The Predictive Ability of the C-reactive Protein to Albumin Ratio As A Mortality Predictor in Hospitalized Severe SARS-CoV-2 Infected Patients with Cardiovascular Diseases (original) (raw)
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Journal of Clinical Medicine of Kazakhstan, 2021
This study aimed to determine the ability of the C-reactive protein (CRP)-to-albumin ratio (CAR) to predict short-term mortality in patients with COVID-19. Material and methods: This retrospective, observational, cohort study included patients with COVID-19. The patients' demographics, clinical characteristics, CRP, albumin, CAR, blood urea nitrogen, creatinine, highsensitive cardiac troponin I and all-cause mortality within 30 days after admission were noted. The receiver operating characteristic curve analysis was performed, and odds ratios (OR) were calculated to determine the discriminative ability of the parameters. Results: A total of 103 patients with a median of age of 57 (25th-75th percentiles: 32-76) years were included in the study. The rate of 30-day mortality was 4.8% for the study cohort. According to the best Youden's index, the cutoff value for CRP was determined as 66.67 (sensitivity: 80%, specificity: 78.6%), and the area under curve (AUC) value was 0.801 (95% confidence interval [CI]: 71.1-87.3). According to the best Youden's index, the cutoff value for CAR was 0.18 (sensitivity: 80%, specificity: 78.6%), and the AUC value was 0.806 (95% CI: 71.6-87.7). There was no statistically significant difference between the AUC values of CRP and CAR (DeLong equality test, p=0.938). The OR of CRP (>66.67 mg/L) and CAR (>0.18) for 30-day mortality were 14.667 (95% CI: 1.555-138.299) and 13.818 (95% CI: 1.468-130.076), respectively. Conclusion: CAR was not useful in predicting 30-day mortality in patients with COVID-19. The calculation of CAR rather than CRP had no clinically significant contribution to the prediction of 30-day mortality in this patient group.
Reviews in Medical Virology, 2022
With COVID-19 still hovering around and threatening the lives of many at-risk patients, an effective, quick, and inexpensive prognostic method is required. Few studies have shown fibrinogen to albumin ratio (FAR) and C-reactive protein to albumin ratio (CAR) to be promising as prognostic markers for COVID-19 disease. However, their implications remain unclear. This meta-analysis aimed to elucidate the prognostic role of FAR and CAR in COVID-19 disease. A systematic literature search was undertaken using PubMed and Embase till April 2022. Inverse variance standardised mean difference (SMD) was calculated to report the overall effect size using random effect models. The generic inverse variance random-effects method was used to pool the area under the curve (AUC) values. All statistical analyses were performed on Revman and MedCalc Software. A total of 23 studies were included. COVID-19 non-survivors had a higher CAR on admission compared with survivors (SMD = 1.79 [1.04, 2.55]; p < 0.00001; I 2 = 97%) and patients with a severe COVID-19 infection had a higher CAR on admission than non-severe patients (SMD = 1.21 [0.54, 1.89]; p = 0.0004; I 2 = 97%). Similarly, higher mean FAR values on admission were significantly associated with COVID-19 mortality (SMD = 0.55 [0.32, 0.78]; p < 0.00001; I 2 = 82%). However, no significant association was found between mean FAR on admission and COVID-19 severity (SMD = 0.54 [−0.09, 1.18]; p = 0.09; I 2 = 91%). The pooled AUC values found that CAR had a good discriminatory-power to predict COVID-19 severity (AUC = 0.81 [0.75, 0.86]; p < 0.00001; I 2 = 80%) and mortality (AUC = 0.81 [0.74, 0.87]; p < 0.00001; I 2 = 86%). FAR had a fair discriminatory-power to predict COVID-19 severity (AUC = 0.73 [0.64, 0.82]; p < 0.00001; I 2 = 89%). Overall, CAR was a good predictor of both severity and mortality associated with COVID-19 infection. Similarly, FAR was a satisfactory predictor of COVID-19 mortality but not severity. Abbreviations: AUC, area under the curve; CAR, C-reactive protein to albumin ratio; COVID-19, coronavirus disease-2019; FAR, fibrinogen to albumin ratio.
