Qualitative and quantitative chest CT parameters as predictors of specific mortality in COVID-19 patients

Prognostication and integration of bedside lung ultrasound and computed tomography imaging findings with clinical features to Predict COVID-19 In-hospital mortality and ICU admission

INTRODUCTION

Bedside lung ultrasound (LUS) and computed tomography (CT) imaging are valuable modalities in screening and diagnosis of pulmonary diseases. This study aims to investigate the prognostic value of integrating LUS and CT imaging findings with clinical features to predict poor outcomes upon ER admission in COVID-19.

METHODS

Patients visiting the study center with clinical presentation and laboratory findings compatible with COVID-19 between April 2020 to January 2022 were considered for this study. Several imaging findings (ground glass opacity, consolidation, atelectatic bands, mosaic attenuation, ARDS pattern, crazy paving, pleural thickening in CT and A-line, comet-tail artifact, confluent B-Line in BLUS, pleural thickening and Consolidation in both modalities) were evaluated, alongside clinical assessments upon admission, to assess their prognostic value. The top radiological, LUS findings, and clinical signs were integrated in a nomogram for predicting mortality.

RESULTS

A total of 1230 patients were included in the analyses. Among the findings, consolidation in BLUS and CT imaging, and absence of A-lines were associated with mortality. In addition to these findings, ground-glass opacities, atelectatic band, mosaic attenuation, crazy paving, and confluent B-line were also associated with ICU hospitalization. Although, the prognostic value of individual markers was poor and comparable (AUC < 0.65), the combined use of top clinical and imaging findings in the associated nomogram led to a high accuracy in predicting mortality (Area under curve: 87.3%).

CONCLUSIONS

BLUS and CT imaging findings alone provide limited utility in stratifying patients for higher mortality and ICU admission risk and should not be used for risk stratification alone outside the context of each patient and their clinical presentations in suspected COVID-19 patients.

[Prevalence and prognostic role of thoracic lymphadenopathy in Covid-19]

PURPOSE

The prevalent coronavirus disease 2019 (COVID-19) pandemic has spread throughout the world and is considered a serious threat to global health. The prognostic role of thoracic lymphadenopathy in COVID-19 is unclear. The aim of the present meta-analysis was to analyze the prognostic role of thoracic lymphadenopathy for the prediction of 30-day mortality in patients with COVID-19.

MATERIALS AND METHODS

The MEDLINE library, Cochrane, and SCOPUS databases were screened for associations between CT-defined features and mortality in COVID-19 patients up to June 2021. In total, 21 studies were included in the present analysis. The quality of the included studies was assessed by the Newcastle-Ottawa Scale. The meta-analysis was performed using RevMan 5.3. Heterogeneity was calculated by means of the inconsistency index I2. DerSimonian and Laird random-effect models with inverse variance weights were performed without any further correction.

RESULTS

The included studies comprised 4621 patients. The prevalence of thoracic lymphadenopathy varied between 1 % and 73.4 %. The pooled prevalence was 16.7 %, 95 % CI = (15.6 %; 17.8 %). The hospital mortality was higher in patients with thoracic lymphadenopathy (34.7 %) than in patients without (20.0 %). The pooled odds ratio for the influence of thoracic lymphadenopathy on mortality was 2.13 (95 % CI = [1.80-2.52], p < 0.001).

CONCLUSION

The prevalence of thoracic lymphadenopathy in COVID-19 is 16.7 %. The presence of thoracic lymphadenopathy is associated with an approximately twofold increase in the risk for hospital mortality in COVID-19.

KEY POINTS

· The prevalence of lymphadenopathy in COVID-19 is 16.7 %.. · Patients with lymphadenopathy in COVID-19 have a higher risk of mortality during hospitalization.. · Lymphadenopathy nearly doubles mortality and plays an important prognostic role..

CITATION FORMAT

· Bucher AM, Sieren M, Meinel F et al. Prevalence and prognostic role of thoracic lymphadenopathy in Covid-19. Rofo 2025; 197: 163 - 171.

Relationship between imaging-derived parameters and circulating microRNAs to study the degree of lung involvement in hospitalized geriatric patients with COVID-19 pneumonia

AIM

Chest computed tomography (CT) scan is useful to evaluate the type and extent of lung lesions in coronavirus disease 2019 (COVID-19) pneumonia. This study explored the association between radiological parameters and various circulating serum-derived markers, including microRNAs, in older patients with COVID-19 pneumonia.

METHODS

A retrospective analysis was designed to study geriatric patients (≥75 years) with COVID-19 pneumonia, who underwent chest CT scan on admission, and for whom clinical data and serum samples were obtained. To quantify the extent of lung involvement, CT-score, the percentage of healthy lung (HL%), the percentage of ground glass opacity (GGO%), and the percentage of lung consolidation were assessed using computer-aided tools. The association of these parameters with two circulating microRNAs, miR-483-5p and miR-320b, previously identified as biomarkers of mortality risk in COVID-19 geriatric patients, was tested.

RESULTS

A total of 73 patients with COVID-19 pneumonia were evaluable (median age 85 years; interquartile range 82-90 years). Among chest CT-derived parameters, the percentage of lung consolidation (HR 1.08, 95% CI 1.02-1.14), CT-score (HR 1.14, 95% CI 1.03-1.25), and HL% (HR 0.97, 95% CI 0.95-0.99) emerged as significant predictors of mortality, whereas non-significant trends toward increased mortality were observed in patients with higher GGO%. We also found a significant positive association between serum miR-483-5p and GGO% (correlation coefficient 0.28; P = 0.018) and a negative association with HL% (correlation coefficient -0.27; P = 0.023).

CONCLUSIONS

Overall, the extent of lung consolidation can be confirmed as a prognostic parameter of COVID-19 pneumonia in older patients. Among various serum-derived markers, miR-483-5p can help in exploring the degree of lung involvement, due to its association with higher GGO% and lower HL%. Geriatr Gerontol Int 2024; 24: 962-972.

