A simple diagnostic scoring system for COVID-19 screening

Background: The COVID-19 pandemic has prompted the world to make various efforts to control its spread by finding ways to diagnose COVID-19 quickly and accurately. Early identification of COVID-19 infection is essential, especially in hospitals with limited resources. We aimed to generate two scores based upon clinical and laboratory findings in patients screen for COVID-19 infection. Methodology: This study used a retrospective cohort design that involved 705 adults (>= 18 y old) admitted in Dr. Sardjito Hospital and Dr. S. Hardjolukito Hospital. The patients' data collected included demographic characteristics, anamnesis on signs and symptoms, history of contact with COVID-19 patients, history of staying or visiting an endemic area, comorbidities, and laboratory and radiologic indicators. All variables with a P < 0.25 on the bivariate test were included in a univariable logistic regression. If the P < 0.05, the variable was included in the multivariable logistic regression with a P < 0.05 considered significant. Receiver Operating Characteristic (ROC) producing an area under the curve (AUC) with 95% confidence intervals (CIs) was used to assess discrimination power. Results: Two scores were generated;score in Model 1 consisted of clinical signs, basic laboratory indicators, and chest X-ray, and in Model 2 consisted of clinical signs, chest X-ray, basic and advanced laboratory indicators, including C-reactive protein (CRP), lactate dehydrogenase (LDH), albumin, and D-dimer. The ROC score of Model 1 was 0.801 (0.764-0. 838), which is considered good discrimination, and of Model 2 had excellent discrimination with a ROC of 0.858 (0.826-0. 891);the differences in the ROC of the two models was statistically significant (P = 0.03). The score of Model 1 more than 5 had 85% sensitivity and 61% specificity of positive COVID-19. A score of Model 2 more than 4 had 83% sensitivity and 72% specificity for diagnosing positive COVID-19. Conclusions: A simple score consisting of clinical symptoms and signs, and simple laboratory indicators can be used to screen for COVID-19 infection.

A simple diagnostic scoring system for COVID-19 screening

Background: The COVID-19 pandemic has prompted the world to make various efforts to control its spread by finding ways to diagnose COVID-19 quickly and accurately. Early identification of COVID-19 infection is essential, especially in hospitals with limited resources. We aimed to generate two scores based upon clinical and laboratory findings in patients screen for COVID-19 infection. Methodology: This study used a retrospective cohort design that involved 705 adults (>= 18 y old) admitted in Dr. Sardjito Hospital and Dr. S. Hardjolukito Hospital. The patients' data collected included demographic characteristics, anamnesis on signs and symptoms, history of contact with COVID-19 patients, history of staying or visiting an endemic area, comorbidities, and laboratory and radiologic indicators. All variables with a P < 0.25 on the bivariate test were included in a univariable logistic regression. If the P < 0.05, the variable was included in the multivariable logistic regression with a P < 0.05 considered significant. Receiver Operating Characteristic (ROC) producing an area under the curve (AUC) with 95% confidence intervals (CIs) was used to assess discrimination power. Result(s): Two scores were generated;score in Model 1 consisted of clinical signs, basic laboratory indicators, and chest X-ray, and in Model 2 consisted of clinical signs, chest X-ray, basic and advanced laboratory indicators, including C-reactive protein (CRP), lactate dehydrogenase (LDH), albumin, and D-dimer. The ROC score of Model 1 was 0.801 (0.764-0. 838), which is considered good discrimination, and of Model 2 had excellent discrimination with a ROC of 0.858 (0.826-0. 891);the differences in the ROC of the two models was statistically significant (P = 0.03). The score of Model 1 more than 5 had 85% sensitivity and 61% specificity of positive COVID-19. A score of Model 2 more than 4 had 83% sensitivity and 72% specificity for diagnosing positive COVID-19. Conclusion(s): A simple score consisting of clinical symptoms and signs, and simple laboratory indicators can be used to screen for COVID-19 infection. Copyright © 2022 Faculty of Anaesthesia, Pain and Intensive Care, AFMS. All rights reserved.

