Diagnosing pulmonary thromboembolism: Concerns and controversies

Resveratrol as an adjuvant prebiotic therapy in the management of pulmonary thromboembolism

Pulmonary thromboembolism (PTE) presents a grave threat to patient lives, often marked by arterial occlusion in the pulmonary vasculature, frequently stemming from deep vein thrombosis (DVT). While current anticoagulant therapies offer temporary relief, they fall short of addressing the long-term management of PTE. Notably, PTE-associated mortality rates continue to rise annually, positioning it as a crucial concern within the cardiovascular landscape. An intriguing suspect underlying compromised prognoses is the intricate interplay between the gut microbiome and PTE outcomes. The gut-derived metabolite, trimethylamine N-oxide (TMAO), has emerged as a direct contributor to accelerated thrombogenesis, thereby heightening PTE susceptibility. In pursuit of remedies, research has delved into diverse prebiotic and probiotic interventions, with Resveratrol (RSV) emerging as a promising candidate. This paper explores the potential of RSV, a polyphenolic compound, as an adjuvant prebiotic therapy. The proposed therapeutic approach not only augments anticoagulant potency through strategic pharmacokinetic interactions but also introduces a novel avenue for attenuating future PTE incidents through deliberate gut microbiome modulation. RSV's multifaceted attributes extend beyond its role in PTE prevention. Recognized for its anti-inflammatory, antioxidant, and cardioprotective properties, RSV stands as a versatile therapeutic candidate. It exhibits the ability to curtail platelet aggregation, augment warfarin bioavailability, and mitigate pulmonary arterial wall thickening - an ensemble of effects that substantiate its potential as an adjunct prebiotic for PTE patients. This literature review weaves together the latest insights, culminating in a compelling proposition: RSV is an instrumental player in the trajectory of PTE management.

Pneumonia and Pulmonary Thromboembolism Classification Using Electronic Health Records

Pneumonia and pulmonary thromboembolism (PTE) are both respiratory diseases; their diagnosis is difficult due to their similarity in symptoms, medical subjectivity, and the large amount of information from different sources necessary for a correct diagnosis. Analysis of such clinical data using computational tools could help medical staff reduce time, increase diagnostic certainty, and improve patient care during hospitalization. In addition, no studies have been found that analyze all clinical information on the Mexican population in the Spanish language. Therefore, this work performs automatic diagnosis of pneumonia and pulmonary thromboembolism using machine-learning tools along with clinical laboratory information (structured data) and clinical text (unstructured data) obtained from electronic health records. A cohort of 173 clinical records was obtained from the Mexican Social Security Institute. The data were preprocessed, transformed, and adjusted to be analyzed using several machine-learning algorithms. For structured data, naïve Bayes, support vector machine, decision trees, AdaBoost, random forest, and multilayer perceptron were used; for unstructured data, a BiLSTM was used. K-fold cross-validation and leave-one-out were used for evaluation of structured data, and hold-out was used for unstructured data; additionally, 1-vs.-1 and 1-vs.-rest approaches were used. Structured data results show that the highest AUC-ROC was achieved by the naïve Bayes algorithm classifying PTE vs. pneumonia (87.0%), PTE vs. control (75.1%), and pneumonia vs. control (85.2%) with the 1-vs.-1 approach; for the 1-vs.-rest approach, the best performance was reported in pneumonia vs. rest (86.3%) and PTE vs. rest (79.7%) using naïve Bayes, and control vs. diseases (79.8%) using decision trees. Regarding unstructured data, the results do not present a good AUC-ROC; however, the best F1-score were scored for control vs. disease (72.7%) in the 1-vs.-rest approach and control vs. pneumonia (63.6%) in the 1-to-1 approach. Additionally, several decision trees were obtained to identify important attributes for automatic diagnosis for structured data, particularly for PTE vs. pneumonia. Based on the experiments, the structured datasets present the highest values. Results suggest using naïve Bayes and structured data to automatically diagnose PTE vs. pneumonia. Moreover, using decision trees allows the observation of some decision criteria that the medical staff could consider for diagnosis.

Determining the diagnostic value of three clinical criteria Wells', YEARS and modified Geneva in pregnant women with suspected pulmonary thromboembolism

BACKGROUND

Diagnosis of pulmonary thromboembolism (PTE) during pregnancy is a challenging medical issue due to complications of X-ray-based imaging studies such as Ct-angiography for neonates and pregnant women. Here we aimed to assess the predictive values of three clinical criteria for diagnosing PTE during pregnancy.

METHODS

This is a retrospective cohort study performed in 2018-2020 on 166 pregnant women suspected of PTE. We reviewed the documents of all patients referred to our medical center with suspected symptoms of PTE. The demographic characteristics of the patients, signs and clinical findings upon the arrival of patients as well as their laboratory tests including D-dimer with a history of abortion or delivery and leg symptoms were entered into the data collection form. Then, according to the information extracted from the patient's files, each patient was evaluated by all clinical PTE criteria, including Wells, YEARS and modified Geneva. For each patient according to clinical criteria and all three algorithms, clinical suspicion for PTE and treatment or non-treatment was determined were compared to the final MDCT result of patients.

RESULTS

The Well's criteria had 100% sensitivity, 6.47% specificity, a positive predictive value of 7.8% and a negative predictive value of 100%. In patients with Well's score of more than four, the sensitivity and specificity of PTE diagnosis were 100% and 6% respectively. The modified Geneva criteria had 100% sensitivity, 8.89% specificity, a positive predictive value of 8.21% and a negative predictive value of 100%. The modified Geneva criteria had 100% sensitivity, 7.74% specificity, positive predictive value of 8.44% and a negative predictive value of 100%.

CONCLUSION

Wells, YEARS and modified Geneva criteria could significantly rule out PTE in pregnancy with 100% sensitivity.