Clinical characteristics of 113 deceased patients with coronavirus disease 2019: retrospective study

ObjectiveTo delineate the clinical characteristics of patients with coronavirus disease 2019 (covid-19) who died.DesignRetrospective case series.SettingTongji Hospital in Wuhan, China.ParticipantsAmong a cohort of 799 patients, 113 who died and 161 who recovered with a diagnosis of covid-19 were analysed. Data were collected until 28 February 2020.Main outcome measuresClinical characteristics and laboratory findings were obtained from electronic medical records with data collection forms.ResultsThe median age of deceased patients (68 years) was significantly older than recovered patients (51 years). Male sex was more predominant in deceased patients (83;73%) than in recovered patients (88;55%). Chronic hypertension and other cardiovascular comorbidities were more frequent among deceased patients (54 (48%) and 16 (14%)) than recovered patients (39 (24%) and 7 (4%)). Dyspnoea, chest tightness, and disorder of consciousness were more common in deceased patients (70 (62%), 55 (49%), and 25 (22%)) than in recovered patients (50 (31%), 48 (30%), and 1 (1%)). The median time from disease onset to death in deceased patients was 16 (interquartile range 12.0-20.0) days. Leukocytosis was present in 56 (50%) patients who died and 6 (4%) who recovered, and lymphopenia was present in 103 (91%) and 76 (47%) respectively. Concentrations of alanine aminotransferase, aspartate aminotransferase, creatinine, creatine kinase, lactate dehydrogenase, cardiac troponin I, N-terminal pro-brain natriuretic peptide, and D-dimer were markedly higher in deceased patients than in recovered patients. Common complications observed more frequently in deceased patients included acute respiratory distress syndrome (113;100%), type I respiratory failure (18/35;51%), sepsis (113;100%), acute cardiac injury (72/94;77%), heart failure (41/83;49%), alkalosis (14/35;40%), hyperkalaemia (42;37%), acute kidney injury (28;25%), and hypoxic encephalopathy (23;20%). Patients with cardiovascular comorbidity were more likely to develop cardiac complications. Regardless of history of cardiovascular disease, acute cardiac injury and heart failure were more common in deceased patients.ConclusionSevere acute respiratory syndrome coronavirus 2 infection can cause both pulmonary and systemic inflammation, leading to multi-organ dysfunction in patients at high risk. Acute respiratory distress syndrome and respiratory failure, sepsis, acute cardiac injury, and heart failure were the most common critical complications during exacerbation of covid-19.

Intravenous Immunoglobulin Therapy for Critically Ill COVID-19 Patients With Different Inflammatory Phenotypes: A Multicenter, Retrospective Study.

Background: The benefits of intravenous immunoglobulin administration are controversial for critically ill COVID-19 patients. Methods: We analyzed retrospectively the effects of immunoglobulin administration for critically ill COVID-19 patients. The primary outcome was 28-day mortality. Inverse probability of treatment weighting (IPTW) with propensity score was used to account for baseline confounders. Cluster analysis was used to perform phenotype analysis. Results: Between January 1 and February 29, 2020, 754 patients with complete data from 19 hospitals were enrolled. Death at 28 days occurred for 408 (54.1%) patients. There were 392 (52.0%) patients who received intravenous immunoglobulin, at 11 (interquartile range (IQR) 8, 16) days after illness onset; 30% of these patients received intravenous immunoglobulin prior to intensive care unit (ICU) admission. By unadjusted analysis, no difference was observed for 28-day mortality between the immunoglobulin and non-immunoglobulin groups. Similar results were found by propensity score matching (n = 506) and by IPTW analysis (n = 731). Also, IPTW analysis did not reveal any significant difference between hyperinflammation and hypoinflammation phenotypes. Conclusion: No significant association was observed for use of intravenous immunoglobulin and decreased mortality of severe COVID-19 patients. Phenotype analysis did not show any survival benefit for patients who received immunoglobulin therapy.

Efficacy and Safety of Anticoagulation Treatment in COVID-19 Patient Subgroups Identified by Clinical-Based Stratification and Unsupervised Machine Learning: A Matched Cohort Study.

