Predictor Of COVID-19 Severity Research Articles

Cluster Analysis of Biochemical Markers as Predictor of COVID-19 Severity

Numerous blood biomarkers are altered in COVID-19 patients; however, no early biochemical markers are currently being used in clinical practice to predict COVID-19 severity. COVID-19, the most recent pandemic, is caused by the SRS-CoV-2 coronavirus. The study was aimed to identify patient groups with a high and low risk of developing COVID-19 using a cluster analysis of several biomarkers. 137 women with confirmed SARS CoV-2 RNA testing were collected and analyzed for biochemical profiles. Two-dimensional automated hierarchy clustering of all biomarkers was applied, and patients were sorted into classes. Biochemistry marker variations (Ferritin, lactate dehydrogenase LDH, D-dimer, and C- reactive protein CRP) have split COVID-19 patients into two groups(severe cases and non-severe cases groups). Ferritin, lactate dehydrogenase LDH, D-dimer and CRP were markedly increased in COVID-19 patients in the first group (severe cases). Our findings imply that early measured levels of (Ferritin, lactate dehydrogenase LDH, D-dimer, and C- reactive protein CRP) are linked to a decreased probability of COVID-19 severity. Elevated levels of this biomarker may predict COVID severity development.

Long noncoding RNA HULC is an independent predictor of COVID-19 severity and mortality in relation to microRNA-9 and IL-6

<abstract> <p>LncRNA HULC regulates inflammation in vascular endothelial cells resulting in their dysfunction. Endothelial dysfunction contributes to severe COVID-19. lncRNA HULC targets miRNA-9 that play roles in the pathogenesis and progression of COVID-19 through the acute inflammatory response mediated by IL-6. This study aimed to evaluate the role of lncRNA HULC, miRNA-9, and IL-6 in estimating the severity and predicting the prognosis of COVID-19. There were 38 non-severe, 38 severe COVID-19 patients, and 38 healthy controls enrolled in this study. Expression of lncRNA HULC and miRNA-9 was performed using RT-qPCR. ELISA was utilized to measure serum IL-6. Expression of lncRNA HULC and IL-6 level were increased in severe patients compared to non-severe patients and controls (p < 0.001). MiRNA-9 showed the lowest expression levels in the severe patients in comparison with non-severe patients and controls (p < 0.001) lncRNA HULC was negatively correlated with miRNA-9 (p < 0.001, r = −0.582) and positively correlated with IL-6 (p < 0.001, r = 0.567). Furthermore, miRNA-9 showed a negative correlation with IL-6 (p < 0.001, r = −0.0466). For severity prediction, lncRNA HULC expression had an adjusted OR of 52.5 (95% CI: 1.43−192.2, p = 0.031). The lncRNA HULC had an adjusted mortality hazard ratio of 1.9 (95% CI: 1.02−3.56, p = 0.043) after the adjustment of IL-6. So, in COVID-19 patients, the lncRNA HULC had a positive correlation with IL-6 and a negative correlation with miRNA-9. The COVID-19 severity and mortality appear to be predicted independently by the lncRNA HULC.</p> </abstract>

Role of immune mediators in predicting hospitalization of SARS-CoV-2 positive patients.

BackgroundSeveral immune mediators (IM) including cytokines, chemokines, and their receptors have been suggested to play a role in COVID-19 pathophysiology and severity.AimTo determine if early IM profiles are predictive of clinical outcome and which of the IMs tested possess the most clinical utility.MethodsA custom bead-based multiplex assay was used to measure IM concentrations in a cohort of SARS-CoV-2 PCR positive patients (n = 326) with varying disease severities as determined by hospitalization status, length of hospital stay, and survival. Patient groups were compared, and clinical utility was assessed. Correlation plots were constructed to determine if significant relationships exist between the IMs in the setting of COVID-19.ResultsIn PCR positive SARS-CoV-2 patients, IL-6 was the best predictor of the need for hospitalization and length of stay. Additionally, MCP-1 and sIL-2Rα were moderate predictors of the need for hospitalization. Hospitalized PCR positive SARS-CoV-2 patients displayed a notable correlation between sIL-2Rα and IL-18 (Spearman’s ρ = 0.48, P=<0.0001).ConclusionsIM profiles between non-hospitalized and hospitalized patients were distinct. IL-6 was the best predictor of COVID-19 severity among all the IMs tested.

