Hypotension during induction of general anesthesia with propofol is common,1 and multiple factors have been implicated.2 Prolonged preoperative fluid abstinence might be expected to exacerbate this complication, as it does during inhaled induction in infants.3 However, no studies have addressed the impact of fluid abstinence on propofol-induced hypotension.
Dehydration may also affect drug pharmacokinetics,4 leading in turn to changes in apparent drug effect. In this prospective observational study, we aimed to determine whether the duration of preoperative fluid abstinence (hereinafter referred to as fasting time) was an independent contributor to the hemodynamic effects of propofol induction or to propofol dose requirements for unconsciousness.
After receiving local research ethics committee approval and written consent, we enrolled 130 ASA I or II patients, ages 18 to 65 years scheduled for surgery under general anesthesia. We excluded patients with a known history of hypertension and patients who on a single reading at preoperative assessment had a systolic arterial blood pressure ≥140 mm Hg or diastolic pressure ≥90 mm Hg.5 Other exclusion criteria included diabetes, pregnancy, deafness, obesity (body mass index >35), patients in whom a rapid sequence induction was indicated, recreational drug use (either regularly or within the last 48 hours), regular medication with central nervous system effects, or a weekly alcohol intake exceeding 21 U per week for men or 14 U per week for women. A unit of alcohol in the United Kingdom is defined as 8 g pure alcohol, namely the approximate alcohol content of a single measure of spirits or a 125-mL glass of wine.
Demographic data were recorded for each patient including age, gender, smoking status, and ethnicity. Patients were asked to select their ethnic group from a preprinted list used by the hospital for ethnic diversity monitoring, consisting of 70 individual designations grouped into 5 broader categories: White, Black/Black British, Asian/Asian British, “mixed background,” or “other ethnic group.” The last time at which the patient drank preoperatively was recorded.
Patients received no premedication. In the induction room, 3-lead electrocardiography, noninvasive blood pressure (NIBP) monitoring, and pulse oximetry were applied. A baseline mean arterial blood pressure (MAP) and heart rate (HR) reading were recorded. Each patient received oxygen at 4 l · min−1 through a Hudson facemask. A bispectral index (BIS) sensor was applied to the patient's forehead and connected to an A-2000 electroencephalographic (EEG) monitor with revision 3.31 software (Aspect Medical Systems, Norwood, Massachusetts). A smoothing time of 15 seconds was selected.
Induction of anesthesia was with Propofol–Lipuro 1% (B Braun Melsungen, Melsungen, Germany) as in a previous study.6 Propofol 40 mg · kg−1 · h−1 was administered using an IV infusion pump connected to a 20-G IV cannula in the patient's antecubital fossa. Where possible, the cannula was sited in the arm contralateral to the blood pressure cuff. NIBP and HR were recorded at 1-minute intervals from the start of propofol infusion. Throughout induction, the same recording was played to each patient at a standard volume, comprising the question “Are you awake?” repeated at 5-second intervals. Patients had been instructed in advance to answer “yes” to this question each time they heard it. The propofol dose at which each patient first failed to respond was recorded.
When the BIS was first below 50, the propofol dose was again noted (PDBIS50—our secondary endpoint) and the infusion rate reduced to 8 mg · kg−1 · h−1. In patients in whom, for surgical circumstances, the NIBP was sited in the same arm as was the IV infusion, recordings of MAP and HR were started at this point (BIS below 50) and continued as for all patients until 15 minutes after the start of induction. All data were recorded and manually transcribed to the patient's research record by an investigator blinded to fasting time.
For rescue treatment of hypotension during induction, we set a standardized threshold of >40% with MAP <70 mm Hg, or MAP <60 mm Hg regardless of baseline,2 at which 6 mg IV ephedrine was to be administered. If required, airway support without instrumentation was provided. Observations were discontinued after 15 minutes from induction. Anesthesia was then administered according to clinical indications and the personal preferences of the assigned anesthesia provider.
From the 15 minutes of data in each patient, we calculated our primary endpoint, the maximal percentage decrease from baseline MAP (max%ΔMAP). We also recorded the maximal percentage decrease in HR (max%ΔHR) and the time taken to reach the maximal percentage decrease in baseline MAP (t max%ΔMAP).
Statistical analysis was performed using SPSS 14.0 for Windows (SPSS, Chicago, Illinois). We first used univariate linear regression analyses to test in turn the effect of fasting time, gender, weight, age, smoking status, Black ethnicity, White ethnicity, baseline MAP, and baseline HR on both max%ΔMAP and PDBIS50. We tested the effect of PDBIS50 on max%ΔMAP in the same way. We also tested for correlation between fasting time and each of the other predictors.
To investigate potential effects of any outliers on the regression analysis of relationships between fasting time and max%ΔMAP and PDBIS50 respectively, we inspected Studentized residuals7 for both endpoints. The effect on the regression coefficients of excluding individuals with values >3 was examined.
