Total Chest Computed Tomography Research Articles

Chest CT scan predictors of intensive care unit admission in hospitalized pregnant women with COVID-19: a case–control study

Purpose To investigate the role of chest computed tomography (CT) scan in the prediction of admission of pregnant women with COVID-19 into intensive care unit (ICU). Methods This was a single-center retrospective case–control study. We included pregnant women diagnosed with COVID-19 by reverse transcriptase polymerase chain reaction between February 2020 and July 2021, requiring hospital admission due to symptoms, who also had a CT chest scan at presentation. Patients admitted to the ICU (case group) were compared with patients who did not require ICU admission (control group). The CT scans were reported by an experienced radiologist, blinded to the patient’s course and outcome, aided by an artificial intelligence software. Total CT scan score, chest CT severity score (CT-SS), total lung volume (TLV), infected lung volume (ILV), and infected-to-total lung volume ratio (ILV/TLV) were calculated. Receiver operating characteristic curves were constructed to test the sensitivity and specificity of each parameter. Results 8/28 patients (28.6%) required ICU admission. These also had lower TLV, higher ILV, and ILV/TLV. The area under the curve (AUC) for these three parameters was 0.789, 0.775, and 0.763, respectively. TLV, ILV, and ILV/TLV had good sensitivity (62.5%, 87.5%, and 87.5%, respectively) and specificity (84.2%, 70%, and 73.7%, respectively) for predicting ICU admission at the following selected thresholds: 2255 mL, 319 mL, and 14%, respectively. The performance of CT-SS, CT scan score, and ILV/TLV in predicting ICU admission was comparable. Conclusion TLV, ILV, and ILV/TLV as measured by an artificial intelligence software on chest CT, may predict ICU admission in hospitalized pregnant women, symptomatic for COVID-19.

Diagnostic efficacy of chest CT imaging in diagnosis of COVID-19 cases based on duration of symptoms.

Chest computed tomography (CT) imaging provides results more rapidly and with higher sensitivity than reverse transcription polymerase chain reaction in diagnosis of COVID-19. To evaluate diagnostic efficacy of chest CT imaging in diagnosis of COVID-19 cases based on age and duration of symptoms. A retrospective study conducted during December 2020 to June 2021 in a tertiary care hospital, India. Total 495 patients with typical clinical symptoms of COVID-19, reverse transcription polymerase chain reaction positive for COVID-19 and had undergone chest CT imaging were included. Descriptive statistical analysis was performed for all the variables. Receiver operating characteristic curve analysis was used to determine threshold value of chest CT severity score (CT_SS) based on duration of symptoms and age to diagnose COVID-19. Mean age of patients was 61.86 ± 10.77 years and 367 (71.4%) patients were male. Ground glass opacities were observed in 456 (92.1%) patients and in 332 (67.1%) patients, multilobes were affected. Total CT_SS showed positive correlation with age (r = 0.257) and duration of symptoms (r = 0.625). Total CT_SS >6 after a duration of 2 days of symptoms identified COVID-19 cases with sensitivity 90.8% (95% confidence interval [CI]: 87.5%-93.5%) and specificity 84.6% (95% CI: 76.2%-90.9%). Total CT_SS >11 in patients aged more than 60 years identified COVID-19 cases with sensitivity 47.4% (95% CI: 41.2%-53.6%) and specificity 87.3% (95% CI: 82.3%-91.4%). Threshold value of CT_SS determined will help to expedite diagnosis of COVID-19 patients by the clinicians in an early stage especially in India and other developing countries which have a high patient volume and limited health resources.

Estimation of biochemical factors affecting survival in intensive care COVID-19 patients undergoing chest CT scoring: A retrospective cross-sectional study.