Fecal Calprotectin as a Predictor of COVID-19 Severity

AbstractCoronavirus disease 2019 (COVID-19) is highly transmittable through contact with respiratory droplets. The virus is also shed in fecal matter. Some patients may present with effects in more than one system; however, there are no defined biomarkers that can accurately predict the course or progression of the disease. The present study aimed to estimate the severity of the disease, to correlate the severity of the disease with biochemical predictors, to identify valuable biomarkers indicative of gastrointestinal disease, and to determine the cutoff values. A cross-sectional study was conducted on COVID-19 patients admitted to the Kafrelsheikh University Hospital (isolation unit) between July 10, 2020, and October 30, 2020. The diagnosis of COVID-19 was confirmed via reverse transcription-polymerase chain reaction (RT-PCR), which was employed for the detection of the viral RNA. We conclude that lymphopenia, elevated C-reactive protein (CRP) level, and liver enzymes were among the most important laboratory findings in COVID-19 patients. Statistically significant differences in platelet count, neutrophil count, D-dimer level, and fecal calprotectin levels were observed among patients presenting with chest symptoms only and patients with both chest and gastrointestinal symptoms (p = 0.004; &lt; 0.001; 0.010; 0.003; and &lt; 0.001, respectively). C-reactive protein, D-dimer, and fecal calprotectin levels positively correlated with disease severity. The cutoff value for fecal calprotectin that can predict gastrointestinal involvement in COVID-19 was 165.0, with a sensitivity of 88.1% and a specificity of 76.5%.

A low aldosterone/renin ratio and high soluble ACE2 associate with COVID-19 severity.

Background:The severity of COVID-19 after SARS-CoV-2 infection is unpredictable. Angiotensin-converting enzyme-2 (ACE2) is the receptor responsible for coronavirus binding, while subsequent cell entry relies on priming by the serine protease TMPRSS2 (transmembrane protease, serine 2). Although renin-angiotensin-aldosterone-system (RAAS) blockers have been suggested to upregulate ACE2, their use in COVID-19 patients is now considered well tolerated. The aim of our study was to investigate parameters that determine COVID-19 severity, focusing on RAAS-components and variation in the genes encoding for ACE2 and TMPRSS2.Methods:Adult patients hospitalized due to SARS-CoV-2 infection between May 2020 and October 2020 in the Haga Teaching Hospital were included, and soluble ACE2 (sACE2), renin, aldosterone (in heparin plasma) and polymorphisms in the ACE2 and TMPRSS2 genes (in DNA obtained from EDTA blood) were determined.Measurements and main results:Out of the 188 patients who were included, 60 were defined as severe COVID-19 (ICU and/or death). These patients more often used antidiabetic drugs, were older, had higher renin and sACE2 levels, lower aldosterone levels and a lower aldosterone/renin ratio. In addition, they displayed the TMPRSS2-rs2070788 AA genotype less frequently. No ACE2 polymorphism-related differences were observed. Multivariate regression analysis revealed independent significance for age, sACE2, the aldosterone/renin ratio, and the TMPRSS2 rs2070788 non-AA genotype as predictors of COVID-19 severity, together yielding a C-index of 0.79. Findings were independent of the use of RAAS blockers.Conclusion:High sACE2, a low aldosterone/renin ratio and having the TMPRSS2 rs2070788 non-AA genotype are novel independent determinants that may help to predict COVID-19 disease severity.Trial registration:retrospectively registered.

Biomarkers for Thrombosis in COVID-19: A Role for High Sensitivity Troponin-I and Immature Platelet Fraction?

