Fibrosis-4 (FIB-4) Index and mortality in COVID-19 patients admitted to the emergency department

Comparison of liver fibrosis scores and fatty liver on computed tomography as risk factors for severity of COVID-19

Background and Aim

Increased liver fibrosis scores (LFS), such as fibrosis-4 index (FIB-4) or non-alcoholic fatty liver disease fibrosis score (NFS), and fatty liver are known risk factors for severe coronavirus disease 2019 (COVID-19). The purpose of this study was to identify the best scores, which predict the prognosis of COVID-19.

Methods

Participants comprised consecutive Japanese COVID-19 patients admitted to our hospital between February 14, 2020, and April 14, 2021. Multivariate logistic regression analysis was performed to evaluate the relationships between LFS (FIB-4, NFS, aspartate aminotransferase-to-platelet ratio index [APRI], BARD score, and hepatic steatosis index [HSI]) or fatty liver on computed tomography (CT), and severity of COVID-19.

Results

Of the 415 patients (mean age, 59 years), 177 patients (42.7%) needed oxygen therapy, 90 patients (21.7%) worsened to severe COVID-19, and 45 patients (10.8%) died during admission. Multivariate logistic regression analysis showed that increased FIB-4 and NFS were risk factors for death, severe COVID-19, and oxygen demand; that increased BARD was a risk factor for severe COVID-19 and oxygen demand; and that increased APRI and HSI were not risk factors for any status of COVID-19. Furthermore, increased NFS or BARD and fatty liver were independent risk factors for severe COVID-19 and oxygen demand.

Conclusions

This study showed that FIB-4 and NFS were the best liver fibrosis scores that predicted worse prognosis for COVID-19, and that increased NFS or BARD and fatty liver evident on CT represented independent risk factors for severe COVID-19 and oxygen demand.

Time‑dependent ROC curve analysis to determine the predictive capacity of seven clinical scales for mortality in patients with COVID‑19: Study of a hospital cohort with very high mortality

Clinical data from hospital admissions are typically utilized to determine the prognostic capacity of Coronavirus disease 2019 (COVID-19) indices. However, as disease status and severity markers evolve over time, time-dependent receiver operating characteristic (ROC) curve analysis becomes more appropriate. The present analysis assessed predictive power for death at various time points throughout patient hospitalization. In a cohort study involving 515 hospitalized patients (General Hospital Number 1 of Mexican Social Security Institute, Colima, Mexico from February 2021 to December 2022) with COVID-19, seven severity indices [Pneumonia Severity Index (PSI) PaO2/FiO2 arterial oxygen pressure/fraction of inspired oxygen (Kirby index), the Critical Illness Risk Score (COVID-GRAM), the National Early Warning Score 2 (NEWS-2), the quick Sequential Organ Failure Assessment score (qSOFA), the Fibrosis-4 index (FIB-4) and the Viral Pneumonia Mortality Score (MuLBSTA were evaluated using time-dependent ROC curves. Clinical data were collected at admission and at 2, 4, 6 and 8 days into hospitalization. The study calculated the area under the curve (AUC), sensitivity, specificity, and predictive values for each index at these time points. Mortality was 43.9%. Throughout all time points, NEWS-2 demonstrated the highest predictive power for mortality, as indicated by its AUC values. PSI and COVID-GRAM followed, with predictive power increasing as hospitalization duration progressed. Additionally, NEWS-2 exhibited the highest sensitivity (>96% in all periods) but showed low specificity, which increased from 22.9% at admission to 58.1% by day 8. PSI displayed good predictive capacity from admission to day 6 and excellent predictive power at day 8 and its sensitivity remained >80% throughout all periods, with moderate specificity (70.6-77.3%). COVID-GRAM demonstrated good predictive capacity across all periods, with high sensitivity (84.2-87.3%) but low-to-moderate specificity (61.5-67.6%). The qSOFA index initially had poor predictive power upon admission but improved after 4 days. FIB-4 had a statistically significant predictive capacity in all periods (P=0.001), but with limited clinical value (AUC, 0.639-0.698), and with low sensitivity and specificity. MuLBSTA and IKIRBY exhibited low predictive power at admission and no power after 6 days. In conclusion, in COVID-19 patients with high mortality rates, NEWS-2 and PSI consistently exhibited predictive power for death during hospital stay, with PSI demonstrating the best balance between sensitivity and specificity.

The FIB-4 scores in the emergency department to predict the outcomes of COVID-19 patients in taiwan

Objective

We aimed to determine the reliability of using the Fibrosis-4 (FIB-4) index in COVID-19 patients without underlying liver illness.

