Automated Blood Pressure Measurement in Public Places
Notice bibliographique
Résumé
Within the past few decades, automated blood pressure (BP) devices began appearing in public places, such as pharmacies and supermarkets, with the goal of detecting undiagnosed, untreated, or inadequately treated hypertension (HTN). The widespread use of such devices in public places raises two important questions: 1) how accurate is this technique in diagnosing HTN? and 2) how effective is this public health strategy in accomplishing its objectives? There are two published guidelines for the validation of automated BP measurement devices, one by the British Hypertension Society1 and one by the American Association for the Advancement of Medical Instrumentation, which has been adopted by the FDA.2 Many automated BP measurement devices are commercially available, yet only a fraction of them have been independently validated.3, 4 Furthermore, even among those devices that have been validated in controlled settings, few have been formally tested in the environments in which they are now commonly being used. In one study that used two automated devices in pharmacies in Toronto, Canada, investigators measured the BP of volunteers according to a strict protocol (i.e., seated, after resting for 2 minutes and excluding subjects with large arm circumferences) and found that neither device met criteria for precision according to either the British or American standards.5, 6 A similar study, performed in grocery stores in Denver, CO, found the device in question to be inconsistent and inaccurate, with a sensitivity of 26% and a negative predictive value of 45% for diagnosing HTN.7 The accuracy of such devices is even more uncertain when utilized in an uncontrolled fashion where there is no standardization for subject position (i.e., sitting vs. standing), arm position (i.e., at the level of the heart), activity level (i.e., at rest), noise level, and arm circumference. Indeed, based on data from the Third National Health and Nutrition Examination Survey (NAHNES III), it is estimated that nearly half of all Americans with HTN have arm circumferences above the limits of one commonly used device.8 Only one study we could find has attempted to measure the efficacy of this public health strategy as a screening tool.9 In this study, automated devices were placed in 13 public places in Exeter, England (including post offices, supermarkets, and homeless shelters) for up to 18 weeks. Of the 769 people who used the devices, 221 recorded elevated BPs (defined as >135/85 mm Hg or systolic BP >160 mm Hg), among whom only 58 were available for follow-up and only 36 subsequently had formal BP measurements by a health care provider. Of this group, 11 subjects (1.4% of all users) were diagnosed with HTN, five of whom started antihypertensive therapy. Another 11 subjects were identified with inadequately treated HTN and five had adjustments made to their antihypertensive regimen, although it is unclear as to whether these changes were prompted by the study measurements. The authors contend that more diagnoses of HTN were likely made among the three quarters of subjects who did not live in Exeter and whose outcomes could not be determined in the study. Conversely, the investigators could not record the incidence of undiagnosed hypertension in patients whose BP readings may have been erroneously normal in the study. Thus, the results of this approach to screening are unclear, since neither its sensitivity nor its specificity is known. Another criticism of this strategy is that no effective protocol exists in most settings for the referral of patients with elevated BP readings to health care providers. In conclusion, automated BP measurement in public places is of questionable accuracy and is of uncertain efficacy as a screening tool. Using such a strategy, some people might be incorrectly labeled as hypertensive and, of greater concern, some people who truly have HTN might be labeled as normal and might then be left with a false sense of security. Even if this strategy is effective in identifying some people with previously undiagnosed HTN, referral systems of such patients to health care providers are not usually in place. There may, however, be a silver lining surrounding this issue. Even if only 1 % of users are newly diagnosed with HTN, this may, potentially, represent thousands of new diagnoses and could result in prevention of large numbers of cardiovascular events, if patients can be referred for follow-up care. At the very least, despite its problems, the measurement of BP in public places serves to promote awareness of HTN as a public health problem.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».