Biomarkers for Thrombosis in COVID-19: A Role for High Sensitivity Troponin-I and Immature Platelet Fraction?IntroductionCoronavirus disease 2019 (COVID-19) is associated with increased risk of thrombosis with both venous and arterial thromboembolism observed. While d-dimer elevation has been shown to be associated with thrombosis, this elevation is present in over 50% of COVID-19 infections demonstrating a clear need for more specific biomarkers of thrombosis in this population. While there are a variety of theories to explain the increased risk for thrombosis: all center on Virchow's triad, specifically hypercoagulability and inflammation. Platelets play a significant role in hypercoagulability. Immature platelets, which are thought to be hyper-reactive, may specifically be associated with thrombosis in COVID-19. It would thus be reasonable to expect immature platelet fraction (IPF) and immature platelet count (IPC) to be predictive biomarkers of thrombosis in this population. Beyond hypercoagulability, High-Sensitivity (HS) Cardiac troponin-I can be a biomarker of inflammation and may also be predictive of thrombotic events in COVID-19. The aim of this study was to evaluate the relationship between IPF, IPC, HS cardiac troponin-I and thrombotic events in COVID-19.MethodsUsing a single center COVID-19 data registry, we extracted all patients with COVID-19 at our single center between May 1, 2020 and January 1, 2021. Patients were stratified into two groups based on thrombotic events during hospitalization, the thrombosis and no thrombosis groups. Biomarker values, including IPF, IPC, platelet counts, d-dimer, and HS cardiac troponin I were extracted. Two-sided Wilcoxon rank test was conducted to test group differences in IPF, IPC, platelet, d-dimer, and HS cardiac troponin-I values.ResultsThere were no significant differences in measurements of IPF at admission, peak IPF , platelet count at admission, peak platelet count, IPC at admission, and peak IPC between the thrombosis and no thrombosis groups. Minimum platelet count values were significantly lower in the thrombosis group compared to the no thrombosis group. D-dimer and troponin values were significantly higher in the thrombosis group than the no thrombosis group. (Table 1)DiscussionTo our knowledge this is the first study assessing the relationship between IPF, IPC, HS cardiac troponin-I and thrombosis in COVID-19. HS cardiac troponin did appear to be a predictive biomarker for thrombosis in COVID-19. This may be related to vascular inflammation playing a significant role in thrombosis. It may also be secondary to myocardial inflammation associated with severe disease in COVID-19.3 Patients with more severe disease are more prone to thrombosis. Unsurprisingly, our study corroborates evidence in the literature that d-dimer is associated with thrombotic events in COVID-19. On the other hand, IPF and IPC do not appear to be predictive biomarkers for thrombosis in this cohort. This appears consistent with the limited data assessing the relationship between IPF, IPC, and thrombosis outside of COVID-19. This does not dispel the importance of immature platelets in COVID-19, however. IPF and IPC are increased in patients with COVID-19, and our published data indicates they are predictors of COVID-19 severity. However, the relationship between immature platelets and outcomes in acute illness can be complex, as in sepsis, where the trend of IPC is associated with mortality, rather than the initial value. Future studies should delineate the relationship between trends in IPF or IPC and outcomes in COVID-19. Furthermore, it is crucial to define biomarkers of thrombosis and disease severity and mortality in COVID-19 which can potentially guide therapeutic interventions. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Assessing influenza vaccination success to inform COVID-19 vaccination campaign.

Given recent downward trends in daily rates of COVID‐19 vaccinations, it is important to reassess strategies to reach those most vulnerable. The success and efficacy of vaccination campaigns for other respiratory illnesses, such as influenza, may help inform messaging around COVID‐19 vaccinations. This cross‐sectional study examines the individual‐level factors associated with, and the spatial distribution of, predictors of COVID‐19 severity, and uptake of influenza and hepatitis B (as a negative control) vaccines across NYC. Data were obtained from the 2018 Community Health Survey (CHS), including self‐reported influenza and hepatitis B vaccine uptake, diabetes, asthma, hypertension, body mass index (BMI), age, race/ethnicity, educational attainment, borough, and United Hospital Fund (UHF) neighborhood of residence. A CDC‐defined COVID‐19 severity risk score was created with variables available in the CHS, including diabetes, asthma, hypertension, BMI ≥ 30 kg/m2, and age ≥65 years old. After adjustment, there was a significant positive association between COVID‐19 severity risk score and influenza vaccine uptake (1: ORadj = 1.49, 95% CI 1.28–1.73; 2: ORadj = 1.99; 95% CI: 1.65–2.41; 3+: ORadj = 2.89; 95% CI: 2.32–3.60, compared to 0). Hepatitis B vaccine uptake was significantly inversely associated with COVID‐19 severity risk score (1: ORadj = 0.67; 95% CI: 0.57–0.79; 2: ORadj = 0.54; 95% CI: 0.44–0.66; 3+: ORadj = 0.45; 95% CI: 0.36–0.56, compared to 0). The influenza vaccination campaign template is effective at reaching those most at risk for serious COVID‐19 and, if implemented, may help reach the most vulnerable that have not yet been vaccinated against COVID‐19.

