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Validation of COPDPredict™: Unique Combination of Remote Monitoring and Exacerbation Prediction to Support Preventative Management of COPD Exacerbations.
Patel, Neil; Kinmond, Kathryn; Jones, Pauline; Birks, Pamela; Spiteri, Monica A.
  • Patel N; Directorate of Respiratory Medicine, University Hospitals of North Midlands NHS Trust, Stoke-on-Trent, Staffordshire, UK.
  • Kinmond K; Directorate of Respiratory Medicine, University Hospitals Birmingham NHS Foundation Trust, Heartlands Hospital, Birmingham, UK.
  • Jones P; Directorate of Respiratory Medicine, University Hospitals of North Midlands NHS Trust, Stoke-on-Trent, Staffordshire, UK.
  • Birks P; Department of Health & Social care, Staffordshire University, Stoke-on-Trent, Staffordshire, UK.
  • Spiteri MA; Directorate of Respiratory Medicine, University Hospitals of North Midlands NHS Trust, Stoke-on-Trent, Staffordshire, UK.
Int J Chron Obstruct Pulmon Dis ; 16: 1887-1899, 2021.
Artículo en Inglés | MEDLINE | ID: covidwho-1902757
ABSTRACT

Background:

COPDPredict™ is a novel digital application dedicated to providing early warning of imminent COPD (chronic obstructive pulmonary disease) exacerbations for prompt intervention. Exacerbation prediction algorithms are based on a decision tree model constructed from percentage thresholds for disease state changes in patient-reported wellbeing, forced expiratory volume in one second (FEV1) and C-reactive protein (CRP) levels. Our study determined the validity of COPDPredict™ to identify exacerbations and provide timely notifications to patients and clinicians compared to clinician-defined episodes.

Methods:

In a 6-month prospective observational study, 90 patients with COPD and frequent exacerbations registered wellbeing self-assessments daily using COPDPredict™ App and measured FEV1 using connected spirometers. CRP was measured using finger-prick testing.

Results:

Wellbeing self-assessment submissions showed 98% compliance. Ten patients did not experience exacerbations and treatment was unchanged. A total of 112 clinician-defined exacerbations were identified in the remaining 80 patients 52 experienced 1 exacerbation; 28 had 2.2±0.4 episodes. Sixty-two patients self-managed using prescribed rescue medication. In 14 patients, exacerbations were more severe but responded to timely escalated treatment at home. Four patients attended the emergency room; with 2 hospitalised for <72 hours. Compared to the 6 months pre-COPDPredict™, hospitalisations were reduced by 98% (90 vs 2, p<0.001). COPDPredict™ identified COPD-related exacerbations at 7, 3 days (median, IQR) prior to clinician-defined episodes, sending appropriate alerts to patients and clinicians. Cross-tabulation demonstrated sensitivity of 97.9% (95% CI 95.7-99.2), specificity of 84.0% (95% CI 82.6-85.3), positive and negative predictive value of 38.4% (95% CI 36.4-40.4) and 99.8% (95% CI 99.5-99.9), respectively.

Conclusion:

High sensitivity indicates that if there is an exacerbation, COPDPredict™ informs patients and clinicians accurately. The high negative predictive value implies that when an exacerbation is not indicated by COPDPredict™, risk of an exacerbation is low. Thus, COPDPredict™ provides safe, personalised, preventative care for patients with COPD.
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Texto completo: Disponible Colección: Bases de datos internacionales Base de datos: MEDLINE Asunto principal: Enfermedad Pulmonar Obstructiva Crónica Tipo de estudio: Estudios diagnósticos / Estudio observacional / Estudio pronóstico / Ensayo controlado aleatorizado Límite: Humanos Idioma: Inglés Revista: Int J Chron Obstruct Pulmon Dis Año: 2021 Tipo del documento: Artículo País de afiliación: COPD.S309372

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Texto completo: Disponible Colección: Bases de datos internacionales Base de datos: MEDLINE Asunto principal: Enfermedad Pulmonar Obstructiva Crónica Tipo de estudio: Estudios diagnósticos / Estudio observacional / Estudio pronóstico / Ensayo controlado aleatorizado Límite: Humanos Idioma: Inglés Revista: Int J Chron Obstruct Pulmon Dis Año: 2021 Tipo del documento: Artículo País de afiliación: COPD.S309372