Coronavirus disease 2019 (COVID-19) is a rapidly spreading deadly respiratory disease that emerged in the city of Wuhan in December 2019. As a result of its rapid and widespread transmission, the WHO declared a pandemic on March 11, 2020 and studies evaluating mortality and prognosis in COVID-19 gained importance. The aim of this study was to determine the factors affecting the survival of COVID-19 patients followed up in a tertiary intensive care unit (ICU) and undergoing chest computed tomography (CT) scoring. This retrospective cross-sectional study was conducted with the approval of Uşak University Medical Faculty Ethics Committee between July and September 2020. It included 187 symptomatic patients (67 females, 120 males) with suspected COVID-19 who underwent chest CT scans in the ICU. Demographics, acute physiology and chronic health evaluation (APACHE II), chest CT scores, COVID-19 real-time polymerase chain reaction (RT PCR) results, and laboratory parameters were recorded. SPSS 15.0 for Windows was used for the data analysis. The ages of the patients ranged from 18 to 94 and the mean age was 68.0 ± 13.9 years. The COVID-19 RT PCR test was positive in 86 (46.0%) patients and 110 patients (58.8%) died during the follow-up. ICU stay (P = .024) and total invasive mechanical ventilation time (P < .001) were longer and blood urea nitrogen (BUN) was higher (P < .001) in the nonsurvivors. Patients with an APACHE II score of 23 and above had a 1.12-fold higher mortality rate (95% CI 0.061–0.263). There was no significant difference in total chest CT score between the survivors and nonsurvivors (P = .210). Chest CT score was not significantly associated with mortality in COVID-19 patients. Our idea that COVID-19 will cause greater mortality in patients with severe chest CT findings has changed. More studies on COVID-19 are needed to reveal the markers that affect prognosis and mortality in this period when new variants are affecting the world.

Association of severe COVID-19 outcomes with radiological scoring and cardiomegaly: findings from the COVID-19 inpatients database, Japan

PurposeWe aimed to characterize novel coronavirus infections based on imaging [chest X-ray and chest computed tomography (CT)] at the time of admission.Materials and methodsWe extracted data from 396 patients with laboratory-confirmed COVID-19 who were managed at 68 hospitals in Japan from January 25 to September 2, 2020. Case patients were categorized as severe (death or treatment with invasive ventilation during hospitalization) and non-severe groups. The imaging findings of the groups were compared by calculating odds ratios (ORs) and 95% confidence intervals (95% CIs), adjusted for sex, age, and hospital size (and radiographic patient positioning for cardiomegaly). Chest X-ray and CT scores ranged from 0 to 72 and 0 to 20, respectively. Optimal cut-off values for these scores were determined by a receiver-operating characteristic (ROC) curve analysis.ResultsThe median age of the 396 patients was 48 years (interquartile range 28–65) and 211 (53.3%) patients were male. Thirty-two severe cases were compared to 364 non-severe cases. At the time of admission, abnormal lesions on chest X-ray and CT were mainly observed in the lower zone/lobe. Among severe cases, abnormal lesions were also seen in the upper zone/lobe. After adjustment, the total chest X-ray and CT score values showed a dose-dependent association with severe disease. For chest X-ray scores, the area under the ROC curve (AUC) was 0.91 (95% CI = 0.86–0.97) and an optimal cut-off value of 9 points predicted severe disease with 83.3% sensitivity and 84.7% specificity. For chest CT scores, the AUC was 0.94 (95% CI = 0.89–0.98) and an optimal cut-off value of 11 points predicted severe disease with 90.9% sensitivity and 82.2% specificity. Cardiomegaly was strongly associated with severe disease [adjusted OR = 24.6 (95% CI = 3.7–166.0)].ConclusionChest CT and X-ray scores and the identification of cardiomegaly could be useful for classifying severe COVID-19 on admission.

Pulmonary Sequelae in Patients After Recovery From Coronavirus Disease 2019: A Follow-Up Study With Chest CT.

Objective: The pulmonary sequelae of coronavirus disease 2019 (COVID-19) have not been comprehensively evaluated. We performed a follow-up study analyzing chest computed tomography (CT) findings of COVID-19 patients at 3 and 6 months after hospital discharge.Methods: Between February 2020 and May 2020, a total of 273 patients with COVID-19 at the Shenzhen Third People's Hospital were recruited and followed for 6 months after discharge. Chest CT scanning was performed with the patient in the supine position at end-inspiration. A total of 957 chest CT scans was obtained at different timepoints. A semi-quantitative score was used to assess the degree of lung involvement.Results: Most chest CT scans showed bilateral lung involvement with peripheral location at 3 and 6 months follow-up. The most common CT findings were ground-glass opacity and parenchymal band, which were found in 136 (55.3%) and 94 (38.2%) of the 246 patients at 3 months follow-up, and 82 (48.2%) and 76 (44.7%) of 170 patients at 6 months follow-up, respectively. The number of lobes involved and the total CT severity score declined over time. The total CT score gradually increased with the increasement of disease severity at both 3 months follow-up (trend test P < 0.001) and 6 months follow-up (trend test P < 0.001). Patients with different disease severity represented diverse CT patterns over time.Conclusions: The most common CT findings were ground-glass opacity and parenchymal bands at the 3 and 6 months follow-up. Patients with different disease severity represent diverse CT manifestations, indicating the necessary for long-term follow-up monitoring of patients with severe and critical conditions.

