Chest CT severity score and radiological patterns as predictors of disease severity, ICU admission, and viral positivity in COVID-19 patients

Assessing the Efficacy of Modified CT Severity Index Versus Conventional CT Severity Index in Determining Severity and Clinical Outcomes of Chronic Obstructive Pulmonary Disease (COPD)

Aims The aim of this study was to evaluate the severity of chronic obstructive pulmonary disease (COPD) using the computed tomography severity index (CTSI) and the modified CTSI (MCTSI) and to assess their correlation with clinical outcome measures. Additionally, the study aimed to compare the diagnostic performance of these indices in predicting moderate to severe COPD, based on patient outcomes. Materials and methods In this prospective study, conducted between November 2023 and March 2024, two radiologists, blinded to clinical outcomes, independently assessed CTSI and MCTSI. Clinical outcomes evaluated included the duration of hospital stay, intensive care unit (ICU) stay, organ failure (OF), evidence of infection, need for intervention, and mortality. Results The study included 60 COPD patients, with a majority being male (40, 66.7%) and a mean age of 65.4 ± 8.6 years. Based on CTSI, severity was classified as mild in 25 (41.7%), moderate in 20 (33.3%), and severe in 15 (25.0%) cases. According to MCTSI, severity was classified as mild in 22 (36.7%), moderate in 15 (25.0%), and severe in 23 (38.3%) cases. MCTSI showed concordance with the Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification in 54 (90.0%) cases, while CTSI was concordant in 47 (78.3%) cases. For predicting moderate to severe COPD, CTSI demonstrated a sensitivity of 91.4%, specificity of 96.0%, positive predictive value (PPV) of 97.1%, and overall accuracy of 98.3%. MCTSI showed a sensitivity of 94.4%, specificity of 92.0%, PPV of 94.4%, and accuracy of 96.7%. Both indices correlated significantly with clinical outcomes, including OF, need for intervention, infection, and mortality (p < 0.001), although no significant correlation was found with ICU stay. Conclusion Both CTSI and MCTSI demonstrated a strong correlation with clinical outcomes in COPD patients and showed good concordance with severity assessments. MCTSI exhibited higher sensitivity, while CTSI demonstrated greater specificity for differentiating mild from moderate/severe COPD cases. These indices can be valuable tools in guiding clinical decision-making and assessing disease severity in COPD.

Significance of Initial Chest CT Severity Score (CTSS) and Patient Characteristics in Predicting Outcomes in Hospitalized COVID-19 Patients: A Single Center Study

The aim of this study is to examine the prognostic role of initial chest computed tomography severity score index (CTSS) and its association with demographic, socio-epidemiological, and clinical parameters in COVID-19 hospitalized patients. A retrospective study included patients who were hospitalized in the COVID Hospital of the Clinical Hospital Center Kosovska Mitrovica from July 2020 to March 2022. We compared patient characteristics and outcome of their hospital stay with values of CT severity score (mild, moderate, and severe form of the disease). Patients with severe disease were statistically significantly older, they treated more days, and they presented statistically significant highest mortality rate compared to mild and moderate forms. Smokers and obese were significantly more frequent among patients with higher CT, while vaccinated patients were more common among those with a mild form. Biochemical parameters at admission also showed statistical significance between the examined groups. We can conclude that by employing the initial CT severity score as the strongest predictor of mortality, it is possible to predict the outcome in hospitalized patients. A comprehensive examination of the patient upon admission, including determining the extent of inflammatory changes in the lungs using computed tomography, the levels of oxygen saturation, and other laboratory parameters, can assist doctors in making an adequate clinical evaluation and apply appropriate therapeutic protocols in the treatment of COVID-19.

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.

