The Official Journal of the Turkish Society Of Clinical Microbiology and Infectious Diseases (KLİMİK)

Original Article

The Role of APRI, FIB-4, and SAD-60 Scores as Predictors of Mortality in COVID-19 Patients

Betül Çopur
×Affiliations
  • Department of Infectious Diseases and Clinical Microbiology, Haseki Training and Research Hospital, İstanbul, Turkey
https://orcid.org/0000-0001-5534-1388
,
Serkan Sürme
×Affiliations
  • Department of Infectious Diseases and Clinical Microbiology, Haseki Training and Research Hospital, İstanbul, Turkey
  • Department of Medical Microbiology, Institute of Graduate Studies, İstanbul University-Cerrahpasa, Istanbul, Turkey
https://orcid.org/0000-0001-7239-1133
,
Gülşah Tunçer
×Affiliations
  • Department of Infectious Diseases and Clinical Microbiology, Bilecik Training and Research Hospital, Bilecik, Turkey
https://orcid.org/0000-0002-9841-9146
,
Osman Faruk Bayramlar
×Affiliations
  • Bakırköy District Health Directorate, İstanbul, Turkey
https://orcid.org/0000-0001-7311-3258

Abstract

Objective: Predictors of mortality that indicate disease severity plays an important role in COVID-19 management and treatment decisions. This study aimed to investigate the association between fibrosis-4 (FIB-4) score, aspartate aminotransferase-to-platelet ratio index (APRI), and novel biomarker-based score (SAD-60) with mortality in COVID-19 patients treated in a tertiary hospital.

Materials and Methods: In this single-center retrospective study, patients ≥18 years of age who were admitted to our hospital for COVID-19 between December 1 and 31, 2021, were included. Patients were divided into two groups as deceased and survived. A comparative analysis was applied. Predictive abilities of the FIB-4, APRI, and SAD-60 scores for in-hospital mortality were evaluated.

Results: Of the 453 patients enrolled in the study, 248 (54.6%) were male, and the mean age was 52.2±14.7 years. Mortality was recorded in 39 (8.5%) of the patients. The median values of APRI (0.43 and 0.58; p=0.001), FIB-4 score (1.66 and 2.91; p<0.001), and SAD-60 (2 and 8.25; p<0.001) were higher in deceased patients than in survivors. The optimal cut-off value for predicting mortality in the receiver operating characteristic (ROC) curve analysis was 0.58  for APRI (sensitivity=56.4%, specificity=63.6%); 2.14 for FIB-4 score (sensitivity=79.5%, specificity=68.2%); 4.25 for SAD-60 (sensitivity=90%, specificity=73.8%). In Cox regression analysis with a model that included gender, chronic obstructive pulmonary disease (COPD), and coronary artery disease (CAD), FIB-4 (hazard ratio [HR]=4.013, 95% confidence interval [CI]=1.643-9.803; p=0.002), and SAD-60 (HR=8.850, 95% CI=1.035-75.696; p=0.046) were independent risk factors for mortality.

Conclusion: SAD-60 and FIB-4 scores are easily applicable and may be used to predict mortality in COVID-19 patients.

Keywords: APRI, FIB-4, SAD-160, COVID-19, mortality, predictors

Highlights

    • Scoring systems that combine biochemical parameters and age seem to be more successful in predicting mortality in patients with COVID-19.
    • Fibrosis-4 (FIB-4) score and novel biomarker-based score (SAD-60) were found to be independent risk factors for mortality in Cox regression analysis.
    • SAD-60 and FIB-4 scores are easy to use and powerful in predicting in-hospital mortality in patients with COVID-19.

Introduction

COVID-19 is a fatal disease affecting all systems, especially the respiratory system. It manifests with different clinical characteristics, from asymptomatic infection to severe infection (1, 2). Since its initial description, the disease has caused devastating effects and many deaths (3). Predictors of mortality that indicate disease severity play an important role in disease management and treatment decisions (4, 5).

Numerous markers and scoring systems have been studied for COVID-19 as predictors of disease severity and poor prognosis (6-10). The aspartate aminotransferase-to-platelet ratio index (APRI) and the fibrosis-4 (FIB-4) scores are two of these that are readily applicable. APRI and FIB-4 scores are noninvasive markers for predicting the development of liver fibrosis in hepatitis C and nonalcoholic fatty liver disease (NAFLD) (11-13). Novel biomarker-based score (SAD-60), on the other hand, was reported as an indicator of mortality in COVID-19 patients in our previous study (7).