Objective: To explore the efficacy of anticoagulation in improving outcomes and safety of Coronavirus disease 2019 (COVID-19) patients in subgroups identified by clinical-based stratification and unsupervised machine learning. Methods: This single-center retrospective cohort study unselectively reviewed 2,272 patients with COVID-19 admitted to the Tongji Hospital between Jan 25 and Mar 23, 2020. The association between AC treatment and outcomes was investigated in the propensity score (PS) matched cohort and the full cohort by inverse probability of treatment weighting (IPTW) analysis. Subgroup analysis, identified by clinical-based stratification or unsupervised machine learning, was used to identify sub-phenotypes with meaningful clinical features and the target patients benefiting most from AC. Results: AC treatment was associated with lower in-hospital death risk either in the PS matched cohort or by IPTW analysis in the full cohort. A higher incidence of clinically relevant non-major bleeding (CRNMB) was observed in the AC group, but not major bleeding. Clinical subgroup analysis showed that, at admission, severe cases of COVID-19 clinical classification, mild acute respiratory distress syndrome (ARDS) cases, and patients with a D-dimer level ≥0.5 µg/mL, may benefit from AC. During the hospital stay, critical cases and severe ARDS cases may benefit from AC. Unsupervised machine learning analysis established a four-class clustering model. Clusters 1 and 2 were non-critical cases and might not benefit from AC, while clusters 3 and 4 were critical patients. Patients in cluster 3 might benefit from AC with no increase in bleeding events. While patients in cluster 4, who were characterized by multiple organ dysfunction (neurologic, circulation, coagulation, kidney and liver dysfunction) and elevated inflammation biomarkers, did not benefit from AC. Conclusions: AC treatment was associated with lower in-hospital death risk, especially in critically ill COVID-19 patients. Unsupervised learning analysis revealed that the most critically ill patients with multiple organ dysfunction and excessive inflammation might not benefit from AC. More attention should be paid to bleeding events (especially CRNMB) when using AC.

Efficacy and Safety of Anticoagulation Treatment in COVID-19 Patient Subgroups Identified by Clinical-Based Stratification and Unsupervised Machine Learning: A Matched Cohort Study

Objective: To explore the efficacy of anticoagulation in improving outcomes and safety of Coronavirus disease 2019 (COVID-19) patients in subgroups identified by clinical-based stratification and unsupervised machine learning. Methods: This single-center retrospective cohort study unselectively reviewed 2,272 patients with COVID-19 admitted to the Tongji Hospital between Jan 25 and Mar 23, 2020. The association between AC treatment and outcomes was investigated in the propensity score (PS) matched cohort and the full cohort by inverse probability of treatment weighting (IPTW) analysis. Subgroup analysis, identified by clinical-based stratification or unsupervised machine learning, was used to identify sub-phenotypes with meaningful clinical features and the target patients benefiting most from AC. Results: AC treatment was associated with lower in-hospital death risk either in the PS matched cohort or by IPTW analysis in the full cohort. A higher incidence of clinically relevant non-major bleeding (CRNMB) was observed in the AC group, but not major bleeding. Clinical subgroup analysis showed that, at admission, severe cases of COVID-19 clinical classification, mild acute respiratory distress syndrome (ARDS) cases, and patients with a D-dimer level ≥0.5 μg/mL, may benefit from AC. During the hospital stay, critical cases and severe ARDS cases may benefit from AC. Unsupervised machine learning analysis established a four-class clustering model. Clusters 1 and 2 were non-critical cases and might not benefit from AC, while clusters 3 and 4 were critical patients. Patients in cluster 3 might benefit from AC with no increase in bleeding events. While patients in cluster 4, who were characterized by multiple organ dysfunction (neurologic, circulation, coagulation, kidney and liver dysfunction) and elevated inflammation biomarkers, did not benefit from AC. Conclusions: AC treatment was associated with lower in-hospital death risk, especially in critically ill COVID-19 patients. Unsupervised learning analysis revealed that the most critically ill patients with multiple organ dysfunction and excessive inflammation might not benefit from AC. More attention should be paid to bleeding events (especially CRNMB) when using AC.

Retrospective Study of Critically Ill COVID-19 Patients With and Without Extracorporeal Membrane Oxygenation Support in Wuhan, China.

