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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.
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Abstract |
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Introduction |
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Most PAS is simply normal thermoregulatory shivering that is triggered by hypothermia and preceded by arteriovenous shunts vasoconstriction. Shivering can also occur in normothermic patients who are developing fever (2). However, some shivering-like tremor during labor (3) and after general anesthesia (4) is not thermoregulatory. Although the etiology of this tremor remains incompletely understood, it is aggravated by inadequate pain control (5). Furthermore, some patients who are distinctly hypothermic do not shiver (6).
A recent meta-analysis of pharmacological interventions to prevent PAS suggested that in a population at risk for PAS, roughly four patients need to receive prophylactic clonidine or meperidine to prevent shivering in one patient (7). Even effective prophylactic interventions may be associated with large numbers-needed-to-treat if the incidence of the outcome of interest is infrequent. An accurate predictive model might reduce the number-needed-to-treat by obviating treatment in patients who are relatively unlikely to shiver. This would mean that fewer patients would need to be exposed to the cost and risk of treatment. Furthermore, a predictive tool can be used for scientific work, e.g., to ensure a homogenous risk for PAS between comparative groups in future studies. The purpose of our study was to develop an algorithm, considering other plausible contributors, for predicting PAS.
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Methods |
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The patients were assessed immediately after arrival in the postoperative care unit (PACU) with respect to the patient’s alertness, motor activity, hemodynamic and respiratory stability, pain, and any nausea or vomiting using the modified Aldrete recovery score (7,8). Core and peripheral temperatures were then recorded using a First Temp Genius Model 3000A aural canal thermometer (Sherwood Medical Company, St. Louis, MO). Tympanic membrane temperature (mean of measurements in both ears) was considered core temperature (Tcore). Mean peripheral skin temperature (Tskin) was derived from 8 measurements of skin temperature at 4 standardized points measured bilaterally using a formula presented by Ramanathan (9): Tskin = 0.3 (chest + deltoid) + 0.2 (thigh + leg).
In the postanesthetic holding area, patients were covered with a blanket but were not actively warmed. Patients were continuously observed for the occurrence of PAS for the first 15 min before and then at 3-min intervals for the following hour. All measurements were performed by the same specially trained observer (FD) to minimize observer bias. The intensity of PAS was graded using the scale described by Crossley and Mahajan (10): 0 = no shivering; 1 = no visible muscle activity but piloerection, peripheral vasoconstriction, or both are present (other causes excluded); 2 = muscular activity in only one muscle group; 3 = moderate muscular activity in more than one muscle group but no generalized shaking; 4 = violent muscular activity that involves the whole body. Patients were judged to have PAS when they displayed grade 3 or 4 activity for at least 3 min. These patients were asked to rate how cold they felt using a simple verbal rating scale (none, mild, moderate, severe). After this evaluation, PAS was treated at the discretion of the nursing staff with 25 mg meperidine (pethidine) or 75 to 150 µg IV clonidine.
Our main outcome was the occurrence of PAS (grade 3 or 4) during the first postoperative hour. Among the 1203 patients who were included in the final analysis, 8 had to be withdrawn from the analysis because of incomplete observational recordings. The data from the remaining 1340 patients were randomly split into an evaluation data set (n = 1000) and a validation data set (n = 340). Figure 1 shows the trial design.
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To reduce the number of variables to be included in the multifactorial model, a univariate statistic (
2 or Fisher’s exact tests for nominal or dichotomous data and Mann-Whitney U-test for continuous data) was calculated for each variable using the data of the evaluation data set. Variables with a P value of 0.20 or less or those identified in previous studies were defined as potentially relevant risk factors and were further subjected to a stepwise backward logistic regression analysis using the maximum likelihood function. Type of surgery was classified mainly according to the frequency, the anatomical location (peripheral, abdominal, urological, surface, head and neck, neurosurgical), the technique of the surgery (laparoscopic, endoscopic), and the complexity of the procedure (minor versus major surgery) based on the average opioid consumption of previous patients. The validity of the model was verified by comparing it with the results of a forward and a mixed forward-backward procedure. The goodness of fit of a model was judged using Nagelkerkes’s R2. Potential interactions between the independent variables were analyzed using the graphical tools provided by the JMP statistical software package (JMP 5.1; SAS Institute Inc., Cary, NC).
