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1.
World J Psychiatry ; 11(7): 365-374, 2021 Jul 19.
Article in English | MEDLINE | ID: covidwho-1335349

ABSTRACT

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has caused major public panic in China. Pregnant women may be more vulnerable to stress, which may cause them to have psychological problems. AIM: To explore the effects of perceived family support on psychological distress in pregnant women during the COVID-19 pandemic. METHODS: A total of 2232 subjects were recruited from three cities in China. Through the online surveys, information on demographic data and health status during pregnancy were collected. Insomnia severity index, generalized anxiety disorder 7-item scale, patient health questionnaire-9, somatization subscale of the symptom check list 90 scale, and posttraumatic stress disorder checklist were used to assess the psychological distress. RESULTS: A total of 1015 (45.4%) women reported having at least one psychological distress. The women who reported having inadequate family support were more likely to suffer from multiple psychological distress (≥ 2 psychological distress) than women who received adequate family support. Among the women who reported less family support, 41.8% reported depression, 31.1% reported anxiety, 8.2% reported insomnia, 13.3% reported somatization and 8.9% reported posttraumatic stress disorder (PTSD), which were significantly higher than those who received strong family support. Perceived family support level was negatively correlated with depressive symptoms (r = -0.118, P < 0.001), anxiety symptoms (r = -0.111, P < 0.001), and PTSD symptoms (r = -0.155, P < 0.001). CONCLUSION: Family support plays an important part on pregnant women's mental health during the COVID-19 pandemic. Better family support can help improve the mental health of pregnant women.

2.
IEEE J Biomed Health Inform ; 25(7): 2363-2373, 2021 07.
Article in English | MEDLINE | ID: covidwho-1328981

ABSTRACT

COVID-19 pneumonia is a disease that causes an existential health crisis in many people by directly affecting and damaging lung cells. The segmentation of infected areas from computed tomography (CT) images can be used to assist and provide useful information for COVID-19 diagnosis. Although several deep learning-based segmentation methods have been proposed for COVID-19 segmentation and have achieved state-of-the-art results, the segmentation accuracy is still not high enough (approximately 85%) due to the variations of COVID-19 infected areas (such as shape and size variations) and the similarities between COVID-19 and non-COVID-infected areas. To improve the segmentation accuracy of COVID-19 infected areas, we propose an interactive attention refinement network (Attention RefNet). The interactive attention refinement network can be connected with any segmentation network and trained with the segmentation network in an end-to-end fashion. We propose a skip connection attention module to improve the important features in both segmentation and refinement networks and a seed point module to enhance the important seeds (positions) for interactive refinement. The effectiveness of the proposed method was demonstrated on public datasets (COVID-19CTSeg and MICCAI) and our private multicenter dataset. The segmentation accuracy was improved to more than 90%. We also confirmed the generalizability of the proposed network on our multicenter dataset. The proposed method can still achieve high segmentation accuracy.


Subject(s)
COVID-19/diagnostic imaging , Deep Learning , Radiographic Image Interpretation, Computer-Assisted/methods , Tomography, X-Ray Computed/methods , Databases, Factual , Humans , Lung/diagnostic imaging
3.
Front Endocrinol (Lausanne) ; 12: 611526, 2021.
Article in English | MEDLINE | ID: covidwho-1305635

ABSTRACT

Background: It has been reported that dyslipidemia is related to coronavirus-related diseases. Critical patients with coronavirus disease 2019 (COVID-19) who suffered from multiple organ dysfunctions were treated in the intensive care unit (ICU) in Wuhan, China. Whether the lipids profile was associated with the prognosis of COVID-19 in critical patients remained unclear. Methods: A retrospective study was performed in critical patients (N=48) with coronavirus disease 2019 in Leishenshan hospital between February and April 2020 in Wuhan. The parameters including lipid profiles, liver function, and renal function were collected on admission day, 2-3days after the admission, and the day before the achievement of clinical outcome. Results: Albumin value and creatine kinase (ck) value were statistically decreased at 2-3 days after admission compared with those on admission day (P<0.05). Low density lipoprotein (LDL-c), high density lipoprotein (HDL-c), apolipoprotein A (ApoA), and apolipoprotein A (Apo B) levels were statistically decreased after admission (P<0.05). Logistic regression showed that HDL-c level both on admission day and the day before the achievement of clinical outcome were negatively associated with mortality in critical patients with COVID-19. Total cholesterol (TC) level at 2-3days after admission was related to mortality in critical patients with COVID-19. Conclusions: There were lipid metabolic disorders in the critical patients with COVID-19. Lower levels of HDL-c and TC were related to the progression of critical COVID-19.


