Key points.
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The clinical effect of drugs can differ markedly in patients at extremes of weight.
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Total body weight does not account for the distribution or metabolic activity of fatty tissue.
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Drugs that do not usually require weight-based dosing may require adjustment at extreme weights.
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Ideal or adjusted body weight is a useful measure to avoid errors in weight-based drug dosing.
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Data are lacking for many drugs at extreme weights, and careful titration to effect is needed.
Learning objectives.
By reading this article, you should be able to:
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Discuss the use of total, predicted, ideal, lean, and adjusted body weights for drug dosing.
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Describe the effects of increasing weight on total body weight, lean body weight, and adiposity.
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Explain the effects of obesity and low body weight on drug pharmacokinetics.
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Use different measures of body weight to calculate doses for drugs with reference to their pharmacological characteristics at extreme weights.
Excessively high or low body weight, with cellular imbalance between nutrient and energy supply and demand, causes physiological changes that may affect drug dosing, handling, and pharmacokinetic properties.1 Furthermore, the pathological changes resulting from extremes of weight and their associated co-morbidities affect the metabolism and elimination of many drugs. This paper summarises the measures used to characterise body weight, the physiological effects of extreme body weights, and their implications for pharmacokinetics and drug dosing in anaesthesia and critical care.
Defining extremes of body weight
Several classifications may be used to describe obesity and malnutrition, but these are deceptively complicated to define and categorise. Many classifications are derived from population-based estimates of ‘normal’ height, weight, and size. The pharmacokinetic implications of being over- and underweight originate from the mass of adipose tissue, its distribution, and coexistent medical problems (such as fatty liver). In practice, clinicians usually define obesity using BMI. BMI is defined as weight (kg)÷height2 (m), with obesity being defined as BMI >30 kg m−2 in the UK (Table 1).2 Although widely used, BMI does not quantify body composition or fat distribution. Individuals with increased body weight because of high muscle mass may be classified as obese by BMI, but may not have the pathophysiological changes related to excess adiposity. Coexistent conditions in those at extremes of body weight, such as chronic kidney disease, fatty liver, marasmus, or kwashiorkor, may also cause variations in underlying pathophysiology and drug handling.
Table 1.
Diagnosis and classification of obesity, and over- and underweight states based on recommendations by the National Institute for Health and Care Excellence CG1892
Category | BMI (kg m−2) | % IBW | |
---|---|---|---|
Underweight | <18.5 | <70–80% IBW | |
Normal | 18.5–24.9 | IBW | |
Overweight | 25–29.9 | ||
Obese | Class 1 | 30–34.9 | >120% IBW |
Morbidly obese | Class 2 | 35–39.9 | |
Class 3 | >40 | ||
Super morbidly obese | >55 |
In view of these shortcomings, other measures, including waist circumference and waist–hip ratio, are sometimes added to BMI for long-term cardiovascular risk stratification. This approach can be a useful alternative to BMI, as it accounts for the distribution of visceral fat and correlates with the risk of obesity-related diseases.2 The fat-free mass index (FFMI) is a measure of nutritional status that accounts for muscle mass and fat. Direct measurement of fat mass is impractical in clinical practice but FFMI can be approximated using the formula FFMI=fat mass (kg)÷height2 (m2).3 FFMI was validated in experimental starvation and is reduced in malnourished or underweight individuals, but is not validated in the diagnosis or categorisation of obesity. Although useful for risk stratification, BMI, FFMI, waist circumference, and waist–hip ratio are not useful for drug dosing.
Low-body-weight states are also challenging to define. Much of the available data are from children in lower-income countries suffering from protein–calorie malnutrition, marasmus, or kwashiorkor. Conversely, low body weight in higher-income countries occurs more often later in life, related to cancer, dementia, and malabsorption syndromes. The underlying aetiologies may affect the growth and development in the young, or contribute to frailty and susceptibility to illness later in life. Malnutrition at all life stages contributes to impaired drug handling.4
The diagnosis of abnormally low body weight can be based on BMI, percentage of ideal body weight (IBW), magnitude of weight loss, or the use of screening tools as composite measures. These tools use a combination of historical weight loss, objective measures, and current illness to define the extent of malnutrition. The subjective global assessment tool grades A–C for nourished, mild undernutrition, and severe malnutrition, respectively, based on weight change, dietary intake, gastrointestinal symptoms, and functional impact.5 Malnutrition leads to changes in composition and size of body compartments, and alterations in proteins and body water that can affect all aspects of drug handling.