Early prediction of COVID-19 severity using extracellular vesicle COPB2.

The clinical manifestations of COVID‐19 vary broadly, ranging from asymptomatic infection to acute respiratory failure and death. But the predictive biomarkers for characterizing the variability are still lacking. Since emerging evidence indicates that extracellular vesicles (EVs) and extracellular RNAs (exRNAs) are functionally involved in a number of pathological processes, we hypothesize that these extracellular components may be key determinants and/or predictors of COVID‐19 severity. To test our hypothesis, we collected serum samples from 31 patients with mild COVID‐19 symptoms at the time of their admission for discovery cohort. After symptomatic treatment without corticosteroids, 9 of the 31 patients developed severe/critical COVID‐19 symptoms. We analyzed EV protein and exRNA profiles to look for correlations between these profiles and COVID‐19 severity. Strikingly, we identified three distinct groups of markers (antiviral response‐related EV proteins, coagulation‐related markers, and liver damage‐related exRNAs) with the potential to serve as early predictive biomarkers for COVID‐19 severity. As the best predictive marker, EV COPB2 protein, a subunit of the Golgi coatomer complex, exhibited significantly higher abundance in patients remained mild than developed severe/critical COVID‐19 and healthy controls in discovery cohort (AUC 1.00 (95% CI: 1.00‐1.00)). The validation set included 40 COVID‐19 patients and 39 healthy controls, and showed exactly the same trend between the three groups with excellent predictive value (AUC 0.85 (95% CI: 0.73‐0.97)). These findings highlight the potential of EV COPB2 expression for patient stratification and for making early clinical decisions about strategies for COVID‐19 therapy.

Analysis of Neutrophil Lymphocyte Ratio and Absolute Lymphocyte Count as Predictors of Severity of COVID-19 Patients

It is important to predict the severity of COVID-19 during the pandemic. Both Neutrophil Lymphocyte Ratio (NLR) andAbsolute Lymphocyte Count (ALC) are two easy, low-cost, and fast inflammatory markers, which positively correlate with theseverity of COVID-19. The purpose of this research was to analyze the value of NLR and ALC as predictors of COVID-19severity. This research was a retrospective study using medical record data of 376 COVID-19 patients duringApril-September 2020 at the Hasanuddin University Hospital and Makassar City Regional Hospital. Patients were classifiedinto non-severe and severe COVID-19. Neutrophil lymphocyte ratio and ALC values were determined based on routineblood test (Sysmex XS-800i) results, statistical analysis using Independent T-test, while NLR and ALC diagnostic values wereanalyzed with Receiver Operating Characteristics (ROC) curve to obtain the cut-off value, p &lt; 0.05 was significant. Thesamples consisted of 372 non-severe and 49 severe COVID-19 patients. Neutrophil lymphocyte ratio value in non-severe(4.02±5.22) was significantly different from severe COVID-19 (9.81±7.06) (p &lt; 0.001), similar to ALC in non-severe(2.00±0.83x103/μL) and severe COVID-19 (1.22±0.78x103/μL) (p &lt; 0.001). Receiver operating characteristics curve showedthat NLR had a sensitivity of 91.8% and specificity of 66.4% with a cut-off ≥ 3.17 with Negative Predict Value (NPV) of 98.2%and Positive Predict Value (PPV) of 29.0%; while ALC had a sensitivity of 81.6% and specificity of 64.8% at cut-off≤ 1.74x103/μL with NPV of 95.9% and PPV of 25.8%. Increased NLR and decreased ALC in severe COVID-19 patientsoccurred due to an increased inflammatory response resulting in a decreased cellular immunity. Receiver operatingcharacteristics curve showed a cut-off for NLR of 3.17 and ALC of 1.74x103/μL, indicating an optimum sensitivity andspecificity. It was concluded that NLR and ALC can be used as predictors of COVID-19 severity with a cut-off ≥ 3.17 and≤ 1.74x103/μL, respectively.

