Background: Over 5,488,000 cases of coronavirus disease-19 (COVID-19) have been reported since December 2019. We aim to explore risk factors associated with mortality in COVID-19 patients and assess the use of D-dimer as a biomarker for disease severity and clinical outcome. Methods: We retrospectively analyzed the clinical, laboratory, and radiological characteristics of 248 consecutive cases of COVID-19 in Renmin Hospital of Wuhan University, Wuhan, China from January 28 to March 08, 2020. Univariable and multivariable logistic regression methods were used to explore risk factors associated with inhospital mortality. Correlations of D-dimer upon admission with disease severity and in-hospital mortality were analyzed. Receiver operating characteristic curve was used to determine the optimal cutoff level for D-dimer that discriminated those survivors versus non-survivors during hospitalization. Results: Multivariable regression that showed D-dimer > 2.0 mg/L at admission was the only variable associated with increased odds of mortality [OR 10.17 (95% CI 1.10-94.38), P = 0.041]. D-dimer elevation (≥ 0.50 mg/L) was seen in 74.6% (185/248) of the patients. Pulmonary embolism and deep vein thrombosis were ruled out in patients with high probability of thrombosis. D-dimer levels significantly increased with increasing severity of COVID-19 as determined by clinical staging (Kendall's tau-b = 0.374, P = 0.000) and chest CT staging (Kendall's tau-b = 0.378, P = 0.000). In-hospital mortality rate was 6.9%. Median D-dimer level in non-survivors (n = 17) was significantly higher than in survivors (n = 231) [6.21 (3.79-16.01) mg/L versus 1.02 (0.47-2.66) mg/L, P = 0.000]. D-dimer level of > 2.14 mg/L predicted in-hospital mortality with a sensitivity of 88.2% and specificity of 71.3% (AUC 0.85; 95% CI = 0.77-0.92). Conclusions: D-dimer is commonly elevated in patients with COVID-19. D-dimer levels correlate with disease severity and are a reliable prognostic marker for in-hospital mortality in patients admitted for COVID-19.

Acute lung injury (ALI) is a critical clinical condition with a high mortality rate, characterized with excessive uncontrolled inflammation and apoptosis. Recently, microRNAs (miRNAs) have been found to play crucial roles in the amelioration of various inflammation-induced diseases, including ALI. However, it remains unknown the biological function and regulatory mechanisms of miRNAs in the regulation of inflammation and apoptosis in ALI. The aim of this study is to identify and evaluate the potential role of miRNAs in ALI and reveal the underlying molecular mechanisms of their effects. Here, we analyzed microRNA expression profiles in lung tissues from LPS-challenged mice using miRNA microarray. Because microRNA-27a (miR-27a) was one of the miRNAs being most significantly downregulated, which has an important role in regulation of inflammation, we investigated its function. Overexpression of miR-27a by agomir-27a improved lung injury, as evidenced by the reduced histopathological changes, lung wet/dry (W/D) ratio, lung microvascular permeability and apoptosis in the lung tissues, as well as ameliorative survival of ALI mice. This was accompanied by the alleviating of inflammation, such as the reduced total BALF cell and neutrophil counts, decreased levels of tumor necrosis factor alpha (TNF-α), interleukin-1 (IL-6) interleukin-1β (IL-1β) and myeloperoxidase (MPO) activity in BAL fluid. Toll-like receptor 4 (TLR4), an important regulator of the nuclear factor kappa-B (NF-κB) signaling pathway, was identified as a novel target of miR-27a in RAW264.7 cells. Furthermore, our results showed that LPS stimulation increased the expression of MyD88 and NF-κB p65 (p-p65), but inhibited the expression of inhibitor of nuclear factor-κB-α (IκB-α), suggesting the activation of NF-κB signaling pathway. Further investigations revealed that agomir-miR-27a reversed the promoting effect of LPS on NF-κB signaling pathway. The results here suggested that miR-27a alleviates LPS-induced ALI in mice via reducing inflammation and apoptosis through blocking TLR4/MyD88/NF-κB activation.

