Machine learning with D-dimer in the risk stratification for pulmonary embolism: a derivation and internal validation study

Performance and costs of rule-out protocols for acute aortic syndromes: analysis of pooled prospective cohorts

BACKGROUND

Acute aortic syndromes (AAS) are deadly conditions causing unspecific symptoms, such as chest/abdominal/back pain, syncope and neurological deficit. They are diagnosed with computed tomography angiography (CTA), but the patient selection is challenging. To support physicians and standardize management, protocols combining a clinical score with D-dimer (DD) have been developed. However, direct comparison of their diagnostic performance and cost-effectiveness is lacking.

METHODS

We used individual patient data from 3 prospective diagnostic studies of patients with suspected AAS, enrolled in 12 centers from 5 countries. Diagnostic accuracy, failure rate and costs were calculated for 5 protocols, applying 3 scores (aortic dissection detection [ADD], AORTAs and Canadian) and 2 DD thresholds (500 ng/mL [DD500], age-adjusted [DDage]). Costs were estimated using Italian and German reimbursements.

RESULTS

Among 4907 patients, 506 (10.3 %) had an AAS. The sensitivity of the diagnostic protocols ranged from 97.6 % for Canadian/DD500 to 99.4 % for AORTAs/DD500 or DDage (P = 0.022). The specificity was lowest for AORTAs/DD500 (46.8 %; P < 0.001 vs AORTAs/DD500) and highest for ADD/DDage (61.5 %; P < 0.001). The number of potential AAS misses was 4-fold higher with Canadian/DD500 vs AORTAs/DD500 or DDage. The net clinical benefit was highest for ADD/DDage. All protocols reduced CTA exams and costs over a CTA-to-all strategy. Numbers of predicted CTA exams and costs per 100 patients were lowest for ADD/DDage (447 CTAs, 34,366 EUR) and highest (579 CTAs, 43,628 EUR) for AORTAs/DD500.

CONCLUSIONS

Guideline-compliant clinical score/DD based protocols are highly sensitive. Differences in specificity and efficiency are present. Data may guide decision-making based on policies and resources.

Correlation Between the Transient Increase of D-Dimer and Thrombolysis at 30d after Anticoagulation Therapy in Patients with Pulmonary Embolism

ObjectiveThis study aimed to investigate the relationship between the transient increase in D-dimer following anticoagulant therapy and thrombolysis at 30 days in patients with pulmonary embolism (PE).MethodPatients diagnosed with PE at our hospital were included in the study. CT pulmonary angiography (CTPA) was performed 7-10 days after starting anticoagulant therapy. Patients were divided into two groups: the change group and the non-change group, based on whether the thrombus had broken into smaller clots and/or dissolved compared to baseline. Plasma D-dimer levels were measured 1-10 days after anticoagulant therapy to observe any transient increase. The correlation between the transient D-dimer increase and thrombolysis at 30 days in PE patients was analyzed.ResultsA total of 172 patients with PE were included. The rate of thrombus change was 63.4% (75/172) at 7-10 days after anticoagulant therapy. The proportion of thrombolysis at 30 days was 68.6% (118/172). Spearman correlation analysis showed a significant correlation between the transient increase in D-dimer and thrombus changes (Rs = 0.482, P < .001), between thrombus changes and thrombolysis at 30 days (Rs = 0.413, P < .001), and between the transient increase in D-dimer and thrombolysis at 30 days (Rs = 0.540, P < .001). ROC curve analysis indicated that the transient increase in D-dimer predicted thrombus changes (AUC: 0.750, 95%CI: 0.673-0.827, P < .001), and predicted thrombolysis at 30 days (AUC: 0.786, 95%CI: 0.714-0.858, P < .001). Thrombus changes also predicted thrombolysis at 30 days (AUC: 0.712, 95%CI: 0.626-0.797, P = .001).ConclusionAfter anticoagulant therapy for PE, D-dimer levels may transiently increase. The rate of thrombolysis at 30 days was higher, and a transient increase in D-dimer indicated a higher likelihood of thrombolysis at 30 days.