The European Society of Parenteral and Enteral Nutrition defines criteria for the diagnosis of malnutrition based on BMI, weight, or FFMI.6 These criteria include: (i) BMI <18.5 kg m−2; (ii) unintentional weight loss >10%, or >5% within the past 3 months combined with either BMI <20 kg m−2 if aged <70 yr, or BMI <22 kg m−2 if aged ≥70 yr; and (iii) FFMI <15 kg m−2 in women and <17 kg m−2 in men.
Table 1 shows the UK guidelines for the diagnosis and classification of obesity, and over- and underweight individuals based on BMI and % IBW.
The use of BMI for diagnosing and categorising extremes of weight indexes disease severity and health risks in some populations, but it does not give a practical measure of body composition for use in drug dosing. Practical methods for drug dosing are shown in Table 2; their formulae use measures readily available in the clinical setting: sex, height, and TBW.
Table 2.
Measures used for calculating drug doses and associated formulae
Term | Derivation | Explanation | Use | Advantages | Disadvantages |
---|---|---|---|---|---|
TBW (kg) | LBW+FW | TBW, as measured, includes both fat and lean weight |
|
|
|
LBW (kg)7 | 1.1×TBW–0.0128×BMI×TBW (male); 1.07×TBW–0.0148×BMI×TBW (female) | LBW comprised of the non-adipose tissues |
|
|
|
IBW (kg)8 | 22×height2 (m) | The weight of an individual based on height and assuming a normal BMI of 22 |
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|
ABW (kg) | LBW+C×(TBW–LBW) (where C is a drug-specific correction based on the solubility of the drug) | Assumes drug distribution to lean tissues and a proportion of the FW depending on the physiochemical properties of the drug |
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|
|
Body surface area9 (m2) | Weight0.425×height0.725×0.007184 | Based on the surface area given height and weight, both of which are easily measured |
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|
PNWT10 (kg) | 1.57×weight−0.0183×BMI×WT−10.5 (male) 1.75×Weight−0.0242×BMI×WT−12.6 (female) |
Consists of lean and fatty weight, corrected for the non-obese individual, an extension of IBW |
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PBW (kg) | 50+[0.91×(height in cm–152.4)] (males); 45.5+[0.91×(height in cm–152.4)] (females) | Based on the original ARDSNet study, normalises to lung size; comparable to IBW |
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|
Effects of weight on body composition
In the normal-weight individual, the TBW comprises 20% fat weight (FW) and 80% lean body weight (LBW), or fat-free mass. In the underweight and malnourished individual, the lines for TBW and LBW converge, as FW accounts for a decreasing proportion of TBW (Fig. 1). Low body weight and protein–calorie malnutrition result in catabolism and loss of fat and lean mass, which affects protein binding, drug distribution, and clearance. During starvation, the loss of fatty tissue is greatest at first, but with increasing starvation, muscle and lean tissues are lost progressively. There is a gradual increase in total body water, with reduced protein synthesis resulting in oedema because of reduced intravascular oncotic pressure. Catabolism affects many metabolic processes, and can reduce metabolism and clearance by inhibiting the synthesis and function of hepatic enzymes.
Fig 1.
Changes in relative proportions of FW, LBW, and TBW with increasing BMI for a male 1.75 m in height.
With increasing TBW in the obese, LBW increases relatively slowly as the majority of the excess weight is because of adipose tissue, so there is a relative increase in both the TBW/LBW ratio (Fig. 1) and FFMI.11 With increasing body weight, total body water also increases, although at extremes of obesity, the proportion of total body water is reduced because most of the excess weight is FW.11
There are limited data describing the effects of severe malnutrition and low body weight on body composition. However, as TBW and LBW converge, the relative fatty mass decreases (Fig. 1).
FW comprises poorly vascularised adipose tissue into which lipid-soluble drugs may be sequestered, particularly with repeated doses or prolonged administration; such drugs have a high volume of distribution VD at steady state. This is of particular relevance in critical care, where prolonged infusion or repeated administration over several days of morphine, propofol, midazolam, and other drugs can result in a significantly delayed offset. After cessation of a prolonged infusion, as plasma or effect-site concentrations decrease, drugs redistribute into plasma and to their effect sites from fatty tissues, prolonging the effect and delaying the terminal elimination. These phenomena are more marked in the obese where FW is higher.
