Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data.

OBJECTIVES: Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients. METHODS: An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19. RESULTS: A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients' to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001). CONCLUSIONS: Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment. KEY POINT: • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.

Machine learning based on clinical characteristics and chest CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19.

OBJECTIVES: To develop and validate a machine learning model for the prediction of adverse outcomes in hospitalized patients with COVID-19. METHODS: We included 424 patients with non-severe COVID-19 on admission from January 17, 2020, to February 17, 2020, in the primary cohort of this retrospective multicenter study. The extent of lung involvement was quantified on chest CT images by a deep learning-based framework. The composite endpoint was the occurrence of severe or critical COVID-19 or death during hospitalization. The optimal machine learning classifier and feature subset were selected for model construction. The performance was further tested in an external validation cohort consisting of 98 patients. RESULTS: There was no significant difference in the prevalence of adverse outcomes (8.7% vs. 8.2%, p = 0.858) between the primary and validation cohorts. The machine learning method extreme gradient boosting (XGBoost) and optimal feature subset including lactic dehydrogenase (LDH), presence of comorbidity, CT lesion ratio (lesion%), and hypersensitive cardiac troponin I (hs-cTnI) were selected for model construction. The XGBoost classifier based on the optimal feature subset performed well for the prediction of developing adverse outcomes in the primary and validation cohorts, with AUCs of 0.959 (95% confidence interval [CI]: 0.936-0.976) and 0.953 (95% CI: 0.891-0.986), respectively. Furthermore, the XGBoost classifier also showed clinical usefulness. CONCLUSIONS: We presented a machine learning model that could be effectively used as a predictor of adverse outcomes in hospitalized patients with COVID-19, opening up the possibility for patient stratification and treatment allocation. KEY POINTS: • Developing an individually prognostic model for COVID-19 has the potential to allow efficient allocation of medical resources. • We proposed a deep learning-based framework for accurate lung involvement quantification on chest CT images. • Machine learning based on clinical and CT variables can facilitate the prediction of adverse outcomes of COVID-19.

Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT.

Background Coronavirus disease 2019 (COVID-19) and pneumonia of other diseases share similar CT characteristics, which contributes to the challenges in differentiating them with high accuracy. Purpose To establish and evaluate an artificial intelligence (AI) system for differentiating COVID-19 and other pneumonia at chest CT and assessing radiologist performance without and with AI assistance. Materials and Methods A total of 521 patients with positive reverse transcription polymerase chain reaction results for COVID-19 and abnormal chest CT findings were retrospectively identified from 10 hospitals from January 2020 to April 2020. A total of 665 patients with non-COVID-19 pneumonia and definite evidence of pneumonia at chest CT were retrospectively selected from three hospitals between 2017 and 2019. To classify COVID-19 versus other pneumonia for each patient, abnormal CT slices were input into the EfficientNet B4 deep neural network architecture after lung segmentation, followed by a two-layer fully connected neural network to pool slices together. The final cohort of 1186 patients (132 583 CT slices) was divided into training, validation, and test sets in a 7:2:1 and equal ratio. Independent testing was performed by evaluating model performance in separate hospitals. Studies were blindly reviewed by six radiologists without and then with AI assistance. Results The final model achieved a test accuracy of 96% (95% confidence interval [CI]: 90%, 98%), a sensitivity of 95% (95% CI: 83%, 100%), and a specificity of 96% (95% CI: 88%, 99%) with area under the receiver operating characteristic curve of 0.95 and area under the precision-recall curve of 0.90. On independent testing, this model achieved an accuracy of 87% (95% CI: 82%, 90%), a sensitivity of 89% (95% CI: 81%, 94%), and a specificity of 86% (95% CI: 80%, 90%) with area under the receiver operating characteristic curve of 0.90 and area under the precision-recall curve of 0.87. Assisted by the probabilities of the model, the radiologists achieved a higher average test accuracy (90% vs 85%, Δ = 5, P < .001), sensitivity (88% vs 79%, Δ = 9, P < .001), and specificity (91% vs 88%, Δ = 3, P = .001). Conclusion Artificial intelligence assistance improved radiologists' performance in distinguishing coronavirus disease 2019 pneumonia from non-coronavirus disease 2019 pneumonia at chest CT. © RSNA, 2020 Online supplemental material is available for this article.

