Prognostic findings for ICU admission in patients with COVID-19 pneumonia: baseline and follow-up chest CT and the added value of artificial intelligence

Classification of the ICU Admission for COVID-19 Patients with Transfer Learning Models Using Chest X-Ray Images

Objectives: Predicting intensive care unit (ICU) admissions during pandemic outbreaks such as COVID-19 can assist clinicians in early intervention and the better allocation of medical resources. Artificial intelligence (AI) tools are promising for this task, but their development can be hindered by the limited availability of training data. This study aims to explore model development strategies in data-limited scenarios, specifically in detecting the need for ICU admission using chest X-rays of COVID-19 patients by leveraging transfer learning and data extension to improve model performance. Methods: We explored convolutional neural networks (CNNs) pre-trained on either natural images or chest X-rays, fine-tuning them on a relatively limited dataset (COVID-19-NY-SBU, n = 899) of lung-segmented X-ray images for ICU admission classification. To further address data scarcity, we introduced a dataset extension strategy that integrates an additional dataset (MIDRC-RICORD-1c, n = 417) with different but clinically relevant labels. Results: The TorchX-SBU-RSNA and ELIXR-SBU-RSNA models, leveraging X-ray-pre-trained models with our training data extension approach, enhanced ICU admission classification performance from a baseline AUC of 0.66 (56% sensitivity and 68% specificity) to AUCs of 0.77-0.78 (58-62% sensitivity and 78-80% specificity). The gradient-weighted class activation mapping (Grad-CAM) analysis demonstrated that the TorchX-SBU-RSNA model focused more precisely on the relevant lung regions and reduced the distractions from non-relevant areas compared to the natural image-pre-trained model without data expansion. Conclusions: This study demonstrates the benefits of medical image-specific pre-training and strategic dataset expansion in enhancing the model performance of imaging AI models. Moreover, this approach demonstrates the potential of using diverse but limited data sources to alleviate the limitations of model development for medical imaging AI. The developed AI models and training strategies may facilitate more effective and efficient patient management and resource allocation in future outbreaks of infectious respiratory diseases.

Machine Learning Based Multi-Parameter Modeling for Prediction of Post-Inflammatory Lung Changes

Objectives: Prediction of lung function deficits following pulmonary infection is challenging and suffers from inaccuracy. We sought to develop machine-learning models for prediction of post-inflammatory lung changes based on COVID-19 recovery data. Methods: In the prospective CovILD study (n = 420 longitudinal observations from n = 140 COVID-19 survivors), data on lung function testing (LFT), chest CT including severity scoring by a human radiologist and density measurement by artificial intelligence, demography, and persistent symptoms were collected. This information was used to develop models of numeric readouts and abnormalities of LFT with four machine learning algorithms (Random Forest, gradient boosted machines, neural network, and support vector machines). Results: Reduced DLCO (diffusion capacity for carbon monoxide <80% of reference) was found in 94 (22%) observations. Those observations were modeled with a cross-validated accuracy of 82-85%, AUC of 0.87-0.9, and Cohen's κ of 0.45-0.5. No reliable models could be established for FEV1 or FVC. For DLCO as a continuous variable, three machine learning algorithms yielded meaningful models with cross-validated mean absolute errors of 11.6-12.5% and R2 of 0.26-0.34. CT-derived features such as opacity, high opacity, and CT severity score were among the most influential predictors of DLCO impairment. Conclusions: Multi-parameter machine learning trained with demographic, clinical, and artificial intelligence chest CT data reliably and reproducibly predicts LFT deficits and outperforms single markers of lung pathology and human radiologist's assessment. It may improve diagnostic and foster personalized treatment.