High Plasma Osteopontin Levels Are Associated with Serious Post-Acute-COVID-19-Related Dyspnea

COVID-19 survivors commonly report persistent symptoms. In this observational study, we investigated the link between osteopontin (OPN) and post-acute COVID-19 symptoms and lung functional/imaging abnormalities. We recorded symptoms and lung imaging/functional data from previously hospitalized COVID-19 patients, who were followed for 4-84 weeks (122 patients/181 visits) post-symptom onset at our outpatient clinic. Circulating OPN was determined using ELISA. Plasma OPN levels were higher in symptomatic patients (compared with the asymptomatic ones); those with dyspnea (compared with those without dyspnea);those with a combination of serious symptoms, i.e., the presence of at least one of the following: dyspnea, fatigue and muscular weakness (compared with those with none of these symptoms); and those with dyspnea and m-MRC > 1 (compared with those with m-MRC = 0-1). Plasma OPN levels were inversely correlated with EQ-VAS (visual analog scale of the EQ-5D-5L health-related quality-of-life questionnaire) values. High-resolution CT or diffusion lung capacity (DLCO) findings were not related to circulating OPN. In the multiple logistic regression, the presence of symptoms, dyspnea, or the combination of serious symptoms were linked to female gender, increased BMI and pre-existing dyspnea (before the acute disease), while increased plasma OPN levels, female gender and pre-existing dyspnea with m-MRC > 1 were independently associated with severe post-COVID-19 dyspnea (m-MRC > 1). Using a correlation matrix to investigate multiple correlations between EQ-VAS, OPN and epidemiological data, we observed an inverse correlation between the OPN and EQ-VAS values. Increased circulating OPN was linked to the persistence of severe exertional dyspnea and impaired quality of life in previously hospitalized COVID-19 patients.

A Novel Combined Model to Predict the Prognosis of COVID-19: Radiologicalmetabolic Scoring

AIM

To investigate the performance of a novel radiological-metabolic scoring (RM-S) system to predict mortality and intensive care unit (ICU) requirements among COVID-19 patients and to compare performance with the chest computed-tomography severity-scoring (C-CT-SS). The RMS was created from scoring systems such as visual coronary-artery-calcification scoring (V-CAC-S), hepatic-steatosis scoring (HS-S) and pancreatic-steatosis scoring (PS-S).

METHODS

Between May 2021 and January 2022, 397 patients with COVID-19 were included in this retrospective cohort study. All demographic, clinical and laboratory data and chest CT images of patients were retrospectively reviewed. RM-S, V-CAC-S, HS-S, PS-S and C-CT-SS scores were calculated, and their performance in predicting mortality and ICU requirement were evaluated by univariate and multivariable analyses.

RESULTS

A total of 32 (8.1%) patients died, and 77 (19.4%) patients required ICU admission. Mortality and ICU admission were both associated with older age (p < 0.001). Sex distribution was similar in the deceased vs. survivor and ICU vs. non-ICU comparisons (p = 0.974 and p = 0.626, respectively). Multiple logistic regression revealed that mortality was independently associated with having a C-CT-SS score of ≥ 14 (p < 0.001) and severe RM-S category (p = 0.010), while ICU requirement was independently associated with having a C-CT-SS score of ≥ 14 (p < 0.001) and severe V-CAC-S category (p = 0.010).

CONCLUSION

RM-S, C-CT-SS, and V-CAC-S are useful tools that can be used to predict patients with poor prognoses for COVID-19. Long-term prospective follow-up of patients with high RM-S scores can be useful for predicting long COVID.

Factors associated with acute kidney injury (AKI) and mortality in COVID-19 patients in a Sub-Saharan African intensive care unit: a single-center prospective study

INTRODUCTION

Acute kidney injury (AKI) is a complication of severe coronavirus disease 2019 (COVID-19). Kidney damage associated with COVID-19 could take specific features due to environmental and socio-cultural factors. This study evaluates the incidence of AKI, the associated factors, and mortality in COVID-19 patients in a Sub-Saharan African intensive care unit.

METHODS

In a prospective cohort study conducted in the intensive care unit (ICU) of the Centre Médical de Kinshasa (CMK), consecutive patients admitted for COVID-19 were screened for the presence of AKI between 27 March, 2020 and 27 January 2022. AKI was defined according to Kidney Disease Improving Global Outcomes (KDIGO) guidelines. The primary outcome was occurrence of AKI. The secondary outcome was 48 days' mortality and recovery of the renal function at intensive care unit (ICU) discharge. Survival (time-to death) curves were built using the Kaplan Meier methods. Multivariate analyses were performed by logistic regression to identify factors associated with AKI and Cox regression to explore the association between AKI and in-hospital mortality. The significance level of the p-value was set at 0.05.