Background: Extracorporeal membrane oxygenation (ECMO) might benefit critically ill COVID-19 patients. But the considerations besides indications guiding ECMO initiation under extreme pressure during the COVID-19 epidemic was not clear. We aimed to analyze the clinical characteristics and in-hospital mortality of severe critically ill COVID-19 patients supported with ECMO and without ECMO, exploring potential parameters for guiding the initiation during the COVID-19 epidemic. Methods: Observational cohort study of all the critically ill patients indicated for ECMO support from January 1 to May 1, 2020, in all 62 authorized hospitals in Wuhan, China. Results: Among the 168 patients enrolled, 74 patients actually received ECMO support and 94 not were analyzed. The in-hospital mortality of the ECMO supported patients was significantly lower than non-ECMO ones (71.6 vs. 85.1%, P = 0.033), but the role of ECMO was affected by patients' age (Logistic regression OR 0.62, P = 0.24). As for the ECMO patients, the median age was 58 (47-66) years old and 62.2% (46/74) were male. The 28-day, 60-day, and 90-day mortality of these ECMO supported patients were 32.4, 68.9, and 74.3% respectively. Patients survived to discharge were younger (49 vs. 62 years, P = 0.042), demonstrated higher lymphocyte count (886 vs. 638 cells/uL, P = 0.022), and better CO2 removal (PaCO2 immediately after ECMO initiation 39.7 vs. 46.9 mmHg, P = 0.041). Age was an independent risk factor for in-hospital mortality of the ECMO supported patients, and a cutoff age of 51 years enabled prediction of in-hospital mortality with a sensitivity of 84.3% and specificity of 55%. The surviving ECMO supported patients had longer ICU and hospital stays (26 vs. 18 days, P = 0.018; 49 vs. 29 days, P = 0.001 respectively), and ECMO procedure was widely carried out after the supplement of medical resources after February 15 (67.6%, 50/74). Conclusions: ECMO might be a benefit for severe critically ill COVID-19 patients at the early stage of epidemic, although the in-hospital mortality was still high. To initiate ECMO therapy under tremendous pressure, patients' age, lymphocyte count, and adequacy of medical resources should be fully considered.

Clinical value of blood markers to assess the severity of coronavirus disease 2019.

BACKGROUND: Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is threatening the world with the symptoms of seasonal influenza. This study was conducted to investigate the patient characteristics and clinical value of blood markers to assess the severity of coronavirus disease 2019 (COVID-19). METHODS: 187 patients, diagnosed with COVID-19 (non-severe and severe cases) and admitted to hospital between January 27th and March 8th of 2020, were enrolled in the present study. RESULTS: A higher proportion of clinical symptoms, including cough, expectoration, myalgia, and fatigue were observed in the non-severe group. The level of white blood cell count, neutrophils, CRP, IL-6 and IL-8 were significantly increased, while the platelet count was remarkedly decreased in the severe group. The risk model based on lymphocyte, IL-6, IL-8, CRP and platelet counts had the highest area under the receiver operator characteristic curve (AUROC). The baseline of IL-6, IL-8 and CRP was positively correlated with other parameters except in the cases of lymphocyte, hemoglobin and platelet counts. The baseline of the platelet count was negatively correlated with other parameters except in the lymphocyte and hemoglobin counts. Additionally, there was no connection between the severity of COVID-19 and cultures of blood, sputum or catheter secretion. CONCLUSIONS: The present study suggested that high leucocyte and low platelets counts were independent predictive markers of the severity of COVID-19.

Clinical characteristics and outcomes of critically ill patients with coronavirus disease 2019 with hypotension in China: a retrospective cohort study.

BACKGROUND: The characteristics of the coronavirus disease 2019 (COVID-19) patients with hypotension are still limited. We aim to describe the clinical features and outcomes of the patients. METHODS: This was a multicenter retrospective study of critically ill patients with COVID-19 from ICUs in 19 hospitals in China. All patients were followed up to day 28 or death, which came first. Clinical and outcome data were collected and analyzed. Patients were classified as early-onset or late-onset hypotension, and clinical characteristics and outcomes were compared. RESULTS: A total of 649 patients were included in the final analysis, and 240 (37.0%) were hypotension patients. The median age of hypotension patients was 67 years (IQR, 60-73 years), and 159 (66.2%) were male. 172 (71.7%) of the hypotension patients had at least one comorbidity. The 28-day mortality of the patients with hypotension was 85.4%, which was significantly higher than that of patients without hypotension. Compared with late-onset hypotension patients, the 28-day mortality of patients with early-onset hypotension was significantly higher (90.1% vs. 78.6%, P=0.02). CONCLUSIONS: Approximately one third critically ill COVID-19 patients progressed to hypotension. The mortality was significantly higher in hypotension patients than that in patients without hypotension. Compared with patients with late-onset hypotension, the mortality of patients with early-onset hypotension was significantly higher.

Early identification of patients with severe COVID-19 at increased risk of in-hospital death: a multicenter case-control study in Wuhan.