Subject(s)
COVID-19/mortality , Dyslipidemias/epidemiology , Hospital Mortality , Multiple Organ Failure/mortality , Aged , Aged, 80 and over , Apolipoproteins A/blood , Apolipoproteins B/blood , COVID-19/blood , COVID-19/epidemiology , China/epidemiology , Cholesterol/blood , Cholesterol, HDL/blood , Cholesterol, LDL/blood , Critical Illness , Dyslipidemias/blood , Female , Humans , Male , Middle Aged , Multiple Organ Failure/blood , Multiple Organ Failure/epidemiology , Retrospective Studies , Risk Factors , SARS-CoV-2 , Severity of Illness Index
4.
Stat Med ; 40(19): 4252-4268, 2021 08 30.
Article in English | MEDLINE | ID: covidwho-1222698

ABSTRACT

Since the outbreak of the new coronavirus disease (COVID-19), a large number of scientific studies and data analysis reports have been published in the International Journal of Medicine and Statistics. Taking the estimation of the incubation period as an example, we propose a low-cost method to integrate external research results and available internal data together. By using empirical likelihood method, we can effectively incorporate summarized information even if it may be derived from a misspecified model. Taking the possible uncertainty in summarized information into account, we augment a logarithm of the normal density in the log empirical likelihood. We show that the augmented log-empirical likelihood can produce enhanced estimates for the underlying parameters compared with the method without utilizing auxiliary information. Moreover, the Wilks' theorem is proved to be true. We illustrate our methodology by analyzing a COVID-19 incubation period data set retrieved from Zhejiang Province and summarized information from a similar study in Shenzhen, China.


Subject(s)
COVID-19 , Humans , Likelihood Functions , Research Design , SARS-CoV-2 , Uncertainty
5.
Biomed Pharmacother ; 139: 111586, 2021 Jul.
Article in English | MEDLINE | ID: covidwho-1188337

ABSTRACT

It has become evident that the actions of pro-inflammatory cytokines and/or the development of a cytokine storm are responsible for the occurrence of severe COVID-19 during SARS-CoV-2 infection. Although immunomodulatory mechanisms vary among viruses, the activation of multiple TLRs that occurs primarily through the recruitment of adapter proteins such as MyD88 and TRIF contributes to the induction of a cytokine storm. Based on this, controlling the robust production of pro-inflammatory cytokines by macrophages may be applicable as a cellular approach to investigate potential cytokine-targeted therapies against COVID-19. In the current study, we utilized TLR2/MyD88 and TLR3/TRIF co-activated macrophages and evaluated the anti-cytokine storm effect of the traditional Chinese medicine (TCM) formula Babaodan (BBD). An RNA-seq-based transcriptomic approach was used to determine the molecular mode of action. Additionally, we evaluated the anti-inflammatory activity of BBD in vivo using a mouse model of post-viral bacterial infection-induced pneumonia and seven severely ill COVID-19 patients. Our study reveals the protective role of BBD against excessive immune responses in macrophages, where the underlying mechanisms involve the inhibition of the NF-κB and MAPK signaling pathways. In vivo, BBD significantly inhibited the release of IL-6, thus resulting in increased survival rates in mice. Based on limited data, we demonstrated that severely ill COVID-19 patients benefited from BBD treatment due to a reduction in the overproduction of IL-6. In conclusion, our study indicated that BBD controls excessive immune responses and may thus represent a cytokine-targeted agent that could be considered to treating COVID-19.