Acute Pulmonary Embolism: Evidence, Innovation, and Horizons

PURPOSE OF REVIEW

Pulmonary embolism (PE) is the third most common cause of cardiovascular morbidity and mortality. The goal of this review is to discuss the most up-to-date literature on epidemiology, diagnosis, risk stratification, and management of acute PE.

RECENT FINDINGS

Despite an increase in annual incidence rate of PE in the United States and development of multiple advanced therapies for treatment of acute PE, PE-related mortality is not consistently decreasing across populations. Although multiple risk stratification schemes have been developed, it is still unclear which advanced therapy should be used for the individual patient and optimal timing. Fortunately, multiple randomized clinical trials are underway to answer these questions. Nevertheless, up to 50% of patients have persistent reduced quality of life 6 months after acute PE, termed post-PE syndrome. Despite advances in therapeutic options for management of acute PE, many questions remain unanswered, including optimal risk stratification and management of acute PE.

Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review

BACKGROUND

 Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific clinical prediction models (CPMs) have been used to assist physicians in decision-making but have several limitations. This systematic review explores if machine learning (ML) can enhance CPMs by analyzing extensive patient data derived from electronic health records. We aimed to explore ML-CPMs' applications in VTE for risk stratification, outcome prediction, diagnosis, and treatment.

METHODS

 Three databases were searched: PubMed, Google Scholar, and IEEE electronic library. Inclusion criteria focused on studies using structured data, excluding non-English publications, studies on non-humans, and certain data types such as natural language processing and image processing. Studies involving pregnant women, cancer patients, and children were also excluded. After excluding irrelevant studies, a total of 77 studies were included.

RESULTS

 Most studies report that ML-CPMs outperformed traditional CPMs in terms of receiver operating area under the curve in the four clinical domains that were explored. However, the majority of the studies were retrospective, monocentric, and lacked detailed model architecture description and external validation, which are essential for quality audit. This review identified research gaps and highlighted challenges related to standardized reporting, reproducibility, and model comparison.

CONCLUSION

 ML-CPMs show promise in improving risk assessment and individualized treatment recommendations in VTE. Apparently, there is an urgent need for standardized reporting and methodology for ML models, external validation, prospective and real-world data studies, as well as interventional studies to evaluate the impact of artificial intelligence in VTE.

Machine learning-based prediction of pulmonary embolism to reduce unnecessary computed tomography scans in gastrointestinal cancer patients: a retrospective multicenter study

This study aimed to develop a machine learning (ML) model for predicting pulmonary embolism (PE) in patients with gastrointestinal cancers, a group at increased risk for PE. We conducted a retrospective, multicenter study analyzing patients who underwent computed tomographic pulmonary angiography (CTPA) between 2010 and 2020. The study utilized demographic and clinical data, including the Wells score and D-dimer levels, to train a random forest ML model. The model's effectiveness was assessed using the area under the receiver operating curve (AUROC). In total, 446 patients from hospital A and 139 from hospital B were included. The training set consisted of 356 patients from hospital A, with internal validation on 90 and external validation on 139 patients from hospital B. The model achieved an AUROC of 0.736 in hospital A and 0.669 in hospital B. The ML model significantly reduced the number of patients recommended for CTPA compared to the conventional diagnostic strategy (hospital A; 100.0% vs. 91.1%, P < 0.001, hospital B; 100.0% vs. 93.5%, P = 0.003). The results indicate that an ML-based prediction model can reduce unnecessary CTPA procedures in gastrointestinal cancer patients, highlighting its potential to enhance diagnostic efficiency and reduce patient burden.

The AI Future of Emergency Medicine

In the coming years, artificial intelligence (AI) and machine learning will likely give rise to profound changes in the field of emergency medicine, and medicine more broadly. This article discusses these anticipated changes in terms of 3 overlapping yet distinct stages of AI development. It reviews some fundamental concepts in AI and explores their relation to clinical practice, with a focus on emergency medicine. In addition, it describes some of the applications of AI in disease diagnosis, prognosis, and treatment, as well as some of the practical issues that they raise, the barriers to their implementation, and some of the legal and regulatory challenges they create.

