1 Department of Anaesthesiology and Intensive Care Medicine, Philipps University, Marburg, 2 Olga-Hospital, Children's Hospital Stuttgart and 3 Ambulatory Surgical Centre Söflingen, Ulm, Germany
* Corresponding author: Department of Anesthesiology and Critical Care Medicine, Philipps University Marburg, Baldingerstrasse, 35043 Marburg, Germany. E-mail: eberhart{at}mailer.uni-marburg.de
Accepted for publication March 25, 2004.
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Abstract |
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Method. The predictive properties of five models were compared with respect to discriminating power (measured by the area under a receiver operating characteristic curve) and calibration (comparison of the predicted and the actual incidences of the disease by weighed linear regression analysis).
Results. The cumulative incidence of PV was 33.2% within 24 h. The discriminating power was low and insufficient in all models tested (0.560.65). Furthermore, the predicted incidences of the scores correlated only vaguely with the actual incidences observed.
Conclusion. Specialized scores for children are required. These might use the history of PV, strabismus surgery, duration of anaesthesia 45 min, age
5 yr and administration of postoperative opioids as independent risk factors.
Keywords: anaesthesia, paediatric ; model, mathematical ; vomiting and nausea, incidence ; vomiting and nausea, patient factors ; vomiting and nausea, surgical factors ; vomiting, patient factors
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Introduction |
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The incidence of this distressing problem can be reduced by using an i.v. anaesthetic technique instead of inhalation agents and by administering antiemetics prophylactically. However, routine efforts to prevent PONV are not indicated because of the potential for adverse effects and increased costs, and the lack of evidence that patient satisfaction is affected.7
For these reasons, tools to predict an increased risk of developing nausea and vomiting are certainly useful in clinical practice. Several scores have been developed for adults in the past, and external validation has demonstrated acceptable predictive properties.8 9 However, such an evaluation has not been performed in paediatric patients. Therefore, in this prospective observational survey, five published risk scores1014 were compared with respect to their ability to predict postoperative vomiting (PV) in children.
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Material and methods |
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After surgery, vomiting or retching was assessed in the postanaesthetic care unit (PACU) by specially instructed nurses or anaesthetists. The children and/or their parents were interviewed 24 h after surgery. Additionally, all medical recordings were screened and the nursing staff were asked not to miss an emetic episode. The parents of patients having day-care surgery were interviewed by telephone using a structured interview on the first postoperative day. The main end-point of the survey was the cumulative incidence of PV within 24 h after surgery.
Several risk models to predict PV or PONV have been published (see references 9 and 15 for an extensive overview). Of these models, a selection was made covering five models that were published as a full paper and allow calculation of an exact risk of PV or PONV during the first 24 h after surgery.1014 Table 1 provides an overview of these identified models. PV was chosen as the main end-point of the survey because nausea is a subjective phenomenon, and the smaller child often may not be able to describe it.16
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Discriminating power
For each patient, two probabilities of PV11 12 and four probabilities of PONV10 1214 were calculated. Using these predicted probabilities and the actual incidences of PV, a receiver operating characteristic (ROC) curve was drawn. The ROC curve can be constructed correlating true- and false-positive rates (sensitivity and 1specificity, respectively) for a series of cut-off points for a test. Here the cut-off point is a predicted risk. In the case of a child exceeding the cut-off point, it is classified as suffering from PV. The area under the ROC curve (AUC) represents the probability that a random pair of test results will be ranked correctly as to their disease state.17 Theoretically, a 45° bisector is a score that predicts no better than a random guess. Thus, the area under this random score would be 0.5. A score predicts significantly better than randomly when the lower limit of the 95% confidence interval for the AUC exceeds 0.5. All statistical calculations were performed using MedCalc (MedCalc Software, Mariakerke, Belgium).
Calibration curves
The calibration curves were constructed by correlating the predicted incidence of PV (or PONV) with the actual incidences of PV. To allow comparison of the different scores, the predicted risk of PONV or PV was clustered into four or five groups. The simplified scores10 12 provided these risk groups without further calculation. For the other three models,11 13 14 the predicted risks were categorized into five groups: 020%, 2040%, 4060%, 6080% and 80100%. This approach resulted in unequal numbers of children in each of the five groups, since most were predicted to have a low to moderate risk. Therefore, weighted linear regression analysis was used to compare the predicted and real incidences (Fig. 1).
