ASSOCIATION OF URIC ACID LEVELS WITH COVID-19 SEVERITY DURING 2020 AND 2021 YEARS

BackgroundThe decrease in uric acid levels attracts more and more attention from clinicians every year [1]. In particular, a factor such as Covid-19 can cause a significant decrease in uric acid due to its increased excretion by the kidneys [2]. This retrospective study aimed to determine changes in the level of uric acid in different years, which allows us to assume the influence of different strains of Covid-19 on uric acid.ObjectivesTo analyze the relationship between uric acid levels through admission to the hospital and Covid-19 severity during 2020 and 2021 years.MethodsOur retrospective study includes 127 hospitalized patients with confirmed Covid-19 in 2021 and 63 patients in 2020 (only patients who didn't receive urate-lowering therapy). Most patients were over 45 years old (84,2% vs 90,5%), women and men almost equally. The severity of Covid-19 we determined by the type and presence of oxygen support ((1) without O2, (2) O2 by mask or nasal cannula, (3) continuous positive airway pressure, (4) positive bi-pressure in the airways or high-flow oxygen, (5) invasive ventilation). A chi-squared test and comparison of means were used.ResultsWe cannot establish the dependence of the uric acid level on the severity of the course of the Covid-19 disease, which is determined by the type of oxygen support in both 2020 and 2021. For example, in 2021, the difference between the least severe type (without O2) and the most severe (invasive ventilation) was almost the same (246.2 vs 277.12 µmol/L), as between O2 by mask or nasal cannula and positive bi-pressure in the airways or high-flow oxygen (257 vs 239.1 µmol/L). However, it was established that in 2020, higher indicators of the level of uric acid were observed for all types of oxygen support. For example, for patients who were without O2, it is higher by 72.95 µmol/L, which is statistically significant. In addition, we analyzed the dependence of the uric acid level on such indicators as the patient's age, the level of lymphocytes, C-reactive protein, and LDH at admission to the hospital. As a result of the analysis, it was found that the dependence is present for the LDH indicator (the lower the LDH, the higher the uric acid: chi-square at the level of 0.05), and for all other indicators, it was absent in 2021. In 2020, a positive relationship between CRP, LDH, and uric acid levels was also observed.ConclusionAlthough there is a trend towards lower uric acid levels in the background of Covid-19, it is not a marker of a severe disease course. The lower uric acid levels in 2021 are likely due to a feature of the strains circulating in 2021 that caused more significant renal excretion of uric acid.References[1]Hu F, Guo Y, Lin J, Zeng Y, Wang J, Li M, Cong L. Association of serum uric acid levels with COVID-19 severity. BMC Endocr Disord. 2021 May 8;21(1):97. DOI: 10.1186/s12902-021-00745-2. PMID: 33964922;PMCID: PMC8106517.[2]Dufour I, Werion A, Belkhir L, Wisniewska A, Perrot M, De Greef J, Schmit G, Yombi JC, Wittebole X, Laterre PF, Jadoul M, Gérard L, Morelle J;CUSL COVID-19 Research Group. Serum uric acid, disease severity, and outcomes in COVID-19. Crit Care. 2021 Jun 14;25(1):212. DOI: 10.1186/s13054-021-03616-3. PMID: 34127048;PMCID: PMC8201458.Acknowledgements:NIL.Disclosure of InterestsNone Declared.

TYPE I INTERFERON SCORE IS ASSOCIATED WITH THE SEVERITY AND POOR PROGNOSIS IN ANTI-MDA5 ANTIBODY-POSITIVE DERMATOMYOSITIS PATIENTS

