Machine-learning-based models assist the prediction of pulmonary embolism in autoimmune diseases:A retrospective,multicenter study

Background:Pulmonary embolism(PE)is a severe and acute cardiovascular syndrome with high mortality among patients with autoimmune inflammatory rheumatic diseases(AIIRDs).Accurate prediction and timely intervention play a pivotal role in enhancing survival rates.However,there is a notable scarcity of practical early prediction and risk assessment systems of PE in patients with AIIRD.Methods:In the training cohort,60 AIIRD with PE cases and 180 age-,gender-,and disease-matched AIIRD non-PE cases were identified from 7254 AIIRD cases in Tongji Hospital from 2014 to 2022.Univariable logistic regression(LR)and least absolute shrinkage and selection operator(LASSO)were used to select the clinical features for further training with machine learning(ML)methods,including random forest(RF),support vector machines(SVM),neural network(NN),logistic regression(LR),gradient boosted decision tree(GBDT),classification and regression trees(CART),and C5.0 models.The performances of these models were subsequently validated using a multicenter validation cohort.Results:In the training cohort,24 and 13 clinical features were selected by univariable LR and LASSO strategies,respectively.The five ML models(RF,SVM,NN,LR,and GBDT)showed promising performances,with an area under the receiver operating characteristic(ROC)curve(AUC)of 0.962-1.000 in the training cohort and 0.969-0.999 in the validation cohort.CART and C5.0 models achieved AUCs of 0.850 and 0.932,respectively,in the training cohort.Using D-dimer as a pre-screening index,the refined C5.0 model achieved an AUC exceeding 0.948 in the training cohort and an AUC above 0.925 in the validation cohort.These results markedly outperformed the use of D-dimer levels alone.Conclusion:ML-based models are proven to be precise for predicting the onset of PE in patients with AIIRD exhibiting clinical suspicion of PE.Trial Registration:Chictr.org.cn:ChiCTR2200059599.

The COVID‐19 vaccine: Attitudes and vaccination in patients with autoimmune inflammatory rheumatic diseases

Background:We examined attitudes toward the COVID‐19 vaccine,potential factors underlying these attitudes,and ways to increase vaccination willingness in autoimmune inflammatory rheumatic diseases(AIIRD)patients.Methods:A multicenter,web‐based,observational survey using an online questionnaire was conducted among AIIRD patients aged≥18 years from May 24,2021,to June 3,2021.Participants were 3104 AIIRD patients(2921 unvaccinated and 183 vaccinated).Results:Of the unvaccinated patients,32.9%were willing to receive the COVID‐19 vaccine,45.0%were uncertain,and 14.8%were unwilling.When vaccination was recommended by physicians,patients'willingness increased to 93.8%.Participants'main concerns were that the vaccine may aggravate AIIRD disease(63.0%)and may cause vaccine‐related adverse events(19.9%).Female patients were less likely to be vaccinated.However,patients who had children aged≤18 years were more willing to be vaccinated.In addition,vaccination willingness was higher in patients with trust in the safety and efficacy of the COVID‐19 vaccine.Notably,183(5.9%)patients were vaccinated.The major vaccination side effects were injection reaction,myalgia,and fatigue.At a median follow‐up of 88(38,131)days,patients'disease activities were stable.Conclusions:The findings show that AIIRD patients were unwilling to receive the COVID‐19 vaccine because of fears of potential disease exacerbation and additional adverse events.Sociodemographic characteristics and concerns about COVID‐19 disease and vaccines had a significant effect on vaccination willingness.