Absolute Lymphocytes, Ferritin, C-Reactive Protein, and Lactate Dehydrogenase Predict Early Invasive Ventilation in Patients With COVID-19

Association between PaO2/(FiO2*PEEP) ratio and in-hospital mortality in COVID-19 patients: A reanalysis of published data from Peru using PaO2/(FiO2*PEEP) ratio in place of PaO2/FaO2 ratio

P/FP [PaO2/(FiO2*PEEP)] is associated with in-hospital mortality in patients with acute respiratory distress syndrome (ARDS). However, to the best of our knowledge, the association between P/FP after 24 hours of invasive mechanical ventilation (IMV) and in-hospital mortality in patients with ARDS due to Coronavirus Disease 2019 (COVID-19) remained unclear. This study aimed to evaluate the relationship between the P/FP after 24 hours of IMV and in-hospital mortality in patients with ARDS due to COVID-19. We reanalyzed previously published data from Peru. Hueda-Zavaleta et al conducted a retrospective cohort study between April 2020 and April 2021 in southern Peru. A total of 200 hospitalized COVID-19 patients requiring IMV were included in this analysis. We used Cox proportional hazard regression models and Kaplan-Meier survival analysis to investigate the effect of P/FP after 24 hours of IMV on in-hospital mortality. We used a restricted cubic spline regression and a two-piecewise Cox proportional hazards model to explore the relationship between P/FP after 24 hours of IMV and in-hospital mortality in patients with ARDS due to COVID-19. Of the 200 patients, 51 (25.50%) died in hospital. The median P/FP was 20.45 mm Hg/cmH2O [interquartile range 15.79-25.21 mm Hg/cmH2O], with a range of 5.67 mm Hg/cmH2O to 51.21 mm Hg/cmH2O. Based on the P/FP ratio, patients were equally divided into 2 groups (low group [P/FP < 20.50 mm Hg/cmH2O] and high group [P/FP ≥ 20.50 mm Hg/cmH2O]). In-hospital mortality was lower in the high P/FP group than in the low P/FP group (12 [12%] vs 39 [39%]; unadjusted hazard ratio [HR]: 0.33, 95% confidence interval [CI]: 0.17-0.63; adjusted HR: 0.10, 95% CI: 0.02-0.47). We also found a nonlinear relationship between P/FP and in-hospital mortality. After adjusting for potential confounders, the HR was 0.67 (95% CI: 0.56-0.79) for P/FP ≤ 22 mm Hg/cmH2O and 1.10 (95% CI: 0.83-1.47) for P/FP > 22 mm Hg/cmH2O. In addition, lymphocytes ≤ 1 × 109/L and acute kidney failure had a higher risk of death. After adjusting for potential confounders, the P/FP after 24 hours of IMV was nonlinearly associated with in-hospital mortality in patients with ARDS due to COVID-19.

Association between Liver Damage and Disease Progression Markers with Mortality Risk and Mechanical Ventilation in Hospitalized COVID-19 Patients: A Nationwide Retrospective SARSTer Study

(1) Background: Liver damage is an important component of acute COVID-19, and the advancement of preexisting liver disease is associated with a worse prognosis; (2) Methods: A nationwide retrospective study including 7444 patients aimed to evaluate levels of selected markers of liver damage and disease advancement and their association with mortality and mechanical ventilation (MV); (3) Results: Elevation of the following markers in multivariate models were associated with increased odds of mortality: Alanine transaminase (ALT), aspartate transaminase (AST), gamma-glutamyltransferase (GGT), lactate dehydrogenase (LDH), fibrosis-4 score (FIB-4), AST-to-platelet ratio index (APRI), and decreased levels of platelet count (PLT). Elevated levels of AST, LDH, APRI, FIB-4, and the AST/ALT ratio and decreased levels of PLT were associated with increased odds of MV in multivariate models. The best predictive accuracy against mortality was achieved with FIB-4 with AUC = 0.733 (95% CI, 0.718-0.749) at the optimal cut-off point of 2.764, while against MV was achieved with LDH with AUC = 0.753 (95% CI, 0.727-0.778) at the optimal cut-off point of 449.5 IU/L. (4) Conclusions: Our study confirms that the advancement of liver damage contributes to a worse prognosis in COVID-19 patients. Markers for liver damage and the advancement of liver disease can provide predictive value in clinical practice among COVID-19 patients.

