Zhumagaliyev, Rassul

Background: The bleeding in cardiac surgery remains a significant clinical problem. There is no "gold standard" method to quantify blood loss. Traditional measurement of drainage volume often underestimates or overestimates, as it does not consider the type of fluid. We hypothesized that blood loss could be more accurately calculated using the Hb/kg index in terms of haemoglobin (Hb) mass loss per kilogram of the patient's body mass. This study aimed to Objective To develop a novel approach for calculating actual blood loss using the Hb/kg index. Methods: This single-center prospective study included 195 patients who underwent cardiac surgery between October 2023 and November 2024. The Hb/kg index was calculated based on intraoperative Hb loss, Hb loss via chest tubes, packed red blood cell transfusions and patient weight. Eighty-six additional clinical predictors were analyzed using conventional statistics and machine learning algorithms. Predictors with statistically significant Spearman correlations were included for further analysis. Results: Lasso regression achieved the best overall performance in predicting Hb/kg index. It yielded the lowest mean squared error (0.08 ± 0.04), mean absolute percentage error (0.18 ± 0.10), with the highest correlation (0.92 ± 0.06) and R² score (0.82 ± 0.13). BMI showed a significant negative relationship (-0.018, p < 0.001). Postoperative Hb and haematocrit values had negative correlation (-0.69, p < 0.001 and -0.07, p < 0.015), while initial Hb was positively correlated (0.85, p < 0.001). Conclusions: This method provides a more reliable and clinically relevant tool to calculate actual blood loss and allows for a more precise assessment and treatment.

Background There is no universally accepted definition of perioperative blood loss in cardiac surgery. Existing methods are based on chest tube output and are not normalised for patient weight. Objective To validate machine learning-derived blood loss severity clusters based on a haemoglobin mass loss per kilogram (Hb/kg index). Methods This single-center prospective study included 195 patients undergoing cardiac surgery between October 2023 and November 2024. Three clusters derived using K-Medoids were mapped to the Hb/kg index to define cut-offs. Cluster discrimination was assessed by receiver operating characteristics (ROC) analysis (area under the curve (AUC)). Group comparisons were performed using analysis of covariance adjusted for age and gender. Associations between the Hb/kg index and clinical outcomes, including transfusion requirements and complications were analysed using Chi-square tests and adjusted two-way Analysis of Covariance (ANCOVA). Results Clustering identified three groups (Mild, Moderate, Severe) defined by optimal Hb/kg thresholds of 1.72 and 2.10. The Severe cluster demonstrated strong discriminative performance (AUC = 0.790, 95% confidence interval 0.721-0.859). Chest tube output did not differ significantly between clusters (= 0.097), while haemoglobin mass loss through chest tubes demonstrated a significant effect (p = 0.011). Conclusions The Hb/kg Index is a validated, data-driven, objective metric for perioperative blood loss, offering greater precision than traditional chest tube drainage volume. It effectively stratifies bleeding severity and identifies high-risk patients with lower BMI.

Postoperative bleeding following cardiopulmonary bypass (CPB) remains a significant challenge.Although viscoelastic testing is increasingly used, the relative contributions of fibrinogen, platelet count and clot firm-ness to blood loss remain debated. We evaluated the diagnostic accuracy of thromboelastometry (ROTEM) comparedwith platelet aggregometry (PA) and standard tests, using the Hb/kg index to quantify blood loss.

Background: Giant descending thoracic aortic aneurysm (GDTAA) is a rare vascular disease char-acterized by an aortic diameter exceeding 10 cm. GDTAA carries a significant risk of rupture and mortality and requires timely diagnosis and intervention. Despite the clinical severity of the disease, the literature on GDTAA remains sparse, particularly in cases with extreme aneurysmal dilatation. Case Presentation: We present the case of a 68-year-old man with a GDTAA of 14.08 × 10.04 cm, one of the largest ever reported. The patient initially presented with recurrent syncope, chronic cough and fatigue. Imaging studies, including Computed Tomography (CT) angiography, revealed a massive aneurysmal dilatation in the distal post-arch segment of the descending aorta with compression of the trachea and bronchi. The patient underwent a successful open surgical repair with a Dacron graft and simultaneous Coronary Artery Bypass Grafting (CABG). Postoperative complications included respiratory acidosis, emphysema and transient haemodynamic instability, which were effectively treated. The patient was discharged in a stable condition on the tenth postoperative day. Conclusion: This case highlights the importance of early recognition and surgical intervention in GDTAA in order to prevent catastrophic consequences. Comprehensive preoperative evaluation, careful surgical plan-ning and attentive postoperative care are essential for optimal recovery. Our results emphasise the importance of modern imaging techniques for accurate anatomical assessment and risk stratification in patients with extreme aneurysm growth. Further research is needed to establish standardised protocols for the treatment of GDTAA.

