Ciobotaru, Octavian Catalin

Background: Cardiogenic shock (CS) is a life-threatening complication of ST-elevation myocardial infarction (STEMI) and remains the leading cause of in-hospital mortality, with rates ranging from 5 to 10% despite advances in reperfusion strategies. Early identification and timely intervention are critical for improving outcomes. This study investigates the utility of machine learning (ML) models for predicting the risk of CS during the early phases of care—prehospital, emergency department (ED), and cardiology-on-call—with a focus on accurate triage and prioritization for urgent angiography. Results: In the prehospital phase, the Extra Trees classifier demonstrated the highest overall performance. It achieved an accuracy (ACC) of 0.9062, precision of 0.9078, recall of 0.9062, F1-score of 0.9061, and Matthews correlation coefficient (MCC) of 0.8140, indicating both high predictive power and strong generalization. In the ED phase, the support vector machine model outperformed others with an ACC of 78.12%. During the cardiology-on-call phase, Random Forest showed the best performance with an ACC of 81.25% and consistent values across other metrics. Quadratic discriminant analysis showed consistent and generalizable performance across all early care stages. Key predictive features included the Killip class, ECG rhythm, creatinine, potassium, and markers of renal dysfunction—parameters readily available in routine emergency settings. The greatest clinical utility was observed in prehospital and ED phases, where ML models could support the early identification of critically ill patients and could prioritize coronary catheterization, especially important for centers with limited capacity for angiography. Conclusions: Machine learning-based predictive models offer a valuable tool for early risk stratification in STEMI patients at risk for cardiogenic shock. These findings support the implementation of ML-driven tools in early STEMI care pathways, potentially improving survival through faster and more accurate decision-making, especially in time-sensitive clinical environments.

Background: While early risk stratification in STEMI is essential, the threat of cardiogenic shock (CS) persists after revascularization due to reperfusion injury and evolving instability. However, risk prediction in later phases—after revascularization—is less explored, despite its importance in guiding intensive care decisions. This study evaluates machine learning (ML) models for dynamic risk assessment in interventional cardiology and cardiac intensive care unit (CICU) phases, where timely detection of deterioration can guide treatment escalation. Methods: We retrospectively analyzed clinical and procedural data from 158 patients diagnosed with STEMI complicated by cardiogenic shock, treated between 2019 and 2022 at the Cardiology Department of the University Emergency Hospital of Bucharest, Romania. Machine learning models—Random Forest (RF), and Quadratic Discriminant Analysis (QDA)—were developed and tested specifically for the interventional cardiology and CICU phases. Model performance was evaluated using area under the receiver operating characteristic curve (ROC-AUC), accuracy (ACC), sensitivity, specificity, and F1-score. Results: In the interventional phase, RF and QDA achieved the highest accuracy, both reaching 87.50%. In the CICU, RF and QDA demonstrate the best performance, reaching ACCs of 0.843. QDA maintained consistent performance across phases. Relevant predictors included reperfusion strategy, TIMI flow before percutaneous coronary intervention (PCI), Killip class, creatinine, and Creatine Kinase Index (CKI)—all parameters routinely assessed in STEMI patients. These models effectively identified patients at risk for post-reperfusion complications and hemodynamic decline, supporting decisions regarding extended monitoring and ICU-level care. Conclusions: Predictive models implemented in advanced STEMI phases can contribute to dynamic, phase-specific risk reassessment and optimize CICU resource allocation. These findings support the integration of ML-based tools into post-PCI workflows, enabling earlier detection of clinical decline and more efficient deployment of intensive care resources. When combined with earlier-stage models, the inclusion of interventional and CICU phases forms a dynamic, end-to-end risk assessment framework. With further refinement, this system could be implemented as a mobile application to support clinical decisions throughout the STEMI care continuum.