Construction and validation of risk prediction models for pulmonary embolism in hospitalized patients based on different machine learning methods

Objective

This study aims to apply different machine learning (ML) methods to construct risk prediction models for pulmonary embolism (PE) in hospitalized patients, and to evaluate and compare the predictive efficacy and clinical benefit of each model.

Methods

We conducted a retrospective study involving 332 participants (172 PE positive cases and 160 PE negative cases) recruited from Guangdong Medical University. Participants were randomly divided into a training group (70%) and a validation group (30%). Baseline data were analyzed using univariate analysis, and potential independent risk factors associated with PE were further identified through univariate and multivariate logistic regression analysis. Six ML models, namely Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM), and AdaBoost were developed. The predictive efficacy of each model was compared using the receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC). Clinical benefit was assessed using decision curve analysis (DCA).

Results

Logistic regression analysis identified lower extremity deep venous thrombosis, elevated D-dimer, shortened activated partial prothrombin time, and increased red blood cell distribution width as potential independent risk factors for PE. Among the six ML models, the RF model achieved the highest AUC of 0.778. Additionally, DCA consistently indicated that the RF model offered the greatest clinical benefit.

Conclusion

This study developed six ML models, with the RF model exhibiting the highest predictive efficacy and clinical benefit in the identification and prediction of PE occurrence in hospitalized patients.

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.

Establishment of machine learning-based tool for early detection of pulmonary embolism

BACKGROUND AND OBJECTIVES

Pulmonary embolism (PE) is a complex disease with high mortality and morbidity rate, leading to increasing society burden. However, current diagnosis is solely based on symptoms and laboratory data despite its complex pathology, which easily leads to misdiagnosis and missed diagnosis by inexperienced doctors. Especially, CT pulmonary angiography, the gold standard method, is not widely available. In this study, we aim to establish a rapid and accurate screening model for pulmonary embolism using machine learning technology. Importantly, data required for disease prediction are easily accessed, including routine laboratory data and medical record information of patients.

METHODS

We extracted features from patients' routine laboratory results and medical records, including blood routine, biochemical group, blood coagulation routine and other test results, as well as symptoms and medical history information. Samples with a feature loss rate greater than 0.8 were deleted from the original database. Data from 4723 cases were retained, 231 of which were positive for pulmonary embolism. 50 features were retained through the positive and negative statistical hypothesis testing which was used to build the predictive model. In order to avoid identification as majority-class samples caused by the imbalance of sample proportion, we used the method of Synthetic Minority Oversampling Technique (SMOTE) to increase the amount of information on minority samples. Five typical machine learning algorithms were used to model the screening of pulmonary embolism, including Support Vector Machines, Logistic Regression, Random Forest, XGBoost, and Back Propagation Neural Networks. To evaluate model performance, sensitivity, specificity and AUC curve were analyzed as the main evaluation indicators. Furthermore, a baseline model was established using the characteristics of the pulmonary embolism guidelines as a comparison model.

RESULTS

We found that XGBoost showed better performance compared to other models, with the highest sensitivity and specificity (0.99 and 0.99, respectively). Moreover, it showed significant improvement in performance compared to the baseline model (sensitivity and specificity were 0.76 and 0.76 respectively). More important, our model showed low missed diagnosis rate (0.46) and high AUC value (0.992). Finally, the calculation time of our model is only about 0.05 s to obtain the possibility of pulmonary embolism.

CONCLUSIONS

In this study, five machine learning classification models were established to assess the likelihood of patients suffering from pulmonary embolism, and the XGBoost model most significantly improved the precision, sensitivity, and AUC for pulmonary embolism screening. Collectively, we have established an AI-based model to accurately predict pulmonary embolism at early stage.