Subject(s)
Anti-Inflammatory Agents/administration & dosage , COVID-19/drug therapy , COVID-19/immunology , Cytokines/immunology , Medicine, Chinese Traditional/methods , Animals , COVID-19/complications , Female , Gene Expression Profiling , Humans , Lung Injury/etiology , Lung Injury/prevention & control , Mice , Mice, Inbred C57BL , Signal Transduction
7.
Preprint | SSRN | ID: ppcovidwho-5315

ABSTRACT

The growing adoption of virtual queues in the service and retail industries has been greatly accelerated by COVID-19 due to the requirement of social distancing. In collaboration with a major ride-sharing platform, we study how the wait time information (WTI) given by the service provider impacts customers' abandonment behavior in virtual queues;the study was conducted through a large-scale randomized field experiment that included 1,425,745 rides: one-third of the rides received a neutral WTI, one-third received a more optimistic WTI shorter than the neutral WTI (hence less frequent updates), and one-third received a pessimistic WTI (hence more frequent updates). We find both the magnitude of the initial WTI and the update frequency of the WTI have a significant impact on customer abandonment. Specifically, when adjusting the initial WTI by 1 minute, it did not impact customer abandonment. We show that this may be because the magnitude effect of the initial WTI is cancelled out by the opposite update-frequency effect. However, when adjusting the WTI by more than 1 minute, the magnitude effect becomes dominant: when comparing the pessimistic WTI of 4 minutes with the neutral initial WTI of 2 minutes, 5 minutes with 3 minutes, and 8 minutes with 5 minutes, customers’ likelihood to abandon increases by 6.2%, 14.1%, and 19.6%, respectively. Similar but opposite effects are found when comparing the optimistic WTI with the neutral WTI. We discuss how firms can use our findings and insights to design and operate better virtual queues.

9.
Front Cardiovasc Med ; 7: 584987, 2020.
Article in English | MEDLINE | ID: covidwho-993346

ABSTRACT

Background: Emerging studies have described and analyzed epidemiological, clinical, laboratory, and radiological features of COVID-19 patients. Yet, scarce information is available regarding the association of lipid profile features and disease severity and mortality. Methods: We conducted a prospective observational cohort study to investigate lipid profile features in patients with COVID-19. From 9 February to 4 April 2020, a total of 99 patients (31 critically ill and 20 severely ill) with confirmed COVID-19 were included in the study. Dynamic alterations in lipid profiles were recorded and tracked. Outcomes were followed up until 4 April 2020. Results: We found that high-density lipoprotein-cholesterol (HDL-C) and apolipoprotein A-1 (apoA-1) levels were significantly lower in the severe disease group, with mortality cases showing the lowest levels (p < 0.0001). Furthermore, HDL-C and apoA-1 levels were independently associated with disease severity (apoA-1: odds ratio (OR): 0.651, 95% confidence interval (CI): 0.456-0.929, p = 0.018; HDL-C: OR: 0.643, 95% CI: 0.456-0.906, p = 0.012). For predicting disease severity, the areas under the receiver operating characteristic curves (AUCs) of HDL-C and apoA-1 levels at admission were 0.78 (95% CI, 0.70-0.85) and 0.85 (95% CI, 0.76-0.91), respectively. For in-hospital deaths, HDL-C and apoA-1 levels demonstrated similar discrimination ability, with AUCs of 0.75 (95% CI, 0.61-0.88) and 0.74 (95% CI, 0.61-0.88), respectively. Moreover, patients with lower serum concentrations of apoA-1 (<0.95 g/L) or HDL-C (<0.84 mmol/l) had higher mortality rates during hospitalization (log-rank p < 0.001). Notably, levels of apoA-1 and HDL-C were inversely proportional to disease severity. The survivors of severe cases showed significant recovery of apoA-1 levels at the end of hospitalization (vs. midterm apoA-1 levels, p = 0.02), whereas the mortality cases demonstrated continuously lower apoA-1 levels throughout hospitalization. Correlation analysis revealed that apoA-1 and HDL-C levels were negatively correlated with both admission levels and highest concentrations of C-reactive protein and interleukin-6. Conclusions: Severely ill COVID-19 patients featured low HDL-C and apoA-1 levels, which were strongly correlated with inflammatory states. Thus, low apoA-1 and HDL-C levels may be promising predictors for severe disease and in-hospital mortality in patients suffering from COVID-19.