BACKGROUND: Most evidence regarding the risk factors for early in-hospital mortality in patients with severe COVID-19 focused on laboratory data at the time of hospital admission without adequate adjustment for confounding variables. A multicenter, age-matched, case-control study was therefore designed to explore the dynamic changes in laboratory parameters during the first 10 days after admission and identify early risk indicators for in-hospital mortality in this patient cohort. METHODS: Demographics and clinical data were extracted from the medical records of 93 pairs of patients who had been admitted to hospital with severe COVID-19. These patients had either been discharged or were deceased by March 3, 2020. Data from days 1, 4, 7, and 10 of hospital admission were compared between survivors and non-survivors. Univariate and multivariate conditional logistic regression analyses were employed to identify early risk indicators of in-hospital death in this cohort. RESULTS: On admission, in-hospital mortality was associated with five risk indicators (ORs in descending order): aspartate aminotransferase (AST, >32 U/L) 43.20 (95% CI: 2.63, 710.04); C-reactive protein (CRP) greater than 100 mg/L 13.61 (1.78, 103.941); lymphocyte count lower than 0.6×109/L 9.95 (1.30, 76.42); oxygen index (OI) less than 200 8.23 (1.04, 65.15); and D-dimer over 1 mg/L 8.16 (1.23, 54.34). Sharp increases in D-dimer at day 4, accompanied by decreasing lymphocyte counts, deteriorating OI, and persistent remarkably high CRP concentration were observed among non-survivors during the early stages of hospital admission. CONCLUSIONS: The potential risk factors of high D-dimer, CRP, AST, low lymphocyte count and OI could help clinicians identify patients at high risk of death early in the hospital admission. This might assist with rationalization of health care resources.

Machine learning discovery of distinguishing laboratory features for severity classification of COVID-19 patients

Abstract The exponential spread of COVID-19 worldwide is evident, with devastating outbreaks primarily in the United States, Spain, Italy, the United Kingdom, France, Germany, Turkey and Russia. As of 1 May 2020, a total of 3,308,386 confirmed cases have been reported worldwide, with an accumulative mortality of 233,093. Due to the complexity and uncertainty of the pathology of COVID-19, it is not easy for front-line doctors to categorise severity levels of clinical COVID-19 that are general and severe/critical cases, with consistency. The more than 300 laboratory features, coupled with underlying disease, all combine to complicate proper and rapid patient diagnosis. However, such screening is necessary for early triage, diagnosis, assignment of appropriate level of care facility, and institution of timely intervention. A machine learning analysis was carried out with confirmed COVID-19 patient data from 10 January to 18 February 2020, who were admitted to Tongji Hospital, in Wuhan, China. A softmax neural network-based machine learning model was established to categorise patient severity levels. According to the analysis of 2662 cases using clinical and laboratory data, the present model can be used to reveal the top 30 of more than 300 laboratory features, yielding 86.30% blind test accuracy, 0.8195 F1-score, and 100% consistency using a two-way patient classification of severe/critical to general. For severe/critical cases, F1-score is 0.9081 (i.e. recall is 0.9050, and precision is 0.9113). This model for classification can be accomplished at a mini-second-level computational cost (in contrast to minute-level manual). Based on available COVID-19 patient diagnosis and therapy, an artificial intelligence model paradigm can help doctors quickly classify patients with a high degree of accuracy and 100% consistency to significantly improve diagnostic and classification efficiency. The discovered top 30 laboratory features can be used for greater differentiation to serve as an essential supplement to current guidelines, thus creating a more comprehensive assessment of COVID-19 cases during the early stages of infection. Such early differentiation will help the assignment of the appropriate level of care for individual patients.

A simple nomogram for predicting failure of non-invasive respiratory strategies in adults with COVID-19: a retrospective multicentre study.

BACKGROUND: Non-invasive respiratory strategies (NIRS) including high-flow nasal cannula (HFNC) and non-invasive ventilation (NIV) have become widely used in patients with COVID-19 who develop acute respiratory failure. However, use of these therapies, if ineffective, might delay initiation of invasive mechanical ventilation (IMV) in some patients. We aimed to determine early predictors of NIRS failure and develop a simple nomogram and online calculator that can identify patients at risk of NIRS failure. METHODS: We did a retrospective, multicentre observational study in 23 hospitals designated for patients with COVID-19 in China. Adult patients (≥18 years) with severe acute respiratory syndrome coronavirus 2 infection and acute respiratory failure receiving NIRS were enrolled. A training cohort of 652 patients (21 hospitals) was used to identify early predictors of NIRS failure, defined as subsequent need for IMV or death within 28 days after intensive care unit admission. A nomogram was developed by multivariable logistic regression and concordance statistics (C-statistics) computed. C-statistics were validated internally by cross-validation in the training cohort, and externally in a validation cohort of 107 patients (two hospitals). FINDINGS: Patients were enrolled between Jan 1 and Feb 29, 2020. NIV failed in 211 (74%) of 286 patients and HFNC in 204 (56%) of 366 patients in the training cohort. NIV failed in 48 (81%) of 59 patients and HFNC in 26 (54%) of 48 patients in the external validation cohort. Age, number of comorbidities, respiratory rate-oxygenation index (ratio of pulse oximetry oxygen saturation/fraction of inspired oxygen to respiratory rate), Glasgow coma scale score, and use of vasopressors on the first day of NIRS in the training cohort were independent risk factors for NIRS failure. Based on the training dataset, the nomogram had a C-statistic of 0·80 (95% CI 0·74-0·85) for predicting NIV failure, and a C-statistic of 0·85 (0·82-0·89) for predicting HFNC failure. C-statistic values were stable in both internal validation (NIV group mean 0·79 [SD 0·10], HFNC group mean 0·85 [0·07]) and external validation (NIV group value 0·88 [95% CI 0·72-0·96], HFNC group value 0·86 [0·72-0·93]). INTERPRETATION: We have developed a nomogram and online calculator that can be used to identify patients with COVID-19 who are at risk of NIRS failure. These patients might benefit from early triage and more intensive monitoring. FUNDING: Ministry of Science and Technology of the People's Republic of China, Key Research and Development Plan of Jiangsu Province, Chinese Academy of Medical Sciences.

