bims-covirf 2021-08-29 papers

Issue of 2021–08–29
five papers selected by
Catherine Rycroft, BresMed

BMC Infect Dis. 2021 Aug 21. 21(1): 855

BACKGROUND: Mortality rates of coronavirus disease-2019 (COVID-19) continue to rise across the world. The impact of several risk factors on coronavirus mortality has been previously reported in several meta-analyses limited by small sample sizes. In this systematic review, we aimed to summarize available findings on the association between comorbidities, complications, smoking status, obesity, gender, age and D-dimer, and risk of mortality from COVID-19 using a large dataset from a number of studies.
METHOD: Electronic databases including Google Scholar, Cochrane Library, Web of Sciences (WOS), EMBASE, Medline/PubMed, COVID-19 Research Database, and Scopus, were systematically searched till 31 August 2020. We included all human studies regardless of language, publication date or region. Forty-two studies with a total of 423,117 patients met the inclusion criteria. To pool the estimate, a mixed-effect model was used. Moreover, publication bias and sensitivity analysis were evaluated.
RESULTS: The findings of the included studies were consistent in stating the contribution of comorbidities, gender, age, smoking status, obesity, acute kidney injury, and D-dimer as a risk factor to increase the requirement for advanced medical care. The analysis results showed that the pooled prevalence of mortality among hospitalized patients with COVID-19 was 17.62% (95% CI 14.26-21.57%, 42 studies and 423,117 patients). Older age has shown increased risk of mortality due to coronavirus and the pooled odds ratio (pOR) and hazard ratio (pHR) were 2.61 (95% CI 1.75-3.47) and 1.31 (95% CI 1.11-1.51), respectively. A significant association were found between COVID-19 mortality and male (pOR = 1.45; 95% CI 1.41-1.51; pHR = 1.24; 95% CI 1.07-1.41), and current smoker (pOR = 1.42; 95% CI 1.01-1.83). Furthermore, risk of mortality among hospitalized COVID-19 patients is highly influenced by patients with Chronic Obstructive Pulmonary Disease (COPD), Cardiovascular Disease (CVD), diabetes, hypertension, obese, cancer, acute kidney injury and increase D-dimer.
CONCLUSION: Chronic comorbidities, complications, and demographic variables including acute kidney injury, COPD, diabetes, hypertension, CVD, cancer, increased D-dimer, male gender, older age, current smoker, and obesity are clinical risk factors for a fatal outcome associated with coronavirus. The findings could be used for disease's future research, control and prevention.

Keywords: Comorbidities; Demographic characteristics; Funnel plot; Heterogeneity; Publication bias; Sensitivity analysis

DOI: https://doi.org/10.1186/s12879-021-06536-3

Infect Dis Rep. 2021 Aug 08. 13(3): 700-711

Unusually High Risks of COVID-19 Mortality with Age-Related Comorbidities: An Adjusted Meta-Analysis Method to Improve the Risk Assessment of Mortality Using the Comorbid Mortality Data.

Andrew Antos, Ming Lai Kwong, Timothy Balmorez, Alyssa Villanueva, Shin Murakami.

