Can we predict the severe course of COVID-19 - a systematic review and meta-analysis of indicators of clinical outcome?

Suicide risk in patients with a current depressive episode during the COVID-19 pandemic

Introduction

The prevalence of major depressive disorder (MDD) increased during the COVID-19 pandemic. Data on suicidality in these patients during the pandemic period remain scarce. The aim of the study was to determine the prevalence and variables associated with serious suicide risk in Russian inpatients with MDD during the COVID-19 pandemic.

Methods

A cross-sectional cohort study with consecutive sampling was conducted from January 1, 2021 to December 31, 2021. All patients completed the Mini International Neuropsychiatric Interview (M.I.N.I.) (including the suicidality module), the Beck Depression Inventory, and the State-Trait Anxiety Inventory, and underwent a semi-structured interview to collect relevant demographic and clinical data. Effect sizes for all independent variables and covariates were calculated using partial eta-squared (ηp2).

Results

Of the 6757 patients with non-psychotic mental disorders assessed, 1605 (23.7%) had MDD confirmed by the M.I.N.I., of whom 17.8% were at serious risk for suicide according to the M.I.N.I. suicidality module. Factors independently associated with serious suicide risk in Russian inpatients with MDD during the pandemic were younger age (ηp2 = 0.021), greater severity of depression (0.038), higher state anxiety (0.003), and nonsuicidal self-injury (NSSI) (0.066). The same variables, except for state anxiety, were independently associated with suicide risk in the subgroup of MDD patients previously infected with SARS-CoV2.

Conclusion

In the COVID-19 pandemic, the proportion of patients with MDD at serious risk of suicide was similar to pre-pandemic data. No associations were found between suicidality in patients with MDD and COVID-related factors. Younger age, greater severity of depression, and especially NSSI were the most significant risk factors for suicide in patients with MDD during the COVID-19 pandemic.

metamedian: An R package for meta-analyzing studies reporting medians

When performing an aggregate data meta-analysis of a continuous outcome, researchers often come across primary studies that report the sample median of the outcome. However, standard meta-analytic methods typically cannot be directly applied in this setting. In recent years, there has been substantial development in statistical methods to incorporate primary studies reporting sample medians in meta-analysis, yet there are currently no comprehensive software tools implementing these methods. In this paper, we present the metamedian R package, a freely available and open-source software tool for meta-analyzing primary studies that report sample medians. We summarize the main features of the software and illustrate its application through real data examples involving risk factors for a severe course of COVID-19.

Short-term predictor for COVID-19 severity from a longitudinal multi-omics study for practical application in intensive care units

BACKGROUND

The COVID-19 pandemic challenged the management of technical and human resources in intensive care units (ICU) across the world. Several long-term predictors for COVID-19 disease progression have been discovered. However, predictors to support short-term planning of resources and medication that can be translated to future pandemics are still missing. A workflow was established to identify a predictor for short-term COVID-19 disease progression in the acute phase of intensive care patients to support clinical decision-making.

METHODS

Thirty-two patients with SARS-CoV-2 infection were recruited on admission to the ICU and clinical data collected. During their hospitalization, plasma samples were acquired from each patient on multiple occasions, excepting one patient for which only one time point was possible, and the proteome (Inflammation, Immune Response and Organ Damage panels from Olink® Target 96), metabolome and lipidome (flow injection analysis and liquid chromatography-mass spectrometry) analyzed for each sample. Patient visits were grouped according to changes in disease severity based on their respiratory and organ function, and evaluated using a combination of statistical analysis and machine learning. The resulting short-term predictor from this multi-omics approach was compared to the human assessment of disease progression. Furthermore, the potential markers were compared to the baseline levels of 50 healthy subjects with no known SARS-CoV-2 or other viral infections.

RESULTS

A total of 124 clinical parameters, 271 proteins and 782 unique metabolites and lipids were assessed. The dimensionality of the dataset was reduced, selecting 47 from the 1177 parameters available following down-selection, to build the machine learning model. Subsequently, two proteins (C-C motif chemokine 7 (CCL7) and carbonic anhydrase 14 (CA14)) and one lipid (hexosylceramide 18:2; O2/20:0) were linked to disease progression in the studied SARS-CoV-2 infections. Thus, a predictor delivering the prognosis of an upcoming worsening of the patient's condition up to five days in advance with a reasonable accuracy (79 % three days prior to event, 84 % four to five days prior to event) was found. Interestingly, the predictor's performance was complementary to the clinicians' capabilities to foresee a worsening of a patient.

CONCLUSION

This study presents a workflow to identify omics-based biomarkers to support clinical decision-making and resource management in the ICU. This was successfully applied to develop a short-term predictor for aggravation of COVID-19 symptoms. The applied methods can be adapted for future small cohort studies.

Characteristics and burden of acute COVID-19 and long-COVID: Demographic, physical, mental health, and economic perspectives

BACKGROUND

COVID-19 infection and its associated consequence, known as long-COVID, lead to a significant burden on the global healthcare system and limitations in people's personal and work lives. This study aims to provide further insight into the impact of acute and ongoing COVID-19 symptoms and investigates the role of patients' gender and vaccination status.

METHODS

416 individuals (73.9% female) between the ages of 16 and 80 years (M = 44.18, SD = 12.90) with self-reported symptoms of long-COVID participated in an online survey conducted between March and May 2022.

RESULTS

6.0%, 74.3%, and 19.7% of all respondents reported having had an asymptomatic, mild, or severe acute illness, respectively. Out of all participants, 7.8% required hospitalization. The most prevalent symptoms during the acute infection (Mdn = 23.50 symptoms, IQR = 13-39) included fatigue, exhaustion, cough, brain fog, and memory problems. The median long-COVID disease duration was 12.10 months (IQR = 2.8-17.4). Among 64 inquired long-COVID symptoms (Mdn = 17.00 symptoms, IQR = 9-27), participants reported fatigue, exhaustion, memory problems, brain fog, and dyspnea as the most common ongoing symptoms, which were generally experienced as fluctuating and deteriorating after physical or cognitive activity. Common consequences of long-COVID included financial losses (40.5%), changes in the participants' profession (41.0%), stress resistance (87.5%), sexual life (38.1%), and mood (72.1%), as well as breathing difficulties (41.3%), or an increased drug intake (e.g., medicine, alcohol; 44.6%). In addition, vaccinated individuals exhibited a shorter acute illness duration and an earlier onset of long-COVID symptoms. In general, women reported more long-COVID symptoms than men.

CONCLUSION

Long-COVID represents a heterogeneous disease and impacts multiple life aspects of those affected. Tailored rehabilitation programs targeting the plurality of physical and mental symptoms are needed.