RESULTS

The median(IQR) sequential organ failure assessment score (SOFA) score and mean age of patients (215) including in our cohort were respectively 3(2-4) and 58.9 ± 14.9 years. The incidence of AKI was 28.4% with stages 1, 2, or 3 AKI accounted for 39.3%, 11.5%, and 49.2%, respectively. Hemodialysis was required in 16 out 215 (7.4%) patients. Dyspnea (adjusted odds ratio (aOR):2.27 [1.1--4.57] p = 0.021), SOFA ≥5 (aOR:3.11[1.29-7.53] p = 0.012), AST/ALT ratio (aOR: 1.53 [1.09-1.79] p = 0.015), N/L ratio (aOR:2.09 [1.09-3.20] p = 0.016), mechanical ventilation (aOR: 3.20 [1.66-10.51] p = 0.005) and Amikacin (aOR: 2.91 [1.37-6.18] p = 0.006) were the main factors associated with AKI. Patients with AKI had a mortality rate of 52.5% and 67.2% of the survivors did not recover kidney function at the end of hospitalization. Adjusted Cox regression analysis revealed that COVID-19-associated AKI was independently associated with in-hospital death (HR:2.96 [1.93-4.65] p = 0.013) compared to non-AKI patients.

CONCLUSIONS

AKI was present in three out of ten COVID-19 patients. The most significant factors associated with AKI were dyspnea, SOFA ≥ 5, AST/ALT and N/L ratio, mechanical ventilation and Amikacin. AKI has been associated with an almost threefold increase in overall mortality and seven out of ten survivors did not recover kidney function after AKI.

IL-6 and Neutrophil/Lymphocyte Ratio as Markers of ICU Admittance in SARS-CoV-2 Patients with Diabetes

Inflammation along with coagulation disturbances has an essential role in the evolution towards a severe disease in patients with the coronavirus disease 2019 (COVID-19). This study aimed to evaluate inflammatory and coagulation biomarkers when predicting the need to visit an intensive care unit (ICU) in diabetes mellitus (DM) patients. In a retrospective study, laboratory parameters were examined for 366 participants: ICU = 90, of which 44 patients had DM and no ICU admittance = 276. The ability of inflammatory and coagulation markers to distinguish the severity of COVID-19 was determined using univariate and multivariate regression analysis. In all patients, lactate dehydrogenase was the only predictor for ICU admittance in the multivariate analysis. In the DM group, the results showed that the interleukin (IL)-6 and neutrophil/lymphocyte ratio (NLR) values at admission could predict the need for ICU admittance. Even though there were significant differences between the ICU and no ICU admittance groups regarding the coagulation markers, they could not predict the severity of the disease in DM patients. The present study showed for the first time that the IL-6 and NLR admission values could predict ICU admittance in DM patients. This finding could help clinicians manage the infection more easily if the COVID-19 pandemic strikes again.

Three-month follow-up after severe COVID-19 infection: are chest CT results associated with respiratory outcomes and respiratory recovery in COVID-19 patients?

BACKGROUND

CT Severity Score (CT-SS) can be used to assess the extent of severe coronavirus disease 19 (COVID-19) pneumonia. Follow-up CT-SS in patients surviving COVID-19-associated hyperinflammation and its correlation with respiratory parameters remains unknown. This study aims to assess the association between CT-SS and respiratory outcomes, both in hospital and at three months after hospitalization.

METHODS

Patients from the COVID-19 High-intensity Immunosuppression in Cytokine storm Syndrome (CHIC) study surviving hospitalization due to COVID-19 associated hyperinflammation were invited for follow-up assessment at three months after hospitalization. Results of CT-SS three months after hospitalization were compared with CT-SS at hospital admission. CT-SS at admission and at 3-months were correlated with respiratory status during hospitalization and with patient reported outcomes as well as pulmonary- and exercise function tests at 3-months after hospitalization.