LBW predominately consists of muscle and vessel-rich organs or tissues, in which drug distribution and elimination are relatively fast. Lean tissues are highly active in metabolic terms, and the proportion of LBW is related to basal metabolic rate, drug metabolism, and clearance. Blood flow to fat accounts for 5% of cardiac output (although this is as little as 2% in the obese), compared to 22% for lean tissues.12, 13
Calculations for predicted body weight (PBW), IBW, and LBW are based on height and sex (Table 2). Therefore, in obesity, the calculated weight for drug dosing using height- and sex-derived measures in an individual of given height remains the same as that for a non-obese individual of the same height. Dosing drugs with high lipid solubility by predicted, ideal, or LBW in the obese could result in lower plasma concentrations compared to the non-obese. Where drugs have a low lipid solubility, dosing by adjusted body weight (ABW) takes into account the distribution into the excess fatty tissue by applying a drug-specific correction factor to the fat mass. Gentamicin, as an example of a hydrophilic aminoglycoside, is dosed by ABW to account for its relatively low distribution into fatty tissue. The use of IBW would result in low plasma concentrations, risking subtherapeutic dosing, as it does not account for the excess fat.
The implications of weight for clinical practice depend on individual patient- and drug-related factors. The lipid or water solubility of a drug is important, and the effects depend on the composition of body compartments. Water-soluble drugs often have a low VD, in contrast to more lipid-soluble drugs, which have a higher VD (Table 3).
Table 3.
Summary of changes in body composition with associated pharmacokinetic effects in obesity and malnutrition
Physiological variable | Change in obesity | Change in malnutrition | Pharmacokinetic consequence |
---|---|---|---|
Total body water | Increased | Increased | Increased VD for water-soluble drugs |
Fat mass | Increased | Reduced | Increased dose in the obese to account for increased VD |
LBW | Increased | Reduced | Increased clearance in obese patients |
Plasma proteins | Increased | Reduced | Increased plasma protein binding in obese patients |
Cardiac output | Increased | Reduced | Increased clearance in obese patients |
Applied pharmacology at extremes of body weight
Drug dosing
The traditional three-compartmental model describes the initial drug distribution into a central compartment before redistribution to peripheral compartments. Drugs may only be cleared or eliminated from the central compartment, although drug effect can terminate when the effect-site concentration decreases because of redistribution to other tissues. Distribution is not uniform, and depends on drug solubility, degree of ionisation, protein binding, and the blood flow between compartments. All of these are affected by obesity, malnutrition, and intercurrent illness. Therefore, the weight used to calculate drug dose is only one determinant of its pharmacological effect.
The combination of patient- and drug-related influences on pharmacokinetics can become significant where a drug has a narrow therapeutic range (risking toxicity), or requires time above a minimum plasma concentration (for some antibiotics). Drugs not usually dosed on a milligrams per kilogram body weight basis may require adjustment to account for the effects of high or low weights (such as the recommended reduction in the dose of paracetamol for patients with a TBW <50 kg).
Several measures based on weight, height, and sex can be used to calculate drug dosage (Table 2).7, 8, 9, 10 Those, such as TBW, fail to account for some changes in compartment size, and therefore, derived indices approximating to compartment size (such as IBW) or incorporating a correction for drug distribution (ABW) is preferable, especially where drug concentration is critical. The distribution and metabolism of adipose tissue are different in males and females; hence, some measures have sex differences.
Another approach is to adjust doses according to LBW, which correlates well with drug clearance. In the non-obese, the TBW approximates to LBW, so is often used without adjustment. However, with increasing weight, the relative proportion of FW increases significantly, so the LBW is much less than the TBW (Fig. 1). In obese patients, it is often preferable to use a measure of LBW, either assuming distribution to lean tissues only (IBW), or to some of the additional fatty mass (ABW), to account for drug lipid solubility. The predicted normal weight (PNWT) corrects for the additional adipose tissue, and takes into account height, weight, and sex. The PBW is derived from the early Acute Respiratory Distress Syndrome Network (ARDSNet) studies as a value to normalise lung size to weight for calculating ventilatory requirements. It includes a calculation of lean body mass, with an estimation of the additional adipose tissue expected in the non-obese individual, and is equivalent to the IBW or LBW. Neither PNWT nor PBW has a role in drug dosing at present.
