Jurėnas, Martynas

Introduction Adequate anticoagulation is crucial for patients with mechanical heart valves, especially during the perioperative period. Heparin is an anticoagulant that binds to antithrombin III, preventing the formation of a clot. Heparin resistance due to antithrombin III deficiency presents a rare but significant clinical challenge that can delay surgery and raise the risk of thrombosis. [1,2] Case Presentation A 60-year-old male patient with a history of mechanical aortic valve replacement was admitted due to aortic valve dysfunction. Cardiac ultrasound revealed impaired opening of the mechanical aortic valve, suspected to be caused by pannus formation. Surgical replacement of the mechanical valve with a biological prosthesis was indicated. Preoperative anticoagulation with unfractionated heparin was initiated; however, therapeutic anticoagulation was not achieved despite adequate heparin dosing. Further examination revealed antithrombin III deficiency, explaining the heparin resistance. Antithrombin III concentrate was unavailable within the department, so it was ordered; it arrived more than 1 week later. During this period, therapeutic anticoagulation was corrected by initiating warfarin therapy with careful INRguided dosing. After reaching the target INR and administering antithrombin III concentrate, antithrombin III levels were restored. The patient then successfully underwent isolated aortic valve replacement surgery with a biological valve. The postoperative period was uneventful. Discussion Hereditary antithrombin III deficiency is a rare autosomal dominant condition. This coagulation disorder may remain undiagnosed for a long time, especially if complications such as deep vein thrombosis or thromboembolism do not occur. Early clarification of family history laboratory tests can help diagnose this condition on time, prevent complications, and select adequate anticoagulant treatment. [3,4] Conclusions This case highlights the importance of recognising antithrombin III deficiency as a cause of heparin resistance in anticoagulation therapy. Early diagnosis and antithrombin III replacement therapy are crucial to ensure effective anticoagulation and avoid postponing surgical intervention.

Background and Objectives: Coronary computed tomography angiography (CCTA) is widely used in the diagnostic evaluation of suspected stable coronary artery disease; however, its agreement with invasive coronary angiography (ICA) remains inconsistent across different levels of analysis. The aim of this study was to evaluate the agreement between CCTA and ICA and to identify the factors associated with discrepancies. Materials and Methods: A single-centre retrospective analysis of 500 patients was performed. All patients underwent CCTA within one year prior to ICA. Coronary stenoses were evaluated at the 11-segment coronary artery, vessel, and patient levels using a ≥50% cut-off. Diagnostic agreement was assessed using the kappa coefficient, while diagnostic performance was evaluated in terms of sensitivity, specificity, positive predictive value, and negative predictive value. Factors associated with discrepancies were evaluated using a logistic regression model. Results: At the segment level, agreement between CCTA and ICA was low to moderate across 11 coronary segments (κ = 0.108–0.461). At the patient level, CCTA identified ≥50% coronary stenosis more frequently than ICA (86.2% vs. 59.4%, p < 0.001), demonstrating high sensitivity (91.3%) but low specificity (21.2%). Diagnostic discrepancies were associated with higher coronary calcium burden, and in multivariable analysis, body mass index > 25 kg/m2, age < 68 years, and multiple comorbidities were independently associated with discordant findings. Conclusions: At the patient level, CCTA demonstrates high sensitivity and represents an appropriate non-invasive method for patient selection for further diagnostic evaluation. However, agreement between CCTA and invasive coronary angiography remains limited at the segment and vessel levels. Diagnostic discrepancies were significantly associated with coronary artery calcification and higher body mass index (BMI), which should be taken into consideration when interpreting CCTA findings.

Pastaraisiais metais kateterinis aortos vožtuvo implantavimas (TAVI) tapo svarbia ir plačiai taikoma chirurginio vožtuvo protezavimo alternatyva. TAVI iš esmės pakeitė sunkios aortos vožtuvo stenozės gydymo paradigmą. Šis metodas iš pradžių buvo skirtas pacientams, kuriems chirurgija buvo absoliučiai kontraindikuotina arba labai didelės rizikos, tačiau vėlesni didelės apimties tyrimai įrodė jo efektyvumą ir vidutinės ar net mažos rizikos populiacijose. Klinikinių tyrimų duomenys ir tarptautinės rekomendacijos rodo, kad ši intervencija gali suteikti reikšmingą klinikinę naudą tinkamai atrinktiems pacientams. Svarbu apžvelgti paciento atrankos kriterijus, vertinimo metodus ir sprendimų priėmimo principus, kurie lemia sėkmingą TAVI rezultatą. Aortos vožtuvo stenozė (AS) yra dažniausia įgyta senyvo amžiaus žmonių vožtuvinė širdies liga, paprastai išsivystanti dėl degeneracinės vožtuvo kalcifikacijos. Ilgą laiką pacientų gydymo aukso standartas buvo chirurginis aortos vožtuvo pakeitimas (AVR), tačiau daugeliui pacientų, ypač vyresnio amžiaus, turintiems daug gretutinių ligų, chirurginis gydymas buvo netinkamas. TAVI atsiradimas 2002 m. sukėlė reikšmingą proveržį, o dideli klinikiniai tyrimai, tokie kaip PARTNER ir SURTAVI, įrodė šios terapijos efektyvumą ir saugumą. Šiandien tarptautinės gairės rekomenduoja TAVI kaip chirurgijos alternatyvą vidutinės ir didelės rizikos pacientams, o kai kuriais atvejais – ir mažos rizikos populiacijai [1, 2, 3].