Mechanistic Investigation of a Self-Assembling Peptide against Escherichia coli.

Because of their distinctive mode of action in targeting bacterial cell membranes, antimicrobial peptides (AMPs) are increasingly regarded as a potential candidate for the development of novel antibiotics to combat the wide spread of bacterial resistance. To date, understanding of the exact molecular process by which AMPs act on the real bacterial envelope remains challenging. Simultaneously, the aggregated state of AMPs upon interaction with bacterial envelopes is still elusive. Previously, we have demonstrated that the potent antibacterial activity of a designed surfactant-like peptide Ac-A9K-NH2 benefited greatly from its high self-assembling ability and appropriate self-assembled morphologies and sizes. By using high-resolution atomic force microscopy, we here not only follow the variations of the Escherichia coli cell envelope in the presence of Ac-A9K-NH2 but also characterize the peptide aggregates on the bacterial surface as well as on the substrate surface. The results, together with those from fluorescence, zeta potential, circular dichroism, and scanning electron microscopy measurements, indicate that both the positively charged peptide monomers and self-assembled nanostructures can directly act on the negatively charged bacterial surface, followed by their insertion into the bacterial membrane, the formation of surface nanopores, and membrane lysis. The mechanism of Ac-A9K-NH2 against E. coli is thus consistent with the detergent-like mode of action. This work enhances our mechanistic understanding of the antibacterial behaviors of self-assembling peptides that will be valuable in exploring their biomedical applications.

Clinical and radiological changes of hospitalised patients with COVID-19 pneumonia from disease onset to acute exacerbation: a multicentre paired cohort study.

OBJECTIVES: To analyse clinical and radiological changes from disease onset to exacerbation in coronavirus infectious disease-19 (COVID-19) patients. METHODS: We reviewed clinical histories of 276 patients with confirmed COVID-19 pneumonia and extracted data on patients who met the diagnostic criteria for COVID-19 severe/fatal pneumonia and had an acute exacerbation starting with mild or common pneumonia. RESULTS: Twenty-four patients were included. Of these, 8% were smokers, 54% had been to Wuhan, and 46% had comorbidities. Before acute exacerbation, elevated lactate dehydrogenase (232.9 ± 88.7) was present, and chest CT scans showed the number of involved lobes was 4 (2-5) and total CT score was 6 (2-8). Following acute exacerbation, patients were likely to have more clinical symptoms (p < 0.01) and abnormal laboratory changes (p < 0.01). The number of involved lobes and CT score after an exacerbation significantly increased to 5 (5-5) and 12 (9-14), respectively. Receiver operating characteristic (ROC) curve showed that, when the cutoff value of CT score was 5, the sensitivity and specificity for severe pneumonia were 90% and 70%, respectively. CT findings of ground glass opacity with consolidations (91.7%), bilateral distribution (100.0%), and multifocal lesion (100.0%) were features in found in patients after exacerbation. CONCLUSIONS: There are significant changes in clinical, laboratory, and CT findings in patients from disease onset to exacerbation. An increase in the number of involved lobes or an increased CT score from the baseline may predict poor clinical outcomes. Combining an assessment of CT changes with clinical and laboratory changes could help clinical teams evaluate the prognosis. KEY POINTS: • The common chest CT signs of COVID-19 pneumonia after exacerbation were ground glass opacity (GGO) with consolidation, bilateral distribution, and multifocal lesions. • An increase in number of involved lobes or an increased CT score from the baseline may predict a poor clinical outcome. • Worsened symptoms and abnormal laboratory results are also associated with poor prognosis.

Effects of salts on the self-assembly behavior and antibacterial activity of a surfactant-like peptide.