Blood Count and Renal Functionality Assessments in the Emergency Section Disclose Morbidity and Mortality in Omicron COVID-19 Patients: A Retrospective Study

Background: SARS-CoV-2 is the coronavirus responsible for the COVID-19 pandemic. Even though we are no longer in a pandemic situation, people are still getting infected, some of them need hospitalization and a few of them die. Methods: We conducted a retrospective study including 445 patients who accessed the Emergency Section of Policlinico Umberto I, Rome, Italy, where they had routine blood exams. In this study, we focused on the complete blood count, serum creatinine and azotemia. The data were analyzed using ANOVA, Spearman correlation and ROC analyses. They were divided into four groups based on their clinical outcomes: (1) the emergency group (patients who had mild forms and were quickly discharged); (2) the hospital ward group (patients who were admitted to the emergency section and were then hospitalized in a COVID-19 ward); (3) the intensive care unit (ICU) group (patients who required intensive assistance after the admission in the emergency section); (4) the deceased group (patients who had a fatal outcome after admission to the emergency section). Results: We found significant changes for creatinine, azotemia, hematocrit, mean corpuscular hemoglobin concentration, basophils, monocytes, red blood cell distribution width, hemoglobin, hematocrit and red blood cell numbers using ANOVA according to their clinical outcomes, particularly for the deceased group. Also, we found linear correlations of clinical outcomes with eosinophils, hemoglobin, hematocrit, mean corpuscular hemoglobin concentration, lymphocyte, neutrophil, platelet and red blood cell number and red blood cell distribution width. Conclusions: This study discloses an early association between "classical" routine blood biomarkers and the severity of clinical outcomes in Omicron patients.

A prognostic model for SARS-CoV-2 breakthrough infection: Analyzing a prospective cellular immunity cohort

BACKGROUND

Following the COVID-19 pandemic, studies have identified several prevalent characteristics, especially related to lymphocyte subsets. However, limited research is available on the focus of this study, namely, the specific memory cell subsets among individuals who received COVID-19 vaccine boosters and subsequently experienced a SARS-CoV-2 breakthrough infection.

METHODS

Flow cytometry (FCM) was employed to investigate the early and longitudinal pattern changes of cellular immunity in patients with SARS-CoV-2 breakthrough infections following COVID-19 vaccine boosters. XGBoost (a machine learning algorithm) was employed to analyze cellular immunity prior to SARS-CoV-2 breakthrough, aiming to establish a prognostic model for SARS-CoV-2 breakthrough infections.

RESULTS

Following SARS-CoV-2 breakthrough infection, naïve T cells and TEMRA subsets increased while the percentage of TCM and TEM cells decreased. Naïve and non-switched memory B cells increased while switched and double-negative memory B cells decreased. The XGBoost model achieved an area under the curve (AUC) of 0.78, with an accuracy rate of 81.8 %, a sensitivity of 75 %, and specificity of 85.7 %. TNF-α, CD27-CD19+cells, and TEMRA subsets were identified as high predictors. An increase in TNF-α, cTfh, double-negative memory B cells, IL-6, IL-10, and IFN-γ prior to SARS-CoV-2 infection was associated with enduring clinical symptoms; conversely, an increase in CD3+ T cells, CD4+ T cells, and IL-2 was associated with clinical with non-enduring clinical symptoms.

CONCLUSION

SARS-CoV-2 breakthrough infection leads to disturbances in cellular immunity. Assessing cellular immunity prior to breakthrough infection serves as a valuable prognostic tool for SARS-CoV-2 infection, which facilitates clinical decision-making.

Thrombo-inflammatory response in hospitalised patients with COVID-19: a single institution experience

BACKGROUND

Severe COVID-19 causes acute inflammation, which is complicated by venous thromboembolism events (VTE). However, it is unclear if VTE risk has evolved over time since the COVID-19 outbreak.

AIMS

To determine markers of thrombo-inflammation and rates of symptomatic VTE in patients hospitalised for COVID-19 in a metropolitan hospital in Sydney, Australia.

METHODS

A retrospective, single-centre, cohort study was performed by reviewing electronic medical records of consecutive patients admitted to Royal Prince Alfred Hospital between March 2020 and September 2021. This period included three waves of COVID-19 outbreaks in Australia with the ancestral, alpha and delta variants. Standard coagulation assays and inflammatory markers were recorded over 4 weeks.

RESULTS

A total of 205 patients were consecutively admitted during the study period. Activated partial thromboplastin time, neutrophil count and C-reactive protein (CRP) were significantly increased in patients hospitalised in the intensive care unit (ICU) compared with non-ICU patients. The use of anti-inflammatory medication increased in 2021 compared with 2020. The mortality rate was 7.3% in our cohort. Ninety-four per cent of patients received anticoagulation with 6.3% of patients developing VTE.

