Abstract
Monitoring intra-abdominal pressure (IAP) in critical care patients is crucial for preventing intra-abdominal hypertension (IAH) and abdominal compartment syndrome (ACS), with their severe consequences. The muscle contraction sensor (MC) introduced in this study offers a novel, non-invasive method with promising accuracy based on previous findings. This study further evaluates the MC accuracy and reproducibility and examines its correlation with objective IAP measurements obtained through a CO(2) insufflator. We enrolled 41 patients undergoing elective laparoscopic gallbladder removal under general anesthesia with complete muscle relaxation. Two MC sensors were placed on the right and left sides of the abdomen, and elevated IAP was induced by insufflating CO(2) into the peritoneal cavity. IAP measurements from the MC sensors were compared to the randomized IAP values set on the CO(2) insufflator. Data from both methods were analyzed to assess the accuracy and agreement with the insufflator measurements. The MC sensor provided continuous and accurate detection of IAP changes. A Pearson correlation coefficient of 0.963 indicated a strong positive linear correlation between the MC sensor readings and the IAP values set on the insufflator. The coefficient of determination (R(2)) was 0.927, showing that the model explains 92.7% of the variation in IAP values based on the MC sensor signals. Receiver operating characteristic analysis demonstrated that the MC sensor system performed exceptionally well in identifying both IAH and ACS cases, with an area under the curve of 0.996 for IAH and 0.981 for ACS. The study introduces a transcutaneous pressure measuring device as an innovative, non-invasive method for assessing IAP. The system strongly correlates with IAP values measured by CO(2) insufflation, indicating its accuracy. It thus could present an alternative to conventional IAP measurement in the future. The MC capability to deliver real-time, continuous data holds substantial potential for proactive patient care. By incorporating advanced analytics like machine learning, the system could detect trends and provide early warnings of dangerous IAP changes, enabling timely, targeted interventions to enhance outcomes for critically ill patients.