Evidence map›Paper›PMID 35898315›Full record

ArticleEClinicalMedicine2022

ACCEPT 2·0: Recalibrating and externally validating the Acute COPD exacerbation prediction tool (ACCEPT).

Abdollah Safari, Amin Adibi, Don D Sin, Tae Yoon Lee, Joseph Khoa Ho, Mohsen Sadatsafavi

Open access · goldAbstract read
In one paragraph

Article in EClinicalMedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 4 pooled it
4.0field-weighted citation impact, top 5% 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

18 citing papers in PubMed, 4 syntheses or guidelines pooled it, 29 citations in OpenAlex.

  1. Pooled it
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  3. Pooled it
  4. Pooled it
  5. Trial
  6. Development of a peripheral blood transcriptomic gene signature to predict bronchopulmonary dysplasia.American journal of physiology. Lung cellular and molecular physiology · 2023
    Trial
  7. Article
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  10. Observational
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  15. Article
  16. Digital health in chronic obstructive pulmonary disease.Chronic diseases and translational medicine · 2023
    Review
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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

6 authors at 1 institution in 2 countries.

Abdollah SafariRespiratory Evaluation Sciences Program, Collaboration for Outcomes Research and Evaluation, Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, Canada.
Amin AdibiRespiratory Evaluation Sciences Program, Collaboration for Outcomes Research and Evaluation, Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, Canada.
Don D SinCentre for Heart Lung Innovation, St. Paul's Hospital and Department of Medicine (Division of Respirology), The University of British Columbia, Vancouver, Canada.
Tae Yoon LeeRespiratory Evaluation Sciences Program, Collaboration for Outcomes Research and Evaluation, Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, Canada.
Joseph Khoa HoRespiratory Evaluation Sciences Program, Collaboration for Outcomes Research and Evaluation, Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, Canada.
Mohsen SadatsafaviRespiratory Evaluation Sciences Program, Collaboration for Outcomes Research and Evaluation, Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, Canada.
University of British Columbia · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The Acute Chronic Obstructive Pulmonary Disease (COPD) Exacerbation Prediction Tool (ACCEPT) was developed for individualised prediction of COPD exacerbations. ACCEPT was well calibrated overall and had a high discriminatory power, but overestimated risk among individuals without recent exacerbations. The objectives of this study were to 1) fine-tune ACCEPT to make better predictions for individuals with a negative exacerbation history, 2) develop more parsimonious models, and 3) externally validate the models in a new dataset. Methods: We recalibrated ACCEPT using data from the Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE, a three-year observational study, 1,803 patients, 2,117 exacerbations) study by applying non-parametric regression splines to the predicted rates. We developed three reduced versions of ACCEPT by removing symptom score and/or baseline medications as predictors. We examined the discrimination, calibration, and net benefit of ACCEPT 2·0 in the placebo arm of the Towards a Revolution in COPD Health (TORCH, a three-year randomised clinical trial of inhaled therapies in COPD, 1,091 patients, 1,064 exacerbations) study. The primary outcome for prediction was the occurrence of ≥2 moderate or ≥1 severe exacerbation in the next 12 months; the secondary outcomes were prediction of the occurrence of any moderate/severe exacerbation or any severe exacerbation. Findings: ACCEPT 2·0 had an area-under-the-curve (AUC) of 0·76 for predicting the primary outcome. Exacerbation history alone (current standard of care) had an AUC of 0·68. The model was well calibrated in patients with positive or negative exacerbation histories. Changes in AUC in reduced versions were minimal for the primary outcome as well as for predicting the occurrence of any moderate/severe exacerbations (ΔAUC<0·011), but more substantial for predicting the occurrence of any severe exacerbations (ΔAUC<0·020). All versions of ACCEPT 2·0 provided positive net benefit over the use of exacerbation history alone for some range of thresholds. Interpretation: ACCEPT 2·0 showed good calibration regardless of exacerbation history, and predicts exacerbation risk better than current standard of care for a range of thresholds. Future studies need to investigate the utility of exacerbation prediction in various subgroups of patients. Funding: This study was funded by a team grant from the Canadian Institutes of Health Research (PHT 178432).

Indexed as

Chronic obstructive pulmonary diseaseClinical prediction modelsPrecision medicinePrognosis

Identifiers

PMID35898315
PMCPMC9309408
OpenAlexW4286602745

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.