The modified NUTRIC score can be used for nutritional risk assessment as well as prognosis prediction in critically ill COVID-19 patients.

BACKGROUND & AIMS: In the newly emerged Coronavirus Disease 2019 (COVID-19) disaster, little is known about the nutritional risks for critically ill patients. It is also unknown whether the modified Nutrition Risk in the Critically ill (mNUTRIC) score is applicable for nutritional risk assessment in intensive care unit (ICU) COVID-19 patients. We set out to investigate the applicability of the mNUTRIC score for assessing nutritional risks and predicting outcomes for these critically ill COVID-19 patients. METHODS: This retrospective observational study was conducted in three ICUs which had been specially established and equipped for COVID-19 in Wuhan, China. The study population was critically ill COVID-19 patients who had been admitted to these ICUs between January 28 and February 21, 2020. Exclusion criteria were as follows: 1) patients of ＜18 years; 2) patients who were pregnant; 3) length of ICU stay of ＜24 h; 4) insufficient medical information available. Patients' characteristics and clinical information were obtained from electronic medical and nursing records. The nutritional risk for each patient was assessed at their ICU admission using the mNUTRIC score. A score of ≥5 indicated high nutritional risk. Mortality was calculated according to patients' outcomes following 28 days of hospitalization in ICU. RESULTS: A total of 136 critically ill COVID-19 patients with a median age of 69 years (IQR: 57-77), 86 (63%) males and 50 (37%) females, were included in the study. Based on the mNUTRIC score at ICU admission, a high nutritional risk (≥5 points) was observed in 61% of the critically ill COVID-19 patients, while a low nutritional risk (<5 points) was observed in 39%. The mortality of ICU 28-day was significantly higher in the high nutritional risk group than in the low nutritional risk group (87% vs 49%, P ＜0.001). Patients in the high nutritional risk group exhibited significantly higher incidences of acute respiratory distress syndrome, acute myocardial injury, secondary infection, shock and use of vasopressors. Additionally, use of a multivariate Cox analysis showed that patients with high nutritional risk had a higher probability of death at ICU 28-day than those with low nutritional risk (adjusted HR = 2.01, 95% CI: 1.22-3.32, P = 0.006). CONCLUSIONS: A large proportion of critically ill COVID-19 patients had a high nutritional risk, as revealed by their mNUTRIC score. Patients with high nutritional risk at ICU admission exhibited significantly higher mortality of ICU 28-day, as well as twice the probability of death at ICU 28-day than those with low nutritional risk. Therefore, the mNUTRIC score may be an appropriate tool for nutritional risk assessment and prognosis prediction for critically ill COVID-19 patients.

Corticosteroid Therapy Is Associated With Improved Outcome in Critically Ill Patients With COVID-19 With Hyperinflammatory Phenotype.

BACKGROUND: Corticosteroid therapy is used commonly in patients with COVID-19, although its impact on outcomes and which patients could benefit from corticosteroid therapy are uncertain. RESEARCH QUESTION: Are clinical phenotypes of COVID-19 associated with differential response to corticosteroid therapy? STUDY DESIGN AND METHODS: Critically ill patients with COVID-19 from Tongji Hospital treated between January and February 2020 were included, and the main exposure of interest was the administration of IV corticosteroids. The primary outcome was 28-day mortality. Marginal structural modeling was used to account for baseline and time-dependent confounders. An unsupervised machine learning approach was carried out to identify phenotypes of COVID-19. RESULTS: A total of 428 patients were included; 280 of 428 patients (65.4%) received corticosteroid therapy. The 28-day mortality was significantly higher in patients who received corticosteroid therapy than in those who did not (53.9% vs 19.6%; P < .0001). After marginal structural modeling, corticosteroid therapy was not associated significantly with 28-day mortality (hazard ratio [HR], 0.80; 95% CI, 0.54-1.18; P = .26). Our analysis identified two phenotypes of COVID-19, and compared with the hypoinflammatory phenotype, the hyperinflammatory phenotype was characterized by elevated levels of proinflammatory cytokines, higher Sequential Organ Failure Assessment scores, and higher rates of complications. Corticosteroid therapy was associated with a reduced 28-day mortality (HR, 0.45; 95% CI, 0.25-0.80; P = .0062) in patients with the hyperinflammatory phenotype. INTERPRETATION: For critically ill patients with COVID-19, corticosteroid therapy was not associated with 28-day mortality, but the use of corticosteroids showed significant survival benefits in patients with the hyperinflammatory phenotype.