Elevated frequencies of CD14+HLA-DRlo/neg MDSCs in COVID-19 patients.

Background: The immune responses, hyper-inflammation or immunosuppression, may be closely related to COVID-19 progression. We aimed to evaluate the changes of frequency of CD14+HLA-DRlo/neg MDSCs, a population of cells with potent immunosuppressive capacity, in COVID-19 patients.Methods: The levels of CD14+HLA-DRlo/neg MDSCs were determined by flow cytometry in 27 COVID-19 patients, and their association with clinical characteristics and laboratory data were analyzed.Results: The frequency of CD14+HLA-DRlo/neg MDSCs was elevated in COVID-19 patients, particularly severe patients. A follow-up comparison revealed a decline of CD14+HLA-DRlo/neg MDSCs percentages in most patients 1 day after testing negative for SARS-CoV-2 nucleic acid, but the levels of CD14+HLA-DRlo/neg MDSCs were still greater than 50.0% in 3 ICU patients 4-10 days after negative SARS-CoV-2 results. Elevated frequency of CD14+HLA-DRlo/neg MDSCs was positively correlated with oropharyngeal viral loads and length of hospital stay, while negatively correlated with lymphocyte counts and serum albumin. Moreover, strong correlations were observed between the frequency of CD14+HLA-DRlo/neg MDSCs and T cell subsets, NK cell counts, and B cell percentages. The frequency of CD14+HLA-DRlo/neg MDSCs could be used as a predictor of COVID-19 severity.Conclusions: A high frequency of CD14+HLA-DRlo/neg MDSCs, especially in severe patients, may indicate an immunoparalysis status and could be a predictor of disease severity and prognosis.

Acute uncontrolled hyperglycemia, a non-specific presentation and predictor of covid-19 severity- a report of three cases

Coronavirus disease 2019 (COVID-19) is a pandemic caused by the severe acute respiratory coronavirus 2 (SARS Cov-2) which currently has caused over 76 million cases with over 1.6 million people dead worldwide. COVID-19 can manifest with several non-specific clinical presentations, posing a diagnostic challenge. Several studies have shown an increased COVID-19 severity in patients with Diabetes mellitus. However, some patients with COVID-19 severity may present with new-onset Diabetes mellitus or worsening blood glucose control in a known diabetic. There are various mechanisms by which the SARS Cov-2 causes hyperglycemia in infected patients. This can lead to hyperglycemia as a presentation of COVID-19 in the absence of specific signs and symptoms. We present three cases: two of them initially presented with acute onset hyperglycemia and were diagnosed with Diabetes mellitus but shortly developed clinical manifestations that led to the suspicion of COVID-19 and a positive COVID-19 RT-PCR test. The third was a diabetic with previously good glycaemic control which suddenly worsened for no reason and shortly after, also developed clinical manifestations that led to the suspicion of COVID-19 which was later confirmed. We recommend that patients with acute hyperglycemia state and/or worsening blood glucose control in patients with previously well controlled Diabetes mellitus should be evaluated for COVID-19 in order to reduce morbidity and mortality as hyperglycemia confers an increased disease severity

Acute uncontrolled hyperglycemia, a non-specific presentation and predictor of covid-19 severity- a report of three cases

Acute uncontrolled hyperglycemia, a non-specific presentation and predictor of covid-19 severity- a report of three cases

Interleukin-6 and C-reactive protein/albumin ratio as predictors of COVID-19 severity and mortality

