Time Series Analysis in Intensive Care Medicine

Abstract

Objectives: Time series analysis techniques facilitate statistical analysis of variables in the course of time. Continuous monitoring of the critically ill in intensive care offers an especially wide range of applications. In an open clinical study time series analysis was applied to the monitoring of lab variables after liver surgery, and to support clinical decision making in the treatment of acute respiratory distress syndrome. Patients and Results: For the analysis of lab variables (blood lactate) in 19 patients after liver resections ARIMA (Auto Regressive Integrated Moving Average) models were developed for an estimation period of at least 14 measurements. Prediction values from these models for the following data points were then compared to the actual lab values. With these models in all cases of hepatic complications pathological changes in the lab values could be differentiated from random variance. In 25 patients with ARDS the effect of therapeutic interventions on pulmonary target variables (PVR, Q S /Q T , AaDO 2 ) was estimated with interrupted ARIMA models. The time series before the therapeutic intervention was compared to changes under intervention using the same model including an intervention regressor. With all therapeutic interventions clinically relevant therapeutic effects could be statistically identified in all patients. Similarly, non-effective therapeutic maneuvers could be detected early, eventually changing therapeutic strategy. Conclusions: Even on the basis of short time series of intensive care monitoring variables ARIMA models could be successfully employed for the analysis of lab variables and of therapeutic interventions. Nevertheless, due to high demands for manpower and to statistical methodological limitations the general use of this methodology in clinical practice apart from controlled clinical studies cannot be recommended today.