CONCLUSION

We observed lower rates of VTE compared to the internationally reported rate for the same period. We conclude that in the setting of controlled hospital admission rate and standard anticoagulation guidelines, COVID-19 resulted in similar thrombo-inflammatory response and VTE rates over the first 1.5 years of the pandemic.

Use of inflammatory markers as predictor for mechanical ventilation in COVID-19 patients with stages IIIb-V chronic kidney disease?

BACKGROUND

Studies have shown elevated C-reactive protein (CRP) to predict mechanical ventilation (MV) in patients with coronavirus disease 2019 (COVID-19). Its utility is unknown in patients with chronic kidney disease (CKD), who have elevated baseline CRP levels due to chronic inflammation and reduced renal clearance.

AIM

To assess whether an association exists between elevated inflammatory markers and MV rate in patients with stages IIIb-V CKD and COVID-19.

METHODS

We conducted a retrospective cohort study on patients with COVID-19 and stages IIIb-V CKD. The primary outcome was the rate of invasive MV, the rate of noninvasive MV, and the rate of no MV. Statistical analyses used unpaired t-test for continuous variables and chi-square analysis for categorical variables. Cutoffs for variables were CRP: 100 mg/L, ferritin: 530 ng/mL, D-dimer: 0.5 mg/L, and lactate dehydrogenase (LDH): 590 U/L.

RESULTS

290 were screened, and 118 met the inclusion criteria. CRP, D-dimer, and ferritin were significantly different among the three groups. On univariate analysis for invasive MV (IMV), CRP had an odds ratio (OR)-5.44; ferritin, OR-2.8; LDH, OR-7.7; D-dimer, OR-3.9, (P < 0.05). The admission CRP level had an area under curve-receiver operator characteristic (AUROC): 0.747 for the IMV group (sensitivity-80.8%, specificity-50%) and 0.663 for the non-IMV (NIMV) group (area under the curve, sensitivity-69.2%, specificity-53%).

CONCLUSION

Our results demonstrate a positive correlation between CRP, ferritin, and D-dimer levels and MV and NIMV rates in CKD patients. The AUROC demonstrates a good sensitivity for CRP levels in detecting the need for MV in patients with stages IIIb-V CKD. This may be because of the greater magnitude of increased inflammation due to COVID-19 itself compared with increased inflammation and reduced clearance due to CKD alone.

Effects of inflammation on thrombosis and outcomes in COVID-19: secondary analysis of the ATTACC/ACTIV-4a trial

Background

Patients hospitalized for COVID-19 are at high risk of thrombotic complications and organ failure, and often exhibit severe inflammation, which may contribute to hypercoagulability.

Objectives

To determine whether patients hospitalized for COVID-19 experience differing frequencies of thrombotic and organ failure complications and derive variable benefits from therapeutic-dose heparin dependent on the extent of systemic inflammation and whether observed benefit from therapeutic-dose anticoagulation varies depending on the degree of systemic inflammation.

Methods

We analyzed data from 1346 patients hospitalized for COVID-19 enrolled in the ATTACC and ACTIV-4a platforms who were randomized to therapeutic-dose heparin or usual care for whom levels of C-reactive protein (CRP) were reported at baseline.

Results

Increased CRP was associated with worse patient outcomes, including a >98% posterior probability of increased organ support requirement, hospital length of stay, risk of 28-day mortality, and incidence of major thrombotic events or death (patients with CRP 40-100 mg/L or ≥100 mg/L compared to patients with CRP <40 mg/L). Patients with CRP 40 to 100 mg/L experienced the greatest degree of benefit from treatment with therapeutic doses of unfractionated or low molecular weight heparin compared with usual-care prophylactic doses. This was most significant for an increase in organ support-free days (odds ratio: 1.63; 95% confidence interval, 1.09-2.40; 97.9% posterior probability of beneficial effect), with trends toward benefit for other evaluated outcomes.

Conclusion

Moderately ill patients hospitalized for COVID-19 with CRP between 40 mg/L and 100 mg/L derived the greatest benefit from treatment with therapeutic-dose heparin.

Multidimensional analysis of immune cells from COVID-19 patients identified cell subsets associated with the severity at hospital admission

BACKGROUND

SARS-CoV-2 emerged as a new coronavirus causing COVID-19, and it has been responsible for more than 760 million cases and 6.8 million deaths worldwide until March 2023. Although infected individuals could be asymptomatic, other patients presented heterogeneity and a wide range of symptoms. Therefore, identifying those infected individuals and being able to classify them according to their expected severity could help target health efforts more effectively.

