scholarly journals Clinical and Laboratory Factors Associated with Severe Disease Course in Turkish Patients with SARS-COV-2 Infection

2021 ◽  
Keyword(s):  
Risk Factors ◽  
Severe Disease ◽  
Laboratory Data ◽  
Course Of Disease ◽  
Neutrophil Lymphocyte Ratio ◽  
Reactive Protein ◽  
Pulmonary Infiltration ◽  
Independent Risk Factors ◽  
Turkish Patients ◽  
D Dimer

Background: Coronavirus disease 2019 (COVID-19) can demonstrate different clinical spectra. Objectives: The current study aimed to analyze the clinical and laboratory risk factors of the severe course of disease in patients with COVID-19. Materials and Methods: Consecutive patients with a diagnosis of COVID-19 pneumonia were included in the present study. The demographic characteristics, comorbid diseases, symptoms, chest computed tomography (CT) findings, laboratory data, oxygen saturation (SpO2), and body temperature of the patients were recorded. The coexistence of pulmonary infiltration in CT and SpO2 of ≤ %93 on fingertip pulse oximeter was defined as the severe course of the disease. Results: A total of 475 patients were included in the current study. The mean age of the patients was 52.02±15.9 years, and 259 (54.5%) participants were male. The disease was mild and severe in 80% (n=380) and 20% (n=95) of the patients. The age of > 50 years, coexistence of hypertension (HT) and diabetes mellitus (DM), neutrophil/lymphocyte ratio (NLR) of > 4, high lactate dehydrogenase (LDH) of > 240 U/L, C-reactive protein (CRP) of > 8 mg/dL, and D-dimer of ≥ 1000 ng/mL were determined to be the risk factors for the severe course of the disease. Conclusion: Age, NLR, CRP, LDH, D-dimer, comorbidity, and coexistence of DM and HT were the independent risk factors for the severe course of the disease. The aforementioned factors should be taken into account during risk stratification and management of patients with COVID-19.

scholarly journals Risk factors for clinical progression in patients with COVID-19: a retrospective study of electronic health record data in the United Kingdom

2020 ◽  
Author(s):  
Robert A Fletcher ◽  
Thomas Matcham ◽  
Marta Tibúrcio ◽  
Arseni Anisimovich ◽  
Stojan Jovanović ◽  
...  
Keyword(s):  
Risk Factors ◽  
Mechanical Ventilation ◽  
Logistic Regression ◽  
Body Temperature ◽  
Blood Cell ◽  
Severe Disease ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
Multivariable Logistic Regression ◽  
D Dimer

