Predictors of the chest CT score in COVID-19 patients: a cross-sectional study

Assessment of the Severity of COVID-19 on the Basis of Examination and Laboratory Diagnostics in Relation to Computed Tomography Imagery of Patients Hospitalised Due to COVID-19-Single-Centre Study

From the moment the SARS-CoV-2 virus was identified in December 2019, the COVID-19 disease spread around the world, causing an increase in hospitalisations and deaths. From the beginning of the pandemic, scientists tried to determine the major cause that led to patient deaths. In this paper, the background to creating a research model was diagnostic problems related to early assessment of the degree of damage to the lungs in patients with COVID-19. The study group comprised patients hospitalised in one of the temporary COVID hospitals. Patients admitted to the hospital had confirmed infection with SARS-CoV-2. At the moment of admittance, arterial blood was taken and the relevant parameters noted. The results of physical examinations, the use of oxygen therapy and later test results were compared with the condition of the patients in later computed tomography images and descriptions. The point of reference for determining the severity of the patient's condition in the computer imagery was set for a mild condition as consisting of a percentage of total lung parenchyma surface area affected no greater than 30%, an average condition of between 30% and 70%, and a severe condition as greater than 70% of the lung parenchyma surface area affected. Patients in a mild clinical condition most frequently had mild lung damage on the CT image, similarly to patients in an average clinical condition. Patients in a serious clinical condition most often had average levels of damage on the CT image. On the basis of the collected data, it can be said that at the moment of admittance, BNP, PE and HCO3- levels, selected due to the form of lung damage, on computed tomography differed from one another in a statistically significant manner (p < 0.05). Patients can qualify for an appropriate group according to the severity of COVID-19 on the basis of a physical examination and applied oxygen therapy. Patients can qualify for an appropriate group according to the severity of COVID-19 on the basis of BNP, HCO3 and BE parameters obtained from arterial blood.

Colliding Challenges: An Analysis of SARS-CoV-2 Infection in Patients with Pulmonary Tuberculosis versus SARS-CoV-2 Infection Alone

Background and Objectives: The concurrent occurrence of tuberculosis and COVID-19 coinfection poses significant clinical complexities, warranting a nuanced approach to diagnosis, management, and patient care. Materials and Methods: A retrospective, cross-sectional study was conducted on two groups: one comprising 32 patients with pulmonary TB (PTB) and COVID-19 co-infection, and one including 100 patients with COVID-19 alone. Data was collected from medical records, including patient history, clinical parameters, laboratory, imaging results, and patient outcome. Results: A lower BMI emerges as a significant marker suggesting underlying PTB in patients with SARS-CoV-2 co-infection. Type 2 diabetes mellitus increases the risk of death in PTB-SARS-CoV-2 co-infection. Co-infected patients show lymphocytopenia and higher neutrophil levels, CRP, transaminases, and D-dimer levels. Elevated CRP and ALT levels are linked to increased co-infection likelihood. Certain parameters like SpO2, CRP, ALT, AST, and D-dimer effectively differentiate between co-infected and COVID-19 patients. Platelet-to-lymphocyte ratio is notably higher in co-infected individuals. Lesion severity on imaging is significantly associated with co-infection, highlighting imaging's diagnostic importance. Longer hospital stays are linked to co-infection but not significantly to death risk. Conclusions: Certain clinical and biological factors may serve as potential indicators of PTB co-infection in patients with SARS-CoV-2.

Evaluation of the value of chest CT severity score in assessment of COVID-19 severity and short-term prognosis

Background

Evaluations have shown that the severity of pulmonary involvement is very important in the mortality rate of patients with coronavirus disease 2019 (COVID-19). The purpose of this study was to evaluate the value of chest CT severity score in assessment of COVID-19 severity and short-term prognosis.

Materials and Methods

This study was a cross-sectional study with a sample size of 197 patients, including all patients admitted to Rasoul Akram Hospital, with positive polymerase chain reaction, to investigate the relationship between computed tomography (CT) severity score and mortality. The demographic data and CT scan findings (including the pattern, side, and distribution of involvement), co-morbidities, and lab data were collected. Finally, gathered data were analyzed by SPSS-26.

