Systematic review and meta-analysis of predictive symptoms and comorbidities for severe COVID-19 infection

Abstract

Background/introductionCOVID-19, a novel coronavirus outbreak starting in China, is now a rapidly developing public health emergency of international concern. The clinical spectrum of COVID-19 disease is varied, and identifying factors associated with severe disease has been described as an urgent research priority. It has been noted that elderly patients with pre-existing comorbidities are more vulnerable to more severe disease. However, the specific symptoms and comorbidities that most strongly predict disease severity are unclear. We performed a systematic review and meta-analysis to identify the symptoms and comorbidities predictive of COVID-19 severity. MethodThis study was prospectively registered on PROSPERO. A literature search was performed in three databases (MEDLINE, EMBASE and Global Health) for studies indexed up to 5 th March 2020. Two reviewers independently screened the literature and both also completed data extraction. Quality appraisal of studies was performed using the STROBE checklist. Random effects meta-analysis was performed for selected symptoms and comorbidities to identify those most associated with severe COVID-19 infection or ICU admission. ResultsOf the 2259 studies identified, 42 were selected after title and abstract analysis, and 7 studies (including 1813 COVID-19 patients) were chosen for inclusion. The ICU group were older (62.4 years) compared to the non-ICU group (46 years), with a significantly higher proportion of males (67.2% vs. 57.1%, p=0.04).Dyspnoea was the only significant symptom predictive for both severe disease (pOR 3.70, 95% CI 1.83 -7.46) and ICU admission (pOR 6.55, 95% CI 4.28-10.0). Notwithstanding the low prevalence of COPD in severe disease and ICUadmitted groups (4.5% and 9.7%, respectively), COPD was the most strongly predictive comorbidity for both severe disease (pOR 6.42, 95% CI 2. 44 -16.9) and ICU admission (pOR 17.8, 95% CI 6.56 -48.2). Cardiovascular disease and All rights reserved. No reuse allowed without permission. perpetuity. : medRxiv preprint hypertension were also strongly predictive for both severe disease and ICU admission. Those with CVD and hypertension were 4.4 (95% CI 2.64 -7.47) and 3.7 (95% CI 2.22 -5.99) times more likely to have an ICU admission respectively, compared to patients without the comorbidity. ConclusionsDyspnoea was the only symptom strongly predictive for both severe disease and ICU admission, and could be useful in guiding clinical management decisions early in the course of illness. When looking at ICU-admitted patients, who represent the more severe end of the spectrum of clinical severity, COPD patients are particularly vulnerable, and those with cardiovascular disease and hypertension are also at a high-risk of severe illness. To aid clinical assessment, risk stratification, efficient resource allocation, and targeted public health interventions, future research must aim to further define those at high-risk of severe illness with COVID-19.

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

“…[43][44][45][46][47] The evidence base for the development and validation of prediction models related to COVID-19 will quickly increase over the coming months. Together with the increasing evidence from predictor finding studies [48][49][50][51][52][53][54] and open peer review initiatives for COVID-19 related publications, 55 data registries 56-60 are being set up. To maximize the new opportunities and to facilitate IPD meta-analyses, the WHO has recently released a new data platform to encourage sharing of anonymized COVID-19 clinical data.…”

The role of comorbidities and clinical predictors of severe disease in COVID-19: a systematic review and meta-analysis

“…Jain et al meta-analysis on seven studies reported dyspnea as the only predictor of severe COVID-19. 24 However, Gong et al found cough, fever, and dyspnea as the most clinical manifestation in severe cases. 25 Guan et al reported that about 25% of studied patients had at least one comorbidity.…”

Risk prediction for poor outcome and death in hospital in-patients with COVID-19: derivation in Wuhan, China and external validation in London, UK

“…chronic lung disease, cardiovascular disease and hypertension) are also reported to have associations with poor prognosis. 2 However, most studies have not described the study population or intended use of prediction models, and external validation is rare and to date done using datasets originating from different Wuhan hospitals. 3 Given different patterns of testing and organisation of healthcare pathways, external validation in datasets from other countries is required.…”

“…This study and others find that relatively simple risk prediction models using demographic, clinical and laboratory data perform well in internal validation but at best moderately in external validation, either because derivation and external validation populations are small (Xie et al 3 ) and/or because they vary greatly in casemix and severity (our study). There are three decision points where risk prediction may be most useful: (1) deciding who to test; (2) deciding which patients in the community are at high-risk of poor outcomes; and (3) identifying patients at high-risk at the point of hospital admission. Larger studies focusing on particular decision points, with rapid external validation in multiple datasets are needed.…”

“…Univariate associations of patient characteristics and outcomes are summarised in Table S1, with findings broadly consistent with previously reported univariate associations. 2,12,13 The strongest observed univariate associations were increased risk of each outcome with increasing age, the presence of prior comorbidity (particularly chronic lung disease, heart disease, immunocompromise, chronic renal disease and hypertension), the presence of dyspnoea, and laboratory values (increased risk with higher neutrophil count, CRP and creatinine, and with lower lymphocyte count and platelet count). Table 2 summarises multivariable odds ratios of models for both outcomes (Table S2 reports the predictor coefficients with more precision), and Table 3 details model performance in internal and external validation.…”