BackgroundCoronavirus disease 2019 (COVID-19) was announced in early December 2019. The pandemic situation is declared. This study aimed to evaluate the role of biomarkers in estimating the severity and predicting the prognosis of COVID-19.ResultsA total of 116 confirmed patients were included in this study. The patients were evaluated clinically. The disease severity was assessed. The measured and calculated laboratory tests were done. The primary outcome is the 30-day mortality. Patients were assigned to the severe (14.7%) and non-severe (85.3%) groups. At IL-6 level of 32.3 pg/mL (the highest Youden’s index = 0.77), IL-6 can differentiate severe from non-severe patients with 82.4% sensitivity and 94.4% specificity. IL-6 can predict the severity [odds ratio of 87.7 (95% CI = 18.9-408.2) (P < 0.0001)]. After adjustment to the significant clinical and laboratory parameters, IL-6 had an adjusted odds ratio of 30.8 (95% CI = 1.1-728.3) (P = 0.046). A high CRP/albumin ratio of > 11.4 was associated with COVID-19 mortality [hazard ratio = 59.9 (95% CI = 7.4–488.3) (P < 0.0001)]. High CRP/albumin ratio had an adjusted hazard ratio of 26.5 (95% CI = 2.6-270.7) after adjustment of age and presence of co-morbidities (P = 0.006).ConclusionIL-6 level could effectively discriminate COVID-19 severity. CRP/albumin ratio was an independent risk factor for 30-day mortality rate in patients with COVID-19. IL-6 and CRP/albumin ratio seem to be valuable biomarkers in evaluating the severity and prognosis of COVID-19, respectively.

Abstract S01-02: Optimizing treatment for COVID-19 using computational modeling: Implications for cancer patients

Abstract Emerging retrospective analyses show that cancer patients are more likely to develop severe COVID-19. The causes for these worse outcomes are unclear, but data suggest that cancer therapies, which can suppress the immune system, are not responsible for increased COVID-19 severity. An alternative hypothesis is that common molecular pathways are altered in cancer and COVID-19, resulting in worsened disease outcomes. Our previous work demonstrated that activated renin angiotensin signaling (RAS) modulates the tumor microenvironment, resulting in worse outcomes and therapy resistance. Inhibition of this pathway using angiotensin receptor blockers (ARBs) or angiotensin converting enzyme inhibitors (ACEIs) can improve the outcomes of cancer therapies. Similarly, there is great interest in understanding the implications of RAS in COVID-19 progression because a key component of this system, ACE2, is also the docking site for the SARS-CoV-2 virus. Indeed, multiple clinical trials are currently evaluating whether ARBs/ACEIs benefit or harm COVID-19 patients. To help guide administration of these drugs, we adapted our existing computational modeling framework of the cancer microenvironment using available data to simulate COVID-19 progression in patients. Using a systems biology approach, we mechanistically modeled the interaction of the RAS and coagulation pathways with COVID-19 infection. We further explored the efficacy of various antiviral, antithrombotic, and RAS-targeted treatment regimens to identify synergistic combinations as well as optimal schedules for therapy. The system is complex, given that viral binding of ACE2 interferes with its antiinflammatory signaling. When ACE2 is bound by the virus, its local activity decreases, leading to immune dysregulation and risk of coagulopathy, predictors of COVID-19 severity and mortality. To optimize combination treatments for cancer patients who contract COVID-19, multiple simulations were run by combining different therapeutics currently in clinical trials to predict their effects on viral load, thrombosis, oxygen saturation, and cytokine levels. These include ARBs, ACEIs, antiviral drugs, antithrombotic agents, and anti-inflammatory drugs (e.g., anti-IL6/6R). Our simulations predict that i) there is an optimal timing for treatment with antiviral drugs such as remdesivir, related to immune activation; ii) combinations of antiviral and antithrombotic drugs are able to prevent lung damage, increase blood oxygen levels, and inhibit thromboembolic events; and iii) RAS modulators can have a positive effect when added to the treatment regimen. Effective strategies for COVID-19 treatment identified by this in silico analysis will be further analyzed in combination with cancer therapeutics (e.g., immune checkpoint blockers, chemotherapy) to provide guidelines for optimal clinical management of both cancer and COVID-19. Citation Format: Chrysovalantis Voutouri, Mohammadreza Nikmaneshi, Melin Khandekar, Ankit B. Patel, Ashish Verma, Sayon Dutta, Triantafyllos Stylianopoulos, Lance L. Munn, Rakesh K. Jain. Optimizing treatment for COVID-19 using computational modeling: Implications for cancer patients [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2020 Jul 20-22. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(18_Suppl):Abstract nr S01-02.

