Critical Care Database Comprising Patients With Infection.

Patients treated in the intensive care unit (ICU) are closely monitored and receive intensive treatment. Such aggressive monitoring and treatment will generate high-granularity data from both electronic healthcare records and nursing charts. These data not only provide infrastructure for daily clinical practice but also can help to inform clinical studies. It is technically challenging to integrate and cleanse medical data from a variety of sources. Although there are several open-access critical care databases from western countries, there is a lack of this kind of database for Chinese adult patients. We established a critical care database involving patients with infection. A large proportion of these patients have sepsis and/or septic shock. High-granularity data comprising laboratory findings, baseline characteristics, medications, international statistical classification of diseases (ICD) code, nursing charts, and follow-up results were integrated to generate a comprehensive database. The database can be utilized for a variety of clinical studies. The dataset is fully accessible at PhysioNet(https://physionet.org/content/icu-infection-zigong-fourth/1.0/).

Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review.

Background: Little is known about the role of artificial intelligence (AI) as a decisive technology in the clinical management of COVID-19 patients. We aimed to systematically review and critically appraise the current evidence on AI applications for COVID-19 in intensive care and emergency settings. Methods: We systematically searched PubMed, Embase, Scopus, CINAHL, IEEE Xplore, and ACM Digital Library databases from inception to 1 October 2020, without language restrictions. We included peer-reviewed original studies that applied AI for COVID-19 patients, healthcare workers, or health systems in intensive care, emergency, or prehospital settings. We assessed predictive modelling studies and critically appraised the methodology and key findings of all other studies. Results: Of fourteen eligible studies, eleven developed prognostic or diagnostic AI predictive models, all of which were assessed to be at high risk of bias. Common pitfalls included inadequate sample sizes, poor handling of missing data, failure to account for censored participants, and weak validation of models. Conclusions: Current AI applications for COVID-19 are not ready for deployment in acute care settings, given their limited scope and poor quality. Our findings underscore the need for improvements to facilitate safe and effective clinical adoption of AI applications, for and beyond the COVID-19 pandemic.

Risk factors for mortality of coronavirus disease 2019 (COVID-19) patients during the early outbreak of COVID-19: a systematic review and meta-analysis.

BACKGROUND: Identification of risk factors for poor prognosis of patients with coronavirus disease 2019 (COVID-19) is necessary to enable the risk stratification and modify the patient's management. Thus, we performed a systematic review and meta-analysis to evaluate the in-hospital mortality and risk factors of death in COVID-19 patients. METHODS: All studies were searched via the PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), VIP, and Wanfang databases. The in-hospital mortality of COVID-19 patients was pooled. Odds ratios (ORs) or mean difference (MD) with 95% confidence intervals (CIs) were calculated for evaluation of risk factors. RESULTS: A total of 80 studies were included with a pooled in-hospital mortality of 14% (95% CI: 12.2-15.9%). Older age (MD =13.32, 95% CI: 10.87-15.77; P<0.00001), male (OR =1.66, 95% CI: 1.37-2.01; P<0.00001), hypertension (OR =2.67, 95% CI: 2.08-3.43; P<0.00001), diabetes (OR =2.14, 95% CI: 1.76-2.6; P<0.00001), chronic respiratory disease (OR =3.55, 95% CI: 2.65-4.76; P<0.00001), chronic heart disease/cardiovascular disease (OR =3.15, 95% CI: 2.43-4.09; P<0.00001), elevated levels of high-sensitive cardiac troponin I (MD =66.65, 95% CI: 16.94-116.36; P=0.009), D-dimer (MD =4.33, 95% CI: 2.97-5.68; P<0.00001), C-reactive protein (MD =48.03, 95% CI: 27.79-68.27; P<0.00001), and a decreased level of albumin at admission (MD =-3.98, 95% CI: -5.75 to -2.22; P<0.0001) are associated with higher risk of death. Patients who developed acute respiratory distress syndrome (OR =62.85, 95% CI: 29.45-134.15; P<0.00001), acute cardiac injury (OR =25.16, 95% CI: 6.56-96.44; P<0.00001), acute kidney injury (OR =22.86, 95% CI: 4.60-113.66; P=0.0001), and septic shock (OR =24.09, 95% CI: 4.26-136.35; P=0.0003) might have a higher in-hospital mortality. CONCLUSIONS: Advanced age, male, comorbidities, increased levels of acute inflammation or organ damage indicators, and complications are associated with the risk of mortality in COVID-19 patients, and should be integrated into the risk stratification system.