10.
Risk Manag Healthc Policy ; 13: 2689-2697, 2020.
Article in English | MEDLINE | ID: covidwho-948006

ABSTRACT

Background: The outbreak of coronavirus disease 2019 (COVID-19) has presented serious threats to people's health and lives. Police officers are bravely fighting on the front lines of the epidemic. The main purpose of this study was to assess the prevalence and severity of psychological responses among police officers during the COVID-19 pandemic and find influencing factors in depression and anxiety. Methods: A cross-sectional online questionnaire was administered to police officers in Wuhu through WeChat, and data were collected between March 10 and 26, 2020. A total of 3,561 questionnaires were received, of which 3,517 were considered valid. The questionnaires included demographic information and a psychological survey. The depression scale of the Patient Health QuestionnaireQ9) and Generalized Anxiety Disorder scale were utilized to assess depression and anxiety, respectively. Results: The mean depression score of participants was 4.10±4.87 (0-27), and 12.17%had moderate-severe depression. The mean anxiety score of participants was 3.59±4.228 (0-21), and 8.79% had moderate-severe anxiety. Older and married police officers were at higher risk of anxiety. Those with a bachelor's degree or above, living near the city center, and taking sleeping pills were at greater risk of depression and anxiety. Auxiliary police had lower depression and anxiety scores. Depression scores were strongly correlated withanxiety scores (r=0.863, p<0.001). Conclusion: Our findings identify factors associated with higher levels of depression and anxiety that can be utilized to develop psychological interventions to improve the mental health of vulnerable populations during the COVID-19 pandemic.

11.
Cell ; 183(6): 1479-1495.e20, 2020 12 10.
Article in English | MEDLINE | ID: covidwho-917236

ABSTRACT

We present an integrated analysis of the clinical measurements, immune cells, and plasma multi-omics of 139 COVID-19 patients representing all levels of disease severity, from serial blood draws collected during the first week of infection following diagnosis. We identify a major shift between mild and moderate disease, at which point elevated inflammatory signaling is accompanied by the loss of specific classes of metabolites and metabolic processes. Within this stressed plasma environment at moderate disease, multiple unusual immune cell phenotypes emerge and amplify with increasing disease severity. We condensed over 120,000 immune features into a single axis to capture how different immune cell classes coordinate in response to SARS-CoV-2. This immune-response axis independently aligns with the major plasma composition changes, with clinical metrics of blood clotting, and with the sharp transition between mild and moderate disease. This study suggests that moderate disease may provide the most effective setting for therapeutic intervention.


Subject(s)
COVID-19 , Genomics , RNA-Seq , SARS-CoV-2 , Single-Cell Analysis , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/blood , COVID-19/immunology , Female , Humans , Male , Middle Aged , SARS-CoV-2/immunology , SARS-CoV-2/metabolism , Severity of Illness Index
12.
J Psychiatr Res ; 137: 542-553, 2021 05.
Article in English | MEDLINE | ID: covidwho-885351