Insights from 3D modeling and fluid dynamics in COVID-19 pneumonia

We address the lack of research regarding aerodynamic events behind respiratory distress at COVID-19. The use of chest CT enables quantification of pneumonia extent; however, there is a paucity of data regarding the impact of airflow changes. We reviewed 31 COVID-19 patients who were admitted in March 2020 with varying severity of pulmonary disease. Lung volumes were segmented and measured on CT images and patient-specific models of the lungs were created. Incompressible, laminar, and three-dimensional Navier-Stokes equations were used for the fluid dynamics (CFD) analyses of ten patients (five mild, five pneumonia). Of 31 patients, 17 were female, 18 had pneumonia, and 2 were deceased. Effective lung volume decreased in the general group, but the involvement of the right lung was prominent in dyspnea patients. CFD analyses revealed that the mass flow distribution was significantly distorted in pneumonia cases with diminished flow rate towards the right lung. In addition, the distribution of flow parameters showed mild group had less airway resistance with higher velocity (1.228 m/s vs 1.572 m/s) and higher static pressure values at airway branches (1.5112 Pa vs 1.3024 Pa). Therefore, we conclude that airway resistance and mass flow rate distribution are as important as the radiological involvement degree in defining the disease severity.

Quantitative CT at Follow-Up of COVID-19 Pneumonia: Relationship with Pulmonary Function Tests

BACKGROUND

The role of quantitative chest computed tomography (CT) is controversial in the follow-up of patients with COVID-19 pneumonia. The aim of this study was to test during the follow-up of COVID-19 pneumonia the association between pulmonary function tests (PFTs) and quantitative parameters extrapolated from follow-up (FU) CT scans performed at least 6 months after COVID-19 onset.

METHODS

The study included patients older than 18 years old, admitted to the emergency department of our institution between 29 February 2020 and 31 December 2020, with a diagnosis of COVID-19 pneumonia, who underwent chest CT at admission and FU CT at least 6 months later; PFTs were performed within 6 months of FU CT. At FU CT, quantitative parameters of well-aerated lung and pneumonia extent were identified both visually and by software using CT density thresholds. The association between PFTs and quantitative parameters was tested by the calculation of the Spearman's coefficient of rank correlation (rho).

RESULTS

The study included 40 patients (38% females; median age 63 years old, IQR, 56-71 years old). A significant correlation was identified between low attenuation areas% (%LAAs) <950 Hounsfield units (HU) and both forced expiratory volume in 1s/forced vital capacity (FEV1/FVC) ratio (rho -0.410, 95% CIs -0.639--0.112, p = 0.008) and %DLCO (rho -0.426, 95% CIs -0.678--0.084, p = 0.017). The remaining quantitative parameters failed to demonstrate a significant association with PFTs (p > 0.05).

CONCLUSIONS

At follow-up, CT scans performed at least 6 months after COVID-19 pneumonia onset showed %LAAs that were inversely associated with %DLCO and could be considered a marker of irreversible lung damage.

Clinical utilization of artificial intelligence-based COVID-19 pneumonia quantification using chest computed tomography - a multicenter retrospective cohort study in Japan

BACKGROUND

Computed tomography (CT) imaging and artificial intelligence (AI)-based analyses have aided in the diagnosis and prediction of the severity of COVID-19. However, the potential of AI-based CT quantification of pneumonia in assessing patients with COVID-19 has not yet been fully explored. This study aimed to investigate the potential of AI-based CT quantification of COVID-19 pneumonia to predict the critical outcomes and clinical characteristics of patients with residual lung lesions.

METHODS

This retrospective cohort study included 1,200 hospitalized patients with COVID-19 from four hospitals. The incidence of critical outcomes (requiring the support of high-flow oxygen or invasive mechanical ventilation or death) and complications during hospitalization (bacterial infection, renal failure, heart failure, thromboembolism, and liver dysfunction) was compared between the groups of pneumonia with high/low-percentage lung lesions, based on AI-based CT quantification. Additionally, 198 patients underwent CT scans 3 months after admission to analyze prognostic factors for residual lung lesions.

RESULTS

The pneumonia group with a high percentage of lung lesions (N = 400) had a higher incidence of critical outcomes and complications during hospitalization than the low percentage group (N = 800). Multivariable analysis demonstrated that AI-based CT quantification of pneumonia was independently associated with critical outcomes (adjusted odds ratio [aOR] 10.5, 95% confidence interval [CI] 5.59-19.7), as well as with oxygen requirement (aOR 6.35, 95% CI 4.60-8.76), IMV requirement (aOR 7.73, 95% CI 2.52-23.7), and mortality rate (aOR 6.46, 95% CI 1.87-22.3). Among patients with follow-up CT scans (N = 198), the multivariable analysis revealed that the pneumonia group with a high percentage of lung lesions on admission (aOR 4.74, 95% CI 2.36-9.52), older age (aOR 2.53, 95% CI 1.16-5.51), female sex (aOR 2.41, 95% CI 1.13-5.11), and medical history of hypertension (aOR 2.22, 95% CI 1.09-4.50) independently predicted persistent residual lung lesions.

CONCLUSIONS

AI-based CT quantification of pneumonia provides valuable information beyond qualitative evaluation by physicians, enabling the prediction of critical outcomes and residual lung lesions in patients with COVID-19.

Are Interstitial Lung Abnormalities a Prognostic Factor of Worse Outcome in COVID-19 Pneumonia?

PURPOSE

To assess the association between interstitial lung abnormalities (ILAs) and worse outcome in patients affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease (COVID-19)-related pneumonia.