Background: The novel coronavirus disease 2019 (COVID-19) outbreak presents a significant threat to global health. A better understanding of patient clinical profiles is essential to drive efficient and timely health service strategies. In this study, we aimed to identify risk factors for a higher susceptibility to symptomatic presentation with COVID-19 and a transition to severe disease. Methods: We analysed data on 2756 patients admitted to Chelsea & Westminster Hospital NHS Foundation Trust between 1st January and 23rd April 2020. We compared differences in characteristics between patients designated positive for COVID-19 and patients designated negative on hospitalisation and derived a multivariable logistic regression model to identify risk factors for predicting risk of symptomatic COVID-19. For patients with COVID-19, we used univariable and multivariable logistic regression to identify risk factors associated with progression to severe disease defined by: 1) admission to the hospital AICU, 2) the need for mechanical ventilation, 3) in-hospital mortality, and 4) at least one measurement of elevated D-dimer (equal or superior to 1,000 ug/L) indicative of increased risk of venous thromboembolism. Results: The patient population consisted of 1148 COVID-19 positive and 1608 COVID-19 negative patients. Age, sex, self-reported ethnicity, C-reactive protein, white blood cell count, respiratory rate, body temperature, and systolic blood pressure formed the most parsimonious model for predicting risk of symptomatic COVID-19 at hospital admission. Among 1148 patients with COVID-19, 116 (10.1%) were admitted to the AICU, 71 (6.2%) required mechanical ventilation, 368 (32.1%) had at least one record of D-dimer levels ≥1,000 μg/L, and 118 patients died. In the multivariable logistic regression, age (OR = 0.953 per 1 year, 95% CI: 0.937-0.968) C-reactive protein (OR = 1.004 per 1 mg/L, 95% CI: 1.002-1.007), and white blood cell counts (OR = 1.059 per 109/L, 95% CI: 1.010-1.111) were found to be associated with admission to the AICU. Age (OR = 0.973 per 1 year, 95% CI: 0.955-0.990), C-reactive protein (OR = 1.003 per 1 mg/L, 95% CI: 1.000-1.006) and sodium (OR = 0.915 per 1 mmol/L, 0.868-0.962) were associated with mechanical ventilation. Age (OR = 1.023 per 1 year, 95% CI: 1.004-1.043), CRP (OR = 1.004 per 1 mg/L, 95% CI: 1.002-1.006), and body temperature (OR = 0.723 per 1oC, 95% CI: 0.541-0.958) were associated with elevated D-dimer. For mortality, we observed associations with age (OR = 1.060 per 1 year, 95% CI: 1.040-1.082), female sex (OR = 0.442, 95% CI: 0.442, 95% CI: 0.245-0.777), Asian ethnic background (OR = 2.237 vs White ethnic background, 95% CI: 1.111-4.510), C-reactive protein (OR = 1.004 per 1 mg/L, 95% CI: 1.001-1.006), sodium (OR = 1.038 per 1 mmol/L, 95% CI: 1.001-1.006), and respiratory rate (OR = 1.054 per 1 breath/min, 95% CI: 1.024-1.087). Conclusion: Our analysis suggests there are several demographic, clinical and laboratory findings associated with a symptomatic presentation of COVID-19. Moreover, significant associations between patient deterioration were found with age, sex and specific blood markers, chiefly C-reactive protein, and could help early identification of patients at risk of poorer prognosis. Further work is required to clarify the extent to which our observations are relevant beyond current settings.

scholarly journals A nomogramic model based on clinical and laboratory parameters at admission for predicting the survival of COVID-19 patients

2020 ◽  
Author(s):  
Xiaojun Ma ◽  
Huifang Wang ◽  
Junwei Huang ◽  
Yan Geng ◽  
Shuqi Jiang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Bootstrap Method ◽  
Characteristic Curve ◽  
Laboratory Data ◽  
Categorical Variables ◽  
P Value ◽  
Continuous Variables ◽  
Model Based ◽  
Independent Risk Factors ◽  
D Dimer

Abstract Background and Aim COVID-19 has become a major global threat. The present study aimed to develop a nomogram model to predict the survival of COVID-19 patients based on their clinical and laboratory data at admission. Methods COVID-19 patients who were admitted at Hankou Hospital and Huoshenshan Hospital in Wuhan, China from January 12, 2020 to March 20, 2020, whose outcome during the hospitalization was known, were retrospectively reviewed. The categorical variables were compared using Pearson’s χ2-test or Fisher’s exact test, and continuous variables were analyzed using Student’s t-test or Mann Whitney U-test, as appropriate. Then, variables with a P-value of ≤0.1 were included in the multivariate model, and merely these independent risk factors were used to establish the nomogram model. The discrimination of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), and internally verified using the Bootstrap method. Results A total of 262 patients (134 surviving and 128 non-surviving patients) were included in the analysis. Seven variables, which included age (odds ratio [OR]: 0.905, 95% confidence interval [CI]: 0.868-0.944; P<0.001), chronic heart disease (CHD, OR: 0.048, 95% CI: 0.013-0.180; P<0.001), the percentage of lymphocytes (Lym%, OR: 1.116, 95% CI: 1.051-1.184; P<0.001), platelets (OR: 1.008, 95% CI: 1.003-1.012; P=0.001), C-reaction protein (OR: 0.982, 95% CI: 0.973-0.991; P<0.001), lactate dehydrogenase (LDH, OR: 0.993, 95% CI: 0.990-0.997; P<0.001) and D-dimer (OR: 0.734, 95% CI: 0.615-0.875; P=0.001), were identified as the independent risk factors. The nomogram model based on these factors exhibited a good discrimination, with an AUC of 0.948 (95% CI: 0.923-0.973). Conclusion A nomogram based on age, CHD, Lym%, platelets, C-reaction protein, LDH and D-dimer was established to accurately predict the prognosis of COVID-19 patients. This can be used as an alerting tool for clinicians to take early intervention measures, when necessary.