Background: Early safety outcomes following transcatheter aortic valve implantation (TAVI) for severe aortic stenosis are critical for patient prognosis. Accurate prediction of adverse events can enhance patient management and improve outcomes. Aim: This study aimed to develop a machine learning model to predict early safety outcomes in patients with severe aortic stenosis undergoing TAVI. Methods: We conducted a retrospective single-centre study involving 224 patients with severe aortic stenosis who underwent TAVI. Seventy-seven clinical and biochemical variables were collected for analysis. To handle unbalanced classification problems, an adaptive synthetic (ADASYN) sampling approach was used. A fined-tuned random forest (RF) machine learning model was developed to predict early safety outcomes, defined as all-cause mortality, stroke, life-threatening bleeding, acute kidney injury (stage 2 or 3), coronary artery obstruction requiring intervention, major vascular complications, and valve-related dysfunction requiring repeat procedures. Shapley Additive Explanations (SHAPs) were used to explain the output of the machine learning model by attributing each variable’s contribution to the final prediction of early safety outcomes. Results: The random forest model identified left femoral artery diameter and aortic valve calcification volume as the most influential predictors of early safety outcomes. SHAPs analysis demonstrated that smaller left femoral artery diameter and higher aortic valve calcification volume were associated with poorer early safety prognoses. Conclusions: The machine learning model highlights of early safety outcomes after TAVI. These findings suggest that incorporating these variables into pre-procedural assessments may improve risk stratification and inform clinical decision-making to enhance patient care.

Jūsų rankose - Lietuvos sveikatos mokslų universiteto vadovėlis „Kardiologijos pagrindai“. Tai platus ir svarus leidinys, apimantis sindrominę diagnostiką, paciento tyrimo metodiką, kardiologijoje naudojamų vaistų klinikinę farmakologiją, vaizdinius širdies ligų tyrimo metodus, genetinius širdies ligų tyrimus bei tam tikrų širdies ir kraujagyslių ligų profilaktiką, diagnostiką, gydymą ir reabilitaciją. Vadovėlį rengė LSMU Kardiologijos klinikos, Vidaus ligų klinikos, Širdies, krūtinės ir kraujagyslių chirurgijos klinikos, Reabilitacijos klinikos, Anesteziologijos klinikos, Genetikos ir molekulinės medicinos klinikos, Onkologijos ir hematologijos klinikos, Fiziologijos ir farmakologijos instituto bei Kardiologijos instituto darbuotojai, iš viso 68 autoriai, todėl kiekvieną skyrių rašė geriausi tos srities specialistai. Pastarasis dešimtmetis medicinoje išsiskyrė įrodymais grįstos medicinos įsigalėjimu, diagnostikai bei gydymui naudojamų technologijų sudėtingumu ir įvairove, labai greitu mokslo bei technikos naujovių įdiegimu į kasdieninę klinikinę praktiką. Todėl tikslinga atnaujinti kardiologijos vadovėlį, kuris taptų ne vien priemone studentams mokyti, bet ir kiekvieno gydytojo rezidento, vidaus ligų gydytojo, kardiologo parankine knyga. Laikas bėga labai greitai, todėl net rengiant šį vadovėlį keitėsi daugelio ligų diagnostikos bei gydymo rekomendacijos, nes greita mokslo pažanga ir nauji duomenys nuolat keičia mūsų požiūrį į daugelį dalykų, kurie atrodė aiškūs ir nusistovėję. Todėl siūlome skaitytojui priimti skaitomą medžiagą kaip geriausią šiuo metu turimą informaciją ir kardiologinės patologijos sampratą, tačiau kartu palikti protą atvirą naujovėms, kurios gali ateiti plečiantis mūsų žinioms visose medicinos srityse. Dėkojame visiems, prisidėjusiems prie vadovėlio „Kardiologijos pagrindai“ išleidimo: autoriams, recenzentams, kalbos redaktorei, leidėjams, o ypač rėmėjams, be kurių pagalbos šis leidinys nebūtų išvydęs dienos šviesos.