Results

119 (60.4%) patients were male, and 78 (39.6%) were female. The mean age was 58.58 ± 17.3 years. Totally, 61 patients died; of those, 41 (67.2%) were admitted to the intensive care unit (ICU), so there was a significant relation between death and ICU admission (P value = 0.000). Diabetes was the most common co-morbidity, followed by hypertension and IHD. There was no significant relation between co-morbidities and death (P value = 0.13). The most common patterns of CTs were interlobular septal thickening and ground glass opacities, and a higher CT severity score was in the second week from the onset of symptoms, which was associated with more mortality (P value < 0.05).

Conclusion

Our study showed that a patient with a higher CT severity score of the second week had a higher risk of mortality. Also, association of the CT severity score, laboratory data, and symptoms could be applicable in predicting the patient's condition.

Correlation of clinical, laboratory, and short-term outcomes of immunocompromised and immunocompetent COVID-19 patients with semi-quantitative chest CT score findings: A case-control study

BACKGROUND

As the effects of immunosuppression are not still clear on COVID-19 patients, we conducted this study to identify clinical and laboratory findings associated with pulmonary involvement in both immunocompromised and immunocompetent patients.

METHODS

A case-control of 107 immunocompromised and 107 immunocompetent COVID-19 patients matched for age and sex with either positive RT-PCR or clinical-radiological findings suggestive of COVID-19 enrolled in the study. Their initial clinical features, laboratory findings, chest CT scans, and short-term outcomes (hospitalization time and intensive care unit [ICU] admission) were recorded. In addition, pulmonary involvement was assessed with the semi-quantitative scoring system (0-25).

RESULTS

Pulmonary involvement was significantly lower in immunocompromised patients in contrast to immunocompetent patients, especially in RLL (p = 0.001), LUL (p = 0.023), and both central and peripheral (p = 0.002), and peribronchovascular (p = 0.004) sites of lungs. Patchy (p < 0.001), wedged (p = 0.002), confluent (p = 0.002) lesions, and ground glass with consolidation pattern (p < 0.001) were significantly higher among immunocompetent patients. Initial signs and symptoms of immunocompromised patients including dyspnea (p = 0.008) and hemoptysis (p = 0.036), respiratory rate of over 25 (p < 0.001), and spo2 of below 93% (p = 0.01) were associated with higher pulmonary involvement. Total chest CT score was also associated with longer hospitalization (p = 0.016) and ICU admission (p = 0.04) among immunocompromised patients.

CONCLUSIONS

Pulmonary involvement score was not significantly different among immunocompromised and immunocompetent patients. Initial clinical findings (dyspnea, hemoptysis, higher RR, and lower Spo2) of immunocompromised patients could better predict pulmonary involvement than laboratory findings.

A Predictive Rule for COVID-19 Pneumonia Among COVID-19 Patients: A Classification and Regression Tree (CART) Analysis Model

BACKGROUND

In this study, we aimed to identify predictive factors for coronavirus disease 2019 (COVID-19) patients with complicated pneumonia and determine which COVID-19 patients should undergo computed tomography (CT) using classification and regression tree (CART) analysis.

METHODS

This retrospective cross-sectional survey was conducted at a university hospital. We recruited patients diagnosed with COVID-19 between January 1 and December 31, 2020. We extracted clinical information (e.g., vital signs, symptoms, laboratory results, and CT findings) from patient records. Factors potentially predicting COVID-19 pneumonia were analyzed using Student's t-test, the chi-square test, and a CART analysis model.

RESULTS

Among 221 patients (119 men (53.8%); mean age, 54.59±18.61 years), 160 (72.4%) had pneumonia. The CART analysis revealed that patients were at high risk of pneumonia if they had C-reactive protein (CRP) levels of >1.60 mg/dL (incidence of pneumonia: 95.7%); CRP levels of ≤1.60 mg/dL + age >35.5 years + lactate dehydrogenase (LDH)>225.5 IU/L (incidence of pneumonia: 95.5%); and CRP levels of ≤1.60 mg/dL + age >35.5 years + LDH≤225.5 IU/L + hemoglobin ≤14.65 g/dL (incidence of pneumonia: 69.6%). The area of the curve of the receiver operating characteristic of the model was 0.860 (95% CI: 0.804-0.915), indicating sufficient explanatory power.

CONCLUSIONS

The present results are useful for deciding whether to perform CT in COVID-19 patients. High-risk patients such as those mentioned above should undergo CT.