scholarly journals A nomogramic model based on clinical and laboratory parameters at admission for predicting the survival of COVID-19 patients

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Xiaojun Ma ◽  
Huifang Wang ◽  
Junwei Huang ◽  
Yan Geng ◽  
Shuqi Jiang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Bootstrap Method ◽  
Characteristic Curve ◽  
Laboratory Data ◽  
Categorical Variables ◽  
Continuous Variables ◽  
Exact Test ◽  
Model Based ◽  
Independent Risk Factors ◽  
D Dimer

Abstract Background COVID-19 has become a major global threat. The present study aimed to develop a nomogram model to predict the survival of COVID-19 patients based on their clinical and laboratory data at admission. Methods COVID-19 patients who were admitted at Hankou Hospital and Huoshenshan Hospital in Wuhan, China from January 12, 2020 to March 20, 2020, whose outcome during the hospitalization was known, were retrospectively reviewed. The categorical variables were compared using Pearson’s χ2-test or Fisher’s exact test, and continuous variables were analyzed using Student’s t-test or Mann Whitney U-test, as appropriate. Then, variables with a P-value of ≤0.1 were included in the log-binomial model, and merely these independent risk factors were used to establish the nomogram model. The discrimination of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), and internally verified using the Bootstrap method. Results A total of 262 patients (134 surviving and 128 non-surviving patients) were included in the analysis. Seven variables, which included age (relative risk [RR]: 0.905, 95% confidence interval [CI]: 0.868–0.944; P < 0.001), chronic heart disease (CHD, RR: 0.045, 95% CI: 0.0097–0.205; P < 0.001, the percentage of lymphocytes (Lym%, RR: 1.125, 95% CI: 1.041–1.216; P = 0.0029), platelets (RR: 1.008, 95% CI: 1.003–1.012; P = 0.001), C-reaction protein (RR: 0.982, 95% CI: 0.973–0.991; P < 0.001), lactate dehydrogenase (LDH, RR: 0.993, 95% CI: 0.990–0.997; P < 0.001) and D-dimer (RR: 0.734, 95% CI: 0.617–0.879; P < 0.001), were identified as the independent risk factors. The nomogram model based on these factors exhibited a good discrimination, with an AUC of 0.948 (95% CI: 0.923–0.973). Conclusions A nomogram based on age, CHD, Lym%, platelets, C-reaction protein, LDH and D-dimer was established to accurately predict the prognosis of COVID-19 patients. This can be used as an alerting tool for clinicians to take early intervention measures, when necessary.

scholarly journals Risk factors of severe cases with COVID-19: a meta-analysis

2020 ◽  
Vol 148 ◽  
Author(s):  
Mingchun Ou ◽  
Jieyun Zhu ◽  
Pan Ji ◽  
Hongyuan Li ◽  
Zhimei Zhong ◽  
...  
Keyword(s):  
Risk Factors ◽  
Platelet Count ◽  
Weighted Mean Difference ◽  
Meta Analysis ◽  
Severe Disease ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
Electronic Search ◽  
Low Platelet Count ◽  
D Dimer

Abstract Our study aimed to systematically analyse the risk factors of coronavirus disease 2019 (COVID-19) patients with severe disease. An electronic search in eight databases to identify studies describing severe or critically ill COVID-19 patients from 1 January 2020 to 3 April 2020. In the end, we meta-analysed 40 studies involving 5872 COVID-19 patients. The average age was higher in severe COVID-19 patients (weighted mean difference; WMD = 10.69, 95%CI 7.83–13.54). Patients with severe disease showed significantly lower platelet count (WMD = −18.63, 95%CI −30.86 to −6.40) and lymphocyte count (WMD = −0.35, 95%CI −0.41 to −0.30) but higher C-reactive protein (CRP; WMD = 42.7, 95%CI 31.12–54.28), lactate dehydrogenase (LDH; WMD = 137.4, 95%CI 105.5–169.3), white blood cell count（WBC), procalcitonin（PCT）, D-dimer, alanine aminotransferase (ALT), aspartate aminotransferase (AST) and creatinine（Cr）. Similarly, patients who died showed significantly higher WBC, D-dimer, ALT, AST and Cr but similar platelet count and LDH as patients who survived. These results indicate that older age, low platelet count, lymphopenia, elevated levels of LDH, ALT, AST, PCT, Cr and D-dimer are associated with severity of COVID-19 and thus could be used as early identification or even prediction of disease progression.