Background. Perioperative blood loss in cardiac surgery remains a significant clinical challenge as there is no “gold standard” for its accurate assessment [1, 2]. Conventional volume-based methods often underestimate or overestimate actual blood loss [3], especially given individual differences in body mass and chest tubes composition (haemorrhagic vs. serous) [4]. We hypothesized that blood loss could be more accurately calculated using the Hb/kg index in terms of haemoglobin (Hb) mass loss per kilogram the patient's body weight. Objective: To develop a novel approach for calculating actual blood loss using the Hb/kg index. Methods. We studied 195 patients undergoing cardiac surgery over 13 months (October 2023–November 2024). The Hb/kg index was calculated using intraoperative Hb loss, Hb loss via chest tubes, packed red blood cell transfusions, and patient weight. Eighty-six additional clinical predictors were analyzed using conventional statistics and machine learning algorithms, including Linear Regression, Lasso, Lasso-OLS, Support Vector Machine, k-Nearest Neighbors, Decision Tree, XGBoost, and Deep Neural Network. Predictors with statistically significant Spearman correlations with the Hb/kg index were included for further analysis. Results: Lasso regression achieved the best overall performance in predicting Hb/kg index. It yielded the lowest mean squared error (0.08 ± 0.04), and mean absolute percentage error (0.18 ± 0.10), with the highest correlation (0.92 ± 0.06) and R² score (0.82 ± 0.13). BMI showed a significant negative relationship (-0.018, p < 0.001). Postoperative Hb and haematocrit values had negative correlation (-0.69, p<0.001 and -0.07, p<0.015), while initial Hb was positively correlated (0.85, p<0.001). Conclusion: This method provides a more reliable and clinically relevant tool to calculate actual blood loss and allows for a more precise assessment and treatment. This approach could be a robust research tool, although further studies are needed to demonstrate the way serves as a predictor of surgical and clinical outcomes.

Introduction Perioperative blood loss in cardiac surgery is still a significant problem [1]. There is no gold standard method to assess actual blood loss [2]. Traditional methods are based on volumetric measurement, which can be inaccurate as they can be under- or overestimated [3]. It is important to understand how the same amount of blood loss affects hemodynamics and postoperative complications in patients with different body mass [4]. Moreover, the fluid draining from the chest tube can be either haemorrhagic, serous or a combination of both [5]. We hypothesized and calculated blood loss using the Hb/kg index, which is based on hemoglobin loss per kilogram and proved to be a more accurate method of calculating actual blood loss. Aim To develop an empirical formula and a novel approach to calculate actual blood loss in terms of hemoglobin loss per kilogram (Hb/kg index). Methods In a prospective observational study, a total of 195 patients who underwent cardiac surgery with cardiopulmonary bypass were included over a period of 13 months (October 2023- November 2024). To calculate the Hb/kg index, hemoglobin loss via chest tube drainage, intraoperative hemoglobin loss, transfusion of packed red blood cells and patient weight were taken into account. An analysis of 86 predictors of the Hb/kg index was performed. Correlation coefficients Pearson r and Spearman rho between the predictors and the Hb/kg index were computed. Predictors that showed statistically significant Spearman correlations were selected for further analysis in Robust Linear Regression model. Results The robust linear regression model was fitted using Huber’s T norm estimator in Ordinary Least Squares (OLS). The determination coefficient R-squared was equal 0.983 and indicated that 98% of the variance in the Hb/kg index was explained by the predictors. The adjusted R-squared was equal 0.981. The linear regression model was statistically significant, F(27,167) = 366.3, p < 0.001. The linear regression analysis revealed several significant predictors of the Hb/kg index. Body mass index (-0.0303, p < 0.001) showed a significant negative relationship, indicating that low body mass index leads to an increase in the Hb/kg index. Predictors such as hemoglobin mass before surgery (0.0430, p < 0.001), hemoglobin loss via chest tube (0.0110, p < 0.001) and intraoperative hemoglobin loss (0.0033, p < 0.001) were found to be highly significant and positively correlated. Conversely, transfusion of hemoglobin mass (packed red blood cells) showed significant negative effects (-0.0136, p < 0.001). Conclusions This method provides a more reliable and clinically relevant tool for calculation actual blood loss, providing a more precise and individualized evaluation. This approach could be a robust research tool, although further studies are needed to demonstrate its reliability.