Does C reactive protein/Albumin ratio have prognostic value in patients with COVID-19
The Journal of Infection in Developing Countries, 2021
Introduction: There is paucity of data regarding C reactive protein/Albumin (CRP/Alb) ratio in patients with SARS-CoV-2 infection. We aimed to evaluate the significance of CRP/Alb ratio in COVID-19 patients. Methodology: Patients hospitalized between March – April 2020 with COVID-19, who had CRP and Albumin levels documented within 24 hours from admission were retrospectively analyzed. Unpaired Student’s t-test was used for continuous and Pearson Chi-square (χ²) test for categorical variables. Univariate and multivariate logistic regression models were developed to assess the relationship between CRP/Alb and mortality. Nonparametric correlations were calculated using Spearman’s Rho correlation coefficient. Results: 75 patients were included. Mean age was 62.92, 26 females (34.67%) and 49 males (65.33%), mean Body Mass Index (BMI) 29.86, mean body temperature 101.3 and mean length of stay (LOS) was 14.80 days. 24 (32%) patients required invasive mechanical ventilation and 51 (68%) di...
Revista da Associação Médica Brasileira, 2021
OBJECTIVE: This study investigates whether C-reactive protein, platelet-lymphocyte ratio, and neutrophil-lymphocyte ratio could be useful to predict mortality in COVID-19. METHODS: Data of 635 patients with COVID-19 followed up in Sinop Ataturk State Hospital from February to May 2020 were evaluated retrospectively. Diagnosis of COVID-19 was made according to the interim guidance of the World Health Organization. Patients were grouped into two groups based on mortality as survived and non-survived patients. Age, gender, neutrophil-lymphocyte ratio, plateletlymphocyte ratio, and C-reactive protein of the groups were investigated and compared. RESULTS: The mean age of the participants was 55.8±22.3 years. Among the patients, 584 survived and 51 patients died. Age was significantly different between the groups, 54.2±22.3 in the survived group and 75.6±11.1 in the dead group (p=0.000). In addition, neutrophil, C-reactive protein, and neutrophil-lymphocyte ratio values were significantly higher in the dead group (p=0.000). plateletlymphocyte ratio was slightly higher in the dead group, but this difference was not significant (p=0.42). The area under the curve values for age, lymphocyte, platelet, C-reactive protein, and neutrophil-lymphocyte ratio are 0.797, 0.424, 0.485, 0.778, and 0.729, respectively. CONCLUSIONS: Our results showed that neutrophil-lymphocyte ratio and C-reactive protein are significantly higher in patients leading to death and could be effective biomarkers in predicting COVID-19 fatality. Furthermore, C-reactive protein could be used as an independent biomarker to predict death in patients with COVID-19, regardless of gender and age (p=0.000).
C-reactive protein as a prognostic marker in coronavirus disease-2019
Eastern Journal of Medical Sciences, 2021
Background: Coronavirus disease-2019 (COVID-19) is a recently emerged viral disease, for which there’s currently no definitive treatment. It is, therefore, necessary to determine biomarkers to know the extent of disease severity so that timely action can be taken to reduce mortality. We aimed to determine the usefulness of C-reactive protein (CRP) levels in assessing COVID-19 disease severity and correlate them with mortality due to the same. Methods: Data for COVID-19 were retrospectively collected and analyzed from May 2020 to October 2020. The CRP value was correlated with disease severity using Karl Pearson’s correlation coefficient. A logistic regression model was adopted to analyze the association between mortality and related factors. Results: Out of 642 patients enrolled, 22 died while 620 recovered. Most of the non-survivors were male. Multivariate analysis showed that age, diabetes, hypertension, and CRP values were significantly associated with mortality. CRP showed a str...