Multimodal data fusion using sparse canonical correlation analysis and cooperative learning: a COVID-19 cohort study

Through technological innovations, patient cohorts can be examined from multiple views with high-dimensional, multiscale biomedical data to classify clinical phenotypes and predict outcomes. Here, we aim to present our approach for analyzing multimodal data using unsupervised and supervised sparse linear methods in a COVID-19 patient cohort. This prospective cohort study of 149 adult patients was conducted in a tertiary care academic center. First, we used sparse canonical correlation analysis (CCA) to identify and quantify relationships across different data modalities, including viral genome sequencing, imaging, clinical data, and laboratory results. Then, we used cooperative learning to predict the clinical outcome of COVID-19 patients: Intensive care unit admission. We show that serum biomarkers representing severe disease and acute phase response correlate with original and wavelet radiomics features in the LLL frequency channel (cor(Xu1, Zv1) = 0.596, p value < 0.001). Among radiomics features, histogram-based first-order features reporting the skewness, kurtosis, and uniformity have the lowest negative, whereas entropy-related features have the highest positive coefficients. Moreover, unsupervised analysis of clinical data and laboratory results gives insights into distinct clinical phenotypes. Leveraging the availability of global viral genome databases, we demonstrate that the Word2Vec natural language processing model can be used for viral genome encoding. It not only separates major SARS-CoV-2 variants but also allows the preservation of phylogenetic relationships among them. Our quadruple model using Word2Vec encoding achieves better prediction results in the supervised task. The model yields area under the curve (AUC) and accuracy values of 0.87 and 0.77, respectively. Our study illustrates that sparse CCA analysis and cooperative learning are powerful techniques for handling high-dimensional, multimodal data to investigate multivariate associations in unsupervised and supervised tasks.

Time-dependent CT score-based model for identifying severe/critical COVID-19 at a fever clinic after the emergence of Omicron variant

Rationale and objectives

The computed tomography (CT) score has been used to evaluate the severity of COVID-19 during the pandemic; however, most studies have overlooked the impact of infection duration on the CT score. This study aimed to determine the optimal cutoff CT score value for identifying severe/critical COVID-19 during different stages of infection and to construct corresponding predictive models using radiological characteristics and clinical factors.

Materials and methods

This retrospective study collected consecutive baseline chest CT images of confirmed COVID-19 patients from a fever clinic at a tertiary referral hospital from November 28, 2022, to January 8, 2023. Cohorts were divided into three subcohorts according to the time interval from symptom onset to CT examination at the hospital: early phase (0-3 days), intermediate phase (4-7 days), and late phase (8-14 days). The binary endpoints were mild/moderate and severe/critical infection. The CT scores and qualitative CT features were manually evaluated. A logistic regression analysis was performed on the CT score as determined by a visual assessment to predict severe/critical infection. Receiver operating characteristic analysis was performed and the area under the curve (AUC) was calculated. The optimal cutoff value was determined by maximizing the Youden index in each subcohort. A radiology score and integrated models were then constructed by combining the qualitative CT features and clinical features, respectively, using multivariate logistic regression with stepwise elimination.

Results

A total of 962 patients (aged, 61.7 ± 19.6 years; 490 men) were included; 179 (18.6%) were classified as severe/critical COVID-19, while 344 (35.8%) had a typical Radiological Society of North America (RSNA) COVID-19 appearance. The AUCs of the CT score models reached 0.91 (95% confidence interval (CI) 0.88-0.94), 0.82 (95% CI 0.76-0.87), and 0.83 (95% CI 0.77-0.89) during the early, intermediate, and late phases, respectively. The best cutoff values of the CT scores during each phase were 1.5, 4.5, and 5.5. The predictive accuracies associated with the time-dependent cutoff values reached 88% (vs.78%), 73% (vs. 63%), and 87% (vs. 57%), which were greater than those associated with universal cutoff value (all P < 0.001). The radiology score models reached AUCs of 0.96 (95% CI 0.94-0.98), 0.90 (95% CI 0.87-0.94), and 0.89 (95% CI 0.84-0.94) during the early, intermediate, and late phases, respectively. The integrated models including demographic and clinical risk factors greatly enhanced the AUC during the intermediate and late phases compared with the values obtained with the radiology score models; however, an improvement in accuracy was not observed.

Conclusion

The time interval between symptom onset and CT examination should be tracked to determine the cutoff value for the CT score for identifying severe/critical COVID-19. The radiology score combining qualitative CT features and the CT score complements clinical factors for identifying severe/critical COVID-19 patients and facilitates timely hierarchical diagnoses and treatment.