METHODOLOGY/PRINCIPAL FINDINGS

Therefore, we wanted to develop a machine learning model to predict those who will develop severe disease at the moment of hospital admission. We recruited 75 individuals and analysed innate and adaptive immune system subsets by flow cytometry. Also, we collected clinical and biochemical information. The objective of the study was to leverage machine learning techniques to identify clinical features associated with disease severity progression. Additionally, the study sought to elucidate the specific cellular subsets involved in the disease following the onset of symptoms. Among the several machine learning models tested, we found that the Elastic Net model was the better to predict the severity score according to a modified WHO classification. This model was able to predict the severity score of 72 out of 75 individuals. Besides, all the machine learning models revealed that CD38+ Treg and CD16+ CD56neg HLA-DR+ NK cells were highly correlated with the severity.

CONCLUSIONS/SIGNIFICANCE

The Elastic Net model could stratify the uninfected individuals and the COVID-19 patients from asymptomatic to severe COVID-19 patients. On the other hand, these cellular subsets presented here could help to understand better the induction and progression of the symptoms in COVID-19 individuals.

Blood Biomarkers from the Emergency Department Disclose Severe Omicron COVID-19-Associated Outcomes

Background: Since its outbreak, Coronavirus disease 2019 (COVID-19), a life-threatening respiratory illness, has rapidly become a public health emergency with a devastating social impact. Lately, the Omicron strain is considered the main variant of concern. Routine blood biomarkers are, indeed, essential for stratifying patients at risk of severe outcomes, and a huge amount of data is available in the literature, mainly for the previous variants. However, only a few studies are available on early routine biochemical blood biomarkers for Omicron-afflicted patients. Thus, the aim and novelty of this study were to identify routine blood biomarkers detected at the emergency room for the early prediction of severe morbidity and/or mortality. Methods: 449 COVID-19 patients from Sapienza University Hospital of Rome were divided into four groups: (1) the emergency group (patients with mild forms who were quickly discharged); (2) the hospital ward group (patients that after the admission in the emergency department were hospitalized in a COVID-19 ward); (3) the intensive care unit (ICU) group (patients that after the admission in the emergency department required intensive assistance); (4) the deceased group (patients that after the admission in the emergency department had a fatal outcome). Results: ANOVA and ROC data showed that high-sensitivity troponin-T (TnT), fibrinogen, glycemia, C-reactive protein, lactate dehydrogenase, albumin, D-dimer myoglobin, and ferritin for both men and women may predict lethal outcomes already at the level of the emergency department. Conclusions: Compared to previous Delta COVID-19 parallel emergency patterns of prediction, Omicron-induced changes in TnT may be considered other early predictors of severe outcomes.

COVID-19 Affects Serum Brain-Derived Neurotrophic Factor and Neurofilament Light Chain in Aged Men: Implications for Morbidity and Mortality

BACKGROUND AND METHODS

Severe COVID-19 is known to induce neurological damage (NeuroCOVID), mostly in aged individuals, by affecting brain-derived neurotrophic factor (BDNF), matrix metalloproteinases (MMP) 2 and 9 and the neurofilament light chain (NFL) pathways. Thus, the aim of this pilot study was to investigate BDNF, MMP-2, MMP-9, and NFL in the serum of aged men affected by COVID-19 at the beginning of the hospitalization period and characterized by different outcomes, i.e., attending a hospital ward or an intensive care unit (ICU) or with a fatal outcome. As a control group, we used a novelty of the study, unexposed age-matched men. We also correlated these findings with the routine blood parameters of the recruited individuals.

RESULTS

We found in COVID-19 individuals with severe or lethal outcomes disrupted serum BDNF, NFL, and MMP-2 presence and gross changes in ALT, GGT, LDH, IL-6, ferritin, and CRP. We also confirmed and extended previous data, using ROC analyses, showing that the ratio MMPs (2 and 9) versus BDNF and NFL might be a useful tool to predict a fatal COVID-19 outcome.

CONCLUSIONS

Serum BDNF and NFL and/or their ratios with MMP-2 and MMP-9 could represent early predictors of NeuroCOVID in aged men.

