Health state magnitude-integrating physiologic parameters

Yoram G. Weiss*, Amit Maliar, Leonid A. Eidelman, Clifford S. Deutschman, C. William Hanson, Gershom Zajicek

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Introduction: Sophisticated monitoring devices present an overwhelming influx of numerical data. We hypothesized that a new measure, the health state magnitude (HSM), reliably integrates numerical data and trends. Methods: Commonly measured physiologic parameters (O2 saturation, systolic blood pressure, heart rate, etc.) were recorded from 10 patients. The means of the variables recorded during a 10 min reference period were used to construct a baseline reference vector, HSV0. Data was collected at 1 min intervals and a series of vectors, HSVX, were constructed. Each HSVx and the HSV0 served to calculate the z score vector. The norm of the z score vector is the HSM, and the derivative of the HSM is dHSM. The HSM was plotted as a function of time. A deviation of the HSM by more then 30% was considered a significant event. Times at which significant HSM deviations occurred were compared with the anesthesia record to correlate with clinical events. A contour plot was used to correlate the HSM with the dHSM. Individual cases were projected on this diagram to identify their relative 'stability'. Results: 1) 57 events were identified by the system. 32 events were recognized by the anesthesiologist. 2) On the contour plot, 90% of all points lay within an area defined by the inner contour line (Fig 1a). To demonstrate the utility of plotting dHSM VS. HSM an individual case, of a relatively "stable" patient is depicted in Fig 1b. Conclusion: The HSM and the correlation between HSM and dHSM reliably represent the patient's hemodynamic state. Data reduction and integration may help optimize clinical decision-making.

Original languageEnglish
Pages (from-to)A113
JournalCritical Care Medicine
Issue number1 SUPPL.
StatePublished - 1999
Externally publishedYes


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