Diagnostic Value of Hematological and Biochemical Parameters Combinations for Predicting Coronavirus Disease 2019 (COVID-19) in Suspected Patients

The Model for End-Stage Liver Disease (MELD) Score Predicting Mortality Due to SARS-CoV-2 in Mexican Patients

Background/Objectives: Coronavirus Disease 2019 (COVID-19) can cause liver injury and a deterioration of hepatic function. The Model for End-Stage Liver Disease (MELD) score is a good predictor for poor prognosis of hospitalized COVID-19 patients in the United States, Egypt and Turkey. Nevertheless, the best cut-off value for the MELD score to predict mortality in the Mexican population has yet to be established. Methods: A total of 234 patients with COVID-19 were studied in a tertiary-level hospital. Patients were stratified into survivors (n = 139) and non-survivors (n = 95). Receiver operating characteristic curves, Cox proportional hazard models, Kaplan-Meier method, and Bonferroni corrections were performed to identify the predictors of COVID-19 mortality. Results: MELD score had an area under the curve of 0.62 (95% CI: 0.56-0.68; p = 0.0009), sensitivity = 53.68%, and specificity = 73.38%. Univariate Cox proportional hazard regression analysis suggested that the leukocytes > 10.6, neutrophils > 8.42, neutrophil-to-lymphocyte ratio (NLR) > 8.69, systemic immune-inflammation index (SII) > 1809.21, MELD score > 9, and leukocyte glucose index (LGI) > 2.41 were predictors for mortality. However, the multivariate Cox proportional hazard model revealed that only the MELD score >9 (Hazard Ratio [HR] = 1.83; 95% confidence interval [CI]: 1.2-2.8; Pcorrected = 0.03) was an independent predictor for mortality of COVID-19. Conclusions: Although the MELD score is used for liver transplantation, we suggest that a MELD score >9 could be an accurate predictor for COVID-19 mortality at admission to ICU requiring mechanical ventilation.

The Role of Aspartate Aminotransferase-to-Lymphocyte Ratio Index (ALRI) in Predicting Mortality in SARS-CoV-2 Infection

COVID-19 has a mortality rate exceeding 5.4 million worldwide. The early identification of patients at a high risk of mortality is essential to save their lives. The AST-to-lymphocyte ratio index (ALRI) is a novel biomarker of survival in patients with hepatocellular carcinoma, an organ susceptible to SARS-CoV-2 infection. For this study, the prognostic value of ALRI as a marker of COVID-19 mortality was evaluated. For this purpose, ALRI was compared with the main biomarkers for COVID-19 mortality (neutrophil-to-lymphocyte ratio [NLR], systemic immune-inflammation index [SII], platelet-to-lymphocyte ratio [PLR], lactate dehydrogenase (LDH)/lymphocyte ratio [LDH/LR]). A retrospective cohort of 225 patients with SARS-CoV-2 infection and without chronic liver disease was evaluated. In the non-survival group, the ALRI, NLR, SII, and LDH/LR were significantly higher than in the survival group (pcorrected < 0.05). ALRI had an area under the curve (AUC) of 0.81, a sensitivity of 70.37%, and a specificity of 75%, with a best cut-off value >42.42. COVID-19 patients with high ALRI levels had a mean survival time of 7.8 days. Multivariate Cox regression revealed that ALRI > 42.42 (HR = 2.32, 95% CI: 1.35-3.97; pcorrected = 0.01) was a prognostic factor of COVID-19 mortality. These findings prove that ALRI is an independent predictor of COVID-19 mortality and that it may help identify high-risk subjects with SARS-CoV-2 infection upon admission.

Evaluation of biochemical characteristics of 183 COVID-19 patients: A retrospective study

Introduction and aim

Coronavirus disease 2019 (COVID-19), with a high mortality rate, has caught the eyes of researchers worldwide and placed a heavy burden on the health care system. Accordingly, this study aimed to evaluate the values of biochemical parameters on the outcomes of COVID-19 patients in Golestan, Iran.

Materials and methods

This retrospective study was conducted on 183 COVID-19 patients (i.e., 94 males and 89 females) between March and September 2020. The biochemical parameters and demographic data of the patients (including age, sex, urea, creatinine [Cr], lactate dehydrogenase [LDH], and creatine kinase [CK]) were obtained from electrical medical records. According to the outcome of COVID-19, the patients were categorized into two groups (i.e., death [n = 63] and survival [n = 120] groups), and the biochemical parameters and outcomes of COVID-19 were analyzed.

Results

Of the 183 patients, 120 (65.5%) had a non-severe type and recovered from COVID-19, and 63 (34.4%) developed into a critically severe type and died. The mean age of all patients was 56.5 years old. The highest mortality was observed in patients with LDH ≥280. The data obtained by the one-sample t-test showed that there were significantly higher mean values of urea, Cr, CK, and LDH in COVID-19 patients when compared to their reference intervals (P˂0.0001 for all).

Conclusions

Some biochemical parameters are effective in the evaluation of dynamic variations in COVID-19 patients. It can be concluded from the results that biochemical parameters and reinforce LDH may be useful for the evaluation of the COVID-19 outcome.

Comparison of clinical, laboratory and radiological features in confirmed and unconfirmed COVID-19 patients

Background: We aimed to compare the clinical, laboratory and radiological findings of confirmed COVID-19 and unconfirmed patients. Methods: This was a single-center, retrospective study. Results: Overall, 620 patients (338 confirmed COVID-19 and 282 unconfirmed) were included. Confirmed COVID-19 patients had higher percentages of close contact with a confirmed or probable case. In univariate analysis, the presence of myalgia and dyspnea, decreased leukocyte, neutrophil and platelet counts were best predictors for SARS-CoV-2 RT-PCR positivity. Multivariate analyses revealed that only platelet count was an independent predictor for SARS-CoV-2 RT-PCR positivity. Conclusion: Routine complete blood count may be helpful for distinguishing COVID-19 from other respiratory illnesses at an early stage, while PCR testing is unique for the diagnosis of COVID-19.