Relationship of chest computed tomography score with disease severity and laboratory values in children with COVID-19.

Although chest computed tomography (CT) score has been well evaluated in adult coronavirus disease (COVID-19), its use in paediatric cases is insufficiently studied. Our aim is to evaluate the relationship of chest CT score with disease severity and laboratory parameters. Seventy-six paediatric patients with confirmed COVID-19 and chest CT evaluation on admission have been included in this study. Chest CT score was calculated for each of the five lobes considering the extent of anatomical involvement, as follows: 0: 0%; 1: <5%; 2: 5%-25%; 3: 26%-50%; 4: 51%-75% and 5: >75%. The resulting total CT score was the sum of each individual lobar score; the range was between 0 and 25. Total chest CT score was found to be positively correlated with alanine aminotransferase and d-dimer, and negatively correlated with lymphocyte count. In receiver operating characteristic analysis, total chest CT score had area under the curve 0.99 (95% confidence interval, 0.98-1.00) at cut-off 2 with 95% sensitivity and 96% specificity for the severe disease. Furthermore, in-depth analysis of lobar CT scores showed a correlation between left upper lobe with lymphocyte count, left lower lobe with d-dimer, right middle and lower lobes with alanine aminotransferase and right upper lobe with leukocyte count. There is a significant relationship between chest CT score and COVID-19 severity and laboratory findings in children. This suggests that chest CT scores can be used to assess the severity of the disease and can play an important role in paediatric clinical practice.

Clinical and radiological characteristics of COVID‑19 patients without comorbidities : Asingle-center study.

ObjectiveTo evaluate the clinical characteristics and detailed imaging features in coronavirus disease 2019 (COVID-19) patients without comorbidities.Material and methodsThis retrospective study included laboratory-confirmed and symptomatic COVID-19 patients without comorbid diseases who were admitted to our second level hospital between March 2020 and September 2020. We assessed the clinical, biochemical and imaging diagnostic parameters on admission. The patients were classified as non-severe and progress to severe group and then the initial parameters were compared.ResultsWe enrolled 135 adult COVID-19 patients, 12 progressed to severe disease during hospitalization. Compared to the non-severe group, patients who progressed to severe were older (p < 0.001) and were more likely to manifest coughing (p = 0.011) and have higher lactate dehydrogenase (LDH) levels (p = 0.011). On chest computed tomography (CT) images, multilobar (p = 0.016), peripherally (p = 0.001) distributed mixed ground glass opacities and consolidation (p < 0.001), crazy paving (p = 0.007) and higher total CT severity score (p < 0.001) were significantly associated with severe disease.ConclusionKnowledge of the clinical and radiological parameters associated with disease severity might be useful to guide clinical decision-making for COVID-19 patients without comorbidities.

An Interpretable Model-Based Prediction of Severity and Crucial Factors in Patients with COVID-19.

This study established an interpretable machine learning model to predict the severity of coronavirus disease 2019 (COVID-19) and output the most crucial deterioration factors. Clinical information, laboratory tests, and chest computed tomography (CT) scans at admission were collected. Two experienced radiologists reviewed the scans for the patterns, distribution, and CT scores of lung abnormalities. Six machine learning models were established to predict the severity of COVID-19. After parameter tuning and performance comparison, the optimal model was explained using Shapley Additive explanations to output the crucial factors. This study enrolled and classified 198 patients into mild (n = 162; 46.93 ± 14.49 years old) and severe (n = 36; 60.97 ± 15.91 years old) groups. The severe group had a higher temperature (37.42 ± 0.99°C vs. 36.75 ± 0.66°C), CT score at admission, neutrophil count, and neutrophil-to-lymphocyte ratio than the mild group. The XGBoost model ranked first among all models, with an AUC, sensitivity, and specificity of 0.924, 90.91%, and 97.96%, respectively. The early stage of chest CT, total CT score of the percentage of lung involvement, and age were the top three contributors to the prediction of the deterioration of XGBoost. A higher total score on chest CT had a more significant impact on the prediction. In conclusion, the XGBoost model to predict the severity of COVID-19 achieved excellent performance and output the essential factors in the deterioration process, which may help with early clinical intervention, improve prognosis, and reduce mortality.