Comparison between clinical characteristics and laboratory findings among patients with complicated and noncomplicated SARS-CoV-2 infection: A single-center experience from Shebin Al-Kom, Egypt.

BACKGROUND: Coronavirus disease 2019 (COVID-19) infection is considered a serious highly infectious disease caused by severe acute respiratory syndrome coronavirus 2, resulting in more than 6.27 million deaths worldwide. AIM OF THE STUDY: The study aimed to compare clinical characteristics and laboratory findings of COVID-19 patients with complications and without complications and discriminate the important risk factors for the complications and deaths. SUBJECTS AND METHODS: This cross-sectional study included 75 confirmed COVID-19 positive patients; out of which 49 were severely-ill cases. Analysis of all patients' clinical and laboratory information on admission including serum ferritin, thrombotic activity (d-dimer), lactate dehydrogenase (LDH), C-reactive protein (CRP), creatinine, aspartate aminotransferase, and alanine aminotransferase were done. RESULTS: Lymphopenia, tachycardia, tachypnea, elevated CRP, d-dimer, serum ferritin, LDH, and decreased SpO2 were significantly associated with complicated cases (p < .05 for all). By using multivariate logistic regression analysis models, elevated serum ferritin and tachycardia were significantly correlated with the increased odds of complicated COVID-19 cases (odds ratio [confidence interval 95%] = 10.42 [2.32-46.89] and 8.01 [1.17-55.99]; respectively) (p = .002 and .007, respectively). CONCLUSION: Lymphocytopenia, d-dimer, LDH, and CRP levels, which were significantly linked to the severity of COVID-19, were the prognostic biomarkers to predict the disease severity.

Establishing Classifiers With Clinical Laboratory Indicators to Distinguish COVID-19 From Community-Acquired Pneumonia: Retrospective Cohort Study.

BACKGROUND: The initial symptoms of patients with COVID-19 are very much like those of patients with community-acquired pneumonia (CAP); it is difficult to distinguish COVID-19 from CAP with clinical symptoms and imaging examination. OBJECTIVE: The objective of our study was to construct an effective model for the early identification of COVID-19 that would also distinguish it from CAP. METHODS: The clinical laboratory indicators (CLIs) of 61 COVID-19 patients and 60 CAP patients were analyzed retrospectively. Random combinations of various CLIs (ie, CLI combinations) were utilized to establish COVID-19 versus CAP classifiers with machine learning algorithms, including random forest classifier (RFC), logistic regression classifier, and gradient boosting classifier (GBC). The performance of the classifiers was assessed by calculating the area under the receiver operating characteristic curve (AUROC) and recall rate in COVID-19 prediction using the test data set. RESULTS: The classifiers that were constructed with three algorithms from 43 CLI combinations showed high performance (recall rate >0.9 and AUROC >0.85) in COVID-19 prediction for the test data set. Among the high-performance classifiers, several CLIs showed a high usage rate; these included procalcitonin (PCT), mean corpuscular hemoglobin concentration (MCHC), uric acid, albumin, albumin to globulin ratio (AGR), neutrophil count, red blood cell (RBC) count, monocyte count, basophil count, and white blood cell (WBC) count. They also had high feature importance except for basophil count. The feature combination (FC) of PCT, AGR, uric acid, WBC count, neutrophil count, basophil count, RBC count, and MCHC was the representative one among the nine FCs used to construct the classifiers with an AUROC equal to 1.0 when using the RFC or GBC algorithms. Replacing any CLI in these FCs would lead to a significant reduction in the performance of the classifiers that were built with them. CONCLUSIONS: The classifiers constructed with only a few specific CLIs could efficiently distinguish COVID-19 from CAP, which could help clinicians perform early isolation and centralized management of COVID-19 patients.

Is the Course of COVID-19 Different during Pregnancy? A Retrospective Comparative Study.