BackgroundAnti-MDA5 antibody-positive dermatomyositis (anti-MDA5+DM) is a rare autoimmune disease associated with a high mortality rate due to rapid-progressive interstitial lung disease (RP-ILD), particularly in East Asia[1]. MDA5, acts as a cytoplasmic sensor of viral RNA, thus activating antiviral responses including the type I interferon (IFN) signaling pathway[2]. The involvement of type 1 IFN in the pathogenesis of MDA5+DM has been proposed based on the significantly elevated expression of its downstream stimulated genes(ISG) in muscle, skin, lung, and peripheral blood[3;4]. Janus kinase inhibitor, which targets the IFN pathway, combined with glucocorticoid could improve the survival of early-stage MDA5+DM-ILD patients[5]. In clinical practice, there is still an urgent demand for sensitive biomarkers to facilitate clinical risk assessment and precise treatment.ObjectivesThis study aimed to investigate the clinical significance of interferon score, especially IFN-I score, in patients with anti-MDA5+DM.MethodsDifferent subtypes of idiopathic inflammatory myopathy, including anti-MDA5+DM(n=61), anti-MDA5-DM(n=20), antisynthetase syndrome(ASS,n=22),polymyositis(PM,n=6) and immune-mediated necrotizing myopathy(IMNM,n=9), and 58 healthy controls were enrolled.. A multiplex quantitative real-time PCR(RT-qPCR) assay using four TaqMan probes was utilized to evaluate two type I ISGs (IFI44, MX1, which are used for IFN-I score), one type II ISG (IRF1), and one housekeeping gene (HRPT1). Clinical features and disease activity index were compared between high and low IFN-I score groups in 61 anti-MDA5+DM patients. The association between laboratory findings and the predictive value of baseline IFN-I score level for mortality was analyzed.ResultsThe IFN scores were significantly higher in patients with anti-MDA5+DM than in HC (Figure 1A). The IFN-I score correlated positively with serum IFN α(r = 0.335, P =0.008), ferritin (r = 0.302, P = 0.018), and Myositis Disease Activity Assessment Visual Analogue Scale (MYOACT) score(r=0.426, P=0.001). Compared with patients with low IFN-I scores, patients with high IFN-I scores showed increased MYOACT score, CRP, AST, ferritin, and the percentages of plasma cells (PC%) but decreased lymphocyte count, natural killer cell count, and monocyte count. The 3-month survival rate was significantly lower in patients with IFN-I score > 4.9 than in those with IFN-I score ≤ 4.9(72.9% vs. 100%, P=0.044)(Figure 1B).ConclusionIFN score, especially IFN-I score, detected by multiplex RT-qPCR, can be a valuable biomarker for monitoring disease activity and predicting mortality in anti-MDA5+DM patients.References[1]I.E. Lundberg, M. Fujimoto, J. Vencovsky, R. Aggarwal, M. Holmqvist, L. Christopher-Stine, A.L. Mammen, and F.W. Miller, Idiopathic inflammatory myopathies. Nat Rev Dis Primers 7 (2021) 86.[2]G. Liu, J.H. Lee, Z.M. Parker, D. Acharya, J.J. Chiang, M. van Gent, W. Riedl, M.E. Davis-Gardner, E. Wies, C. Chiang, and M.U. Gack, ISG15-dependent activation of the sensor MDA5 is antagonized by the SARS-CoV-2 papain-like protease to evade host innate immunity. Nat Microbiol 6 (2021) 467-478.[3]G.M. Moneta, D. Pires Marafon, E. Marasco, S. Rosina, M. Verardo, C. Fiorillo, C. Minetti, L. Bracci-Laudiero, A. Ravelli, F. De Benedetti, and R. Nicolai, Muscle Expression of Type I and Type II Interferons Is Increased in Juvenile Dermatomyositis and Related to Clinical and Histologic Features. Arthritis Rheumatol 71 (2019) 1011-1021.[4]Y. Ye, Z. Chen, S. Jiang, F. Jia, T. Li, X. Lu, J. Xue, X. Lian, J. Ma, P. Hao, L. Lu, S. Ye, N. Shen, C. Bao, Q. Fu, and X. Zhang, Single-cell profiling reveals distinct adaptive immune hallmarks in MDA5+ dermatomyositis with therapeutic implications. Nat Commun 13 (2022) 6458.[5]Z. Chen, X. Wang, and S. Ye, Tofacitinib in Amyopathic Dermatomyositis–Associated Interstitial Lung Disease. New England Journal of Medicine 381 (2019) 291-293.AcknowledgementsThis work was supported by the National Natural Science Foundation of China [81974251], and Shanghai Hospital Develop ent Center, Joint Research of New Advanced Technology Project [SHDC12018106]Disclosure of InterestsNone Declared.

IDENTIFICATION OF FACTORS ASSOCIATED WITH A WORSE OUTCOME -EXITUS- IN PATIENTS WITH RHEUMATOID ARTHRITIS AND SEVERE INFECTION

BackgroundInfections constitute an important and frequent cause of morbidity and mortality in patients with chronic inflammatory and systemic autoimmune rheumatic diseases. In rheumatoid arthritis (RA), this increased risk has been related to the immune system alterations inherent to the disease, the drugs used to control it (corticosteroids, DMARDs and immunosuppressants) and associated comorbidities. Most studies focus on the search for factors associated with the development of infections but do not explore the worst outcome: patient failure.ObjectivesTo identify factors that help to predict an unfavorable outcome (exitus) after a severe infection in patients with rheumatoid arthritis.MethodsThis study was a retrospective case-control study at a single institution over a 10-year period. Patients with a diagnosis of rheumatoid arthritis with hospital admission for infection from January 1, 2010, to December 31, 2019 (pre-pandemic SARS-COV-2) were selected. The main variable was exitus due to the infectious episode. We collected: age, sex, time of evolution of RA, previous treatment and at the time of admission, number of admissions for infection, location of the infection, comorbidities, and other associated serious diseases. The statistics included a descriptive analysis of the different variables (expressed as median and interquartile range -IR- for quantitative variables and percentages for qualitative variables), and the association study using the χ2 test or Fisher's exact test for qualitative variables, and t-student or Mann-Whitney U and Kruskal Wallis for quantitative variables.ResultsWe obtained 152 patients (71.7% female, 28.3% male), with a total of 214 episodes of admission for infection (115 patients with 1 episode (75.7%), 25 (16.4%) with 2 episodes, 6 being the maximum number of episodes recorded). The median age at admission was 77 years, and the median time of RA evolution was 8 years (IR 4-16). The location of the infection responsible for admission was mainly respiratory and urinary. Forty-eight patients died in the episode (31.6% of the sample, 15 males and 33 females, median age 81.5 years (IR 69.5-86.5)). Comparing the patients with unfavorable outcomes (exitus) with the rest, we only found a statistically significant difference in the number of previous admissions (p=0.011), and in the coexistence of some other serious disease (exitus 85.4%, rest 61.5% p=0.003). There were no differences by sex, age, time of RA evolution, drugs, location of the infection, or comorbidities.ConclusionA history of hospital admission due to infection, and having another serious disease, are factors associated with an unfavorable outcome (exitus) in patients with RA admitted for an infectious process.References[1] Listing J, Gerhold K, Zink A. The risk of infections associated with rheumatoid arthritis, with its comorbidity and treatment. Rheumatology 2013;52(1):53-61.[2] George MD, Baker JF, Winthrop K, Hsu JY, Wu Q, Chen L, et al. Risk for serious infection with low-dose glucocorticoids in patients with Rheumatoid Arthritis: A cohort study. Ann Intern Med. 2020;173(11):870-8.[3] Singh JA, Cameron C, Noorbaloochi S, Cullis T, Tucker M, Christensen R, et al. Risk of serious infection in biological treatment of patients with rheumatoid arthritis: A systematic review and meta-analysis. The Lancet. 2015;386(9990):258-65.Acknowledgements:NIL.Disclosure of InterestsNone Declared.

