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

Sidhaye, Venkataramana K.; Nishida, Kristine; Martínez, Fernando J.

doi:10.1183/16000617.0022-2018

Cited by 76 publications

(62 citation statements)

References 131 publications

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“…Another could be the unusually overrepresented patients in IMPACT with FEV1>50% predicted and two or more exacerbations in the previous year. The data could be reanalysed to explore these factors and others, including thresholds of airflow limitation and eosinophil counts, particularly higher cut-offs such as 4% or 300 cells•μL −1 [7,[17][18][19]. It would also be valuable to see whether the pattern over time also emerges with the frequency of exacerbations and to compare the characteristics of early and late exacerbators.…”

Section: Resultsmentioning

confidence: 99%

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Triple therapy trials in COPD: a precision medicine opportunity

Suissa

Ariel

2018

Eur Respir J

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Section: Resultsmentioning

confidence: 99%

“…The design of the IMPACT trial raised some methodological issues that limited the interpretation of its findings [5]. However, these limitations provide some opportunities to better understand the potential for triple therapy to advance precision medicine, which has been lagging behind in COPD [6,7].…”

Section: Introductionmentioning

confidence: 99%

Triple therapy trials in COPD: a precision medicine opportunity

Suissa

Ariel

2018

Eur Respir J

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“…COPD is a heterogeneous disease that differs in etiology, progression, and prognosis from one patient to the other [28,29]. About 37% of patients with COPD display a persistently elevated eosinophil count [14].…”

Section: Discussionmentioning

confidence: 99%

Blood eosinophil levels and prognosis of hospitalized patients with acute exacerbation of chronic obstructive pulmonary disease

Zhang

Fang

et al. 2020

Preprint

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Background: Studies about the clinical significance of high eosinophil levels in chronic obstructive pulmonary disease (COPD) are conflicting, and it has been less studied in hospitalized patients with acute exacerbation of COPD (AECOPD).This study was to examine blood eosinophil levels in relation to the prognosis of hospitalized patients with AECOPD.Methods: This was a retrospective cohort study of patients with AECOPD as their primary diagnosis and admitted to Beijing Shijitan Hospital, Capital Medical University, from January 2010 to December 2016. The patients were assigned according to the proportion and count of eosinophil in peripheral blood at their first hospitalization. Patients were grouped as ≤100, 100-300, and ≥300 eosinophils/µL of peripheral blood. The use of glucocorticoids, duration of hospitalization, in-hospital mortality, and re-hospitalization were examined.Results: Compared with the 100-300 eosinophils/µL group, the ≤100 eosinophils/µL group showed higher frequencies of fever, respiratory failure, and the use of systemic glucocorticoids. Eosinophil counts were not associated with in-hospital mortality and duration of hospitalization. The multivariable analysis showed that GOLD3/4 (odds ratio (OR)=2.04, 95%CI: 1.20-3.44, P=0.008), neutrophil count (OR=1.21, 95%CI: 1.03-1.41, P=0.019), systemic glucocorticoids (OR=1.84, 95%CI: 1.41-2.98, P=0.012), mechanical ventilation (OR=2.66, 95%CI: 1.36-5.18, P=0.004), and acute exacerbation in the past year before hospitalization (OR=2.03, 95%CI: 1.27-3.23, P=0.003) were independently associated with acute exacerbation within 1 year after discharge. Eosinophil count was not associated with acute exacerbation within 1 year after discharge.Conclusion: Peripheral blood eosinophil counts are not associated with the 1-year AECOPD prognosis.

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“…Phenotypes are de ned as the observable properties of disorders that are produced by the interactions of the genotype and the environment (8). They represent groups with similar clinical characteristics, prognosis and/or therapeutic needs (9) and therefore, can be utilized in the clinical management of the disorders. In many common respiratory disorders, like asthma and chronic obstructive pulmonary disease, several distinct phenotypes have been documented (8,10).…”

Section: Introductionmentioning

confidence: 99%

Cluster analysis in 975 patients with current cough identifies a phenotype with several cough triggers, many background disorders, and low quality of life

Koskela

Selander

Lätti

2020

Preprint

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Background Recognition of disorder phenotypes may help to estimate prognosis and to guide the clinical management. Current cough management guidelines classify patients according to the duration of the cough episode. However, this classification is not based on phenotype analyses. The present study aimed to identify cough phenotypes by clustering. Methods An email survey among employed, working-age subjects identified 975 patients with current cough. All filled in a comprehensive 80-item questionnaire including the Leicester Cough Questionnaire. Phenotypes were identified utilizing K-means partitional clustering. A subgroup filled in a follow-up questionnaire 12 months later to investigate the possible differences in the prognosis between the phenotypes. Results Two clusters were found. The cluster A included 608 patients (62.4% of the population) and the cluster B 367 patients (37.6%). The three most important variables to separate the clusters were the number of the triggers of cough (mean 2.63 (SD 2.22) vs . 6.95 (2.30), respectively, p<0.001), the number of the cough background disorders (chronic rhinosinusitis, current asthma, gastroesophageal reflux disease, 0.29 (0.50) vs . 1.28 (0.75), respectively, p<0.001), and the Leicester Cough Questionnaire physical domain (5.33 (0.76) vs . 4.25 (0.84), respectively, p<0.001). There were significant interrelationships between these three variables (each p<0.001). Duration of the episode was not among the most important variables to separate the clusters. At 12 months, 27.0% of the patients of the cluster A and 46.1% of the patients of the cluster B suffered from cough that had continued without interruptions from the first survey (p<0.001). Conclusions Two cough phenotypes could be identified. Cluster A represents phenotype A, which includes the majority of patients and has a tendency to heal by itself. The authors propose that cluster B represents phenotype TBQ (Triggers, Background disorders, Quality of life impairment). Given the poor prognosis of this phenotype, it urges a prompt and comprehensive clinical evaluation regardless of the duration of the cough episode.

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

Cited by 76 publications

References 131 publications

Triple therapy trials in COPD: a precision medicine opportunity

Triple therapy trials in COPD: a precision medicine opportunity

Blood eosinophil levels and prognosis of hospitalized patients with acute exacerbation of chronic obstructive pulmonary disease

Cluster analysis in 975 patients with current cough identifies a phenotype with several cough triggers, many background disorders, and low quality of life

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