Values of the Wells and Revised Geneva Scores Combined with D-dimer in Diagnosing Elderly Pulmonary Embolism Patients

Background： Pulmonary embolism （PE） can be difficult to diagnose in elderly patients because of the coexistent diseases and the combination of drugs that they have taken. We aimed to compare the clinical diagnostic values of the Wells score, the revised Geneva score and each of them combined with D-dimer for suspected PE in elderly patients. Methods： Three hundred and thirty-six patients who were admitted for suspected PE were enrolled retrospectively and divided into two groups based on age （≥65 or 〈65 years old）. The Wells and revised Geneva scores were applied to evaluate the clinical probability of PE, and the positive predictive values of both scores were calculated using computed tomography pulmonary arteriography as a gold standard： overall accuracy was evaluated by the area under the curve （AUC） of receiver operator characteristic curve： the negative predictive values of D-dimcr, the Wells score combined with D-dimer, and the revised Geneva score combined with D-dimer were calculated. Results： Ninety-six cases （28.6%） were definitely diagnosed as PE among 336 cases, among them 56 cases （58.3%） were 〉65 years old. The positive predictive values of Wells and revised Geneva scores were 65.8% and 32.4%, respectively （P 〈 0.05） in the elderly patients; the AUC for the Wells score and the revised Geneva score in elderly was 0.682 （95% confidence interval [CI] ： 0.612- 0.746） and 0.655 （95% CI： 0.584-0.722）, respectively （P = 0.389）. The negative predictive values of D-dimer. the Wells score combined with D-dimer, and the revised Geneva score combined with D-dimer were 93.7%, 100%, and 100% in the elderly, respectively. Conclusions： The diagnostic value of the Wells score was higher than the revised Geneva score for the elderly cases with suspected PE. The combination of either the Wells score or the revised Geneva score with a normal D-dimer concentration is a sate strategy to rule out PE.

A machine learning model for diagnosing acute pulmonary embolism and comparison with Wells score,revised Geneva score,and Years algorithm

Background:Acute pulmonary embolism(APE)is a fatal cardiovascular disease,yet missed diagnosis and misdiagnosis often occur due to non-specific symptoms and signs.A simple,objective technique will help clinicians make a quick and precise diagnosis.In population studies,machine learning(ML)plays a critical role in characterizing cardiovascular risks,predicting outcomes,and identifying biomarkers.This work sought to develop an ML model for helping APE diagnosis and compare it against current clinical probability assessment models.Methods:This is a single-center retrospective study.Patients with suspected APE were continuously enrolled and randomly divided into two groups including training and testing sets.A total of 8 ML models,including random forest(RF),Naïve Bayes,decision tree,K-nearest neighbors,logistic regression,multi-layer perceptron,support vector machine,and gradient boosting decision tree were developed based on the training set to diagnose APE.Thereafter,the model with the best diagnostic performance was selected and evaluated against the current clinical assessment strategies,including the Wells score,revised Geneva score,and Years algorithm.Eventually,the ML model was internally validated to assess the diagnostic performance using receiver operating characteristic(ROC)analysis.Results:The ML models were constructed using eight clinical features,including D-dimer,cardiac troponin T(cTNT),arterial oxygen saturation,heart rate,chest pain,lower limb pain,hemoptysis,and chronic heart failure.Among eight ML models,the RF model achieved the best performance with the highest area under the curve(AUC)(AUC=0.774).Compared to the current clinical assessment strategies,the RF model outperformed the Wells score(P=0.030)and was not inferior to any other clinical probability assessment strategy.The AUC of the RF model for diagnosing APE onset in internal validation set was 0.726.Conclusions:Based on RF algorithm,a novel prediction model was finally constructed for APE diagnosis.When compared to the current clinical assessment strategies,the RF model achieved better diagnostic efficacy and accuracy.Therefore,the ML algorithm can be a useful tool in assisting with the diagnosis of APE.