Method

We employed multivariate logistic regression to identify variables that exhibited statistically significant influence on the ultimate outcome. Multilayer perceptron analysis was employed to develop a prediction model for the FIB-4 index concerning ICU admission and intubation rates. However, the scarcity of cases rendered the assessment of the mortality rate unfeasible. We plotted ROC curves to analyze the predictive strength of the FIB-4 index across various age groups.

Result

In univariate logistic regression, only the FIB-4 index and respiratory rate demonstrated statistical significance on all poor outcomes. The FIB-4 index for mortality prediction had an ROC and AUC of 0.863 (95% CI: 0.781-0.9444). It demonstrates predictive power across age groups, particularly for age ≥65 (AUC: 0.812, 95% CI: 0.6571-0.9673) and age <65 (AUC: 0.878, 95% CI: 0.8012-0.9558). Its sensitivity for intubation and ICU admission prediction is suboptimal.

Conclusion

FIB-4 index had promising power in prediction of mortality rate in all age groups.

QTc Prolongation to Predict Mortality in Patients Admitted with COVID-19 Infection: An Observational Study

BACKGROUND

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) causes Coronavirus disease 2019 (COVID-19), characterized by pulmonary infection ranging from asymptomatic forms to respiratory insufficiency and death. Evidence of cardiac involvement in COVID-19 is increasing, and systemic inflammation or direct heart damage by SARS-CoV-2 can prolong the corrected QT interval (QTc).

METHODS

In this observational study, a total of 333 consecutive patients admitted to the Covid Center of Verona University Hospital from November 2020 to April 2021 were included. Patients with bundle branch block, pacemaker-controlled heart rhythm and heart rate >120 beats/min were excluded. A complete electrocardiogram (ECG) was performed at admission, and QTc values of ≥440 ms for males and ≥460 ms for females were considered prolonged.

RESULTS

Overall, 153 patients had prolonged QTc (45.5%). In multivariate logistic regression analysis, male sex (odds ratio (OR)=6.612, p=0.046), troponin (OR=1.04, p=0.015) and lymphocyte count (OR=3.047, p=0.019) were independently associated with QTc prolongation. Multivariate logistic regression showed that QTc was independently associated with mortality (OR=4.598, p=0.036). Age, sex, the ratio between the partial pressure of oxygen (PaO2) and the fraction of inspired oxygen (FiO2) (P/F), and fibrosis-4 index for liver fibrosis (FIB-4) were also independently associated with mortality.

CONCLUSION

QTc interval prolongation appears to be a frequent finding in patients with COVID-19. Moreover, prolonged QTc may be predictive of more severe forms of COVID-19 and worse outcome.

The mortality of hospitalized patients with COVID-19 and non-cirrhotic chronic liver disease: a retrospective multi-center study

Background

Patients with chronic liver disease (CLD) have a higher risk of mortality when infected with severe acute respiratory syndrome coronavirus 2. Although the fibrosis-4 (FIB-4) index, aspartate aminotransferase-to-platelet ratio index (APRI), and albumin-bilirubin grade (ALBI) score can predict mortality in CLD, their correlation with the clinical outcomes of CLD patients with coronavirus disease 2019 (COVID-19) is unclear. This study aimed to investigate the association between the liver severity and the mortality in hospitalized patients with non-cirrhotic CLD and COVID-19.

Methods

This retrospective study analyzed 231 patients with non-cirrhotic CLD and COVID-19. Clinical characteristics, laboratory data, including liver status indices, and clinical outcomes were assessed to determine the correlation between liver status indices and the mortality among patients with non-cirrhotic CLD and COVID-19.

Results

Non-survivors had higher levels of prothrombin time-international normalized ratio (PT-INR), alanine aminotransferase, aspartate aminotransferase, and high-sensitivity C-reactive protein (hs-CRP) and lower albumin levels. Multivariable analysis showed that ALBI grade 3 (odds ratio (OR): 22.80, 95% confidence interval (CI) [1.70-305.38], p = 0.018), FIB-4 index ≥ 3.25 (OR: 10.62, 95% CI [1.12-100.31], p = 0.039), PT-INR (OR: 19.81, 95% CI [1.31-299.49], p = 0.031), hs-CRP (OR: 1.02, 95% CI [1.01-1.02], p = 0.001), albumin level (OR: 0.08, 95% CI [0.02-0.39], p = 0.002), and use of vasopressors (OR: 4.98, 95% CI [1.27-19.46], p = 0.021) were associated with the mortality.

Conclusion

The ALBI grade 3 and FIB-4 index ≥ 3.25, higher PT-INR, hsCRP levels and lower albumin levels could be associated with mortality in non-cirrhotic CLD patients with COVID-19. Clinicians could assess the ALBI grade, FIB-4 index, PT-INR, hs-CRP, and albumin levels of patients with non-cirrhotic CLD upon admission.