Hyperosmolarity Deserves More Attention in Critically Ill COVID-19 Patients with Diabetes: A Cohort-Based Study.

PURPOSE: Recently, a cluster of pneumonia caused by SARS-CoV-2 were identified in Wuhan and spread throughout the world. More information about risk factors for mortality of critically ill patients infected with SARS-CoV-2 remain to be evaluated. METHODS: We included adult patients confirmed with SARS-CoV-2 infection who were critically ill and admitted to the intensive care unit (ICU) of Tongji Hospital in Wuhan from Feb 4, 2020 to Feb 20, 2020. Data were collected and compared between patients who died and improved. Logistic regression was used to explore the risk factors for death of SARS-CoV-2-infected critically ill patients. RESULTS: A total of 160 critically ill patients with SARS-CoV-2 infection were included, of which 146 patients with appeared outcomes were included into the final analysis. The random blood glucose, serum sodium and effective plasma osmolarity were higher in deceased patients, especially in patients with diabetes. There were 7 patients with diabetes with hyperosmolar status and all of them were deceased. Multivariable regression revealed that older age (odds ratio 4.28, 95% CI 1.01-18.20; p = 0.049), higher C-reactive protein (odds ratio 1.01, 1.00-1.03; p = 0.024), higher interleukin-6 (odds ratio 1.01, 1.00-1.03; p = 0.0323), and d-dimer greater than 1 µg/mL (odds ratio 1.10, 1.01-1.20; p = 0.032) at admission were associated with increased odds of death. CONCLUSION: In conclusion, hyperosmolarity needs more attention and may contribute to mortality in critically ill patients with COVID-19, especially in those with diabetes. Older age, inflammatory response, and thrombosis may be risk factors for death of critically ill patients with SARS-CoV-2 infection.

Characteristics of Liver Function in Patients With SARS-CoV-2 and Chronic HBV Coinfection.

BACKGROUND & AIMS: Coronavirus disease 2019 (COVID-19) is a major global health threat. We aimed to describe the characteristics of liver function in patients with SARS-CoV-2 and chronic hepatitis B virus (HBV) coinfection. METHODS: We enrolled all adult patients with SARS-CoV-2 and chronic HBV coinfection admitted to Tongji Hospital from February 1 to February 29, 2020. Data of demographic, clinical characteristics, laboratory tests, treatments, and clinical outcomes were collected. The characteristics of liver function and its association with the severity and prognosis of disease were described. RESULTS: Of the 105 patients with SARS-CoV-2 and chronic HBV coinfection, elevated levels of liver test were observed in several patients at admission, including elevated levels of alanine aminotransferase (22, 20.95%), aspartate aminotransferase (29, 27.62%), total bilirubin (7, 6.67%), gamma-glutamyl transferase (7, 6.67%), and alkaline phosphatase (1, 0.95%). The levels of the indicators mentioned above increased substantially during hospitalization (all P < .05). Fourteen (13.33%) patients developed liver injury. Most of them (10, 71.43%) recovered after 8 (range 6-21) days. Notably the other, 4 (28.57%) patients rapidly progressed to acute-on-chronic liver failure. The proportion of severe COVID-19 was higher in patients with liver injury (P = .042). Complications including acute-on-chronic liver failure, acute cardiac injury and shock happened more frequently in patients with liver injury (all P < .05). The mortality was higher in individuals with liver injury (28.57% vs 3.30%, P = .004). CONCLUSION: Liver injury in patients with SARS-CoV-2 and chronic HBV coinfection was associated with severity and poor prognosis of disease. During the treatment of COVID-19 in chronic HBV-infected patients, liver function should be taken seriously and evaluated frequently.

Proteomic characteristics of bronchoalveolar lavage fluid in critical COVID-19 patients.

