Temperature curve complexity predicts survival in critically ill patients.

Rationale: Temperature curve complexity is inversely related to clinical status in critically ill patients.
Objective: To study if temperature curve complexity analysis predicts clinical outcome and how this test compares to other well-established conventional measures.
Methods: Temperature was continuously recorded in 50 patients with multiple organ failure. Time-series complexity was analyzed using hourly approximate entropy (ApEn) and detrended fluctuation analysis (DFA) values. Sequential Organ Failure Assessment (SOFA) score was obtained every other day, and correlation between complexity and SOFA values was evaluated. Differences in complexity between nonsurviving and surviving patients were likewise analyzed. Logistic regression models were calculated to predict outcome, and receiver operating characteristic (ROC) curves were plotted to compare the predictive power of complexity values versus SOFA.
Measurements and Results: There was good correlation between complexity results and clinical scores for each patient. Nonsurvivors exhibited lower complexity values than survivors (minimum ApEn = 0.230 vs. 0.378; maximum DFA = 1.636 vs. 1.507; mean ApEn = 0.459 vs. 0.596; mean DFA = 1.376 vs. 1.288; p < 0.001 for all comparisons). In the logistic regression model, a change of 0.1 in the minimum complexity resulted in severe increases in the odds ratio of dying (7.6-fold for ApEn, 5.4-fold for DFA). In terms of predicting outcome, there were no significant differences in the areas under the ROC curves for complexity values versus SOFA scores.
Conclusions: 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.