MATERIALS AND METHODS

The study included patients older than 18 years, who were admitted at the emergency department between February 29 and April 30, 2020 with findings of COVID-19 pneumonia at chest computed tomography (CT), with positive reverse-transcription polymerase chain reaction nasal-pharyngeal swab for SARS-CoV-2, and with the availability of prepandemic chest CT. Prepandemic CTs were reviewed for the presence of ILAs, categorized as fibrotic in cases with associated architectural distortion, bronchiectasis, or honeycombing. Worse outcome was defined as intensive care unit (ICU) admission or death. Cox proportional hazards regression analysis was used to test the association between ICU admission/death and preexisting ILAs.

RESULTS

The study included 147 patients (median age 73 y old; 95% CIs: 71-76-y old; 29% females). On prepandemic CTs, ILA were identified in 33/147 (22%) of the patients, 63% of which were fibrotic ILAs. Fibrotic ILAs were associated with higher risk of ICU admission or death in patients with COVID-19 pneumonia (hazard ratios: 2.73, 95% CIs: 1.50-4.97, P =0.001).

CONCLUSIONS

In patients affected by COVID-19 pneumonia, preexisting fibrotic ILAs were an independent predictor of worse prognosis, with a 2.7 times increased risk of ICU admission or death. Chest CT scans obtained before the diagnosis of COVID-19 pneumonia should be carefully reviewed for the presence and characterization of ILAs.

A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumonia

BACKGROUND

The role of computed tomography (CT) in the diagnosis and characterization of coronavirus disease 2019 (COVID-19) pneumonia has been widely recognized. We evaluated the performance of a software for quantitative analysis of chest CT, the LungQuant system, by comparing its results with independent visual evaluations by a group of 14 clinical experts. The aim of this work is to evaluate the ability of the automated tool to extract quantitative information from lung CT, relevant for the design of a diagnosis support model.

METHODS

LungQuant segments both the lungs and lesions associated with COVID-19 pneumonia (ground-glass opacities and consolidations) and computes derived quantities corresponding to qualitative characteristics used to clinically assess COVID-19 lesions. The comparison was carried out on 120 publicly available CT scans of patients affected by COVID-19 pneumonia. Scans were scored for four qualitative metrics: percentage of lung involvement, type of lesion, and two disease distribution scores. We evaluated the agreement between the LungQuant output and the visual assessments through receiver operating characteristics area under the curve (AUC) analysis and by fitting a nonlinear regression model.

RESULTS

Despite the rather large heterogeneity in the qualitative labels assigned by the clinical experts for each metric, we found good agreement on the metrics compared to the LungQuant output. The AUC values obtained for the four qualitative metrics were 0.98, 0.85, 0.90, and 0.81.

CONCLUSIONS

Visual clinical evaluation could be complemented and supported by computer-aided quantification, whose values match the average evaluation of several independent clinical experts.

KEY POINTS

We conducted a multicenter evaluation of the deep learning-based LungQuant automated software. We translated qualitative assessments into quantifiable metrics to characterize coronavirus disease 2019 (COVID-19) pneumonia lesions. Comparing the software output to the clinical evaluations, results were satisfactory despite heterogeneity of the clinical evaluations. An automatic quantification tool may contribute to improve the clinical workflow of COVID-19 pneumonia.

Clinical Characteristics and Predictors of In-Hospital Mortality of Patients Hospitalized with COVID-19 Infection

Background: The identification of parameters that would serve as predictors of prognosis in COVID-19 patients is very important. In this study, we assessed independent factors of in-hospital mortality of COVID-19 patients during the second wave of the pandemic. Material and methods: The study group consisted of patients admitted to two hospitals and diagnosed with COVID-19 between October 2020 and May 2021. Clinical and demographic features, the presence of comorbidities, laboratory parameters, and radiological findings at admission were recorded. The relationship of these parameters with in-hospital mortality was evaluated. Results: A total of 1040 COVID-19 patients (553 men and 487 women) qualified for the study. The in-hospital mortality rate was 26% across all patients. In multiple logistic regression analysis, age ≥ 70 years with OR = 7.8 (95% CI 3.17−19.32), p < 0.001, saturation at admission without oxygen ≤ 87% with OR = 3.6 (95% CI 1.49−8.64), p = 0.004, the presence of typical COVID-19-related lung abnormalities visualized in chest computed tomography ≥40% with OR = 2.5 (95% CI 1.05−6.23), p = 0.037, and a concomitant diagnosis of coronary artery disease with OR = 3.5 (95% CI 1.38−9.10), p = 0.009 were evaluated as independent risk factors for in-hospital mortality. Conclusion: The relationship between clinical and laboratory markers, as well as the advancement of lung involvement by typical COVID-19-related abnormalities in computed tomography of the chest, and mortality is very important for the prognosis of these patients and the determination of treatment strategies during the COVID-19 pandemic.

Quantitative chest computed tomography combined with plasma cytokines predict outcomes in COVID-19 patients

Despite extraordinary international efforts to dampen the spread and understand the mechanisms behind SARS-CoV-2 infections, accessible predictive biomarkers directly applicable in the clinic are yet to be discovered. Recent studies have revealed that diverse types of assays bear limited predictive power for COVID-19 outcomes. Here, we harness the predictive power of chest computed tomography (CT) in combination with plasma cytokines using a machine learning and k-fold cross-validation approach for predicting death during hospitalization and maximum severity degree in COVID-19 patients. Patients (n = 152) from the Mount Sinai Health System in New York with plasma cytokine assessment and a chest CT within five days from admission were included. Demographics, clinical, and laboratory variables, including plasma cytokines (IL-6, IL-8, and TNF-α), were collected from the electronic medical record. We found that CT quantitative alone was better at predicting severity (AUC 0.81) than death (AUC 0.70), while cytokine measurements alone better-predicted death (AUC 0.70) compared to severity (AUC 0.66). When combined, chest CT and plasma cytokines were good predictors of death (AUC 0.78) and maximum severity (AUC 0.82). Finally, we provide a simple scoring system (nomogram) using plasma IL-6, IL-8, TNF-α, ground-glass opacities (GGO) to aerated lung ratio and age as new metrics that may be used to monitor patients upon hospitalization and help physicians make critical decisions and considerations for patients at high risk of death for COVID-19.