RESULTS

A total of 113 patients were included. Mean CT-SS decreased by 40.4% (SD 27.6) in three months (P < 0.001). CT-SS during hospitalization was higher in patients requiring more oxygen (P < 0.001). CT-SS at 3-months was higher in patients with more dyspnoea (CT-SS 8.31 (3.98) in patients with modified Medical Council Dyspnoea scale (mMRC) 0-2 vs. 11.03 (4.47) in those with mMRC 3-4). CT-SS at 3-months was also higher in patients with a more impaired pulmonary function (7.4 (3.6) in patients with diffusing capacity for carbon monoxide (DLCO) > 80%pred vs. 14.3 (3.2) in those with DLCO < 40%pred, P = 0.002).

CONCLUSION

Patients surviving hospitalization for COVID-19-associated hyperinflammation with higher CT-SS have worse respiratory outcome, both in-hospital and at 3-months after hospitalization. Strict monitoring of patients with high CT-SS is therefore warranted.

Multi-omics and immune cells' profiling of COVID-19 patients for ICU admission prediction: in silico analysis and an integrated machine learning-based approach in the framework of Predictive, Preventive, and Personalized Medicine

Background

Intensive care unit admission (ICUA) triage has been urgent need for solving the shortage of ICU beds, during the coronavirus disease 2019 (COVID-19) surge. In silico analysis and integrated machine learning (ML) approach, based on multi-omics and immune cells (ICs) profiling, might provide solutions for this issue in the framework of predictive, preventive, and personalized medicine (PPPM).

Methods

Multi-omics was used to screen the synchronous differentially expressed protein-coding genes (SDEpcGs), and an integrated ML approach to develop and validate a nomogram for prediction of ICUA. Finally, the independent risk factor (IRF) with ICs profiling of the ICUA was identified.

Results

Colony-stimulating factor 1 receptor (CSF1R) and peptidase inhibitor 16 (PI16) were identified as SDEpcGs, and each fold change (FC_ij) of CSF1R and PI16 was selected to develop and validate a nomogram to predict ICUA. The area under curve (AUC) of the nomogram was 0.872 (95% confidence interval (CI): 0.707 to 0.950) on the training set, and 0.822 (95% CI: 0.659 to 0.917) on the testing set. CSF1R was identified as an IRF of ICUA, expressed in and positively correlated with monocytes which had a lower fraction in COVID-19 ICU patients.

Conclusion

The nomogram and monocytes could provide added value to ICUA prediction and targeted prevention, which are cost-effective platform for personalized medicine of COVID-19 patients. The log₂fold change (log₂FC) of the fraction of monocytes could be monitored simply and economically in primary care, and the nomogram offered an accurate prediction for secondary care in the framework of PPPM.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00317-5.

Computed tomography severity score as a predictor of disease severity and mortality in COVID-19 patients: A systematic review and meta-analysis

BACKGROUND

Prediction of outcomes in severe COVID-19 patients using chest computed tomography severity score (CTSS) may enable more effective clinical management and early, timely ICU admission. We conducted a systematic review and meta-analysis to determine the predictive accuracy of the CTSS for disease severity and mortality in severe COVID-19 subjects.

METHODS

The electronic databases PubMed, Google Scholar, Web of Science, and the Cochrane Library were searched to find eligible studies that investigated the impact of CTSS on disease severity and mortality in COVID-19 patients between 7 January 2020 and 15 June 2021. Two independent authors looked into the risk of bias using the Quality in Prognosis Studies (QUIPS) tool.

RESULTS

Seventeen studies involving 2788 patients reported the predictive value of CTSS for disease severity. The pooled sensitivity, specificity, and summary area under the curve (sAUC) of CTSS were 0.85 (95% CI 0.78-0.90, I² =83), 0.86 (95% CI 0.76-0.92, I² =96) and 0.91 (95% CI 0.89-0.94), respectively. Six studies involving 1403 patients reported the predictive values of CTSS for COVID-19 mortality. The pooled sensitivity, specificity, and sAUC of CTSS were 0.77 (95% CI 0.69-0.83, I² = 41), 0.79 (95% CI 0.72-0.85, I² = 88), and 0.84 (95% CI 0.81-0.87), respectively.

DISCUSSION

Early prediction of prognosis is needed to deliver the better care to patients and stratify them as soon as possible. Because different CTSS thresholds have been reported in various studies, clinicians are still determining whether CTSS thresholds should be used to define disease severity and predict prognosis.

