Risk stratification scores for hospitalization duration and disease progression in moderate and severe patients with COVID-19

A Model for Predicting the Duration of Viral Shedding in Patients Who Had Been Hospitalized with Mild COVID-19: A Single-Center Retrospective Study

Background

Clinical decision-making is enhanced by the development of a mathematical model for prognosis prediction. Screening criteria associated with viral shedding time and developing a prediction model facilitate clinical decision-making and are, thus, of great medical value.

Methods

This study comprised 631 patients who were hospitalized with mild COVID-19 from a single center and 30 independent variables included. The data set was randomly divided into the training set (80%) and the validation set (20%). The outcome variable included viral shedding time and whether the viral shedding time >14 days, LASSO was used to screen the influencing factors.

Results

There were 321 males and 310 females among the 631 cases, with an average age of 62.1 years; the median viral shedding time was 12 days, and 68.8% of patients experienced viral shedding within 14 days, with fever (50.9%) and cough (44.2%) being the most common clinical manifestations. Using LASSO with viral shedding time as the outcome variable, the model with lambda as 0.1592 (λ = 0.1592) and 13 variables (eg the time from diagnosis to admission, constipation, cough, hs-CRP, IL-8, IL-1β, etc.) was more accurate. Factors were screened by LASSO and multivariable logistic regression with whether the viral shedding time >14 days as the outcome variable, five variables, including the time from diagnosis to admission, CD4 cell count, Ct value of ORF1ab, constipation, and IL-8, were included, and a nomogram was drawn; after model validation, the consistency index was 0.888, the AUC was 0.847, the sensitivity was 0.744, and the specificity was 0.830.

Conclusion

A clinical model developed after LASSO regression was used to identify the factors that influence the viral shedding time. The predicted performance of the model was good, and it was useful for the allocation of medical resources.

Development and validation of a scoring system to predict mortality in patients hospitalized with COVID-19: A retrospective cohort study in two large hospitals in Ecuador

OBJECTIVE

To develop and validate a scoring system to predict mortality among hospitalized patients with COVID-19.

METHODS

Retrospective cohort study. We analyzed 5,062 analyzed hospitalized patients with COVID-19 treated at two hospitals; one each in Quito and Guayaquil, from February to July 2020. We assessed predictors of mortality using survival analyses and Cox models. We randomly divided the database into two sets: (i) the derivation cohort (n = 2497) to identify predictors of mortality, and (ii) the validation cohort (n = 2565) to test the discriminative ability of a scoring system. After multivariate analyses, we used the final model's β-coefficients to build the score. Statistical analyses involved the development of a Cox proportional hazards regression model, assessment of goodness of fit, discrimination, and calibration.

RESULTS

There was a higher mortality risk for these factors: male sex [(hazard ratio (HR) = 1.32, 95% confidence interval (95% CI): 1.03-1.69], per each increase in a quartile of ages (HR = 1.44, 95% CI: 1.24-1.67) considering the younger group (17-44 years old) as the reference, presence of hypoxemia (HR = 1.40, 95% CI: 1.01-1.95), hypoglycemia and hospital hyperglycemia (HR = 1.99, 95% CI: 1.01-3.91, and HR = 1.27, 95% CI: 0.99-1.62, respectively) when compared with normoglycemia, an AST-ALT ratio >1 (HR = 1.55, 95% CI: 1.25-1.92), C-reactive protein level (CRP) of >10 mg/dL (HR = 1.49, 95% CI: 1.07-2.08), arterial pH <7.35 (HR = 1.39, 95% CI: 1.08-1.80) when compared with normal pH (7.35-7.45), and a white blood cell count >10 × 103 per μL (HR = 1.76, 95% CI: 1.35-2.29). We found a strong discriminative ability in the proposed score in the validation cohort [AUC of 0.876 (95% CI: 0.822-0.930)], moreover, a cutoff score ≥39 points demonstrates superior performance with a sensitivity of 93.10%, a specificity of 70.28%, and a correct classification rate of 72.66%. The LR+ (3.1328) and LR- (0.0981) values further support its efficacy in identifying high-risk patients.

CONCLUSION

Male sex, increasing age, hypoxemia, hypoglycemia or hospital hyperglycemia, AST-ALT ratio >1, elevated CRP, altered arterial pH, and leucocytosis were factors significantly associated with higher mortality in hospitalized patients with COVID-19. A statistically significant Cox regression model with strong discriminatory power and good calibration was developed to predict mortality in hospitalized patients with COVID-19, highlighting its potential clinical utility.

Normal high-sensitivity cardiac troponin for ruling-out inpatient mortality in acute COVID-19

INTRODUCTION

Assessment of inpatient mortality risk in COVID-19 patients is important for guiding clinical decision-making. High sensitivity cardiac troponin T (hs-cTnT) is a biomarker of cardiac injury associated with a worse prognosis in COVID-19. We explored how hs-cTnT could potentially be used in clinical practice for ruling in and ruling out mortality in COVID-19.

METHOD

We tested the diagnostic value of hs-cTnT in laboratory-confirmed COVID-19 patients (≥18 years old) admitted to the Royal Berkshire Hospital (UK) between 1st March and 10th May 2020. A normal hs-cTnT was defined as a value within the 99th percentile of healthy individuals (≤14 ng/L), and an elevated hs-cTnT was defined as >14 ng/L. Adverse clinical outcome was defined as inpatient mortality related to COVID-19.