The COVID-19 pandemic has challenged health systems around the world. Maternal-foetal medicine, which has been particularly affected, must consider scientific data on the physiological processes occurring in the pregnant woman's body to develop relevant standards of care. Our study retrospectively compared the clinical and laboratory characteristics of 52 COVID-19 pregnant patients with 53 controls. Most of the pregnant patients required medical attention during the third trimester and therefore we propose that vaccination is needed prior to the 30th week of pregnancy. We found no differences between the 2 groups in the course of illness classification system, days of hospital stay, need for oxygen supplementation, need for mechanical ventilation, and ICU admission. Moreover, clinical manifestations and imaging findings were comparable. Pregnant patients needed a greater oxygen flow rate and required high flow oxygen therapy more frequently. Considering pregnancy-related physiological adaptations, we found that COVID-19 infection in pregnant patients is associated with higher levels of inflammatory markers, apart from serum ferritin, than in non-pregnant women, and concluded that biomarkers of cardiac and muscle injury, as well as kidney function, may not be good predictors of COVID-19 clinical course in pregnant patients at the time of admission, but more research needs to be conducted on this topic.

Cautions on the laboratory indicators of COVID-19 patients on and during admission.

BACKGROUND: Different disease severities of COVID-19 patients could be reflected on clinical laboratory findings. METHODS: In this single-centered retrospective study, demographic, clinical, and laboratory indicators on and during admission were compared among 74 participants with mild, moderate, critical severe, or severe classification. Risk factors associated with disease severity were analyzed by multivariate analyses. The AUC and 95% CI of the ROC curve were calculated. RESULTS: The most common manifestations of these patients were fever and cough. Critical severe or severe group owned the longest length of stay (23 (19,31), p < 0.001). After multivariate logistic regression, independent influence factors on admission for severity of disease were CK-MB (OR 0.674; 95% CI 0.489-0.928; p = 0.016), LDH (OR 1.111 or 1.107; 95% CI 1.026-1.204 or 1.022-1.199; p = 0.009 or 0.013), normal T-BIL (OR 4.58 × 10-8 ; 95% CI 3.05 × 10-9 -6.88 × 10-7 ; p < 0.001), LYM% (OR 0.008; 95% CI 0-0.602; p = 0.029), and normal ESR (OR 0.016; 95% CI 0-0.498; p = 0.019). Factors during hospitalization were normal T-BIL (OR 8.56 × 10-9 ; 95% CI 8.30 × 10-10 -8.83 × 10-8 ; p < 0.001), LYM (OR 0.068; 95% CI 0.005-0.934; p = 0.044), albumin (OR 0.565; 95% CI 0.327-0.977; p = 0.041), and normal NEU% (OR 0.013; 95% CI 0.000-0.967; p = 0.048). Combined indicators of AUC were 0.860 (LYM, LDH, and normal ESR on admission, p < 0.001) and 0.750 (CK-MB, LDH, and normal T-BIL during hospitalization, p = 0.020) when predicting for severe or critical severe patients. CONCLUSION: To pay close attention to the progression of COVID-19 and take measures promptly, we should be cautious of the laboratory indicators when patients on admission especially CK-MB, LDH, LYM%, T-BIL as well as ESR; and T-BIL, LYM, albumin, NEU% with the process of disease.

Characterisation of clinical, laboratory and imaging factors related to mild vs. severe covid-19 infection: a systematic review and meta-analysis.