The factors included in the initial model were used to calculate the probability of shivering for each patient of the validation data set. The discriminating properties of the predictive model were judged by calculating the area under a receiver operating characteristic (ROC) curve, which was constructed by correlating true-positive and false-positive rates (sensitivity plotted against 1 – specificity) for a series of cut-off points defined as the predicted risk (Fig. 2). The area under the ROC curve represents the probability that a patient experiences PAS has a higher value than one who does not experience it (11). Theoretically a 45° bisector would yield a prediction score that was no better than a random guess. Thus, the area under this "random score" would be 0.5. A score performing significantly better than chance has an area under the ROC curve more than 0.5 with the lower limit of the 95% confidence interval (CI) exceeding the value of 0.5 (12).
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Calibration was judged by plotting predicted against the actual incidences. For this purpose, PAS risk was calculated for each patient in the validation data set. The patients were then divided into quintiles (39 patients each) of increasing risk. Predicted risk in each quintile was then plotted against observed risk in each group. Calibration characteristics were expressed as the slope (ideal: 1.0) and the offset (ideal: no offset) of the regression line. The Spearman rank correlation procedure was used to test the significance of agreement between the predicted and the actual incidences.
To set up a risk model and to extract variables with significant impact on PAS, an arbitrary number of 1000 patients was chosen. Based on results from a pilot study, we assumed there would be a 12% incidence of PAS. Thus, if 300 patients were used for validation of the risk model the power would be 98% to achieve an area under a ROC curve of 0.7 or higher (two-sided alternative hypothesis). We thus intended to include approximately 1300 patients in this survey in order to have 1000 for evaluation and about 300 for validation.
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Results |
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Based on the univariate statistical analysis for all recorded data, we determined that 26 factors were potentially related to the occurrence of PAS. These, and some basic biometric data of the patients, are listed in Table 1. Results of the postoperative assessment using the modified Aldrete score are presented in Table 2. Among the 26 potentially relevant factors that were included in the stepwise logistic regression analysis, 23 were removed because they proved to be no significant predictors at the 5% level. An identical model was achieved using a forward and a mixed forward-backward logistic regression analysis.
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The remaining 3 factors accounted for approximately 20% of the observed variation of the data (Nagelkerkes’s R2 = 0.192). These 3 variables are presented in Table 3, along with their odds ratios and 95% CIs. The ß-coefficient and the constant allow the calculation of a linear term z of which the negative value is used as the exponent of the logit equation: predicted risk = 100%/(1+e-z). Variables with a negative ß or an odds-ratio <1.0 are associated with reduced risk of PAS; these were older age and higher core temperature at PACU admission. Factors with a positive sign and an odds ratio more than 1.0, respectively, are associated with an increased risk of PAS. In the final model, endoprosthetic surgery compared with all other kinds of procedures (Table 1) increases the incidence of PAS: z = 17.5 – (0.531 · age in decades) – (0.462 · core temperature in °C) + [1.23 · (1 for endoprosthetic surgery or 0 for other surgery)].
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For example, when a 22-yr-old patient is admitted normothermic (36.5°C core temperature) to the recovery room after a nonendoprosthetic surgery then the linear term z can be calculated as follows: z = 17.5 – (0.531 · 3) – (0.462 · 36.5) + (1.23 · 0) = – 0.956.
When the negative of z (+ 0.956) is inserted into the logit equation then the predicted risk is 27.8%. A 72-yr-old patient under the same preconditions has a risk for PAS of only 8.5%. If both patients are admitted hypothermic (core temperature 35.0°C), then the risk to develop PAS for the young patient is increased to 43.5% and that for the older patient is increased to 15.6%.