Acta Cardiologica, 2020
Background: to identify the potential cardiovascular risk factors associated with mortality in hospitalised COVID-19 patients. Methods: All consecutive patients admitted to intensive care unit (ICU) of our institute for COVID-19 from 1 April 2020 to 20 May 2020 were included. Patient characteristics including complete medical history and comorbid diseases, admission and 7th day blood test results and clinical characteristics were compared between survivors and non-survivors. Results: There were no significant difference between survivors and non-survivors regarding age, gender, and pre-existing coronary artery disease, hypertension, diabetes, heart failure, coronary artery bypass grafting surgery, percutaneous coronary intervention and coronary stenting. Admission D-dimer and NT-proBNP levels of non-survivors were significantly higher than survivors. CRP, procalcitonin, creatine kinase (CK) and troponin I levels on 7th day of admission were significantly higher in non-survivors compared to survivors. In addition, both admission and 7th day lymphocyte count were lower in non-survivors compared to that of the survivors. CRP declined from admission to 7th day of hospitalisation in survivors, whereas a median 6.75 mg/L increase was observed in non survivors. The peak and minimum CRP, procalcitonin and levels were significantly higher in non-survivors than survivors. The peak NT-proBNP level of non-survivors was also significantly higher than that of the survivors. Intubation, lower GFR values and higher NT-proBNP values were predictive for death. Conclusion: The prothrombotic coagulopathy mediated by the endothelial interaction with SARS-CoV-2 may also have role in unfavourable prognosis in COVID-19. These readily available biomarkers might be useful in risk stratification of COVID-19 cases.
Journal of Istanbul Faculty of Medicine / İstanbul Tıp Fakültesi Dergisi, 2022
Objective: Early detection of mortality risk is important in patients diagnosed with of coronavirus disease 2019 (COVID-19). Therefore, we aimed to evaluate the predictive value of different clinical and laboratory parameters in disease severity and mortality in patients with COVID-19. Materials and Methods: Patients admitted to hospital with a diagnosis of COVID-19 were evaluated retrospectively. The patients' admission date, discharge date, intensive care transfer/death date, contact history, smoking, symptoms at the time admission, vital markers at admission, and laboratory parameters were recorded. Results: The study included a total of 347 patients, of whom 168 (48.4%) were women. The mean age of the patients was 59.69 +/- 16.87 (14-97) years, while 40.9% (n=142) were aged over 65 years. Overall, 10.1% (n=35) of the patients required transfer to an intensive care unit and 8.4% (n=29) were deceased. When clinical parameters were evaluated at the time of admission, oxygen sat...
Risk Factors and a Novel Score (CARI-65) Predicting Mortality in COVID-19 Patients
Indian Journal of Respiratory Care
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) was identified as a novel coronavirus toward the end of the year 2019. After its recognition as an etiology for a cluster of pneumonia patients in China, this unknown disease named coronavirus disease 2019 (COVID-19) spread expeditiously across the globe, hence being declared a pandemic by the
The role of hemogram parameters and C‐reactive protein in predicting mortality in COVID‐19 infection
International Journal of Clinical Practice
This study aimed to investigate hemogram parameters and CRP that can be used in clinical practice to predict mortality in hospitalized patients with a diagnosis of COVID-19. Methods: This cohort study was conducted at University Hospital, which is a designated hospital for COVID-19 patients. Adult patients who were admitted to our hospital emergency department with suspected COVID-19 and who were hospitalized in our institution with a COVID-19 diagnosis were analysed. Results: There were 148 patients hospitalized with COVID-19. All-cause mortality of follow-up was 12.8%. There were statistically significant results between the 2 groups (survivors and nonsurvivors), which were classified based on hospital mortality rates, in terms of the lymphocyte to C-reactive protein ratio (LCRP), Systemic immune inflammation index (SII), , neutrophil-tolymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), CRP concentration and comorbid disease. In a Receiver operating characteristic (ROC)". curve analysis, LCRP, NLR, PLR, and SII Accepted Article This article is protected by copyright. All rights reserved area under the curve (AUC) for in-hospital mortality were 0.817, 0.816, 0.733 and, 0,742 respectively. Based on an LCRP value of 1 for in-hospital mortality, the sensitivity, and specificity rates were 100%, 86.8% respectively. Based on the average SII of 2699 for in-hospital mortality, the sensitivity, specificity, and accuracy rates were 68,4%, 77,5%, and 76,3%, respectively A total of 19 patients died during hospitalization. All of these patients had an LCRP level ≤ 1; 14 had an NLR level ≤ 10.8; 13 had a SII ≥ 2699 (Fisher's exact test, p = 0.000). Independent predictors of in-hospital mortality rates were LCRP < 1, PLR, SII ≥ 2699, white blood cell count, CRP, age, comorbidities, and ICU stay. Conclusions: We concluded that inflammatory parameters, such as LRCP, SII and NLR, were associated with disease severity and could be used as potentially important risk factors for COVID-19 progression.