Correlation between serum carbohydrate antigen 19-9 levels and computed tomography severity score in patients with nontuberculous mycobacterial pulmonary disease

There is no validated clinical biomarker for disease severity or treatment response for nontuberculous mycobacterial pulmonary disease (NTM-PD). We investigated the correlation between elevated serum carbohydrate antigen (CA) 19-9 levels and NTM-PD disease activity, defined using an imaging severity score based on chest computed tomography (CT). We retrospectively examined 79 patients with NTM-PD who underwent serum CA19-9 level assessments and chest CT less than 1 month apart. NTM-PD severity was rated using a CT-based scoring system. The correlation between the CT score and serum CA19-9 levels was evaluated. Chest CT revealed nodular bronchiectasis without cavitation in most patients (78.5%). Serum CA19-9 levels were elevated in 19 (24%) patients. Serum CA19-9 levels were positively correlated with the total CT score and bronchiectasis, bronchiolitis, cavity, and consolidation subscores. Partial correlation analysis revealed a significant positive correlation between serum CA19-9 levels and CT scores for total score and bronchiectasis, bronchiolitis, cavitation, and consolidation subscores after controlling for age, sex, and BMI. Serum CA19-9 levels were positively correlated with the CT severity score for NTM-PD. Serum CA19-9 may be useful in evaluating disease activity or therapeutic response in patients with NTM-PD.

Correlation between age, sex, and severity of Coronavirus disease-19 based on chest computed tomography severity scoring system

BackgroundCoronavirus disease 2019 (COVID-19) is the most recent global health emergency; early diagnosis of COVID-19 is very important for rapid clinical interventions and patient isolation; chest computed tomography (CT) plays an important role in screening, diagnosis, and evaluating the progress of the disease. According to the results of different studies, due to high severity of the disease, clinicians should be aware of the different potential risk factors associated with the fatal outcome, so chest CT severity scoring system was designed for semi-quantitative assessment of the severity of lung disease in COVID-19 patients, ranking the pulmonary involvement on 25 points severity scale according to extent of lung abnormalities; this study aims to evaluate retrospectively the relationship between age and severity of COVID-19 in both sexes based on chest CT severity scoring system.ResultsAge group C (40–49 year) was the commonest age group that was affected by COVID-19 by 21.3%, while the least affected group was group F (≥ 70 years) by only 6.4%. As regards COVID-RADS classification, COVID-RADS-3 was the most commonly presented at both sexes in all different age groups. Total CT severity lung score had a positive strong significant correlation with the age of the patient (r = 0.64, P < 0.001). Also, a positive strong significant correlation was observed between CT severity lung score and age in both males and females (r = 0.59, P < 0.001) and (r = 0.69, P < 0.001) respectively.ConclusionWe concluded that age can be considered as a significant risk factor for the severity of COVID-19 in both sexes. Also, CT can be used as a significant diagnostic tool for the diagnosis of COVID-19 and evaluation of the progression and severity of the disease.

Correlation between CT findings and outcomes in 46 patients with coronavirus disease 2019

The aim of this study was to analyze initial chest computed tomography (CT) findings in COVID-19 pneumonia and identify features associated with poor prognosis. Patients with RT-PCR-confirmed COVID-19 infection were assigned to recovery group if they made a full recovery and to death group if they died within 2 months of hospitalization. Chest CT examinations for ground-glass opacity, crazy-paving pattern, consolidation, and fibrosis were scored by two reviewers. The total CT score comprised the sum of lung involvement (5 lobes, scores 1–5 for each lobe, range; 0, none; 25, maximum). 40 patients who recovered from COVID-19 and six patients who died were enrolled. The initial chest CTs showed 27 (58.7%) patients had ground-glass opacity, 19 (41.3%) had ground glass and consolidation, and 35 (76.1%) patients had crazy-paving pattern. None of the patients who died had fibrosis in contrast to six (15%) patients who recovered from COVID-19. Most patients had subpleural lesions (89.0%) as well as bilateral (87.0%) and lower (93.0%) lung lobe involvement. Diffuse lesions were present in four (67%) patients who succumbed to coronavirus but only one (2.5%) patient who recovered (p < 0.001). In the death group of patients, the total CT score was higher than that of the recovery group (p = 0.005). Patients in the death group had lower lymphocyte count and higher C-reactive protein than those in the recovery group (p = 0.011 and p = 0.041, respectively). A high CT score and diffuse distribution of lung lesions in COVID-19 are indicative of disease severity and short-term mortality.

