PurposeSodium glucose cotransporter 2 (SGLT2) inhibitors are shown to cause small, but significant changes of lipid profiles, we aim to investigate whether such altered lipid profiles can be translated into clinically meaningful changes in dyslipidemia.MethodsPubMed, Embase, and Cochrane Central Register of Controlled Trials (CENTRAL) were searched for randomized controlled trials (RCTs) that compared SGLT2 inhibitors with placebo or other oral glucose‐lowering drugs in patients with type 2 diabetes mellitus and reported the events of dyslipidemia. A random‐effect meta‐analysis was performed to calculate the pooled estimates with risk ratio (RR) for dyslipidemia risk and weighted mean difference for lipid profiles with their 95% confidential intervals (CIs).ResultsOf 2427 studies identified, 15 RCTs involving 7578 patients were included. This meta‐analysis found no association between SGLT2 inhibitors and risk of dyslipidemia (RR: 1.13; 95% CI: 0.91‐1.40). However, SGLT2 inhibitors were significantly associated with increases in total cholesterol by 0.15 mmol/L, low‐density lipoprotein cholesterol by 0.12 mmol/L, and high‐density lipoprotein cholesterol by 0.07 mmol/L while they can significantly decrease triglycerides by −0.12 mmol/L compared to controls.ConclusionsSGLT2 inhibitors were not associated with increased risk of dyslipidemia. Further trials with longitudinal assessment are needed to assess the effect of SGLT2 inhibitors on trajectories of changes of lipid metabolism.
Background: Thrombolysis with r-tPA is recommended for patients after acute ischemic stroke (AIS) within 4.5 h of symptom onset. However, only a few patients benefit from this therapeutic regimen. Thus, we aimed to develop an interpretable machine learning (ML)–based model to predict the thrombolysis effect of r-tPA at the super-early stage.Methods: A total of 353 patients with AIS were divided into training and test data sets. We then used six ML algorithms and a recursive feature elimination (RFE) method to explore the relationship among the clinical variables along with the NIH stroke scale score 1 h after thrombolysis treatment. Shapley additive explanations and local interpretable model–agnostic explanation algorithms were applied to interpret the ML models and determine the importance of the selected features.Results: Altogether, 353 patients with an average age of 63.0 (56.0–71.0) years were enrolled in the study. Of these patients, 156 showed a favorable thrombolysis effect and 197 showed an unfavorable effect. A total of 14 variables were enrolled in the modeling, and 6 ML algorithms were used to predict the thrombolysis effect. After RFE screening, seven variables under the gradient boosting decision tree (GBDT) model (area under the curve = 0.81, specificity = 0.61, sensitivity = 0.9, and F1 score = 0.79) demonstrated the best performance. Of the seven variables, activated partial thromboplastin clotting time (time), B-type natriuretic peptide, and fibrin degradation products were the three most important clinical characteristics that might influence r-tPA efficiency.Conclusion: This study demonstrated that the GBDT model with the seven variables could better predict the early thrombolysis effect of r-tPA.
Background: Fibrin degradation products (FDPs) are fragments released by the plasmin-mediated degradation of fibrinogen or fibrin. Whether plasma levels of these fragments can predict the thrombolytic effect of recombinant tissue plasminogen activator (r-tPA) remains unknown.Methods: We performed a hospital-based study of patients with acute ischemic stroke (AIS) to explore the relationship between FDP levels at admission and the NIH Stroke Scale (NIHSS) score 1 h after thrombolysis treatment. In this retrospective, single-center study, the data of all patients with AIS who received r-tPA treatment at Beijing Tiantan Hospital from January 2019 to October 2020 were collected and analyzed. Demographic and clinical data, including laboratory examinations, were also analyzed.Results: A total of 339 patients with AIS were included in this study. Of these, 151 showed favorable effects of r-tPA, and 188 showed unsatisfactory effects at 1 h after thrombolysis. Overall, we found an inverse relationship between the FDPs levels at admission and the NIHSS score. A significant difference was observed when using the interquartile range of the FDPs levels (1.31 μg/mL) as a cutoff value (P = 0.003, odds ratio [OR] = 1.95, 95% confidence interval [CI]: 1.26–3.01), even after adjusting for confounding factors (P = 0.003, OR = 2.23, 95% CI: 1.31–3.77). In addition, significant associations were observed in the tertile (T3) and quartile (Q3, Q4) FDP levels when compared with T1 or Q1. A nomogram was also employed to create a model to predict an unsatisfactory effect of r-tPA. We found that FDP levels, white blood cell count, age, D-dimer level, and body mass index could influence the thrombolytic effect of r-tPA.Conclusion: In conclusion, the present study demonstrated that the levels of FDPs at admission can be used as a prognostic factor to predict the curative effect of r-tPA.
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