This study aimed to investigate the association between FIB-4 score, APRI, and SAD-60 with mortality in COVID-19 patients hospitalized in a tertiary hospital.

Materials and Methods

Patients at age ≥18 years who were admitted to our hospital for COVID-19 between December 1 and 31, 2021, were included in this single-center retrospective study. Patients with chronic liver disease, malignancies, HIV/AIDS, and pregnant women were excluded. Laboratory diagnosis of COVID-19 was made by SARS-CoV-2 PCR test using nasopharyngeal/oropharyngeal swab specimens. The diagnosis of pneumonia was confirmed by chest CT in patients with the following symptoms: fever, cough, shortness of breath, and tachypnea. Patient demographic, clinical, and laboratory data were retrospectively obtained from patient records. Patient vital signs such as body temperature, peripheral capillary oxygen saturation, respiratory rate per minute at the time of initial admission, and especially blood counts, aspartate aminotransferase (AST), alanine aminotransferase (ALT), albumin and D-dimer levels, and other biochemical test results used in the calculation of scores were recorded. The FIB-4 score and APRI were calculated according to the following formulas:

FIB-4  Score= Age (years)×AST (U/L) / [PLT (109/L)×ALT1/2 (U/L)]

APRI= [AST (U/L) / (AST (Upper Limit of Normal) (U/L)] / Platelet Count (109/L)×100

For the SAD-60, the sum of the values of O2 saturation (2.5 points  90%), albumin (2 points < 3.5 g/dL), D-dimer (3.5 points ≥ 0.9µg/ml), and age (2 points ≥ 60 years) were used. Patients were divided into two groups as deceased and survived. A comparative analysis was applied. The predictive ability of the FIB-4 score, APRI, and SAD-60 for in-hospital mortality was evaluated.

The statistical analyses were performed using the Statistical Package for Social Sciences (SPSS) 15.0 (IBM Corp., Armonk, NY, USA). For categorical variables, numbers and percentages were used. The median and interquartile range were used for continuous variables. Rates in groups were compared with the chi-square test. Comparisons of numerical variables between two independent groups were performed with the Student’s t-test for normally distributed data, otherwise with the Mann-Whitney U test. For the prediction of mortality, the FIB-4 score, APRI, and SAD-60 were evaluated by the receiving operating characteristic (ROC) curve. Kaplan-Meier survival analysis was calculated using the breakpoints of the scores. Cox regression analysis was performed to identify risk factors. Results were presented as hazard ratio (HR) and 95% confidence interval (CI). The statistical significance was set as p<0.05. 

This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Haseki Training and Research Hospital on August 24, 2022, with decision number 148-2022. Because this was a retrospective study, patient consent was not obtained.

Results

Table 1. Demographic and clinical characteristics of patients diagnosed with COVID-19.

Table 2. Laboratory characteristics of patients diagnosed with COVID-19.

Table 3. Relationships of FIB-4 score, SAD-60, and APRI with mortality.

Figure 1. ROC curve analyses of APRI, FIB-4, and SAD-60 scores for predicting mortality in COVID-1.

Figure 2. Probability of survival of COVID-19 patients during hospitalization based on APRI, FIB-4, and SAD-60 scores.

Of the 453 patients enrolled in the study, 248 (54.6%) were male and the mean age was 52.5 (41-62) years. Mortality was recorded in 39 (8.5%) of the patients. Causes of death were secondary bacterial pneumonia in 12 (30.8%) patients, multi-organ failure in 10 (25.6%), macrophage activation syndrome in 8 (20.5%), pulmonary embolism in 4 (10.3%), bacteremia in 3 (7.7%), myocardial infarction in 2 (5.1%). The mean age of the deceased group was significantly higher compared with the survivor group (67 years vs. 50 years, p<0.001). The prevalence of chronic obstructive pulmonary disease (COPD) (14 (3.4%) vs. 4 (10.3%); p=0.035) and coronary artery disease (CAD) (30 (7.2%) vs. 7 (17.9%); p=0.030) was higher in the deceased group than in the survived group. In total, 439 had COVID-19 pneumonia and four patients had mild COVID-19 without pneumonia (Table 1). Laboratory results of the patients are shown in Table 2. The scores for APRI (0.43 and 0.58; p=0.001), FIB-4 (1.66 and 2.91, p<0.001), and SAD-60 (2 and 8.25; p<0.001) were higher in deceased patients than in survivors. The optimal cut-off values for predicting mortality in the ROC curve analysis were 0.58 for APRI (sensitivity=56.4%, specificity=63.6%); 2.14 for FIB-4 (sensitivity=79.5%, specificity=68.2%); 4.25 for SAD-60 (sensitivity=90%, specificity=73.8%) (Figure 1). In the Kaplan-Meier survival analysis, the mortality rate in patients with high APRI scores was similar to those with low APRI scores (p=0.546). The mortality rate was higher in patients with high SAD-60 and FIB-4 scores (p=0.017 and p=0.002, respectively) (Figure 2). FIB-4 (HR=4.013, 95% CI=1.643-9.803; p=0.002) and SAD-60 (HR=8.850, 95% CI=1.035-75.696; p=0.046) were found to be independent risk factors for mortality in Cox regression analysis with a model that included gender, COPD, and CAD. APRI (HR=1.105 95% CI=0.546-2.237; p=0.854) was not associated with mortality (Table 3). 

