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As of March 9, 2020, more than 100,000 cases of coronavirus disease-2019 were reported in more than 100 countries with thousands deaths globally. It is now known that Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is a new type of coronavirus causing COVID-19 infection (1). The most common clinical feature of SARS-CoV-2 infection is fever (2). Moreover, acute respiratory distress syndrome (ARDS) is the most frequent cause of admission to intensive care unit in COVID-19 patients (1). Lactate dehydrogenase (LDH), a key enzyme in the glycolytic pathway and a cytoplasmic enzyme found in most organs, has been linked to inflammation response and cell damage. Currently, the role of serum LDH levels in ARDS patients infected by SARS-CoV-2 is unclear.Between January 30 and Feb 22, 2020, 77 fever patients diagnosed with SARS-CoV-2 infection were admitted to the hospital of Changsha Public Health Center. In all patients, fever was defined assessed as follows: reported a fever history during the time from the onset symptom to admission, fever was defined as a rise in body temperature and presence of axillary temperature ≥37.0 ℃. Exclusion criteria included onset symptoms without fever, and patients with cancer. Clinical information of COVID-19 patients such as age, gender, days from onset of symptoms, medical history, physical examination, clinical presentation, laboratory tests, and imaging studies during admission were collected. Laboratory findings including erythrocyte sedimentation rate, C-reactive protein, procalcitonin,

Background. Early and accurate evaluation of severity and prognosis in acute pancreatitis (AP), especially at the time of admission is very significant. This study was aimed to develop an artificial neural networks (ANN) model for early prediction of in-hospital mortality in AP. Methods. Patients with AP were identified from the Medical Information Mart for Intensive Care-III (MIMIC-III) database. Clinical and laboratory data were utilized to perform a predictive model by back propagation ANN approach. Results. A total of 337 patients with AP were analyzed in the study, and the in-hospital mortality rate was 11.2%. A total of 12 variables that differed between patients in survivor group and nonsurvivor group were applied to construct ANN model. Three independent variables were identified as risk factors associated with in-hospital mortality by multivariate logistic regression analysis. The predictive performance based on the area under the receiver operating characteristic curve (AUC) was 0.769 for ANN model, 0.607 for logistic regression, 0.652 for Ranson score, and 0.401 for SOFA score. Conclusion. An ANN predictive model for in-hospital mortality in patients with AP in MIMIC-III database was first performed. The patients with high risk of fatal outcome can be screened out easily in the early stage of AP by our model.

Deep vein thrombosis (DVT) is a serious complication in patients with acute ischemic stroke (AIS). Early prediction of DVT could enable physicians to perform a proper prevention strategy. We analyzed the association of clinical and laboratory variables with DVT to evaluate the risk of DVT in patients after AIS. AIS patients admitted to the Changsha Central Hospital between January 2017 and December 2019 with length of stay in hospital ≥7 days were included. Clinical and laboratory variables for DVT at baseline were collected, and the diagnosis of DVT was confirmed by ultrasonography. Independent factors were developed by Multivariate logistic regression analysis. A total of 101 patients were included in the study. The in-hospital incidence of DVT after AIS was 19.8%(20/101). The average level of D-dimer when DVT detected was significant increased around 4-fold than that on admission ( P < .001). Pulmonary infection (odds ratio [OR] = 5.4, 95%CI:1.10–26.65, P = .037)) and increased muscle tone (OR = 0.11, 95%CI:0.02–0.58, P = .010) as independent relevant factors for DVT were confirmed. Pulmonary infection as a risk factor and increased muscle tone as a protective factor for DVT were identified in patients after AIS. The level of D-dimer which increased around 4-fold compared to the initial level could be an indicator for DVT occurrence.

Objective: The aim of this study was to formulate and validate an individualized predictive nomogram for in-hospital incidence of acute respiratory distress syndrome (ARDS) in patients with acute pancreatitis(AP). Design: AP patients were randomly distributed into primary cohort and validation cohort. Based on the primary cohort, risk factors were identified by logistic regression model and a nomogram was performed. The nomogram was validated in the primary and validation cohort by the bootstrap validation method. The calibration curve was applied to evaluate the consistency between nomogram and ideal observation. Setting: Departments of Emergency Medicine of two university-affiliated tertiary hospitals. Participants: From January 2017 to December 2018, 779 individuals with AP were included in this study. Primary outcome measures: The in-hospital incidence of ARDS was assessed. Results: There were 728 patients in the non-ARDS group and 51patients in the ARDS group, with an incidence rate of about 6.55%. Five independent factors including white blood counts(WBC),prothrombin time(PT),albumin(ALB),serum creatinine(SCR)and triglyceride (TG) were associated with in-hospital incidence of ARDS in AP patients. A nomogram was constructed based on the five independent factors with primary cohort of AUC 0.821 and validation cohort of AUC 0.822. Calibration curve analysis indicated that the predicted probability was in accordance with the observed probability in both primary and validation cohorts. Conclusions: The study developed an intuitive nomogram with easily available laboratory parameters for the prediction of in-hospital incidence of ARDS in patients with AP. The incidence of ARDS for an individual patient can be fast and conveniently evaluated by our nomogram.

