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GENERAL ARTICLES

Independent Risk Factors for Postoperative Shivering

Department of Anesthesiology and Critical Care, Philipps-University Marburg, Germany; Department of Anesthesiology, University of Würzburg, Germany; Outcomes Research ^TM Institute and Departments of Anesthesiology and Pharmacology, University of Louisville, Louisville, Kentucky

Address correspondence and reprint requests to Leopold Eberhart, MD, Department of Anesthesiology and Critical Care, Philipps-University Marburg, Baldingerstr. 1, D–35033 Marburg, Germany. Address e-mail to eberhart{at}mailer.uni-marburg.de.

Postoperative shivering (PAS) is uncomfortable for patients and potentially risky. In this observational trial we sought to identify independent risk factors for PAS after general anesthesia. Potential risk factors for PAS were recorded in 1340 consecutive patients. Signs of shivering, peripheral and core temperature, and thermal comfort were recorded in the postanesthetic care unit. The data were split into an evaluation data set (n = 1000) and a validation data set (n = 340). The first was used to identify independent risk factors for PAS and to formulate a risk score using backward-elimination logistic regression analysis. The proposed model was subsequently tested for its discrimination and calibration properties using receiver operating characteristic (ROC)-curve analysis and linear correlation between the predicted and the actual incidences of PAS in the validation group. The incidence of PAS was 11.6%. There were three major risk factors: young age, endoprosthetic surgery, and core hypothermia, with age being the most important. The risk score derived from this analysis had a reasonable discriminating power, with an area under the ROC-curve of 0.69 (95% confidence interval, 0.60–0.78; P < 0.0001). Furthermore the equation of the calibration curve (y = 0.69x + 6; R² = 0.82; P < 0.05) indicated a good and statistically significant agreement between predicted and actual PAS incidence. Postoperative shivering can be predicted with acceptable accuracy using the four risk factors identified in the present study. The presented model may serve as a clinical tool to help clinicians to rationally administer prophylactic antishivering drugs.

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