Comparison of Clinical Features and CT Temporal Changes Between Familial Clusters and Non-familial Patients With COVID-19 Pneumonia.

Purpose: This study aimed to compare the clinical characteristics, laboratory findings, and chest computed tomography (CT) findings of familial cluster (FC) and non-familial (NF) patients with coronavirus disease 2019 (COVID-19) pneumonia. Methods: This retrospective study included 178 symptomatic adult patients with laboratory-confirmed COVID-19. The 178 patients were divided into FC (n = 108) and NF (n = 70) groups. Patients with at least two confirmed COVID-19 cases in their household were classified into the FC group. The clinical and laboratory features between the two groups were compared and so were the chest CT findings on-admission and end-hospitalization. Results: Compared with the NF group, the FC group had a longer period of exposure (13.1 vs. 8.9 days, p < 0.001), viral shedding (21.5 vs. 15.9 days, p < 0.001), and hospital stay (39.2 vs. 22.2 days, p < 0.001). The FC group showed a higher number of involved lung lobes on admission (3.0 vs. 2.3, p = 0.017) and at end-hospitalization (3.6 vs. 1.7, p < 0.001) as well as higher sum severity CT scores at end-hospitalization (4.6 vs. 2.7, p = 0.005) than did the NF group. Conversely, the FC group had a lower lymphocyte count level (p < 0.001) and a significantly lower difference in the number of involved lung lobes (Δnumber) between admission and discharge (p < 0.001). Notably, more cases of severe or critical illness were observed in the FC group than in the NF group (p = 0.036). Conclusions: Patients in the FC group had a worse clinical course and outcome than those in the NF group; thus, close monitoring during treatment and follow-ups after discharge would be beneficial for patients with familial infections.

CT-based radiomics for predicting the rapid progression of coronavirus disease 2019 (COVID-19) pneumonia lesions.

OBJECTIVES: To develop and validate a radiomic model to predict the rapid progression (defined as volume growth of pneumonia lesions > 50% within seven days) in patients with coronavirus disease 2019 (COVID-19). METHODS: Patients with laboratory-confirmed COVID-19 who underwent longitudinal chest CT between January 01 and February 18, 2020 were included. A total of 1316 radiomic features were extracted from the lung parenchyma window for each CT. The least absolute shrinkage and selection operator (LASSO), Relief, Las Vegas Wrapper (LVW), L1-norm-Support Vector Machine (L1-norm-SVM), and recursive feature elimination (RFE) were applied to select the features that associated with rapid progression. Four machine learning classifiers were used for modeling, including Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and Decision Tree (DT). Accordingly, 20 radiomic models were developed on the basis of 296 CT scans and validated in 74 CT scans. Model performance was determined by the receiver operating characteristic curve. RESULTS: A total of 107 patients (median age, 49.0 years, interquartile range, 35-54) were evaluated. The patients underwent a total of 370 chest CT scans with a median interval of 4 days (interquartile range, 3-5 days). The combination methods of L1-norm SVM and SVM with 17 radiomic features yielded the highest performance in predicting the likelihood of rapid progression of pneumonia lesions on next CT scan, with an AUC of 0.857 (95% CI: 0.766-0.947), sensitivity of 87.5%, and specificity of 70.7%. CONCLUSIONS: Our radiomic model based on longitudinal chest CT data could predict the rapid progression of pneumonia lesions, which may facilitate the CT follow-up intervals and reduce the radiation. ADVANCES IN KNOWLEDGE: Radiomic features extracted from the current chest CT have potential in predicting the likelihood of rapid progression of pneumonia lesions on the next chest CT, which would improve clinical decision-making regarding timely treatment.

Three-Dimensional CT for Quantification of Longitudinal Lung and Pneumonia Variations in COVID-19 Patients.