CONCLUSION

Early prediction of prognosis is needed to deliver optimal care and timely stratification of patients.  CTSS has strong discriminating power for the prediction of disease severity and mortality in patients with COVID-19.

CT severity score: an imaging biomarker to estimate the severity of COVID-19 pneumonia in vaccinated and non-vaccinated population

Background

In India, two vaccines received emergent use authorization, namely Covishield (a brand of the Oxford—Astra Zeneca vaccine manufactured by the Serum institute of India) and Covaxin (developed by Bharat Biotech) against COVID-19 disease. Chest CT is an objective way to assess the extent of pulmonary parenchymal involvement. This study aims to estimate the disease severity and outcome due to COVID-19 among vaccinated and non-vaccinated symptomatic patients and compare the same in Covishield versus Covaxin recipients using CT severity score.

Results

A total of 306 patients were retrospectively evaluated. The mean age was 62.56 ± 8.9 years, and males [n-208 (67.97%)] were commonly affected. Of 306 patients, 143 were non-vaccinated (47%), 124 were partially vaccinated (40%), and 39 were completely vaccinated (13%). CT severity scores were reduced in both Covishield and Covaxin recipients in comparison with the non-vaccinated group [χ² (2) = 16.32, p < 0.001]. There is a reduction in LOS among the vaccinated group, predominantly among the Covishield recipients.

Conclusion

Vaccination confers protection from severe SARS-CoV2 infection and is associated with an overall reduction in mortality.

Pancreatic Steatosis Evaluated by Automated Volumetric CT Fat Fraction of the Pancreas: Association with Severity in COVID-19 Pneumonia

This study investigated the relationship between the severity of pneumonia based on chest CT findings and that of pancreatic steatosis assessed using an automated volumetric measurement of the CT fat volume fraction (CT-FVF) of the pancreas, using unenhanced three-dimensional CT in polymerase chain reaction (PCR)-confirmed COVID-19 patients. The study population consisted of 128 patients with PCR-confirmed COVID-19 infection who underwent CT examinations. The CT-FVF of the pancreas was calculated using a histogram analysis for the isolation of fat-containing voxels in the pancreas. The CT-FVF (%) of the pancreas had a significantly positive correlation with the lung severity score on CT (ρ = 0.549, p < 0.01). CT-FVF (%) of the pancreas in the severe pneumonia group was significantly higher than that of the non-severe pneumonia group (21.7% vs. 7.8%, p < 0.01). The area under the curve of CT-FVF (%) of the pancreas in predicting the severity of pneumonia on CT was calculated to be 0.82, with a sensitivity of 88% and a specificity of 68% at a threshold for the severity score of 12.3. The automated volumetric measurement of the CT-FVF of the pancreas using unenhanced CT can help estimate disease severity in patients with COVID-19 pneumonia based on chest CT findings.

Pneumonia Frequency and Severity in Patients With Symptomatic COVID-19: Impact of mRNA and Adenovirus Vector Vaccines

