Estimation of risk factors for COVID-19 mortality - preliminary results

Caramelo, Francisco; Ferreira, Noely Evangelista; Oliveiros, Bárbara

doi:10.1101/2020.02.24.20027268

Cited by 237 publications

(279 citation statements)

References 10 publications

(10 reference statements)

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“…2 0 0 4 J u n ; 3 0 ( 6 ) : 1 2 2 8 -3 . Overall (I-squared = 91.5%, p = 0.000) Chen R et al 2020 Caramelo F et al 2020Caramelo F et al 2020 Search terms for Scopus #1 ("COVID-19" OR "severe acute respiratory syndrome coronavirus 2" OR "SARS-CoV-2" OR "novel coronavirus" OR "2019-nCoV"): Title/Abstract/Keyword #2 ("death" OR "mortality" OR "survival" OR "fatal outcome"): Title/Abstract/Keyword #1 AND #2 Search terms for Web of Science #1. TS = ("COVID-19" OR "severe acute respiratory syndrome coronavirus 2" OR "SARS-CoV-2" OR "novel coronavirus" OR "2019-nCoV") #2.…”

Section: Resultsmentioning

confidence: 97%

Risk factors for mortality in patients with Coronavirus disease 2019 (COVID-19) infection: a systematic review and meta-analysis of observational studies

Parohan¹,

Yaghoubi

Seraj

et al. 2020

Preprint

101

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24Background: Coronavirus disease 2019 is an emerging disease that was first reported 25 in Wuhan city, the capital of Hubei province in China, and has subsequently spread worldwide. 26Risk factors for mortality have not been well summarized. Current meta-analysis of retrospective 27 cohort studies was done to summarize available findings on the association between age, gender, 28 comorbidities and risk of death from COVID-19 infection. 29 Methods: Online databases including Web of Science, PubMed, Scopus and Google scholar were 30 searched to detect relevant publications up to 22 March 2020, using relevant keywords. To pool 31 data, random-effects model was used. Furthermore, sensitivity analysis and publication bias test 32 were also done. 33 Results: In total, six retrospective studies with 22,350 COVID-19 infected patients and 741 cases 34 of death were included in the current meta-analysis. A significant positive association was found 35 between older age (≥65 years old) and COVID-19 mortality (combined effect size=2.39 (over 36 twofold), 95% CIs=1.75-3.28, p<0.001). Such finding was also seen for hypertension (combined 37 effect size=3.29 (over threefold), 95% CIs=1.54-7.05, p=0.002), diabetes (combined effect 38 size=3.11 (over threefold), 95% CIs=1.10-8.80, p=0.032), chronic obstructive pulmonary disease 39 (COPD) (combined effect size=7.69 (over sevenfold), 95% CIs=5.65-10.47, p<0.001) and 40 cardiovascular diseases (CVDs) (combined effect size=7.39 (over sevenfold), 95% CIs=2.88-41 18.96, p<0.001).42 Conclusions: Older age, hypertension, diabetes, COPD and CVDs were associated with greater 43 risk of death from COVID-19 infection. These findings could help clinicians to identify patients 44 with poor prognosis at an early stage.45

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Section: Resultsmentioning

confidence: 97%

Risk factors for mortality in patients with Coronavirus disease 2019 (COVID-19) infection: a systematic review and meta-analysis of observational studies

Parohan¹,

Yaghoubi

Seraj

et al. 2020

Preprint

101

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“…Risk stratification scores that identify patients at high risk of deterioration or that qualify patients for empiric therapies like hydroxychloroquine also need to reconsider adding patients with cerebrovascular disease. 8…”

Section: Discussionmentioning

confidence: 99%

Scoping review of prevalence of neurologic comorbidities in patients hospitalized for COVID-19

