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@#BACKGROUND: Hyperkalemia is common among patients in emergency department and is associated with mortality. While, there is a lack of good evaluation and prediction methods for the efficacy of potassium-lowering treatment, making the drug dosage adjustment quite difficult. We aimed to develop a predictive model to provide early forecasting of treating effects for hyperkalemia patients. METHODS: Around 80% of hyperkalemia patients (n=818) were randomly selected as the training dataset and the remaining 20% (n=196) as the validating dataset. According to the serum potassium (K+) levels after the first round of potassium-lowering treatment, patients were classified into the effective and ineffective groups. Multivariate logistic regression analyses were performed to develop a prediction model. The receiver operating characteristic (ROC) curve and calibration curve analysis were used for model validation. RESULTS: In the training dataset, 429 patients had favorable effects after treatment (effective group), and 389 had poor therapeutic outcomes (ineffective group). Patients in the ineffective group had a higher percentage of renal disease (P=0.007), peripheral edema (P<0.001), oliguria (P=0.001), or higher initial serum K+ level (P<0.001). The percentage of insulin usage was higher in the effective group than in the ineffective group (P=0.005). After multivariate logistic regression analysis, we found age, peripheral edema, oliguria, history of kidney transplantation, end-stage renal disease, insulin, and initial serum K+ were all independently associated with favorable treatment effects. CONCLUSION: The predictive model could provide early forecasting of therapeutic outcomes for hyperkalemia patients after drug treatment, which could help clinicians to identify hyperkalemia patients with high risk and adjust the dosage of medication for potassium-lowering.
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BACKGROUNDCorona Virus Disease 2019 (COVID-19) is spreading worldwide. Effective screening for patients is important to limit the epidemic. However, some defects make the currently applied diagnosis methods are still not very ideal for early warning of patients. We aimed to develop a diagnostic model that allows for the quick screening of highly suspected patients using easy-to-get variables. METHODSA total of 1,311 patients receiving severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleicacid detection were included, whom with a positive result were classified into COVID-19 group. Multivariate logistic regression analyses were performed to construct the diagnostic model. Receiver operating characteristic (ROC) curve analysis were used for model validation. RESULTSAfter analysis, signs of pneumonia on CT, history of close contact, fever, neutrophil-to-lymphocyte ratio (NLR), Tmax and sex were included in the diagnostic model. Age and meaningful respiratory symptoms were enrolled into COVID-19 early warning score (COVID-19 EWS). The areas under the ROC curve (AUROC) indicated that both of the diagnostic model (training dataset 0.956 [95%CI 0.935-0.977, P < 0.001]; validation dataset 0.960 [95%CI 0.919-1.0, P < 0.001]) and COVID-19 EWS (training dataset 0.956 [95%CI 0.934-0.978, P < 0.001]; validate dataset 0.966 [95%CI 0.929-1, P < 0.001]) had good discrimination capacity. In addition, we also obtained the cut-off values of disease severity predictors, such as CT score, CD8+ T cell count, CD4+ T cell count, and so on. CONCLUSIONSThe new developed COVID-19 EWS was a considerable tool for early and relatively accurately warning of SARS-CoV-2 infected patients.
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Tripterygium wilfordii Hook F has significant anti-inflammatory and immunosuppressive properties and is widely used for treating autoimmune and inflammatory diseases such as rheumatoid arthritis, systemic lupus erythematosus, and kidney disease, especially in traditional Chinese medicine. The mechanisms underlying its effects may be diverse but they remain unclear, and its toxicity and side effects limit its wider clinical application. This review summarizes the clinical application of Tripterygium wilfordii Hook F in recent years, as well as the results of studies into its mechanisms and toxicity, to provide a reference for its future clinical application.
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Tripterygium wilfordii Hook F has significant anti-inflammatory and immunosuppressive properties and is widely used for treating autoimmune and inflammatory diseases such as rheumatoid arthritis, systemic lupus erythematosus, and kidney disease, especially in traditional Chinese medicine. The mechanisms underlying its effects may be diverse but they remain unclear, and its toxicity and side effects limit its wider clinical application. This review summarizes the clinical application of Tripterygium wilfordii Hook F in recent years, as well as the results of studies into its mechanisms and toxicity, to provide a reference for its future clinical application.