LABORATORY PARAMETERS ASSOCIATED WITH CODIV-19 CYTOKINE STORM DEVELOPMENT

BackgroundSince the end of 2019, physicians became more and more familiar with SARS-CoV-2 infection and the variety of forms in which it may present and evolve. There have been a lot of studies trying to understand and predict why some patients develop a dysregulation of the immune response, with an exaggerated release of pro-inflammatory cytokines, called cytokine storm (1–4). There is scarce evidence in Romania regarding this aspect.ObjectivesThis study aims to verify the correlation between some laboratory parameters and the development of cytokine storm in SARS-CoV-2 infection in a cohort of over 200 patients admitted in a tertiary hospital from Romania, hoping that early identification of these risk factors of progression to a severe form of the disease can bring considerable benefit to patient care.MethodsThis is an analytical, observational, case-control study which includes 219 patients (all COVID-19 hospitalized patients on the Internal Medicine 3 department of Colentina Clinical Hospital, Bucharest, from 01 March 2020 to 1 April 2021). A series of data were collected, the laboratory parameters being the most important, including: albumin, lymphocyte (percentage), neutrophil (absolute value), aspartate aminotransferase, alanine aminotransferase, D-dimers, lactate dehydrogenase (LDH), anionic gap, chloremia, potassium and the BUN:creatinine ratio (BUN - blood urea nitrogen). The laboratory parameters used for the statistical analysis represent the average values of the first 7 days of hospitalization for those who did not develop cytokine storm, respectively until the day of its development, for the others. Patients were classified into these groups, those who developed cytokine storm, respectively those who did not have this complication taking into account the clinical and paraclinical criteria (impairment of respiratory function, elevations of certain markers 2-3 times above the upper limit of normal, those who died as a result of SARS-CoV-2 infection). Then Binary Univariate Logistic Regression was applied in order to verify the individual impact of every laboratory parameter on cytokine storm development. Furthermore, all laboratory parameters were subsequently included in the multivariate analysis, using the backward selection technique to achieve a model as predictive as possible.ResultsWe mention that the analysis of demographic data was previously performed, showing no statistically significant relationship between patient gender, age or comorbidities (history of neoplasm, lung diseases, cardiac pathology, obesity, type II diabetes and hypertension) and their evolution to cytokine storm. After performing binary univariate logistic regression we concluded that 8 of the 13 laboratory analyzes have had a significant change between groups (ferritin, PCR, albumin, Lymphocyte, Neutrophils, TGO, LDH, BUN:creatinine ratio). Only 150 patients were then included in the multivariate analysis. After the analysis, some of the variables lost their statistical significance, the final model including C-reactive protein, neutrophilia, LDH, ferritin and the BUN:creatinine ratio. This model correctly predicts the development of cytokine storm in 88% of cases.ConclusionHigh C-reactive protein, neutrophilia, LDH, ferritin and the BUN:creatinine ratio are risk factors for cytokine storm development and should be monitored in all COVID-19 patients in order to predict their evolution.References[1]Pedersen SF et all. SARS-CoV-2: A storm is raging[2]Mehta P et al. COVID-19: consider cytokine storm syndromes and immunosuppression[3]Hu B et al. The cytokine storm and COVID-19.[4]Caricchio R et al. Preliminary predictive criteria for COVID-19 cytokine stormAcknowledgements:NIL.Disclosure of InterestsNone Declared.