Co-Administration of Remdesivir and Azithromycin May Protect against Intensive Care Unit Admission in COVID-19 Pneumonia Requiring Hospitalization: A Real-Life Observational Study

The benefits of remdesivir treatment, with or without co-administration of antibiotics such as azithromycin, are uncertain in COVID-19 pneumonia. The aim of this retrospective single-center study was to assess the effects of remdesivir, with or without azithromycin, on hospital mortality, intensive care unit (ICU) admission, and need of non-invasive ventilation. The clinical records of the COVID-19 patients hospitalized in an Italian ward in March 2021 were analyzed, and data on comorbidities and clinical, radiological, and laboratory presentation of the disease were collected. Among 394 participants (234 M), 173 received remdesivir (43.9%), including 81 with azithromycin (20.5%). Remdesivir recipients were younger, with less comorbidities, and had better PaO₂/FiO₂ and clinical outcomes, including reduced mortality, but the differences were not independent of covariates. Rates of ICU transferal were 17%, 9%, and 1% in the no remdesivir, remdesivir without azithromycin, and remdesivir/azithromycin groups, respectively. In a stepwise multivariate logistic regression model, remdesivir/azithromycin co-treatment was independently associated with reduced ICU admission (vs remdesivir alone, OR 0.081, 95% CI 0.008-0.789, p = 0.031; vs no remdesivir, OR 0.060, 95% CI 0.007-0.508, p = 0.010). These data suggest that the therapeutical effect of remdesivir in COVID-19 pneumonia may be potentiated by azithromycin. The association between the two drugs should be further investigated.

Chest CT Total Severity Score on Admission to Predict In-Hospital Mortality in COVID-19 Patients with Acute and Chronic Renal Impairment

Aim: To identify the predictors of in-hospital mortality in patients with coronavirus disease of 2019 (COVID-19) and acute renal impairment (ARI) or chronic kidney disease (CKD), and to evaluate the performance and inter-reader concordance of chest CT total severity scores (TSSs). Methods: This retrospective single-center study was conducted on symptomatic COVID-19 patients with renal impairment (either acute or chronic) and a serum creatinine of >2 mg/dL at the time of admission. The patients’ demographic characteristics, clinical data, and laboratory data were extracted from the clinical computerized medical records. All chest CT images obtained at the time of hospital admission were analyzed. Two radiologists independently assessed the pulmonary abnormalities and scored the severity using CT chest total severity score (TSS). Univariate logistic regression analysis was used to determine factors associated with in-hospital mortality. A receiver operating characteristic (ROC) curve analysis was performed for the TSS in order to identify the cut-off point that predicts mortality. Bland−Altman plots were used to evaluate agreement between the two radiologists assessing TSS. Results: A total of 100 patients were included, with a mean age of 60 years, 54 were males, 53 had ARI, and 47 had CKD. In terms of in-hospital mortality, 60 patients were classified in the non-survivor group and 40 were classified in the survivor group. The mortality rate was higher for those with ARI compared to those with CKD (p = 0.033). The univariate regression analysis showed an increasing odds of in-hospital mortality associated with higher respiratory rate (OR 1.149, 95% CI 1.057−1.248, p = 0.001), total bilirubin (OR 2.532, 95% CI 1.099−5.836, p = 0.029), lactate dehydrogenase (LDH) (OR 1.001, 95% CI 1.000−1.003, p = 0.018), CRP (OR 1.010, 95% CI 1.002−1.017, p = 0.012), invasive mechanical ventilation (MV) (OR 7.667, 95% CI 2.118−27.755, p = 0.002), a predominant pattern of pulmonary consolidation (OR 21.714, 95% CI 4.799−98.261, p < 0.001), and high TSS (OR 2.082, 95% CI 1.579−2.745, p < 0.001). The optimum cut-off value of TSS used to predict in-hospital mortality was 8.5 with a sensitivity of 86.7% and a specificity of 87.5%. There was excellent interobserver agreement (ICC > 0.9) between the two independent radiologists in their quantitative assessment of pulmonary changes using TSS. Conclusions: In-hospital mortality is high in COVID-19 patients with ARI/CKD, especially for those with ARI. High serum bilirubin, a predominant pattern of pulmonary consolidation, and TSS are the most significant predictors of mortality in these patients. Patients with a higher TSS may require more intensive hospital care. TSS is a reliable and helpful auxiliary tool for risk stratification among COVID-19 patients with ARI/CKD.

Different Lung Parenchyma Quantification Using Dissimilar Segmentation Software: A Multi-Center Study for COVID-19 Patients

Background: Chest Computed Tomography (CT) imaging has played a central role in the diagnosis of interstitial pneumonia in patients affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and can be used to obtain the extent of lung involvement in COVID-19 pneumonia patients either qualitatively, via visual inspection, or quantitatively, via AI-based software. This study aims to compare the qualitative/quantitative pathological lung extension data on COVID-19 patients. Secondly, the quantitative data obtained were compared to verify their concordance since they were derived from three different lung segmentation software. Methods: This double-center study includes a total of 120 COVID-19 patients (60 from each center) with positive reverse-transcription polymerase chain reaction (RT-PCR) who underwent a chest CT scan from November 2020 to February 2021. CT scans were analyzed retrospectively and independently in each center. Specifically, CT images were examined manually by two different and experienced radiologists for each center, providing the qualitative extent score of lung involvement, whereas the quantitative analysis was performed by one trained radiographer for each center using three different software: 3DSlicer, CT Lung Density Analysis, and CT Pulmo 3D. Results: The agreement between radiologists for visual estimation of pneumonia at CT can be defined as good (ICC 0.79, 95% CI 0.73–0.84). The statistical tests show that 3DSlicer overestimates the measures assessed; however, ICC index returns a value of 0.92 (CI 0.90–0.94), indicating excellent reliability within the three software employed. ICC was also performed between each single software and the median of the visual score provided by the radiologists. This statistical analysis underlines that the best agreement is between 3D Slicer “LungCTAnalyzer” and the median of the visual score (0.75 with a CI 0.67–82 and with a median value of 22% of disease extension for the software and 25% for the visual values). Conclusions: This study provides for the first time a direct comparison between the actual gold standard, which is represented by the qualitative information described by radiologists, and novel quantitative AI-based techniques, here represented by three different commonly used lung segmentation software, underlying the importance of these specific values that in the future could be implemented as consistent prognostic and clinical course parameters.