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Results |
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The calibration curves (Fig. 1AF) compare the predicted with the actual incidences of PV/PONV. This type of analysis is useful when a group of patients rather than a single child is concerned. The curve is expressed by the slope and the offset, where y is the actual incidence of PV observed in the described paediatric population and x is the predicted incidence of PV or PONV calculated according to the risk score (Fig. 1).
Koivuranta's score to predict vomiting (Fig. 1E) shows the best calibration properties of all evaluated models. However, even with this risk score, the equation of the calibration curve yactual risk of PV=1.18xpredicted risk of PV+8 underestimates the actual risk by up to a quarter (e.g. the actual incidence of PV may be 80% when the predicted risk is 61%).
The explorative logistic regression analysis of potential relevant risk factors for the occurrence of PV in our paediatric population revealed that five variables were independent predictors of PV in children. These five risk factors are listed in Table 4 with their odds ratios and 95% confidence interval.
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Discussion |
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All risk scores that were evaluated in the present trial allow calculation of an exact risk. Previous external evaluations of these models using data from adult patients suggested that the discriminating power of the scores should exceed 0.650.7 before it can be judged as useful for clinical practice.8 9 Furthermore, it has been pointed out that it is of great importance that a risk model could be used easily in clinical practice to guarantee widespread use and acceptance by clinicians. In this context, two criteria are of great importance. First, there should be as few factors as possible that need to be remembered when the score is applied to a patient. Secondly, the recorded risk factors must be easily translatable into the patient's individual risk without complicated calculations. Only the score of Koivuranta and colleagues12 and the simplified score of Apfel and colleagues10 meet these requirements. To calculate both risk models, five factors must be explored. According to the number of factors present, the predicted risk can be determined (Table 1). For instance, using Koivuranta's score in adults, the expected incidence of PONV is 17% if no risk factor applies, and 18, 42, 54, 74 and 87% if one to five factors are present respectively.12 The other scores11 13 14 use a logit equation that cannot be calculated without the aid of a pocket calculator and thus hamper their clinical usefulness. The use of the model of Sinclair and colleagues14 is further complicated by the large number of factors (12) and the use of logical Boolean arithmetic.
Comparing the five available scores for predicting the probability of PV in paediatric patients undergoing general anaesthesia revealed that all models predict the occurrence of PV better than a random guess. This feature of a prognostic tool is called discrimination. It is represented by the area under a ROC curve that can be constructed correlating true- and false-positive rates (sensitivity and 1specificity, respectively). In all evaluated models this value exceeds 0.5 (even if the worst scenario is assumed, e.g. the lower limit of the 95% confidence interval is used). This 50% chance is the pre hoc probability that a random pair of two children (one child who will vomit after surgery and the other one will not) will be correctly classified as to their disease state. However, though statistically significant, a model that improves this pre hoc probability from 50% to values between 56%13 and 65%14 is far from being clinically relevant, even though surveying the five risk factors is not very time-consuming, and no invasive or expensive diagnostic procedures are needed. The two simplified scores have a discriminating power of 0.5810 and 0.6112 respectively and are thus not useful in children despite their ease of use.
In addition to discriminating power, calibration is the other feature of a prognostic score. Calibration is defined as the fit of the predicted incidences of a group of patients with the actual incidence of an event. As seen in Fig. 1, there is evidence that an increased risk, as predicted by the different scores, correlates with the actual incidence of PV. However, calculating a linear equation indicates that the calibration of all models was far from optimal. An optimal fit of this curve would result in a slope of 1.0 with no offset (e.g. resulting in the linear equation y=1.0x+0). The slope of most evaluated models was flat between 0.51 and 0.64,10 11 13 14 indicating that these scores overestimate the increase in PV when more risk factors are present. Only the PV model of Koivuranta and colleagues had a slope greater than 1.0,12 resulting in systematic underestimation of the actual rate of PV. All scores had a positive offset of 830%, indicating that a notable incidence of PV was present, even though these scores predicted a low risk.