Fib-4 score is able to predict intra-hospital mortality in 4 different SARS-COV2 waves

Increased values of the FIB-4 index appear to be associated with poor clinical outcomes in COVID-19 patients. This study aimed to develop and validate predictive mortality models, using data upon admission of hospitalized patients in four COVID-19 waves between March 2020 and January 2022. A single-center cohort study was performed on consecutive adult patients with Covid-19 admitted at the Fondazione Policlinico Gemelli IRCCS (Rome, Italy). Artificial intelligence and big data processing were used to retrieve data. Patients and clinical characteristics of patients with available FIB-4 data derived from the Gemelli Generator Real World Data (G2 RWD) were used to develop predictive mortality models during the four waves of the COVID-19 pandemic. A logistic regression model was applied to the training and test set (75%:25%). The model's performance was assessed by receiver operating characteristic (ROC) curves. A total of 4936 patients were included. Hypertension (38.4%), cancer (12.15%) and diabetes (16.3%) were the most common comorbidities. 23.9% of patients were admitted to ICU, and 12.6% had mechanical ventilation. During the study period, 762 patients (15.4%) died. We developed a multivariable logistic regression model on patient data from all waves, which showed that the FIB-4 score > 2.53 was associated with increased mortality risk (OR = 4.53, 95% CI 2.83-7.25; p ≤ 0.001). These data may be useful in the risk stratification at the admission of hospitalized patients with COVID-19.

Fibrosis-4 (FIB-4) index as a predictor for mechanical ventilation and 30-day mortality across COVID-19 variants

Background

The Fibrosis-4 (FIB-4) index, a simple index that includes age, liver enzymes, and platelet count has been studied as a tool to identify patients at a risk of requiring mechanical ventilation due to its high negative predictive value. It is unknown if FIB-4 remains useful to predict the severity of respiratory disease requiring mechanical ventilation amongst new Coronavirus disease 2019 (COVID-19) variants and whether a relationship also exists between FIB-4 and 30-day mortality. The main objective was to determine if FIB-4 can predict mechanical ventilation requirements and 30-day mortality from COVID-19 across variants including Alpha, Delta, and Omicron.

Methods

This was a population-based, retrospective cohort analysis of 232,364 hospitalized patients in the National COVID-19 Cohort Collaborative between the age of 18-90 who tested positive for COVID-19 between April 27, 2020 and June 25, 2022. The primary outcome was association between FIB-4 and need for mechanical ventilation. Secondary measures included the association of FIB-4 with 30-day mortality.

Results

A FIB-4 > 2.67 had 1.8 times higher odds of requiring mechanical ventilation across all variants of COVID-19 (OR 1.81; 95% CI: [1.76, 1.86]). The area under the ROC curve showed high diagnostic accuracy with values ranging between 0.79 (Omicron wave) and 0.97 (delta wave). Increased FIB-4 was associated with 30-day mortality across the variates.

Conclusion

The FIB-4 was consistently associated with both increased utilization of mechanical ventilation and 30-day mortality among COVID-19 patients across all waves in both adjusted and unadjusted models. This provides a simple tool for risk-stratification for front-line health care professionals.

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

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.

Obesity-Associated Hepatic Steatosis, Somatotropic Axis Impairment, and Ferritin Levels Are Strong Predictors of COVID-19 Severity

The full spectrum of SARS-CoV-2-infected patients has not yet been defined. This study aimed to evaluate which parameters derived from CT, inflammatory, and hormonal markers could explain the clinical variability of COVID-19. We performed a retrospective study including SARS-CoV-2-infected patients hospitalized from March 2020 to May 2021 at the Umberto I Polyclinic of Rome. Patients were divided into four groups according to the degree of respiratory failure. Routine laboratory examinations, BMI, liver steatosis indices, liver CT attenuation, ferritin, and IGF-1 serum levels were assessed and correlated with severity. Analysis of variance between groups showed that patients with worse prognoses had higher BMI and ferritin levels, but lower liver density, albumin, GH, and IGF-1. ROC analysis confirmed the prognostic accuracy of IGF-1 in discriminating between patients who experienced death/severe respiratory failure and those who did not (AUC 0.688, CI: 0.587 to 0.789, p < 0.001). A multivariate analysis considering the degrees of severity of the disease as the dependent variable and ferritin, liver density, and the standard deviation score of IGF-1 as regressors showed that all three parameters were significant predictors. Ferritin, IGF-1, and liver steatosis account for the increased risk of poor prognosis in COVID-19 patients with obesity.