In summary, the TBW is used where absolute plasma concentration is less important, and for drugs with a wide therapeutic window (e.g. succinylcholine) with a risk of overdosing hydrophilic drugs and underdosing hydrophobic drugs. The lean body mass approximates to the distribution of hydrophilic drugs and is best suited to their dosing (e.g. atracurium). The ABW is used for drugs with a narrow therapeutic window, where the effects of lipid distribution may significantly affect toxicity (e.g. gentamicin).
Pharmacokinetic changes at extremes of weight
Absorption and distribution
Gastrointestinal function is largely preserved in the obese with no significant differences in absorption or first-pass metabolism. However, in the low-body-weight patient, especially those who are malnourished, gastrointestinal absorption is reduced, which may be because of underlying malabsorption or as a consequence of refeeding syndrome.
The pharmacokinetic changes in drug distribution in the obese and malnourished are influenced by changes in the size and composition of tissue compartments and plasma protein binding. Increased peripheral compartment volumes can exacerbate drug sequestration within poorly perfused tissues in the obese; an example is the delayed offset of fentanyl after prolonged infusion because of saturation of poorly perfused peripheral compartments.14 In the malnourished, the increased total body water, reduced fat and lean masses, and reduction in protein synthesis result in an increased free fraction and VD of many drugs.
Metabolism
Drug metabolism depends on the site (hepatic, renal, and other sites), extraction ratio, and drug delivery. In obesity, cardiac output and hepatic and renal blood flow are all increased, so drug delivery for metabolism in these organs is greater. Therefore, the clearance and metabolism of flow-limited drugs with a high extraction ratio (e.g. propofol, morphine, and ketamine) are increased. Conversely, metabolic pathways may be inhibited by the effects of fatty liver disease or coexistent chronic kidney disease, or increased because of the effects of other medications, alcohol, or smoking. The effects of obesity on hepatic metabolism are inconsistent.
In extreme low-body-weight states, catabolism causes a loss of LBW and adipose tissue, accompanied by reduced albumin and total protein. The net result is a reduced resting metabolic rate, glomerular filtration rate (GFR), and protein binding. Reduced protein binding increases the fraction of unbound drug available for hepatic metabolism. Therefore, for drugs with a high extraction ratio, metabolism is relatively unaffected, whereas for those with a low extraction ratio, the higher free concentration may exceed the capacity for hepatic metabolism, resulting in higher plasma concentrations, a delayed offset, or prolonged terminal elimination.
Elimination and clearance
Clearance of drugs has been shown to correlate directly with LBW, which increases in obese states, and is reduced in the underweight because of catabolism. The increase in cardiac output related to obesity increases the GFR. In malnutrition, the GFR is relatively well preserved until the late stages.
Specific examples in anaesthesia
This section describes commonly used drugs in anaesthesia and critical care. I.V. anaesthetic agents, neuromuscular blocking drugs, local anaesthetics, and volatile agents are used to illustrate the general principles and differences between drugs (see Supplementary Table 1). For other drugs not discussed here, the same principles apply. Drugs should be dosed with caution where they have a narrow therapeutic window (e.g. gentamicin and vancomycin), and therapeutic drug monitoring is used where an assay is available. The interested reader is directed to sources from the UK Clinical Pharmacy Association, the Association of Anaesthetists, and the British National Formulary; these provide information on a wider range of drugs, the details of which are outside the scope of this paper.15, 16, 17
I.V. anaesthetic agents
The effect of an i.v. anaesthetic agent is determined by its effect-site concentration, which in turn depends on the initial dose, VD, and lipid solubility. Offset of clinical effect is determined by redistribution, which reduces the effect-site concentration. Cardiac output is the most important determinant of the onset and offset of i.v. anaesthetic agents; this correlates with LBW.
As both cardiac output and VD are increased in obesity, predictably, a higher bolus dose is required to achieve a given effect. The increased clearance and redistribution are proportional to LBW; therefore, LBW is recommended for use in bolus dosing the obese patient, and is supported in studies of propofol in obese patients.18 Studies examining the pharmacokinetics of anaesthetic agents in malnutrition are limited, but there is some evidence of enhanced effects of propofol for a given dose, as predicted by the reduced central VD.19
Where TIVA is used, the available models are all based on assumptions from the normal-weight population, but estimate the effect-site and plasma concentrations differently. Of the common TIVA models, none of the Marsh, Schnider, or Minto models are validated at extremes of weight; all require adjustment and careful titration.