scholarly journals A nomogramic model based on clinical and laboratory parameters at admission for predicting the survival of COVID-19 patients

2020 ◽  
Author(s):  
Xiaojun Ma ◽  
Huifang Wang ◽  
Junwei Huang ◽  
Yan Geng ◽  
Shuqi Jiang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Bootstrap Method ◽  
Characteristic Curve ◽  
Laboratory Data ◽  
Categorical Variables ◽  
Continuous Variables ◽  
Exact Test ◽  
Model Based ◽  
Independent Risk Factors ◽  
D Dimer

Abstract BackgroundCOVID-19 has become a major global threat. The present study aimed to develop a nomogram model to predict the survival of COVID-19 patients based on their clinical and laboratory data at admission.MethodsCOVID-19 patients who were admitted at Hankou Hospital and Huoshenshan Hospital in Wuhan, China from January 12, 2020 to March 20, 2020, whose outcome during the hospitalization was known, were retrospectively reviewed. The categorical variables were compared using Pearson’s χ2-test or Fisher’s exact test, and continuous variables were analyzed using Student’s t-test or Mann Whitney U-test, as appropriate. Then, variables with a P-value of ≤0.1 were included in the log-binomial model, and merely these independent risk factors were used to establish the nomogram model. The discrimination of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), and internally verified using the Bootstrap method.ResultsA total of 262 patients (134 surviving and 128 non-surviving patients) were included in the analysis. Seven variables, which included age (relative risk [RR]: 0.905, 95% confidence interval [CI]: 0.868-0.944; P<0.001), chronic heart disease (CHD, RR: 0.045, 95% CI: 0.0097-0.205; P<0.001, the percentage of lymphocytes (Lym%, RR: 1.125, 95% CI: 1.041-1.216; P=0.0029), platelets (RR: 1.008, 95% CI: 1.003-1.012; P=0.001), C-reaction protein (RR: 0.982, 95% CI: 0.973-0.991; P<0.001), lactate dehydrogenase (LDH, RR: 0.993, 95% CI: 0.990-0.997; P<0.001) and D-dimer (RR: 0.734, 95% CI: 0.617-0.879; P<0.001), were identified as the independent risk factors. The nomogram model based on these factors exhibited a good discrimination, with an AUC of 0.948 (95% CI: 0.923-0.973).ConclusionsA nomogram based on age, CHD, Lym%, platelets, C-reaction protein, LDH and D-dimer was established to accurately predict the prognosis of COVID-19 patients. This can be used as an alerting tool for clinicians to take early intervention measures, when necessary

scholarly journals A nationwide survey concerning the mortality and risk of progressing severity due to arterial and venous thromboembolism in inflammatory bowel disease in Japan

2021 ◽  
Author(s):  
Katsuyoshi Ando ◽  
Mikihiro Fujiya ◽  
Kenji Watanabe ◽  
Sakiko Hiraoka ◽  
Hisashi Shiga ◽  
...  
Keyword(s):  
Risk Factors ◽  
Inflammatory Bowel Disease ◽  
Risk Factor ◽  
Venous Thromboembolism ◽  
Bowel Disease ◽  
Severe Disease ◽  
Laboratory Data ◽  
Nationwide Survey ◽  
Inflammatory Bowel ◽  
Independent Risk Factors