ABSTRACT

BACKGROUND: The current COVID pandemic is happening while the long-term effects of coronavirus infection remain poorly understood. The present article meta-analyzed mental health outcomes (anxiety, depression, etc.) from a previous coronavirus outbreak in China (2002). METHOD: CNKI, Wanfang, PubMed/Medline, Scopus, Web of Science, Baidu Scholar, and Google Scholar were searched up to early June 2020 for articles in English or Chinese reporting mental illness symptoms of SARS patients. Main outcome measures include SCL-90, SAS, SDS, and IES-R scales. 29 papers met the inclusion criteria. The longest follow-up time included in the analysis was 46 months. FINDINGS: The systematic meta-analysis indicated that mental health problems were most serious before or at hospital discharge and declined significantly during the first 12 months after hospital discharge. Nevertheless, average symptom levels remained above healthy norms even at 12 months and continued to improve, albeit slowly, thereafter. INTERPRETATION: The adverse mental health impact of being hospitalized with coronavirus infection long outlasts the physical illness. Mental health issues were the most serious for coronavirus infected patients before (including) hospital discharge and improved continuously during the first 12 months after hospital discharge. If COVID-19 infected patients follow a similar course of mental health development, most patients should recover to normal after 12 months of hospital discharge.


Subject(s)
COVID-19/epidemiology , COVID-19/psychology , Mental Health/statistics & numerical data , Survivors/psychology , Survivors/statistics & numerical data , Survivorship , China/epidemiology , Humans
13.
Health Inf Sci Syst ; 8(1): 28, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-805373

ABSTRACT

The novel coronavirus (COVID-19) is continuing its spread across the world, claiming more than 160,000 lives and sickening more than 2,400,000 people as of April 21, 2020. Early research has reported a basic reproduction number (R0) between 2.2 to 3.6, implying that the majority of the population is at risk of infection if no intervention measures were undertaken. The true size of the COVID-19 epidemic remains unknown, as a significant proportion of infected individuals only exhibit mild symptoms or are even asymptomatic. A timely assessment of the evolving epidemic size is crucial for resource allocation and triage decisions. In this article, we modify the back-calculation algorithm to obtain a lower bound estimate of the number of COVID-19 infected persons in China in and outside the Hubei province. We estimate the infection density among infected and show that the drastic control measures enforced throughout China following the lockdown of Wuhan City effectively slowed down the spread of the disease in two weeks. We also investigate the COVID-19 epidemic size in South Korea and find a similar effect of its "test, trace, isolate, and treat" strategy. Our findings are expected to provide guidelines and enlightenment for surveillance and control activities of COVID-19 in other countries around the world.

14.
Front Med (Lausanne) ; 7: 541, 2020.
Article in English | MEDLINE | ID: covidwho-769242

ABSTRACT

Background: Lung mechanics during invasive mechanical ventilation (IMV) for both prognostic and therapeutic implications; however, the full trajectory lung mechanics has never been described for novel coronavirus disease 2019 (COVID-19) patients requiring IMV. The study aimed to describe the full trajectory of lung mechanics of mechanically ventilated COVID-19 patients. The clinical and ventilator setting that can influence patient-ventilator asynchrony (PVA) and compliance were explored. Post-extubation spirometry test was performed to assess the pulmonary function after COVID-19 induced ARDS. Methods: This was a retrospective study conducted in a tertiary care hospital. All patients with IMV due to COVID-19 induced ARDS were included. High-granularity ventilator waveforms were analyzed with deep learning algorithm to obtain PVAs. Asynchrony index (AI) was calculated as the number of asynchronous events divided by the number of ventilator cycles and wasted efforts. Mortality was recorded as the vital status on hospital discharge. Results: A total of 3,923,450 respiratory cycles in 2,778 h were analyzed (average: 24 cycles/min) for seven patients. Higher plateau pressure (Coefficient: -0.90; 95% CI: -1.02 to -0.78) and neuromuscular blockades (Coefficient: -6.54; 95% CI: -9.92 to -3.16) were associated with lower AI. Survivors showed increasing compliance over time, whereas non-survivors showed persistently low compliance. Recruitment maneuver was not able to improve lung compliance. Patients were on supine position in 1,422 h (51%), followed by prone positioning (499 h, 18%), left positioning (453 h, 16%), and right positioning (404 h, 15%). As compared with supine positioning, prone positioning was associated with 2.31 ml/cmH2O (95% CI: 1.75 to 2.86; p < 0.001) increase in lung compliance. Spirometry tests showed that pulmonary functions were reduced to one third of the predicted values after extubation. Conclusions: The study for the first time described full trajectory of lung mechanics of patients with COVID-19. The result showed that prone positioning was associated with improved compliance; higher plateau pressure and use of neuromuscular blockades were associated with lower risk of AI.