Discussion

Our study showed that FIB-4 and SAD-60 scores may be used as predictors of mortality in hospitalized patients with COVID-19. FIB-4 (HR=4.013, 95% CI=1.643-9.803; p=0.002) and SAD-60 (HR=8.850, 95% CI=1.035-75.696; p=0.046) were found to be independent risk factors for mortality in Cox regression analysis.

Although the FIB-4 score is a noninvasive tool primarily used to diagnose liver fibrosis, studies are showing an association between FIB-4 and some chronic diseases and cardiovascular diseases (14-16). Recently, the association between the FIB-4 score and the severity and prognosis of COVID-19 has been the subject of numerous studies (10,17-19). COVID-19 damages not only the lungs but also the liver and other solid organs (20, 21). The impact of liver injury on the prognosis of COVID-19 can be severe, as can that of pneumonia. Although COVID-19 is reported to have more severe consequences in patients with chronic liver disease and a high FIB-4 score, large-scale studies on this topic are needed (22-24). For example, in patients with hepatitis C and SARS-CoV-2 infection, the FIB-4 score was associated with hospitalization but not mortality (6.6% vs. 6.5%; p=0.9) (22). In another study, the FIB-4 score was described as a noninvasive marker for the diagnosis of NAFLD, and an elevated FIB-4 score was also associated with the severity of COVID-19 in these patients (17).

APRI, another score that can be used to determine liver fibrosis, has also been associated with the severity and prognosis of COVID-19. APRI has been frequently compared with FIB-4 in many studies (10, 18, 19, 24, 25). In a study involving 202 hospitalized patients with COVID-19, further analysis that also accounted for gender and liver disease found that each unit increase in FIB-4 was an independent predictor of mortality (odds ratio [OR]=1.79, 95% CI=1.36-2.35; p<0.001). In the same study, APRI did not prove successful in predicting mortality (p=0.054) (10). In another study of COVID-19, containing patients admitted to the emergency department, the FIB-4 score (area under ROC curve [AUC]=0.689, 95% CI=0.659-0.718; p<0.0001) was superior to the APRI in predicting mortality (AUC=0.58, 95% CI=0.553-0.615; p=0.0008). In the same study, FIB-4 was shown to be significantly higher in patients requiring high-flow oxygen and mechanical ventilation, and FIB-4 was also associated with COVID-19 disease severity (18). In a meta-analysis of thirteen studies examining the association between FIB-4 and COVID-19 prognosis, a high FIB-4 score was found to increase mortality 3-fold (OR=3.01, 95% CI=2.21-4.09; p<0.001) (19). Woo-Kim et al. observed 10-fold increased deaths in COVID-19 patients with a high FIB-4 score and diabetes mellitus than in patients with a low FIB-4 score without diabetes mellitus (24). As a result, different cut-off values for FIB-4 and APRI scores were used in mortality studies. 

In our study, the presence of any chronic disease (at least one or more) was not associated with mortality, whereas FIB-4 and APRI scores were higher in the deceased group. In the Kaplan-Meier analysis, a higher mortality rate was found in those with FIB-4 score>2.14, but this difference was not found for APRI. 

