R. Ross Kennedy

The Impact of Obesity on Outcome after Major Colorectal Surgery

PurposeThere is an epidemic of obesity in the Western world and its associated substantial morbidity and mortality. This review examines the data on the impact of obesity on perioperative morbidity and mortality specifically in the context of colorectal surgery.MethodsMEDLINE, PUBMED, and the Cochrane library were searched for relevant articles. A manual search for other pertinent papers also was performed.ResultsThere is good evidence that obesity is a risk factor for wound infection after colorectal surgery. Obesity may increase the risk of wound dehiscence, incisional site herniation, and stoma complications. Obesity is linked to anastomotic leak, and obese patient undergoing rectal resections may be at particular risk. There is little data on the impact of obesity on pulmonary and cardiovascular complications after colorectal surgery. Operation times are longer for rectal procedures in obese patients, but hospital stay is not prolonged. Obese patients undergoing laparoscopic colorectal surgery are at increased risk of conversion to an open procedure.ConclusionsObesity has a negative impact on outcome after colorectal surgery. To further clarify the impact of obesity on surgical outcome, it is recommended that future studies examine grades of obesity and include measures of abdominal obesity.

The effect of epidural analgesia on postoperative outcome after colorectal surgery.

Objective  The aim of this review was to determine the effects of epidural analgesia as it relates to outcome after colorectal surgery.

Changing patterns in anesthetic fresh gas flow rates over 5 years in a teaching hospital.

BACKGROUND:Reducing anesthetic fresh gas flows can reduce volatile anesthetic consumption without affecting drug delivery to the patient. Delivery systems with electronic flow transducers permit the simple and accurate collection of fresh gas flow information. In a 2001 audit of fresh gas flow, we found little response to interventions designed to foster more efficient use of fresh gas. We compared current practice with our earlier results. METHODS:Flow data were collected in areas with a mix of general and acute surgery in March and November 2001, and again during 2006, by recording directly from the Datex ADU to a computer every 10 s. We extracted the distribution of flow rates when a volatile anesthetic was being administered. Data collection in March 2001 and 2006 was not advertised. RESULTS:In 2001, the mean flow rates were 1.95 and 2.1 L/min with a median flow of 1.5 L/min. In 2006, the mean was 1.27 and the median in the range 0.5-1.0 L/min. Isoflurane use decreased from 47% in 2001 to 4% in 2006. CONCLUSIONS:Fresh gas flows used in our department have decreased by 35% over 4 years. Although the absolute change in flow rate is not large, this represents potential annual savings of more than

The effect of a model-based predictive display on the control of end-tidal sevoflurane concentrations during low-flow anesthesia

US130,000. This occurred without specific initiatives, suggesting an evolution in practice towards lower fresh gas flow. Improvements in equipment and monitoring, including a locally developed system, which displays forward predictions of end-tidal and effect-site vapor concentrations, may be factors in this change.

Predictive accuracy of a model of volatile anesthetic uptake

We have shown that a multicompartment model accurately predicts end-tidal (ET) sevoflurane (sevo) and isoflurane concentrations. The model has been adapted to use real-time fresh gas flow and vaporizer settings to display a 10-min prediction of ET sevo concentrations. In this study, we evaluated the effect of the predictive display on the speed and accuracy of changes in ET sevo by the anesthesiologist. Fifteen patients were studied in whom sevo-based anesthesia was expected to last more than 2 h. Four step changes of target ET concentration (+0.5, +1.0, −1.0, and −0.5 vol%) were made either unaided or with the prediction display. Fresh gas flow was 1 L/min. Response time, maximum overshoot, and stability in the 5 min after the target was achieved were compared by using two-tailed paired Student’s t-tests. Changes were made on average 1.5–2.3 times faster with the predictive display than without it. These differences were statistically significant (P < 0.05) for the +0.5, +1.0, and −0.5 vol% step changes but not for the −1.0 vol% change. There were no differences in the degree of overshoot or stability. These differences are comparable to those seen with an automatic feedback control system. This system may simplify the administration of volatile anesthesia and the use of low-flow anesthesia.