Development of a novel machine learning model based on laboratory and imaging indices to predict acute cardiac injury in cancer patients with COVID-19 infection: a retrospective observational study

PURPOSE

Due to the increased risk of acute cardiac injury (ACI) and poor prognosis in cancer patients with COVID-19 infection, our aim was to develop a novel and interpretable model for predicting ACI occurrence in cancer patients with COVID-19 infection.

METHODS

This retrospective observational study screened 740 cancer patients with COVID-19 infection from December 2022 to April 2023. The least absolute shrinkage and selection operator (LASSO) regression was used for the preliminary screening of the indices. To enhance the model accuracy, we introduced an alpha index to further screen and rank the indices based on their significance. Random forest (RF) was used to construct the prediction model. The Shapley Additive Explanation (SHAP) and Local Interpretable Model-Agnostic Explanation (LIME) methods were utilized to explain the model.

RESULTS

According to the inclusion criteria, 201 cancer patients with COVID-19, including 36 variables indices, were included in the analysis. The top eight indices (albumin, lactate dehydrogenase, cystatin C, neutrophil count, creatine kinase isoenzyme, red blood cell distribution width, D-dimer and chest computed tomography) for predicting the occurrence of ACI in cancer patients with COVID-19 infection were included in the RF model. The model achieved an area under curve (AUC) of 0.940, an accuracy of 0.866, a sensitivity of 0.750 and a specificity of 0.900. The calibration curve and decision curve analysis showed good calibration and clinical practicability. SHAP results demonstrated that albumin was the most important index for predicting the occurrence of ACI. LIME results showed that the model could predict the probability of ACI in each cancer patient infected with COVID-19 individually.

CONCLUSION

We developed a novel machine-learning model that demonstrates high explainability and accuracy in predicting the occurrence of ACI in cancer patients with COVID-19 infection, using laboratory and imaging indices.

Do the oxidative stress biomarkers predict COVID-19 outcome? An in-hospital cohort study

In SARSCoV-2 infections, excessive activation of the immune system dramatically elevates reactive oxygen species levels, harms cell structures, and directly increases disease severity and mortality. We aimed to evaluate whether plasma oxidative stress biomarker levels could predict mortality in adults admitted with Coronavirus Disease 2019 (COVID-19), considering potential confounders. We conducted a cohort study of 115 adults (62.1 ± 17.6 years, 65 males) admitted to a Brazilian public hospital for severely symptomatic COVID-19. Serum levels of α-tocopherol, glutathione, superoxide dismutase, 8-hydroxy-2'-deoxyguanosine, malondialdehyde, and advanced oxidation protein products were quantified at COVID-19 diagnosis using real-time polymerase chain reaction. Serum levels of α-tocopherol, glutathione, superoxide dismutase, and advanced oxidation protein products differed significantly between survivors and non-survivors. Serum glutathione levels below 327.2 μmol/mL were associated with a significant risk of death in COVID-19 patients, even after accounting for other factors (adjusted hazard ratio = 3.12 [95% CI: 1.83-5.33]).

C-reactive protein and clinical outcome in COVID-19 patients: the importance of harmonized measurements

C-reactive protein (CRP) is a cytokine-mediated acute phase reactant with a recognized role in inflammatory conditions and infectious disease. In coronavirus disease 2019 (COVID-19), elevated CRP concentrations in serum were frequently detected and significantly associated with poor outcome in terms of disease severity, need for intensive care, and in-hospital death. For these reasons, the marker was proposed as a powerful test for prognostic classification of COVID-19 patients. In most of available publications, there was however confounding information about how interpretative criteria for CRP in COVID-19 should be derived, including quality of employed assays and optimal cut-off definition. Assuring result harmonization and controlling measurement uncertainty in terms of performance specifications are fundamental to allow worldwide application of clinical information according to specific CRP thresholds and to avoid risk of patient misclassification.

Prevalence and Mortality Risk of Neurological Disorders during the COVID-19 Pandemic: An Umbrella Review of the Current Evidence

INTRODUCTION

Coronavirus disease 2019 (COVID-19), a global pandemic, has infected approximately 10% of the world's population. This comprehensive review aimed to determine the prevalence of various neurological disorders in COVID-19 without overlapping meta-analysis errors.

METHODS

We searched for meta-analyses on neurological disorders following COVID-19 published up to March 14, 2023. We obtained 1,184 studies, of which 44 meta-analyses involving 9,228,588 COVID-19 patients were finally included. After confirming the forest plot of each study and removing overlapping individual studies, a re-meta-analysis was performed using the random-effects model.

RESULTS

The summarized combined prevalence of each neurological disorder is as follows: stroke 3.39% (95% confidence interval, 1.50-5.27), dementia 6.41% (1.36-11.46), multiple sclerosis 4.00% (2.50-5.00), epilepsy 5.36% (-0.60-11.32), Parkinson's disease 0.67% (-1.11-2.45), encephalitis 0.66% (-0.44-1.77), and Guillain-Barré syndrome 3.83% (-0.13-7.80). In addition, the mortality risk of patients with comorbidities of COVID-19 is as follows: stroke OR 1.63 (1.23-2.03), epilepsy OR 1.71 (1.00-2.42), dementia OR 1.90 (1.31-2.48), Parkinson's disease OR 3.94 (-2.12-10.01).

CONCLUSION

Our results show that the prevalence and mortality risk may increase in some neurological diseases during the COVID-19 pandemic. Future studies should elucidate the precise mechanisms for the link between COVID-19 and neurological diseases, determine which patient characteristics predispose them to neurological diseases, and consider potential global patient management.

Clinical expressions, characteristics and treatments of confirmed COVID-19 in nursing home residents: a systematic review

BACKGROUND

The coronavirus 2019 (COVID-19) pandemic has led to a high rate of infections, frequent outbreaks, and high mortality rates in nursing homes (NH) worldwide. To protect and improve the treatment and care of the vulnerable NH population, it is pivotal to systematise and synthesise data from cases of COVID-19 among NH residents. In our systematic review, we therefore aimed to describe the clinical expressions, characteristics, and treatments of NH residents confirmed to have COVID-19.

METHODS

We conducted two comprehensive literature searches in several electronic databases: (1) PubMed, (2) CINAHL, (3) AgeLine, (4) Embase, and (5) PsycINFO in April and July 2021. Of the 438 articles screened, 19 were included in our sample, and we used the Newcastle-Ottawa Assessment Scale to assess the quality of the reported studies. A weighted mean (M_weighted), was calculated to account for the large variation in sample sizes of the studies, and due to heterogeneity among the studies, we report our findings in a narrative synthesis.