BACKGROUND. Additional evidence of the role of COVID-19 vaccination in reducing pneumonia frequency and severity in the setting of breakthrough infection could help combat ongoing vaccine hesitancy. OBJECTIVE. The purpose of this article was to compare the frequency and severity of pneumonia on chest CT in patients with confirmed COVID-19 between patients who are unvaccinated and those who are fully vaccinated by messenger RNA (mRNA) or adenovirus vector vaccines. METHODS. This retrospective single-center study included 467 patients (250 men, 217 women; mean age, 65 ± 17 [SD] years) who underwent chest CT between December 15, 2021, and February 18, 2022, during hospitalization for symptomatic COVID-19, confirmed by reverse transcriptase-polymerase chain reaction assay. A total of 216 patients were unvaccinated, and 167 and 84 patients were fully vaccinated (defined as receipt of the second dose at least 14 days before COVID-19 diagnosis) by the BNT162b2 mRNA vaccine or the ChAdOx1-S adenovirus vector vaccine, respectively. Semiquantitative CT severity scores (CT-SS; 0-25 scale) were determined; CT-SS of 0 indicated absence of pneumonia. Presence of bilateral involvement was assessed in patients with pneumonia. Associations were explored between vaccination status and CT findings. RESULTS. The frequency of the absence of pneumonia was 15% (32/216) in unvaccinated patients, 29% (24/84) in patients fully vaccinated with ChAdOx1-S vaccine, and 51% (85/167) in patients fully vaccinated with BNT162b2 vaccine (unvaccinated and ChAdOx1-S vs BNT162b2: p < .001; unvaccinated vs ChAdOx1-S: p = .08). Mean CT-SS was significantly higher in unvaccinated patients (9.7 ± 6.1) than in patients fully vaccinated with BNT162b2 (5.2 ± 6.1) or ChAdOx1-S (6.2 ± 5.9) vaccine (both p < .001). Full vaccination was significantly associated with CT-SS independent of patient age and sex (estimate = -4.46; p < .001). Frequency of bilateral lung involvement was significantly higher in unvaccinated patients (158/184, 86%) and in patients fully vaccinated with ChAdOx1-S vaccine (54/60, 90%) than in patients fully vaccinated with BNT162b2 vaccine (47/82, 57%) (both p < .001). CONCLUSION. Pneumonia frequency and severity were lower in patients with full vaccination by mRNA and adenovirus vector vaccines who experienced breakthrough infections in comparison with unvaccinated patients. CLINICAL IMPACT. The visual observation by radiologic imaging of the protective effect of vaccination on lung injury in patients with breakthrough infections provides additional evidence supporting the clinical benefit of vaccination.

Chest CT Severity Score and Systemic Inflammatory Biomarkers as Predictors of the Need for Invasive Mechanical Ventilation and of COVID-19 Patients' Mortality

Background: Numerous tools, including inflammatory biomarkers and lung injury severity scores, have been evaluated as predictors of disease progression and the requirement for intensive therapy in COVID-19 patients. This study aims to verify the predictive role of inflammatory biomarkers [monocyte to lymphocyte ratio (MLR), neutrophil to lymphocyte ratio (NLR), systemic inflammatory index (SII), Systemic Inflammation Response Index (SIRI), Aggregate Index of Systemic Inflammation (AISI), and interleukin-6 (IL-6)] and the total system score (TSS) in the need for invasive mechanical ventilation (IMV) and mortality in COVID-19 patients. Methods: The present study was designed as an observational, analytical, retrospective cohort study and included all patients over 18 years of age with a diagnosis of COVID-19 pneumonia, confirmed through real time-polymerase chain reaction (RT-PCR) and radiological chest CT findings admitted to County Emergency Clinical Hospital of Targu-Mureș, Romania, and Modular Intensive Care Unit of UMFST “George Emil Palade” of Targu Mures, Romania between January 2021 and December 2021. Results: Non-Survivors patients were associated with higher age (p = 0.01), higher incidence of cardiac disease [atrial fibrillation (AF) p = 0.0008; chronic heart failure (CHF) p = 0.01], chronic kidney disease (CKD; p = 0.02), unvaccinated status (p = 0.001), and higher pulmonary parenchyma involvement (p < 0.0001). Multivariate analysis showed a high baseline value for MLR, NLR, SII, SIRI, AISI, IL-6, and TSS independent predictor of adverse outcomes for all recruited patients. Moreover, the presence of AF, CHF, CKD, and dyslipidemia were independent predictors of mortality. Furthermore, AF and dyslipidemia were independent predictors of IMV need. Conclusions: According to our findings, higher MLR, NLR, SII, SIRI, AISI, IL-6, and TSS values at admission strongly predict IMV requirement and mortality. Moreover, patients above 70 with AF, dyslipidemia, and unvaccinated status highly predicted IMV need and fatality. Likewise, CHF and CKD were independent predictors of increased mortality.