COVID-19: A qualitative chest CT model to identify severe form of the disease

PurposeThe purpose of this study was to identify clinical and chest computed tomography (CT) features associated with a severe form of coronavirus disease 2019 (COVID-19) and to propose a quick and easy to use model to identify patients at risk of a severe form. Materials and methodsA total of 158 patients with biologically confirmed COVID-19 who underwent a chest CT after the onset of the symptoms were included. There were 84 men and 74 women with a mean age of 68±14 (SD) years (range: 24–96years). There were 100 non-severe and 58 severe cases. Their clinical data were recorded and the first chest CT examination was reviewed using a computerized standardized report. Univariate and multivariate analyses were performed in order to identify the risk factors associated with disease severity. Two models were built: one was based only on qualitative CT features and the other one included a semi-quantitative total CT score to replace the variable representing the extent of the disease. Areas under the ROC curves (AUC) of the two models were compared with DeLong's method. ResultsCentral involvement of lung parenchyma (P<0.001), area of consolidation (P<0.008), air bronchogram sign (P<0.001), bronchiectasis (P<0.001), traction bronchiectasis (P<0.011), pleural effusion (P<0.026), large involvement of either one of the upper lobes or of the middle lobe (P<0.001) and total CT score≥15 (P<0.001) were more often observed in the severe group than in the non-severe group. No significant differences were found between the qualitative model (large involvement of either upper lobes or middle lobe [odd ratio (OR)=2.473], central involvement [OR=2.760], pleural effusion [OR=2.699]) and the semi-quantitative model (total CT score≥15 [OR=3.342], central involvement [OR=2.344], pleural effusion [OR=2.754]) with AUC of 0.722 (95% CI: 0.638–0.806) vs. 0.739 (95% CI: 0.656–0.823), respectively (P=0.209). ConclusionWe have developed a new qualitative chest CT-based multivariate model that provides independent risk factors associated with severe form of COVID-19.

Chest CT imaging features and severity scores as biomarkers for prognostic prediction in patients with COVID-19.

BackgroundCoronavirus disease 2019 (COVID-19) has become a pandemic. Few studies have explored the role of chest computed tomography (CT) features and severity scores for prognostic prediction. In this study, we aimed to investigate the role of chest CT severity score and imaging features in the prediction of the prognosis of COVID-19 patients.MethodsA total of 134 patients (62 recovered and 72 deceased patients) with confirmed COVID-19 were enrolled. The clinical, laboratory, and chest CT (316 scans) data were retrospectively reviewed. Demographics, symptoms, comorbidities, and temporal changes of laboratory results, CT features, and severity scores were compared between recovered and deceased groups using the Mann-Whitney U test and logistic regression to identify the risk factors for poor prognosis.ResultsMedian age was 48 and 58 years for recovered and deceased patients, respectively. More patients had at least one comorbidity in the deceased group than the recovered group (60% vs. 29%). Leukocytes, neutrophil, high-sensitivity C-reactive protein (hsCRP), prothrombin, D-dimer, serum ferritin, interleukin (IL)-2, and IL-6 were significantly elevated in the deceased group than the recovered group at different stages. The total CT score at the peak stage was significantly greater in the deceased group than the recovered group (20 vs. 11 points). The optimal cutoff value of the total CT scores was 16.5 points, achieving 69.4% sensitivity and 82.2% specificity for the prognostic prediction. The crazy-paving pattern and consolidation were more common in the deceased patients than those in the recovered patients. Linear opacities significantly increased with the disease course in the recovered patients. Sex, age, neutrophil, IL-2, IL-6, and total CT scores were independent risk factors for the prognosis with odds ratios of 3.8 to 8.7.ConclusionsSex (male), older age (>60 years), elevated neutrophil, IL-2, IL-6 level, and total CT scores (≥16) were independent risk factors for poor prognosis in patients with COVID-19. Temporal changes of chest CT features and severity scores could be valuable for early identification of severe cases and eventually reducing the mortality rate of COVID-19.