Self-assembling peptides have become one of the most promising antibacterial agents due to their superior properties, such as simple molecular composition, favorable assembly structures, and rich designability. For maximum application in vivo, their activities in the presence of salts are desirable, however, the potent correlation between peptide nanostructures, antibacterial activity, and salt resistance behavior remains poorly explored. Previously, we have demonstrated that the potent antibacterial activity of a designed surfactant-like peptide Ac-A9K-NH2 benefited from its high self-assembly ability and appropriate size of its self-assembled nanostructures. In this study, we investigated the effect of salts on its self-assembly behavior and antibacterial activity. The results indicated that the flexible and long nanofibrils formed by Ac-A9K-NH2 in the presence of CaCl2 were adverse to its membrane insertion, leading to the reduction of antibacterial activity. Comparatively, Ac-A9K-NH2 maintained its potent antibacterial activity in the presence of NaCl due to its suitable shape and size of nanostructures. The newly formed nanofibers and nanorods facilitated the penetration of peptides into the bacterial membrane, forming nanopores and eventually leading to the lysis of bacteria. The high antibacterial activity and NaCl tolerance of Ac-A9K-NH2 make it a promising antibacterial agent at elevated salt concentrations.

Relation Between Chest CT Findings and Clinical Conditions of Coronavirus Disease (COVID-19) Pneumonia: A Multicenter Study.

OBJECTIVE. The increasing number of cases of confirmed coronavirus disease (COVID-19) in China is striking. The purpose of this study was to investigate the relation between chest CT findings and the clinical conditions of COVID-19 pneumonia. MATERIALS AND METHODS. Data on 101 cases of COVID-19 pneumonia were retrospectively collected from four institutions in Hunan, China. Basic clinical characteristics and detailed imaging features were evaluated and compared between two groups on the basis of clinical status: nonemergency (mild or common disease) and emergency (severe or fatal disease). RESULTS. Patients 21-50 years old accounted for most (70.2%) of the cohort, and five (5.0%) patients had disease associated with a family outbreak. Most patients (78.2%) had fever as the onset symptom. Most patients with COVID-19 pneumonia had typical imaging features, such as ground-glass opacities (GGO) (87 [86.1%]) or mixed GGO and consolidation (65 [64.4%]), vascular enlargement in the lesion (72 [71.3%]), and traction bronchiectasis (53 [52.5%]). Lesions present on CT images were more likely to have a peripheral distribution (88 [87.1%]) and bilateral involvement (83 [82.2%]) and be lower lung predominant (55 [54.5%]) and multifocal (55 [54.5%]). Patients in the emergency group were older than those in the non-emergency group. Architectural distortion, traction bronchiectasis, and CT involvement score aided in evaluation of the severity and extent of the disease. CONCLUSION. Patients with confirmed COVID-19 pneumonia have typical imaging features that can be helpful in early screening of highly suspected cases and in evaluation of the severity and extent of disease. Most patients with COVID-19 pneumonia have GGO or mixed GGO and consolidation and vascular enlargement in the lesion. Lesions are more likely to have peripheral distribution and bilateral involvement and be lower lung predominant and multifocal. CT involvement score can help in evaluation of the severity and extent of the disease.

Chest CT findings and clinical features of coronavirus disease 2019 in children. / 儿童2019å† çŠ¶ç— æ¯’ç— æ‚£è€ çš„èƒ¸éƒ¨CT表现和临床特征.

OBJECTIVES: To describe the CT features and clinical characteristics of pediatric patients with coronavirus disease 2019 (COVID-19). METHODS: A total of 9 COVID-19 infected pediatric patients were included in this study. Clinical history, laboratory examination, and detailed CT imaging features were analyzed. All patients underwent the first CT scanning on the same day of being diagnosed by real-time reverse-transcription polymerase chain reaction (rRT-PCR). A low-dose CT scan was performed during follow-up. RESULTS: All the child patients had positive results. Four patients had cough and one patient had fever. One patient presented both cough and fever. Two children presented other symptoms like sore throat and stuffy nose. One child showed no clinical symptom. Five patients had positive initial CT findings with subtle lesions like ground-glass opacity (GGO) or spot-like mixed consolidation. Three patients were reported with negative results in the initial and follow-up CT examination. One patient was reported with initial negative CT findings but turning positive during the first follow-up. All patients had absorbed lesions on follow-up CT images after treatment. CONCLUSIONS: Pediatric COVID-19 patients have certain imaging and clinical features as well as disease prognosis. Children with COVID-19 tend to have normal or subtle CT findings and relatively better outcome.

Multistage CT features of coronavirus disease 2019. / 2019å† çŠ¶ç— æ¯’ç— å¤šé˜¶æ®µCT影像学特征.