Assessment of oxidative stress markers in elderly patients with SARS-CoV-2 infection and potential prognostic implications in the medium and long term

We aimed to evaluate the correlation of plasma levels of thiobarbituric acid reactive substances (TBARS) and reduced thiols with morbidity, mortality and immune response during and after SARS-CoV-2 infection. This was an observational study that included inpatients with SARS-CoV-2 infection older than 65 years. The individuals were followed up to the twelfth month post-discharge. Plasma levels of TBARS and reduced thiols were quantified as a measure of lipid and protein oxidation, respectively. Fatal and non-fatal events were evaluated during admission and at the third, sixth and twelfth month post-discharge. Differences in oxidative stress markers between the groups of interest, time to a negative RT-qPCR and time to significant anti-SARS-CoV-2 IgM titers were assessed. We included 61 patients (57% women) with a mean age of 83 years old. After multivariate analysis, we found differences in TBARS and reduced thiol levels between the comparison groups in fatal and non-fatal events during hospital admission. TBARS levels were also correlated with fatal events at the 6th and 12th months post-discharge. One year after hospital discharge, other predictors rather than oxidative stress markers were relevant in the models. The median time to reach significant anti-SARS-CoV-2 IgM titers was lower in patients with low levels of reduced thiols. Assessment of some parameters related to oxidative stress may help identify groups of patients with a higher risk of morbidity, mortality and delayed immune response during and after SARS-CoV-2 infection.

The role of exhaled nitric oxide (FeNO) in the evaluation of lung parenchymal involvement in COVID-19 patients

The inflammatory balance is an important factor in the clinical course of COVID-19 (SARS-CoV-2) infection, which has affected over 300 million people globally since its appearance in December 2019. This study aimed to evaluate the correlation between exhaled nitric oxide (FeNO) level and parenchymal involvement in COVID-19. The study included 106 patients with the delta variant of COVID-19 identified by real-time PCR as well as 40 healthy control groups between October 2021 and March 2022. The patients were analyzed in three groups: moderate COVID-19 (group 1), severe COVID-19 without macrophage activation syndrome (MAS) (group 2), and severe COVID-19 with MAS (group 3). FeNO and CT scores were significantly higher in groups 2 and 3 at admission and discharge compared to group 1 (p = 0.001 for all). In addition, CT score at admission and CT score and FeNO level at discharge were higher in group 3 than in group 2 (p = 0.001 for all). It was found that the FeNO levels were higher in Groups 2 and 3 than in the control group (p = 0.001) during the admission. FeNO and CT scores showed strong positive correlation at admission and discharge (r = 0.917, p = 0.001; r = 0.790, p = 0.001). In receiver operating characteristic curve analysis for prediction of MAS, FeNO at a cut-off of 10.5 ppb had 66% sensitivity and 71% specificity. COVID-19 causes more severe lung involvement than other viral lower respiratory tract infections, leading to the frequent use of chest CT in these patients. FeNO assessment is a practical and noninvasive method that may be useful in evaluating for parenchymal infiltration in the diagnosis and follow-up of COVID-19 patients.

Multisite implementation of a workflow-integrated machine learning system to optimize COVID-19 hospital admission decisions

Demand has outstripped healthcare supply during the coronavirus disease 2019 (COVID-19) pandemic. Emergency departments (EDs) are tasked with distinguishing patients who require hospital resources from those who may be safely discharged to the community. The novelty and high variability of COVID-19 have made these determinations challenging. In this study, we developed, implemented and evaluated an electronic health record (EHR) embedded clinical decision support (CDS) system that leverages machine learning (ML) to estimate short-term risk for clinical deterioration in patients with or under investigation for COVID-19. The system translates model-generated risk for critical care needs within 24 h and inpatient care needs within 72 h into rapidly interpretable COVID-19 Deterioration Risk Levels made viewable within ED clinician workflow. ML models were derived in a retrospective cohort of 21,452 ED patients who visited one of five ED study sites and were prospectively validated in 15,670 ED visits that occurred before (n = 4322) or after (n = 11,348) CDS implementation; model performance and numerous patient-oriented outcomes including in-hospital mortality were measured across study periods. Incidence of critical care needs within 24 h and inpatient care needs within 72 h were 10.7% and 22.5%, respectively and were similar across study periods. ML model performance was excellent under all conditions, with AUC ranging from 0.85 to 0.91 for prediction of critical care needs and 0.80-0.90 for inpatient care needs. Total mortality was unchanged across study periods but was reduced among high-risk patients after CDS implementation.