TIME MANAGEMENT AND RISK FACTORS IN LIFE-THREATENING ANCA-VASCULITIS

BackgroundAccuracy of diagnosis and prompt therapeutic intervention are the mainstay in patients with ANCA-associated vasculitis(AAV) suffering from life-threatening complications [1].However, there is no definition of therapeutic window in vital AAV, nor its impact on patient outcome regarding length of hospital stay, intensive care unit(ICU) admission or survival.ObjectivesThe aim of the study is to analyze the process of care from the perspective of time management in vital organ involvement AAV patients and to identify potential risk factors for ICU admission.MethodsA retrospective multicenter study identified AAV patients with life-threatening organ involvement, defined as alveolar hemorrhage, rapidly progressive renal failure, myocarditis and cerebral granuloma. Demographic data was collected. Key time frames were recorded, namely the interval from acute symptom onset to hospital presentation, days until imaging(plain X-ray, cardiac ultrasound, CT-scan), time to therapeutic intervention with corticosteroids or biologic/non-biologic immunosuppression(cyclophosphamide or rituximab) and to renal replacement therapy(RRT) or plasmapheresis. Time to ICU admission, hospital length-of-stay, Birmingham Vasculitis Activity Score(BVAS) were also noted. Statistical analysis was performed using SPSS and Chi-square and Pearson correlation tests were applied.Results66 patients with AAV were enrolled, out of which 17 fulfilled inclusion criteria. Mean age in the study group was 58.6±11.1 years old,10 patients(58.8%) were females and 7 (41.2%) males.11(64.7%) patients were c-ANCA positive, while 6 (35.3%) had p-ANCA and all were diagnosed with AAV prior to life-threatening event. Two patients had COVID-19 triggered AAV.In the study group, the most frequent critical organ suffering was rapidly progressive renal failure(12), followed by alveolar hemorrhages(10), 2 cerebral granulomas and one acute myocarditis. Three patients(17.6%) had more than one vital manifestation. Ten patients(58.8%) had more than three additional non-organ-threatening manifestations. Mean interval from AAV diagnosis to emergency admission was 30.1± 61.1 days, median 3 and from severe episode onset to hospitalization 1.65±0.18 days, median 1. There was only one death in the study group. Three patients were admitted in the ICU in 0.59±1.5 days following hospital presentation and required either RRT or plasma exchange within 2.66 days. Imaging examination was performed unanimously the day upon hospital admission. All patients received corticosteroids in the first 5.95±14.3 days, while immunosuppression was given to 13(76.5%) patients within 11.5±15.5 days from hospitalization.12 patients(70.5%) suffered from associated infections. Mean BVAS(13.6±6.76) correlated to ICU admission(p 0.013, r 0.58).Patients in ICU revealed higher BVAS(22±9.53) versus non-ICU(11.8±4.76).Hospital length of stay was 14.7±10.7 days(median 14) and showed no relationship to the type of severe organ involvement. The need for ICU caring was dominant in males(p 0.05) and confirmed in patients with proteinuria(p 0.012) and at least two major organ damage.ConclusionThis study shows that severity risk factors for potential ICU admission for life-threatening AAV appear to be male gender, proteinuria and the number of affected organs.Moreover, BVAS should be considered a useful tool to predict patients' risk for intensive care management since a higher score indicates a more aggressive disease.However, time to investigational or therapeutic intervention did not correlate to patient outcome in AAV.References[1]Geetha, D., Seo, P. (2011). Life-Threatening Presentations of ANCA-Associated Vasculitis. In: Khamashta, M., Ramos-Casals, M. (eds) Autoimmune Diseases. Springer, London. https://doi.org/10.1007/978-0-85729-358-9_8Acknowledgements:NIL.Disclosure of InterestsNone Declared.

Biruni Index as a Novel Diagnostic Tool for the Early Prediction of Mortality in Critical Patients With COVID-19: A Cohort Study.

BACKGROUND: The aim of this study was to find out the potential risk factors associated with mortality in severe coronavirus disease 2019 (COVID-19) patients hospitalized due to viral bronchopneumonia, and to establish a novel COVID-19 mortality index for daily use. METHODS: The study included 431 quantitative real-time polymerase chain reaction (qRT-PCR)-confirmed COVID-19-positive patients admitted to the intensive care unit in a tertiary care hospital. Patients were divided into training and validation cohorts at random (n= 285 and n= 130, respectively). Biruni Index was developed by multivariate logistic regression analysis for predicting COVID-19-related mortality. RESULTS: In univariate logistic regression analysis, age, systolic and diastolic blood pressures, respiratory and pulse rates per minute, D-dimer, pH, urea, ferritin, and lactate dehydrogenase levels at first admission were statistically significant factors for the prediction of mortality in the training cohort. By using multivariate logistic regression analysis, all of these statistically significant parameters were used to produce Biruni Index. Statistically significant differences in Biruni Index were observed between ex and non-ex groups in both training and validation cohorts (P < 0.001 for both comparisons). Areas under receiver operating characteristic (ROC) curve for Biruni Index were 0.901 (95CI%: 0.864-0.938, P < 0.001) and 0.860 (95CI%: 0.795-0.926, P < 0.001) in training and validation cohorts, respectively. CONCLUSION: As a pioneering clinical study, Biruni Index may be a useful diagnostic tool for clinicians to predict the mortality in critically ill patients with COVID-19 hospitalized due to severe viral bronchopneumonia. However, Biruni Index should be validated with larger series of multicenter prospective clinical studies.

Prognostic Factors for Covid-19 on Admission Profile and Air Pollutants.

It has been reported that the severity and lethality of Covid-19 are associated with coexisting underlying diseases (hypertension, diabetes, etc.) and cardiovascular diseases (coronary artery disease, atrial fibrillation, heart failure, etc.) that increase with age, but environmental exposure such as air pollutants may also be a risk factor for mortality. In this study, we investigated patient characteristics at admission and prognostic factors of air pollutants in Covid-19 patients using a machine learning (random forest) prediction model. Age, Photochemical oxidant concentration one month prior to admission, and level of care required were shown to be highly important for the characteristics, while the cumulative concentrations of air pollutants SPM, NO2, and PM2.5 one year prior to admission were the most important characteristics for patients aged 65 years and older, suggesting the influence of long-term exposure.

