ADVANCING THE UNDERSTANDING OF CLINICAL SEPSIS USING GENE EXPRESSION-DRIVEN MACHINE LEARNING TO IMPROVE PATIENT OUTCOMES.

ABSTRACT: Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of machine learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. The lack of a clear definition of sepsis is highlighted as a major hurdle, but ML models offer a workaround by focusing on endpoint prediction. We emphasize the significance of gene transcript information and its use in ML models to provide insights into sepsis pathophysiology and biomarker identification. Temporal analysis and integration of gene expression data further enhance the accuracy and predictive capabilities of ML models for sepsis. Although challenges such as interpretability and bias exist, ML research offers exciting prospects for addressing critical clinical problems, improving sepsis management, and advancing precision medicine approaches. Collaborative efforts between clinicians and data scientists are essential for the successful implementation and translation of ML models into clinical practice. Machine learning has the potential to revolutionize our understanding of sepsis and significantly improve patient outcomes. Further research and collaboration between clinicians and data scientists are needed to fully understand the potential of ML in sepsis management.

Intraoperative Blood Pressure Monitoring in Obese Patients.

BACKGROUND: The optimal method for blood pressure monitoring in obese surgical patients remains unknown. Arterial catheters can cause potential complications, and noninvasive oscillometry provides only intermittent values. Finger cuff methods allow continuous noninvasive monitoring. The authors tested the hypothesis that the agreement between finger cuff and intraarterial measurements is better than the agreement between oscillometric and intraarterial measurements. METHODS: This prospective study compared intraarterial (reference method), finger cuff, and oscillometric (upper arm, forearm, and lower leg) blood pressure measurements in 90 obese patients having bariatric surgery using Bland-Altman analysis, four-quadrant plot and concordance analysis (to assess the ability of monitoring methods to follow blood pressure changes), and error grid analysis (to describe the clinical relevance of measurement differences). RESULTS: The difference (mean ± SD) between finger cuff and intraarterial measurements was -1 mmHg (± 11 mmHg) for mean arterial pressure, -7 mmHg (± 14 mmHg) for systolic blood pressure, and 0 mmHg (± 11 mmHg) for diastolic blood pressure. Concordance between changes in finger cuff and intraarterial measurements was 88% (mean arterial pressure), 85% (systolic blood pressure), and 81% (diastolic blood pressure). In error grid analysis comparing finger cuff and intraarterial measurements, the proportions of measurements in risk zones A to E were 77.1%, 21.6%, 0.9%, 0.4%, and 0.0% for mean arterial pressure, respectively, and 89.5%, 9.8%, 0.2%, 0.4%, and 0.2%, respectively, for systolic blood pressure. For mean arterial pressure and diastolic blood pressure, absolute agreement and trending agreement between finger cuff and intraarterial measurements were better than between oscillometric (at each of the three measurement sites) and intraarterial measurements. Forearm performed better than upper arm and lower leg monitoring with regard to absolute agreement and trending agreement with intraarterial monitoring. CONCLUSIONS: The agreement between finger cuff and intraarterial measurements was better than the agreement between oscillometric and intraarterial measurements for mean arterial pressure and diastolic blood pressure in obese patients during surgery. Forearm oscillometry exhibits better measurement performance than upper arm or lower leg oscillometry.