Caracterización y factores pronóstico de mortalidad en pacientes ingresados en UCI por COVID-19 en un hospital público de referencia en Bogotá, Colombia

Introducción

Objetivo

Materiales y métodos

Resultados

Conclusión

Serum NGF and BDNF in Long-COVID-19 Adolescents: A Pilot Study

COVID-19 (COronaVIrus Disease 19) is an infectious disease also known as an acute respiratory syndrome caused by the SARS-CoV-2. Although in children and adolescents SARS-CoV-2 infection produces mostly mild or moderate symptoms, in a certain percentage of recovered young people a condition of malaise, defined as long-COVID-19, remains. To date, the risk factors for the development of long-COVID-19 are not completely elucidated. Neurotrophins such as NGF (Nerve Growth Factor) and BDNF (Brain-Derived Neurotrophic Factor) are known to regulate not only neuronal growth, survival and plasticity, but also to influence cardiovascular, immune, and endocrine systems in physiological and/or pathological conditions; to date only a few papers have discussed their potential role in COVID-19. In the present pilot study, we aimed to identify NGF and BDNF changes in the serum of a small cohort of male and female adolescents that contracted the infection during the second wave of the pandemic (between September and October 2020), notably in the absence of available vaccines. Blood withdrawal was carried out when the recruited adolescents tested negative for the SARS-CoV-2 ("post-infected COVID-19"), 30 to 35 days after the last molecular test. According to their COVID-19 related outcomes, the recruited individuals were divided into three groups: asymptomatics, acute symptomatics and symptomatics that over time developed long-COVID-19 symptoms ("future long-COVID-19"). As a control group, we analyzed the serum of age-matched healthy controls that did not contract the infection. Inflammatory biomarkers (TNF-α, TGF-β), MCP-1, IL-1α, IL-2, IL-6, IL-10, IL-12) were also analyzed with the free oxygen radicals' presence as an oxidative stress index. We showed that NGF serum content was lower in post-infected-COVID-19 individuals when compared to healthy controls; BDNF levels were found to be higher compared to healthy individuals only in post-infected-COVID-19 symptomatic and future long-COVID-19 girls, leaving the BDNF levels unchanged in asymptomatic individuals if compared to controls. Oxidative stress and inflammatory biomarkers were unchanged in male and female adolescents, except for TGF-β that, similarly to BDNF, was higher in post-infected-COVID-19 symptomatic and future long-COVID-19 girls. We predicted that NGF and/or BDNF could be used as early biomarkers of COVID-19 morbidity in adolescents.

Safety and long-term improvement of mesenchymal stromal cell infusion in critically COVID-19 patients: a randomized clinical trial

BACKGROUND

COVID-19 is a multisystem disease that presents acute and persistent symptoms, the postacute sequelae (PASC). Long-term symptoms may be due to consequences from organ or tissue injury caused by SARS-CoV-2, associated clotting or inflammatory processes during acute COVID-19. Various strategies are being chosen by clinicians to prevent severe cases of COVID-19; however, a single treatment would not be efficient in treating such a complex disease. Mesenchymal stromal cells (MSCs) are known for their immunomodulatory properties and regeneration ability; therefore, they are a promising tool for treating disorders involving immune dysregulation and extensive tissue damage, as is the case with COVID-19. This study aimed to assess the safety and explore the long-term efficacy of three intravenous doses of UC-MSCs (umbilical cord MSCs) as an adjunctive therapy in the recovery and postacute sequelae reduction caused by COVID-19. To our knowledge, this is one of the few reports that presents the longest follow-up after MSC treatment in COVID-19 patients.

METHODS

This was a phase I/II, prospective, single-center, randomized, double-blind, placebo-controlled clinical trial. Seventeen patients diagnosed with COVID-19 who require intensive care surveillance and invasive mechanical ventilation-critically ill patients-were included. The patient infusion was three doses of 5 × 105 cells/kg UC-MSCs, with a dosing interval of 48 h (n = 11) or placebo (n = 6). The evaluations consisted of a clinical assessment, viral load, laboratory testing, including blood count, serologic, biochemical, cell subpopulation, cytokines and CT scan.

RESULTS

The results revealed that in the UC-MSC group, there was a reduction in the levels of ferritin, IL-6 and MCP1-CCL2 on the fourteen day. In the second month, a decrease in the levels of reactive C-protein, D-dimer and neutrophils and an increase in the numbers of TCD3, TCD4 and NK lymphocytes were observed. A decrease in extension of lung damage was observed at the fourth month. The improvement in all these parameters was maintained until the end of patient follow-up.

CONCLUSIONS

UC-MSCs infusion is safe and can play an important role as an adjunctive therapy, both in the early stages, preventing severe complications and in the chronic phase with postacute sequelae reduction in critically ill COVID-19 patients. Trial registration Brazilian Registry of Clinical Trials (ReBEC), UTN code-U1111-1254-9819. Registered 31 October 2020-Retrospectively registered, https://ensaiosclinicos.gov.br/rg/RBR-3fz9yr.