[Impact of days elapsed from the onset of symptoms to hospitalization in COVID-19 in-hospital mortality: Time matters]. / Impacto de los días transcurridos desde el inicio de los síntomas hasta la hospitalización en la mortalidad hospitalaria por COVID-19: el tiempo importa.

Background: COVID-19 shows different clinical and pathophysiological stages over time. Theeffect of days elapsed from the onset of symptoms (DEOS) to hospitalization on COVID-19prognostic factors remains uncertain. We analyzed the impact on mortality of DEOS to hospital-ization and how other independent prognostic factors perform when taking this time elapsedinto account. Methods: This retrospective, nationwide cohort study, included patients with confirmed COVID-19 from February 20th and May 6th, 2020. The data was collected in a standardized online datacapture registry. Univariate and multivariate COX-regression were performed in the generalcohort and the final multivariate model was subjected to a sensitivity analysis in an earlypresenting (EP; < 5 DEOS) and late presenting (LP; ≥5 DEOS) group. Results: 7915 COVID-19 patients were included in the analysis, 2324 in the EP and 5591 in theLP group. DEOS to hospitalization was an independent prognostic factor of in-hospital mortalityin the multivariate Cox regression model along with other 9 variables. Each DEOS incrementaccounted for a 4.3% mortality risk reduction (HR 0.957; 95% CI 0.93---0.98). Regarding variationsin other mortality predictors in the sensitivity analysis, the Charlson Comorbidity Index onlyremained significant in the EP group while D-dimer only remained significant in the LP group. Conclusion: When caring for COVID-19 patients, DEOS to hospitalization should be consideredas their need for early hospitalization confers a higher risk of mortality. Different prognosticfactors vary over time and should be studied within a fixed timeframe of the disease.

Evaluation of the Relationship Between Disease Severity and Viral Cycle Threshold Value in COVID-19 Patients

Objective: There is a positive and significant relationship between severity and viral load in some viral diseases. Studies on the relationship between SARS-CoV-2 viral load at diagnosis and severity of coronavirus disease-2019 (COVID-19) have yielded conflicting results. Therefore, we aimed to evaluate the relationship between viral load and the clinical status of patients with COVID-19.Methods: Data of the patients diagnosed with COVID-19 and admitted to our center between May 01 and June 31, 2020, were retrospectively reviewed. The patients were divided into two groups according to their clinical character-istics as mild-moderate and severe. The demographic, laboratory, clinical, and radiological data were retrieved from electronic folders.Results: The entire cohort included 285 patients;254 had a mild-moderate clinical course, and 31 had a severe course. Statistical analyses revealed that SARS-CoV-2 viral load was not associated with symptom duration and clinical status (p>0.05). According to multivariate logistic regression analysis, only ferritin, C-reactive protein, and lactate dehydro-genase elevations were positively correlated with severe clinical course. (p<0.05).Conclusion: We do not recommend using viral load to predict disease severity in COVID-19. We also found that only ferritin, C-reactive protein, and lactate dehydrogenase accompanied severe clinical course. Keywords: cycle threshold, COVID-19, clinical severity

COVID-19 in sub-intensive care unit: An italian single-center experience along the three epidemic waves

The goal was to characterize COVID-19 patients who needed treatment in Sub-Intensive Care Units (SICUs) for hypoxemic respiratory failure, describe their six-month mortality, and identify clinical and laboratory characteristics that were associated with death. Data from 216 consecutive patients admitted to the COVID-SICU of Turin's San Giovanni Bosco Hospital were analyzed retrospectively. A total of 216 patients (24.5% of whom were female) were enrolled. The average age was 63 +/- 11.9 years. In the three waves, the six-month mortality rate was 32.8%, 35.1%, and 26.6%, respectively (p=0.52). The mortality rate was significantly higher in intubated patients compared to those not requiring intubation (60.8% versus 29.9%, p<0.01). On admission, deceased patients were older (69 +/- 7.7 versus 60.2 +/- 12.6 y.o., p<0.01), with a higher prevalence of dyslipidemia, coronary artery disease, chronic heart failure, and higher serum creatinine. However, only age was predictive of death at multivariate analysis (OR 5.29, p<0.01), with 63 years old as the best cut-point. At six months, mortality in COVID patients managed in a SICU is around 30%. Age is a significant negative prognostic factor, with 63 years of age being the best predicting cut-off.