RESULTS

According to the mean weights (M_weighted), common symptoms and signs in NH residents confirmed to have COVID-19 were fever (53.7%), cough (56.5%), hypoxia (32.3%), and delirium or confusion (31.2%). Common comorbidities were hypertension (78.6%), dementia or cognitive impairment (55.3%), and cardiovascular diseases (52.0%). Six studies presented data concerning medical and pharmacological treatments, such as inhalers, oxygen supplementation, anticoagulation, and parenteral/enteral fluids or nutrition. The treatments were used to improve outcomes, as part of palliative care, or as end-of-life treatment. Transfers to hospital for NH residents with confirmed COVID-19 were reported in six of the included studies, and the rate of hospital transfers ranged from 6.9% to 50% in this population. In the 17 studies reporting mortality, 40.2% of the NH residents died during the studies' observation periods.

CONCLUSIONS

Our systematic review allowed us to summarise important clinical findings about COVID-19 among NH residents and to identify the population's risk factors for serious illness and death caused by the disease. However, the treatment and care of NH residents with severe COVID-19 warrant further investigation.

D-dimer levels in non-COVID-19 ARDS and COVID-19 ARDS patients: A systematic review with meta-analysis

BACKGROUND

Hypercoagulability and thrombo-inflammation are the main reasons for death in COVID-19 patients. It is unclear whether there is a difference between D-dimer levels in patients without or with COVID-19 acute respiratory distress syndrome (ARDS).

METHODS

We searched PubMed, EMBASE, and ClinicalTrails.gov databases looking for studies reporting D-dimer levels in patients without or with COVID-19 ARDS. Secondary endpoints included length of hospital stay, and mortality data at the longest follow-up available.

RESULTS

We included 12 retrospective and 3 prospective studies with overall 2,828 patients, of whom 1,404 (49.6%) had non-COVID-19 ARDS and 1,424 had COVID-19 ARDS. D-dimer levels were not significantly higher in non-COVID-19 ARDS than in COVID-19 ARDS patients (mean 7.65 mg/L vs. mean 6.20 mg/L MD 0.88 [CI: -0.61 to 2.38] p = 0.25; I² = 85%) while the length of hospital stay was shorter (non-COVID-19 mean 37.4 days vs. COVID-19 mean 48.5 days, MD -10.92 [CI: -16.71 to -5.14] p < 0.001; I² = 44%). No difference in mortality was observed: non-COVID-19 ARDS 418/1167 (35.8%) vs. COVID-19 ARDS 467/1201 (38.8%).

CONCLUSIONS

We found no difference in the mean D-dimer levels between non-COVID-19 ARDS and COVID-19 ARDS patients.

Standard error estimation in meta-analysis of studies reporting medians

We consider the setting of an aggregate data meta-analysis of a continuous outcome of interest. When the distribution of the outcome is skewed, it is often the case that some primary studies report the sample mean and standard deviation of the outcome and other studies report the sample median along with the first and third quartiles and/or minimum and maximum values. To perform meta-analysis in this context, a number of approaches have recently been developed to impute the sample mean and standard deviation from studies reporting medians. Then, standard meta-analytic approaches with inverse-variance weighting are applied based on the (imputed) study-specific sample means and standard deviations. In this article, we illustrate how this common practice can severely underestimate the within-study standard errors, which results in poor coverage for the pooled mean in common effect meta-analyses and overestimation of between-study heterogeneity in random effects meta-analyses. We propose a straightforward bootstrap approach to estimate the standard errors of the imputed sample means. Our simulation study illustrates how the proposed approach can improve the estimation of the within-study standard errors and consequently improve coverage for the pooled mean in common effect meta-analyses and estimation of between-study heterogeneity in random effects meta-analyses. Moreover, we apply the proposed approach in a meta-analysis to identify risk factors of a severe course of COVID-19.

Recent developments in the immunopathology of COVID-19

There has been an important change in the clinical characteristics and immune profile of Coronavirus disease 2019 (COVID-19) patients during the pandemic thanks to the extensive vaccination programs. Here, we highlight recent studies on COVID-19, from the clinical and immunological characteristics to the protective and risk factors for severity and mortality of COVID-19. The efficacy of the COVID-19 vaccines and potential allergic reactions after administration are also discussed. The occurrence of new variants of concerns such as Omicron BA.2, BA.4, and BA.5 and the global administration of COVID-19 vaccines have changed the clinical scenario of COVID-19. Multisystem inflammatory syndrome in children (MIS-C) may cause severe and heterogeneous disease but with a lower mortality rate. Perturbations in immunity of T cells, B cells, and mast cells, as well as autoantibodies and metabolic reprogramming may contribute to the long-term symptoms of COVID-19. There is conflicting evidence about whether atopic diseases, such as allergic asthma and rhinitis, are associated with a lower susceptibility and better outcomes of COVID-19. At the beginning of pandemic, the European Academy of Allergy and Clinical Immunology (EAACI) developed guidelines that provided timely information for the management of allergic diseases and preventive measures to reduce transmission in the allergic clinics. The global distribution of COVID-19 vaccines and emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with reduced pathogenic potential dramatically decreased the morbidity, severity, and mortality of COVID-19. Nevertheless, breakthrough infection remains a challenge for disease control. Hypersensitivity reactions (HSR) to COVID-19 vaccines are low compared to other vaccines, and these were addressed in EAACI statements that provided indications for the management of allergic reactions, including anaphylaxis to COVID-19 vaccines. We have gained a depth knowledge and experience in the over 2 years since the start of the pandemic, and yet a full eradication of SARS-CoV-2 is not on the horizon. Novel strategies are warranted to prevent severe disease in high-risk groups, the development of MIS-C and long COVID-19.

Correlates of Coronavirus Disease 2019 Inpatient Mortality at a Southern California Community Hospital With a Predominantly Hispanic/Latino Adult Population

Background

Studies of inpatient coronavirus disease 2019 (COVID-19) mortality risk factors have mainly used data from academic medical centers or large multihospital databases and have not examined populations with large proportions of Hispanic/Latino patients. In a retrospective cohort study of 4881 consecutive adult COVID-19 hospitalizations at a single community hospital in Los Angeles County with a majority Hispanic/Latino population, we evaluated factors associated with mortality.

Methods

Data on demographic characteristics, comorbidities, laboratory and clinical results, and COVID-19 therapeutics were abstracted from the electronic medical record. Cox proportional hazards regression modeled statistically significant, independently associated predictors of hospital mortality.

Results

Age ≥65 years (hazard ratio [HR] = 2.66; 95% confidence interval [CI] = 1.90-3.72), male sex (HR = 1.31; 95% CI = 1.07-1.60), renal disease (HR = 1.52; 95% CI = 1.18-1.95), cardiovascular disease (HR = 1.45; 95% CI = 1.18-1.78), neurological disease (HR = 1.84; 95% CI = 1.41-2.39), D-dimer ≥500 ng/mL (HR = 2.07; 95% CI = 1.43-3.0), and pulse oxygen level <88% (HR = 1.39; 95% CI = 1.13-1.71) were independently associated with increased mortality. Patient household with (1) multiple COVID-19 cases and (2) Asian, Black, or Hispanic compared with White non-Hispanic race/ethnicity were associated with reduced mortality. In hypoxic COVID-19 inpatients, remdesivir, tocilizumab, and convalescent plasma were associated with reduced mortality, and corticosteroid use was associated with increased mortality.

