Evidence map›Paper›PMID 34188465›Full record

Observational studyInternational journal of chronic obstructive pulmonary disease2021

Validation of COPDPredict™: Unique Combination of Remote Monitoring and Exacerbation Prediction to Support Preventative Management of COPD Exacerbations.

Neil Patel, Kathryn Kinmond, Pauline Jones, Pamela Birks, Monica A Spiteri

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in International journal of chronic obstructive pulmonary disease, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 2 pooled it
3.4field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 34 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 1 country.

Neil PatelDirectorate of Respiratory Medicine, University Hospitals of North Midlands NHS Trust, Stoke-on-Trent, Staffordshire, UK.ORCID 0000-0001-6218-1036
Kathryn KinmondDirectorate of Respiratory Medicine, University Hospitals of North Midlands NHS Trust, Stoke-on-Trent, Staffordshire, UK.ORCID 0000-0001-8105-6011
Pauline JonesDirectorate of Respiratory Medicine, University Hospitals of North Midlands NHS Trust, Stoke-on-Trent, Staffordshire, UK.
Pamela BirksDirectorate of Respiratory Medicine, University Hospitals of North Midlands NHS Trust, Stoke-on-Trent, Staffordshire, UK.
Monica A SpiteriDirectorate of Respiratory Medicine, University Hospitals of North Midlands NHS Trust, Stoke-on-Trent, Staffordshire, UK.
University Hospitals of North Midlands NHS Trust · GBUniversity Hospitals Birmingham NHS Foundation Trust · GBUniversity of Staffordshire · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 (FEV 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 FEV 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.

Indexed as

Pulmonary Disease, Chronic ObstructiveDisease ProgressionForced Expiratory VolumeHospitalizationHumansPredictive Value of TestsRespiratory Function Testsautomated health-status algorithmsCOPD acute eventsdiagnostic accuracydigital enabled-healthcarepreventative carereduced hospitalisations

Identifiers

PMID34188465
PMCPMC8232856
OpenAlexW3173300309

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.