3044 Cases reveal important prognosis signatures of COVID-19 patients.

Critical patients and intensive care unit (ICU) patients are the main population of COVID-19 deaths. Therefore, establishing a reliable method is necessary for COVID-19 patients to distinguish patients who may have critical symptoms from other patients. In this retrospective study, we firstly evaluated the effects of 54 laboratory indicators on critical illness and death in 3044 COVID-19 patients from the Huoshenshan hospital in Wuhan, China. Secondly, we identify the eight most important prognostic indicators (neutrophil percentage, procalcitonin, neutrophil absolute value, C-reactive protein, albumin, interleukin-6, lymphocyte absolute value and myoglobin) by using the random forest algorithm, and find that dynamic changes of the eight prognostic indicators present significantly distinct within differently clinical severities. Thirdly, our study reveals that a model containing age and these eight prognostic indicators can accurately predict which patients may develop serious illness or death. Fourthly, our results demonstrate that different genders have different critical illness rates compared with different ages, in particular the mortality is more likely to be attributed to some key genes (e.g. ACE2, TMPRSS2 and FURIN) by combining the analysis of public lung single cells and bulk transcriptome data. Taken together, we urge that the prognostic model and first-hand clinical trial data generated in this study have important clinical practical significance for predicting and exploring the disease progression of COVID-19 patients.

Prognostic Factors for Predicting COVID-19 Severity and Mortality

Background: COVID-19 has become a serious health problem worldwide. Objective(s): The current study investigated the prognostic factors associated with demographical parameters, clinical and vital signs, and laboratory results for predicting severity and mortality in patients infected with COVID-19. Method(s): This retrospective analysis was conducted on the medical records of 372 COVID-19-positive patients hospitalized at the Khatam al-Anbiya Hospital, Shoushtar, Iran, from Sep 2020 to Sep 2021. The association of demographic parameters, clinical and vital signs, and laboratory results with severity and patients' outcomes (survival/mortality) was studied. The patients were divided into the non-severe group (n = 275) and the severe group (n = 97). COVID-19 disease severity was determined based on the severity of pulmonary involvement using CT chest images. The collected data were analyzed using IBM SPSS software for Windows (version 18). Logistic regression analysis was employed using the Forward LR method to predict COVID-19 severity and mortality. Result(s): The rates of mortality and the severe form of the disease were 87.1% (n = 324) and 12.9% (n = 48), respectively. A prognostic value was observed in predicting COVID-19 severity and mortality for some clinical and vital signs (diabetes (P < 0.001, P = 0.019), hypertension (P = 0.024, P = 0.012), pulmonary diseases (P = 0.038, P < 0.001), and anosmia (P = 0.043, P = 0.044) and paraclinical parameters (FBS (P = 0.014, P = 0.045), BUN (P = 0.045, 0.001), Cr (P = 0.027, P = 0.047), Neut (P = 0.002, P = 0.005), and SpO2 (P = 0.014, P = 0.001)). Cardiovascular disorders (P = 0.037), fever (P = 0.008), and dyspnea (P = 0.020) were also effective at predicting disease-related mortality. Multiple logistic regression analyses showed that diabetes disease, the place of residence, PCO2, and BUN with R2 = 0.18, and age, pulmonary diseases, and BUN with R2 = 0.21 were involved in predicting the severity and mortality, respectively. Conclusion(s): It seems that in addition to the BUN, diabetes and pulmonary diseases play a more significant role in predicting the severity and mortality due to COVID-19, respectively.Copyright © 2023, Author(s).

Biomarkers Predicting Poor Prognosis in Covid-19 Patients: A Survival Analysis.

Introduction With the spread of the Covid-19 pandemic and its overwhelming impact on health systems in several countries, the importance of identifying predictors of severity is of paramount importance. The objective of this study is to determine the relationship between death and the biological parameters of patients with Covid-19. Materials and methods This is an analytical retrospective cohort study conducted on 326 patients admitted to the Mohammed VI University Hospital in Oujda, Morocco. The statistical analysis concerned the biological parameters carried out on the admission of the patients, in addition to age and sex. The comparison between the two surviving and non-surviving groups was made by a simple analysis than a multivariate analysis by logistic regression. Next, a survival analysis was performed by the Kaplan-Meier method and then by Cox regression. Results A total of 326 patients were included in the study, including 108 fatal cases. The mean age was 64.66 ± 15.51 and the sex ratio was 1.08:1 (M:F). Age, procalcitonin, liver enzymes, and coagulation factors were significantly higher in patients who died of Covid-19 and are therefore considered to be the main prognostic factors identified in this study. Conclusion Knowledge and monitoring of predictive biomarkers of poor prognosis in patients with Covid-19 could be of great help in the identification of patients at risk and in the implementation of an effective diagnostic and therapeutic strategy to predict severe disease forms.

Impact of days elapsed from the onset of symptoms to hospitalization in COVID-19 in-hospital mortality: time matters.

BACKGROUND: COVID-19 shows different clinical and pathophysiological stages over time. The effect of days elapsed from the onset of symptoms (DEOS) to hospitalization on COVID-19 prognostic factors remains uncertain. We analyzed the impact on mortality of DEOS to hospitalization and how other independent prognostic factors perform when taking this time elapsed into account. METHODS: This retrospective, nationwide cohort study, included patients with confirmed COVID-19 from February 20th and May 6th, 2020. The data was collected in a standardized online data capture registry. Univariate and multivariate COX-regression were performed in the general cohort and the final multivariate model was subjected to a sensitivity analysis in an early presenting (EP; <5 DEOS) and late presenting (LP; ≥5 DEOS) group. RESULTS: 7915 COVID-19 patients were included in the analysis, 2324 in the EP and 5591 in the LP group. DEOS to hospitalization was an independent prognostic factor of in-hospital mortality in the multivariate Cox regression model along with other 9 variables. Each DEOS increment accounted for a 4.3% mortality risk reduction (HR 0.957; 95% CI 0.93-0.98). Regarding variations in other mortality predictors in the sensitivity analysis, the Charlson Comorbidity Index only remained significant in the EP group while D-dimer only remained significant in the LP group. CONCLUSION: When caring for COVID-19 patients, DEOS to hospitalization should be considered as their need for early hospitalization confers a higher risk of mortality. Different prognostic factors vary over time and should be studied within a fixed timeframe of the disease.

