Statistical pattern detection in univariate time series of intensive care on-line monitoring data

To determine how different mathematical time series approaches can be implemented for the detection of qualitative patterns in physiologic monitoring data, and which of these approaches could be suitable as a basis for future bedside time series analysis.

Off-line time series analysis.

Surgical intensive care unit of a teaching hospital.

19 patients requiring hemodynamic monitoring with a pulmonary artery catheter.

None.

Hemodynamic data were acquired in 1-min intervals from a clinical information system and exported into statistical software for further analysis. Altogether, 134 time series for heart rate, mean arterial pressure, and mean pulmonary artery pressure were visually classified by a senior intensivist into five patterns: no change, outlier, temporary level change, permanent level change, and trend. The same series were analyzed with low-order autoregressive (AR) models and with phase space (PS) models. The resulting classifications from both models were compared to the initial classification. Outliers and level changes were detected in most instances with both methods. Trend detection could only be done indirectly. Both methods were more sensitive to pattern changes than they were clinically relevant. Especially with outlier detection, 95% confidence intervals were too close. AR models require direct user interaction, whereas PS models offer opportunities for fully automated time series analysis in this context.

Statistical patterns in univariate intensive care time series can reliably be detected with AR models and with PS models. For most bedside problems both methods are too sensitive. AR models are highly interactive, and both methods require that users have an explicit knowledge of statistics. While AR models and PS models can be extremely useful in the scientific off-line analysis, routine bedside clinical use cannot yet be recommended.