The atherogenic index of plasma as a predictor of mortality in patients with COVID-19
Heart & Lung, 2021
Background: Coronavirus disease 2019 (COVID-19) has become a global health threat, and thus, an early and effective set of predictors is needed to manage the course of the disease. Objectives: We aim to determine the effect of SARS-CoV-2 on lipid profile and to evaluate whether the atherogenic index of plasma (AIP) could be used to predict in-hospital mortality in COVID-19 patients. Methods: In this retrospective chart review study, a total of 139 confirmed COVID-19 patients, whose diagnoses are confirmed by PCR and computerized tomography results, are enrolled. The study population is divided into two groups: the deceased patient group and the survivor group. For each patient, fasting total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and the triglyceride values are obtained from the laboratory tests required at the admission to hospital. Finally, the AIP is calculated as the base 10 logarithm of the triglyceride to HDL-C ratio. Distributional normality of the data is checked and depending on the normality of the data, either T test or Mann Whithey U test is employed to compare the two aforementioned study groups. Results: Mean age of the study population is 49.2 § 20.8 and 61.2% (n = 85) is male. Out of the 139 patients 26 have deceased and the remaining 113 patients survived the disease. Mean age of the deceased patients was 71.8*8.9 and mean age of the survivor patients is 44.0*19.2 (p < 0.001). The deceased group had more patients with hypertension (50.0% vs. 23.0, p = 0.006), diabetes mellitus (35.6% vs. 10.6%, p = 0.002), cardiovascular diseases (23.1% vs. 4.4%, p = 0.001), chronic renal insufficiency (11.5% vs. 0.9%, p = 0.003) and atrial fibrillation (7.7% vs 0%, p = 0.003). The AIP values in the deceased group are found to be statistically higher (p < 0.001) than the survivor group. As a measure of mortality, the area under the operating characteristic curve for the AIP is calculated as 0.850 (95% confidence interval: 0.772À0.928) along with the optimal cutoff value of 0.6285 (78.6% sensitivity and 80.5% specificity). Furthermore, the AIP value is observed to be elevated in patients with pneumonia, intubation history, and intensive care admission during hospital stay (p = 0.002, p < 0.001 and p < 0.001, respectively). Finally, compared to the survivor group, total cholesterol, HDL-C, LDL-C values are lower (p = 0.004, p < 0.001 and p < 0.001, respectively) and triglyceride levels are higher (p < 0.001) in deceased patients. Conclusion: In this study, we show that the AIP levels higher than 0.6285 can predict in-hospital mortality for COVID-19 patients. Moreover, the AIP emerges as a good candidate to be used as an early biomarker to predict pneumonia, intubation and intensive care need. Hence, regular check of the AIP levels in COVID-19 patients can improve management of these patients and prevent deterioration of the disease.