Conclusions

We corroborate several previously identified mortality risk factors and find evidence that the combination of factors associated with mortality differ between populations.

Mental Health Impact of Early Stages of the COVID-19 Pandemic on Individuals with Pre-Existing Mental Disorders: A Systematic Review of Longitudinal Research

In view of disease-related threats, containment measures, and disrupted healthcare, individuals with pre-existing mental illness might be vulnerable to adverse effects of the COVID-19 pandemic. Previous reviews indicated increased mental distress, with limited information on peri-pandemic changes. In this systematic review, we aimed to identify longitudinal research investigating pre- to peri-pandemic and/or peri-pandemic changes of mental health in patients, focusing on the early phase and considering specific diagnoses. PsycINFO, Web of Science, the WHO Global literature on coronavirus disease database, and the Cochrane COVID-19 Study Register weresearched through 31 May 2021. Studies were synthesized using vote counting based on effect direction. We included 40 studies mostly from Western, high-income countries. Findings were heterogeneous, with improving and deteriorating mental health observed compared to pre-pandemic data, partly depending on underlying diagnoses. For peri-pandemic changes, evidence was limited, with some suggestion of recovery of mental distress. Study quality was heterogeneous; only few studies investigated potential moderators (e.g., chronicity of mental illness). Mental health effects on people with pre-existing conditions are heterogeneous within and across diagnoses for pre- to peri-pandemic and peri-pandemic comparisons. To improve mental health services amid future global crises, forthcoming research should understand medium- and long-term effects, controlling for containment measures.

Anti-SARS-CoV-2 antibody-containing plasma improves outcome in patients with hematologic or solid cancer and severe COVID-19: a randomized clinical trial

Patients with cancer are at high risk of severe coronavirus disease 2019 (COVID-19), with high morbidity and mortality. Furthermore, impaired humoral response renders severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines less effective and treatment options are scarce. Randomized trials using convalescent plasma are missing for high-risk patients. Here, we performed a randomized, open-label, multicenter trial ( https://www.clinicaltrialsregister.eu/ctr-search/trial/2020-001632-10/DE ) in hospitalized patients with severe COVID-19 (n = 134) within four risk groups ((1) cancer (n = 56); (2) immunosuppression (n = 16); (3) laboratory-based risk factors (n = 36); and (4) advanced age (n = 26)) randomized to standard of care (control arm) or standard of care plus convalescent/vaccinated anti-SARS-CoV-2 plasma (plasma arm). No serious adverse events were observed related to the plasma treatment. Clinical improvement as the primary outcome was assessed using a seven-point ordinal scale. Secondary outcomes were time to discharge and overall survival. For the four groups combined, those receiving plasma did not improve clinically compared with those in the control arm (hazard ratio (HR) = 1.29; P = 0.205). However, patients with cancer experienced a shortened median time to improvement (HR = 2.50; P = 0.003) and superior survival with plasma treatment versus the control arm (HR = 0.28; P = 0.042). Neutralizing antibody activity increased in the plasma cohort but not in the control cohort of patients with cancer (P = 0.001). Taken together, convalescent/vaccinated plasma may improve COVID-19 outcomes in patients with cancer who are unable to intrinsically generate an adequate immune response.

Machine Learning Successfully Detects Patients with COVID-19 Prior to PCR Results and Predicts Their Survival Based on Standard Laboratory Parameters in an Observational Study

INTRODUCTION

In the current COVID-19 pandemic, clinicians require a manageable set of decisive parameters that can be used to (i) rapidly identify SARS-CoV-2 positive patients, (ii) identify patients with a high risk of a fatal outcome on hospital admission, and (iii) recognize longitudinal warning signs of a possible fatal outcome.

METHODS

This comparative study was performed in 515 patients in the Maria Skłodowska-Curie Specialty Voivodeship Hospital in Zgierz, Poland. The study groups comprised 314 patients with COVID-like symptoms who tested negative and 201 patients who tested positive for SARS-CoV-2 infection; of the latter, 72 patients with COVID-19 died and 129 were released from hospital. Data on which we trained several machine learning (ML) models included clinical findings on admission and during hospitalization, symptoms, epidemiological risk, and reported comorbidities and medications.

RESULTS

We identified a set of eight on-admission parameters: white blood cells, antibody-synthesizing lymphocytes, ratios of basophils/lymphocytes, platelets/neutrophils, and monocytes/lymphocytes, procalcitonin, creatinine, and C-reactive protein. The medical decision tree built using these parameters differentiated between SARS-CoV-2 positive and negative patients with up to 90-100% accuracy. Patients with COVID-19 who on hospital admission were older, had higher procalcitonin, C-reactive protein, and troponin I levels together with lower hemoglobin and platelets/neutrophils ratio were found to be at highest risk of death from COVID-19. Furthermore, we identified longitudinal patterns in C-reactive protein, white blood cells, and D dimer that predicted the disease outcome.

CONCLUSIONS

Our study provides sets of easily obtainable parameters that allow one to assess the status of a patient with SARS-CoV-2 infection, and the risk of a fatal disease outcome on hospital admission and during the course of the disease.

Depression, aging, and immunity: implications for COVID-19 vaccine immunogenicity

The aging process can have detrimental effects on the immune system rendering the elderly more susceptible to infectious disease and less responsive to vaccination. Major depressive disorder (MDD) has been hypothesized to show characteristics of accelerated biological aging. This raises the possibility that depressed individuals will show some overlap with elderly populations with respect to their immune response to infection and vaccination. Here we provide an umbrella review of this literature in the context of the SARS CoV-2 pandemic. On balance, the available data do indeed suggest that depression is a risk factor for both adverse outcomes following COVID-19 infection and for reduced COVID-19 vaccine immunogenicity. We conclude that MDD (and other major psychiatric disorders) should be recognized as vulnerable populations that receive priority for vaccination along with other at-risk groups.

Predicting COVID-19 progression in hospitalized patients in Belgium from a multi-state model

Objectives

To adopt a multi-state risk prediction model for critical disease/mortality outcomes among hospitalised COVID-19 patients using nationwide COVID-19 hospital surveillance data in Belgium.

Materials and methods

Information on 44,659 COVID-19 patients hospitalised between March 2020 and June 2021 with complete data on disease outcomes and candidate predictors was used to adopt a multi-state, multivariate Cox model to predict patients' probability of recovery, critical [transfer to intensive care units (ICU)] or fatal outcomes during hospital stay.