Early Routine Biomarkers of SARS-CoV-2 Morbidity and Mortality: Outcomes from an Emergency Section

Background. COVID-19 is a severe acute respiratory disease caused by SARS-CoV-2, a virus belonging to the Coronaviridae family. This disease has spread rapidly around the world and soon became an international public health emergency leading to an unpredicted pressure on the hospital emergency units. Early routine blood biomarkers could be key predicting factors of COVID-19 morbidity and mortality as suggested for C-reactive protein (CRP), IL-6, prothrombin and D-dimer. This study aims to identify other early routine blood biomarkers for COVID-19 severity prediction disclosed directly into the emergency section. Methods. Our research was conducted on 156 COVID-19 patients hospitalized at the Sapienza University Hospital “Policlinico Umberto I” of Rome, Italy, between March 2020 and April 2020 during the paroxysm’s initial phase of the pandemic. In this retrospective study, patients were divided into three groups according to their outcome: (1) emergency group (patients who entered the emergency room and were discharged shortly after because they did not show severe symptoms); (2) intensive care unit (ICU) group (patients who attended the ICU after admission to the emergency unit); (3) the deceased group (patients with a fatal outcome who attended the emergency and, afterward, the ICU units). Routine laboratory tests from medical records were collected when patients were admitted to the emergency unit. We focused on Aspartate transaminase (AST), Alanine transaminase (ALT), Lactate dehydrogenase (LDH), Creatine kinase (CK), Myoglobin (MGB), Ferritin, CRP, and D-dimer. Results. As expected, ANOVA data show an age morbidity increase in both ICU and deceased groups compared with the emergency group. A main effect of morbidity was revealed by ANOVA for all the analyzed parameters with an elevation between the emergency group and the deceased group. Furthermore, a significant increase in LDH, Ferritin, CRP, and D-dimer was also observed between the ICU group and the emergency group and between the deceased group and ICU group. Receiver operating characteristic (ROC) analyses confirmed and extended these findings. Conclusions. This study suggests that the contemporaneous presence of high levels of LDH, Ferritin, and as expected, CRP, and D-dimer could be considered as potential predictors of COVID-19 severity and death.

Diabetes mellitus and COVID-19: review of a lethal interaction from the cellular and molecular level to the bedside

INTRODUCTION

While the main mode of transmission of coronavirus disease 2019 (COVID-19) is close contact with other individuals, the presence of chronic underlying diseases such as Diabetes Mellitus (DM) increases the chance of hospitalization and mortality rate due to infection.

AREAS COVERED

To investigate the effects of COVID-19 infection in DM patients, we reviewed literature from Google Scholar search engine and PubMed database from '2013 to 2020' using the terms "COVID-19; SARS-CoV-2; Diabetes mellitus; obesity; Angiotensin-converting enzyme 2; ACE2; Insulin and Metformin. Evidence suggests that COVID-19 exacerbates the course of diabetes. Presence of pro-inflammatory conditions, increased expression of receptors, and more difficult control of glucose levels in diabetics COVID-19 patients are some of the problems that diabetic patients may face. Also, psychological problems caused by the COVID-19 epidemic in diabetic patients is one of the most important problems in these patients, which is less covered.

EXPERT OPINION

DM is a strong and independent risk factor with a poor prognosis, which increases the risk of COVID-19 infection, the need for emergency services, the rate of hospitalization in the intensive care unit and also increases the mortality rate of COVID-19 patients.

Comparative evaluation of semi-quantitative CT-severity scoring versus serum lactate dehydrogenase as prognostic biomarkers for disease severity and clinical outcome of COVID-19 patients

Background

Coronavirus disease 2019 pandemic causes significant strain on healthcare infrastructure and medical resources. So, it becomes crucial to identify reliable predictor biomarkers for COVID-19 disease severity and short term mortality. Many biomarkers are currently investigated for their prognostic role in COVID-19 patients. Our study is retrospective and aims to evaluate role of semi-quantitative CT-severity scoring versus LDH as prognostic biomarkers for COVID-19 disease severity and short-term clinical outcome.