OBJECTIVES: To determine imaging features of coronavirus disease 2019 (COVID-19) in different stages, and to provide foundations for early diagnosis and treatment. METHODS: CT image data of 187 COVID-19 patients were analyzed in the period of hospitalization. CT scanning was performed on admission and repeated every 3 days. The improvement time of clinical symptoms and the image changes of follow-up CT were statistically analyzed. RESULTS: All 187 patients' nucleic acid test were positive to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The early CT images of lung in 187 cases (100%) showed multiple patchy and ground-glass opacities with fine mesh and consolidation shade, which mainly distributed in pulmonary band or under the pleura. In the progressive stage, the pulmonary lesions in 146 cases (78.1%) were mainly consolidation, accompanied by air bronchogram, thickening of blood vessels, and interstitial changes. Severe pulmonary CT images in 15 cases (8%) showed diffuse lesions in both lungs, displaying consolidation, or "white lung". The CT imaging features in 185 cases (98.9%) at the absorptive period showed that the lesions diminished and fibrogenesis. The imaging features of 6 times of lung CT examination in one case showed continuous progress. The original lesion in one case was obviously absorbed, but new lesions appeared under the pleura of both lungs at the third review of CT scanning. The changes of CT imaging lesions during follow-up were significantly different in different clinical symptoms improvement time (P< 0.01). CONCLUSIONS: Images of COVID-19 in various stages have special characteristics. The change of clinical symptoms is synchronous with the change of reexamination CT. Follow-up CT can reflect the trend of clinical changes. Repeat CT examination plays an important role in the early clinical diagnosis and the evaluation for the therapeutic effect on COVID-19 patient.

Generation of high-quality tunable Airy beams with an adaptive deformable mirror.

Self-accelerating Airy beams have emerged to hold great promise in wide applications due to their non-diffraction and self-healing characteristics. The generation of Airy beams with high quality and high efficiency still remains challenging for conventional diffractive or refractive optical elements. In this Letter, tunable Airy beams with high quality are realized using a reflective adaptive deformable mirror (DM). Both the controllable cubic phase and the compensatory phase for aberrations of the optical system are generated simultaneously to ensure high quality of the resultant Airy beam. Continuous cubic phases with different amplitudes and rotation angles can be readily generated, demonstrating exceptional tunability of the generated Airy beams using a DM device. The intensity profiles and propagation trajectories of the experimentally generated Airy beams are in good agreement with the theoretical results. Benefiting from the intrinsic superiority of a DM with high reflectivity and a high damage threshold, our proposed method for dynamic generation of Airy beams opens up an avenue to plenty of applications such as ultrahigh-power laser shaping, laser fabrication, and optical manipulation.

[Effect of Jianpi Shengxue Granule on Fertility and Early Embryo Development Toxicity of Rats].

Objective To assess the effects of Jianpi Shengxue Granule (JSG) on the fertility and early embryo development in rats. Methods Totally 176 SD rats were stratified by sex, and divided into 4 groups, i.e., the control group, low, medium, high dose JSG treatment groups. Rats in the 3 JSG treatment groups were administered with JSG at 0, 1. 16, 3. 48, 5. 80 g/kg per day by gastrogavage, re- spectively. After 2 weeks of administration for females and 4 weeks of administration for males, males and females were caged in the ratio of 1:1. Females were administered to the gestation day 7. Males were administered for 59 -63 days until the day before anatomy. All parental generations were anatomized to observe signs and morphologies. Pathological examination was performed. General toxicity was detected. The testis and epididymis were weighed. Spermatozoa number was counted from epididymis, and repro- duction toxicity of sperm motility was checked. The numbers of corpus luteum, live fetus, dead fetus, and absorbed fetus were counted. The implantation number was calculated to observe early embryo de- velopment toxicity. Results (1) General toxicity: The body weight growth slowed down in male rats of the high dose JSG treatment group. No abnormality was found in low and medium dose JSG treatment groups. (2)Fertility toxicity: There was no obvious toxic effect on testis, epididymis, and spermatozoa number from epididymis sperm in all JSG treatment groups. (3) Early embryo development toxicity: No significant effect on the formation of early embryos was found in all JSG treatment groups. Conclusions Certain gastrointestinal toxicity of JSG might exist to some extent. The dose for non-adverse effect of JSG on fertility and early embryo development was 3. 38 g/kg per day. No obvious fertility or early embryo development toxicity occurred in each JSG treatment group.