BackgroundAcute lung injury (ALI) and acute respiratory distress syndrome (ARDS) are severe inflammatory lung diseases. Methylprednisolone (MP) is a common drug against inflammation in clinic. In this study, we aim to investigate the protective effect of MP on ALI and potential mechanisms.MethodsMale BABL/c mice were injected through tail vein using lipopolysaccharide (LPS, 5 mg/kg) with or without 5 mg/kg MP. Lung mechanics, tissue injury and inflammation were examined. Macrophage subsets in the lung were identified by flow cytometry. Macrophages were cultured from bone marrow of mice with or without MP. Then, we analyzed and isolated the subsets of macrophages. These isolated macrophages were then co-cultured with CD4+ T cells, and the percentage of regulatory T cells (Tregs) was examined. The expression of IL-10 and TGF-β in the supernatant was measured. The Tregs immunosuppression function was examined by T cell proliferation assay. To disclose the mechanism of the induction of Tregs by M2c, we blocked IL-10 or/and TGF-β using neutralizing antibody.ResultsRespiratory physiologic function was significantly improved by MP treatment. Tissue injury and inflammation were ameliorated in the MP-treated group. After MP treatment, the number of M1 decreased and M2 increased in the lung. In in vitro experiment, MP promoted M2 polarization rather than M1. We then induced M1, M2a and M2c from bone marrow cells. M1 induced more Th17 while M2 induced more CD4+CD25+Fxop3+ Tregs. Compared with M2a, M2c induced more Tregs, and this effect could be blocked by anti-IL-10 and anti-TGF-β antibodies. However, M2a and M2c have no impact on Tregs immunosuppression function.ConclusionIn conclusion, MP ameliorated ALI by promoting M2 polarization. M2, especially M2c, induced Tregs without any influence on Tregs immunosuppression function.

Background: Coronavirus disease 2019 , caused by a novel coronavirus (designated as SARS-CoV-2) has become a pandemic worldwide. Based on the current reports, hypertension may be associated with increased risk of sever condition in hospitalized COVID-19 patients. Angiotensin-converting enzyme 2 (ACE2) was recently identified to functional receptor of SARS-CoV-2. Previous experimental data revealed ACE2 level was increased following treatment with ACE inhibitors (ACEIs) and angiotensin receptor blockers (ARBs). Currently doctors concern whether these commonly used renin-angiotensin system (RAS) blockers-ACEIs/ARBs may increase the severity of COVID-19.Methods: We extracted data regarding 50 hospitalized hypertension patients with laboratory confirmed COVID-19 in the Renmin Hospital of Wuhan University from Feb 7 to Mar 03, 2020. These patients were grouped into RAS blockers group (Group A, n=20) and non-RAS blockers group (Group B, n=30) according to the basic blood pressure medications. All patients continued to use pre-admission antihypertensive drugs.Clinical severity (symptoms, laboratory and chest CT findings, etc.), clinical course, and short time outcome were analyzed after hospital admission.Results: Ten (50%) and seventeen (56.7%) of the Group A and Group B participants were males (P=0.643), and the average age was 52.65±13.12 and 67.77±12.84 years (P=0.000), respectively. The blood pressure of both groups was under effective control. There was no significant difference in clinical severity, clinical course and in-hospital mortality between Group A and Group B. Serum cardiac troponin I (cTnI) (P=0.03), and N-terminal (NT)-pro hormone BNP (NT-proBNP) (P=0.04) showed significant lower level in Group A than in Group B. But the patients with more than 0.04ng/mL or elevated NT-proBNP level had no statistical significance between the two groups. In patients over 65 years or under 65 years, cTnI or NT-proBNP level showed no difference between the two groups. Conclusions:We observed there was no obvious difference in clinical characteristics between RAS blockers and non-RAS blockers groups. These data suggest ACEIs/ARBs may have few effects on increasing the clinical severe conditions of COVID-19.

It is increasingly important to accurately and comprehensively estimate the effects of particular clinical treatments. Although randomization is the current gold standard, randomized controlled trials (RCTs) are often limited in practice due to ethical and cost issues. Observational studies have also attracted a great deal of attention as, quite often, large historical datasets are available for these kinds of studies. However, observational studies also have their drawbacks, mainly including the systematic differences in baseline covariates, which relate to outcomes between treatment and control groups that can potentially bias results.Propensity score methods, which are a series of balancing methods in these studies, have become increasingly popular by virtue of the two major advantages of dimension reduction and design separation. Within this approach, propensity score matching (PSM) has been empirically proven, with outstanding performances across observational datasets. While PSM tutorials are available in the literature, there is still room for improvement. Some PSM tutorials provide step-by-step guidance, but only one or two packages have been covered, thereby limiting their scope and practicality. Several articles and books have expounded upon propensity scores in detail, exploring statistical principles and theories; however, the lack of explanations on function usage in programming language has made it difficult for researchers to understand and follow these materials. To this end, this tutorial was developed with a six-step PSM framework, in which we summarize the recent updates and provide step-by-step guidance to the R programming language. This tutorial offers researchers with a broad survey of PSM, ranging from data preprocessing to estimations of propensity scores, and from matching to analyses. We also explain generalized propensity scoring for multiple or continuous treatments, as well as time-dependent PSM. Lastly, we discuss the advantages and disadvantages of propensity score methods.