A modified Trigg's Tracking Variable as an ‘advisory’ alarm during anaesthesia

A computer program that models anesthetic uptake and distribution has been in use in our department for 20 yr as a teaching tool. New anesthesia machines that electronically measure fresh gas flow rates and vaporizer settings allowed us to assess the performance of this model during clinical anesthesia. Gas flow, vaporizer settings, and end-tidal concentrations were collected from the anesthesia machine (Datex S/5 ADU) at 10-s intervals during 30 elective anesthetics. These were entered into the uptake model. Expired anesthetic vapor concentrations were calculated and compared with actual values as measured by the patient monitor (Datex AS/3). Sevoflurane was used in 16 patients and isoflurane in 14 patients. For all patients, the median performance error was −0.24%, the median absolute performance error was 13.7%, divergence was 2.3%/h, and wobble was 3.1%. There was no significant difference between sevoflurane and isoflurane. This model predicted expired concentrations well in these patients. These results are similar to those seen when comparing calculated and actual propofol concentrations in propofol infusion systems and meet published guidelines for the accuracy of models used in target-controlled anesthesia systems. This model may be useful for predicting responses to changes in fresh gas and vapor settings.

Seeing the future of anesthesia drug dosing: moving the art of anesthesia from impressionism to realism.

We wished to assess the accuracy of a modified form of Triggs Tracking Variable (TTV) at detecting the onset of changes in systolic blood pressure. A computer model generated systolic blood pressures which changed to a new value after period of stability. A separate algorithm based on TTV indicated when a ‘significant’ change had been detected. This signal occurred when TTV had exceeded a set limit (0.8–0.99) a predetermined number of times (1–10). Five anaesthetists were shown 40 sets of data generated by same the computer model and asked to indicate the onset of changes. The greatest number of changes (94.1%) were correctly identified when TTV exceeded 0.92 on 4 consecutive determinations. The onset of the trend was detected after an average delay of 140s. The anaesthetists correctly detected 85% of the changes after an average delay of 162s. There was no statistically significant difference between the anaesthetists and the algorithm, although only one anaesthetist performed better than the modified TTV. The modified TTV detected changes in simulated invasive systolic blood pressures faster and more accurately than four of a group of five anaesthetists. Such simple trend detection systems may be useful as ‘advisory’ alarms.

Preoperative core temperatures in elective surgical patients show an unexpected skewed distribution

The relationship among drug dosing, drug concentration, and drug response in anesthesia is complex and differs considerably from the steady-state equilibrium pharmacokinetics that we were taught in medical school. With inhalation anesthetics, end-tidal monitoring can give us an estimate of drug concentrations in the body, but we do not have any real-time monitor of drug concentrations for IV agents. We learn largely empirically the drug doses and concentrations that produce appropriate responses (or lack thereof) in a wide variety of patients; this is part of the art of anesthesia. The relationships between dose or concentration and response can be illustrated for teaching and in clinical practice using software packages such as StanPump, RUGLOOP, IVA-SIM, and TIVATrainer, which have been reviewed recently. These applications have been available online for some years and incorporate a wide range of pharmacokinetic and pharmacodynamic models. However, they remain essentially tools for teaching, research, or the enthusiast. It has been the incorporation of this technology into commercial clinical devices that has introduced and demonstrated these concepts to a wide range of users. This process began in 1996 in many parts of the world with the introduction of the Diprifusor (Astra Zeneca, United Kingdom) for administering a target-controlled infusion (TCI) of propofol. TCI devices allow the user to select a drug concentration that the pump tries to attain using a built-in pharmacokinetic model of the drug. The user can observe how the infusion rate changes to meet the set target. Many TCI pumps incorporate estimates of effectsite concentrations and some graphically display both the history and predictions of future concentrations (at the current infusion setting). More recently, “open TCI” pumps have expanded the concept to a wider range of drugs. Some of these devices allow the user to choose between various pharmacokinetic models for a drug. Different models of the same drug deliver different amounts of drug to attain the same (modeled) concentration. This is confusing for users, and poses the question of which model is the most accurate. In this issue, Masui et al. have explored this question by examining the performance of 4 different propofol models under a range of conditions. They looked at 3 conventional compartmental models and 1 physiologically based recirculation model. They also reviewed the history and derivation of these models, providing useful background information and context for the various models. Masui et al. concluded that “the Schnider model. . . has the fewest shortcomings.” This guarded recommendation suggests that we still have a way to go to find ideal models. Despite these limitations, TCI has been shown to be a useful approach to drug delivery. When a TCI pump is not available for a particular drug, or for volatile anesthetics for which target control of volatile concentration is relatively new, an alternative “advisory” approach utilizes embedded models that show drug concentrations in various compartments along with their history and predictions in the near future. The user adjusts drug delivery to achieve the desired predicted drug concentration. We have developed this approach for use with inhalation anesthetics. In our system, measured fresh gas flow rates and vaporizer settings are used as inputs to a validated uptake model, which is used to predict both end-tidal and effect-site concentrations over the following 10 minutes. Because the model is updated every 10 seconds, determining the inputs that produce a desired pattern, such as adjusting the vaporizer after reducing fresh gas flows or anticipating various stages of surgery or anesthesia, is straightforward. We have shown that this approach enables the user to make changes more rapidly and may facilitate the reduction of flow rates with a consequent significant cost reduction. Although TCI frees the user from the complexities of dosing, especially during induction, most systems attempt to achieve a given target as rapidly as possible. With an advisory system the user can directly control the rate of change, which may be an advantage at some stages of anesthesia. All of the above goes a long way to revealing the mysteries of pharmacokinetics in a clinically useful “realtime” setting for individual drugs. As has been discussed in the preceding editorials, and is illustrated by articles in this issue and others, drug interactions have a significant influence on various anesthetic end points. New devices incorporating these interactions, SmartPilot View (Drager Medical, Lubeck, Germany) and Navigator Suite (GE Healthcare, Helsinki, Finland), are the latest step in From the Department of Anaesthesia, Christchurch Hospital and University of Otago, Christchurch, New Zealand.