Abstract Background The mortality and risk factors of severe disease and death due to arterial and venous thromboembolism (ATE and VTE, respectively) in patients with inflammatory bowel disease (IBD) remain unclear, especially in Asia. Aims This study aimed to reveal the mortality and risk factors of TE in IBD patients in Japan. Methods In the primary surveillance, responses to questionnaires regarding the number of cases of severe TE and TE-associated death in IBD patients in a span of over the past 10 years were obtained from 32 institutions in Japan. In the secondary surveillance, detailed data about IBD patients with TE were collected. The characteristics, laboratory data, therapy status, and situation at the time of TE development were retrospectively collected, and the data were compared between the patients with and without severe TE and TE-associated death. Results The incidence of TE was 1.89% among 31,940 IBD patients. The frequencies of severe TE and TE-associated mortality were 10.7% and 1.0% among the total IBD and TE with IBD patients, respectively. The only risk factor for severe ATE and ATE-associated death was ischemic heart disease. The independent risk factors for severe VTE and VTE-associated death were age (≤ 45 years old), the site of VTE, and disease severity, with anti-TNF therapy as a potential negative risk factor. Patients with severe VTE had a high risk of developing persistent VTE and sequelae. Conclusion Unlike ATE, the incidence of VTE was comparable in Asian and Western countries. Therapeutic and prophylactic strategies for managing IBD-associated TE in Asia are urgently needed.

scholarly journals A nomogramic model based on clinical and laboratory parameters at admission for predicting the survival of COVID-19 patients

2020 ◽  
Author(s):  
Xiaojun Ma ◽  
Huifang Wang ◽  
Junwei Huang ◽  
Yan Geng ◽  
Shuqi Jiang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Bootstrap Method ◽  
Characteristic Curve ◽  
Laboratory Data ◽  
Categorical Variables ◽  
Continuous Variables ◽  
Exact Test ◽  
Model Based ◽  
Independent Risk Factors ◽  
D Dimer

Abstract Background and AimCOVID-19 has become a major global threat. The present study aimed to develop a nomogram model to predict the survival of COVID-19 patients based on their clinical and laboratory data at admission.MethodsCOVID-19 patients who were admitted at Hankou Hospital and Huoshenshan Hospital in Wuhan, China from January 12, 2020 to March 20, 2020, whose outcome during the hospitalization was known, were retrospectively reviewed. The categorical variables were compared using Pearson’s χ2-test or Fisher’s exact test, and continuous variables were analyzed using Student’s t-test or Mann Whitney U-test, as appropriate. Then, variables with a P-value of ≤0.1 were included in the log-binomial model, and merely these independent risk factors were used to establish the nomogram model. The discrimination of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), and internally verified using the Bootstrap method.ResultsA total of 262 patients (134 surviving and 128 non-surviving patients) were included in the analysis. Seven variables, which included age (relative risk [RR]: 0.905, 95% confidence interval [CI]: 0.868-0.944; P<0.001), chronic heart disease (CHD, RR: 0.045, 95% CI: 0.0097-0.205; P<0.001, the percentage of lymphocytes (Lym%, RR: 1.125, 95% CI: 1.041-1.216; P=0.0029), platelets (RR: 1.008, 95% CI: 1.003-1.012; P=0.001), C-reaction protein (RR: 0.982, 95% CI: 0.973-0.991; P<0.001), lactate dehydrogenase (LDH, RR: 0.993, 95% CI: 0.990-0.997; P<0.001) and D-dimer (RR: 0.734, 95% CI: 0.617-0.879; P<0.001), were identified as the independent risk factors. The nomogram model based on these factors exhibited a good discrimination, with an AUC of 0.948 (95% CI: 0.923-0.973).ConclusionA nomogram based on age, CHD, Lym%, platelets, C-reaction protein, LDH and D-dimer was established to accurately predict the prognosis of COVID-19 patients. This can be used as an alerting tool for clinicians to take early intervention measures, when necessary.

scholarly journals Clinical Characteristics and Risk Factors of Mortality Among Severe COVID-19 Patients

2021 ◽  
Author(s):  
Reham Mohamed Elmorshedy ◽  
Maha Mohamed El-kholy ◽  
Alaa Eldin AbdelMoniem ◽  
Shimaa Abbas Hassan ◽  
Samiaa Hamdy Sadek
Keyword(s):  
Risk Factors ◽  
Clinical Characteristics ◽  
Laboratory Data ◽  
Ground Glass Opacity ◽  
Older Age ◽  
Ground Glass ◽  
Neutrophil Lymphocyte Ratio ◽  
Lymphocyte Ratio ◽  
Increased Risk ◽  
D Dimer

