Defining the “Frequent Exacerbator” Phenotype in COPD

Chenivesse, Cécile; Roche, Nicolás; Cortot, A.; Tillie‐Leblond, I.; Masure, F.; Pérez, Thierry; Boucot, I.; Hamouti, Latifa; Ostinelli, J.; Pribil, C.; Poutchnine, C.; Schück, Stéphane; Pouriel, Mathilde; Housset, B.

doi:10.1016/j.chest.2017.10.009

Cited by 71 publications

(65 citation statements)

References 17 publications

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“…A recent study of chronic obstructive pulmonary disease patients adopted a similar approach to define 'frequent exacerbator' phenotype. 18 We found that ≥2 HF admissions per year was the best threshold that defined 'frequent admitter' phenotype. It was associated with increased mortality compared to patients who accrued ≤1 HF admission per year.…”

Section: Discussionmentioning

confidence: 78%

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Defining a ‘frequent admitter’ phenotype among patients with repeat heart failure admissions

Sellmair

Allen

et al. 2018

European J of Heart Fail

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Aims We aimed to identify a ‘frequent admitter’ phenotype among patients admitted for acute decompensated heart failure (HF). Methods and results We studied 10 363 patients in a population‐based prospective HF registry (2008–2012), segregated into clusters based on their 3‐year HF readmission frequency trajectories. Using receiver‐operating characteristic analysis, we identified the index year readmission frequency threshold that most accurately predicts HF admission frequency clusters. Two clusters of HF patients were identified: a high frequency cluster (90.9%, mean 2.35 ± 3.68 admissions/year) and a low frequency cluster (9.1%, mean 0.50 ± 0.81 admission/year). An index year threshold of two admissions was optimal for distinguishing between clusters. Based on this threshold, ‘frequent admitters’, defined as patients with ≥ 2 HF admissions in the index year (n = 2587), were of younger age (68 ± 13 vs 69 ± 13 years), more often male (58% vs. 54%), smokers (38.4% vs. 34.4%) and had lower left ventricular ejection fraction (37 ± 17 vs. 41 ± 17%) compared to ‘non‐frequent admitters’ (< 2 HF admissions in the index year; n = 7776) (all P < 0.001). Despite similar rates of advanced care utilization, frequent admitters had longer length of stay (median 4.3 vs. 4.0 days), higher annual inpatient costs (€ 7015 vs. € 2967) and higher all‐cause mortality at 3 years compared to the non‐frequent admitters (adjusted odds ratio 2.33, 95% confidence interval 2.11–2.58; P < 0.001). Conclusion ‘Frequent admitters’ have distinct clinical characteristics and worse outcomes compared to non‐frequent admitters. This study may provide a means of anticipating the HF readmission burden and thereby aid in healthcare resource distribution relative to the HF admission frequency phenotype.

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Section: Discussionmentioning

confidence: 78%

“…Indeed, machine learning methods, specifically KmL, has been used in other fields to define disease phenotype. A recent study of chronic obstructive pulmonary disease patients adopted a similar approach to define ‘frequent exacerbator’ phenotype …”

Section: Discussionmentioning

confidence: 99%

Defining a ‘frequent admitter’ phenotype among patients with repeat heart failure admissions

Sellmair

Allen

et al. 2018

European J of Heart Fail

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“…Frequent exacerbators are a group of subjects with 2 or more exacerbations per year. 70 , 82 Although recent data suggest that the frequent exacerbator phenotype is quite infrequent in a large cohort study, this phenotype seems to be quite stable over time because the best predictor for exacerbations is past history of exacerbations. 83 Other circulating biomarkers have been examined as part of the SPIROMICS and COPDGene cohorts to characterize this phenotype, and except for high levels of decorin and α 2 -macroglobulin, none have proved to be robust enough to be replicated across cohorts.…”

Section: Recognizing Subpopulations Within Copd: “Taxonomy” Of Airwaymentioning

confidence: 99%

Chronic obstructive pulmonary disease subpopulations and phenotyping

Martínez

2018

Journal of Allergy and Clinical Immunology

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The diagnosis and treatment of chronic obstructive pulmonary disease (COPD) has been based largely on a one-size-fits-all approach. Diagnosis of COPD is based on meeting the physiologic criteria of fixed obstruction in forced expiratory flows and treatment focus on symptomatic relief, with limited effect on overall prognosis. However, patients with COPD have distinct features that determine very different evolutions of the disease. In this review we highlight distinct subgroups of COPD characterized by unique pathophysiologic derangements, response to treatment, and disease progression. It is likely that identification of subgroups of COPD will lead to discovery of much needed disease-modifying therapeutic approaches. We argue that a precision approach that integrates multiple dimensions (clinical, physiologic, imaging, and endotyping) is needed to move the field forward in the treatment of this disease.

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“…This was further explored in the Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE) cohort, demonstrating that severity and frequency of exacerbations correlated with the severity of COPD and that across all GOLD stages, the single best predictor of exacerbations was a history of exacerbations [ 15 ]. Similarly, study of an unbiased prospective cohort COPD patients independently suggested that a history of two moderate or severe exacerbations was the best predictor of subsequent events [ 16 ]. However, a recent large observational cohort suggested that individuals meeting this threshold are rare and that variability in exacerbation rate over time is significant [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Precision medicine in COPD: where are we and where do we need to go?

2018

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Chronic obstructive pulmonary disease (COPD) was the fourth leading cause of death worldwide in 2015. Current treatments for patients ease discomfort and help decrease disease progression; however, none improve lung function or change mortality. COPD is heterogeneous in its molecular and clinical presentation, making it difficult to understand disease aetiology and define robust therapeutic strategies. Given the complexity of the disease we propose a precision medicine approach to understanding and better treating COPD. It is possible that multiOMICs can be used as a tool to integrate data from multiple fields. Moreover, analysis of electronic medical records could aid in the treatment of patients and in the predictions of outcomes. The Precision Medicine Initiative created in 2015 has made precision medicine approaches to treat disease a reality; one of these diseases being COPD.

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Defining the “Frequent Exacerbator” Phenotype in COPD

Cited by 71 publications

References 17 publications

Defining a ‘frequent admitter’ phenotype among patients with repeat heart failure admissions

Defining a ‘frequent admitter’ phenotype among patients with repeat heart failure admissions

Chronic obstructive pulmonary disease subpopulations and phenotyping

Precision medicine in COPD: where are we and where do we need to go?

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