Up to 10-20% of patients with coronavirus disease 2019 (COVID-19) develop a severe pulmonary disease due to immune dysfunction and cytokine dysregulation. However, the extracellular proteomic characteristics in respiratory tract of these critical COVID-19 patients still remain to be investigated. In the present study, we performed a quantitative proteomic analysis of the bronchoalveolar lavage fluid (BALF) from patients with critical COVID-19 and from non-COVID-19 controls. Our study identified 358 differentially expressed BALF proteins (P < 0.05), among which 41 were significantly changed after using the Benjamini-Hochberg correction (q < 0.05). The up-regulated signaling was found to be mainly involved in inflammatory signaling and response to oxidative stress. A series of increased extracellular factors including Tenascin-C (TNC), Mucin-1 (KL-6 or MUC1), Lipocalin-2 (LCN2), periostin (POSTN), Chitinase 3-like 1 (CHI3L1 or YKL40), and S100A12, and the antigens including lymphocyte antigen 6D/E48 antigen (LY6D), CD9 antigen, CD177 antigen, and prostate stem cell antigen (PSCA) were identified, among which the proinflammatory factors TNC and KL-6 were further validated in serum of another thirty-nine COVID-19 patients and healthy controls, showing high potentials of being biomarkers or therapeutic candidates for COVID-19. This BALF proteome associated with COVID-19 would also be a valuable resource for researches on anti-inflammatory medication and understanding the molecular mechanisms of host response. DATABASE: Proteomic raw data are available in ProteomeXchange (http://proteomecentral.proteomexchange.org) under the accession number PXD022085, and in iProX (www.iprox.org) under the accession number IPX0002429000.

Coagulopathy of Patients with COVID-19 is Associated with Infectious and Inflammatory Markers.

BACKGROUND: SARS-CoV-2 infection activates coagulation and stimulates innate immune system. Little is known about coagulopathy and response of inflammation and infection in ICU patients with COVID-19. Derangement of coagulation and markers of infection and inflammation induced by SARS-CoV-2 infection, as well as their correlations were elucidated. METHODS: One hundred eight ICU patients with COVID-19 (28 survivors and 80 non-survivors) in Tongji hospital and Wuhan Jinyintan hospital, in Wuhan, China were included. Coagulation parameters, infectious and inflammatory markers were dynamically analysed. The correlation between coagulopathy of patients and infectious and inflammatory markers was verified. RESULTS: SARS-CoV-2-associated coagulopathy occurred in most cases of critical illness. Raised values of d-dimer and FDP were measured in all patients, especially in non-survivors, who had longer PT, APTT, INR, as well as TT, and lower PTA and AT compared to survivors. SIC and DIC mostly occurred in non-survivors. CRP, ESR, serum ferritin, IL-8, and IL-2R increased in all patients, and were much higher in non-survivors who had significantly higher levels of IL-6 and IL-10. D-dimer was positively associated with CRP, serum ferritin (p = 0.02), PCT (p < 0.001), and IL-2R (p = 0.007). SIC scores were positively correlated with CRP (p = 0.006), PCT (p = 0.0007), IL-1ß (p = 0.048), and IL-6 (p = 0.009). DIC scores were positively associated with CRP (p < 0.0001), ESR (p = 0.02), PCT (p < 0.0001), serum ferritin (p < 0.0001), IL-10 (p = 0.02), and IL-2R (p = 0.0005). CONCLUSION: Prothrombotic state, SIC, and DIC are the characteristics of coagulation in ICU patients with COVID-19. CRP, ESR, serum ferritin, IL-8, IL-2R, IL-6, and PCT were stimulated by SARS-CoV-2 infection. CRP, PCT, serum ferritin, and IL-2R indicate the coagulopathy severity of patients with COVID-19.

COVID-19: a risk factor for fatal outcomes in patients with comorbid cardiovascular disease.

OBJECTIVES: To evaluate the fatal impact of COVID-19 on patients with comorbid cardiovascular disease (CVD). RESULTS: Overall, the 28-day mortality of patients with comorbid CVD was 3.25 times of that of patients without comorbid CVD (40.63% vs 12.50%, P=0.011). Clinic symptoms on admission were similar for the two groups. However, patients with comorbid CVD had higher levels of Interleukin-10 (22.22% vs 0%, P=0.034), procalcitonin (22.6% vs 3.13%, P<0.001), high-sensitivity troponin I (20 pg/mL vs 16.05 pg/mL, P=0.019), and lactic dehydrogenase (437 U/L vs 310 U/L, P=0.015). In addition, patients with comorbid CVD experienced a high incidence of acute respiratory distress syndrome (59.38% vs 15.63%, P<0.001), and required more invasive mechanical ventilation (40.63% vs 12.50%, P=0.011). Methylprednisolone was found to improve the survival of patients without comorbid CVD (p = 0.05). CONCLUSIONS: Comorbid CVD resulted in a higher mortality rate for COVID-19 patients. Acute respiratory distress syndrome was the primary reason of death for COVID-19 patients with comorbid CVD, followed by acute myocardial infarction. METHODS: This retrospective study used propensity score matching to divide 64 COVID-19 patients into two groups with and without comorbid CVD. Clinic symptoms, laboratory features, treatments, and 28-day mortality were compared between the two groups.