BACKGROUND: Early detection of disease progression associated with severe COVID-19, and access to proper medical care lowers fatality rates of severe cases. Currently, no studies had systematically examined the variables in detecting severe COVID-19. METHOD: Systematic searching of electronic databases identified observational studies which recruited participants with confirmed COVID-19 infection who were divided into different groups according to disease severity were identified. RESULTS: To analysis 41 studies with 5064 patients were included.Patients who are elderly (SMD, 1.90; 95% CI, 1.01 to 2.8), male (OR, 1.71; 95% CI, 1.39 to 2.11) and have comorbidities or flu-like symptoms were significantly associated with the development to severe cases. Severe cases were associated with significant increased WBC (OR, 5.83; 95% CI, 2.76 to 12.32), CRP (OR, 3.62; 95% CI, 1.62 to 8.03), D-dimer (SMD, 1.69; 95% CI, 1.09 to 2.28), AST (OR, 4.64; 95% CI, 3.18 to 6.77) and LDH (OR, 7.94; 95% CI, 2.09 to 30.21). CT manifestation of bilateral lung involvement (OR, 4.55; 95% CI, 2.17 to 9.51) was associated with the severe cases. Conclusions and Relevance: Our findings offer guidance for a wide spectrum of clinicians to early identify severe COVID-19 patients, transport to specialised centres, and initiate appropriate treatment. Key Messages This systematic review and meta-analysis examined 41 studies including 5,064 patients with confirmed COVID-19. Severe cases were associated with age, male gender, and with fever, cough and respiratory diseases, increased WBC, CRP, D-dimer, AST and LDH levels. Furthermore, CT manifestation of bilateral lung involvement was associated with the severe cases. These findings provide guidance to health professionals with early identification of severe COVID-19 patients, transportation to specialised care and initiate appropriate supportive treatment.

Establishing a model for predicting the outcome of COVID-19 based on combination of laboratory tests.

INTRODUCTION: There are currently no satisfactory methods for predicting the outcome of Coronavirus Disease-2019 (COVID-19). The aim of this study is to establish a model for predicting the prognosis of the disease. METHODS: The laboratory results were collected from 54 deceased COVID-19 patients on admission and before death. Another 54 recovered COVID-19 patients were enrolled as control cases. RESULTS: Many laboratory indicators, such as neutrophils, AST, γ-GT, ALP, LDH, NT-proBNP, Hs-cTnT, PT, APTT, D-dimer, IL-2R, IL-6, IL-8, IL-10, TNF-α, CRP, ferritin and procalcitonin, were all significantly increased in deceased patients compared with recovered patients on admission. In contrast, other indicators such as lymphocytes, platelets, total protein and albumin were significantly decreased in deceased patients on admission. Some indicators such as neutrophils and procalcitonin, others such as lymphocytes and platelets, continuously increased or decreased from admission to death in deceased patients respectively. Using these indicators alone had moderate performance in differentiating between recovered and deceased COVID-19 patients. A model based on combination of four indicators (P = 1/[1 + e-(-2.658+0.587×neutrophils - 2.087×lymphocytes - 0.01×platelets+0.004×IL-2R)]) showed good performance in predicting the death of COVID-19 patients. When cutoff value of 0.572 was used, the sensitivity and specificity of the prediction model were 90.74% and 94.44%, respectively. CONCLUSIONS: Using the current indicators alone is of modest value in differentiating between recovered and deceased COVID-19 patients. A prediction model based on combination of neutrophils, lymphocytes, platelets and IL-2R shows good performance in predicting the outcome of COVID-19.

Analysis of heart injury laboratory parameters in 273 COVID-19 patients in one hospital in Wuhan, China.

An outbreak of severe acute respiratory syndrome novel coronavirus (SARS-CoV-2) epidemic spreads rapidly worldwide. SARS-CoV-2 infection caused mildly to seriously and fatally respiratory, enteric, cardiovascular, and neurological diseases. In this study, we detected and analyzed the main laboratory indicators related to heart injury, creatine kinase isoenzyme-MB (CK-MB), myohemoglobin (MYO), cardiac troponin I (ultra-TnI), and N-terminal pro-brain natriuretic peptide (NT-proBNP), in 273 patients with COVID-19 and investigated the correlation between heart injury and severity of the disease. It was found that higher concentration in venous blood of CK-MB, MYO, ultra-TnI, and NT-proBNP were associated with the severity and case fatality rate of COVID-19. Careful monitoring of the myocardiac enzyme profiles is of great importance in reducing the complications and mortality in patients with COVID-19.