Comparison of COVID-19 disease between young and elderly patients: Hidden viral shedding of COVID-19

ObjectivesThe symptoms of Coronavirus disease 2019 (COVID-19) vary among patients. The aim of this study was to investigate the clinical manifestation and disease duration in young versus elderly patients. MethodsWe retrospectively analyzed 187 patients (87 elderly and 100 young patients) with confirmed COVID-19. The clinical characteristics and chest computed tomography (CT) extent as defined by a score were compared between the two groups. ResultsThe numbers of asymptomatic cases and severe cases were significantly higher in the elderly group (elderly group vs. young group; asymptomatic cases, 31 [35.6%] vs. 10 [10%], p < 0.0001; severe cases, 25 [28.7%] vs. 8 [8.0%], p = 0.0002). The proportion of asymptomatic patients and severe patients increased across the 10-year age groups. There was no significant difference in the total CT score and number of abnormal cases. A significant positive correlation between the disease duration and patient age was observed in asymptomatic patients (ρ = 0.4570, 95% CI 0.1198–0.6491, p = 0.0034). ConclusionsAlthough the extent of lung involvement did not have a significant difference between the young and elderly patients, elderly patients were more likely to have severe clinical manifestations. Elderly patients were also more likely to be asymptomatic and a source of COVID-19 viral shedding.

Different computed tomography patterns of Coronavirus Disease 2019 (COVID-19) between survivors and non-survivors

This study aimed to compare the chest computed tomography (CT) findings between survivors and non-survivors with Coronavirus Disease 2019 (COVID-19). Between 12 January 2020 and 20 February 2020, the records of 124 consecutive patients diagnosed with COVID-19 were retrospectively reviewed and divided into survivor (83/124) and non-survivor (41/124) groups. Chest CT findings were qualitatively compared on admission and serial chest CT scans were semi-quantitively evaluated between two groups using curve estimations. On admission, significantly more bilateral (97.6% vs. 73.5%, p = 0.001) and diffuse lesions (39.0% vs. 8.4%, p < 0.001) with higher total CT score (median 10 vs. 4, p < 0.001) were observed in non-survivor group compared with survivor group. Besides, crazy-paving pattern was more predominant in non-survivor group than survivor group (39.0% vs. 12.0%, p < 0.001). From the prediction of curve estimation, in survivor group total CT score increased in the first 20 days reaching a peak of 6 points and then gradually decreased for more than other 40 days (R2 = 0.545, p < 0.001). In non-survivor group, total CT score rapidly increased over 10 points in the first 10 days and gradually increased afterwards until ARDS occurred with following death events (R2 = 0.711, p < 0.001). In conclusion, persistent progression with predominant crazy-paving pattern was the major manifestation of COVID-19 in non-survivors. Understanding this CT feature could help the clinical physician to predict the prognosis of the patients.

Are COVID-19 patients with hypertension at higher risk in China?

Are COVID-19 patients with hypertension at higher risk in China?

Structural determinants of long-term functional outcomes in young children with cystic fibrosis.

Accelerated lung function decline in individuals with cystic fibrosis (CF) starts in adolescence with respiratory complications being the most common cause of death in later life. Factors contributing to lung function decline are not well understood, in particular its relationship with structural lung disease in early childhood. Detection and management of structural lung disease could be an important step in improving outcomes in CF patients. Annual chest computed tomography (CT) scans were available from 2005 to 2016 as a part of the AREST CF cohort for children aged 3 months to 6 years. Annual spirometry measurements were available for 89.77% of the cohort (167 children aged 5-6 years) from age 5 to 15 years through outpatient clinics at Perth Children's Hospital (Perth, Australia) and The Royal Children's Hospital in Melbourne (Melbourne, Australia) (697 measurements, mean±sd age 9.3±2.1 years). Children with a total CT score above the median at age 5-6 years were more likely to have abnormal forced expiratory volume in 1 s (FEV1) (adjusted hazard ratio 2.67 (1.06-6.72), p=0.037) during the next 10 years compared to those below the median chest CT score. The extent of all structural abnormalities except bronchial wall thickening were associated with lower FEV1 Z-scores. Mucus plugging and trapped air were the most predictive sub-score (adjusted mean change -0.17 (-0.26 - -0.07) p<0.001 and -0.09 (-0.14 - -0.04) p<0.001, respectively). Chest CT identifies children at an early age who have adverse long-term outcomes. The prevention of structural lung damage should be a goal of early intervention and can be usefully assessed with chest CT. In an era of therapeutics that might alter disease trajectories, chest CT could provide an early readout of likely long-term success.