Coronary artery calcium score as a prognostic factor of adverse outcomes in patients with COVID-19: a comprehensive review

BACKGROUND AND AIM

The association of known cardiovascular risk factors and poor prognosis of coronavirus disease 2019 (COVID-19) has been recently emphasized. Coronary artery calcium (CAC) score is considered to be a risk predictor of cardiovascular events. Therefore, we have conducted a review of literature on the predictive value of CAC score predictive value in COVID-19 outcome.

METHOD

A search of literature was conducted, aiming for articles published until December 2021 on PubMed and Scopus to identify potentially eligible studies.

DISCUSSION

A total of 18 articles were reviewed for association between higher CAC score and adverse outcomes in COVID-19.

CONCLUSION

The coronary calcium score could be considered as a new radiological marker for risk assessment in COVID-19 patients and providing additional information in fields of prognosis and possible cardiovascular complications. High CAC score is associated with higher in-hospital death and adverse clinical outcomes in patients with confirmed COVID-19, which highlights the importance of calcium load testing for hospitalized COVID-19 patients and calls for attention to patients with high CAC scores.

Biomarker evaluation for prognostic stratification of patients with COVID-19: the added value of quantitative chest CT

Aim: Pulmonary disease burden and biomarkers are possible predictors of outcomes in patients with COVID-19 and provide complementary information. In this study, the prognostic value of adding quantitative chest computed tomography (CT) to a multiple biomarker approach was evaluated among 148 hospitalized patients with confirmed COVID-19. Materials & methods: Patients admitted between March and July 2020 who were submitted to chest CT and biomarker measurement (troponin I, D-dimer and C-reactive protein) were retrospectively analyzed. Biomarker and tomographic data were compared and associated with death and intensive care unit admission. Results: The number of elevated biomarkers was significantly associated with greater opacification percentages, lower lung volumes and higher death and intensive care unit admission rates. Total lung volume <3.0 l provided further stratification for mortality when combined with biomarker evaluation. Conclusion: Adding automated CT data to a multiple biomarker approach may provide a simple strategy for enhancing risk stratification of patients with COVID-19.

SARS-CoV-2 management in Emergency Department: risk stratification and care setting identification proposal based on first pandemic wave in Pisa University Hospital

SARS-CoV-2 management in Emergency Department: risk stratification and care setting identification proposal based on first pandemic wave in Pisa University Hospital Background: COVID-19 patients require early treatment and admission to an appropriate care setting, considering possible rapid and unpredictable to Severe Acute Respiratory Syndrome. Objective: A flow-chart was developed by a multidisciplinary team of Emergency Department (ED) clinicians, intensivists and radiologists aiming to provide tools for disease severity stratification, appropriate ventilation strategy and hospitalization setting identification. Methods: We conducted a retrospective application of our model on 313 hospitalized patients at Pisa University Hospital including 222 patients admitted to ED for respiratory failure between March and April 2020. Risk stratification score was based on respiratory and chest imaging parameters, while management strategy on comorbidities and age.  Results: Age, comorbidities, clinical respiratory and arterial blood gas parameters, semi-quantitative chest computed tomography score were significant predictors of mortality (p<0,05). Mortality rate was higher in patients treated in intensive care units (26,5%) and undergoing endo-tracheal intubation (32,7%), compared to medical area (21,3%). We verified a good concordance (81,7%) between the proposed model and actual evaluation in ED. Outcomes analysis of subgroups of patients homogeneous for baseline features allowed to verify safety of our model: in non-elderly and/or non-comorbid patients (15% mortality) our scheme overestimates the risk in 30% of cases, but it suggests non-intensive management in patients with reduced functional reserve, elderly and with comorbidities (50% mortality). Conclusion: Correct management of respiratory failure COVID-19 patients is crucial in this unexpected pandemic. Our flow-chart, despite retrospectively application in small sample, could represents a valid and safe proposal for evaluation in ED.

Coronavirus Disease 2019 Phenotypes, Lung Ultrasound, Chest Computed Tomography and Clinical Features in Critically Ill Mechanically Ventilated Patients

Chest computed tomography (CT) may provide insights into the pathophysiology of coronavirus disease 2019 (COVID-19), although it is not suitable for a timely bedside dynamic assessment of patients admitted to intensive care unit (ICU); therefore, lung ultrasound (LUS) has been proposed as a complementary diagnostic tool. The aims of this study were to investigate different lungs phenotypes in patients with COVID-19 and to assess the differences in CT and LUS scores between ICU survivors and non-survivors. We also explored the association between CT and LUS, and oxygenation (arterial partial pressure of oxygen [PaO₂]/fraction of inspired oxygen [FiO_2]) and clinical parameters. The study included 39 patients with COVID-19. CT scans revealed types 1, 2 and 3 phenotypes in 62%, 28% and 10% of patients, respectively. Among survivors, pattern 1 was prevalent (p < 0.005). Chest CT and LUS scores differed between survivors and non-survivors both at ICU admission and 10 days after and were associated with ICU mortality. Chest CT score was positively correlated with LUS findings at ICU admission (r = 0.953, p < 0.0001) and was inversely correlated with PaO₂/FiO₂ (r = -0.375, p = 0.019) and C-reactive protein (r = 0.329, p = 0.041). LUS score was inversely correlated with PaO₂/FiO₂ (r = -0.345, p = 0.031). COVID-19 presents distinct phenotypes with differences between survivors and non-survivors. LUS is a valuable monitoring tool in an ICU setting because it may correlate with CT findings and mortality, although it cannot predict oxygenation changes.

