Online ISSN: 2515-8260

Keywords : CT score

Studying the correlation of cycle threshold value of RT-PCR and computed tomography score of CT-Scan in covid-19 infection

Prashantakumar B. Jaikar, Neha G. Patil, Giridhar Patil

European Journal of Molecular & Clinical Medicine, 2023, Volume 10, Issue 1, Pages 3163-3172

The COVID-19 pandemic has unfolded as one of the world’s worst health crisis. Viral RT-PCR, CRP, and CT scan thorax are the most common tools used for its diagnosis, prognosis and severity assessment. Hence, a parallel between these parameters can aid in better understanding and management of COVID-19 infection.
Methodology: Demographic data, history, cycle threshold values of RT-PCR from nasopharyngeal and oropharyngeal swabs, CRP and computed tomography score were obtained from 108 adult participants. Statistical analysis was performed using python programming (python 3.7) and inbuilt libraries.
Results: Mean age of the study group was 51.05 years. 63.89% were males. The mean CT score was 15.417 indicating severe disease. Men had a higher CRP. Cycle threshold value of N gene was directly proportional to CT score. Lower cycle threshold values were associated with higher CRP. Of the 37 deaths, 62.16% were males. Cycle threshold in non-survivors was significantly higher than survivors indicating lower nasopharyngeal viral load in non-survivors. Diabetes was the most common comorbidity associated with mortality.
Conclusion: Nasopharyngeal load can be low even with severe radiological CT findings probably due to migration of the virus to lower respiratory tract in later stages of the disease. Low nasopharyngeal viral loads cannot negate the possibility of a severe pulmonary infection. CRP values may not always correlate with CT findings in recovering stages of disease. Comorbidities adversely affect the disease outcome. These parameters should be used in conjunction to assess and veer the progression, management and outcome of patients with COVID-19 infection.

Laboratory Markers Versus Ct Severity Score In Predicting Mortality In Covid 19

B.S.Gopala Krishna; P.Pranay Krishna; V.Ravi Sankar; Kondle Raghu; A.Siva Kumar; M. Srikanth; V. Satyanarayana; P. Siri Priya

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 10, Pages 1824-1831

Aim: Predicting the mortality of COVID-19 with a spectrum of complications is a difficult task for prognostication and management. When individual level data of COVID-19 patients were not yet available, there is a need for risk predictors to support the treatment decisions. The study aims to identify the high accurate marker to measure the prognosis and outcome of COVID19.
COVID-19 course is divided into four stages, according to chest computed tomography (C.T.) progress. The demographics, disease exposure history, clinical condition, laboratory tests, computed tomographic chest scan, and outcome data were collected and measured their correlation to assess the risk predictor.
The 10.4% mortality (n=52) was observed in total population. D-dimer (μg/dL) levels observed as 0.75 ± 0.65 in expired patients. NLR ratio observed as 17.1 in expired patients. Ferritin levels were observed as 49.8 ± 32.5 in expired patients. A D-dimer positive predictive value of 72.5% and a negative predictive value of 88% for a predictor of mortality. Ferritin positive predictive value of 35.5% and a negative predictive value of 76.5% for the predictor of mortality. Hence, the AUC of serum ferritin 0.598 represents the poor ability to discriminate the prediction for the cause of death than D-dimer levels. D‐dimer > 2 μg/dL on admission was associated with in‐hospital death. These main findings indicate that D‐dimer on admission >2.0 μg/dl was the independent predictor of hospital death in patients with Covid‐19. A D-dimer has the highest positive predictive value than serum ferritin levels.
Conclusion: The AUC for D- dimer at admission was 0.880, with an optimal cutoff of 2.2 μg/dL in predicting the cause of mortality. D‐dimer on admission > 2.0 μg/mL (fourfold increase) is the best predict in‐hospital mortality and a helpful marker to improve the management of Covid‐19.