This calculation was performed in each of the 340 patients of the validation data set. These patients did not differ from those in the evaluation data set with respect to biometric data or type and duration of anesthesia and surgery. The incidence of PAS grade 3 and 4 in. this validation data set was 12.7% and was thus very similar to the rate of the evaluation data set (11.6%). Figure 2 shows the calculated ROC curve. The area under the curve was 0.69 (95% CI, 0.60–0.78), indicating that PAS could be predicted with a moderate accuracy in these patients (P < 0.0001). The gray line indicates the decision criterion that can be used to judge the sensitivity and specificity respectively for each cut-off point. For example, if patients are classified as "shivering" even when the expected risk is low (e.g., 10%) then the score has a high sensitivity but a corresponding low specificity (the ROC curve is in the upper right corner of the diagram). Conversely, if patients are classified as shivering only when a high cut-off level (e.g., 40%) is exceeded, then the decision criterion has a low sensitivity but a high specificity (ROC curve runs in the lower left corner of the figure).
Figure 3 shows the results of the calibration analysis. Here, the predicted incidences (x-axis) that were grouped within five predicted risk quintiles are plotted against the observed incidences PAS of these patients (y-axis). Calibration characteristics are expressed as the slope and the offset of the regression line. This equation can be described by y = 0.69x + 6. The correlation coefficient indicates a moderate but statistically significant agreement between the predicted and the actual incidences (R2 = 0.82; P < 0.05).
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In Table 4 those 10 factors potentially influencing the occurrence of PAS that were removed during the last steps of the logistic regression analysis are presented. For these variables the P value and the step at which they were removed from the model are listed.
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Discussion |
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Using stepwise multivariate analysis, we identified three independent risk predictors of PAS: younger age, endoprosthetic surgery, and low core body temperature. Age proved to be by far the most important risk factor for PAS, accounting for more than 70% of the predictive power of our entire model. This was not surprising because the thermoregulatory responses to cold and heat are attenuated in older patients (2). For example, the vasoconstriction threshold during nitrous oxide/isoflurane anesthesia (14) and the shivering threshold during spinal anesthesia (15) are each decreased by about 1°C in the elderly.
It is difficult to extract independent risk factors for PAS in the perioperative setting, as numerous variables influence the postoperative course of the patients, perhaps most importantly, thermoregulatory impairment resulting from residual volatile (16) or IV anesthetics and sedatives (17). The situation is further complicated by co-linearities between risk factors. Using a univariate analysis, almost all drugs taken regularly by the patients (e.g., antihypertensive and antidiabetic drugs, cumarine derivates) seem to offer significant protection against PAS. This observation is not new. In fact, a significantly less frequent incidence of PAS in patients taking propranolol was reported in 1986 (18). Obviously, this kind of chronic medication treatment is much more frequent in older patients than in younger ones. Another example for co-linearity within the data set was the ASA physical status of the patients. Higher ASA classification is positively correlated with age and is thus supposed to be a significant protective factor in a univariate analysis, although it has only a minor impact in the multivariate model. Thus, by appropriately including age into the multifactorial model, numerous supposedly protective factors were removed early during the stepwise regression analysis.
The hypothermia that develops within the first hour after induction of general (19) or neuraxial anesthesia (20) results primarily from core-to-peripheral redistribution of body heat. Shivering and vasoconstriction are each 80% controlled by core temperature, with the remaining 20% being derived from mean skin temperature (21). It is thus reasonable to assume that hypothermia contributes to development of PAS, and normothermia is indeed protective in the absence of surgery (22). In the data analyzed by Crossley (13), core temperature of most patients who did not shiver postoperatively was not recorded and were thus unavailable for analysis. Most of our patients entered the PACU slightly hypothermic (with a mean core temperature of 35.8°C). However, 11.1% of the patients had core temperatures <35°C and core temperature was <34°C in 1% of the patients. When core temperature of patients who developed shivering and those who did not were compared, there was neither a clinically relevant nor a statistically significant difference (Table 1). However, when included in the multifactorial regression model, a significant influence was detected.
Our observation that core temperature has only a slight influence on PAS development compared with age as the most important determinate is consistent with other studies that have shown that PAS is poorly predicted by either core or peripheral body temperature (10,23). According to a multifactorial logistic regression analysis, absolute postoperative core temperature was not directly related to postoperative shivering in children whereas relative perioperative temperature change was one of three independent predictors (24). However, patients with a body temperature <36°C shivered for a longer time than those who were warmer (25), and at least one study found a linear relationship between PAS and esophageal temperature (26). A confounding factor in many studies, including ours, is suboptimal measurement of core temperature. Among infrared aural canal thermometers, the one we used (Kendall GENIUS) is among the most accurate (27). However, it is important to recognize that any inaccuracy in core temperature measurements degrades the apparent contribution of temperature compared with factors when measurement accuracy is minimal such as patient age.
