spectrum effect and spectrum bias were found in oscillometric ABI to detect PAD, potentially affecting clinical decisions, especially for positive tests. Information about spectrum variables and the application of specific subgroups indicators are necessary. © 2018 Elsevier B.V. | |
dc.language.iso | en | |
dc.publisher | Elsevier Ireland Ltd | |
dc.subject | Ankle-brachial index | |
dc.subject | Oscillometry | |
dc.subject | Peripheral artery disease | |
dc.subject | Spectrum bias | |
dc.subject | Spectrum effect | |
dc.subject | age | |
dc.subject | aged | |
dc.subject | ankle brachial index | |
dc.subject | Article | |
dc.subject | cardiovascular risk | |
dc.subject | clinical decision making | |
dc.subject | clinical evaluation | |
dc.subject | cross-sectional study | |
dc.subject | diabetes mellitus | |
dc.subject | diagnostic accuracy | |
dc.subject | diagnostic test accuracy study | |
dc.subject | Doppler flowmetry | |
dc.subject | dyslipidemia | |
dc.subject | false positive result | |
dc.subject | female | |
dc.subject | gender | |
dc.subject | human | |
dc.subject | hypertension | |
dc.subject | logistic regression analysis | |
dc.subject | major clinical study | |
dc.subject | male | |
dc.subject | multicenter study | |
dc.subject | multivariate analysis | |
dc.subject | obesity | |
dc.subject | oscillometry | |
dc.subject | peripheral occlusive artery disease | |
dc.subject | physical parameters | |
dc.subject | predictive value | |
dc.subject | primary medical care | |
dc.subject | priority journal | |
dc.subject | prospective study | |
dc.subject | sensitivity and specificity | |
dc.subject | smoking | |
dc.subject | spectrum bias | |
dc.subject | spectrum effect | |
dc.subject | statistical bias | |
dc.subject | univariate analysis | |
dc.subject | algorithm | |
dc.subject | brachial artery | |
dc.subject | diagnostic imaging | |
dc.subject | Doppler ultrasonography | |
dc.subject | middle aged | |
dc.subject | oscillometry | |
dc.subject | peripheral occlusive artery disease | |
dc.subject | regression analysis | |
dc.subject | signal processing | |
dc.subject | statistical bias | |
dc.subject | statistics | |
dc.subject | very elderly | |
dc.subject | Aged | |
dc.subject | Aged, 80 and over | |
dc.subject | Algorithms | |
dc.subject | Ankle Brachial Index | |
dc.subject | Bias | |
dc.subject | Brachial Artery | |
dc.subject | Cross-Sectional Studies | |
dc.subject | Female | |
dc.subject | Humans | |
dc.subject | Male | |
dc.subject | Middle Aged | |
dc.subject | Multivariate Analysis | |
dc.subject | Oscillometry | |
dc.subject | Peripheral Arterial Disease | |
dc.subject | Predictive Value of Tests | |
dc.subject | Regression Analysis | |
dc.subject | Sensitivity and Specificity | |
dc.subject | Signal Processing, Computer-Assisted | |
dc.subject | Smoking | |
dc.subject | Statistics as Topic | |
dc.subject | Ultrasonography, Doppler | |
dc.title | Spectrum effect and spectrum bias in the oscillometric ankle brachial index to diagnose peripheral arterial disease: Clinical implications | |
dc.type | Article |