The Acute COPD Exacerbation Prediction Tool (ACCEPT): a modelling study

Adibi, Amin; Sin, Don D.; Safari, Abdollah; Johnson, Kate; Aaron, Shawn D.; FitzGerald, J. Mark

doi:10.1016/s2213-2600(19)30397-2

Cited by 81 publications

(70 citation statements)

References 22 publications

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“…This can be achieved through the use of nuanced clinical prediction models that rely not just on AECOPD history but on a multitude of other predictor variables. A recent risk prediction model for AECOPD has demonstrated that the use of easily verifiable patient characteristics can significantly improve the predictability of AECOPDs, especially severe ones (an improvement in c-statistics of 0.11 was observed in this study) [20]. With the availability of multidimensional data, one can move beyond easily verifiable predictors to include values from imaging (e.g., bronchiectasis), blood or sputum biomarkers (e.g., eosinophils), comorbidity from electronic charts, and others.…”

Section: Discussionsupporting

confidence: 57%

Should the number of acute exacerbations in the previous year be used to guide treatments in COPD?

et al. 2020

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BackgroundIn contemporary management of COPD, the frequent exacerbator phenotype based on a 12-month history of acute exacerbations (AECOPD) is a major determinant of therapeutic recommendations. However, there is considerable debate on the stability of this phenotype over time.MethodsWe used fundamental principles in time-to-event analysis to demonstrate that variation in the frequent exacerbator phenotype has two major sources: variability in the underlying AECOPD rate and the randomness in the occurrence of individual AECOPDs. We re-analysed data from two large cohorts (ECLIPSE and SPIROMICS) using a Bayesian model that separated these sources of variability. We then evaluated the stability of the frequent exacerbator phenotype based on these results.ResultsIn both cohorts, the pattern of AECOPDs strongly supported the presence of an individual-specific underlying AECOPD rate which is stable over time (Bayes Factor <0.001). Despite this, the observed AECOPD rate can markedly vary year-to-year within individual patients. For those with an underlying rate between 0.8–3.1/year, the frequent exacerbator classification, based on the observed rate, changes >30% of the time over two consecutive years due to chance alone. This value increases to >45% for those with an underlying rate between 1.2–2.2/year.ConclusionWhile the underlying AECOPD rate is a stable trait, the frequent exacerbator phenotype based on observed AECOPD patterns is unstable, so much so that its suitability for informing treatment decisions should be questioned. Whether evaluating AECOPD history over longer durations or the use of multivariate prediction models can result in more stable phenotyping needs to be evaluated.

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Section: Discussionsupporting

confidence: 57%

Should the number of acute exacerbations in the previous year be used to guide treatments in COPD?

et al. 2020

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“…Initial attempts using Deep Belief Networks and clinical factors have shown an accuracy of 92%, which is superior to prior attempts to predict exacerbations using support vector machine classifiers 67 . These kinds of approaches could augment and support more standard models that have been recently proposed from pooled clinical trials 68 .…”

Section: Diagnosis and Outcome Predictionmentioning

confidence: 94%

Artificial Intelligence in COPD: New Venues to Study a Complex Disease

Estépar¹

2021

BRN

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“…Overall, none of these models revealed to adequately predict exacerbations and are therefore not of clinical use [ 64 ]. Recently, the Acute COPD Exacerbation Prediction Tool (ACCEPT) was introduced to predict the individualized rate and severity of COPD exacerbations [ 65 ]. ACCEPT uses demographical and clinical variables, such as sex, smoking status, lung function parameters, and medication use, to estimate the 1-year exacerbation risk [ 65 ].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, the Acute COPD Exacerbation Prediction Tool (ACCEPT) was introduced to predict the individualized rate and severity of COPD exacerbations [ 65 ]. ACCEPT uses demographical and clinical variables, such as sex, smoking status, lung function parameters, and medication use, to estimate the 1-year exacerbation risk [ 65 ]. This tool has significant limitations as it does not differentiate between the different biological exacerbation clusters, neither does it include biomarkers or enable individualized prediction and early identification.…”

Section: Introductionmentioning

confidence: 99%

Unmet needs in the management of exacerbations of chronic obstructive pulmonary disease

et al. 2021

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Exacerbations of chronic obstructive pulmonary disease (COPD) are episodes of acute worsening of respiratory symptoms that require additional therapy. These events play a pivotal role in the natural course of the disease and are associated with a progressive decline in lung function, reduced health status, a low physical activity level, tremendous health care costs, and increased mortality. Although most exacerbations have an infectious origin, the underlying mechanisms are heterogeneous and specific predictors of their occurrence in individual patients are currently unknown. Accurate prediction and early diagnosis of exacerbations is essential to develop novel targets for prevention and personalized treatments to reduce the impact of these events. Several potential biomarkers have previously been studied, these however lack specificity, accuracy and do not add value to the available clinical predictors. At present, microbial composition and host-microbiome interactions in the lung are increasingly recognized for their role in affecting the susceptibility to exacerbations, and may steer towards a novel direction in the management of COPD exacerbations. This narrative review describes the current challenges and unmet needs in the management of acute exacerbations of COPD. Exacerbation triggers, biological clusters, current treatment strategies, and their limitations, previously studied biomarkers and prediction tools, the lung microbiome and its role in COPD exacerbations as well as future directions are discussed.

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The Acute COPD Exacerbation Prediction Tool (ACCEPT): a modelling study

Cited by 81 publications

References 22 publications

Should the number of acute exacerbations in the previous year be used to guide treatments in COPD?

Should the number of acute exacerbations in the previous year be used to guide treatments in COPD?

Artificial Intelligence in COPD: New Venues to Study a Complex Disease

Unmet needs in the management of exacerbations of chronic obstructive pulmonary disease

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