17.
Chin J Integr Med ; 26(9): 648-655, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-648556

ABSTRACT

OBJECTIVES: To develop a new Chinese medicine (CM)-based drug and to evaluate its safety and effect for suppressing acute respiratory distress syndrome (ARDS) in COVID-19 patients. METHODS: A putative ARDS-suppressing drug Keguan-1 was first developed and then evaluated by a randomized, controlled two-arm trial. The two arms of the trial consist of a control therapy (alpha interferon inhalation, 50 µg twice daily; and lopinavir/ritonavir, 400 and 100 mg twice daily, respectively) and a testing therapy (control therapy plus Keguan-1 19.4 g twice daily) by random number table at 1:1 ratio with 24 cases each group. After 2-week treatment, adverse events, time to fever resolution, ARDS development, and lung injury on newly diagnosed COVID-19 patients were assessed. RESULTS: An analysis of the data from the first 30 participants showed that the control arm and the testing arm did not exhibit any significant differences in terms of adverse events. Based on this result, the study was expanded to include a total of 48 participants (24 cases each arm). The results show that compared with the control arm, the testing arm exhibited a significant improvement in time to fever resolution (P=0.035), and a significant reduction in the development of ARDS (P=0.048). CONCLUSIONS: Keguan-1-based integrative therapy was safe and superior to the standard therapy in suppressing the development of ARDS in COVID-19 patients. (Trial registration No. NCT04251871 at www.clinicaltrials.gov ).


Subject(s)
Coronavirus Infections/drug therapy , Drugs, Chinese Herbal/administration & dosage , Interferon-alpha/administration & dosage , Lopinavir/administration & dosage , Pneumonia, Viral/drug therapy , Severe Acute Respiratory Syndrome/drug therapy , Administration, Inhalation , Adult , COVID-19 , China , Coronavirus Infections/diagnosis , Coronavirus Infections/mortality , Dose-Response Relationship, Drug , Drug Administration Schedule , Female , Follow-Up Studies , Humans , Integrative Medicine , Male , Middle Aged , Pandemics , Pneumonia, Viral/diagnosis , Pneumonia, Viral/mortality , Risk Assessment , Severe Acute Respiratory Syndrome/diagnosis , Severe Acute Respiratory Syndrome/mortality , Severity of Illness Index , Survival Rate
18.
Cell Host Microbe ; 28(1): 124-133.e4, 2020 07 08.
Article in English | MEDLINE | ID: covidwho-378130

ABSTRACT

Since December 2019, a novel coronavirus SARS-CoV-2 has emerged and rapidly spread throughout the world, resulting in a global public health emergency. The lack of vaccine and antivirals has brought an urgent need for an animal model. Human angiotensin-converting enzyme II (ACE2) has been identified as a functional receptor for SARS-CoV-2. In this study, we generated a mouse model expressing human ACE2 (hACE2) by using CRISPR/Cas9 knockin technology. In comparison with wild-type C57BL/6 mice, both young and aged hACE2 mice sustained high viral loads in lung, trachea, and brain upon intranasal infection. Although fatalities were not observed, interstitial pneumonia and elevated cytokines were seen in SARS-CoV-2 infected-aged hACE2 mice. Interestingly, intragastric inoculation of SARS-CoV-2 was seen to cause productive infection and lead to pulmonary pathological changes in hACE2 mice. Overall, this animal model described here provides a useful tool for studying SARS-CoV-2 transmission and pathogenesis and evaluating COVID-19 vaccines and therapeutics.