Characteristics and Outcomes of Coronavirus Disease- 2019 Among Pregnant Women in Saudi Arabia; a Retrospective Study

Background

Pregnancy-related coronavirus disease 2019 infection ranges from asymptomatic to very serious illness. This study aimed to determine the impact of the COVID-19 infection on pregnant women in the Jazan region of Saudi Arabia.

Methods

Retrospective observational study of women who had COVID-19 positive test in pregnancy admitted in King Fahd Hospital, Abu Arish General Hospital, and Sabya General Hospital, Jazan, Saudi Arabia during the period between March 2020 and March 2022. Data were extracted from the patient's records. Frequency and percentage distributions were calculated for categorical variables. Descriptive studies and regression analysis were conducted to evaluate the association between selected variables and pregnancy outcomes.

Results

Of the 33 pregnant women with confirmed infection, the majority were in their second and third trimester, with approximately 42.4% requiring intensive care unit (ICU) admission and oxygen therapy. The most prevalent symptoms were high respiratory rate and low blood pressure, often accompanied by fever, cough, and shortness of breath. Live births resulted in 54.5% of the cases, while two maternal deaths were reported. Significant associations were found between the need for non-invasive ventilation and timing of infection (p = 0.026), the mode of delivery and timing of infection (p = 0.036), and the mode of delivery and body mass index (BMI) (p = 0.007).

Conclusion

COVID-19 poses significant risks to pregnant women, particularly in the third trimester, and emphasized the importance of early identification of high-risk pregnancies, strategic planning, and enhanced monitoring during antenatal care.

Quantitative Computed Tomography Lung COVID Scores with Laboratory Markers: Utilization to Predict Rapid Progression and Monitor Longitudinal Changes in Patients with Coronavirus 2019 (COVID-19) Pneumonia

Coronavirus disease 2019 (COVID-19), is an ongoing issue in certain populations, presenting rapidly worsening pneumonia and persistent symptoms. This study aimed to test the predictability of rapid progression using radiographic scores and laboratory markers and present longitudinal changes. This retrospective study included 218 COVID-19 pneumonia patients admitted at the Chungnam National University Hospital. Rapid progression was defined as respiratory failure requiring mechanical ventilation within one week of hospitalization. Quantitative COVID (QCOVID) scores were derived from high-resolution computed tomography (CT) analyses: (1) ground glass opacity (QGGO), (2) mixed diseases (QMD), and (3) consolidation (QCON), and the sum, quantitative total lung diseases (QTLD). Laboratory data, including inflammatory markers, were obtained from electronic medical records. Rapid progression was observed in 9.6% of patients. All QCOVID scores predicted rapid progression, with QMD showing the best predictability (AUC = 0.813). In multivariate analyses, the QMD score and interleukin(IL)-6 level were important predictors for rapid progression (AUC = 0.864). With >2 months follow-up CT, remained lung lesions were observed in 21 subjects, even after several weeks of negative reverse transcription polymerase chain reaction test. AI-driven quantitative CT scores in conjugation with laboratory markers can be useful in predicting the rapid progression and monitoring of COVID-19.

Multimodal Biomedical Data Fusion Using Sparse Canonical Correlation Analysis and Cooperative Learning: A Cohort Study on COVID-19

Through technological innovations, patient cohorts can be examined from multiple views with high-dimensional, multiscale biomedical data to classify clinical phenotypes and predict outcomes. Here, we aim to present our approach for analyzing multimodal data using unsupervised and supervised sparse linear methods in a COVID-19 patient cohort. This prospective cohort study of 149 adult patients was conducted in a tertiary care academic center. First, we used sparse canonical correlation analysis (CCA) to identify and quantify relationships across different data modalities, including viral genome sequencing, imaging, clinical data, and laboratory results. Then, we used cooperative learning to predict the clinical outcome of COVID-19 patients. We show that serum biomarkers representing severe disease and acute phase response correlate with original and wavelet radiomics features in the LLL frequency channel (corr(Xu1, Zv1) = 0.596, p-value < 0.001). Among radiomics features, histogram-based first-order features reporting the skewness, kurtosis, and uniformity have the lowest negative, whereas entropy-related features have the highest positive coefficients. Moreover, unsupervised analysis of clinical data and laboratory results gives insights into distinct clinical phenotypes. Leveraging the availability of global viral genome databases, we demonstrate that the Word2Vec natural language processing model can be used for viral genome encoding. It not only separates major SARS-CoV-2 variants but also allows the preservation of phylogenetic relationships among them. Our quadruple model using Word2Vec encoding achieves better prediction results in the supervised task. The model yields area under the curve (AUC) and accuracy values of 0.87 and 0.77, respectively. Our study illustrates that sparse CCA analysis and cooperative learning are powerful techniques for handling high-dimensional, multimodal data to investigate multivariate associations in unsupervised and supervised tasks.

