Catheter directed thrombolytic therapy and aspiration thrombectomy in intermediate pulmonary embolism with long term results

Background: Catheter directed thrombolysis (CDT) and thrombectomy represent well established techniques for the treatment of intermediate pulmonary embolism (IPE). The long-term effect of catheterdirected thrombolysis of IPE is unknown.Methods: Clinical, interventional and echocardiographic data from 80 consecutive patients with IPE who were treated with CDT were evaluated. Primary end-points were technical success and major adverse events. Secondary end-points were cardiovascular mortality, all-cause mortality, clinical success, rate of bleeding complications, improvement in pulmonary pressure and echocardiography parameters. CDT completed with alteplase (10 mg bolus and 1 mg/h maintenance dose) through a pig-tail catheter for 24 h. After 24 h, control pulmonary angiography was performed. Results: In total, 80 patients with a mean age of 59.0 ± 16.8 years were treated. CDT was successful after the first post-operative day in 72 (90%) patients, but thrombus aspiration and fragmentation was performed due to failed thrombolysis in 8 (10%) patients. Final technical and clinical success was reached in 79 (98.8%) and 77 (96.3%) patients, respectively. The mean CDT time in IPE was 27.8 ± 9.6 h. Invasive pulmonary pressure dropped from 57.5 ± 16.7 to 38.9 ± 13.5 (p < 0.001). A caval filter was implanted in 4 (5%) patients. The 1-year major adverse events and cardiovascular mortality rate was 4.0% and 1.4%, respectively. Access site complications (6 major and 6 minor) were encountered in 12 (16.2%) patients.Conclusions: Catheter directed thrombolysis in submassive pulmonary embolism had excellent results. However, additional mechanical thrombectomy was necessary in some patients to achieve good clinical outcomes

Predictors of mortality and outcomes after retrograde endovascular angioplasty in patients with peripheral artery disease

Introduction: Endovascular revascularization (ER) techniques in patients with peripheral artery disease (PAD) have been developed and became more accessible in recent years. The ER is a first-line treatment in the majority of patients with symptomatic PAD. However, data on assessment of predictors of long-term outcomes of retrograde ER in patients with PAD are scarce. Aim: To evaluate predictors of long-term outcomes of retrograde ER in patients with chronic total occlusion in lower limb arteries. Material and methods: We analyzed data of 834 patients who underwent retrograde ER. Baseline clinical characteristics and procedural data were collected. Patients were followed up for 36 months, and the primary endpoint was all-cause mortality. Results: All patients were symptomatic and had failed antegrade ER. The procedural success rate was 92%. Cumulative all-cause mortality was 13.4% at 36-month follow-up. In multivariate analysis history of stroke, Rutherford category, chronic limb ischemia, chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD) and previous ER of other lesion were independent predictors of a higher mortality rate after 36 months (hazard ratio (HR) for stroke 2.4, 95% confidence interval (CI): 1.55–3.66; p = 0.0002; HR for age per 10 years 1.37, 95% CI: 1.15–1.64; p = 0.0002; HR for Rutherford category 1.63, 95% CI: 1.35–1.98; p < 0.0001, HR for chronic limb ischemia 0.44, 95% CI: 0.25–0.8, p = 0.007; HR for CKD 1.73, 95% CI: 1.14–2.56, p = 0.01; HR for COPD 2.4, 95% CI: 1.5–3.7, p = 0.0004; HR for previous ER 0.59, 95% CI: 0.35–0.94, p = 0.02). Conclusions: History of stroke, Rutherford category, chronic limb ischemia, CKD, COPD, and previous ER of other lesion were independently associated with increased risk of all-cause death

Body mass index and long-term outcomes in patients with chronic total occlusions undergoing retrograde endovascular revascularization of the infra-inguinal lower limb arteries