Those variables for which univariate analysis demonstrated a significant effect (P < 0.1) were incorporated simultaneously with fasting time into a multivariate model for the relevant endpoint. Sum of squares tests were also conducted. Colinearity between a covariate and the others was determined using the tolerance statistic, calculated as 1 – R2, where R2 is the variation in the covariate explained by the others. Tolerance can lie between 0 and 1, with lower values indicating stronger colinearity. A tolerance below 0.2 was taken to indicate potential colinearity to be investigated.
The possibility of a nonlinear relationship between fasting time and our primary endpoint, max%ΔMAP, was investigated by testing an additional quadratic term (the square of fasting time in minutes) in the relevant multivariate model. In addition, the residuals from the multivariate model which adjusted for all significant factors except fasting time were plotted against fasting time. Quadratic and linear fit lines were added to this plot to allow any relationships to be visualized.
Demographic details and baseline characteristics are presented in Table 1. Mean (SD) fluid abstinence time was 696 (231) minutes (range = 115 to 1263 minutes). Transient BIS failure occurred during induction in 16 patients. None of the patients developed clinically significant hypotension, according to our chosen definition, requiring ephedrine during the study period. Airway support was necessary in 52 patients, and no one required manual assisted ventilation.
Preliminary linear regression analyses between predictors indicated significant correlation between fasting time and baseline MAP (r = 0.25), and between fasting time and weight (r = 0.25). Given this finding, a multivariate analysis was appropriate.
Mean (SD) max%ΔMAP was 25.6% (7.3%) and PDBIS50 was 143.9 (46.3) mg. On univariate analysis, duration of fluid abstinence (in minutes) was not significantly correlated with max%ΔMAP (in percentage) or PDBIS50 (in milligrams). Unstandardized regression coefficients (95% confidence intervals) were, respectively, 0.003% (−0.002% to + 0.009%) and 0.021 mg (−0.017 mg to + 0.059 mg). Figure 1 illustrates the lack of correlation between time of fasting and max%ΔMAP. The mean (SD) propofol dose at which each patient first failed to respond was 1.37 (0.33) mg · kg−1, tmax%ΔMAP was 10.5 (3.9) minutes and max%ΔHR was 12.8% (16.2%).
After univariate linear regression, all significant (P < 0.1) predictors of max%ΔMAP were then incorporated in a multivariate model with fasting time (Table 2). In this model, baseline MAP and weight retained significance. Table 2 shows that the regression coefficient for fasting time was little changed after adjustment for other, significant predictors with the effect of a 1-hour increase in fasting time on max%ΔMAP being −0.01% (95% CI: −0.26% to + 0.24%).
Table 2 also illustrates all significant univariate predictors of PDBIS50, which were subsequently included in the multivariate model with fasting time. Gender, weight, and baseline HR remained significant on multivariate analysis, PDBIS50 being greater in men and increasing with weight and baseline HR. As with max%ΔMAP, adjustment for significant predictors in the PDBIS50 model had little effect on the regression coefficient for fasting time, a 1-hour increase changing PDBIS50 by −0.38 mg (95% CI: −2.34 mg to + 1.58 mg).
Tolerance values for predictors included in the multivariate models were all >0.7, indicating no evidence of colinearity present.
We investigated further the influence of fasting time in the context of predictors in the multivariate models using sum of squares (SS) tests. These showed no significant association between fasting time and max%ΔMAP, or PDBIS50, whether assessed before or after adjustment of other significant predictors. Type II SS P values were, respectively, 0.94 and 0.70.
Analysis of outliers identified 1 individual in the max%ΔMAP distribution and 2 in the PDBIS50 distribution with Studentized residuals >3. Their exclusion from univariate regression analyses against fasting time had negligible impact on effect sizes. The effect of a 1-hour increase in fasting time on max%ΔMAP, excluding outliers, was 0.0% (95% CI: −0.23% to + 0.24%) and on PDBIS50, −0.11 mg (95%CI: −1.75 mg to + 1.52 mg) in the multivariate analysis.
To ascertain whether fasting time might be related to max%ΔMAP in a nonlinear fashion, the square of fasting time in minutes was added into the relevant multivariate model. No significant association was detected (P = 0.44). Figure 2 illustrates the relationship between residuals of the model (excluding fasting) and duration of fasting with quadratic and linear fit lines.
Our data indicate that fasting time in a relatively young, healthy adult population is not significantly correlated with the hemodynamic response to a rapid propofol infusion or with propofol dose requirements. In our cohort, fasting times ranged from just under 2 hours to >21 hours.
It is widely assumed that prolonged preoperative fasting can cause a decrease in circulating blood volume as a result of ongoing urine production and insensible perspiration.8 The autonomic and hormonal effects of dehydration on the cardiovascular system are well known and might be expected to exacerbate propofol-induced hypotension, which is itself partly mediated by the sympathetic nervous system.