Could initial CT chest manifestation in patients hospitalized with COVID 19 pneumonia predict outcome on short term basis

Chest computed tomography (CT) can be used to monitor the course of the disease or response to therapy. Therefore, our study was designed to identify chest CT manifestations that can predict the outcome of patients on short term follow-up. This was a retrospective study wherein we reviewed chest CT scans of 112 real-time reverse transcription polymerase chain reaction positive patients admitted to our hospital. All 112 patients underwent follow-up chest CT at a time interval of 4 to 42 days. Our study included 83 male and 29 female who were positive for COVID 19 infection and admitted to the hospital with positive chest CT findings. All patients underwent follow-up chest CT, and the outcomes were categorized as resolution, regression, residual fibrosis, progression, or death. These proportions were 5.4%, 48.2%, 24.1%, 14.3%, and 8%, respectively. The only significant factor in determining the complete resolution of chest CT was oligo-segmental affection (P = .0001). The main CT feature that significantly affected the regression of chest CT manifestations was diffuse nodular shadows (P = .039). The CT features noted in patients with residual fibrosis were interstitial thickening, with a P value of .017. The mono-segmental process significantly affected progression (P = .044). The significant factors for fatality were diffuse crazy paving, pleural effusion, and extra-thoracic complications (P = .033, .029, and .007, respectively). The prognostic value of the first admission CT can help assess disease outcomes in the earliest phases of onset. This can improve resource distribution.

Randomized Trial of Anticoagulation Strategies for Noncritically Ill Patients Hospitalized With COVID-19

BACKGROUND

Prior studies of therapeutic-dose anticoagulation in patients with COVID-19 have reported conflicting results.

OBJECTIVES

We sought to determine the safety and effectiveness of therapeutic-dose anticoagulation in noncritically ill patients with COVID-19.

METHODS

Patients hospitalized with COVID-19 not requiring intensive care unit treatment were randomized to prophylactic-dose enoxaparin, therapeutic-dose enoxaparin, or therapeutic-dose apixaban. The primary outcome was the 30-day composite of all-cause mortality, requirement for intensive care unit-level of care, systemic thromboembolism, or ischemic stroke assessed in the combined therapeutic-dose groups compared with the prophylactic-dose group.

RESULTS

Between August 26, 2020, and September 19, 2022, 3,398 noncritically ill patients hospitalized with COVID-19 were randomized to prophylactic-dose enoxaparin (n = 1,141), therapeutic-dose enoxaparin (n = 1,136), or therapeutic-dose apixaban (n = 1,121) at 76 centers in 10 countries. The 30-day primary outcome occurred in 13.2% of patients in the prophylactic-dose group and 11.3% of patients in the combined therapeutic-dose groups (HR: 0.85; 95% CI: 0.69-1.04; P = 0.11). All-cause mortality occurred in 7.0% of patients treated with prophylactic-dose enoxaparin and 4.9% of patients treated with therapeutic-dose anticoagulation (HR: 0.70; 95% CI: 0.52-0.93; P = 0.01), and intubation was required in 8.4% vs 6.4% of patients, respectively (HR: 0.75; 95% CI: 0.58-0.98; P = 0.03). Results were similar in the 2 therapeutic-dose groups, and major bleeding in all 3 groups was infrequent.

CONCLUSIONS

Among noncritically ill patients hospitalized with COVID-19, the 30-day primary composite outcome was not significantly reduced with therapeutic-dose anticoagulation compared with prophylactic-dose anticoagulation. However, fewer patients who were treated with therapeutic-dose anticoagulation required intubation and fewer died (FREEDOM COVID [FREEDOM COVID Anticoagulation Strategy]; NCT04512079).