Predictors of intubation and mortality in COVID-19 patients: a retrospective study

Background

Estimating the risk of intubation and mortality among COVID-19 patients can help clinicians triage these patients and allocate resources more efficiently. Thus, here we sought to identify the risk factors associated with intubation and intra-hospital mortality in a cohort of COVID-19 patients hospitalized due to hypoxemic acute respiratory failure (ARF).

Results

We included retrospectively a total of 187 patients admitted to the subintensive and intensive care units of the University Hospital “Maggiore della Carità” of Novara between March 1st and April 30th, 2020. Based on these patients’ demographic characteristics, early clinical and laboratory variables, and quantitative chest computerized tomography (CT) findings, we developed two random forest (RF) models able to predict intubation and intra-hospital mortality. Variables independently associated with intubation were C-reactive protein (p < 0.001), lactate dehydrogenase level (p = 0.018) and white blood cell count (p = 0.026), while variables independently associated with mortality were age (p < 0.001), other cardiovascular diseases (p = 0.029), C-reactive protein (p = 0.002), lactate dehydrogenase level (p = 0.018), and invasive mechanical ventilation (p = 0.001). On quantitative chest CT analysis, ground glass opacity, consolidation, and fibrosis resulted significantly associated with patient intubation and mortality. The major predictors for both models were the ratio between partial pressure of arterial oxygen and fraction of inspired oxygen, age, lactate dehydrogenase, C-reactive protein, glycemia, CT quantitative parameters, lymphocyte count, and symptom onset.

Conclusions

Altogether, our findings confirm previously reported demographic, clinical, hemato-chemical, and radiologic predictors of adverse outcome among COVID-19-associated hypoxemic ARF patients. The two newly developed RF models herein described show an overall good level of accuracy in predicting intra-hospital mortality and intubation in our study population. Thus, their future development and implementation may help not only identify patients at higher risk of deterioration more effectively but also rebalance the disproportion between resources and demand.

Supplementary Information

The online version contains supplementary material available at 10.1186/s44158-021-00016-5.

Accuracy of Machine Learning Models to Predict Mortality in COVID-19 Infection Using the Clinical and Laboratory Data at the Time of Admission

Aim

This study aimed to develop a predictive model to predict patients’ mortality with coronavirus disease 2019 (COVID-19) from the basic medical data on the first day of admission.

Methods

The medical data including the demographic, clinical, and laboratory features on the first day of admission of clinically diagnosed COVID-19 patients were documented. The outcome of patients was also recorded as discharge or death. Feature selection models were then implemented and different machine learning models were developed on top of the selected features to predict discharge or death. The trained models were then tested on the test dataset.

Results

A total of 520 patients were included in the training dataset. The feature selection demonstrated 22 features as the most powerful predictive features. Among different machine learning models, the naive Bayes demonstrated the best performance with an area under the curve of 0.85. The ensemble model of the naive Bayes and neural network combination had slightly better performance with an area under the curve of 0.86. The models had relatively the same performance on the test dataset.

Conclusion

Developing a predictive machine learning model based on the basic medical features on the first day of admission in COVID-19 infection is feasible with acceptable performance.

Coronary calcium score as a predictor of outcomes in the hypertensive Covid-19 population: results from the Italian (S) Core-Covid-19 Registry

Hypertension is associated with more severe disease and adverse outcomes in COVID-19 patients. Recent investigations have indicated that hypertension might be an independent predictor of outcomes in COVID-19 patients regardless of other cardiovascular and noncardiovascular comorbidities. We explored the significance of coronary calcifications in 694 hypertensive patients in the Score-COVID registry, an Italian multicenter study conducted during the first pandemic wave in the Western world (March-April 2020). A total of 1565 patients admitted with RNA-PCR-positive nasopharyngeal swabs and chest computed tomography (CT) at hospital admission were included in the study. Clinical outcomes and cardiovascular calcifications were analyzed independently by a research core lab. Hypertensive patients had a different risk profile than nonhypertensive patients, with more cardiovascular comorbidities. The deceased hypertensive patients had a greater coronary calcification burden at the level of the anterior descending coronary artery. Hypertension status and the severity cutoffs of coronary calcifications were used to stratify the clinical outcomes. For every 100-mm³ increase in coronary calcium volume, hospital mortality in hypertensive patients increased by 8%, regardless of sex, age, diabetes, creatinine, and lung interstitial involvement. The coronary calcium score contributes to stratifying the risk of complications in COVID-19 patients. Cardiovascular calcifications appear to be a promising imaging marker for providing pathophysiological insight into cardiovascular risk factors and COVID-19 outcomes.