Despite this, studies addressing prolonged fasting and the hemodynamic responses to induction of anesthesia have been limited to the pediatric population.3,9 Friesen et al.3 found limited evidence for an effect of fasting time on hypotension during halothane induction in infants. The authors conceded that despite the enrollment of 250 patients, the study was not adequately powered because of multiple age grouping and great variability in the number of subjects in each fasting group.
Our failure to demonstrate a relationship between fasting time and max%ΔMAP or PDBIS50 raises the question of statistical power in the current study. Post hoc power calculations indicate that our sample size of 130 provided 99% power to detect correlation at R2 = 0.13 between a single predictor, such as fasting time, and max%ΔMAP. In the multivariate model we had a >90% power to predict a correlation at R2 = 0.13 with at least 3 predictors. Our failure to detect a significant correlation at R2 = 0.13 between fasting time and either of our 2 endpoints does not preclude a smaller effect. However, the study has provided 95% CIs within which the effect in the wider population is estimated to lie: the effect of a 1-hour increase in fasting time on max%ΔMAP was −0.01% (−0.26% to 0.24%) and on PDBIS50, −0.38 mg (−2.34 mg to + 1.58 mg). Independent of statistical analysis, Figure 1 would also indicate that if there was a relationship between max%ΔMAP and fasting time, this relationship would be small.
It is possible that the use of invasive, rather than noninvasive, arterial blood pressure monitoring may have better captured the max%ΔMAP. However, studies have demonstrated that in the majority of people, hypotension occurring after propofol induction is not precipitous.1,2 Bearing in mind the ethical implications of arterial cannulation in our healthy patients, we chose to use frequent cuff measurements instead.
Our induction protocol, with an initial infusion rate of 40 mg · kg−1 · h−1, may have led to less profound cardiovascular effects than would have been seen with a propofol bolus. Evidence from several studies suggests that induction speed does not influence propofol's effect on arterial blood pressure in patients ages 18 to 65 years,10 18 to 60 years,11 18 to 55 years,12 18 to 50 years, 13and 60 years,13 though an effect is seen in older patients.14 This does not preclude the possibility of an effect on blood pressure caused by an interaction between induction speed and fasting time in younger patients.
In this study, fasting time did not influence propofol dose requirements for a designated EEG effect. Propofol dose requirements for induction are predicted by several factors. These include central blood volume15 and, as has been shown in animal models, increased sympathetic activity induced by catecholamine infusions that alter the drug's pharmacokinetics.16 Although dehydration might be expected to influence both central blood volume and plasma catecholamine concentrations, we suspect that these changes were too modest to result in any subsequent pharmacokinetic changes and thus clinical sequelae.
Of the variables we tested in the current study, the significant univariate and multivariate predictors of max%ΔMAP and PDBIS50 were the same as in a previous study.6 In the multivariate models, baseline MAP and HR were positively associated with max%ΔMAP and PDBIS50, respectively. Though we did not measure anxiety scores, it is possible that the more anxious patients in the current study had higher baseline MAP and HR. An acutely raised baseline MAP, consequent on anxiety, might conceivably exacerbate the hypotensive effect of propofol at induction. A similar anxiety-related tachycardia might increase the dose of propofol required to attain a given level of EEG suppression, by pharmacokinetic or pharmacodynamic means. Whichever is the case, our finding with respect to HR is consistent with the observed reduction in propofol dose required for induction in patients receiving esmolol.17 Using the State–Trait Anxiety Inventory (STAI), we have previously failed to demonstrate an independent effect of anxiety on either max%ΔMAP or PDBIS50 during propofol induction. This may, however, have been due to limitations of the STAI in the preoperative setting.6
Though PDBIS50 was not itself significantly correlated with max%ΔMAP in the current study, and others have found the propofol induction dose to be unrelated to the likelihood of hypotension,1,2 our finding that weight was an independent predictor of both max%ΔMAP and PDBIS50 implies that propofol dose may indeed be relevant to both of these phenomena. We also found PDBIS50 to be less in women than in men. This concurs with the finding by others that the predicted propofol effect-site concentration at which 50% of individuals lose consciousness is higher in men than in women.18
A final point relates to our treatment of fasting time as an independent variable. While it might be argued that clinicians somehow determine fasting time, we believe that the likelihood that this introduced significant bias in our study is small. The great majority of our patients were ambulatory, and all received the same information regarding preoperative fasting before the day of surgery. They were instructed to fast from 6 hours before the proposed time of surgery, excepting water, which they were permitted until 2 hours before. The fact that many fasted considerably longer than this may have been due to a number of factors, including lack of understanding or unanticipated changes in the order of patients on operating lists.
Our finding that fasting time was significantly correlated with 2 other potential predictors, weight and MAP, was unexpected. There are a number of possible physiological explanations that might merit subsequent investigation, but our intention in using multivariate analysis was to consider any possible relationships between predictors in determining which were independently associated with max%ΔMAP and PSBIS50.
In conclusion, in young healthy patients receiving a rapid propofol infusion for induction of anesthesia, we failed to demonstrate significant relationships between fasting time and changes in MAP or propofol dose requirements to achieve a BIS50.
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