Recent studies have shown that APRI can be used as an early predictor of poor prognosis in COVID-19 patients. However, FIB-4 performs superior to APRI. In a study examining the relationship between liver fibrosis scores and mortality in COVID-19, FIB-4 (C-index=0.74) was found to be superior to APRI (C-index=0.657) (26). In the study of Crisan et al., FIB-4 and APRI scores were found to be significantly higher in the deceased group in patients with COVID-19 (27). The researchers demonstrated that a FIB-4 score of >3.25 increased mortality 1.3-fold (OR=1.353; p=0.046). However, they found that the APRI score was not associated with mortality (OR=0.21; p=0.119) (27). Consistent with the general literature, in our study FIB-4 was superior to APRI in predicting mortality in COVID-19. The predictive value for mortality of FIB-4 (AUC=0.793) and SAD-60 (AUC=0.870) scores seems also to be higher than other previously tested scores for mortality in COVID-19 patients, namely the World Health Organization (WHO) severity scale (AUC=0.654), the national early warning score (NEWS) (AUC=0.649 ), the CURB-65 score (AUC=0.591) and the acute physiological assessment and chronic health evaluation (APACHE) score (AUC=0.651) (28). In our previous multicenter study including 1013 patients, the SAD-60 (AUC=0.776) score was more successful than NEWS (AUC=0.686), CURB-65 (AUC=0.753), and quick sepsis-related organ failure (q-SOFA) (AUC=0.628) scores in predicting mortality in hospitalized patients with COVID-19 (7). 

Objective results may not always be obtained with scores that include vital signs. This is because environmental and personal factors may play a role in measuring and recording vital signs in emergency departments and health centers with heavy workloads. Perhaps for this reason, scores obtained with noninterpretable parameters such as laboratory values and age are more successful in predicting disease severity. In our study, the SAD-60 score proved to be superior to FIB-4 and APRI in predicting mortality in hospitalized patients with COVID-19 (AUC=0.870, sensitivity=90%, specificity=73.8%). Further analysis by gender, COPD, and CAD showed that mortality increased about 4-fold in patients with FIB-4 >2.14 and 9-fold in patients with SAD-60 score >4.25. 

Since the beginning of the pandemic, advanced age has been known to be associated with increased mortality for COVID-19 (1, 29). Studies evaluating COVID-19 predictors of disease severity have pointed out that age and the presence of chronic disease are important individual factors determining disease severity (30). On the other hand, biochemical tests indicating organ damage and the scores generated from these tests are likely to be successful in predicting disease severity and mortality at COVID-19. The addition of the age factor to the formulas is expected to increase the success of these scores in predicting disease severity. The low success of APRI compared with FIB-4 and SAD-60 may also be due to this reason. Consistent with this, in a multicenter study of 353 patients with COVID-19, the AUC value of APRI (0.66; 95% C= 0.56-0.76) was lower than the AUC value of FIB-4 (0.72; 95% CI= 0.61-0.82) for predicting mortality. However, the APRI-plus AUC value of 0.90 (95%CI= 0.86-0.95), obtained by adding age and other parameters to the APRI, was much more successful than FIB-4 in predicting mortality (25).

This study has several limitations. First, our results may not be generalizable because the study was conducted in a single center. Second, the scores were calculated based on biochemical parameters at the time of initial admission. Therefore, a longitudinal evaluation was not performed. Third, NAFLD patients could not be excluded because routine abdominal ultrasonography was not performed in COVID-19 patients. Nevertheless, this study has some strengths. First, several variables, such as demographic features, clinical characteristics, and laboratory test results, were included in the study, and multivariable analyses were performed. Second, our study population was homogenous including only COVID-19 patients confirmed by the nasopharyngeal/oropharyngeal SARS-CoV-2 PCR testing.

In conclusion, the performance of SAD-60 and FIB-4 scores, composed of age and biochemical parameters, seems to be better than that of scores containing vital signs in predicting a poor prognosis for COVID-19. Therefore, SAD-60 and FIB-4 scores are easily applicable and may be used to predict mortality in COVID-19 patients.

Ethical Approval: The Ethics Committee of Haseki Training and Research Hospital approved the study on August 24, 2022, with decision number 148-2022.

Informed Consent: N.A.

Peer-review: Externally peer-reviewed

Author Contributions: Concept – B.Ç., S.S.; Design – B.Ç., S.S., G.T., O.F.B.; Data Collection and/or Processing – B.Ç., G.T.; Analysis and/or Interpretation – B.Ç., S.S., O.F.B.; Literature Review – B.Ç., S.S.; Writer – B.Ç., S.S.; Critical Reviews – B.Ç., S.S., G.T., O.F.B.