Background. Ischaemia-reperfusion (I/R) injury leads to cell death and triggers inflammatory responses. Cardioplegic solutions are essential in cardiac surgery to induce controlled cardiac arrest and protect the myocardium from ischemic damage and ischaemia-reperfusion injury. Aim. The aim of this study was to evaluate the impact of different cardioplegic solutions — del Nido, Custodiol HTK, and St. Thomas on cytokines response in an immature rat heart using the Langendorff preparation. Methods. 30 male Wistar albino rats (up to 1 month age) were used. A median sternotomy was performed. After the heart was removed, the aorta was gently cannulated and ligated using double 5-0 silk sutures. We employed the retrograde heart perfusion model described by O. Langendorff. Experimental animals were divided into three groups: St. Thomas (CP1), Custodiol HTK (CP2), and del Nido (CP3) group. Moreover, each of these groups was divided into three groups according to ischaemia time: 1 h ischaemia with 20 min reperfusion time, 2 h ischaemia with 40 min reperfusion time, 4 h ischaemia with 80 min reperfusion, and control groups (KPRF) with 30 minutes of perfusion was performed in the control group. Cardioplegic solutions approximately 10 ml, were infused into the aorta. Ventricular biopsies were harvested and stored at –80 o C until analysis. Results. Pro-inflammatory cytokines TNF-alpha and IL-6 and anti-inflammatory cytokines IL-4 and IL-10 were evaluated. The results showed that there was no statistically significant difference between the groups (p > 0.05). However, under conditions of prolonged ischaemia, inflammation was reduced the most in the CP2 group. It is clearly seen that the number of anti-inflammatory cytokines decreases over time in all cardioplegic groups. Looking at IL-10, the amount in the CP3 group remains the highest after four hours, while the results in the CP2 group are more stable. However, for IL-4, CP2 and CP3 appear similar, while CP1 shows higher values at 2 h but experiences larger drops at 4 h of ischaemia. Conclusion. Although no statistically significant differences were found between the groups, cytokine analysis revealed that, anti-inflammatory cytokines more accurately reflect the initial changes in the immature myocardium.

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.

Introduction: Primary malignant aortic tumors are rare and diagnosis can be difficult due to the variety of clinical manifestations. This malignant disease, which originates in the intima or medial layers of the aorta, presents a complex diagnostic and therapeutic dilemma. Due to their insidious growth and nonspecific symptoms, they are often diagnosed postmortem. Case Presentation: We present the case of a sarcoma of the descending part of thoracic aorta in a 69-year-old man. The initial symptoms were hypertension crisis, chest pain, shortness of breath, and swollen legs. A computed tomography scan of the chest and abdomen revealed an obstructive mass in the thoracic aorta. Treatment consisted of performing prosthesis of the thoracic aorta and removal of thrombi/tumor masses from the thoracoabdominal aorta and iliac artery on both sides, as well as chemotherapy. Conclusion: Aortic sarcoma should be recognized as a potential cause of a large thrombus in the aorta. Palliative procedures, including open aortic replacement, may also serve as alternative treatment options for aortic sarcoma.

Background: Transcatheter aortic valve implantation (TAVI) has emerged as a pivotal intervention for managing severe aortic stenosis in high-risk surgical patients. Objective: This study aimed to evaluate the impacts of procedural factors and patient characteristics on TAVI outcomes, with a focus on survival rates, cardiac mortality, and associated complications. Methods: A retrospective, single-center study involving 224 patients who underwent TAVI at the Lithuanian University of Health Sciences from September 2021 to April 2023 was conducted. Data encompassing demographic characteristics, medical history, procedural specifics, and follow-up outcomes were analyzed. Survival and adverse events were assessed at 30 days, 6 months, and 12 months post-TAVI. Results: The study included 224 patients. The mean age in the non-death group was 80 ± 6.17 years (range, 49–91), while that in the cardiac death group was 81.5 ± 6.14 years (range, 70–94; p = 0.079). Males accounted for 37.7% of the non-death group and 50% of the cardiac death group (p = 0.304). Statistical analyses identified factors significantly associated with mortality and complications. The overall survival rate was 88.8%, with cardiac-related mortality observed in 8% of patients. Increased fluoroscopy time (p < 0.001), a higher contrast volume (p = 0.005), and less improvement in aortic valve velocity post-TAVI (p = 0.031) were significantly associated with cardiac mortality. Advanced age and a reduced left ventricular ejection fraction (<50%) were prominent predictors of adverse outcomes. Patients with non-coronary cusp calcification exhibited lower cardiac mortality (p = 0.005), while mitral valve regurgitation was linked to poorer outcomes (p = 0.015). Logistic regression analysis underscored the incremental risks posed by procedural complexities and comorbidities. Conclusions: Procedural factors such as fluoroscopy duration and contrast volume, along with patient-specific attributes including age, left ventricular function, and valve calcification patterns, critically influence TAVI outcomes. These findings emphasize the need for tailored procedural strategies and patient management protocols to mitigate risks and enhance the efficacy of TAVI interventions.