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.

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.

Acute 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. Male 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. Respiratory 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. In conclusion, MP ameliorated ALI by promoting M2 polarization. M2, especially M2c, induced Tregs without any influence on Tregs immunosuppression function.

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.

Veno-arterial extracorporeal membrane oxygenation (VA-ECMO) is an effective mechanical circulatory support modality that rapidly restores systemic perfusion for circulatory failure in patients. Given the huge increase in VA-ECMO use, its optimal management depends on continuous and discrete hemodynamic monitoring. This article provides an overview of VA-ECMO pathophysiology, and the current state of the art in hemodynamic monitoring in patients with VA-ECMO.

Acute kidney injury (AKI) following cardiac surgery is common and associated with poor patient outcomes. Early risk assessment for development of AKI remains a challenge. The combination of urinary tissue inhibitor of metalloproteinase 2 (TIMP-2) and insulin-like growth factor binding protein 7 (IGFBP7) has been shown to be an excellent predictor of AKI following cardiac surgery, but reported studies are for predominately non-Asian populations. Adult patients were prospectively enrolled at Zhongshan hospital in Shanghai, China. The primary analysis was prediction of AKI and stage 2–3 AKI by [TIMP-2]*[IGFBP7] measured 4 h after postoperative ICU admission assessed using receiver operating characteristic curve (ROC) analysis. Kinetics of [TIMP-2]*[IGFBP7] following ICU admission were also examined. We prospectively enrolled 57 cardiac surgery patients, of which 20 (35%) developed AKI and 6 (11%) developed stage 2–3 AKI. The area under the ROC curve (AUC) of [TIMP-2]*[IGFBP7] at 4 h after ICU admission was 0.80 (95% confidence interval (CI): 0.68–0.91) for development of AKI and 0.83 (95% CI: 0.69–0.96) for development of stage 2–3 AKI. Urinary [TIMP-2]*[IGFBP7] values at 4 h after ICU admission were significantly (P < 0.001) higher in patients who developed AKI than in patients who did not develop AKI (mean (standard error) of 1.08 (0.34) (ng/mL)2/1000 and 0.29 (0.05) (ng/mL)2/1000, respectively). The time-profile of [TIMP-2]*[IGFBP7] suggests the markers started to elevate by the time of ICU admission in patients who developed AKI and either decreased or remained flat in patients without AKI. The combination of urinary TIMP-2 and IGFBP7 4 h after postoperative ICU admission identifies patients at risk for developing AKI, not just stage 2–3 AKI following cardiac surgery.