Abstract Background:The novel corona virus is attacking several millions of people worldwide, resulting in death of almost a million and a half-humans. The rational of the current study was to detect clinical characteristics of severe COVID- 19 patients, and assessment of risk factors for death.Methodology:This retrospective cohort study included all laboratory confirmed COVID-19 patients with severe disease admitted to critical care unit in June and July 2020. All recorded data were collected,which included clinincal, radiological, and laboratory data, in addition to the outcome and duration of ICU stay.Statistical analysis was performed for obtaining descriptive information, comparison between living and dead patients,in addition to regression analysis to identify risk factors for mortality.Results:One hundred and three patients were included in the current study;cough and fever were the most common clinical presentations, and bilateral ground glass opacity was the most common radiological presentation. Patients had elevated values of neutrophils, neutrophil lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), serum ferritin, CRP, and D-dimer, also had longer ICU stay ,with reduced values of lymphocytes, and PaO2/FIO2 ratio. Most of these variables were more exaggerated in dead patients compared to living ones. Older age, lower values of PaO2/FIO2 ratio, and higher values of neutrophils, NLR, and D-dimer were predictors for death.Conclusion: Cough, fever and bilateral ground glass opacity were the most common clinical and radiological presentation of severe COVID 19. Older age, lower value of PaO2/FIO2 ratio, and higher values of D- dimer, neutrophil and NLR were risk factors associated with increased risk of mortality.

scholarly journals A nomogramic model based on clinical and laboratory parameters at admission for predicting the survival of COVID-19 patients

2020 ◽  
Author(s):  
Xiaojun Ma ◽  
Huifang Wang ◽  
Junwei Huang ◽  
Yan Geng ◽  
Shuqi Jiang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Characteristic Curve ◽  
Laboratory Data ◽  
Categorical Variables ◽  
Continuous Variables ◽  
Exact Test ◽  
Model Based ◽  
Chronic Heart Disease ◽  
Independent Risk Factors ◽  
D Dimer

Abstract Background and AimCOVID-19 has become a major global threat. The present study aimed to develop a nomogram model to predict the survival of COVID-19 patients based on their clinical and laboratory data at admission.Methods COVID-19 patients who were admitted at Hankou Hospital and Huoshenshan Hospital in Wuhan, China from January 12, 2020 to March 20, 2020, whose outcome during the hospitalization was known, were retrospectively reviewed. The categorical variables were compared using Pearson’s χ2-test or Fisher’s exact test, and continuous variables were analyzed using Student’s t-test or Mann Whitney U-test, as appropriate. Then, variables with a P-value of ≤0.1 were included in the log-binomial model, and merely these independent risk factors were used to establish the nomogram model. The discrimination of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), and internally verified using the Bootstrap method.Results A total of 262 patients (134 surviving and 128 non-surviving patients) were included in the analysis. Seven variables, which included age (relative risk [RR]: 0.905, 95% confidence interval [CI]: 0.868-0.944; P<0.001), chronic heart disease (CHD, RR: 0.045, 95% CI: 0.0097-0.205; P<0.001, the percentage of lymphocytes (Lym%, RR: 1.125, 95% CI: 1.041-1.216; P=0.0029), platelets (RR: 1.008, 95% CI: 1.003-1.012; P=0.001), C-reaction protein (RR: 0.982, 95% CI: 0.973-0.991; P<0.001), lactate dehydrogenase (LDH, RR: 0.993, 95% CI: 0.990-0.997; P<0.001) and D-dimer (RR: 0.734, 95% CI: 0.617-0.879; P<0.001), were identified as the independent risk factors. The nomogram model based on these factors exhibited a good discrimination, with an AUC of 0.948 (95% CI: 0.923-0.973). ConclusionA nomogram based on age, CHD, Lym%, platelets, C-reaction protein, LDH and D-dimer was established to accurately predict the prognosis of COVID-19 patients. This can be used as an alerting tool for clinicians to take early intervention measures, when necessary.