Background Escherichia coli (E. coli) is an important pathogen in sepsis. This study aimed to explore the factors which were associated with in-hospital mortality in adult sepsis with E. coli infection based on a public database. Methods All sepsis patients with E. coli infection in MIMIC-III were included in this study. Clinical characteristics between the survivor and non-survivor groups were analyzed. Factors associated with in-hospital mortality were identified by multivariate logistic regression. Results A total of 199 patients were eventually included and divided into two groups: a survivor group (n = 167) and a non-survivor group (n = 32). RDW and HCT were identified as the factors with clinical outcomes. The area under the ROC curve (AUC) were 0.633 and 0.579, respectively. When combined RDW and HCT for predicting in-hospital mortality, the AUC was 0.772, which was significantly superior to SOFA and APACHEII scores. Conclusion RDW and HCT were identified as factors associated with in-hospital mortality in adult sepsis patients with E. coli infection. Our findings will be of help in early and effective evaluation of clinical outcomes in those patients.

Objective Early identifying sepsis patients who had higher risk of poor prognosis was extremely important. The aim of this study was to develop an artificial neural networks (ANN) model for early predicting clinical outcomes in sepsis. Methods This study was a retrospective design. Sepsis patients from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were enrolled. A predictive model for predicting 30-day morality in sepsis was performed based on the ANN approach. Results A total of 2874 patients with sepsis were included and 30-day mortality was 29.8%. The study population was categorized into the training set (n = 1698) and validation set (n = 1176) based on the ratio of 6:4. 11 variables which showed significant differences between survivor group and nonsurvivor group in training set were selected for constructing the ANN model. In training set, the predictive performance based on the area under the receiver-operating characteristic curve (AUC) were 0.873 for ANN model, 0.720 for logistic regression, 0.629 for APACHEII score and 0.619 for SOFA score. In validation set, the AUCs of ANN, logistic regression, APAHCEII score, and SOFA score were 0.811, 0.752, 0.607, and 0.628, respectively. Conclusion An ANN model for predicting 30-day mortality in sepsis was performed. Our predictive model can be beneficial for early detection of patients with higher risk of poor prognosis.

Objective This study aimed to explore the relationship between albumin level with short- and long-term outcomes in sepsis patients admitted in the intensive care unit (ICU) based on a large public database to provide clinical evidence for physicians to make individualized plans of albumin supplementation. Methods Sepsis patients admitted in the ICU in MIMIC-IV were included. Different models were performed to investigate the relationships between albumin and mortalities of 28-day, 60-day, 180-day and 1-year. Smooth fitting curves were performed. Results A total of 5357 sepsis patients were included. Mortalities of 28-day, 60-day, 180-day and 1-year were 29.29% (n = 1569), 33.92% (n = 1817), 36.70% (n = 1966) and 37.71% (n = 2020). In the fully adjusted model (adjusted for all potential confounders), with each 1g/dL increment in albumin level, the risk of mortality in 28-day, 60-day, 180-day and 1-year decreased by 39% (OR = 0.61, 95% CI: 0.54–0.69), 34% (OR = 0.66, 95% CI: 0.59–0.73), 33% (OR = 0.67, 95% CI: 0.60–0.75), and 32% (OR = 0.68, 95% CI: 0.61–0.76), respectively. The non-linear negative relationships between albumin and clinical outcomes were confirmed by smooth fitting curves. The turning point of albumin level was 2.6g/dL for short- and long-term clinical outcomes. When albumin level ≤2.6g/dL, with each 1g/dL increment in albumin level, the risk of mortality in 28-day, 60-day, 180-day and 1-year decreased by 59% (OR = 0.41, 95% CI: 0.32–0.52), 62% (OR = 0.38, 95% CI: 0.30–0.48), 65% (OR = 0.35, 95% CI: 0.28–0.45), and 62% (OR = 0.38, 95% CI: 0.29–0.48), respectively. Conclusion Albumin level was associated with short- and long-term outcomes in sepsis. Albumin supplementation might be beneficial for septic patients with serum albumin<2.6g/dL.