Practice Changes Among Patients Without COVID-19 Receiving Mechanical Ventilation During the Early COVID-19 Pandemic

The COVID-19 pandemic led to rapid changes in care delivery for critically ill patients, due to factors including increased numbers of ICU patients, shifting staff roles, and changed care locations. As these changes may have impacted the care of patients without COVID-19, we assessed changes in common ICU practices for mechanically ventilated patients with non-COVID acute respiratory failure at the onset of and during the COVID-19 pandemic.

DESIGN

Interrupted time series analysis, adjusted for seasonality and autocorrelation where present, evaluating trends in common ICU practices prior to the pandemic (March 2016 to February 2020), at the onset of the pandemic (April 2020) and intra-pandemic (April 2020 to December 2020).

SETTING

Premier Healthcare Database, containing data from 25% of U.S. discharges from January 1, 2016, to December 31, 2020.

PATIENTS

Patients without COVID-19 receiving mechanical ventilation for acute respiratory failure.

INTERVENTIONS

We assessed monthly rates of chest radiograph (CXR), chest CT scans, lower extremity noninvasive vascular testing (LENI), bronchoscopy, arterial catheters, and central venous catheters.

MEASUREMENTS AND MAIN RESULTS

We identified 742,096 mechanically ventilated patients without COVID-19 at 545 hospitals. At the onset of the pandemic, CXR (-0.5% [-0.9% to -0.2%; p = 0.001]), LENI (LENI: -2.1% [-3.3% to -0.9%; p = 0.001]), and bronchoscopy rates (-1.0% [-1.5% to -0.6%; p < 0.001]) decreased; use of chest CT increased (1.5% [0.5-2.5%; p = 0.006]). Use of arterial lines and central venous catheters did not change significantly. Intra-pandemic, LENI (0.5% [0.3-0.7%; p < 0.001]/mo) and bronchoscopy (0.1% [0.05-0.2%; p < 0.001]/mo) trends increased relative to pre-pandemic trends, while the remainder of practices did not change significantly.

CONCLUSIONS

We observed several statistically significant changes to practice patterns among patients without COVID-19 early during the pandemic. However, most of the changes were small or temporary, suggesting that routine practices in the care of mechanically ventilated patients in the ICU was not drastically affected by the pandemic.

A deep convolutional neural network ensemble for composite identification of pulmonary nodules and incidental findings on routine PET/CT

AIM

To evaluate primary and secondary pathologies of interest using an artificial intelligence (AI) platform, AI-Rad Companion, on low-dose computed tomography (CT) series from integrated positron-emission tomography (PET)/CT to detect CT findings that might be overlooked.

MATERIALS AND METHODS

One hundred and eighty-nine sequential patients who had undergone PET/CT were included. Images were evaluated using an ensemble of convolutional neural networks (AI-Rad Companion, Siemens Healthineers, Erlangen, Germany). The primary outcome was detection of pulmonary nodules for which the accuracy, identity, and intra-rater reliability was calculated. For secondary outcomes (binary detection of coronary artery calcium, aortic ectasia, vertebral height loss), accuracy and diagnostic performance were calculated.

RESULTS

The overall per-nodule accuracy for detection of lung nodules was 0.847. The overall sensitivity and specificity for detection of lung nodules was 0.915 and 0.781. The overall per-patient accuracy for AI detection of coronary artery calcium, aortic ectasia, and vertebral height loss was 0.979, 0.966, and 0.840, respectively. The sensitivity and specificity for coronary artery calcium was 0.989 and 0.969. The sensitivity and specificity for aortic ectasia was 0.806 and 1.

CONCLUSION

The neural network ensemble accurately assessed the number of pulmonary nodules and presence of coronary artery calcium and aortic ectasia on low-dose CT series of PET/CT. The neural network was highly specific for the diagnosis of vertebral height loss, but not sensitive. The use of the AI ensemble can help radiologists and nuclear medicine physicians to catch CT findings that might be overlooked.

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.