A Brief Analysis of a New Device to Prevent Early Intubation in Hypoxemic Patients: An Observational Study

The need for mechanical ventilation is one of the main concerns related to the care of patients with COVID-19. The aim of this study is to evaluate the efficacy of a bubble device for oxygen supplementation. This device was implemented for the selected patients hospitalized with severe COVID-19 pneumonia with persistent low oxygen saturation. Patients were selected in three major COVID-19 hospitals of Bahia state in Brazil from July to November 2020, where they remained with the device for seven days and were monitored for different factors, such as vital signs, oximetry evaluation, and arterial blood gasometry. Among the 51 patients included in the study, 68.63% successfully overcame hypoxemia without the necessity to be transferred to mechanical ventilation, whereas 31.37% required tracheal intubation (p value < 0.05). There was no difference of note on the analysis of the clinical data, chemistry, and hematological evaluation, with the exception of the SpO2 on follow-up days. Multivariate analysis revealed that the independent variable, male sex, SpO2, and non-inhaled mask, was associated with the necessity of requiring early mechanical ventilation. We concluded that this bubble device should be a prior step to be utilized before indication of mechanical ventilation in patients with persistent hypoxemia of severe COVID-19 pneumonia.

Application of the advanced lung cancer inflammation index for patients with coronavirus disease 2019 pneumonia: Combined risk prediction model with lung cancer inflammation index, computed tomography and chest radiograph

The purpose of the present study was to evaluate the feasibility of applying the advanced lung cancer inflammation index (ALI) in patients with coronavirus disease 2019 (COVID-19) and to establish a combined ALI and radiologic risk prediction model for disease exacerbation. The present study included patients diagnosed with COVID-19 infection in our single institution from March to October 2020. Patients without clinical information and/or chest computed tomography (CT) upon admission were excluded. A radiologist assessed the CT severity score and abnormality on chest radiograph. The combined ALI and radiologic risk prediction model was developed via random forest classification. Among 79 patients (age, 43±19 years; male/female, 45:34), 72 experienced improvement and seven patients experienced exacerbation after admission. Significant differences were observed between the improved and exacerbated groups in the ALI (median, 47.6 vs. 13.2; P=0.011), frequency of chest radiograph abnormality (24.7 vs. 83.3%; P<0.001), and chest CT score (CCTS; median, 1 vs. 9; P<0.001). For the accuracy of predicting exacerbation, the receiver-operating characteristic curve analysis demonstrated an area under the curve of 0.79 and 0.92 for the ALI and CCTS, respectively. The combined ALI and radiologic risk prediction model had a sensitivity of 1.00 and a specificity of 0.81. Overall, ALI alone and CCTS alone modestly predicted the exacerbation of COVID-19, and the combined ALI and radiologic risk prediction model exhibited decent sensitivity and specificity.

Prediction of mortality in COVID-19 through combing CT severity score with NEWS, qSOFA, or peripheral perfusion index

INTRODUCTION

The assessment of disease severity and the prediction of clinical outcomes at early disease stages can contribute to decreased mortality in patients with Coronavirus disease 2019 (COVID-19). This study was conducted to develop and validate a multivariable risk prediction model for mortality with using a combination of computed tomography severity score (CT-SS), national early warning score (NEWS), and quick sequential (sepsis-related) organ failure assessment (qSOFA) in COVID-19 patients.

METHODS

We retrospectively collected medical data from 655 adult COVID-19 patients admitted to our hospital between July and November 2020. Data on demographics, clinical characteristics, and laboratory and radiological findings measured as part of standard care at admission were used to calculate NEWS, qSOFA score, CT-SS, peripheral perfusion index (PPI) and shock index (SI). Logistic regression and Cox proportional hazard models were used to predict mortality, which was our primary outcome. The predictive accuracy of distinct scoring systems was evaluated by the receiver-operating characteristic (ROC) curve analysis.

RESULTS

The median age was 50.0 years [333 males (50.8%), 322 females (49.2%)]. Higher NEWS and SI was associated with time-to-death within 90-days, whereas higher age, CT-SS and lower PPI were significantly associated with time-to-death within both 14 days and 90 days in the adjusted Cox regression model. The CT-SS predicted different mortality risk levels within each stratum of NEWS and qSOFA and improved the discrimination of mortality prediction models. Combining CT-SS with NEWS score yielded more accurate 14 days (DBA: -0.048, p = 0.002) and 90 days (DBA: -0.066, p < 0.001) mortality prediction.

CONCLUSION

Combining severity tools such as CT-SS, NEWS and qSOFA improves the accuracy of predicting mortality in patients with COVID-19. Inclusion of these tools in decision strategies might provide early detection of high-risk groups, avoid delayed medical attention, and improve patient outcomes.