Automated Quantitative Lung CT Improves Prognostication in Non-ICU COVID-19 Patients beyond Conventional Biomarkers of Disease

(1) Background: COVID-19 continues to represent a worrying pandemic. Despite the high percentage of non-severe illness, a wide clinical variability is often reported in real-world practice. Accurate predictors of disease aggressiveness, however, are still lacking. The purpose of our study was to evaluate the impact of quantitative analysis of lung computed tomography (CT) on non-intensive care unit (ICU) COVID-19 patients' prognostication; (2) Methods: Our historical prospective study included fifty-five COVID-19 patients consecutively submitted to unenhanced lung CT. Primary outcomes were recorded during hospitalization, including composite ICU admission for the need of mechanical ventilation and/or death occurrence. CT examinations were retrospectively evaluated to automatically calculate differently aerated lung tissues (i.e., overinflated, well-aerated, poorly aerated, and non-aerated tissue). Scores based on the percentage of lung weight and volume were also calculated; (3) Results: Patients who reported disease progression showed lower total lung volume. Inflammatory indices correlated with indices of respiratory failure and high-density areas. Moreover, non-aerated and poorly aerated lung tissue resulted significantly higher in patients with disease progression. Notably, non-aerated lung tissue was independently associated with disease progression (HR: 1.02; p-value: 0.046). When different predictive models including clinical, laboratoristic, and CT findings were analyzed, the best predictive validity was reached by the model that included non-aerated tissue (C-index: 0.97; p-value: 0.0001); (4) Conclusions: Quantitative lung CT offers wide advantages in COVID-19 disease stratification. Non-aerated lung tissue is more likely to occur with severe inflammation status, turning out to be a strong predictor for disease aggressiveness; therefore, it should be included in the predictive model of COVID-19 patients.

Cardiovascular findings on chest computed tomography associated with COVID-19 adverse clinical outcomes

STUDY OBJECTIVE

Chest computed tomography (chest CT) is routinely obtained to assess disease severity in COVID-19. While pulmonary findings are well-described in COVID-19, the implications of cardiovascular findings are less well understood. We evaluated the impact of cardiovascular findings on chest CT on the adverse composite outcome (ACO) of hospitalized COVID-19 patients.

SETTING/PARTICIPANTS

245 COVID-19 patients who underwent chest CT at Rush University Health System were included.

DESIGN

Cardiovascular findings, including coronary artery calcification (CAC), aortic calcification, signs of right ventricular strain [right ventricular to left ventricular diameter ratio, pulmonary artery to aorta diameter ratio, interventricular septal position, and inferior vena cava (IVC) reflux], were measured by trained physicians.

INTERVENTIONS/MAIN OUTCOME MEASURES

These findings, along with pulmonary findings, were analyzed using univariable logistic analysis to determine the risk of ACO defined as intensive care admission, need for non-invasive positive pressure ventilation, intubation, in-hospital and 60-day mortality. Secondary endpoints included individual components of the ACO.

RESULTS

Aortic calcification was independently associated with an increased risk of the ACO (odds ratio 1.86, 95% confidence interval (1.11-3.17) p < 0.05). Aortic calcification, CAC, abnormal septal position, or IVC reflux of contrast were all significantly associated with 60-day mortality and major adverse cardiovascular events. IVC reflux was associated with in-hospital mortality (p = 0.005).

CONCLUSION

Incidental cardiovascular findings on chest CT are clinically important imaging markers in COVID-19. It is important to ascertain and routinely report cardiovascular findings on CT imaging of COVID-19 patients as they have potential to identify high risk patients.

In-hospital prognostic role of coronary atherosclerotic burden in COVID-19 patients

AIMS

Currently, there are few available data regarding a possible role for subclinical atherosclerosis as a risk factor for mortality in Coronavirus Disease 19 (COVID-19) patients. We used coronary artery calcium (CAC) score derived from chest computed tomography (CT) scan to assess the in-hospital prognostic role of CAC in patients affected by COVID-19 pneumonia.

METHODS

Electronic medical records of patients with confirmed diagnosis of COVID-19 were retrospectively reviewed. Patients with known coronary artery disease (CAD) were excluded. A CAC score was calculated for each patient and was used to categorize them into one of four groups: 0, 1-299, 300-999 and at least 1000. The primary endpoint was in-hospital mortality for any cause.

RESULTS

The final population consisted of 282 patients. Fifty-seven patients (20%) died over a follow-up time of 40 days. The presence of CAC was detected in 144 patients (51%). Higher CAC score values were observed in nonsurvivors [median: 87, interquartile range (IQR): 0.0-836] compared with survivors (median: 0, IQR: 0.0-136). The mortality rate in patients with a CAC score of at least 1000 was significantly higher than in patients without coronary calcifications (50 vs. 11%) and CAC score 1-299 (50 vs. 23%), P < 0.05. After adjusting for clinical variables, the presence of any CAC categories was not an independent predictor of mortality; however, a trend for increased risk of mortality was observed in patients with CAC of at least 1000.

CONCLUSION

The correlation between CAC score and COVID-19 is fascinating and under-explored. However, in multivariable analysis, the CAC score did not show an additional value over more robust clinical variables in predicting in-hospital mortality. Only patients with the highest atherosclerotic burden (CAC ≥1000) could represent a high-risk population, similarly to patients with known CAD.

Quantitative chest CT combined with plasma cytokines predict outcomes in COVID-19 patients

Despite extraordinary international efforts to dampen the spread and understand the mechanisms behind SARS-CoV-2 infections, accessible predictive biomarkers directly applicable in the clinic are yet to be discovered. Recent studies have revealed that diverse types of assays bear limited predictive power for COVID-19 outcomes. Here, we harness the predictive power of chest CT in combination with plasma cytokines using a machine learning approach for predicting death during hospitalization and maximum severity degree in COVID-19 patients. Patients (n=152) from the Mount Sinai Health System in New York with plasma cytokine assessment and a chest CT within 5 days from admission were included. Demographics, clinical, and laboratory variables, including plasma cytokines (IL-6, IL-8, and TNF-α) were collected from the electronic medical record. We found that chest CT combined with plasma cytokines were good predictors of death (AUC 0.78) and maximum severity (AUC 0.82), whereas CT quantitative was better at predicting severity (AUC 0.81 vs 0.70) while cytokine measurements better predicted death (AUC 0.70 vs 0.66). Finally, we provide a simple scoring system using plasma IL-6, IL-8, TNF-α, GGO to aerated lung ratio and age as novel metrics that may be used to monitor patients upon hospitalization and help physicians make critical decisions and considerations for patients at high risk of death for COVID-19.