Background: The aim of the present study is to assess the relationship between body mass index (BMI) and long-term clinical outcomes in retrograde endovascular recanalization (ER) regarding chronic total occlusions (CTOs) of the infra-inguinal lower limb arteries. Methods: The study included patients who underwent retrograde ER of CTOs localized in superficial, popliteal or below-the-knee arteries. During follow-up, major adverse cardiac and cerebrovascular and major adverse lower limb events (MALE) were evaluated. MALE was defined as amputation, target lesion re-intervention, target vessel re-intervention and surgical treatment. Results: The study included 405 patients at the mean age of 67.2 ± 10.4. The authors divided the overall group of patients according to BMI into &lt; 25 (n = 156, 38.5%) and ≥ 25 kg/m2 (n = 249, 61.5%), and then into &lt; 30 (n = 302, 75.8%) and ≥ 30 kg/m2 (n = 103, 24.2%). During the average follow-up 1,144.9 ± 664.3 days, the mortality rate was higher in the group of patients with BMI &lt; 25 kg/m2 (10.5% vs. 5.3%, p = 0.051), and in the group of patients with BMI &lt; 30 kg/m2 (8.7% vs. 2.9%, p = 0.048). The comparison of Kaplan-Meier curves revealed borderline differences when assessing months to death for the BMI &lt; 25 kg/m2 (p = 0.057) and BMI &lt; 30 kg/m2 (p = 0.056) grouping variables. Conclusions: Obese and overweight patients undergoing CTO ER of the lower limb arteries from retrograde access are related to lower death rates during long-term follow-up

A Pharmaceutical Paradigm for Cardiovascular Composite Risk Assessment Using Novel Radiogenomics Risk Predictors in Precision Explainable Artificial Intelligence Framework: Clinical Trial Tool

Background: Cardiovascular disease (CVD) is challenging to diagnose and treat since symptoms appear late during the progression of atherosclerosis. Conventional risk factors alone are not always sufficient to properly categorize at-risk patients, and clinical risk scores are inadequate in predicting cardiac events. Integrating genomic-based biomarkers (GBBM) found in plasma/serum samples with novel non-invasive radiomics-based biomarkers (RBBM) such as plaque area, plaque burden, and maximum plaque height can improve composite CVD risk prediction in the pharmaceutical paradigm. These biomarkers consider several pathways involved in the pathophysiology of atherosclerosis disease leading to CVD. Objective: This review proposes two hypotheses: (i) The composite biomarkers are strongly correlated and can be used to detect the severity of CVD/Stroke precisely, and (ii) an explainable artificial intelligence (XAI)-based composite risk CVD/Stroke model with survival analysis using deep learning (DL) can predict in preventive, precision, and personalized (aiP 3 ) framework benefiting the pharmaceutical paradigm. Method: The PRISMA search technique resulted in 214 studies assessing composite biomarkers using radiogenomics for CVD/Stroke. The study presents a XAI model using AtheroEdge TM 4.0 to determine the risk of CVD/Stroke in the pharmaceutical framework using the radiogenomics biomarkers. Conclusions: Our observations suggest that the composite CVD risk biomarkers using radiogenomics provide a new dimension to CVD/Stroke risk assessment. The proposed review suggests a unique, unbiased, and XAI model based on AtheroEdge TM 4.0 that can predict the composite risk of CVD/Stroke using radiogenomics in the pharmaceutical paradigm

Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review

Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans

Pilot analysis of the usefulness of mortality risk score systems at resuscitated patients

Introduction: Sudden cardiac death is one of the most significant cardiovascular causes of death worldwide. Although there have been immense methodological and technical advances in the field of cardiopulmonary resuscitation and following intensive care in the last decade, currently there are only a few validated risk-stratification scoring systems for the quick and reliable estimation of the mortality risk of these patients at the time of admission to the intensive care unit. Objective: Our aim was to correlate the mortality prediction risk points calculated by CardShock Risk Score (CSRS) and modified (m) CSRS based on the admission data of the post-cardiac arrest syndrome (PCAS) patients. Methods: The medical records of 172 out-of-hospital resuscitated cardiac arrest patients, who were admitted at the Heart and Vascular Centre of Semmelweis University, were screened retrospectively. Out of the 172 selected patients, 123 were eligible for inclusion to calculate CSRS and mCSRS. Based on CSRS score, we generated three different groups of patients, with scores 1 to 3, 4 to 6, and 7+, respectively. Mortality data of the groups were compared by log-rank test. Results: Mean age of the patients was 63.6 years (69% male), the cause of sudden cardiac death was acut coronary syndrome in 80% of the cases. The early and late mortality was predicted by neurological status, serum lactate level, renal function, initial rhythm, and the need of catecholamines. Using mCSRS, a significant survival difference was proven in between the groups "1-3" vs "4-6" (p Conclusion: Compared to the CSRS, the mCSRS expanded with the 2 additional weighting points differentiates more specifically the low-moderate and high survival groups in the PCAS patient population treated in our institute.Peer reviewe

Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study

A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients