Pulmonary Embolism in Acute Asthma Exacerbation: Clinical Characteristics, Prediction Model and Hospital Outcomes

Adult patients with an exacerbation of asthma and a higher risk for pulmonary embolism: a cluster analysis

OBJECTIVE

Current literature acknowledges the complexity of exacerbation triggers in patients with asthma. We studied the clinical heterogeneity of patients with asthma exacerbation suspected of having pulmonary embolism using cluster analysis and compared the clusters regarding of the risks for pulmonary embolism.

METHODS

In a secondary analysis of a dataset from the University of Florida, USA, individuals who experienced asthma exacerbation between June 2011 and October 2018 were included. All patients had undergone pulmonary CT angiography. Overall, 18 variables consisting of demographic, clinical, comorbidity, and therapeutic characteristics were used to cluster patients. The clusters were then profiled and compared in the percentages of pulmonary embolism.

RESULTS

In total, 758 patients (226; 29.8% men) with an exacerbation of asthma were included in the analysis. The frequency of a confirmed pulmonary embolism was 145 (19.1%). Two distinct clusters were identified with a statistically significant difference in pulmonary embolism [p < 0.001, odds ratio (95%CI)=2.24 (1.55, 3.24)]. We developed a high-performance classifier to profile the low- and high-risk clusters (area under the curve = 0.923, positive likelihood ratio = 20.2). The three top important variables discriminating the two clusters were age, heart rate, and body mass index. Older age, lower heart rate, higher body mass index, black race, and positive medical history (including atrial fibrillation) were more frequent in the high-risk group. Despite the higher percentage of women in the high-risk group, the sex ratios were not significantly different between the clusters.

CONCLUSION

There are two clusters in patients with an exacerbation of asthma with different prognoses percentages of pulmonary embolism. The clusters can be well identified based on patient characteristics.

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.

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.

Venous thromboembolism associated with severe dyspnoea and asthma in 102 792 adults

Background

The most recent guideline on acute pulmonary embolism (PE) indicates possible long-term sequelae such as dyspnoea and chronic thromboembolic pulmonary hypertension after a PE event. However, effects on lung function or asthma risk have not been evaluated in the general population.

Methods

We tested whether individuals with a venous thromboembolism (VTE) encompassing PE and deep vein thrombosis (DVT) have reduced lung function, or greater risks of dyspnoea and asthma using data from 102 792 adults from the Copenhagen General Population Study. Diagnoses of PE, DVT and asthma were collected from the national Danish Patient Registry. Factor V Leiden and prothrombin G20210A gene variants were determined using TaqMan assays.

Results

Prevalences of PE, DVT and VTE were 2.2%, 3.6% and 5.2%, respectively. Individuals with VTE had forced expiratory volume in 1 s of 92% predicted compared with 96% pred in individuals without VTE (p<0.001). Individuals with VTE versus those without had adjusted OR (95% CI) for light, moderate and severe dyspnoea of 1.4 (1.2-1.6), 1.6 (1.4-1.8) and 1.7 (1.5-1.9), respectively. Individuals with VTE versus those without had an adjusted OR for asthma of 1.6 (95% CI 1.4-1.8). Factor V Leiden and prothrombin G20210A genotype also associated with increased risk of asthma (p for trend=0.002). Population-attributable fractions of severe dyspnoea and asthma due to VTE were 3.5% and 3.0%, respectively, in the population.

Conclusion

Individuals with VTE have worse lung function and higher risks of severe dyspnoea and asthma, and may account for 3.5% and 3.0% of people with severe dyspnoea and asthma, respectively, in the general population.

Automated detection of airflow obstructive diseases: A systematic review of the last decade (2013-2022)

BACKGROUND AND OBJECTIVE

Obstructive airway diseases, including asthma and Chronic Obstructive Pulmonary Disease (COPD), are two of the most common chronic respiratory health problems. Both of these conditions require health professional expertise in making a diagnosis. Hence, this process is time intensive for healthcare providers and the diagnostic quality is subject to intra- and inter- operator variability. In this study we investigate the role of automated detection of obstructive airway diseases to reduce cost and improve diagnostic quality.

METHODS

We investigated the existing body of evidence and applied Preferred Reporting Items for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search records in IEEE, Google scholar, and PubMed databases. We identified 65 papers that were published from 2013 to 2022 and these papers cover 67 different studies. The review process was structured according to the medical data that was used for disease detection. We identified six main categories, namely air flow, genetic, imaging, signals, and miscellaneous. For each of these categories, we report both disease detection methods and their performance.

RESULTS

We found that medical imaging was used in 14 of the reviewed studies as data for automated obstructive airway disease detection. Genetics and physiological signals were used in 13 studies. Medical records and air flow were used in 9 and 7 studies, respectively. Most papers were published in 2020 and we found three times more work on Machine Learning (ML) when compared to Deep Learning (DL). Statistical analysis shows that DL techniques achieve higher Accuracy (ACC) when compared to ML. Convolutional Neural Network (CNN) is the most common DL classifier and Support Vector Machine (SVM) is the most widely used ML classifier. During our review, we discovered only two publicly available asthma and COPD datasets. Most studies used private clinical datasets, so data size and data composition are inconsistent.

CONCLUSIONS

Our review results indicate that Artificial Intelligence (AI) can improve both decision quality and efficiency of health professionals during COPD and asthma diagnosis. However, we found several limitations in this review, such as a lack of dataset consistency, a limited dataset and remote monitoring was not sufficiently explored. We appeal to society to accept and trust computer aided airflow obstructive diseases diagnosis and we encourage health professionals to work closely with AI scientists to promote automated detection in clinical practice and hospital settings.

An efficient machine learning framework to identify important clinical features associated with pulmonary embolism

A misdiagnosis of pulmonary embolism (PE) can have severe consequences such as disability or death. It's crucial to accurately identify key clinical features of PE in clinical practice to promptly identify potential PE patients who may present asymptomatically, and to prevent misdiagnosing PE as asthma exacerbation in patients with symptoms like dyspnea or chest pain. However, reliably identifying these important features can be challenging due to many factors influencing the likelihood of PE development in complex fashions (e.g., the interactions among these factors). To address this difficulty, we presented an effective framework using the deep neural network (DNN) model and the permutation-based feature importance test (PermFIT) procedure, i.e., PermFIT-DNN. We applied the PermFIT-DNN framework to the analysis of data from a PE study for asthma exacerbation patients. Our analysis results show that the PermFIT-DNN framework can robustly identify key features for classifying PE status. The important features identified can also aid in accurately predicting the PE risk.

Study of the Significance of Thromboelastography Changes in Patients with Dyslipidemia

Purpose

To investigate the changes in thromboelastography (TEG) in patients with dyslipidemia to study its effect on the blood coagulation status.

Methods

131 patients hospitalized in Fujian Provincial Jinshan Hospital from January 2018 to December 2020 were selected, and 64 cases in the hyperlipidemia (HL) group and 67 cases in the non-HL group were set according to whether their blood lipids were abnormal. By measuring the changes of each parameter of TEG in patients, the relevant parameters R value, K value, α angle, and MA value were calculated. And routine blood coagulation (PT, APTT, INR, FIB, and TT) and routine blood (platelet count) tests were performed on all study subjects to analyze the changes of each index of the coagulation function and each parameter of TED in both groups and explore the clinical value of TEG on HL diseases.

Results

Compared with the non-HL group, R and K values decreased, and angle and MA values increased in the HL group (P < 0.05). PT, APTT, and INR values decreased, and FIB values increased in the HL group compared with the nonhyperlipidemic group (P < 0.05). The TT levels were similar in the non-HL group and the HL group (P > 0.05). Compared with the non-HL group, PLT values decreased, and PDW and MPV values increased in the HL group (P < 0.05). R value was positively correlated with APTT, r= 0.373, P=0.002. K value was negatively correlated with PLT, r= -0.399, P=0.002. α angle and MA values were positively correlated with PLT, r= 0.319/0.475, P=0.010/P < 0.001. The rest of the indexes did not correlate with each parameter of TEG significant correlation.

Conclusion

TEG can predict the hypercoagulability and hypocoagulability of blood by the changes of R value, K value, α angle, and MA to evaluate the effect of hyperlipidemia on the coagulation status, which is important for guiding the adjustment of lipid-lowering, antithrombotic, and anticoagulation programs in patients with atherosclerosis combined with hyperlipidemia or postsurgery combined with hyperlipidemia.

Evaluating cardiopulmonary function following acute pulmonary embolism

INTRODUCTION

Pulmonary embolism is a common cause of cardiopulmonary mortality and morbidity worldwide. Survivors of acute pulmonary embolism may experience dyspnea, report reduced exercise capacity, or develop overt pulmonary hypertension. Clinicians must be alert for these phenomena and appreciate the modalities and investigations available for evaluation.

AREAS COVERED

In this review, the current understanding of available contemporary imaging and physiologic modalities is discussed, based on available literature and professional society guidelines. The purpose of the review is to provide clinicians with an overview of these modalities, their strengths and disadvantages, and how and when these investigations can support the clinical work-up of patients post-pulmonary embolism.

EXPERT OPINION

Echocardiography is a first test in symptomatic patients post-pulmonary embolism, with ventilation/perfusion scanning vital to determination of whether there is chronic residual emboli. The role of computed tomography and magnetic resonance in assessing the pulmonary arterial tree in post-pulmonary embolism patients is evolving. Functional testing, in particular cardiopulmonary exercise testing, is emerging as an important modality to quantify and determine cause of functional limitation. It is possible that future investigations of the post-pulmonary embolism recovery period will better inform treatment decisions for acute pulmonary embolism patients.

Venous Thromboembolic Disease in Chronic Inflammatory Lung Diseases: Knowns and Unknowns

Persistent inflammation within the respiratory tract underlies the pathogenesis of numerous chronic pulmonary diseases. There is evidence supporting that chronic lung diseases are associated with a higher risk of venous thromboembolism (VTE). However, the relationship between lung diseases and/or lung function with VTE is unclear. Understanding the role of chronic lung inflammation as a predisposing factor for VTE may help determine the optimal management and aid in the development of future preventative strategies. We aimed to provide an overview of the relationship between the most common chronic inflammatory lung diseases and VTE. Asthma, chronic obstructive pulmonary disease, interstitial lung diseases, or tuberculosis increase the VTE risk, especially pulmonary embolism (PE), compared to the general population. However, high suspicion is needed to diagnose a thrombotic event early as the clinical presentation inevitably overlaps with respiratory disorders. PE risk increases with disease severity and exacerbations. Hence, hospitalized patients should be considered for thromboprophylaxis administration. Conversely, all VTE patients should be asked for lung comorbidities before determining anticoagulant therapy duration, as those patients are at increased risk of recurrent PE episodes rather than DVT. Further research is needed to understand the underlying pathophysiology of in-situ thrombosis in those patients.