Longitudinal investigation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in older patients in the province of Palermo (Southern Italy) during the early wave of the pandemic.

Introduction: Clinical presentation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in old adults from Southern Italy is little known. This study aims to investigate the mortality risk related to risk factors, therapy and clinical course and to suggest prognostic indicators based on day-to-day follow-up of clinical and laboratory findings. Material and methods: It was designed as a retrospective longitudinal cohort study of adult SARS-CoV-2 patients admitted at Partinico COVID Hospital in Palermo, Southern Italy. Patients were recruited between 4 March and 25 April and followed up until 31 May 2020, day-to-day until death or hospital discharge. Clinical data, laboratory tests and treatment data were extracted from medical records and epidemiologic information was obtained by clinical history and the medical interview. Results: Forty-seven patients (median age = 75 IQR: 59.50-86.00) were followed up during a 87 days observation period, accounting for a total of 1,035 person days. At the end of follow-up, 28 (60%) patients were discharged and 19 (40%) died, so that the estimated incidence density rate was 0.018 deaths per day (18 SARS-CoV-2-related deaths per 1,000 patient days). Diabetes (HR = 8.13, 95% CI: 1.91-34.67), chronic kidney failure (HR = 5.86, 95% CI: 1.36-25.21), dementia (HR = 7.84, 95% CI: 1.80-34.20), and neutrophil/lymphocyte ratio > 7 (HR = 10.37, 95% CI: 2.24-48.14) were found as significant prognostic factors. Conclusions: The joint evaluation of dementia, diabetes, chronic kidney failure and neutrophil/lymphocyte ratio showed an optimal prognostic value already in the first week of follow-up. The day-to-day follow-up provides essential information for clinical monitoring and treatment of the disease in a hospital setting and improves the disease's home management, especially for older patients with frailty.

Do prior neurological comorbidities predict COVID-19 severity and death? A 25-month cross-sectional multicenter study on 7370 patients.

BACKGROUND: The prognosis of COVID-19 cases that suffer from particular comorbidities is worse. The impact of chronic neurological disorders (CNDs) on the outcome of COVID-19 patients is not clear yet. This study aimed to assess whether CNDs can predict in-hospital mortality or severity in COVID-19 patients. METHODS: Following a cross-sectional design, all consecutive hospitalized patients with PCR-confirmed COVID-19 who were hospitalized at three centers from February 20th, 2020 to March 20th, 2022, were studied. CND was defined as neurological conditions resulting in permanent disability. Data on demographic and clinical characteristics, COVID-19 severity, treatment, and laboratory findings were evaluated. A multivariate Cox-regression log-rank test was used to assess the primary outcome, which was in-hospital all-cause mortality. The relationship among CND, COVID-19 severity and abnormal laboratory findings was analyzed as a secondary endpoint. RESULTS: We studied 7370 cases, 43.6% female, with a mean age of 58.7 years. 1654 (22.4%) patients had one or more CNDs. Patients with CNDs had higher age, were more disabled at baseline, and had more vascular risk factors and comorbidities. The ICU admission rate in CND patients with 59.7% was more frequent than the figure among non-CND patients with 20.3% (p = 0.044). Mortality of those with CND was 43.4%, in comparison with 12.8% in other participants (p = 0.005). Based on the Cox regression analysis, CND could independently predict death (HR 1.198, 95% CI 1.023-3.298, p = 0.003). CONCLUSION: CNDs could independently predict the death and severity of COVID-19. Therefore, early diagnosis of COVID-19 should be considered in CND patients.

Investigation of the changes in the general characteristics of COVID19 patients with the vaccination program

Since the onset of the Coronavirus Disease 2019 (COVID-19) pandemic, many vaccine research studies have started. Changes in the demographic characteristics of the patients hospitalized from the emergency room to the intensive care unit due to COVID-19 has caught our attention since the vaccination program began in Turkey. The purpose of this study is to investigate whether our investigation is scientifically valid and meaningful. Thus, it will be helpful to investigate the effect of priority ordering in vaccination programs in future pandemics. Demographic characteristics and hospitalization processes of patients hospitalized in the intensive care unit before and after vaccination were compared. For comparison, Charlson Comorbidity Index (CCI) and Acute Physiologic Assessment and Chronic Health Evaluation (APACHE) scores, as well as intensive care unit duration of stay and mortality were used. While age [mean (SD);70,8 (12,2) vs 66,2 (15,2), p=0,032] and duration of intensive care stay [day;mean (SD);6,4 (6,3) vs 9,4 (7,4);p<0,001] increased in the post-vaccination group, a statistically significant decrease was observed in APACHE [mean (SD);26,9 (9,2) vs 20,9 (9,0);p=0,008] and CCI scores [mean (SD);4,3 (2,2) vs 3,6 (2,7);p<0,001]. Regulating the priorities of those to be vaccinated causes rapid changes in the patient population. For this reason, vaccination of vulnerable groups will contribute to the operation of the health system properly. Copyright © 2022 Ondokuz Mayis Universitesi. All rights reserved.