Corticosteroid therapy in critically ill patients with COVID-19: a multicenter, retrospective study.

BACKGROUND: Corticoid therapy has been recommended in the treatment of critically ill patients with COVID-19, yet its efficacy is currently still under evaluation. We investigated the effect of corticosteroid treatment on 90-day mortality and SARS-CoV-2 RNA clearance in severe patients with COVID-19. METHODS: 294 critically ill patients with COVID-19 were recruited between December 30, 2019 and February 19, 2020. Logistic regression, Cox proportional-hazards model and marginal structural modeling (MSM) were applied to evaluate the associations between corticosteroid use and corresponding outcome variables. RESULTS: Out of the 294 critically ill patients affected by COVID-19, 183 (62.2%) received corticosteroids, with methylprednisolone as the most frequently administered corticosteroid (175 accounting for 96%). Of those treated with corticosteroids, 69.4% received corticosteroid prior to ICU admission. When adjustments and subgroup analysis were not performed, no significant associations between corticosteroids use and 90-day mortality or SARS-CoV-2 RNA clearance were found. However, when stratified analysis based on corticosteroid initiation time was performed, there was a significant correlation between corticosteroid use (≤ 3 day after ICU admission) and 90-day mortality (logistic regression adjusted for baseline: OR 4.49, 95% CI 1.17-17.25, p = 0.025; Cox adjusted for baseline and time varying variables: HR 3.89, 95% CI 1.94-7.82, p < 0.001; MSM adjusted for baseline and time-dependent variants: OR 2.32, 95% CI 1.16-4.65, p = 0.017). No association was found between corticosteroid use and SARS-CoV-2 RNA clearance even after stratification by initiation time of corticosteroids and adjustments for confounding factors (corticosteroids use ≤ 3 days initiation vs no corticosteroids use) using MSM were performed. CONCLUSIONS: Early initiation of corticosteroid use (≤ 3 days after ICU admission) was associated with an increased 90-day mortality. Early use of methylprednisolone in the ICU is therefore not recommended in patients with severe COVID-19.

Cumulative oxygen deficit is a novel predictor for the timing of invasive mechanical ventilation in COVID-19 patients with respiratory distress.

BACKGROUND AND OBJECTIVES: The timing of invasive mechanical ventilation (IMV) is controversial in COVID-19 patients with acute respiratory hypoxemia. The study aimed to develop a novel predictor called cumulative oxygen deficit (COD) for the risk stratification. METHODS: The study was conducted in four designated hospitals for treating COVID-19 patients in Jingmen, Wuhan, from January to March 2020. COD was defined to account for both the magnitude and duration of hypoxemia. A higher value of COD indicated more oxygen deficit. The predictive performance of COD was calculated in multivariable Cox regression models. RESULTS: A number of 111 patients including 80 in the non-IMV group and 31 in the IMV group were included. Patients with IMV had substantially lower PaO2 (62 (49, 89) vs. 90.5 (68, 125.25) mmHg; p < 0.001), and higher COD (-6.87 (-29.36, 52.38) vs. -231.68 (-1040.78, 119.83) mmHg·day) than patients without IMV. As compared to patients with COD < 0, patients with COD > 30 mmHg·day had higher risk of fatality (HR: 3.79, 95% CI [2.57-16.93]; p = 0.037), and those with COD > 50 mmHg·day were 10 times more likely to die (HR: 10.45, 95% CI [1.28-85.37]; p = 0.029). CONCLUSIONS: The study developed a novel predictor COD which considered both magnitude and duration of hypoxemia, to assist risk stratification of COVID-19 patients with acute respiratory distress.

Lung Mechanics of Mechanically Ventilated Patients With COVID-19: Analytics With High-Granularity Ventilator Waveform Data.

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.

Identification of risk factors for mortality associated with COVID-19.