Background: Acute Myocardial Infarction (AMI) represents a significant clinical challenge with diverse outcomes. Predicting patient-oriented composite endpoints (POCE) (all-cause death, any stroke, any AMI, or any revascularization) can significantly enhance post-AMI care. Incorporating clinical data with neuroendocrine biomarkers may offer improved prognostic capabilities. Objective: To develop and validate a machine learning model to predict POCE in AMI patients, utilizing clinical parameters and neuroendocrine biomarkers. Methods: There was prospective, observational, single-center study. Three approaches have been used to obtain the predictions for POCE event. First, as the dataset was imbalanced, an adaptive synthetic (ADASYN) sampling approach was used to generate synthetic instances, particularly focusing on those that are difficult-to-learn by using a weighted distribution. Then, random forest classifier (RF) was used to build a machine learning model to predict POCE. The model was tuned via a grid-search algorithm for optimal hyperparameters and validated using a 10-fold stratified cross-validation. Finally, the feature importance was determined by Shapley Additives, which measure the average marginal contribution of a feature value across all potential feature combinations. For the comparative purpose, the Gini index, which also shows the feature importance but in terms of mean decrease in impurity, was calculated. Results: The study incorporated data from 315 patients, examining 47 variables and identifying 72 instances of POCE. After applying the ADASYN algorithm, the class distribution within the dataset was effectively equalized, facilitating the training of a robust RF model. Upon training with 252 instances, the model distinguished POCE with an accuracy of 83.8%, demonstrating a sensitivity of 80% and a specificity of 86%. During 10-fold cross-validation, the model's accuracy slightly dipped to 75%, with a sensitivity of 56% and specificity of 82%, indicating a balance between generalizability and overfitting. Testing mirrored these results, underscoring the model’s consistent performance. Notably, SHAP value analysis highlighted C-Reactive Protein (CRP) and alanine transaminase (ALT) as the most influential features, underscoring their significance in the context of AMI (Figure 1-2). The predictive power of each feature was further demonstrated by the mean decrease in Gini index (Figure 1-2). Conclusion: Our study highlights the feasibility of employing machine learning to predict POCE post-AMI using an integrative dataset of clinical and neuroendocrine markers. The predictive model has the potential to revolutionize post-AMI care by allowing clinicians to identify high-risk patients early, tailor interventions, and allocate resources efficiently. Future research could explore the integration of this model into clinical workflows and its impact on patient outcomes.

WHY THIS STUDY?

The VIVA First-in-Human feasibility study aimed to investigate the safety and efficacy of the pre-mounted dry-pericardium self-expandable Vienna aortic valve TAVI system in patients with severe symptomatic aortic stenosis (SSAS), categorized as intermediate or high surgical risk. The innovation of the dehydrated pericardium facilitates immediate use and potentially reduces procedural errors, time, and resources, making it a practical option in areas where resource constraints are a significant concern.

HOW WAS IT EXECUTED?

This prospective, non-randomized, single-arm, single-center study evaluated the Vienna aortic valve system in 10 patients with SSAS. The Valve Academic Research Consortium 3-defined safety outcomes were evaluated up to 1-year follow-up. The major outcomes included all-cause mortality, neurological events, overt bleeding, cardiac structural complications, valve-related complications, conduction disturbances, myocardial infarction, acute kidney injury and structural valve deterioration. Technical success (at exit from procedure room), device success (at 30 days), early safety (at 30 days) and clinical efficacy (at 1 year) were also evaluated. Hemodynamic outcomes were assessed up to one year. The study was prospectively registered at ClinicalTrials.gov (NCT04861805).

WHAT WERE THE ESSENTIAL RESULTS?

Over one-year, significant hemodynamic improvements were observed: mean aortic valve pressure gradient decreased from 48.7±10.8 mmHg to 8.1±3.09 mmHg, aortic valve area increased from 0.75±0.18 cm² to 1.91±0.19 cm², and maximum jet velocity through the aortic valve reduced from 4.41±0.35 to 1.95±0.36 (p<0.0001). No moderate/severe paravalvular leakage was noted at 12 months. CT scans at 3 months post-TAVI revealed a significant reduction in aortic annular eccentricity from pre-implantation 22.3±7.3% to 10.4±6.5% at the valve inflow (p=0.04) and no evidence of hypoattenuated leaflet thickening in all cases, indicating an absence of subclinical leaflet thrombosis. At the 12-month follow-up, there were no cases of cardiovascular mortality, with one non-cardiovascular death reported, confirmed as unrelated to the device. Complications included one overt bleeding event, two cases requiring post-TAVI pacemaker implantation, and three ischemic events, with only one resulting in lasting neurological impairment. Improvements in quality of life and functional status were observed but were not statistically significant.