Early prediction of mortality risk among patients with severe COVID-19, using machine learning.

BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 infection, has been spreading globally. We aimed to develop a clinical model to predict the outcome of patients with severe COVID-19 infection early. METHODS: Demographic, clinical and first laboratory findings after admission of 183 patients with severe COVID-19 infection (115 survivors and 68 non-survivors from the Sino-French New City Branch of Tongji Hospital, Wuhan) were used to develop the predictive models. Machine learning approaches were used to select the features and predict the patients' outcomes. The area under the receiver operating characteristic curve (AUROC) was applied to compare the models' performance. A total of 64 with severe COVID-19 infection from the Optical Valley Branch of Tongji Hospital, Wuhan, were used to externally validate the final predictive model. RESULTS: The baseline characteristics and laboratory tests were significantly different between the survivors and non-survivors. Four variables (age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level) were selected by all five models. Given the similar performance among the models, the logistic regression model was selected as the final predictive model because of its simplicity and interpretability. The AUROCs of the external validation sets were 0.881. The sensitivity and specificity were 0.839 and 0.794 for the validation set, when using a probability of death of 50% as the cutoff. Risk score based on the selected variables can be used to assess the mortality risk. The predictive model is available at [https://phenomics.fudan.edu.cn/risk_scores/]. CONCLUSIONS: Age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level of COVID-19 patients at admission are informative for the patients' outcomes.

Impact of Shenfu injection on a composite of organ dysfunction development in critically ill patients with coronavirus disease 2019 (COVID-19): A structured summary of a study protocol for a randomized controlled trial.

OBJECTIVES: This study aims to determine the protection provided by Shenfu injection (a traditional Chinese medicine) against development of organ dysfunction in critically ill patients with coronavirus disease 2019 (COVID-19). TRIAL DESIGN: This study is a multicenter, randomized, controlled, open-label, two-arm ratio 1:1, parallel group clinical trial. PARTICIPANTS: The patients, who are aged from 18 to 75 years old, with a confirmed or suspected diagnosis of severe or critical COVID-19, will be consecutively recruited in the study, according to the guideline on diagnosis and treatment of COVID-19 (the 7th version) issued by National Health Commission of the People's Republic of China. Exclusion criteria include pregnant and breastfeeding women, atopy or allergies to Shenfu Injection (SFI), severe underlying disease (malignant tumor with multiple metastases, uncontrolled hemopathy, cachexia, severe malnutrition, HIV), active bleeding, obstructive pneumonia caused by lung tumor, severe pulmonary interstitial fibrosis, alveolar proteinosis and allergic alveolitis, continuous use of immunosuppressive drugs in last 6 months, organ transplantation, expected death within 48 hours, the patients considered unsuitable for this study by researchers. The study is conducted in 11 ICUs of designated hospitals for COVID-19, located in 5 cities of China. INTERVENTION AND COMPARATOR: The enrolled patients will randomly receive 100 ml SFI (study group) or identical volume of saline (control group) twice a day for seven consecutive days. Patients in the both groups will be given usual care and the necessary supportive therapies as recommended by the latest edition of the management guidelines for COVID-19 (the 7th version so far). MAIN OUTCOMES: The primary endpoint is a composite of newly developed or exacerbated organ dysfunction. This is defined as an increase in the sequential organ failure assessment (SOFA) score of two or more, indicating sepsis and involvement of at least one organ. The SOFA score will be measured for the 14 days after enrolment from the baseline (the score at randomization). The secondary endpoints are shown below: • SOFA score in total • Pneumonia severity index score • Dosage of vasoactive drugs • Ventilation free days within 28 days • Length of stay in intensive care unit • Total hospital costs to treat the patient • 28-day mortality • The incidence of adverse drug events related to SFI RANDOMISATION: The block randomization codes were generated by SAS V.9.1 for allocation of participants in this study. The ratio of random distribution is 1:1. The sealed envelope method is used for allocation concealment. BLINDING (MASKING): The patients and statistical personnel analyzing study data are both blinded. The blinding of group assignment is not adopted for the medical staff. NUMBERS TO BE RANDOMISED (SAMPLE SIZE): This study is expected to recruit 300 patients with COVID-19, (150 in each group). TRIAL STATUS: Protocol version 2.0, February 15, 2020. Patient recruitment started on February 25, and will end on August 31, 2020. TRIAL REGISTRATION: Chinese Clinical Trial Registry: ChiCTR2000030043. Registered February 21, 2020, http://www.chictr.org.cn/showprojen.aspx?proj=49866 FULL PROTOCOL: The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this letter serves as a summary of the key elements of the full protocol.