BACKGROUND: Physiologic monitors present an influx of numerical data that can be overwhelming to the clinician. We combined several parameters in an effort to reduce the amount of information that must be continuously monitored including oxyhemoglobin saturation by pulse oximetry, end-tidal CO2 concentration, arterial blood pressure, and heart rate into an integrated measure--the health stability magnitude (HSM). The HSM is computed for a predetermined basal period, the reference HSM (RHSM), and recalculated continuously for comparison with the baseline value. In this study we present the HSM concept and examine changes in the HSM during abdominal aortic aneurysm surgery. MATERIALS AND METHODS: After IRB approval, nine patients were studied. The anesthesiologist recorded all significant intra-operative events. Within a defined time interval, data were recorded and used to calculate a combined parameter, the HSM. The baseline or reference value of this index (RHSM) was calculated after the induction of anesthesia. Individual HSM values were repeatedly calculated for ten second periods after the RHSM value was established. A > 30% deviation of the HSM from the RHSM was considered significant. Deviations in the HSM were compared with events recorded by the anesthesiologist on a paper record and with the record from an electronic record-keeping system. The deviation observed between two consecutive HSMs, called dHSM, was plotted against HSM to construct a contour diagram of data from all patients to which individual cases could be compared. RESULTS: The plot showed that dHSM vs. HSM values were tightly clustered. The inner contour on the distribution plot contained 90% of values. Individual patient's time course, projected on this diagram, revealed deviations form "normal" physiology. Fifty-nine events led to > 30% deviations in the HSM; 27 were anticipated events and 32 were unanticipated. CONCLUSION: The correlation between HSM and dHSM depicts changes in multiple monitored parameters that can be viewed using a single graphical representation. Projection of individual cases on the contour diagram may help the clinician to distinguish relative intraoperative stability from important events. Data reduction in this manner may guide clinical decision-making in response to unanticipated or unrecognized events.
BACKGROUND: Physiologic monitors present an influx of numerical data that can be overwhelming to the clinician. We combined several parameters in an effort to reduce the amount of information that must be continuously monitored including oxyhemoglobin saturation by pulse oximetry, end-tidal CO2 concentration, arterial blood pressure, and heart rate into an integrated measure--the health stability magnitude (HSM). The HSM is computed for a predetermined basal period, the reference HSM (RHSM), and recalculated continuously for comparison with the baseline value. In this study we present the HSM concept and examine changes in the HSM during abdominal aortic aneurysm surgery. MATERIALS AND METHODS: After IRB approval, nine patients were studied. The anesthesiologist recorded all significant intra-operative events. Within a defined time interval, data were recorded and used to calculate a combined parameter, the HSM. The baseline or reference value of this index (RHSM) was calculated after the induction of anesthesia. Individual HSM values were repeatedly calculated for ten second periods after the RHSM value was established. A > 30% deviation of the HSM from the RHSM was considered significant. Deviations in the HSM were compared with events recorded by the anesthesiologist on a paper record and with the record from an electronic record-keeping system. The deviation observed between two consecutive HSMs, called dHSM, was plotted against HSM to construct a contour diagram of data from all patients to which individual cases could be compared. RESULTS: The plot showed that dHSM vs. HSM values were tightly clustered. The inner contour on the distribution plot contained 90% of values. Individual patient's time course, projected on this diagram, revealed deviations form "normal" physiology. Fifty-nine events led to > 30% deviations in the HSM; 27 were anticipated events and 32 were unanticipated. CONCLUSION: The correlation between HSM and dHSM depicts changes in multiple monitored parameters that can be viewed using a single graphical representation. Projection of individual cases on the contour diagram may help the clinician to distinguish relative intraoperative stability from important events. Data reduction in this manner may guide clinical decision-making in response to unanticipated or unrecognized events.