Low levels of complexity in the temperature curve are indicators of poor prognosis in patients with multiple organ failure. The predictive ability of temperature curve complexity is similar to that of the SOFA score.

Objective: To investigate glycemic dynamics and its relation with mortality in critically ill patients. We searched for differences in complexity of the glycemic profile between survivors and nonsurvivors in patients admitted to a multidisciplinary intensive care unit.Design: Prospective, observational study, convenience sample. Settings: Multidisciplinary intensive care unit of a teaching hospital in Madrid, Spain.Patients: A convenience sample of 42 patients, aged 29 to 86 yrs, admitted to an intensive care unit with an Acute Physiology and Chronic Health Evaluation II score of >14 and with an anticipated intensive care unit stay of >72 hrs.Interventions: A continuous glucose monitoring system was used to measure subcutaneous interstitial fluid glucose levels every 5 mins for 48 hrs during the first days of intensive care unit stay. A 24-hr period (n = 288 measurements) was used as time series for complexity analysis of the glycemic profile. n recent years, there has been a growing interest in hyperglycemia associated with critical illness. Hyperglycemia in critically ill patients is a consequence of several factors, including increased cortisol, catecholamines, glucagon, growth hormone, gluconeogenesis, and glycogenolysis (1, 2). In addition, insulin resistance has been demonstrated in >80% of critically ill patients (3).

An attempt was made to develop a truly quantitative approach to temperature, based on models derived from nonlinear dynamics and chaos theory. Three different procedures for measuring the degree of complexity of the temperature curve were compared, and the possible correlations between these measurements and certain physiopathologically relevant parameters in healthy subjects were examined. Twenty-three healthy subjects (10 males, 13 females) between 18 and 85 years of age had their temperature measured every 10 min for at least 30 h. These time series were used to determine the approximate entropy (ApEn), a detrended fluctuation analysis (DFA), and the fractal dimension by the compass method (FD(c)). There was good correlation between the different methods of measuring the complexity of the curve [ r=-0.603 for ApEn vs. DFA ( p=0.002), r=0.438 for ApEn vs. FDc ( p=0.04) and r=-0.647 for DFA vs. FDc ( p=0.0008)]. Both the fractal dimension and the approximate entropy were inversely correlated with age [ r=-0.637 ( p=0.001) and r=-0.417 ( p=0.03), respectively], while the DFA increased with age ( r=0.413, p=0.04). The results thus suggest that complexity of the temperature curve decreases with age. The complexity of the temperature curve can be quantified in a consistent fashion. Age is associated with lower complexity of the temperature curve.

The clinical status of patients suffering multiple organ failure is inversely correlated to the complexity of the temperature curve expressed as approximate entropy. Reduced complexity has dismal prognostic implications. Its assessment is noninvasive and inexpensive and allows for real-time continuous monitoring of clinical status.