WHY IT IS IMPORTANT?

The Vienna aortic valve system demonstrated promising safety and efficacy profiles, as evidenced by the absence of cardiovascular mortality and robust hemodynamic performance in the one-year follow-up. These initial findings suggest that the pre-mounted dry-pericardium Vienna aortic valve is a viable option for further investigation in ongoing long-term multi-center clinical trials. While the economic aspects, such as direct comparisons of different TAVI systems for economic efficiency, were not the focus of the current study, they represent a crucial area for future research. The Vienna valve's unique pre-mounted design suggests a potential reduction in healthcare costs by minimizing the need for extensive human and logistic resources, equipment, and potential procedural errors. This study underscores the critical need for future comparative economic assessments, aiming to optimize healthcare efficiency and affordability.

Background and Objectives: The combination of aortic valve stenosis (AS) and ischemic heart disease (IHD) is quite common and is associated with myocardial fibrosis (MF). The purpose of this study was to evaluate the association between the histologically verified left ventricular (LV) MF and its geometry and function in isolated AS and AS within IHD groups. Materials and Methods: In a single-center, prospective trial, 116 patients underwent aortic valve replacement (AVR) with/without concomitant surgery. The study population was divided into groups of isolated AS with/without IHD. Echocardiography was used, and LV measurements and aortic valve parameters were obtained from all patients. Myocardial tissue was procured from all study patients undergoing elective surgery. Results: There were no statistical differences between isolated AS and AS+IHD groups in LV parameters or systolic and diastolic functions during the study periods. The collagen volume fraction was significantly different between the isolated AS and AS+IHD groups and was 7.3 ± 5.6 and 8.3 ± 6.4, respectively. Correlations between MF and left ventricular end-diastolic diameter (LVEDD) (r = 0.59, p = < 0.001), left ventricular mass (LVM) (r = 0.42, p = 0.011), left ventricular ejection fraction (LVEF) (r = −0.67, p < 0.001) and an efficient orifice area (EOA) (r = 0.371, p = 0.028) were detected in isolated AS during the preoperative period; the same was observed for LVEDD (r = 0.45, p = 0.002), LVM (r = 0.36, p = 0.026), LVEF (r = −0.35, p = 0.026) and aortic annulus (r = 0.43, p = 0.018) in the early postoperative period; and LVEDD (r = 0.35, p ≤ 0.05), LVM (r = 0.43, p = 0.007) and EOA (r = 0.496, p = 0.003) in the follow-up period. In the group of AS and IHD, correlations were found only with LV posterior wall thickness (r = 0.322, p = 0.022) in the follow-up period. Conclusions: Histological MF in AS was correlated with LVM and LVEDD in all study periods. No correlations between MF and LV parameters were found in aortic stenosis in the ischemic heart disease group across all study periods.

Introduction Paclitaxel, a chemotherapy agent, can rarely cause acute ST-elevation myocardial infarction (STEMI) [1]. Paclitaxel can induce cardiac ischemia, known as Paclitaxel Induced Cardiac Ischemia (PICI) due to coronary thrombosis [2,3]. The most common treatment for STEMI is Percutaneous Coronary Intervention (PCI) [4]. Case presentation A 65 year old lung cancer patient experienced STEMI during Paclitaxel treatment. Post Paclitaxel infusion, the patient had severe chest pain and ECG showed ST elevations in anterior leads. COG showed acute occlusion of the proximal LAD and chronic RCA occlusion. During PCI two stents were implanted, Aspirin, Clopidogrel were prescribed and Apixaban for atrial fibrillation was continued. After 15 minutes, patient began experiencing chest pains again. A repeat PCI showed stent thrombosis. Thrombectomy was performed and Glycoprotein IIb/IIIa inhibitor was injected into the LCA. Due to its faster onset of action, a loading dose of Ticagrelor was additionally prescribed. Ten days later the patient was diagnosed with NSTEMI. COG revealed a chronic occlusion of the RCA with collateral flow leading to the decision to apply only medical treatment. [...].

The dry-pericardium Vienna transcatheter aortic valve system is repositionable and retrievable, already premounted on the delivery system, eliminating the need for assembly and crimping of the device before valve implantation.