A Systematic Review of the Relationship between Chest CT Severity Score and Laboratory Findings and Clinical Parameters in COVID-19 Pneumonia

The COVID-19 virus has infected millions of people and became a global pandemic in 2020. The efficacy of laboratory and clinical parameters in the diagnosis and monitoring of COVID-19 has been established. The CT scan has been identified as a crucial tool in the prognostication of COVID-19 pneumonia. Moreover, it has been proposed that the CT severity score can be utilized for the diagnosis and prognostication of COVID-19 disease severity and exhibits a correlation with laboratory findings such as inflammatory markers, blood glucose levels, and clinical parameters such as endotracheal intubation, oxygen saturation, mortality, and hospital admissions. Nevertheless, the correlation between the CT severity score and clinical or laboratory parameters has not been firmly established. The objective of this study is to provide a comprehensive review of the aforementioned association. This review used a systematic approach to collate and assess the existing literature that investigates the correlation between CT severity score and laboratory and clinical parameters. The search was conducted using Embase Ovid, MEDLINE Ovid, and PubMed databases, covering the period from inception to 20 May 2023. This review identified 20 studies involving more than 8000 participants of varying designs. The findings showed that the CT severity score is positively associated with laboratory and clinical parameters in COVID-19 patients. The findings indicate that the CT severity score exhibits a satisfactory level of prognostic accuracy in predicting mortality among patients with COVID-19.

Predicting omicron pneumonia severity and outcome: a single-center study in Hangzhou, China

Background

In December 2022, there was a large Omicron epidemic in Hangzhou, China. Many people were diagnosed with Omicron pneumonia with variable symptom severity and outcome. Computed tomography (CT) imaging has been proven to be an important tool for COVID-19 pneumonia screening and quantification. We hypothesized that CT-based machine learning algorithms can predict disease severity and outcome in Omicron pneumonia, and we compared its performance with the pneumonia severity index (PSI)-related clinical and biological features.

Methods

Our study included 238 patients with the Omicron variant who have been admitted to our hospital in China from 15 December 2022 to 16 January 2023 (the first wave after the dynamic zero-COVID strategy stopped). All patients had a positive real-time polymerase chain reaction (PCR) or lateral flow antigen test for SARS-CoV-2 after vaccination and no previous SARS-CoV-2 infections. We recorded patient baseline information pertaining to demographics, comorbid conditions, vital signs, and available laboratory data. All CT images were processed with a commercial artificial intelligence (AI) algorithm to obtain the volume and percentage of consolidation and infiltration related to Omicron pneumonia. The support vector machine (SVM) model was used to predict the disease severity and outcome.

Results

The receiver operating characteristic (ROC) area under the curve (AUC) of the machine learning classifier using PSI-related features was 0.85 (accuracy = 87.40%, p < 0.001) for predicting severity while that using CT-based features was only 0.70 (accuracy = 76.47%, p = 0.014). If combined, the AUC was not increased, showing 0.84 (accuracy = 84.03%, p < 0.001). Trained on outcome prediction, the classifier reached the AUC of 0.85 using PSI-related features (accuracy = 85.29%, p < 0.001), which was higher than using CT-based features (AUC = 0.67, accuracy = 75.21%, p < 0.001). If combined, the integrated model showed a slightly higher AUC of 0.86 (accuracy = 86.13%, p < 0.001). Oxygen saturation, IL-6, and CT infiltration showed great importance in both predicting severity and outcome.

Conclusion

Our study provided a comprehensive analysis and comparison between baseline chest CT and clinical assessment in disease severity and outcome prediction in Omicron pneumonia. The predictive model accurately predicts the severity and outcome of Omicron infection. Oxygen saturation, IL-6, and infiltration in chest CT were found to be important biomarkers. This approach has the potential to provide frontline physicians with an objective tool to manage Omicron patients more effectively in time-sensitive, stressful, and potentially resource-constrained environments.

Prediction of prognosis in patients with severe COVID-19 pneumonia using CT score by emergency physicians: a single-center retrospective study

We aimed to develop a method to determine the CT score that can be easily obtained from CT images and examine its prognostic value for severe COVID pneumonia. Patients with COVID pneumonia who required ventilatory management by intubation were included. CT score was based on anatomical information in axial CT images and were divided into three sections of height from the apex to the bottom. The extent of pneumonia in each section was rated from 0 to 5 and summed. The primary outcome was the prediction of patients who died or were managed on extracorporeal membrane oxygenation (ECMO) based on the CT score at admission. Of the 71 patients included, 12 (16.9%) died or required ECMO management, and the CT score predicted death or ECMO management with ROC of 0.718 (0.561-0.875). The death or ECMO versus survival group (median [quartiles]) had a CT score of 17.75 (14.75-20) versus 13 (11-16.5), p = 0.017. In conclusion, a higher score on our generated CT score could predict the likelihood of death or ECMO management. A CT score at the time of admission allows for early preparation and transfer to a hospital that can manage patients who may need ECMO.