Results

Two hundred sixty-six patients between April 2020 and November 2020 with positive RT-PCR results underwent non-enhanced CT scan chest in our hospital and were retrospectively evaluated for CT severity scoring and serum LDH level measurement. Data were correlated with clinical disease severity. CT severity score and LDH were significantly higher in severe and critical cases compared to mild cases (P value < 0.001). High predictive significance of CT severity score for COVID-19 disease course noted, with cut-off value ≥ 13 highly predictive of severe disease (96.96% accuracy); cut-off value ≥ 16 highly predictive of critical disease (94.21% accuracy); and cut-off value ≥ 19 highly predictive of short-term mortality (92.56% accuracy). CT severity score has higher sensitivity, specificity, positive, and negative predictive values as well as overall accuracy compared to LDH level in predicting severe, critical cases, and short-term mortality.

Conclusion

Semi-quantitative CT severity scoring has high predictive significance for COVID-19 disease severity and short-term mortality with higher sensitivity, specificity, and overall accuracy compared to LDH. Our study strongly supports the use of CT severity scoring as a powerful prognostic biomarker for COVID-19 disease severity and short-term clinical outcome to allow triage of need for hospital admission, earlier medical interference, and to effectively prioritize medical resources for cases with high mortality risk for better decision making and clinical outcome.

A machine learning based exploration of COVID-19 mortality risk

Early prediction of patient mortality risks during a pandemic can decrease mortality by assuring efficient resource allocation and treatment planning. This study aimed to develop and compare prognosis prediction machine learning models based on invasive laboratory and noninvasive clinical and demographic data from patients' day of admission. Three Support Vector Machine (SVM) models were developed and compared using invasive, non-invasive, and both groups. The results suggested that non-invasive features could provide mortality predictions that are similar to the invasive and roughly on par with the joint model. Feature inspection results from SVM-RFE and sparsity analysis displayed that, compared with the invasive model, the non-invasive model can provide better performances with a fewer number of features, pointing to the presence of high predictive information contents in several non-invasive features, including SPO2, age, and cardiovascular disorders. Furthermore, while the invasive model was able to provide better mortality predictions for the imminent future, non-invasive features displayed better performance for more distant expiration intervals. Early mortality prediction using non-invasive models can give us insights as to where and with whom to intervene. Combined with novel technologies, such as wireless wearable devices, these models can create powerful frameworks for various medical assignments and patient triage.

Predicting the severity of disease progression in COVID-19 at the individual and population level: A mathematical model

The impact of COVID-19 disease on health and economy has been global, and the magnitude of devastation is unparalleled in modern history. Any potential course of action to manage this complex disease requires the systematic and efficient analysis of data that can delineate the underlying pathogenesis. We have developed a mathematical model of disease progression to predict the clinical outcome, utilizing a set of causal factors known to contribute to COVID-19 pathology such as age, comorbidities, and certain viral and immunological parameters. Viral load and selected indicators of a dysfunctional immune response, such as cytokines IL-6 and IFNα, which contribute to the cytokine storm and fever, parameters of inflammation d-dimer and ferritin, aberrations in lymphocyte number, lymphopenia, and neutralizing antibodies were included for the analysis. The model provides a framework to unravel the multi-factorial complexities of the immune response manifested in SARS-CoV-2 infected individuals. Further, this model can be valuable to predict clinical outcome at an individual level, and to develop strategies for allocating appropriate resources to mitigate severe cases at a population level.

Predicting the Severity of Disease Progression in COVID-19 at the Individual and Population Level: A Mathematical Model

The impact of COVID-19 disease on health and economy has been global, and the magnitude of devastation is unparalleled in modern history. Any potential course of action to manage this complex disease requires the systematic and efficient analysis of data that can delineate the underlying pathogenesis. We have developed a mathematical model of disease progression to predict the clinical outcome, utilizing a set of causal factors known to contribute to COVID-19 pathology such as age, comorbidities, and certain viral and immunological parameters. Viral load and selected indicators of a dysfunctional immune response, such as cytokines IL-6 and IFNα which contribute to the cytokine storm and fever, parameters of inflammation D-Dimer and Ferritin, aberrations in lymphocyte number, lymphopenia, and neutralizing antibodies were included for the analysis. The model provides a framework to unravel the multi-factorial complexities of the immune response manifested in SARS-CoV-2 infected individuals. Further, this model can be valuable to predict clinical outcome at an individual level, and to develop strategies for allocating appropriate resources to manage severe cases at a population level.

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

OBJECTIVE

To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease.

DESIGN

Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group.

DATA SOURCES

PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020.

STUDY SELECTION

Studies that developed or validated a multivariable covid-19 related prediction model.

DATA EXTRACTION

At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool).

RESULTS

37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models.

CONCLUSION

Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline.

SYSTEMATIC REVIEW REGISTRATION

Protocol https://osf.io/ehc47/, registration https://osf.io/wy245.

READERS' NOTE

This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.