Early identification of macrophage activation syndrome secondary to systemic lupus erythematosus with machine learning.

OBJECTIVE: The macrophage activation syndrome (MAS) secondary to systemic lupus erythematosus (SLE) is a severe and life-threatening complication. Early diagnosis of MAS is particularly challenging. In this study, machine learning models and diagnostic scoring card were developed to aid in clinical decision-making using clinical characteristics. METHODS: We retrospectively collected clinical data from 188 patients with either SLE or the MAS secondary to SLE. 13 significant clinical predictor variables were filtered out using the Least Absolute Shrinkage and Selection Operator (LASSO). These variables were subsequently utilized as inputs in five machine learning models. The performance of the models was evaluated using the area under the receiver operating characteristic curve (ROC-AUC), F1 score, and F2 score. To enhance clinical usability, we developed a diagnostic scoring card based on logistic regression (LR) analysis and Chi-Square binning, establishing probability thresholds and stratification for the card. Additionally, this study collected data from four other domestic hospitals for external validation. RESULTS: Among all the machine learning models, the LR model demonstrates the highest level of performance in internal validation, achieving a ROC-AUC of 0.998, an F1 score of 0.96, and an F2 score of 0.952. The score card we constructed identifies the probability threshold at a score of 49, achieving a ROC-AUC of 0.994 and an F2 score of 0.936. The score results were categorized into five groups based on diagnostic probability: extremely low (below 5%), low (5-25%), normal (25-75%), high (75-95%), and extremely high (above 95%). During external validation, the performance evaluation revealed that the Support Vector Machine (SVM) model outperformed other models with an AUC value of 0.947, and the scorecard model has an AUC of 0.915. Additionally, we have established an online assessment system for early identification of MAS secondary to SLE. CONCLUSION: Machine learning models can significantly improve the diagnostic accuracy of MAS secondary to SLE, and the diagnostic scorecard model can facilitate personalized probabilistic predictions of disease occurrence in clinical environments.

Therapeutic efficacy of clomiphene citrate combined with metformin in patients with polycystic ovary syndrome.

WHAT IS KNOWN AND OBJECTIVE: Although several clinical trials have compared the clinical efficacy of clomiphene citrate (CC) combined with metformin (MET) in the treatment of women with polycystic ovary syndrome (PCOS), the results are controversial. Therefore, this study was designed to conduct a pooled analysis to evaluate the efficacy of CC combined with MET versus CC in these patients. METHODS: Computerized searches of the PubMed, Web of Science, Embase and Cochrane Library databases were conducted to identify eligible randomized controlled trials (RCTs) from the data obtained up to June 2021. The Cochrane Collaboration risk of bias tool was used to assess the risk of bias in individual RCTs, and RevMan 5.4 was used for data statistical analysis. RESULTS AND DISCUSSION: A total of 13 RCTs were included in the meta-analysis. These studies involved 1,353 patients, 707 of these were in the combination group and 646 in the monotherapy group. The results indicated a higher clinical pregnancy rate (risk ratio [RR] 1.28, 95% confidence interval [CI] 1.06-1.54, p = 0.01) in the combined group compared to the monotherapy group. However, no significant differences were observed in the ovulation rate (RR 1.13, 95% CI 0.98-1.30, p = 0.10), live birth rate (RR 1.13, 95% CI 0.89-1.42, p = 0.32), multiple pregnancy rate (RR 0.58, 95% CI 0.19-1.73, p = 0.33) and abortion rate (RR 1.26, 95% CI 0.86-1.84, p = 0.23) between the two groups. WHAT IS NEW AND CONCLUSION: CC combined with MET has an advantage in improving the clinical pregnancy rate compared to CC alone; however, there is no significant difference in the rate of ovulation. For better management of PCOS, a high-quality RCT is needed to demonstrate the safety of the combination.