OBJECTIVES: Coronavirus Disease 2019 (COVID-19) has become a pandemic outbreak. Risk stratification at hospital admission is of vital importance for medical decision making and resource allocation. There is no sophisticated tool for this purpose. This study aimed to develop neural network models with predictors selected by genetic algorithms (GA). METHODS: This study was conducted in Wuhan Third Hospital from January 2020 to March 2020. Predictors were collected on day 1 of hospital admission. The primary outcome was the vital status at hospital discharge. Predictors were selected by using GA, and neural network models were built with the cross-validation method. The final neural network models were compared with conventional logistic regression models. RESULTS: A total of 246 patients with COVID-19 were included for analysis. The mortality rate was 17.1% (42/246). Non-survivors were significantly older (median (IQR): 69 (57, 77) vs. 55 (41, 63) years; p < 0.001), had higher high-sensitive troponin I (0.03 (0, 0.06) vs. 0 (0, 0.01) ng/L; p < 0.001), C-reactive protein (85.75 (57.39, 164.65) vs. 23.49 (10.1, 53.59) mg/L; p < 0.001), D-dimer (0.99 (0.44, 2.96) vs. 0.52 (0.26, 0.96) mg/L; p < 0.001), and α-hydroxybutyrate dehydrogenase (306.5 (268.75, 377.25) vs. 194.5 (160.75, 247.5); p < 0.001) and a lower level of lymphocyte count (0.74 (0.41, 0.96) vs. 0.98 (0.77, 1.26) × 109/L; p < 0.001) than survivors. The GA identified a 9-variable (NNet1) and a 32-variable model (NNet2). The NNet1 model was parsimonious with a cost on accuracy; the NNet2 model had the maximum accuracy. NNet1 (AUC: 0.806; 95% CI [0.693-0.919]) and NNet2 (AUC: 0.922; 95% CI [0.859-0.985]) outperformed the linear regression models. CONCLUSIONS: Our study included a cohort of COVID-19 patients. Several risk factors were identified considering both clinical and statistical significance. We further developed two neural network models, with the variables selected by using GA. The model performs much better than the conventional generalized linear models.

A bibliometric analysis using VOSviewer of publications on COVID-19.

BACKGROUND: As a global pandemic, COVID-19 has aroused great concern in the last few months and a growing number of related researches have been published. Therefore, a bibliometric analysis of these publications may provide a direction of hot topics and future research trends. METHODS: The global literatures about COVID-19 published between 2019 and 2020 were scanned in the Web of Science collection database. "COVID-19" "Novel Coronavirus" "2019-nCoV" and "SARS-CoV-2" were used as the keywords to reach the relevant publications. VOSviewer was applied to perform the bibliometric analysis of these articles. RESULTS: Totally 3,626 publications on the topic of COVID-19 were identified and "COVID-19" with a total link strength of 2,649 appeared as the most frequent keyword, which had a strong link to "pneumonia" and "epidemiology". The mean citation count of the top 100 most cited articles was 96 (range, 26-883). Most of them were descriptive studies and concentrated on the clinical features. The highest-ranking journal was British medical journal with 211 publications and the most cited journal was Lancet with 2,485 citation counts. Eleven articles written by Christian Drosten from Berlin Institute of Virology have been cited for 389 times and 40 articles from Chinese Academy of Sciences have been cited for 1,597 times which are the most cited author and organization. The number of collaborators with China is 44 and the total link strength is 487. The main partners of China are USA, England and Germany. The published literatures have focused on three topics: disease management, clinical features and pathogenesis. CONCLUSIONS: The current growth trends predict a large increase in the number of global publications on COVID-19. China made the most outstanding contribution within this important field. Disease treatment, spike protein and vaccine may be hotspots in the future.

Clinical characteristics and treatment of critically ill patients with COVID-19 in Hebei.

BACKGROUND: In December, 2019, a novel coronavirus disease 2019 (COVID-19) emerged in Wuhan, China. We aimed to clarify the epidemiology, laboratory examinations, imaging findings, and treatment of critically ill patients with COVID-19 in Hebei province, China. METHODS: In this retrospective study, the demographic, laboratory and imaging, and treatment data of patients with severe COVID-19 treated in 13 designated hospitals in Hebei were collected and analyzed. RESULTS: A total of 319 severe COVID-19 patients were treated at the 13 designated hospitals between 22 January, 2020 and 25 March, 2020. Eventually, 51 critically ill (31 severe cases and 20 critically severe cases) patients were included in the analysis. The patients had an average age of 58.9±13.7 years, and 27 (52.9%) were men. Twenty-one (41.2%) were familial cluster, and 33 (64.7%) had chronic illnesses. The patients in critically severe group had longer duration from symptom to confirmation, more severe infections, more severe lung injury, and a lower percentage of lymphocytes. All 51 patients received antiviral drugs, 47 (92.2%) received antibacterial agents, 49 (96.1%) received traditional Chinese drugs, and 46 (90.2%) received methylprednisolone. The critically severe patients received more fluid and more diuretic treatment; 14 (70.0%) required invasive mechanical ventilation, and 13 (65.0%) developed extrapulmonary complications. CONCLUSIONS: COVID-19 patients who had underlying diseases and longer confirmation times were more likely to progress to critically severe COVID-19. These patients also presented with a higher risk of respiratory depression, circulatory collapse, extrapulmonary complications, and infection.