BACKGROUND: The pandemic of Coronavirus Disease 2019 (COVID-19) has been a threat to global health. In the US, the Centers for Disease Control and Prevention (CDC) has listed 12 comorbidities within the first tier that increase with the risk of severe illness from COVID-19, including the comorbidities that are common with increasing age (referred to as age-related comorbidities) and other comorbidities. However, the current method compares a population with and without a particular disease (or disorder), which may result in a bias in the results. Thus, comorbidity risks of COVID-19 mortality may be underestimated.
OBJECTIVE: To re-evaluate the mortality data from the US and estimate the odds ratios of death by major comorbidities with COVID-19, we incorporated the control population with no comorbidity reported and assessed the risk of COVID-19 mortality with a comorbidity.
METHODS: We collected all the comorbidity data from the public health websites of fifty US States and Washington DC (originally accessed on December 2020). The timing of the data collection should minimize bias from the COVID-19 vaccines and new COVID-19 variants. The comorbidity demographic data were extracted from the state public health data made available online. Using the inverse variance random-effects model, we performed a comparative analysis and estimated the odds ratio of deaths by COVID-19 with pre-existing comorbidities.
RESULTS: A total of 39,451 COVID-19 deaths were identified from four States that had comorbidity data, including Alabama, Louisiana, Mississippi, and New York. 92.8% of the COVID-19 deaths were associated with a pre-existing comorbidity. The risk of mortality associated with at least one comorbidity combined was 1113 times higher than that with no comorbidity. The comparative analysis identified nine comorbidities with odds ratios of up to 35 times higher than no comorbidities. Of them, the top four comorbidities were: hypertension (odds ratio 34.73; 95% CI 3.63-331.91; p = 0.002), diabetes (odds ratio 20.16; 95% CI 5.55-73.18; p < 0.00001), cardiovascular disease (odds ratio 18.91; 95% CI 2.88-124.38; p = 0.002), and chronic kidney disease (odds ratio 12.34; 95% CI 9.90-15.39; p < 0.00001). Interestingly, lung disease added only a modest increase in risk (odds ratio 6.69; 95% CI 1.06-42.26; p < 0.00001).
CONCLUSION: The aforementioned comorbidities show surprisingly high risks of COVID-19 mortality when compared to the population with no comorbidity. Major comorbidities were enriched with pre-existing comorbidities that are common with increasing age (cardiovascular disease, diabetes, and hypertension). The COVID-19 deaths were mostly associated with at least one comorbidity, which may be a source of the bias leading to the underestimation of the mortality risks previously reported. We note that the method has limitations stemming primarily from the availability of the data. Taken together, this type of study is useful to approximate the risks, which most likely provide an updated awareness of age-related comorbidities.

Keywords: COVID-19; age-related comorbidity; mortality; risk assessment

DOI: https://doi.org/10.3390/idr13030065

BMC Med. 2021 Aug 27. 19(1): 212

Pre-existing health conditions and severe COVID-19 outcomes: an umbrella review approach and meta-analysis of global evidence.

Marina Treskova-Schwarzbach, Laura Haas, Sarah Reda, Antonia Pilic, Anna Borodova, Kasra Karimi, Judith Koch, Teresa Nygren, Stefan Scholz, Viktoria Schönfeld, Sabine Vygen-Bonnet, Ole Wichmann, Thomas Harder.

BACKGROUND: This study applies an umbrella review approach to summarise the global evidence on the risk of severe COVID-19 outcomes in patients with pre-existing health conditions.
METHODS: Systematic reviews (SRs) were identified in PubMed, Embase/Medline and seven pre-print servers until December 11, 2020. Due to the absence of age-adjusted risk effects stratified by geographical regions, a re-analysis of the evidence was conducted. Primary studies were extracted from SRs and evaluated for inclusion in the re-analysis. Studies were included if they reported risk estimates (odds ratio (OR), hazard ratio (HR), relative risk (RR)) for hospitalisation, intensive care unit admission, intubation or death. Estimated associations were extracted from the primary studies for reported pre-existing conditions. Meta-analyses were performed stratified for each outcome by regions of the World Health Organization. The evidence certainty was assessed using GRADE. Registration number CRD42020215846.
RESULTS: In total, 160 primary studies from 120 SRs contributed 464 estimates for 42 pre-existing conditions. Most studies were conducted in North America, European, and Western Pacific regions. Evidence from Africa, South/Latin America, and the Eastern Mediterranean region was scarce. No evidence was available from the South-East Asia region. Diabetes (HR range 1.2-2.0 (CI range 1.1-2.8)), obesity (OR range 1.5-1.75 (CI range 1.1-2.3)), heart failure (HR range 1.3-3.3 (CI range 0.9-8.2)), COPD (HR range 1.12-2.2 (CI range 1.1-3.2)) and dementia (HR range 1.4-7.7 (CI range 1.2-39.6)) were associated with fatal COVID-19 in different regions, although the estimates varied. Evidence from Europe and North America showed that liver cirrhosis (OR range 3.2-5.9 (CI range 0.9-27.7)) and active cancer (OR range 1.6-4.7 (CI range 0.5-14.9)) were also associated with increased risk of death. Association between HIV and undesirable COVID-19 outcomes showed regional heterogeneity, with an increased risk of death in Africa (HR 1.7 (CI 1.3-2.2)). GRADE certainty was moderate to high for most associations.
CONCLUSION: Risk of undesirable COVID-19 health outcomes is consistently increased in certain patient subgroups across geographical regions, showing high variability in others. The results can be used to inform COVID-19 vaccine prioritisation or other intervention strategies.