Because simply covering patients with a blanket was reported to reduce PAS without altering core temperature (28), skin temperature has also been implicated as a causative factor for PAS; our data refute this assertion. In fact, only core temperature was retained in our final model whereas peripheral skin temperature and the derived mean body temperature were removed as insignificant variables. However, it is important to recognize that skin temperature dropped out because skin and core temperature are usually well correlated. There is no question that sufficiently increasing skin temperature alone can stop PAS (29).
We confirmed the previous finding (13) that orthopedic surgery, particularly endoprosthetic surgery using bone cement is an independent risk factor for the development of PAS. However the underlying biological reasons for this remain unclear. One possible explanation is that bone cement (polymethyl-methacrylate), which is often used in arthroplastic surgery, stimulates the release of cytokines such as 
-tissue necrosis factor and interleukin-6 (30), both of which can increase the set point of the thermoregulatory system postoperatively.
The question that arises from these possibilities is whether shivering in normothermic patients is caused by unknown intrinsic mechanisms of certain anesthetics or by an increase of the individual thermoregulatory set point that, in other words, might be described as continuing "postoperative fever." The first explanation is unlikely because we found no association between a certain anesthesia technique or anesthetic drugs and an increased incidence of PAS. For example, maintenance of anesthesia (volatile anesthetics versus propofol) was removed at step 19 with a P value of 0.39. However, Frank et al. (31) found that even in the absence of clinical signs of infection, 50% of postoperative patients reach core temperatures >38°C–38.5°C within the first 24 hours postoperatively. Furthermore, in the same study there was a positive association between the maximum temperature reached during the postoperative course and younger age and, as already discussed, younger age was the most important predictor for PAS in our model. In this context, it is interesting that all variables that were removed during the last steps of the regression analysis with P values close to 0.05 are indicators for an invasive, more painful surgery. Longer duration of surgery was associated with more frequent PAS, although this effect was corrected for a slightly lower core temperature in these patients by having the temperature variable still included at this step. After more invasive procedures more frequent PAS was observed, although this influence could not be proven on the 0.05% level. One possible explanation as to why duration of surgery predicts PAS is that these procedures are usually more complex and invasive. Injured tissue can release pyrogenic substances that, in turn, increase the set point of the thermoregulatory system postoperatively and, thus, induce PAS (31). In support of this theory, increased plasma concentrations of interleukin-6 are more often observed after longer and more invasive procedures than shorter ones and are associated with higher body temperatures during the postoperative period (31).
Several potential risk factors for PAS identified in previous studies were rejected by our analysis, including pain, male sex, and type of anesthesia. One review suggested that shivering, as an integral part of the thermoregulatory system, is tightly linked to other homeostatic systems, including pain control (2). The authors argue that pain and temperature signals are transmitted along similar fiber systems that both synapse in dorsal root horn regions. Furthermore most antishivering drugs have weak or moderate analgesic action (7,32). However, we did not observe any association between postoperative pain intensity and PAS. Pain, though, was well controlled in most of our patients, perhaps obscuring the importance of this factor in our analysis.
Male sex was also thought to contribute to the incidence of PAS (13), but we did not find that this to be an important factor. It was removed at an early step during the regression procedure. It may be that shivering is not more common in males but is more apparent to casual clinical observation because the larger muscle mass of males makes severe PAS especially impressive.
A limitation of our study is that we can not test the tempting hypothesis that postoperative inflammation may play an important role with our data because we did not measure temperature or inflammatory mediator concentrations as a function of postoperative time.
In conclusion, PAS can be predicted with moderate discriminating power using four risk factors derived from a logistic regression analysis. Age of the patient was the variable with the most predictive power by far. Other risk factors included low core temperature at admission to the PACU, prolonged surgery, and orthopedic surgery.
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Footnotes |
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Accepted for publication June 29, 2005.
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