Association between Fibrinogen-to-Albumin Ratio and Prognosis of Hospitalized Patients with COVID-19: A Systematic Review and Meta-Analysis

Although the fibrinogen-to-albumin ratio (F/R ratio) has been used as an inflammation marker to predict clinical outcomes in patients with cardiovascular diseases, its association with the prognosis of patients with coronavirus disease 2019 (COVID-19) remains unclear. Electronic databases including EMBASE, MEDLINE, Google Scholar, and Cochrane Library were searched from inception to 20 June 2022. The associations of F/R ratio with poor prognosis (defined as the occurrence of mortality or severe disease) were investigated in patients with COVID-19. A total of 10 studies (seven from Turkey, two from China, one from Croatia) involving 3675 patients published between 2020 and 2022 were eligible for quantitative syntheses. Merged results revealed a higher F/R ratio in the poor prognosis group (standardized mean difference: 0.529, p < 0.001, I2 = 84.8%, eight studies) than that in the good prognosis group. In addition, a high F/R ratio was associated with an increased risk of poor prognosis (odds ratio: 2.684, I2 = 59.5%, five studies). Pooled analysis showed a sensitivity of 0.75, specificity of 0.66, and area under curve of 0.77 for poor prognosis prediction. In conclusion, this meta-analysis revealed a positive correlation between F/A ratio and poor prognostic outcomes of COVID-19. Because of the limited number of studies included, further investigations are warranted to support our findings.

Association of Prognostic Nutritional Index with Severity and Mortality of Hospitalized Patients with COVID-19: A Systematic Review and Meta-Analysis

The associations of prognostic nutritional index (PNI) with disease severity and mortality in patients with coronavirus disease 2019 (COVID-19) remain unclear. Electronic databases, including MEDLINE, EMBASE, Google scholar, and Cochrane Library, were searched from inception to 10 May 2022. The associations of PNI with risk of mortality (primary outcome) and disease severity (secondary outcome) were investigated. Merged results from meta-analysis of 13 retrospective studies (4204 patients) published between 2020 and 2022 revealed a lower PNI among patients in the mortality group [mean difference (MD): −8.65, p < 0.001] or severity group (MD: −5.19, p < 0.001) compared to those in the non-mortality or non-severity groups. A per-point increase in PNI was associated with a reduced risk of mortality [odds ratio (OR) = 0.84, 95% CI: 0.79 to 0.9, p < 0.001, I2 = 67.3%, seven studies] and disease severity (OR = 0.84, 95% CI: 0.77 to 0.92, p < 0.001, I2 = 83%, five studies). The pooled diagnostic analysis of mortality yielded a sensitivity of 0.76, specificity of 0.71, and area under curve (AUC) of 0.79. Regarding the prediction of disease severity, the sensitivity, specificity, and AUC were 0.8, 0.61, and 0.65, respectively. In conclusion, this study demonstrated a negative association between PNI and prognosis of COVID-19. Further large-scale trials are warranted to support our findings.

Quantitative Chest CT Analysis to Measure Short-Term Sequelae of COVID-19 Pneumonia: A Monocentric Prospective Study

(1) Background: Quantitative CT analysis (QCT) has demonstrated promising results in the prognosis prediction of patients affected by COVID-19. We implemented QCT not only at diagnosis but also at short-term follow-up, pairing it with a clinical examination in search of a correlation between residual respiratory symptoms and abnormal QCT results. (2) Methods: In this prospective monocentric trial performed during the “first wave” of the Italian pandemic, i.e., from March to May 2020, we aimed to test the relationship between %deltaCL (variation of %CL-compromised lung volume) and variations of symptoms-dyspnea, cough and chest pain-at follow-up clinical assessment after hospitalization. (3) Results: 282 patients (95 females, 34%) with a median age of 60 years (IQR, 51–69) were included. We reported a correlation between changing lung abnormalities measured by QCT, and residual symptoms at short-term follow up after COVID-19 pneumonia. Independently from age, a low percentage of surviving patients (1–4%) may present residual respiratory symptoms at approximately two months after discharge. QCT was able to quantify the extent of residual lung damage underlying such symptoms, as the reduction of both %PAL (poorly aerated lung) and %CL volumes was correlated to their disappearance. (4) Conclusions QCT may be used as an objective metric for the measurement of COVID-19 sequelae.