An artificial neural network model to predict the mortality of COVID-19 patients using routine blood samples at the time of hospital admission: Development and validation study

Background:

In a pandemic situation (e.g., COVID-19), the most important issue is to select patients at risk of high mortality at an early stage and to provide appropriate treatments. However, a few studies applied the model to predict in-hospital mortality using routine blood samples at the time of hospital admission. This study aimed to develop an app, name predict the mortality of COVID-19 patients (PMCP) app, to predict the mortality of COVID-19 patients at hospital-admission time.

Methods:

We downloaded patient records from 2 studies, including 361 COVID-19 patients in Wuhan, China, and 106 COVID-19 patients in 3 Korean medical institutions. A total of 30 feature variables were retrieved, consisting of 28 blood biomarkers and 2 demographic variables (i.e., age and gender) of patients. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared with each other across 2 scenarios using

An app for predicting the mortality of COVID-19 patients was developed using the model's estimated parameters for the prediction and classification of PMCP at an earlier stage. Feature variables and prediction results were visualized using the forest plot and category probability curves shown on Google Maps.

Results:

We observed that

Conclusions:

Our new PMCP app with ANN model accurately predicts the mortality probability for COVID-19 patients. It is publicly available and aims to help health care providers fight COVID-19 and improve patients’ classifications against treatment risk.

The role of bronchoscopy in patients with SARS-CoV-2 pneumonia

BACKGROUND

The role of bronchoscopy in coronavirus disease 2019 (COVID-19) is a matter of debate.

PATIENTS AND METHODS

This observational multicentre study aimed to analyse the prognostic impact of bronchoscopic findings in a consecutive cohort of patients with suspected or confirmed COVID-19. Patients were enrolled at 17 hospitals from February to June 2020. Predictors of in-hospital mortality were assessed by multivariate logistic regression.

RESULTS

A total of 1027 bronchoscopies were performed in 515 patients (age 61.5±11.2 years; 73% men), stratified into a clinical suspicion cohort (n=30) and a COVID-19 confirmed cohort (n=485). In the clinical suspicion cohort, the diagnostic yield was 36.7%. In the COVID-19 confirmed cohort, bronchoscopies were predominantly performed in the intensive care unit (n=961; 96.4%) and major indications were: difficult mechanical ventilation (43.7%), mucus plugs (39%) and persistence of radiological infiltrates (23.4%). 147 bronchoscopies were performed to rule out superinfection, and diagnostic yield was 42.9%. There were abnormalities in 91.6% of bronchoscopies, the most frequent being mucus secretions (82.4%), haematic secretions (17.7%), mucus plugs (17.6%), and diffuse mucosal hyperaemia (11.4%). The independent predictors of in-hospital mortality were: older age (OR 1.06; p<0.001), mucus plugs as indication for bronchoscopy (OR 1.60; p=0.041), absence of mucosal hyperaemia (OR 0.49; p=0.041) and the presence of haematic secretions (OR 1.79; p=0.032).

CONCLUSION

Bronchoscopy may be indicated in carefully selected patients with COVID-19 to rule out superinfection and solve complications related to mechanical ventilation. The presence of haematic secretions in the distal bronchial tract may be considered a poor prognostic feature in COVID-19.

COVID-19 pneumonia-ultrasound, radiographic, and computed tomography findings: a comprehensive pictorial essay

Ultrasound, chest X-ray, and computed tomography (CT) have been used with excellent results in diagnosis, first assessment, and follow-up of COVID-19 confirmed and suspected patients. Ultrasound and chest X-ray have the advantages of the wide availability and acquisition at the patient's bed; CT showed high sensitivity in COVID-19 diagnosis. Ground-glass opacities and consolidation are the main CT and X-ray features; the distribution of lung abnormalities is typically bilateral and peripheral. Less typical findings, including pleural effusion, mediastinal lymphadenopathies, the bubble air sign, and cavitation, can also be visible on chest CT. Radiologists should be aware of the advantages and limitations of the available imaging techniques and of the different pulmonary aspects of COVID-19 infection.

Lung Lesion Burden found on Chest CT as a Prognostic Marker in Hospitalized Patients with High Clinical Suspicion of COVID-19 Pneumonia: a Brazilian experience

OBJECTIVE

To investigate the relationship between lung lesion burden (LLB) found on chest computed tomography (CT) and 30-day mortality in hospitalized patients with high clinical suspicion of coronavirus disease 2019 (COVID-19), accounting for tomographic dynamic changes.

METHODS

Patients hospitalized with high clinical suspicion of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in a dedicated and reference hospital for COVID-19, having undergone at least one RT-PCR test, regardless of the result, and with one CT compatible with COVID-19, were retrospectively studied. Clinical and laboratory data upon admission were assessed, and LLB found on CT was semi-quantitatively evaluated through visual analysis. The primary outcome was 30-day mortality after admission. Secondary outcomes, including the intensive care unit (ICU) admission, mechanical ventilation used, and length of stay (LOS), were assessed.

RESULTS

A total of 457 patients with a mean age of 57±15 years were included. Among these, 58% presented with positive RT-PCR result for COVID-19. The median time from symptom onset to RT-PCR was 8 days [interquartile range 6-11 days]. An initial LLB of ≥50% using CT was found in 201 patients (44%), which was associated with an increased crude at 30-day mortality (31% vs. 15% in patients with LLB of <50%, p<0.001). An LLB of ≥50% was also associated with an increase in the ICU admission, the need for mechanical ventilation, and a prolonged LOS after adjusting for baseline covariates and accounting for the CT findings as a time-varying covariate; hence, patients with an LLB of ≥50% remained at a higher risk at 30-day mortality (adjusted hazard ratio 2.17, 95% confidence interval 1.47-3.18, p<0.001).

CONCLUSION

Even after accounting for dynamic CT changes in patients with both clinical and imaging findings consistent with COVID-19, an LLB of ≥50% might be associated with a higher risk of mortality.