The effect of cardiac output changes on end-expired volatile anaesthetic concentrations - a theoretical study

PurposeTo document the preoperative core temperature of adult elective surgical patients.MethodsA prospective audit obtained sublingual temperatures from 446 adult elective surgical patients on arrival in the preoperative holding area.ResultsTemperatures ranged from 35.7°C to 37.8°C with a mean of 36.5°C (0.4 SD). The median was 36.4°C and the mode was 36.1°C. There was a skewed distribution with a clustering of values at the lower end of the range. All recordings were within the accepted normothermic range.ConclusionThe asymmetric distribution we observed differs from previously published normothermia data which shows a symmetrical distribution of temperatures. This skewed distribution has not previously been documented and we interpret it as being due to the effect of preoperative cooling factors.RésuméObjectifDocumenter la température centrale préopératoire de patients adultes en chirurgie élective.MéthodeUn audit prospectif a fourni les températures sublinguales de 446 patients adultes en chirurgie élective au moment de leur arrivée à l’unité préopératoire.RésultatsLes températures allaient de 35,7°Cà37,8°C selon une moyenne de 36,5 °C (écart type de 0,4). La médiane a été de 36,4 °C et le mode a été de 36,1 °C. Il y a eu une distribution asymétrique et un regroupement des valeurs à l’extrémité inférieure de la distribution. Toutes les valeurs enregistrées se situaient à l’intérieur d’un intervalle normothermique accepté.ConclusionLa distribution asymétrique observée diffère des données normothermiques publiées antérieurement et qui montraient une distribution symétrique des températures. Cette distribution asymétrique n’a pas été documentée antérieurement et nous croyons qu’elle résulte de l’effet de facteurs de refroidissement préopératoire.

The effect of using different values for the effect-site equilibrium half-time on the prediction of effect-site sevoflurane concentration: a simulation study.

Cardiac output is one of the major determinants of the rate of uptake, and therefore the end‐expired concentration (FE′) of volatile anaesthetic agents. The purpose of this theoretical study was to explore the effect of cardiac output changes on FE′ for a range of volatile anaesthetics. A multicompartment model of anaesthetic uptake and distribution which produces constant values of FE′ was used. The minimum detectable change in cardiac output was determined for a variety of anaesthetic agents for four patterns of cardiac output change. The effect of a step change in cardiac output from 5 to 10 l.min−1 was also recorded. The smallest cardiac output changes (average 33%) were detected with isoflurane. As blood solubility increased or decreased, larger cardiac output changes were needed before they could be detected. With a large step change in cardiac output and with increasing solubility, the final change in FE′ increased but the initial rate of change of FE′ is decreased. A significant cardiac output change will produce a change in volatile anaesthetic uptake. An unexpected change in FE′ should be considered as a possible signal of a sudden cardiac output change. The difference between agents may represent a balance between the amount of agent taken up and the size of the tissue ‘sink’ for that agent.