2020

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Objective: The emergence of COVID-19 presents a challenge for neurologists caring for patients with pre-existing neurological conditions hospitalized for COVID-19 or for evaluation of patients who suffer neurological complications during COVID-19 infection. We conducted a scoping review of available literature on COVID-19 to assess the potential impact on neurologists in terms of prevalent comorbidities and incidence of new neurological events in patients hospitalized with COVID-19. Methods:We searched Medline/PubMed, CINAHL (EBSCO), and SCOPUS databases for adult patients with pre-existing neurologic disease that were diagnosed and hospitalized for COVID-19, or reported incidence of secondary neurologic events following diagnosis of COVID-19. Pooled descriptive statistics of clinical data and comorbidities were examined.Results: Among screened articles, 322 of 4014 (8.0 %) of hospitalized patients diagnosed and treated for COVID-19 had a pre-existing neurological illness. Four retrospective studies demonstrated an increased risk of secondary neurological complications in hospitalized patients with COVID-19 (incidence of 6%, 20% and 36.4%, respectively). Inconsistent reporting and limited statistical analysis among these studies did not allow for assessment of comparative outcomes.Conclusion: Emerging literature suggests a daunting clinical relationship between COVID-19 and neurological illness. Neurologists need to be prepared to reorganize their consultative practices to serve the neurological needs of patients during this pandemic. Herman

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“…One study used U.S. Medicare claims data from 2015 to 2016 to estimate COVID-19 vulnerability, 8 two studies used control CT scans from the USA or Switzerland, 11 25 and one study used simulated data. 18 All but one study 24 developed prediction models for use in adults. The median age varied between studies (from 34 to 65 years, see Supplementary Table 1), as did the percentage of men (from 41% to 61%).…”

Section: Primary Datasetsmentioning

confidence: 99%

“…Bai, Fang, et al 9 low unclear unclear high 38 Caramelo, Ferreira, et al 18 high high high high Gong, Ou, et al 32 low unclear unclear high Lu, Hu, et al 19 low low low high Qi, Jiang, et al 20 unclear low low high Shi, Yu, et al 37 high high high high Xie, Hungerford, et al 7 low low low high Yan, Zhang, et al 21 low high low high Yuan, Yin, et al 22 low high low high *1 Risk of bias high due to not evaluating calibration. If this criterion is not taken into account, analysis risk of bias would have been unclear.…”

Section: Prognosismentioning

confidence: 99%

Systematic review and critical appraisal of prediction models for diagnosis and prognosis of COVID-19 infection

Wynants

Calster

Bonten

et al. 2020

Preprint

167

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words)Objective To review and critically appraise published and preprint reports of models that aim to predict either (i) presence of existing COVID-19 infection, (ii) future complications in individuals already diagnosed with COVID-19, or (iii) models to identify individuals at high risk for COVID-19 in the general population.Design Rapid systematic review and critical appraisal of prediction models for diagnosis or prognosis of COVID-19 infection. Data sourcesPubMed, EMBASE via Ovid, Arxiv, medRxiv and bioRxiv until 24 th March 2020. Study selectionStudies that developed or validated a multivariable COVID-19 related prediction model. Two authors independently screened titles, abstracts and full text. Data extractionData from included studies were extracted independently by at least two authors based on the CHARMS checklist, and risk of bias was assessed using PROBAST.Data were extracted on various domains including the participants, predictors, outcomes, data analysis, and prediction model performance.Results 2696 titles were screened. Of these, 27 studies describing 31 prediction models were included for data extraction and critical appraisal. We identified three models to predict hospital admission from pneumonia and other events (as a proxy for covid-19 pneumonia) in the general population; 18 diagnostic models to detect COVID-19 infection in symptomatic individuals (13 of which were machine learning utilising computed tomography (CT) results); and ten prognostic models for predicting mortality risk, progression to a severe state, or length of hospital stay. Only one of these studies used data on COVID-19 cases outside of China.Most reported predictors of presence of COVID-19 in suspected patients included age, body temperature, and signs and symptoms. Most reported predictors of severe prognosis in

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Estimation of risk factors for COVID-19 mortality - preliminary results

Abstract: Since late December 2019 a new epidemic outbreak has emerged from Whuhan, China. Rapidly the new coronavirus has spread worldwide. China

Cited by 237 publications

References 10 publications

Risk factors for mortality in patients with Coronavirus disease 2019 (COVID-19) infection: a systematic review and meta-analysis of observational studies

Risk factors for mortality in patients with Coronavirus disease 2019 (COVID-19) infection: a systematic review and meta-analysis of observational studies

Scoping review of prevalence of neurologic comorbidities in patients hospitalized for COVID-19

Systematic review and critical appraisal of prediction models for diagnosis and prognosis of COVID-19 infection

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