Conflict of Interest: The authors declare no conflict of interest.

Financial Disclosure: The authors declared that this study has received no financial support.

Scientific Presentation: The study was presented at the BUHASDER 2022 Congress on November 10-13, 2022.

Show References

References

  1. Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: Summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention. JAMA. 2020;323(13):1239-42. [CrossRef]
  2. Yang X, Yu Y, Xu J, Shu H, Xia J, Liu H, et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. Lancet Respir Med. 2020;8(5):475-81. Erratum in: Lancet Respir Med. 2020;8(4):e26. [CrossRef]
  3. WHO Coronavirus (COVID-19) Dashboard [Internet]. Geneva: World Health Organization (WHO). [cited April 1, 2023]. Available from: https://covid19.who.int/?mapFilter=deaths
  4. Gacche RN, Gacche RA, Chen J, Li H, Li G. Predictors of morbidity and mortality in COVID-19. Eur Rev Med Pharmacol Sci. 2021;25(3):1684-707. [CrossRef]
  5. Incerti D, Rizzo S, Li X, Lindsay L, Yau V, Keebler D, et al. Prognostic model to identify and quantify risk factors for mortality among hospitalised patients with COVID-19 in the USA. BMJ Open. 2021;11(4):e047121. [CrossRef]
  6. Lombardi Y, Azoyan L, Szychowiak P, Bellamine A, Lemaitre G, Bernaux M, et al; AP-HP/Universities/INSERM COVID-19 Research Collaboration AP-HP COVID CDR Initiative. External validation of prognostic scores for COVID-19: a multicenter cohort study of patients hospitalized in Greater Paris University Hospitals. Intensive Care Med. 2021;47(12):1426-39. [CrossRef]
  7. Surme S, Tuncer G, Bayramlar OF, Copur B, Zerdali E, Nakir IY, et al. Novel biomarker-based score (SAD-60) for predicting mortality in patients with COVID-19 pneumonia: a multicenter retrospective cohort of 1013 patients. Biomark Med. 2022;16(8):577-88. [CrossRef]
  8. Tuncer G, Surme S, Bayramlar OF, Karanalbant HK, Copur B, Yazla M, et al. National Early Warning Score 2 and laboratory predictors correlate with clinical deterioration in hospitalized patients with COVID-19. Biomark Med. 2021;15(11):807-20. [CrossRef]
  9. Çelik ÇO, Özer N, Çiftci O, Torun Ş, Yavuz-Çolak M, Müderrisoğlu İH. Evaluation of inflammation- based prognostic risk scores in predicting in-hospital mortality risk in hospitalized COVID-19 patients: A cross-sectional retrospective study. Infect Dis Clin Microbiol. 2023;1:4-12. [CrossRef]
  10. Li Y, Regan J, Fajnzylber J, Coxen K, Corry H, Wong C, et al. Liver Fibrosis Index FIB-4 is associated with mortality in COVID-19. Hepatol Commun. 2021;5(3):434-45. [CrossRef]
  11. Cusi K, Isaacs S, Barb D, Basu R, Caprio S, Garvey WT, et al. American Association of Clinical Endocrinology Clinical Practice Guideline for the Diagnosis and Management of Nonalcoholic Fatty Liver Disease in Primary Care and Endocrinology Clinical Settings: Co-Sponsored by the American Association for the Study of Liver Diseases (AASLD). Endocr Pract. 2022;28(5):528-62. [CrossRef]
  12. Chou R, Wasson N. Blood tests to diagnose fibrosis or cirrhosis in patients with chronic hepatitis C virus infection: a systematic review. Ann Intern Med. 2013;158(11):807-20. Erratum in: Ann Intern Med. 2013;159(4):308. [CrossRef]
  13. Lin ZH, Xin YN, Dong QJ, Wang Q, Jiang XJ, Zhan SH, et al. Performance of the aspartate aminotransferase-to-platelet ratio index for the staging of hepatitis C-related fibrosis: an updated meta-analysis. Hepatology. 2011;53(3):726-36. [CrossRef]
  14. Sterling RK, Lissen E, Clumeck N, Sola R, Correa MC, Montaner J, et al; APRICOT Clinical Investigators. Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology. 2006;43(6):1317-25. [CrossRef]