Source tracing with cadmium isotope and risk assessment of heavy metals in sediment of an urban river, China.

The Nanfei River was one of dominant inflowing rivers of the fifth largest freshwater Chaohu Lake in China, which had been subjected to increasing nutrients and contaminants from population expansion, rapid industrialization and agricultural intensification in recent decades. In present study, surface sediment from the Nanfei River was collected to investigate the anthropogenic impact on distribution and bioavailability of heavy metals. Possible Cd sources along the river were constrained by using Cd isotope signatures and labile concentrations of heavy metals in sediment were determined through the DGT technique for risk assessment. Results showed that Cd in river sediment showed greatest enrichment (EF 0.8-9.4), indicating massive pollution from anthropogenic activities. Among the various possible Cd source materials, urban road dust, industrial soil and chicken manure, displayed higher Cd abundance and enrichment that might contribute to Cd accumulation in river sediment. Cadmium isotopic composition in river sediment was ranged from -0.21 ± 0.01‰ to 0.13 ± 0.03‰, whereas yielded relative variation from -0.31 ± 0.02‰ to 0.23 ± 0.01‰ in source materials. Accordingly, Cd sources along the river were constrained, i.e. traffic and industrial activities in the upper and middle reaches whereas agricultural activities in the lower reaches. Furthermore, the evaluation on ecological risk of heavy metals in sediment on basis of SQGs and DGT-labile concentrations demonstrated that Pb and Zn might pose higher risk on aquatic species. The present study confirmed that Cd isotopes were promising source tracer in environmental studies.

Correlation between early features and prognosis of symptomatic COVID-19 discharged patients in Hunan, China.

To determine the correlation between the clinical, laboratory, and radiological findings and the hospitalization days in Coronavirus Infectious Disease-19 (COVID-19) discharged patients. We retrospectively identified 172 discharged patients with COVID-19 pneumonia from January 10, 2020, to February 28, 2020, in Hunan province. The patients were categorized into group 1 (≤ 19 days) and group 2 (> 19 days) based on the time from symptom onset to discharge. Cough during admission occurred more commonly in group 2 (68.4%) than in group 1 (53.1%, p = 0.042). White blood cell (p = 0.045), neutrophil counts (p = 0.023), Alanine aminotransferase (p = 0.029), Aspartate aminotransferase (p = 0.027) and Lactate dehydrogenase (p = 0.021) that were above normal were more common in group 2. Patients with single lesions were observed more in group 1(17.7%, p = 0.018) and multiple lesions observed more in group 2(86.8%, p = 0.012). The number of lobes involved (p = 0.008) in the CT score (p = 0.001) for each patient was all differences between the two groups with a statistically significant difference. Mixed ground-glass opacity (GGO) and consolidation appearances were observed in most patients. GGO components > consolidation appearance was more common in group 1 (25.0%) than in group 2 (8.0%) with a significant difference (0.015), GGO < consolidation was more common in group 2(71.1%, p = 0.012). From the logistic regression analysis, the CT score (OR, 1.223; 95% CI, 1.004 to 1.491, p = 0.046) and the appearance of GGO > consolidation (OR, 0.150; 95% CI, 0.034 to 0.660, p = 0.012) were independently associated with the hospitalization days. Thus, special attention should be paid to the role of radiological features in monitoring the disease prognosis.

Relationship between ABO blood group distribution and clinical characteristics in patients with COVID-19.