Lactate dehydrogenase and PaO2/FiO2 ratio at admission helps to predict CT score in patients with COVID-19: An observational study

INTRODUCTION

Since the beginning of the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) pandemic an important tool for patients with Coronavirus Disease 2019 (COVID-19) has been the computed tomography (CT) scan, but not always available in some settings The aim was to find a cut-off that can predict worsening in patients with COVID-19 assessed with a computed tomography (CT) scan and to find laboratory, clinical or demographic parameters that may correlate with a higher CT score.

METHODS

We performed a multi-center, observational, retrospective study involving seventeen COVID-19 Units in southern Italy, including all 321 adult patients hospitalized with a diagnosis of COVID-19 who underwent at admission a CT evaluated using Pan score.

RESULTS

Considering the clinical outcome and Pan score, the best cut-off point to discriminate a severe outcome was 12.5. High lactate dehydrogenase (LDH) serum value and low PaO2/FiO2 ratio (P/F) resulted independently associated with a high CT score. The Area Under Curve (AUC) analysis showed that the best cut-off point for LDH was 367.5 U/L and for P/F 164.5. Moreover, the patients with LDH> 367.5 U/L and P/F < 164.5 showed more frequently a severe CT score than those with LDH< 367.5 U/L and P/F> 164.5, 83.4%, vs 20%, respectively.

CONCLUSIONS

A direct correlation was observed between CT score value and outcome of COVID-19, such as CT score and high LDH levels and low P/F ratio at admission. Clinical or laboratory tools that predict the outcome at admission to hospital are useful to avoiding the overload of hospital facilities.

Evaluation of Follow-Up CT Scans in Patients with Severe Initial Pulmonary Involvement by COVID-19

Objective

To investigate the predictive factors of residual pulmonary opacity on midterm follow-up CT scans in patients hospitalized with COVID-19 pneumonia.

Materials and Methods

This prospective study was conducted in a tertiary referral university hospital in Iran, from March 2020 to December 2020. Patients hospitalized due to novel coronavirus pneumonia with bilateral pulmonary involvement in the first CT scan were included and underwent an 8-week follow-up CT scan. Pulmonary involvement (PI) severity was assessed using a 25-scale semiquantitative scoring system. Density of opacities was recorded using the Hounsfield unit (HU).

Results

The chest CT scans of 50 participants (mean age = 54.4 ± 14.2 years, 72% male) were reviewed, among whom 8 (16%) had residual findings on follow-up CT scans. The most common residual findings were faint ground-glass opacities (GGOs) (14%); fibrotic-like changes were observed in 2 (4%) patients. Demographic findings, underlying disease, and laboratory findings did not show significant association with remaining pulmonary opacities. The total PI score was significantly higher in participants with remaining parenchymal involvement (14.5 ± 6.5 versus 10.2 ± 3.7; P=0.02). On admission, the HU of patients with remaining opacities was significantly higher (-239.8 ± 107.6 versus -344.0 ± 157.4; P=0.01). Remaining pulmonary findings were more frequently detected in patients who had received antivirals, steroid pulse, or IVIG treatments (P=0.02, 0.02, and 0.001, respectively). Only the PI score remained statistically significant in multivariate logistic regression with 88.1% accuracy (OR = 1.2 [1.01-1.53]; P=0.03).

Conclusion

Pulmonary opacities are more likely to persist in midterm follow-up CT scans in patients with severe initial pulmonary involvement.

Seroconversion following the first, second, and third dose of SARS-CoV-2 vaccines in immunocompromised population: a systematic review and meta-analysis

BACKGROUND

Immunocompromised (IC) patients are at higher risk of more severe COVID-19 infections than the general population. Special considerations should be dedicated to such patients. We aimed to investigate the efficacy of COVID-19 vaccines based on the vaccine type and etiology as well as the necessity of booster dose in this high-risk population.