Chest CT scoring for evaluation of lung sequelae in congenital diaphragmatic hernia survivors.

Data on long-term structural lung abnormalities in survivors of congenital diaphragmatic hernia (CDH) is scarce. The purpose of this study was to develop a chest computed tomography (CT) score to assess the structural lung sequelae in CDH survivors and to study the correlation between the CT scoring and clinical parameters in the neonatal period and at 1 year of follow-up. A prospective, clinical follow-up program is organised for CDH survivors at the University Hospital of Leuven including a chest CT at the age of 1 year. The CT scoring used and evaluated, named CDH-CT score, was adapted from the revised Aukland score for chronic lung disease of prematurity. Thirty-five patients were included. All CT scans showed some pulmonary abnormalities, ranging from very mild to severe. The mean total CT score was 16 (IQR: 9-23), with the greatest contribution from the subscores for decreased attenuation (5; IQR: 2-8), subpleural linear and triangular opacities (4; IQR: 3-5), and atelectasis/consolidation (2; IQR: 1-3). Interobserver and intraobserver agreement was very good for the total score (ICC coefficient > 0.9). Total CT score correlated with number of neonatal days ventilated/on oxygen as well as with respiratory symptoms and feeding problems at 1 year of age. The CDH-CT scoring tool has a good intraobserver and interobserver repeatability and correlates with relevant clinical parameters. This holds promise for its use in clinical follow-up and as outcome parameter in clinical interventional studies.

Abstract 17257: Does Repeat Imaging Validate Visual Coronary Artery Calcium Scoring on Non-Electrocardiogram Gated Chest Computed Tomography?

Aim: To investigate if estimation of the amount of calcium in the coronary system utilizing a visual coronary artery calcium scoring (CACS) system (Kirsch et al. Detection of coronary calcium during standard chest computed tomography correlates with multi-detector computed tomography coronary artery calcium score) in non - electrocardiogram (ECG) gated chest computed tomography (CT) scans maintains its strong correlation with the Agatston CACS on dedicated ECG gated cardiac CT exams across two or more points in time per patient. Methods: A visual scoring system (Weston score, WCS) was previously developed assigning a score (0-3, total score 0-12) for each coronary vessel including the left main trunk. Predetermined score cutoffs in both Agatston and Weston scoring methods were designated for risk stratification. Following institutional review board approval, we retrospectively analyzed 127 total body CT scans on 53 low-risk patients referred by executive health who had received repeat CT scans between 2007 and 2018 and compared the traditional Agatston CACS with the Weston score. Analysis: The data were gathered from a total of 53 executive health patients ages 33 to 71, with an average age of 51. 83.6% were male and 16.4% were female. 118 scans were examined, taken between 307 and 2,792 days apart, with an average of 937 days between scans. The Weston and Agatston scores predicted the same risk classification for 79.7% of the first set of scans, with 10.2% uprisked and 10.2% downrisked by their Weston scores. For the second set of scans, 78.0% had the same classification, with 10.2% uprisked and 11.9% downrisked by their Weston scores. Weston scores predicted 100% of scans within one risk classification of the corresponding Agatston score across all pairs of scans.The average CACS utilizing the Agatston scoring method was 54.3, with a standard deviation of 148.6 due to a large positive skew in the data. The corresponding Weston scores had an average of 1.1, with a standard deviation of 2.14. Conclusion: A visual CACS system (WCS) performed on non-gated CT scans allows risk stratification for cardiac events in a similar manner as the Agatston CACS on ECG-gated CT scans even at separate points in time.