Results

Median length of hospital stay was 9 days (interquartile range: 5-14). After admission, approximately 82% of the COVID-19 patients were discharged alive, 15% of patients were admitted to ICU, and 15% died in the hospital. The main predictors of an increased probability for recovery were younger age, and to a lesser extent, a lower number of prevalent comorbidities. A patient's transition to ICU or in-hospital death had in common the following predictors: high levels of c-reactive protein (CRP) and lactate dehydrogenase (LDH), reporting lower respiratory complaints and male sex. Additionally predictors for a transfer to ICU included middle-age, obesity and reporting loss of appetite and staying at a university hospital, while advanced age and a higher number of prevalent comorbidities for in-hospital death. After ICU, younger age and low levels of CRP and LDH were the main predictors for recovery, while in-hospital death was predicted by advanced age and concurrent comorbidities.

Conclusion

As one of the very few, a multi-state model was adopted to identify key factors predicting COVID-19 progression to critical disease, and recovery or death.

N, Gautam N, Sunny AK, Thakur N, Subeedee KC, Mandal SK, Bastola A. Audit of Clinical Care Received by COVID-19 Patients Treated at a Tertiary Care Hospital of Nepal in 2021

Like the world over, Nepal was also hard hit by the second wave of COVID-19. We audited the clinical care provided to COVID-19 patients admitted from April to June 2021 in a tertiary care hospital of Nepal. This was a cohort study using routinely collected hospital data. There were 620 patients, and most (458, 74%) had severe illness. The majority (600, 97%) of the patients were eligible for admission as per national guidelines. Laboratory tests helping to predict the outcome of COVID-19, such as D-dimer and C-reactive protein, were missing in about 25% of patients. Nearly all (>95%) patients with severe disease received corticosteroids, anticoagulants and oxygen. The use of remdesivir was low (22%). About 70% of the patients received antibiotics. Hospital exit outcomes of most (>95%) patients with mild and moderate illness were favorable (alive and discharged). Among patients with severe illness, about 25% died and 4% were critically ill, needing further referral. This is the first study from Nepal to audit and document COVID-19 clinical care provision in a tertiary care hospital, thus filling the evidence gap in this area from resource-limited settings. Adherence to admission guidelines was excellent. Laboratory testing, access to essential drugs and data management needs to be improved.

ISPE-Endorsed Guidance in Using Electronic Health Records for Comparative Effectiveness Research in COVID-19: Opportunities and Trade-Offs

As the scientific research community along with healthcare professionals and decision makers around the world fight tirelessly against the coronavirus disease 2019 (COVID-19) pandemic, the need for comparative effectiveness research (CER) on preventive and therapeutic interventions for COVID-19 is immense. Randomized controlled trials markedly under-represent the frail and complex patients seen in routine care, and they do not typically have data on long-term treatment effects. The increasing availability of electronic health records (EHRs) for clinical research offers the opportunity to generate timely real-world evidence reflective of routine care for optimal management of COVID-19. However, there are many potential threats to the validity of CER based on EHR data that are not originally generated for research purposes. To ensure unbiased and robust results, we need high-quality healthcare databases, rigorous study designs, and proper implementation of appropriate statistical methods. We aimed to describe opportunities and challenges in EHR-based CER for COVID-19-related questions and to introduce best practices in pharmacoepidemiology to minimize potential biases. We structured our discussion into the following topics: (1) study population identification based on exposure status; (2) ascertainment of outcomes; (3) common biases and potential solutions; and (iv) data operational challenges specific to COVID-19 CER using EHRs. We provide structured guidance for the proper conduct and appraisal of drug and vaccine effectiveness and safety research using EHR data for the pandemic. This paper is endorsed by the International Society for Pharmacoepidemiology (ISPE).

Early predictors of clinical deterioration in a cohort of outpatients with COVID-19 in southern Italy: A multicenter observational study

Data regarding early predictors of clinical deterioration in patients with infection of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is still scarce. The aim of the study is to identify early symptoms or signs that may be associated with severe coronavirus disease 2019 (COVID-19). We conducted a multicentre prospective cohort study on a cohort of patients with COVID-19 in home isolation from March 2020 to April 2021. We assessed longitudinal clinical data (fever, dyspnea, need for hospitalization) through video calls at three specific time points: the beginning of symptoms or the day of the first positivity of the nasopharyngeal swab for SARS-CoV-2-RNA (t₀ ), and 3 (t₃ ) and 7 (t₇ ) days after the onset of symptoms. We included 329 patients with COVID-19: 182 (55.3%) males, mean age 53.4 ± 17.4 years, median Charlson comorbidity index (CCI) of 1 (0-3). Of the 329 patients enrolled, 171 (51.98%) had a mild, 81 (24.6%) a moderate, and 77 (23.4%) a severe illness; 151 (45.9%) were hospitalized. Compared to patients with mild COVID-19, moderate and severe patients were older (p < 0.001) and had more comorbidities, especially hypertension (p < 0.001) and cardiovascular diseases (p = 0.01). At t₃ and t₇ , we found a significant higher rate of persisting fever (≥37°C) among patients with moderate (91.4% and 58.0% at t₃ and t₇ , respectively; p < 0.001) and severe outcome (75.3% and 63.6%, respectively; p < 0.001) compared to mild COVID-19 outcome (27.5% and 11.7%, respectively; p < 0.001). Factors independently associated with a more severe outcome were persisting fever at t₃ and t₇ , increasing age, and CCI above 2 points. Persisting fever at t₃ and t₇ seems to be related to a more severe COVID-19. This data may be useful to assess hospitalization criteria and optimize the use of resources in the outpatient setting.

Predictors of mortality among inpatients with COVID-19 infection in a tertiary referral center in the Philippines

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Mortality data were comparable to those of early reports relating to the wild-type SARS-CoV-2.

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Clinical and laboratory monitoring is critical during the 2nd to 3rd week of illness.

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Common and inexpensive laboratory tests may aid in the monitoring of patients.

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Clinical pathways can be adapted to local data, especially in resource-poor settings.

Objectives

The aim of this study was to determine the predictors of mortality and describe laboratory trends among adults with confirmed COVID-19.

Methods

The medical records of adult patients admitted to a referral hospital with COVID-19 were retrospectively reviewed. Demographic and clinical characteristics, and laboratory parameters, were compared between survivors and non-survivors. Predictors of mortality were determined by multivariate analysis. Mean laboratory values were plotted across illness duration.