Subject(s)
Betacoronavirus/physiology , Coronavirus Infections , Disease Models, Animal , Mice, Inbred C57BL , Pandemics , Pneumonia, Viral , Aging , Angiotensin-Converting Enzyme 2 , Animals , Brain/virology , COVID-19 , CRISPR-Cas Systems , Coronavirus Infections/pathology , Coronavirus Infections/virology , Cytokines/blood , Gene Knock-In Techniques , Lung/pathology , Lung/virology , Lung Diseases, Interstitial/pathology , Nose/virology , Peptidyl-Dipeptidase A/genetics , Peptidyl-Dipeptidase A/metabolism , Pneumonia, Viral/pathology , Pneumonia, Viral/virology , RNA, Viral/analysis , SARS-CoV-2 , Stomach/virology , Trachea/virology , Viral Load , Virus Replication
19.
Int J Infect Dis ; 96: 294-297, 2020 Jul.
Article in English | MEDLINE | ID: covidwho-356171

ABSTRACT

OBJECTIVE: To describe the clinical features of coronavirus disease 2019 (COVID-19). METHODS: We recruited 73 patients with COVID-19 [49 men and 24 women; average age: 58.36 years (SD: 14.31)] admitted to the intensive care unit of Wuhan Jinyintan Hospital from December 30, 2019 to February 16, 2020. Demographics, underlying diseases, and laboratory test results on admission were collected and analyzed. Data were compared between survivors and non-survivors. RESULTS: The non-survivors were older (65.46 [SD 9.74]vs 46.23 [12.01]) and were more likely to have chronic medical illnesses. Non-survivors tend to develop more severe lymphopenia, with higher C-reactive protein, interleukin-6, D-dimer, and hs-Troponin I(hs-TnI) levels. Patients with elevated hs-TnI levels on admission had shorter duration from symptom onset to death. Increased hs-TnI level was related to dismal prognosis. Death risk increased by 20.8% when the hs-TnI level increased by one unit. After adjusting for inflammatory or coagulation index, the independent predictive relationship between hs-TnI and death disappeared. CONCLUSIONS: Cardiac injury may occur at the early stage of COVID-19, which is associated with high mortality. Inflammatory factor cascade and coagulation abnormality may be the potential mechanisms of COVID-19 combined with cardiac injury.


Subject(s)
Betacoronavirus , Coronavirus Infections/complications , Heart Diseases/etiology , Pneumonia, Viral/complications , Troponin I/blood , Adult , Aged , C-Reactive Protein/analysis , COVID-19 , Coronavirus Infections/blood , Coronavirus Infections/mortality , Female , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/blood , Pneumonia, Viral/mortality , SARS-CoV-2
20.
Cell ; 181(6): 1423-1433.e11, 2020 06 11.
Article in English | MEDLINE | ID: covidwho-116588

ABSTRACT

Many COVID-19 patients infected by SARS-CoV-2 virus develop pneumonia (called novel coronavirus pneumonia, NCP) and rapidly progress to respiratory failure. However, rapid diagnosis and identification of high-risk patients for early intervention are challenging. Using a large computed tomography (CT) database from 3,777 patients, we developed an AI system that can diagnose NCP and differentiate it from other common pneumonia and normal controls. The AI system can assist radiologists and physicians in performing a quick diagnosis especially when the health system is overloaded. Significantly, our AI system identified important clinical markers that correlated with the NCP lesion properties. Together with the clinical data, our AI system was able to provide accurate clinical prognosis that can aid clinicians to consider appropriate early clinical management and allocate resources appropriately. We have made this AI system available globally to assist the clinicians to combat COVID-19.


Subject(s)
Artificial Intelligence , Coronavirus Infections/diagnosis , Pneumonia, Viral/diagnosis , Tomography, X-Ray Computed , COVID-19 , China , Cohort Studies , Coronavirus Infections/pathology , Coronavirus Infections/therapy , Datasets as Topic , Humans , Lung/pathology , Models, Biological , Pandemics , Pilot Projects , Pneumonia, Viral/pathology , Pneumonia, Viral/therapy , Prognosis , Radiologists , Respiratory Insufficiency/diagnosis
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