Determinants of 30-day mortality in elderly patients admitted to a cardiovascular surgery intensive care unit.

BACKGROUND: This study aims to identify the factors influencing 30-day morbidity and mortality in patients aged 65 and older undergoing cardiovascular surgery. METHODS: Data from 360 patients who underwent cardiac surgery between January 2012 and August 2021 in the Cardiovascular Surgery Intensive Care Unit (CVS ICU) were analyzed. Patients were categorized into two groups: "mortality+" (33 patients) and "mortality-" (327 patients). Factors influencing mortality, including preoperative, intraoperative, and postoperative risk factors, complications, and outcomes, were assessed. RESULTS: Significant differences were observed between the two groups in factors affecting mortality, including extubation time, ICU stay duration, blood transfusion, surgical reexploration, aortic clamp duration, glomerular filtration rate (GFR), blood urea nitrogen (BUN), creatinine, hemoglobin A1c (HbA1c) levels, and the lowest systolic blood pressure during the first 24 hours in the ICU (p<0.05). The "mortality+" group had longer extubation times and ICU stays, required more blood transfusions, and had higher BUN-creatinine ratios, but lower systolic blood pressures, GFR, and HbA1c levels. Mortality was also higher in patients needing noradrenaline infusions and those who underwent reoperation for bleeding (p<0.05). CONCLUSION: By optimizing preoperative renal function, minimizing extubation time, shortening ICU stays, and carefully managing blood transfusions, surgical reexplorations, aortic clamp duration, and HbA1c levels, we believe that the mortality rate can be reduced in elderly patients. Key strategies include shortening aortic clamp times, reducing perioperative blood transfusions, and ensuring effective bleeding control.

Evaluating the accuracy of ChatGPT-4 in predicting ASA scores: A prospective multicentric study ChatGPT-4 in ASA score prediction.

BACKGROUND: This study investigates the potential of ChatGPT-4, developed by OpenAI, in enhancing medical decision-making processes, particularly in preoperative assessments using the American Society of Anesthesiologists (ASA) scoring system. The ASA score, a critical tool in evaluating patients' health status and anesthesia risks before surgery, categorizes patients from I to VI based on their overall health and risk factors. Despite its widespread use, determining accurate ASA scores remains a subjective process that may benefit from AI-supported assessments. This research aims to evaluate ChatGPT-4's capability to predict ASA scores accurately compared to expert anesthesiologists' assessments. METHODS: In this prospective multicentric study, ethical board approval was obtained, and the study was registered with clinicaltrials.gov (NCT06321445). We included 2851 patients from anesthesiology outpatient clinics, spanning neonates to all age groups and genders, with ASA scores between I-IV. Exclusion criteria were set for ASA V and VI scores, emergency operations, and insufficient information for ASA score determination. Data on patients' demographics, health conditions, and ASA scores by anesthesiologists were collected and anonymized. ChatGPT-4 was then tasked with assigning ASA scores based on the standardized patient data. RESULTS: Our results indicate a high level of concordance between ChatGPT-4 predictions and anesthesiologists' evaluations, with Cohen's kappa analysis showing a kappa value of 0.858 (p = 0.000). While the model demonstrated over 90% accuracy in predicting ASA scores I to III, it showed a notable variance in ASA IV scores, suggesting a potential limitation in assessing patients with more complex health conditions. DISCUSSION: The findings suggest that ChatGPT-4 can significantly contribute to the medical field by supporting anesthesiologists in preoperative assessments. This study not only demonstrates ChatGPT-4's efficacy in medical data analysis and decision-making but also opens new avenues for AI applications in healthcare, particularly in enhancing patient safety and optimizing surgical outcomes. Further research is needed to refine AI models for complex case assessments and integrate them seamlessly into clinical workflows.

Using the Perfusion Index for Block Success in Pediatric Patients: A Retrospective Study.

STUDY OBJECTIVE: To investigate the efficacy of the perfusion index in assessing block success in pediatric patients undergoing elective supracondylar fracture repair surgery. METHODS: It was a retrospective study in a tertiary-care center. Twenty-eight pediatric supracondylar humerus fracture patients who underwent elective surgery for fracture repair were evaluated. Perfusion index, pulse rate, pleth variability index (PVi), and oxygen saturation were measured at different time intervals before and after the coracoid infraclavicular block procedure. MAIN RESULTS:  The changes in perfusion index (PI) values were found to be statistically significant (p˂0.05). The Bonferroni analysis revealed that the results obtained at three separate measurement times differed significantly (p˂0.05). On the other hand, changes in other variables were not statistically significant (p˃0.05). CONCLUSIONS:  The perfusion index can be used as an indicator of block success in elective surgeries of the upper extremities in pediatric patients.