scholarly journals OP0313 PRELIMINARY CRITERIA FOR MACROPHAGE ACTIVATION SYNDROME ASSOCIATED WITH CORONAVIRUS DISEASE-19

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 191.1-192
Author(s):  
S. Amikishiyev ◽  
M. G. Gunver ◽  
M. Bektas ◽  
S. Aghamuradov ◽  
B. Ince ◽  
...  
Keyword(s):  
Macrophage Activation Syndrome ◽  
Macrophage Activation ◽  
Laboratory Data ◽  
Disease Onset ◽  
Dynamic Changes ◽  
Ferritin Concentration ◽  
Neutrophil Lymphocyte Ratio ◽  
Significant Difference ◽  
D Dimer ◽  
Activation Syndrome

Background:COVID-19 runs a severe disease associated with acute respiratory distress syndrome in a subset of patients, and a hyperinflammatory response developing in the second week contributes to the worse outcome. Inflammatory features are mostly compatible with macrophage activation syndrome (MAS) observed in other viral infections despite resulting in milder changes. Early detection and treatment of MAS may be associated with a better outcome. However, available criteria for MAS associated with other causes have not been helpful.Objectives:To identify distinct features of MAS associated with COVID-19 using a large database enabling to assess of dynamic changes.Methods:PCR-confirmed hospitalized COVID-19 patients followed between March and September 2020 constituted the discovery set. Patients considered to have findings of MAS by experienced physicians and given anakinra or tocilizumab were classified as the MAS group and the remaining patients as the non-MAS group. The MAS group was then re-grouped as the cases with exact-MAS and borderline-MAS cases by the study group. Clinical and laboratory data including the Ct values of the PCR test were obtained from the database, and dynamic changes were evaluated especially for the first 14 days of the hospitalization. The second set of 162 patients followed between September-December 2020 were used as the replication group to test the preliminary criteria. In the second set, hospitalization rules were changed, and all patients required oxygen support and received dexamethasone 6mg/day or equivalent glucocorticoids. Daily changes were calculated for the laboratory items in MAS, borderline, and non-MAS groups to see the days differentiating the groups, and ROC curves and lower and upper limits (10-90%) of the selected parameters were calculated to determine the cutoff values.Results:A total of 769 PCR-confirmed hospitalized patients were analysed, and 77 of them were classified as MAS and 83 as borderline MAS patients. There was no statistically significant difference in the baseline viral loads of MAS patients compared to the non-MAS group according to the Ct values. Daily dynamic changes in the MAS group differed from the non-MAS group especially around the 6th day of hospitalization, and more than a twofold increase in ferritin and a 1.5-fold increase in D-dimer levels compared to the baseline values help to define the MAS group. Twelve items selected for the criteria are given in Table 1 below. The total score of 45 provided 79.6% sensitivity for the MAS (including borderline cases) and 81.3% specificity around days 5 and 6 in the discovery set, and a score of 60 increased the specificity to 94.9% despite a decrease in sensitivity to 40.8%. The same set provided a similar sensitivity (80.3%) in the replication, but a lower specificity (47.4-66% on days 6 to 9) due to a group of control patients with findings of MAS possibly masked by glucocorticoids.Table 1.Preliminary Criteria for Macrophage Activation Syndrome Associated with Coronavirus Disease-191.Fever (>37.0 °C)2.Ferritin concentration > 550 ng/mL3.More than 2 times increase of ferritin concentration within 7 days of disease onset4.Neutrophil count > 6000 cell/mm35.Lymphopenia < 1000 cell/mm36.Neutrophil/lymphocyte ratio > 67.D-dimer concentration > 1000 ng/ml8.More than 50% increase of D-dimer concentration within 7 days of disease onset9.CRP concetration > 50 mg/L10.LDH concentration > 300 U/L11.ALT or AST concentration > 50 U/L12.Procalcitonin concentration < 1.21 point for each positive item assessed on Days 5-7Score calculation: Total points / 12 x 100Possible MAS ≥45 and Definite MAS ≥60Conclusion:This study defined a set of preliminary criteria using the most relevant items of MAS according to the dynamic changes in the parameters in a group of COVID-19 patients. A score of 45 would be helpful to define a possible MAS group with reasonable sensitivity and specificity to start necessary treatments as early as possible.Disclosure of Interests:None declared.

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