  15. Shah AG, Lydecker A, Murray K, Tetri BN, Contos MJ, Sanyal AJ; Nash Clinical Research Network. Comparison of noninvasive markers of fibrosis in patients with nonalcoholic fatty liver disease. Clin Gastroenterol Hepatol. 2009;7(10):1104-12. [CrossRef]
  16. Yan Z, Liu Y, Li W, Zhao X, Lin W, Zhang J, et al. Liver fibrosis scores and prognosis in patients with cardiovascular diseases: A systematic review and meta-analysis. Eur J Clin Invest. 2022;52(11):e13855. [CrossRef]
  17. Targher G, Mantovani A, Byrne CD, Wang XB, Yan HD, Sun QF, et al. Risk of severe illness from COVID-19 in patients with metabolic dysfunction-associated fatty liver disease and increased fibrosis scores. Gut. 2020;69(8):1545-7. [CrossRef]
  18. Bucci T, Galardo G, Gandini O, Vicario T, Paganelli C, Cerretti S, et al; Research On Medical patients Admitted to the Emergency Department (ROMA-ED) study group. Fibrosis-4 (FIB-4) Index and mortality in COVID-19 patients admitted to the emergency department. Intern Emerg Med. 2022;17(6):1777-84. [CrossRef]
  19. Ao G, Li A, Li J, Du X, Wang Y, Tran C, Qi X. The Association between the Fibrosis-4 Index and COVID-19: A Systematic Review and Meta-Analysis. Ann Clin Lab Sci. 2022;52(5):781-7.
  20. Yu D, Du Q, Yan S, Guo XG, He Y, Zhu G, et al. Liver injury in COVID-19: clinical features and treatment management. Virol J. 2021;18(1):121. [CrossRef]
  21. Zhou F, Xia J, Yuan HX, Sun Y, Zhang Y. Liver injury in COVID-19: Known and unknown. World J Clin Cases. 2021;9(19):4980-9. [CrossRef]
  22. Butt AA, Yan P, Chotani RA, Shaikh OS. Mortality is not increased in SARS-CoV-2 infected persons with hepatitis C virus infection. Liver Int. 2021;41(8):1824-31. [CrossRef]
  23. Elfeki MA, Robles J, Akhtar Z, Ullah F, Ganapathiraju I, Tran C, et al. Impact of fibrosis-4 index prior to COVID-19 on outcomes in patients at risk of non-alcoholic fatty liver disease. Dig Dis Sci. 2022;67(7):3333-9. [CrossRef]
  24. Kim SW, Jeon JH, Moon JS, Kim MK. High fibrosis-4 index is related with worse clinical outcome in patients with coronavirus disease 2019 and diabetes mellitus: A multicenter observational study. Endocrinol Metab (Seoul). 2021;36(4):800-9. [CrossRef]
  25. Madian A, Eliwa A, Abdalla H, A Azeem Aly H. Aspartate transferase-to-platelet ratio index-plus: A new simplified model for predicting the risk of mortality among patients with COVID-19. World J Gastroenterol. 2022;28(16):1671-80. [CrossRef]
  26. Zhang J, Liu F, Song T, Li Z, Xia P, Tang X, et al. Liver fibrosis scores and clinical outcomes in patients with COVID-19. Front Med (Lausanne). 2022;9:829423. [CrossRef]
  27. Crisan D, Avram L, Grapa C, Dragan A, Radulescu D, Crisan S, et al. Liver injury and elevated FIB-4 define a high-risk group in patients with COVID-19. J Clin Med. 2021;11(1):153. [CrossRef]
  28. Covino M, De Matteis G, Polla DAD, Santoro M, Burzo ML, Torelli E, et al. Predictors of in-hospital mortality AND death RISK STRATIFICATION among COVID-19 PATIENTS aged ≥ 80 YEARs OLD. Arch Gerontol Geriatr. 2021;95:104383. [CrossRef]
  29. COVID-19 Forecasting Team. Variation in the COVID-19 infection-fatality ratio by age, time, and geography during the pre-vaccine era: a systematic analysis. Lancet. 2022;399(10334):1469-88. Erratum in: Lancet. 2022 Apr 16;399(10334):1468. [CrossRef]
  30. Gallo Marin B, Aghagoli G, Lavine K, Yang L, Siff EJ, Chiang SS, et al. Predictors of COVID-19 severity: A literature review. Rev Med Virol. 2021;31(1):1-10. [CrossRef]