Results

Of 1215 patients, 203 (16.7%) had mild, 488 (40.2%) moderate, 183 (15.1%) severe, and 341 (28.1%) critical COVID-19 on admission. In-hospital mortality was 18.2% (0% mild, 6.1% moderate, 15.8% severe, 47.5% critical). Predictors of mortality were age ≥ 60 years, COPD, qSOFA score ≥ 2, WBC > 10 × 10⁹/L, absolute lymphocyte count < 1000, neutrophil ≥ 70%, PaO₂/FiO₂ ratio ≤ 200, eGFR < 90 mL/min/1.73 m², LDH > 600 U/L, and CRP > 12 mg/L. Non-survivors exhibited an increase in LDH and decreases in PaO₂/FiO₂ ratio and eGFR during the 2nd–3rd week of illness.

Conclusion

The overall mortality rate was high. Predictors of mortality were similar to those of other reports globally. Marked inflammation and worsening pulmonary and renal function were evident among non-survivors by the 2nd–3rd week of illness.

A Machine Learning Model for Predicting Hospitalization in Patients with Respiratory Symptoms during the COVID-19 Pandemic

A machine learning approach is a useful tool for risk-stratifying patients with respiratory symptoms during the COVID-19 pandemic, as it is still evolving. We aimed to verify the predictive capacity of a gradient boosting decision trees (XGboost) algorithm to select the most important predictors including clinical and demographic parameters in patients who sought medical support due to respiratory signs and symptoms (RAPID RISK COVID-19). A total of 7336 patients were enrolled in the study, including 6596 patients that did not require hospitalization and 740 that required hospitalization. We identified that patients with respiratory signs and symptoms, in particular, lower oxyhemoglobin saturation by pulse oximetry (SpO₂) and higher respiratory rate, fever, higher heart rate, and lower levels of blood pressure, associated with age, male sex, and the underlying conditions of diabetes mellitus and hypertension, required hospitalization more often. The predictive model yielded a ROC curve with an area under the curve (AUC) of 0.9181 (95% CI, 0.9001 to 0.9361). In conclusion, our model had a high discriminatory value which enabled the identification of a clinical and demographic profile predictive, preventive, and personalized of COVID-19 severity symptoms.

Postacute Sequelae of SARS-CoV-2 Infection-Lessons Learned From a Coordinated Health System Response

Objective

To outline a consensus-designed process for triaging and managing patients with post-coronavirus disease (COVID-19) syndrome at Mayo Clinic.

Patients and Methods

We convened a central multidisciplinary team including members from the departments of general internal medicine, occupational medicine, physical medicine and rehabilitation, psychology, allergy and immunology, infectious disease, pulmonology, neurology, cardiology, and pediatrics and otorhinolaryngology with membership from all Mayo Clinic sites in Arizona, Florida, Iowa, Minnesota, and Wisconsin.

Results

Consensus recommendations were made for the best practice guidelines on triaging and managing patients. Several innovations were agreed upon, including a postacute sequelae of COVID-19-specific appointment request form for data collection, a bioregistry, a biorepository, and a postacute sequelae of COVID-19-specific treatment program.

Conclusion

Given that each clinical site had individual clinical practices, these recommendations were implemented using different models, which may provide broad applicability to other clinical settings.

Personal Protective Measures during the COVID-19 Pandemic in Germany

OBJECTIVES

The coronavirus SARS-CoV-2 is highly contagious and can only be contained if the majority of the population takes measures to protect themselves against infection. The present study aimed to investigate personal protective measures, their development over the course of the pandemic in Germany, and potential differences in behavior in terms of sex, age, and education.

METHODS

Data from 20 waves of the serial cross-sectional study "BfR-Corona-Monitor" were analyzed. The total sample consisted of N = 20,317 respondents (about 1000 per wave). Data were collected through telephone surveys between June 2020 and March 2021.

RESULTS

To protect themselves from infection, participants primarily relied on wearing covers for mouth and nose, keeping their distance from other individuals, and washing their hands thoroughly. Analyses over time showed a strong positive correlation between the number of measures taken and the national incidence rate. Sociodemographic differences also emerged, with women and those who are higher educated as well as younger respondents taking a higher number of protective measures.

CONCLUSIONS

Our results indicated that in times of greater infection risks, individuals adapted accordingly and took more protective measures. However, on the basis of sociodemographic differences, campaigns should especially focus on older individuals, the male sex, and those with lower education to enhance their protective behavior.

SARS-CoV-2 Infection and Vaccination Coverage among Fragile Populations in a Local Health Area of Northern Italy

Italy was dramatically hit by the COVID-19 pandemic, and the province of Brescia was one of the epicenters of the outbreak. Furthermore, Brescia has one of the highest incidences of people living with HIV (PLWH) and a substantial presence of migrants. We conducted a retrospective cohort study involving all citizens connected to the Brescia Health Protection Agency, assessing the SARS-CoV-2 burden, COVID-19 prevalence, and vaccination coverage. A total of 1,004,210 persons were included, 3817 PLWH and 134,492 foreigners. SARS-CoV-2 infection, hospitalizations and death were more frequent among Italians than foreigners. SARS-CoV-2 infections and deaths were more frequent in HIV-uninfected people than in PLWH. PLWH and foreigners were less likely to have a SARS-CoV-2 diagnosis compared to HIV-negative patients. Migrants were more likely to be hospitalized but had a lower risk of death compared to HIV-negative patients. Regarding vaccination, 89.1% of the population received at least one dose of vaccine, while 70.4% of the Italian citizens and 36.3% of the foreigner subjects received three doses of vaccine. Foreigners showed a lower risk of being diagnosed with SARS-CoV-2 but a higher risk of complications. HIV infection was not associated with a higher risk of SARS-CoV-2 severe manifestations compared to the general population. COVID-19 vaccine hesitancy was not different between PLWH and HIV uninfected people, but foreigners were more hesitant.

Capturing the value of vaccination: impact of vaccine-preventable disease on hospitalization

Evidence from epidemiological studies suggests that vaccine-preventable disease (VPD) such as influenza or herpes zoster contribute significantly to the increased risk of older adults for cardiovascular, cerebrovascular, neurological, and renal complications in the period after illnesses. However, since the period of elevated risk can persist well beyond the duration of the acute illness, the connection is not always recognized. To obtain insights into the relationship between diagnoses for vaccine-preventable disease and for other conditions, we analyzed principal and secondary diagnoses for 3,127,768 inpatient admissions of adults 50 years and older in the United States, using medical insurance claims drawn from the IBM® MarketScan® Research Databases (Marketscan). The Marketscan data indicated that overall, 3.1% of these hospitalizations had a principal diagnosis of VPD with variation by month of admission, and age. However, hospitalizations with a principal non-VPD diagnosis but secondary VPD diagnoses were 2.8 times more frequent, with particularly high rates in those whose principal diagnoses were non-VPD respiratory or circulatory disease. Hospitalized patients with a secondary VPD diagnosis tended to have poorer discharge outcomes, and longer length of stay in comparison to hospitalized patients without a secondary VPD diagnosis. In total, these data are consistent with suggestions that VPDs play a significant and potentially under-estimated role in hospitalization and outcomes, which may be potentially preventable by improved vaccination coverage.