Prevalence and impact of acute renal impairment on COVID-19: a systematic review and meta-analysis.

BACKGROUND: The aim of this study is to assess the prevalence of abnormal urine analysis and kidney dysfunction in COVID-19 patients and to determine the association of acute kidney injury (AKI) with the severity and prognosis of COVID-19 patients. METHODS: The electronic database of Embase and PubMed were searched for relevant studies. A meta-analysis of eligible studies that reported the prevalence of abnormal urine analysis and kidney dysfunction in COVID-19 was performed. The incidences of AKI were compared between severe versus non-severe patients and survivors versus non-survivors. RESULTS: A total of 24 studies involving 4963 confirmed COVID-19 patients were included. The proportions of patients with elevation of sCr and BUN levels were 9.6% (95% CI 5.7-13.5%) and 13.7% (95% CI 5.5-21.9%), respectively. Of all patients, 57.2% (95% CI 40.6-73.8%) had proteinuria, 38.8% (95% CI 26.3-51.3%) had proteinuria +, and 10.6% (95% CI 7.9-13.3%) had proteinuria ++ or +++. The overall incidence of AKI in all COVID-19 patients was 4.5% (95% CI 3.0-6.0%), while the incidence of AKI was 1.3% (95% CI 0.2-2.4%), 2.8% (95% CI 1.4-4.2%), and 36.4% (95% CI 14.6-58.3%) in mild or moderate cases, severe cases, and critical cases, respectively. Meanwhile, the incidence of AKI was 52.9%(95% CI 34.5-71.4%), 0.7% (95% CI - 0.3-1.8%) in non-survivors and survivors, respectively. Continuous renal replacement therapy (CRRT) was required in 5.6% (95% CI 2.6-8.6%) severe patients, 0.1% (95% CI - 0.1-0.2%) non-severe patients and 15.6% (95% CI 10.8-20.5%) non-survivors and 0.4% (95% CI - 0.2-1.0%) survivors, respectively. CONCLUSION: The incidence of abnormal urine analysis and kidney dysfunction in COVID-19 was high and AKI is closely associated with the severity and prognosis of COVID-19 patients. Therefore, it is important to increase awareness of kidney dysfunction in COVID-19 patients.

Clinical characteristics of Coronavirus Disease 2019 and development of a prediction model for prolonged hospital length of stay.

BACKGROUND: The epidemic of Coronavirus Disease 2019 (COVID-19) has become a global health emergency, but the clinical characteristics of COVID-19 are not fully described. We aimed to describe the clinical characteristics of COVID-19 outside of Wuhan city; and to develop a multivariate model to predict the risk of prolonged length of stay in hospital (ProLOS). METHODS: The study was conducted in a tertiary care hospital in Zhejiang province from January to February 20, 2020. Medical records of all confirmed cases of COVID-19 were retrospectively reviewed. Patients were categorized into the ProLOS and non-ProLOS groups by hospital length of stay greater and less than 14 days, respectively. Conventional descriptive statistics were applied. Multivariate regression model was built to predict the risk of ProLOS, with variables selected using stepwise approach. RESULTS: A total of 75 patients with confirmed COVID-19 were included for quantitative analysis, including 25 (33%) patients in the ProLOS group. ProLOS patients were more likely to have history of traveling to Wuhan (68% vs. 28%; P=0.002). Patients in the ProLOS group showed lower neutrophil counts [median (IQR): 2.50 (1.77-3.23) ×109/L vs. 2.90 (2.21-4.19) ×109/L; P=0.048], higher partial thrombin time (PT) (13.42±0.63 vs. 13.10±0.48 s; P=0.029), lower D-Dimer [0.26 (0.22-0.46) vs. 0.44 (0.32-0.84) mg/L; P=0.012]. There was no patient died and no severe case in our cohort. The overall LOS was 11 days (IQR, 5-15 days). The median cost for a hospital stay was 7,388.19 RMB (IQR, 5,085.39-11,145.44). The prediction model included five variables of procalcitonin, heart rate, epidemiological history, lymphocyte count and cough. The discrimination of the model was 84.8% (95% CI: 75.3% to 94.4%). CONCLUSIONS: Our study described clinical characteristics of COVID-19 outside of Wuhan city and found that the illness was less severe than that in the core epidemic region. A multivariate model was developed to predict ProLOS, which showed good discrimination.