Efficacy of tocilizumab therapy in severe COVID-19 pneumonia patients and determination of the prognostic factors affecting 30 days mortality

Objective: In coronavirus disease - 19 (COVID-19) patients, cytokine storm develops due to the increase of pro-inflammatory cytokines. Tocilizumab (TU.), has been used in the treatment of COVID-19 patients and successful results have been obtained. The aim of this study was to determine the efficacy of TCZ and also investigate the prognostic factors affecting the success of treatment and mortality in COVID-19 patients treated with TCZ. Patients and Methods: Between March 2020 and August 2021, a total of 326 confirmed severe COVID-19 pneumonia patients, treated in the intensive care unit, were included in the study. Results: The mean age of the patients was 63.02 +/- 11.58 years, and 203 (62.3%) of the patients were male. Patients treated with TCZ had a longer survival time compared with the standard therapy (p=0.012). It was found that type of respiratory support (HR:2.19, CI:1.10-4.36, p=0.025) and hyperlactatemia on the day of TCZ therapy admission (HR:2.93 CI:1.53-5.64, p=0.001) were the significant and independent prognostic factors of survival in severe COVID-19 pneumonia patients treated with TCZ. Conclusion: Tocilizumab therapy improved 30-days survival in critically ill COVID-19 pneumonia patients. Also, among the patients with TCZ, types of respiratory support and hyperlactatemia on the day of TCZ admission were the independent prognostic factors.

Development and validation of a prognostic model for the early identification of COVID-19 patients at risk of developing common long COVID symptoms.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic demands reliable prognostic models for estimating the risk of long COVID. We developed and validated a prediction model to estimate the probability of known common long COVID symptoms at least 60 days after acute COVID-19. METHODS: The prognostic model was built based on data from a multicentre prospective Swiss cohort study. Included were adult patients diagnosed with COVID-19 between February and December 2020 and treated as outpatients, at ward or intensive/intermediate care unit. Perceived long-term health impairments, including reduced exercise tolerance/reduced resilience, shortness of breath and/or tiredness (REST), were assessed after a follow-up time between 60 and 425 days. The data set was split into a derivation and a geographical validation cohort. Predictors were selected out of twelve candidate predictors based on three methods, namely the augmented backward elimination (ABE) method, the adaptive best-subset selection (ABESS) method and model-based recursive partitioning (MBRP) approach. Model performance was assessed with the scaled Brier score, concordance c statistic and calibration plot. The final prognostic model was determined based on best model performance. RESULTS: In total, 2799 patients were included in the analysis, of which 1588 patients were in the derivation cohort and 1211 patients in the validation cohort. The REST prevalence was similar between the cohorts with 21.6% (n = 343) in the derivation cohort and 22.1% (n = 268) in the validation cohort. The same predictors were selected with the ABE and ABESS approach. The final prognostic model was based on the ABE and ABESS selected predictors. The corresponding scaled Brier score in the validation cohort was 18.74%, model discrimination was 0.78 (95% CI: 0.75 to 0.81), calibration slope was 0.92 (95% CI: 0.78 to 1.06) and calibration intercept was -0.06 (95% CI: -0.22 to 0.09). CONCLUSION: The proposed model was validated to identify COVID-19-infected patients at high risk for REST symptoms. Before implementing the prognostic model in daily clinical practice, the conduct of an impact study is recommended.

Long-Term Survival among Patients Hospitalized for COVID-19 during the First Three Epidemic Waves: An Observational Study in a Northern Italy Hospital.

The mortality rate of hospitalized COVID-19 patients differed strongly between the first three pandemic waves. Nevertheless, their long-term survival has been poorly assessed. The aim of this study was to compare the clinical characteristics and mortality rates of 825 patients with coronavirus disease 2019 (COVID-19) infection who were hospitalized at the Alessandria hub hospital, in Northern Italy, during the first fifty days of the first three pandemic waves. Each subject was followed in terms of vital status for six months from the date of hospital admission or until deceased. Patients admitted during the three waves differed in age (p = 0.03), disease severity (p < 0.0001), Charlson comorbidity index (p = 0.0002), oxygen therapy (p = 0.002), and invasive mechanical ventilation (p < 0.0001). By the end of follow-up, 309 deaths (38.7%) were observed, of which 186 occurred during hub hospitalization (22.5%). Deaths were distributed differently among the waves (p < 0.0001), resulting in being higher amongst those subjects admitted during the first wave. The COVID-19 infection was reported as the main cause of death and patients with a higher mortality risk were those aged ≥65 years [adjusted HR = 3.40 (95% CI 2.20-5.24)], with a higher disease severity [adjusted HR = 1.87 (95%CI 1.43-2.45)], and those requiring oxygen therapy [adjusted HR = 2.30 (95%CI 1.61-3.30)]. In conclusion, COVID-19 patients admitted to our hub hospital during the second and the third waves had a lower risk of long-term mortality than those admitted during the first. Older age, more severe disease, and the need for oxygen therapy were among the strongest risk factors for poor prognosis.