Characteristics of COVID-19 Inpatients in Rehabilitation Units during the First Pandemic Wave: A Cohort Study from a Large Hospital in Champagne Region

Background: Data describing patients hospitalized in medical rehabilitation wards after the acute phase of COVID-19 could help to better understand the rehabilitation needs in the current pandemic situation. Methods: Cohort including all patients with COVID-19 hospitalized in a single, large university hospital in Northeast France from 25 February to 30 April 2020. Results: 479 patients were admitted with COVID-19 during the study period, of whom 128 died (26.7%). Among the 351 survivors, 111 were referred to rehabilitation units, including 63 (17.9%) referred to physical and rehabilitation medicine (PRM) units. The median age of patients referred to rehabilitation units was 72 years. Patients who had been in intensive care, or who had had a long hospital stay, required referral to PRM units. Two biomarkers were associated with referral to rehabilitation units, namely, elevated troponin (p = 0.03) and impaired renal function (p = 0.03). Age was associated with referral to PRM units (p = 0.001). Conclusions: Almost one-third of COVID-19 patients required post-acute care, but only one-fifth had access to PRM units. The optimal strategy for post-acute management of COVID-19 patients remains to be determined. The need for rehabilitation wards during a pandemic is a primary concern in enabling the long-term functioning of infected patients.

Association between 25-hydroxyvitamin D levels and COVID-19 severity

Background and aims

Despite reports on the impact of vitamin D status on coronavirus disease 2019 (COVID-19) severity, the association between low vitamin D status and severe COVID-19 remains unclear. Moreover, researchers have not determined the aforementioned association in Japanese patients. This study aimed to investigate the association between 25-hydroxyvitamin D [25(OH)D] levels and COVID-19 severity in Japanese patients.

Methods

This retrospective observational study included 117 consecutive patients with COVID-19 admitted to the Kobe City Medical Center General Hospital between October 01, 2020, and January 31, 2021. We measured the serum 25(OH)D levels using blood specimens collected within 5 days of hospital admission using liquid chromatography-tandem mass spectrometry.

Results

There were 21 (17.9%), 73 (62.4%), 19 (16.2%) and 4 (3.4%) patients with severe deficiency (<10 ng/mL), deficiency (10–<20 ng/mL), insufficiency (20–<30 ng/mL), and sufficiency (≥30 ng/mL) of vitamin D, respectively. In univariate logistic regression analyses, lower serum 25(OH)D levels [odds ratio (OR) 1.18 per 1 ng/mL decrease, 95% confidence interval (CI) 1.04–1.33, p = 0.007] were significantly associated with invasive mechanical ventilation (IMV) or death. In a multivariate logistic regression analysis, low serum 25(OH)D levels [OR 1.22 per 1 ng/mL decrease, 95% CI 1.06–1.40, p = 0.005] were significantly associated with IMV or death. The cut-off value of serum 25(OH)D levels was 10.4 ng/mL, calculated by the receiver operating characteristic curve to detect the requirement for IMV or death.

Conclusions

To the best of our knowledge, this is the first study to assess the association between vitamin D status and COVID-19 severity in Japanese patients. Low serum 25(OH)D level was detected as an independent risk factor for severe COVID-19 among Japanese patients.

Prediction of the need for intensive oxygen supplementation during hospitalisation among subjects with COVID-19 admitted to an academic health system in Texas: a retrospective cohort study and multivariable regression model

OBJECTIVE

SARS-CoV-2 has caused a pandemic claiming more than 4 million lives worldwide. Overwhelming COVID-19 respiratory failure placed tremendous demands on healthcare systems increasing the death toll. Cost-effective prognostic tools to characterise the likelihood of patients with COVID-19 to progress to severe hypoxemic respiratory failure are still needed.

DESIGN

We conducted a retrospective cohort study to develop a model using demographic and clinical data collected in the first 12 hours of admission to explore associations with severe hypoxemic respiratory failure in unvaccinated and hospitalised patients with COVID-19.

SETTING

University-based healthcare system including six hospitals located in the Galveston, Brazoria and Harris counties of Texas.

PARTICIPANTS

Adult patients diagnosed with COVID-19 and admitted to one of six hospitals between 19 March and 30 June 2020.

PRIMARY OUTCOME

The primary outcome was defined as reaching a WHO ordinal scale between 6 and 9 at any time during admission, which corresponded to severe hypoxemic respiratory failure requiring high-flow oxygen supplementation or mechanical ventilation.

RESULTS

We included 329 participants in the model cohort and 62 (18.8%) met the primary outcome. Our multivariable regression model found that lactate dehydrogenase (OR 2.36), Quick Sequential Organ Failure Assessment score (OR 2.26) and neutrophil to lymphocyte ratio (OR 1.15) were significant predictors of severe disease. The final model showed an area under the curve of 0.84. The sensitivity analysis and point of influence analysis did not reveal inconsistencies.

CONCLUSIONS

Our study suggests that a combination of accessible demographic and clinical information collected on admission may predict the progression to severe COVID-19 among adult patients with mild and moderate disease. This model requires external validation prior to its use.

A Model Predicting Mortality of Hospitalized Covid-19 Patients Four Days After Admission: Development, Internal and Temporal-External Validation

Objective

To develop and validate a prognostic model for in-hospital mortality after four days based on age, fever at admission and five haematological parameters routinely measured in hospitalized Covid-19 patients during the first four days after admission.

Methods

Haematological parameters measured during the first 4 days after admission were subjected to a linear mixed model to obtain patient-specific intercepts and slopes for each parameter. A prediction model was built using logistic regression with variable selection and shrinkage factor estimation supported by bootstrapping. Model development was based on 481 survivors and 97 non-survivors, hospitalized before the occurrence of mutations. Internal validation was done by 10-fold cross-validation. The model was temporally-externally validated in 299 survivors and 42 non-survivors hospitalized when the Alpha variant (B.1.1.7) was prevalent.

Results

The final model included age, fever on admission as well as the slope or intercept of lactate dehydrogenase, platelet count, C-reactive protein, and creatinine. Tenfold cross validation resulted in a mean area under the receiver operating characteristic curve (AUROC) of 0.92, a mean calibration slope of 1.0023 and a Brier score of 0.076. At temporal-external validation, application of the previously developed model showed an AUROC of 0.88, a calibration slope of 0.95 and a Brier score of 0.073. Regarding the relative importance of the variables, the (apparent) variation in mortality explained by the six variables deduced from the haematological parameters measured during the first four days is higher (explained variation 0.295) than that of age (0.210).