RESULTS

A total of 191 COVID-19 patients (62% male; age 66±16 years) had hs-cTnT measured on admission. Of these patients, 124 (65%) had elevated hs-cTnT and 67 (35%) had normal hs-cTnT. On a group level, patients with elevated hs-cTnT had worse inpatient survival (p = 0.0014; Kaplan-Meier analysis) and higher risk of inpatient mortality (HR 5.84 [95% CI 1.29-26.4]; p = 0.02; Cox multivariate regression) compared to patients with normal hs-cTnT. On a per-patient level, a normal hs-cTnT had a negative predictive value of 94% (95% CI: 85-98%) for ruling out mortality, whilst an elevated hs-cTnT had a low positive predictive value of 38% (95% CI: 39-47%) for ruling in mortality.

CONCLUSIONS

In this study cohort of COVID-19 patients, the potential clinical utility of hs-cTnT appears to rest in ruling out inpatient mortality. This finding, if prospectively validated in a larger study, may allow hs-cTnT to become an important biomarker to facilitate admission-avoidance and early safe discharge.

Predictors of Poor Outcome among Critically Ill COVID-19 Patients: A Nationally Representative Sample of the Saudi Arabian Population

The outbreak and continuing impact of COVID-19 have significantly increased the rates of hospitalization and admissions to intensive care units (ICU). This study evaluates clinical outcomes in critically ill patients and investigates variables tied to poor prognosis. A secondary database analysis was conducted to investigate the predictors of poor outcome among critically ill COVID-19 patients in Saudi Arabia. Multivariable logistic regression analysis was used to assess the association between various demographic characteristics, comorbidities, and COVID-19 symptoms and patients’ poor prognosis, as a composite outcome. A total of 2257 critically ill patients were identified (male (71.8%), and elderly (37.3%)). The mortality rate was 50.0%, and the composite poor outcome was 68.4%. The predictors of poor outcome were being elderly (OR = 4.79, 95%CI 3.19−7.18), obesity (OR = 1.43, 95%CI 1.1−1.87), having a severe or critical case at admission (OR = 6.46, 95%CI 2.34−17.8; OR = 22.3, 95%CI 11.0−45, respectively), and some signs and symptoms of COVID-19 such as shortness of breath, feeling fatigued or headache, respiratory rate ≥ 30/min, PaO2/FiO2 ratio < 300, and altered consciousness. In conclusion, identifying high-risk populations that are expected to have a poor prognosis based on their criteria upon admission helps policymakers and practitioners better triage patients when faced with limited healthcare resources.

Right ventricle early inflow-outflow index may inform about the severity of pneumonia in patients with COVID-19

BACKGROUND

Echocardiography is generally used in our daily practice to detect cardiovascular complications in COVID-19 patients and for etiological research in the case of worsened clinical status. Many echocardiographic parameters have been the subject of investigation in previous studies on COVID-19. Recently, the right ventricle early inflow-outflow (RVEIO) index has been identified as a possible and indirect marker of the severity of tricuspid regurgitation and right ventricular dysfunction in pulmonary embolism. In this study, we aimed to investigate the relationship between the severity of pneumonia in COVID-19 patients and the RVEIO index.

METHODS

A total of 54 patients diagnosed with COVID-19 pneumonia were enrolled in this study. Our study population was separated into two groups as severe pneumonia and nonsevere pneumonia based on computed tomography imaging.

RESULTS

Saturation O₂ , C-reactive protein, D-dimer, deceleration time, tricuspid annular plane systolic excursion, tricuspid lateral annular systolic velocity, and RVEIO index values were found to be significantly different between severe and nonsevere pneumonia groups. The result of the multivariate logistic regression test revealed that saturation O2, D-dimer, Sm, and RVEIO index were the independent predictive parameters for severe pneumonia. Receiver operating characteristic curve analysis demonstrated that RVEIO index >4.2 predicted severe pneumonia with 77% sensitivity and 79% specificity.

CONCLUSION

The RVEIO index can be used as a bedside, noninvasive, easily accessible, and useful marker to identify the COVID-19 patient group with widespread pneumonia and, therefore high risk of complications, morbidity, and mortality.

Elevated fecal and serum calprotectin in COVID-19 are not consistent with gastrointestinal symptoms

Intestinal epithelial cell damage caused by SARS-CoV-2 infection was thought to be associated with gastrointestinal symptoms and decreased fecal consistency. The association of the gastrointestinal symptoms with the COVID-19-mediated inflammatory response triggered by the gastrointestinal immune system was investigated in this paper. Intestinal inflammation marker fecal calprotectin along with serum calprotectin and other inflammatory markers were measured in COVID-19 cases with and without GI manifestations as well as healthy individuals. Analyses were performed to compare COVID-19 patient subgroups and healthy controls and examine the relationship between fecal and serum calprotectin levels with gastrointestinal symptoms and disease severity. COVID-19 patients (n = 70) were found to have markedly elevated median levels of fecal (124.3 vs. 25.0 µg/g; P < 0/0001) and serum calprotectin (3500 vs. 1060 ng/mL; P < 0/0001) compared with uninfected controls. Fecal and serum calprotectin levels were not significantly different between COVID-19 patients who displayed GI symptoms and those who did not. Compared with other acute phase markers, both fecal and serum calprotectin were superior in identifying COVID-19 patients who progressed to severe illness. Although the progression of COVID-19 disease is marked by an elevation of fecal and serum calprotectin, gastrointestinal symptoms or diarrhea were not correlated with calprotectin increase level.