Implementation of pressure monitoring and a risk algorithm to evaluate pre- and post-interventions in the community
Notice bibliographique
Résumé
Introduction: individual's residing in the community spend a relatively small amount of time with healthcare practitioners and often rely on informal or formal carers.1 Those with mobility impairments can spend prolonged periods in bed or chair, posing them at risk of pressure ulcers (PUs).2 The quality improvement project, ‘Pressure Reduction through cOntinuous Monitoring In the community SEtting’ (PROMISE), implemented the use of continuous pressure monitoring (CPM) technology in the community to inform support surface selection, posture and pressure relieving movements.3 The present study aimed to evaluate continuous pressure monitoring data using a novel algorithm developed by the researchers, to assess for changes pre- and post-PROMISE quality improvement intervention.4<br/><br/>Method: patients were selected from recruited community residents, whose pressure data were captured pre- and post-PROMISE intervention. Pressure data were collected with a commercial pressure monitoring (ForesitePT, Xsensor, Canada). Data was analysed with an intelligent algorithm5 to determine duration and magnitude of peak pressure gradient and peak pressure index at the buttock area. A risk algorithm,4 based on the sigmoid relationship between pressure and time to stratify exposure to prolonged pressures, determined the ‘low’ (green), ‘moderate’ (yellow), ‘high’ (orange), and ‘very high’ (red) categories of pressures and time exposure. Sigmoid curves were developed for both pressure gradients and peak pressure index parameters.<br/><br/>Results: in all, 17 patients were included in the study. The percentage monitoring time in each exposure category (green, yellow, orange, red), pre- and post-PROMISE intervention was assessed, with respect to duration and magnitude of peak pressure gradient and peak pressure index. Some patients spent most of their time in the ‘at risk’ categories both pre- and post-intervention. By contrast, other patients revealed mobility and pressure signatures falling in the green category for >80% of their time. There was a trend in reduced exposure categories from pre-to post-PROMISE intervention, as depicted by a shift in categories.<br/><br/>Conclusion: patients had a PU at the time of monitoring pre-PROMISE intervention and many exhibited trends which exposed their skin to prolonged pressures during static postures. The algorithm depicted high exposure to prolonged pressures, which showed trends of improvement post-PROMISE intervention. Further development is required to establish subject-specific sigmoids. This could be integrated in a novel sensing array for community monitoring use, and aid targeted intervention and clinical decision-making.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, pas un consensus.
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