Conclusions

The presented model requires only variables routinely acquired in hospitals, which allows immediate and wide-spread use as a decision support for earlier discharge of low-risk patients to reduce the burden on the health care system.

Clinical Trial Registration

Austrian Coronavirus Adaptive Clinical Trial (ACOVACT); ClinicalTrials.gov, identifier NCT04351724.

A model of infection and immune response to low dose radiation

PURPOSE

Low dose radiation therapy (LDRT) using doses in the range of 30-150 cGy has been proposed as a means of mitigating the pneumonia associated with COVID-19. However, preliminary results from ongoing clinical trials have been mixed. The aim of this work is to develop a mathematical model of the viral infection and associated systemic inflammation in a patient based on the time evolution of the viral load. The model further proposes an immunomodulatory response to LDRT based on available data. Inflammation kinetics are then explored and compared to clinical results.

METHODS

The time evolution of a viral infection, inflammatory signaling factors, and inflammatory response are modeled by a set of coupled differential equations. Adjustable parameters are taken from the literature where available and otherwise iteratively adjusted to fit relevant data. Simple functions modeling both the suppression of pro-inflammatory signal factors and the enhancement of anti-inflammatory factors in response to low doses of radiation are developed. The inflammation response is benchmarked against C-reactive protein (CRP) levels measured for cohorts of patients with severe COVID-19.

RESULTS

The model fit the time-evolution of viral load data, cytokine data, and inflammation (CRP) data. When LDRT was applied early, the model predicted a reduction in peak inflammation consistent with the difference between the non-surviving and surviving cohorts. This reduction of peak inflammation diminished as the application of LDRT was delayed.

CONCLUSION

The model tracks the available data on viral load, cytokine levels, and inflammatory biomarkers well. An LDRT effect is large enough in principle to provide a life-saving immunomodulatory effect, though patients treated with LDRT already near the peak of their inflammation trajectory are unlikely to see drastic reductions in that peak. This result potentially explains some discrepancies in the preliminary clinical trial data.

Rectus Sheath Hematoma in COVID-19 Patients as a Mortal Complication: A Retrospective Report

BACKGROUND

Rectus sheath hematoma is a rare self-limited presentation that has become a concern in hospitalized COVID-19 patients receiving anticoagulant therapies.

METHOD

A retrospective multicentric study was conducted in referral hospitals affiliated with the Tehran University of Medical Science, Tehran, Iran, between June and August 2021. Patients with a confirmed diagnosis of COVID-19 that were complicated with rectus sheath hematoma during hospitalization were included. Median (lower quartile to upper quartile) was used to report the distribution of the results.

RESULT

This study was conducted on nine patients with confirmed COVID-19 pneumonia, including eight females and one male. The severity of viral pneumonia was above average in eight patients. The median age and median body mass index were 65 (55.5 to 78) years and 29.38 (23.97 to 31.71) kg/m². The duration of anticoagulant therapy was 10 (6 to 14) days, and the median length of hospital stay was 20 (10 to 23.5) days. Rectus sheath hematoma occurred after a median reduction of 4 (2.7 to 6.6) units in blood hemoglobin. Although 66.7% received ICU care and all of them were under full observation in well-equipped hospitals, the mortality rate was 55.6%.

CONCLUSION

In summary, increased levels of inflammatory markers such as lactic acid dehydrogenase along with an abrupt decrease in blood hemoglobin in COVID-19 patients should be considered as predisposing factors for rectus sheath hematoma, especially in patients with moderate to severe COVID-19 pneumonia under anticoagulant therapy. This complication had been considered a self-limited condition; however, it seems to be fatal in patients with COVID-19 pneumonia. Further studies in larger sample sizes should be conducted to find out suitable management for this complication.

Gamma-glutamyl-transferase may predict COVID-19 outcomes in hospitalised patients

AIM

In this study, we aimed to define the predictive role of liver function tests at admission to the hospital in outcomes of hospitalised patients with COVID-19.

MATERIAL AND METHOD

In this multicentric retrospective study, a total of 269 adult patients (≥18 years of age) with confirmed COVID-19 who were hospitalised for the treatment were enrolled. Demographic features, complete medical history and laboratory findings of the study participants at admission were obtained from the medical records. Patients were grouped regarding their intensive care unit (ICU) requirements during their hospitalisation periods.

RESULTS

Among all 269 participants, 106 were hospitalised in the ICU and 66 died. The patients hospitalised in ICU were older than patients hospitalised in wards (P = .001) and expired patients were older than alive patients (P = .001). Age, elevated serum D-dimer, creatinine and gamma-glutamyl transferase (GGT) levels at admission were independent factors predicting ICU hospitalisation and mortality in COVID-19 patients.

CONCLUSION

In conclusion, in hospitalised patients with COVID-19, laboratory data on admission, including serum, creatinine, GGT and d-dimer levels have an important predictive role for the ICU requirement and mortality. Since these tests are readily available in all hospitals and inexpensive, some predictive formulas may be calculated with these parameters at admission, to define the patients requiring intensive care.

The MaD-CLINYC score: An easy tool for the prediction of the outcome of hospitalized COVID-19 patients

BACKGROUND

Most outcome-predictive models for COVID-19 patients use hospital admission data, offering a spontaneous mortality risk estimation. We aimed to elaborate on a tool that could be applied repeatedly, thus being more suitable for these patients' rapidly changing clinical course.

METHODS

In this prospective study, we evaluated 560 samples derived from 156 patients hospitalized for COVID-19 in a single center. Age >61 years, male sex, comorbidities >2, need for intensive care unit admission, lactate dehydrogenase (LDH) >408 U/L, Neutrophil/Lymphocyte Ratio (NLR) >17, C-reactive protein (CRP) >10 mg/dl, and D-dimers >3,200 ng/ml were incorporated in an eight-scale score (MaD-CLINYC) after optimal scaling, ridge regression, and bootstrapping, which was documented to correlate with outcome independently of one or more samples analyzed, day from admission at sampling, and need for delivery. Validation process was performed over 574 samples derived from three centers.

RESULTS

The developing and the validation cohort Area under Curve (AUC) was 0.90 (95 % Confidence Interval: 0.82-0.98) and 0.91 (0.88-0.94), respectively (p =0.822). A MaD-CLINYC score ≥4 had 75 % sensitivity and 81 % specificity to predict fatal outcome.

CONCLUSIONS

MaD-CLINYC score is a powerful, feasible, easy-to-use, dynamic tool to assess the risk of the outcome, thus assisting clinicians in close monitoring and timely decisions in COVID-19 hospitalized patients. HIPPOKRATIA 2021, 25 (3):119-125.