Keywords: COVID-19; Comorbidities; Death; Hospitalisation; Pre-existing health conditions; SARS-CoV-2; Umbrella review

DOI: https://doi.org/10.1186/s12916-021-02058-6

Influenza Other Respir Viruses. 2021 Aug 25.

Population-based assessment of risks for severe COVID-19 disease outcomes.

Ousseny Zerbo, Ned Lewis, Bruce Fireman, Kristin Goddard, Jacek Skarbinski, James J Sejvar, Eduardo Azziz-Baumgartner, Nicola P Klein.

Among approximately 4.6 million members of Kaiser Permanente Northern California, we examined associations of severe COVID-19 with demographic factors and comorbidities. As of July 23, 2021, 16 182 had been hospitalized, 2416 admitted to an ICU, and 1525 died due to COVID-19. Age was strongly associated with hospitalization, ICU admission, and death. Black persons and Hispanic ethnicity had higher risk of death compared with Whites. Among the comorbidities examined, Alzheimer's disease was associated with the highest risk for hospitalization (aHR 3.19, CI: 2.88-3.52) and death (aHR 4.04, CI: 3.32-4.91). Parkinson's disease had the second highest risk of death (aHR = 2.07, CI: 1.50-2.87).

Keywords: COVID-19; comorbidities; race/ethnicity; risk factors; severe disease

DOI: https://doi.org/10.1111/irv.12901

Diagnostics (Basel). 2021 Jul 31. pii: 1383. [Epub ahead of print]11(8):

Machine Learning Approaches to Identify Patient Comorbidities and Symptoms That Increased Risk of Mortality in COVID-19.

Sakifa Aktar, Ashis Talukder, Md Martuza Ahamad, A H M Kamal, Jahidur Rahman Khan, Md Protikuzzaman, Nasif Hossain, A K M Azad, Julian M W Quinn, Mathew A Summers, Teng Liaw, Valsamma Eapen, Mohammad Ali Moni.

Providing appropriate care for people suffering from COVID-19, the disease caused by the pandemic SARS-CoV-2 virus, is a significant global challenge. Many individuals who become infected may have pre-existing conditions that may interact with COVID-19 to increase symptom severity and mortality risk. COVID-19 patient comorbidities are likely to be informative regarding the individual risk of severe illness and mortality. Determining the degree to which comorbidities are associated with severe symptoms and mortality would thus greatly assist in COVID-19 care planning and provision. To assess this we performed a meta-analysis of published global literature, and machine learning predictive analysis using an aggregated COVID-19 global dataset. Our meta-analysis suggested that chronic obstructive pulmonary disease (COPD), cerebrovascular disease (CEVD), cardiovascular disease (CVD), type 2 diabetes, malignancy, and hypertension as most significantly associated with COVID-19 severity in the current published literature. Machine learning classification using novel aggregated cohort data similarly found COPD, CVD, CKD, type 2 diabetes, malignancy, and hypertension, as well as asthma, as the most significant features for classifying those deceased versus those who survived COVID-19. While age and gender were the most significant predictors of mortality, in terms of symptom-comorbidity combinations, it was observed that Pneumonia-Hypertension, Pneumonia-Diabetes, and Acute Respiratory Distress Syndrome (ARDS)-Hypertension showed the most significant associations with COVID-19 mortality. These results highlight the patient cohorts most likely to be at risk of COVID-19-related severe morbidity and mortality, which have implications for prioritization of hospital resources.

Keywords: COVID-19; SARS-CoV-2; comorbidities; machine learning; meta-analysis

DOI: https://doi.org/10.3390/diagnostics11081383