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The American Society of Anesthesiologists Standards for Basic Monitoring recommends blood pressure (BP) measurement every 5 min. Research has shown distractions or technical factors can cause prolonged measurement intervals exceeding 5 min. We investigated the relationship between prolonged non-invasive BP (NIBP) measurement interval and the incidence of hypotension, detected postinterval. Our secondary outcome was to determine independent predictors of these prolonged NIBP measurement intervals. Retrospective data were analyzed from 139,509 general anesthesia cases from our institution’s Anesthesia Information Management System (AIMS). Absolute hypotension (AH) was defined a priori as a systolic BP < 80 mmHg and relative hypotension (RH) was defined as a 40% decrease in systolic BP from the preoperative baseline. Odds ratios (OR) with 95% confidence intervals and Pearson’s Chi square Test reported the association of prolonged NIBP measurement intervals on hypotension detected post-NIBP measurement interval. Logistic regression models were developed to determine independent predictors of NIBP measurement intervals. The analysis revealed that NIBP measurement intervals greater than 6 and 10 min are associated with an approximately four times higher incidence of a patient transitioning into hypotension (AH/RH > 6 min OR 4.0 / 3.6; AH/RH > 10 min OR 4.3 / 3.9; p< 0.001). A key finding was that the “> 10-minute AH model” indicated that age 41–80, increased co-morbidity profile, obesity and turning (repositioning) of the operative room table were significant predictors of prolonged NIBP measurement intervals (p < 0.001). While we do not suggest NIBP measurement intervals cause hypotension, intervals greater than 6 and 10 min are associated with a fourfold increase in the propensity of an undetected transition into both RH or AH. These data support current monitoring guidelines.

Automated physiologic alarms are available in most commercial physiologic monitors. However, due to the variability of data coming from the physiologic sensors describing the state of patients, false positive alarms frequently occur. Each alarm requires review and documentation, which consumes clinicians' time, may reduce patient safety through 'alert fatigue' and makes automated physician paging infeasible. To address these issues a computerized architecture based on simple reactive intelligent agent technology has been developed and implemented in a live critical care unit to facilitate the investigation of deterministic algorithms for the improvement of the sensitivity and specificity of physiologic alarms. The initial proposed algorithm uses a combination of median filters and production rules to make decisions about what alarms to generate. The alarms are used to classify the state of patients and alerts can be easily viewed and distributed using standard network, SQL database and Internet technologies. To evaluate the proposed algorithm, a 28 day study was conducted in the University of Michigan Medical Center's 14 bed Cardiothoracic Intensive Care Unit. Alarms generated by patient monitors, the intelligent agent and alerts documented on patient flow sheets were compared. Significant improvements in the specificity of the physiologic alarms based on systolic and mean blood pressure was found on average to be 99% and 88% respectively. Even through significant improvements were noted based on this algorithm much work still needs to be done to ensure the sensitivity of alarms and methods to handle spurious sensor data due to patient or sensor movement and other influences.