Unequal Impact of Structural Health Determinants and Comorbidity on COVID-19 Severity and Lethality in Older Mexican Adults: Considerations Beyond Chronological Aging.

BackgroundCOVID-19 has had a disproportionate impact on older adults. Mexico’s population is younger, yet COVID-19’s impact on older adults is comparable to countries with older population structures. Here, we aim to identify health and structural determinants that increase susceptibility to COVID-19 in older Mexican adults beyond chronological aging.MethodsWe analyzed confirmed COVID-19 cases in older adults using data from the General Directorate of Epidemiology of Mexican Ministry of Health. We modeled risk factors for increased COVID-19 severity and mortality, using mixed models to incorporate multilevel data concerning healthcare access and marginalization. We also evaluated structural factors and comorbidity profiles compared to chronological age for COVID-19 mortality risk prediction.ResultsWe analyzed 20 804 confirmed SARS-CoV-2 cases in adults aged 60 and older. Male sex, smoking, diabetes, and obesity were associated with pneumonia, hospitalization, and intensive care unit (ICU) admission in older adults, CKD and COPD were associated with hospitalization. High social lag indexes and access to private care were predictors of COVID-19 severity and mortality. Age was not a predictor of COVID-19 severity in individuals without comorbidities and combination of structural factors and comorbidities were better predictors of COVID-19 lethality and severity compared to chronological age alone. COVID-19 baseline lethality hazards were heterogeneously distributed across Mexican municipalities, particularly when comparing urban and rural areas.ConclusionsStructural factors and comorbidity explain excess risk for COVID-19 severity and mortality over chronological age in older Mexican adults. Clinical decision-making related to COVID-19 should focus away from chronological aging onto more a comprehensive geriatric care approach.

Inflammatory markers as predictors of COVID-19 severity: A review of literature

Background: COVID-19 (severe acute respiratory syndrome [SARS] COV-2), which is now a global pandemic, continues to spread across countries and continents, bringing along with it untold economic hardship and a high mortality rate. Many biochemical changes have been associated with COVID-19. This study is aimed to establish an association between various inflammatory markers and the severity of COVID-19 to provide knowledge for the clinicians and help professionals that manage the disease. Methods: A search in PubMed/Medline, Google scholar, and Journal Storage (JSTOR) databases was conducted from May 15, 2020 to June 15, 2020, for studies that reported serum levels of inflammatory markers in COVID-19. Search terms included a combination of “medical laboratory diagnosis, inflammatory markers, cytokines, acute-phase reactants, biomarkers and COVID-19, SARS-COV-2, and coronavirus.” Results: Four hundred and twelve (412) articles were retrieved following the removal of duplicates, of which 15 articles were included in this study after meeting the study inclusion criteria. The included studies comprised 2828 COVID-19 positives made of 1472 (52.1%) male and 1356 (47.9%) female patients. The most prevalent laboratory finding was increased interleukin-6 (IL) (100%), erythrocyte sedimentation rate (88.9%), and procalcitonin (63.6%). Levels of ferritin, IL-2, tissue necrotic factor (TNF)-α, TNF-γ, serum amyloid A, interferon gamma, IL-4, IL-8, and IL-10 were also increased. Conclusion: This study provides enough evidence that inflammatory markers are associated with the severity and prognosis of COVID-19. Inflammatory markers are, therefore, necessary if not the most important assays in the management of COVID-19 patients. Patients with elevated inflammatory markers should be given adequate attention and proper management to avert deterioration.