Objectives: Visual chest CT is subjective with interobserver variability. We aimed to quantify the dynamic changes of lung and pneumonia on three-dimensional CT (3D-CT) images in coronavirus disease 2019 (COVID-19) patients during hospitalization. Methods: A total of 110 laboratory-confirmed COVID-19 patients who underwent chest CT from January 3 to February 29, 2020 were retrospectively reviewed. Pneumonia lesions were classified as four stages: early, progressive, peak, and absorption stages on chest CT. A computer-aided diagnostic (CAD) system calculated the total lung volume (TLV), the percentage of low attenuation areas (LAA%), the volume of pneumonia, the volume of ground-glass opacities (GGO), the volume of consolidation plus the GGO/consolidation ratio. The CT score was visually assessed by radiologists. Comparisons of lung and pneumonia parameters among the four stages were performed by one-way ANOVA with post-hoc tests. The relationship between the CT score and the volume of pneumonia, and between LAA% and the volume of pneumonia in four stages was assessed by Spearman's rank correlation analysis. Results: A total of 534 chest CT scans were performed with a median interval of 4 days. TLV, LAA%, and the GGO/consolidation ratio were significantly decreased, while the volume of pneumonia, GGO, and consolidation were significantly increased in the progressive and peak stages (for all, P < 0.05). The CT score was significantly correlated with the pneumonia volume in the four stages (r = 0.731, 0.761, 0.715, and 0.669, respectively, P < 0.001). Conclusion: 3D-CT could be used as a useful quantification method in monitoring the dynamic changes of COVID-19 pneumonia.

Clinical Utility of a Nomogram for Predicting 30-Days Poor Outcome in Hospitalized Patients With COVID-19: Multicenter External Validation and Decision Curve Analysis.

Aim: Early detection of coronavirus disease 2019 (COVID-19) patients who are likely to develop worse outcomes is of great importance, which may help select patients at risk of rapid deterioration who should require high-level monitoring and more aggressive treatment. We aimed to develop and validate a nomogram for predicting 30-days poor outcome of patients with COVID-19. Methods: The prediction model was developed in a primary cohort consisting of 233 patients with laboratory-confirmed COVID-19, and data were collected from January 3 to March 20, 2020. We identified and integrated significant prognostic factors for 30-days poor outcome to construct a nomogram. The model was subjected to internal validation and to external validation with two separate cohorts of 110 and 118 cases, respectively. The performance of the nomogram was assessed with respect to its predictive accuracy, discriminative ability, and clinical usefulness. Results: In the primary cohort, the mean age of patients was 55.4 years and 129 (55.4%) were male. Prognostic factors contained in the clinical nomogram were age, lactic dehydrogenase, aspartate aminotransferase, prothrombin time, serum creatinine, serum sodium, fasting blood glucose, and D-dimer. The model was externally validated in two cohorts achieving an AUC of 0.946 and 0.878, sensitivity of 100 and 79%, and specificity of 76.5 and 83.8%, respectively. Although adding CT score to the clinical nomogram (clinical-CT nomogram) did not yield better predictive performance, decision curve analysis showed that the clinical-CT nomogram provided better clinical utility than the clinical nomogram. Conclusions: We established and validated a nomogram that can provide an individual prediction of 30-days poor outcome for COVID-19 patients. This practical prognostic model may help clinicians in decision making and reduce mortality.

Treatment With Convalescent Plasma for Critically Ill Patients With Severe Acute Respiratory Syndrome Coronavirus 2 Infection.

As of March 24, 2020, novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been responsible for 379,661 infection cases with 16,428 deaths globally, and the number is still increasing rapidly. Herein, we present four critically ill patients with SARS-CoV-2 infection who received supportive care and convalescent plasma. Although all four patients (including a pregnant woman) recovered from SARS-CoV-2 infection eventually, randomized trials are needed to eliminate the effect of other treatments and investigate the safety and efficacy of convalescent plasma therapy.

Positive rectal swabs in young patients recovered from coronavirus disease 2019 (COVID-19).

OBJECTIVES: To investigate the widely concerned issue about positive real-time reverse transcription polymerase chain reaction (RT-PCR) test results after discharge in patients recovered from coronavirus disease 2019 (COVID-19). METHODS: We identified seven cases of COVID-19 who was readmitted to hospital because of positive RT-PCR after discharge, including three pediatrics and four young adult patients. RESULTS: Six patients had positive rectal swabs but negative throat swabs, and one patient had positive throat swabs. All the patients continued to be asymptomatic and had unchanged chest computed tomography from previous images. The time from hospital discharge to positive RT-PCR after recovery was 7-11 days. The time from positive to negative rectal swabs was 5-23 days. CONCLUSION: The study might suggest the positive RT-PCR after recovery did not mean disease relapse or virus reinfection. Adding RT-PCR test of rectal swabs to the criteria for discharge or discontinuation of quarantine might be necessary.