This means that extrapolation of the risk that has to be expected for children with a certain mix of risk factors is far from being exact. At best, this information can be used to prepare guidelines when antiemetic prophylaxis is advisable. For instance, if there is agreement that children who have a risk of PV of 50% or even higher should receive antiemetic prophylaxis, this should be performed in children who present with four or more of the risk factors identified in the simplified model of Koivuranta and colleagues (Table 1). Using the latter example, this decision criteria would result in a sensitivity of only 5% but the specificity would be 98%. This means that only a few children would be falsely classified as vomiters, and thus the likelihood that a child receives an antiemetic without indication would be very low. On the other hand, many children will be falsely classified as non-vomiters but will develop PV.
There are several reasons for the disappointing predictive properties when the scores developed for adult patients were applied to children. Some of the risk factors used in the available risk scores to predict PV or PONV in adults are difficult to assess or do not usually apply in children. For instance, smoking has been identified as a protective factor in several investigations.11 12 14 Among our paediatric patients there was only one 12-yr-old boy who admitted smoking regularly. However, we did not ask about passive smoking. Younger age is a risk factor identified in adults;11 14 this applies in reverse to children. In agreement with previous findings,19 our data demonstrate that toddlers are less susceptible to emetic stimuli than schoolchildren.19 The odds ratio (OR) for developing PV is 1.6 in children aged 6 yr compared with younger children. Furthermore, even in older children sex does not play a major role in the occurrence of PV.20 In our stepwise logistic regression analysis, gender was removed from the model early as not being significant.
Certain types of surgery have often been considered to be associated with an increased risk of PV or PONV. However, of the risk models that were evaluated in the present analysis, only that of Sinclair and colleagues included some of these types of surgery.14 Of these, only ophthalmic procedures were identified as relevant in our paediatric patients. Since more than 90% of these procedures were strabismus surgery, a second analysis with this specific procedure revealed that, in our population, strabismus surgery but not eye surgery per se was associated with an increased risk (odds ratio 3.6). This is comparable with the result of Sinclair's model, with which ophthalmic procedures had an odds ratio of 5.9. Obviously, this agreement is one reason why the Sinclair score provided the best discrimination power of all tested risk scores.
Despite these limitations of the scores initially developed for adults, there are also several parallels between risk factors in children and adults. One example is a positive history of PV after previous surgery or motion sickness, which was observed in all but one model for adults. Obviously, the incidence of patients who had undergone previous surgery is lower than in an adult population. Thus, children are more likely to be classified as having a negative history of PV than adult patients. Furthermore, a history of motion sickness may be difficult to elicit in smaller children. Nevertheless, in the explorative logistic regression analysis, a history of previous PV or motion sickness was identified as an independent risk factor present also in children (odds ratio 2.6).
The administration of postoperative opioids and a longer duration of anaesthesia are two further examples of risk factors present in children and in adults. As in other investigations,10 11 we did not differentiate between different kinds of opioid analgesics. This was done for practical reasons but also under the assumption that there is no relevant difference in the emetogenic effects of different opioids.21 Administration of postoperative opioids was associated with an odds ratio of 1.8. The duration of anaesthesia was an even stronger risk factor for PV in children. Although duration of anaesthesia and surgery are highly correlated, previous risk models uniformly use duration of surgery to describe a higher risk in adults.11 12 14 In our patients, 45 min was the optimal cut-off that achieved the best prediction. The odds ratio for this risk factor was 2.8.
Because of the ease of calculation, only the models of Koivuranta and colleagues and Apfel and colleagues are of potential clinical relevance. However, none of the scores discriminated sufficiently in our study. Thus, specialized scores for children are required. These future risk models should consider the history of PV and of motion sickness, strabismus surgery, duration of anaesthesia, school age and the administration of postoperative opioids as potentially highly relevant risk factors. Furthermore, additional attention should be paid to other environmental and genetic factors, e.g. passive smoking and the susceptibility of close relatives to PONV.
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References |
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