MATERIALS AND METHODS

We searched PubMed, Web of Science, and Scopus databases for observational studies published between June 1st, 2020, and September 1st, 2021, which investigated the seroconversion after COVID-19 vaccine administration in adult patients with IC conditions. For investigation of sources of heterogeneity, subgroup analysis and sensitivity analysis were conducted. Statistical analysis was performed using R software.

RESULTS

According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, we included 81 articles in the meta-analysis. The overall crude prevalence of seroconversion after the first (n: 7460), second (n: 13,181), and third (n: 909, all population were transplant patients with mRNA vaccine administration) dose administration was 26.17% (95% CI 19.01%, 33.99%, I² = 97.1%), 57.11% (95% CI: 49.22%, 64.83%, I² = 98.4%), and 48.65% (95% CI: 34.63%, 62.79%, I² = 94.4%). Despite the relatively same immunogenicity of mRNA and vector-based vaccines after the first dose, the mRNA vaccines induced higher immunity after the second dose. Regarding the etiologic factor, transplant patients were less likely to develop immunity after both first and second dose rather than patients with malignancy (17.0% vs 37.0% after first dose, P = 0.02; 38.3% vs 72.1% after second dose, P < 0.001) or autoimmune disease (17.0% vs 36.4%, P = 0.04; 38.3% vs 80.2%, P < 0.001). To evaluate the efficacy of the third dose, we observed an increasing trend in transplant patients after the first (17.0%), second (38.3%), and third (48.6%) dose.

CONCLUSION

The rising pattern of seroconversion after boosting tends to be promising. In this case, more attention should be devoted to transplant patients who possess the lowest response rate.

Predictors for COVID-19 Complete Remission with HRCT Pattern Evolution: A Monocentric, Prospective Study

There are growing concerns that some COVID-19 survivors may acquire fibrosis and other irreversible lung abnormalities. The purpose of this prospective study was to assess the rate and predictors of complete resolution of COVID-19 pneumonia by pursuing a hypothetical relation between time and imaging pattern evolution using HRCT findings. A monocentric prospective cohort study with a consecutive-case enrolment design was implemented during a five-month period, having a total of 683 post-COVID patients eligible for inclusion and 635 evaluations with complete follow-up for chest HRCT. The target for post-COVID evaluations consisted of performing HRCT 90 days after a confirmed SARS-CoV-2 infection. The studied patients had an average age of 54 years, ranging between 18 and 85 years old, and an average duration from the first symptoms until HRCT was performed of 74 days. At the post-COVID follow-up, 25.8% had a complete imagistic remission. The most common appearance with HRCT was “ground glass” in 86.6% in patients with persistent COVID-19, followed by reticulations, present in 78.8%, and respectively pleural thickening in 41.2% of cases. The mean total HRCT scores were statistically significantly higher in patients older than 65 years (10.6 ± 6.0) compared to the 40−65 group (6.1 ± 6.1) and the 18−40 age group (2.7 ± 4.8) (p < 0.001). Chest HRCT is a “time window” in documenting temporal persistent radiologic features of lung injury 90 days after SARS-CoV-2 infection, determining the pathologic basis of so-called “long COVID”. The complete remission was associated with a significantly higher average follow-up period and a significantly lower average patient age. Persistent HRCT features of ground glass, reticulation, and pleural thickening are associated with a higher total CT score and older age.

Is target sign (bull's eye appearance) associated with adverse outcomes in COVID-19 patients? A case series and literature review

BACKGROUND

In COVID-19 pneumonia, chest CT scan plays a crucial role in diagnosing and closely monitoring lung parenchyma. The main reportedly chest CT features of novel coronavirus pneumonia (NCP) have been fully discussed in the literature, but there is still a paucity of reports on uncommon CT manifestations.

CASE PRESENTATION

Herewith, we have reported ten rRT-PCR confirmed COVID-19 patients with CT target signs (bull's eye appearance); additionally, we have reviewed previously reported cases. Reviewing the literature, we found eight COVID-19 patients with target sign in the literature. 18 patients were included with a median age of 43. 11 (61%) patients were males. In 87% of patients, the lesions developed within the second-week post symptom onset. These patients mostly experienced an extended hospital stay (median = 10 days), with 53.8% of cases being admitted in ICU. The in-hospital mortality rate was 23%.

CONCLUSION

Our findings indicate that lesions with a bull's eye appearance are not significantly associated with higher mortality in hospitalized COVID-19 patients.