Echinococcosis represents a major public health issue and continues to be endemic in various regions around the world. Hydatid cysts are commonly located in the liver (primary site), followed by the lungs (secondary site). However, they are also found in less typical organs such as the spleen, kidneys, peritoneum, retroperitoneum, pancreas, gallbladder, and various other organs, as well as in striate muscles. Hydatid cysts involving the psoas muscle are rare, and in the past five years, only a few articles have addressed this condition due to its low incidence. Symptoms can be quite vague, and serological testing might return negative, making imaging studies crucial for an accurate diagnosis. Treatment options involve surgery alongside antiparasitic agents. Despite having a low mortality rate, the recurrence of hydatid disease remains high. This paper aims to provide new clinical data through our case presentation, while also offering a review of the cases of hydatid cysts involving the psoas muscle that have been published over the past five years.

Laparoscopic cholecystectomy is characterized by reduced postoperative pain, shorter hospital stays, rapid return to preoperative physical activity, and less psychological impact on the patient. During laparoscopic cholecystectomy, the intra-abdominal insufflation of carbon dioxide with secondary increase in intra-abdominal pressure can cause important hemodynamic consequences, like decreased cardiac output and blood pressure, as well as compensatory increase in heart rate. The purpose of this study is to evaluate changes in cardiovascular parameters during general anesthesia in patients undergoing laparoscopic cholecystectomy. Retrospective data from 342 patients with cholecystectomy for cholelithiasis performed at Railway Hospital Galati, Romania, were reviewed. All patients received the same intraoperative anesthetics. Female patients were 85.7% (n = 293). More than half of the patients, 53.51% (n = 183), were 40–59 years old, and only 16.37% (n = 56) were under 40 years old. Patients with a normal body mass index (BMI) represented 45.6% (n = 156), 33.3% (n = 114) were underweight, and 12% (n = 42) had grade 1 obesity (BMI 25–29.9 kg/m2). The minimum intraoperative blood pressure correlated with patient gender (p 0.015 < 0.005), with men having a higher blood pressure than women (p 0.006 < 0.05), and for BMI, a higher BMI was associated with elevated blood pressure (p 0.025 < 0.05). Older age correlated with an increased maximum intraoperative blood pressure (p < 0.001 < 0.05) and with maximum intraoperative heart rate (p 0.015 < 0.05). Patients undergoing laparoscopic cholecystectomy experienced significant hemodynamic changes with pneumoperitoneum, but this type of surgical intervention was safe for patients regardless of their age.

Background: Infective endocarditis (IE) management is challenging, usually requiring multidisciplinary collaboration from cardiologists, infectious disease specialists, interventional cardiologists, and cardiovascular surgeons, as more than half of the cases will require surgical procedures. Therefore, it is essential for all healthcare providers involved in managing IE to understand the disease’s characteristics, potential complications, and treatment options. While systemic embolization is one of the most frequent complications of IE, the coronary localization of emboli causing acute myocardial infarction (AMI) is less common, with an incidence ranging from 1% to 10% of cases, but it has a much higher rate of morbidity and mortality. There are no guidelines for this type of AMI management in IE. Methods: This narrative review summarizes the current knowledge regarding septic coronary embolization in patients with IE. Additionally, this paper highlights the diagnosis and management challenges in such cases, particularly due to the lack of protocols or consensus in the field. Results: Data extracted from case reports indicate that septic coronary embolization often occurs within the first two weeks of the disease. The aortic valve is most commonly involved with vegetation, and the occluded vessel is frequently the left anterior descending artery. Broad-spectrum antibiotic therapy followed by targeted antibiotic therapy for infection control is essential, and surgical treatment offers promising results through surgical embolectomy, concomitant with valve replacement or aspiration thrombectomy, with or without subsequent stent insertion. Thrombolytics are to be avoided due to the increased risk of bleeding. Conclusions: All these aspects should constitute future lines of research, allowing the integration of all current knowledge from multidisciplinary team studies on larger patient cohorts and, subsequently, creating a consensus for assessing the risk and guiding the management of this potentially fatal complication.