BackgroundCardiac surgery–associated acute kidney injury (CSA‐AKI) is a common complication with a poor prognosis. In order to identify modifiable perioperative risk factors for AKI, which existing risk scores are insufficient to predict, a dynamic clinical risk score to allow clinicians to estimate the risk of CSA‐AKI from preoperative to early postoperative periods is needed.Methods and ResultsA total of 7233 cardiac surgery patients in our institution from January 2010 to April 2013 were enrolled prospectively and distributed into 2 cohorts. Among the derivation cohort, logistic regression was used to analyze CSA‐AKI risk factors preoperatively, on the day of ICU admittance and 24 hours after ICU admittance. Sex, age, valve surgery combined with coronary artery bypass grafting, preoperative NYHA score >2, previous cardiac surgery, preoperative kidney (without renal replacement therapy) disease, intraoperative cardiopulmonary bypass application, intraoperative erythrocyte transfusions, and postoperative low cardiac output syndrome were identified to be associated with CSA‐AKI. Among the other 1152 patients who served as a validation cohort, the point scoring of risk factor combinations led to area under receiver operator characteristics curves (AUROC) values for CSA‐AKI prediction of 0.74 (preoperative), 0.75 (on the day of ICU admission), and 0.82 (postoperative), and Hosmer–Lemeshow goodness‐of‐fit tests revealed a good agreement of expected and observed CSA‐AKI rates.ConclusionsThe first dynamic predictive score system, with Kidney Disease: Improving Global Outcomes (KDIGO) AKI definition, was developed and predictive efficiency for CSA‐AKI was validated in cardiac surgery patients.

Background: Extubation failure (EF) can lead to an increased chance of ventilator-associated pneumonia, longer hospital stays, and a higher mortality rate. This study aimed to develop and validate an accurate machine-learning model to predict EF in intensive care units (ICUs).Methods: Patients who underwent extubation in the Medical Information Mart for Intensive Care (MIMIC)-IV database were included. EF was defined as the need for ventilatory support (non-invasive ventilation or reintubation) or death within 48 h following extubation. A machine-learning model called Categorical Boosting (CatBoost) was developed based on 89 clinical and laboratory variables. SHapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance and the recursive feature elimination (RFE) algorithm was used to select key features. Hyperparameter optimization was conducted using an automated machine-learning toolkit (Neural Network Intelligence). The final model was trained based on key features and compared with 10 other models. The model was then prospectively validated in patients enrolled in the Cardiac Surgical ICU of Zhongshan Hospital, Fudan University. In addition, a web-based tool was developed to help clinicians use our model.Results: Of 16,189 patients included in the MIMIC-IV cohort, 2,756 (17.0%) had EF. Nineteen key features were selected using the RFE algorithm, including age, body mass index, stroke, heart rate, respiratory rate, mean arterial pressure, peripheral oxygen saturation, temperature, pH, central venous pressure, tidal volume, positive end-expiratory pressure, mean airway pressure, pressure support ventilation (PSV) level, mechanical ventilation (MV) durations, spontaneous breathing trial success times, urine output, crystalloid amount, and antibiotic types. After hyperparameter optimization, our model had the greatest area under the receiver operating characteristic (AUROC: 0.835) in internal validation. Significant differences in mortality, reintubation rates, and NIV rates were shown between patients with a high predicted risk and those with a low predicted risk. In the prospective validation, the superiority of our model was also observed (AUROC: 0.803). According to the SHAP values, MV duration and PSV level were the most important features for prediction.Conclusions: In conclusion, this study developed and prospectively validated a CatBoost model, which better predicted EF in ICUs than other models.

Pneumocystis jirovecii pneumonia (PJP) is a severe and life-threatening complication in immunocompromised patients. Trimethoprim/sulfamethoxazole (TMP-SMZ) is well known for its effectiveness as prophylaxis of PJP. However, the use of TMP-SMZ is associated with various adverse effects that may not be tolerated by critically ill patients. Caspofungin is recommended for invasive fungal infections, but the treatment of PJP after solid organ transplantation (SOT) is an off-label use of this drug. In this study, three cases of severe PJP in renal transplant recipients treated with a combination of caspofungin and low-dose TMP-SMZ were presented. Initial findings indicated that the combined treatment may be beneficial for the treatment of PJP and decrease the incidence of TMP-SMZ-related adverse effects.