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Association between serum albumin-to-creatinine ratio and clinical outcomes among patients with ST-elevation myocardial infarction after percutaneous coronary intervention: a secondary analysis based on Dryad databases. Frontiers in cardiovascular medicine Background:The prognostic value of the serum albumin-to-creatinine ratio (sACR) in patients with ST-elevation myocardial infarction (STEMI) remains unclear. This study aims to investigate the impact of the sACR on incident major adverse cardiovascular events (MACEs) among revascularized patients with STEMI at long-term follow-up. Methods:A total of 461 patients with STEMI who underwent successful primary percutaneous coronary intervention (PCI) were enrolled to explore the association between the sACR and MACE during a 30-month follow-up. The Cox regression proportional hazard model was used to evaluate the prognostic value of the sACR. Heterogeneity among specific groups was investigated by subgroup analysis. Results:A total of 118 patients developed MACE during the follow-up. A negative association between the sACR and MACE was found after adjusting for other MACE-related risk factors. In subgroup analyses, the sACR was inversely associated with MACE in patients aged ≥ 60 years [hazard ratio (HR), 0.478; 95% confidence interval (CI), 0.292-0.784], male (HR, 0.528; 95% CI, 0.327-0.851), with hypertension history (HR, 0.470; 95% CI, 0.271-0.816), and with anterior wall myocardial infarction (HR, 0.418; 95% CI, 0.239-0.730). Meanwhile, the negative association between the sACR and MACE remained significant in a sensitivity analysis that excluded patients with low serum albumin levels (HR, 0.553; 95% CI, 0.356-0.860). Conclusions:Patients with STEMI who underwent successful PCI with a low sACR had a higher risk of developing MACE, indicating that the sACR could be used to identify patients with STEMI who are at high risk of developing MACE. 10.3389/fcvm.2023.1191167
A clinical model to predict the progression of knee osteoarthritis: data from Dryad. Journal of orthopaedic surgery and research BACKGROUND:Knee osteoarthritis (KOA) is a multifactorial, slow-progressing, non-inflammatory degenerative disease primarily affecting synovial joints. It is usually induced by advanced age and/or trauma and eventually leads to irreversible destruction of articular cartilage and other tissues of the joint. Current research on KOA progression has limited clinical application significance. In this study, we constructed a prediction model for KOA progression based on multiple clinically relevant factors to provide clinicians with an effective tool to intervene in KOA progression. METHOD:This study utilized the data set from the Dryad database which included patients with Kellgren-Lawrence (KL) grades 2 and 3. The KL grades was determined as the dependent variable, while 15 potential predictors were identified as independent variables. Patients were randomized into training set and validation set. The training set underwent LASSO analysis, model creation, visualization, decision curve analysis and internal validation using R language. The validation set is externally validated and F1-score, precision, and recall are computed. RESULT:A total of 101 patients with KL2 and 94 patients with KL3 were selected. We randomly split the data set into a training set and a validation set by 8:2. We filtered "BMI", "TC", "Hypertension treatment", and "JBS3 (%)" to build the prediction model for progression of KOA. Nomogram used to visualize the model in R language. Area under ROC curve was 0.896 (95% CI 0.847-0.945), indicating high discrimination. Mean absolute error (MAE) of calibration curve = 0.041, showing high calibration. MAE of internal validation error was 0.043, indicating high model calibration. Decision curve analysis showed high net benefit. External validation of the metabolic syndrome column-line graph prediction model was performed by the validation set. The area under the ROC curve was 0.876 (95% CI 0.767-0.984), indicating that the model had a high degree of discrimination. Meanwhile, the calibration curve Mean absolute error was 0.113, indicating that the model had a high degree of calibration. The F1 score is 0.690, the precision is 0.667, and the recall is 0.714. The above metrics represent a good performance of the model. CONCLUSION:We found that KOA progression was associated with four variable predictors and constructed a predictive model for KOA progression based on the predictors. The clinician can intervene based on the nomogram of our prediction model. KEY INFORMATION:This study is a clinical predictive model of KOA progression. KOA progression prediction model has good credibility and clinical value in the prevention of KOA progression. 10.1186/s13018-023-04118-4