Chronic obstructive pulmonary disease is associated with severe coronavirus disease 2019 (COVID-19)

Lippi, Giuseppe; Henry, Brandon Michael

doi:10.1016/j.rmed.2020.105941

Cited by 351 publications

(295 citation statements)

References 13 publications

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“…In this study, we observed that hypertension carries a nearly 2.5 -fold higher risk of developing severe COVID -19 or dying of SARS -CoV -2 infection (FIGurE 1). Although this association seems weaker than that reported earlier for other comorbidities, such as chronic obstructive pulmonary disease (over 5 -fold higher risk) 27 or chronic kidney disease (over 3 -fold higher risk), 28 it still carries important clinical implications.…”

Section: Article Information Conflict Of Interest None Declaredmentioning

confidence: 60%

Hypertension and its severity or mortality in Coronavirus Disease 2019 (COVID-19): a pooled analysis

Lippi¹,

Wong²,

Henry³

2020

Polish Archives of Internal Medicine

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390

296

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Section: Article Information Conflict Of Interest None Declaredmentioning

confidence: 60%

Hypertension and its severity or mortality in Coronavirus Disease 2019 (COVID-19): a pooled analysis

Lippi¹,

Wong²,

Henry³

2020

Polish Archives of Internal Medicine

Self Cite

390

296

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“…these parameters are underestimates. Nevertheless, the observed differences across scenarios remained unchanged overall when considering a much higher and unlikely (26,41,42) diagnosis rate of 1 in 10, except for the efficacy of the post-quarantine screening on the course of the epidemic, which was much greater under this assumption, contributing to the robustness of our conclusions. Second, the contact matrix was approximated using multiple assumptions for each type of contact.…”

Section: Discussionmentioning

confidence: 67%

Facing the COVID-19 epidemic in NYC: a stochastic agent-based model of various intervention strategies

Hoertel

Blachier²,

Blanco

et al. 2020

Preprint

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AcknowledgmentsWe thank Pr Melanie Wall, Pr Yuanjia Wang and Mrs Marina Sánchez Rico for their helpful comments on early versions of this manuscript. Conflicts of interestThe authors declare no competing interests. DisclaimerThe views and opinions expressed in this report are those of the authors and should not be construed to represent the views of any of the sponsoring organizations, agencies, or the US government.Authorship HL designed the model and performed the analysis. NH wrote the first draft of the manuscript and critically revised the model. MB critically revised the model and manuscript for scientific content; CB, MO, MM and FL critically revised the manuscript for scientific content. All authors have contributed to and have approved the current version of the manuscript. AbstractGlobal spread of coronavirus disease 2019 (COVID-19) has created an unprecedented infectious disease crisis worldwide. Despite uncertainties about COVID-19, model-based forecasting of competing mitigation measures on its course is urgently needed to inform mitigation policy. We used a stochastic agent-based microsimulation model of the COVID-19 epidemic in New York City and evaluated the potential impact of quarantine duration (from 4 to 16 weeks), quarantine lifting type (1-step lifting for all individuals versus a 2-step lifting according to age), post-quarantine screening, and use of a hypothetical effective treatment against COVID-19 on the disease's cumulative incidence and mortality, and on ICU-bed occupancy. The source code of the model has been deposited in a public source code repository (GitHub®). The model calibrated well and variation of model parameter values had little impact on outcome estimates. While quarantine is efficient to contain the viral spread, it is unlikely to prevent a rebound of the epidemic once lifted. We projected that lifting quarantine in a single step for the full population would be unlikely to substantially lower the cumulative mortality, regardless of quarantine duration. By contrast, a two-step quarantine lifting according to age was associated with a substantially lower cumulative mortality and incidence, up to 71% and 23%, respectively, as well as lower ICU-bed occupancy. Although post-quarantine screening was associated with diminished epidemic rebound, this strategy may not prevent ICUs from being overcrowded. It may even become deleterious after a 2-step quarantine lifting according to age if the herd immunity effect does not had sufficient time to become established in the younger population when the quarantine is lifted for the older population. An effective treatment against COVID-19 would considerably reduce the consequences of the epidemic, even more so if ICU capacity is not exceeded.

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“…29,30 Recent meta-analyses have identi ed that cardiovascular diseases and COPD can greatly affect the severity of COVID-19. 31,32 Renal disease, dementia and cancer could be also important risk factors for severe COVID-19. [33][34][35] The effects of each comorbidity on the COVID-19 mortality have been observed in our results as well, and if they are combined, the effect will be stronger on the severity of COVID-19.…”

Section: Discussionmentioning

confidence: 99%

Impact of comorbidity burden on mortality in patients with COVID-19: a retrospective analysis of the Korean health insurance database

Cho

Yoon

Lee

2020

Preprint

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We aimed to investigate the impact of comorbidity burden on mortality in patients with COVID-19. We analyzed the COVID-19 data from the nationwide health insurance claims of South Korea. Data on demographic characteristics, comorbidities, and mortality records of patients with COVID-19 were extracted from the database. The odds ratios of mortality according to comorbidities in patients with COVID-19 with and without adjustment for age and sex were calculated. The predictive value of the original Charlson comorbidity index (CCI) and the age-adjusted CCI (ACCI) for mortality in patients with COVID-19 were investigated using the receiver operating characteristic (ROC) curve analysis. Among 7,590 patients with COVID-19, 227 (3.0%) had died. After age and sex adjustment, hypertension, diabetes mellitus, congestive heart failure, dementia, chronic pulmonary disease, liver, renal, and cancer were significant risk factors for mortality. The ROC curve analysis showed that ACCI threshold ≥4 yielded the best cut-off point for predicting mortality (area under the ROC 0.92; 95% CI, 0.91–0.94). Our study revealed multiple risk factors that were associated with mortality in patients with COVID-19. The high predictive power of the ACCI for mortality in our results could support the importance of old age and comorbidities in the severity of COVID-19.

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Chronic obstructive pulmonary disease is associated with severe coronavirus disease 2019 (COVID-19)

Cited by 351 publications

References 13 publications

Hypertension and its severity or mortality in Coronavirus Disease 2019 (COVID-19): a pooled analysis

Hypertension and its severity or mortality in Coronavirus Disease 2019 (COVID-19): a pooled analysis

Facing the COVID-19 epidemic in NYC: a stochastic agent-based model of various intervention strategies

Impact of comorbidity burden on mortality in patients with COVID-19: a retrospective analysis of the Korean health insurance database

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