OBJECTIVE: To explore ABO blood group distribution and clinical characteristics in patients with COVID-19. METHODS: The clinical data of 187 patients with COVID-19 seen between January 20, 2020 and March 5, 2020 at the First Hospital of Changsha were retrospectively analyzed. The differences in the ABO blood group distribution between COVID-19 patients and the control group (1991 cases) were analyzed. The relationship between blood type and clinical characteristics was analyzed. RESULTS: Of the 187 patients with COVID-19, 69 had type A (36.90%), 63 had type B (33.69%), 41 had type O (21.92%), and 14 had type AB blood (7.49%). The proportion of patients with type A blood in the COVID-19 group was significantly higher than that in the control group (36.90% vs. 27.47%, P = 0.006), while the proportion of patients with type O blood in the COVID-19 group was significantly lower than that in the control group (21.92% vs. 30.19%, P = 0.018). The risk of COVID-19 was higher for individuals with blood group A than for those with blood group O (OR = 1.849, 95% CI = 1.228-2.768, P = 0.003). The risk of COVID-19 was higher for patients with blood group A than for those with a blood group other than A (OR = 1.544, 95% CI = 1.122-2.104, P = 0.006). Patients with blood group O had a lower risk of COVID-19 than non-O blood group patients (OR = 0.649, 95% CI = 0.457-0.927, P = 0.018). The ABO blood group distribution was related to COVID-19 status. CONCLUSIONS: Patients with blood group A had an increased risk for infection with SARS-CoV-2, whereas blood group O was associated with a decreased risk, indicating that certain ABO blood groups were correlated with SARS-CoV-2 susceptibility. Blood type was related to some clinical characteristics of patients with COVID-19.

CT Scans of Patients with 2019 Novel Coronavirus (COVID-19) Pneumonia.

Rationale: The increasing speed of confirmed 2019 novel coronavirus (COVID-19) cases is striking in China. The purpose of this study is to summarize the outcomes of patients with novel COVID-19 pneumonia (NCP) at our institution. Methods: In this single-center study, we retrospectively included 118 cases of NCP, from January 16, 2020 to February 4, 2020. The clinical outcomes were monitored up to February 11, 2020. The outcomes of NCP patients were phase summarized at our institution. Three kinds of responses to clinical treatment were defined and evaluated: 1) good, symptoms continually improved; 2) fair, symptoms not improved or relapsed; 3) poor, symptoms aggravated. The risk factors, including basal clinical characteristics, CT imaging features, and follow-up CT changes (no change, progress, and improvement) related to poor/fair outcomes, were also investigated. Results: Six patients were improved to no-emergency type, 2 remained the same, and 2 progressed to fatal type. Besides, 13 patients progressed from the common type group to the emergency group (3 in fatal type and 10 in severe type). Forty-two (35.6%) patients were discharged with a median hospital stay of 9.5 days (range, 4.0-15.0 days). Thus, the numbers in different responses were, 73 patients in good response group (4 emergency cases, 69 no-emergency cases), 28 in fair response group (3 emergency cases, 25 no-emergency cases), and 17 in poor response group (3 emergency cases, 14 no-emergency cases). No patient has died in our hospital to date. The median duration of progress observed from CT scans was 6 days (range, 2-14 days). The progression in abnormal imaging findings indicate a poor/fair response, whereas the alleviated symptoms seen from CT suggest a good response. Conclusion: Most cases are no-emergency type and have a favorable response to clinical treatment. Follow-up CT changes during the treatment can help evaluate the treatment response of patients with NCP.

Association between incubation period and clinical characteristics of patients with COVID-19.

PURPOSE: To investigate associations between the clinical characteristics and incubation periods of patients infected with coronavirus disease 2019 (COVID-19) in Wuhan, China. METHODS: Complete clinical and epidemiological data from 149 patients with COVID-19 at a hospital in Hunan Province, China, were collected and retrospectively analyzed. RESULTS: Analysis of the distribution and receiver operator characteristic curve of incubation periods showed that 7 days was the optimal cut-off value to assess differences in disease severity between groups. Patients with shorter (≤7 days) incubation periods (n = 79) had more severe disease, longer durations of hospitalization, longer times from symptom onset to discharge, more abnormal laboratory findings, and more severe radiological findings than patients with longer (>7 days) incubation periods. Regression and correlation analyses also showed that a shorter incubation period was associated with longer times from symptom onset to discharge. CONCLUSION: The associations between the incubation periods and clinical characteristics of COVID-19 patients suggest that the incubation period may be a useful marker of disease severity and prognosis.

Clinical Factors Associated with Progression and Prolonged Viral Shedding in COVID-19 Patients: A Multicenter Study.

Coronavirus disease 2019 (COVID-19) is a global pandemic associated with a high mortality. Our study aimed to determine the clinical risk factors associated with disease progression and prolonged viral shedding in patients with COVID-19. Consecutive 564 hospitalized patients with confirmed COVID-19 between January 17, 2020 and February 28, 2020 were included in this multicenter, retrospective study. The effects of clinical factors on disease progression and prolonged viral shedding were analyzed using logistic regression and Cox regression analyses. 69 patients (12.2%) developed severe or critical pneumonia, with a higher incidence in the elderly and in individuals with underlying comorbidities, fever, dyspnea, and laboratory and imaging abnormalities at admission. Multivariate logistic regression analysis indicated that older age (odds ratio [OR], 1.04; 95% confidence interval [CI], 1.02-1.06), hypertension without receiving angiotensinogen converting enzyme inhibitors or angiotensin receptor blockers (ACEI/ARB) therapy (OR, 2.29; 95% CI, 1.14-4.59), and chronic obstructive pulmonary disease (OR, 7.55; 95% CI, 2.44-23.39) were independent risk factors for progression to severe or critical pneumonia. Hypertensive patients without receiving ACEI/ARB therapy showed higher lactate dehydrogenase levels and computed tomography (CT) lung scores at about 3 days after admission than those on ACEI/ARB therapy. Multivariate Cox regression analysis revealed that male gender (hazard ratio [HR], 1.22; 95% CI, 1.02-1.46), receiving lopinavir/ritonavir treatment within 7 days from illness onset (HR, 0.75; 95% CI, 0.63-0.90), and receiving systemic glucocorticoid therapy (HR, 1.79; 95% CI, 1.46-2.21) were independent factors associated with prolonged viral shedding. Our findings presented several potential clinical factors associated with developing severe or critical pneumonia and prolonged viral shedding, which may provide a rationale for clinicians in medical resource allocation and early intervention.

Clinical characteristics of older and younger patients infected with SARS-CoV-2.

BACKGROUND: SARS-CoV-2 causes high mortality risk in older patients. This study aims to characterize the clinical features of older and younger SARS-CoV-2 infected patients. RESULTS: A total of 239 patients were divided into the younger group (<60 years; n=181) and the older group (≥60 years; n=58). In both groups, fever and cough were common symptoms. However, dyspnea was more frequent in older patients than younger patients (20.7% versus 9.9%, p=0.032). Compared with younger patients, older patients harbored more severe cases (37.9% versus 17.1%, p=0.001) and comorbidities (58.6% versus 21.0%, p<0.001) such as hypertension and diabetes. The baseline values of eosinophils and C-reactive protein were abnormal in older and younger groups. From baseline to day 14, significant decreases of three biomarkers (C-reactive protein, hemoglobin, albumin) and dramatic increases of three biomarkers (lymphocytes, platelets, blood urea nitrogen) were observed in older patients. CONCLUSION: Older and younger patients exhibited differences in dyspnea, comorbidities, and proportions of severe cases. Moreover, the disease progression of SARS-CoV-2 in older patients is observed with the dynamics of laboratory biomarkers, supporting their potential use in disease monitoring. METHODS: We retrieved clinical symptoms, laboratory findings, comorbidities, and hospitalization information of SARS-CoV-2 cases in Changsha.

Early prediction of disease progression in COVID-19 pneumonia patients with chest CT and clinical characteristics.

The outbreak of coronavirus disease 2019 (COVID-19) has rapidly spread to become a worldwide emergency. Early identification of patients at risk of progression may facilitate more individually aligned treatment plans and optimized utilization of medical resource. Here we conducted a multicenter retrospective study involving patients with moderate COVID-19 pneumonia to investigate the utility of chest computed tomography (CT) and clinical characteristics to risk-stratify the patients. Our results show that CT severity score is associated with inflammatory levels and that older age, higher neutrophil-to-lymphocyte ratio (NLR), and CT severity score on admission are independent risk factors for short-term progression. The nomogram based on these risk factors shows good calibration and discrimination in the derivation and validation cohorts. These findings have implications for predicting the progression risk of COVID-19 pneumonia patients at the time of admission. CT examination may help risk-stratification and guide the timing of admission.