Target-controlled infusion (TCI) propofol is commonly infused using the Marsh and Schnider models. The former is based solely on total weight and may cause overdosing unless a weight adjusted to IBW is input. The Schnider model uses age, height, weight, and sex to derive compartment sizes, and calculates a keo value (the rate constant for equilibration between plasma and effect-site concentrations) for individual patients; this results in lesser degrees of overshoot and cardiovascular instability. The Schnider model assumes a fixed central compartment size, whereas the Marsh model tailors this to patient's weight, causing significant differences between the models for doses to induce anaesthesia. Apparent differences in the calculated effect and plasma concentrations are less significant when dosing for prolonged infusions.
The Schnider model calculates the LBW from the TBW using James's formula. When the Schnider model is used, the anaesthetist inputs the patient's TBW, but should be aware that the internal algorithm is actually based on a calculated LBW. Conversely, the Marsh model uses no correction, so the LBW should be entered directly. However, both the Schnider and Marsh models become inaccurate at a BMI >42 kg m−2 for males and >37 kg m−2 for females. Hence, an alternative approach, if using the Marsh model, is to use Servin's formula [IBW+0.4×(TBW–IBW)]; this has been validated for propofol infusions in obese individuals. Servin's formula adjusts the IBW to account for the distribution of lipophilic drug into some of the excess fatty tissue, but the overall dose is increased compared with using the calculated LBW or IBW without adjustment. The Servin adjustment results in higher plasma concentrations, and this could cause exaggerated cardiovascular effects in the unfit, elderly, or compromised patient. These models have not been evaluated in low-body-weight states; the Schnider model administers less propofol for a given effect-site concentration and may be more appropriate for underweight patients. However, the use of TIVA at extremes of weight, regardless of model, requires caution, and depth of anaesthesia monitoring is recommended to prevent awareness and haemodynamic instability.
Opioids
Fentanyl is highly lipid soluble with a large VD, and its actions are dose dependent. After a bolus, it has a rapid onset and offset because of redistribution into peripheral tissues, and an increased clearance in obesity.20 However, high lipid solubility and VD cause significant accumulation in peripheral tissues after high doses, repeated boluses, or an infusion, resulting in prolonged elimination half-life, context-sensitive half-time and delayed offset of clinical effect.
The offset of alfentanil also increases after multiple doses or prolonged infusions, although this is related to its low intrinsic clearance. After bolus doses, the plasma concentration of alfentanil should be reduced because of increased cardiac output.21 In general, the context-sensitive half-time of less lipid-soluble drugs should be lower than that of more soluble drugs in obesity, because there is a reduced peripheral accumulation, but limited data are available.
Remifentanil differs in its pharmacokinetics, as it undergoes extensive and rapid tissue metabolism. Studies of the pharmacokinetics of remifentanil in the obese have shown that dosing by TBW can produce cardiovascular depression, and remifentanil dosing should be calculated using LBW.22 For TCI, remifentanil is modelled by the Minto model, which uses weight, height, and age, and corrects to LBW. In all patients, cautious titration is advisable.
Morphine is a classical opioid with a moderate onset and a slow offset of action. Its metabolites include the active compound morphine-6-gluconuride. The obese are at risk from opioid-related respiratory depression because of coexistent obstructive sleep apnoea and the risks of accumulation of both morphine and its active metabolite. This necessitates dosing by LBW and cautious titration and monitoring. Codeine is metabolised to morphine and presents similar risks of respiratory depression, particularly in those with obstructive sleep apnoea, further complicated by pharmacogenetic variations in metabolism to its active form.
Tramadol is an atypical opioid receptor agonist that also inhibits noradrenaline uptake. As a relatively weak opioid agonist, the risks of respiratory depression are theoretically lower. However, the active metabolite, O-desmethyltramadol, can cause significant respiratory compromise, and careful monitoring is required. There are no studies of the pharmacokinetics of tramadol in the obese.
Neuromuscular blocking agents
The metabolism of succinylcholine by plasma pseudocholinesterase is largely independent of organ function. However, reduced pseudocholinesterase caused by hepatic dysfunction may slow metabolism. Therefore, in the obese patient, where LBW, FW, and plasma volume increase, dosing by TBW ensures that an adequate dose is given, accounts for increased pseudocholinesterase activity, and ensures optimal intubation conditions.23
The non-depolarising neuromuscular blocking agents (NMBAs) are polar molecules that typically have a small VD and undergo hepatic and renal metabolism. The rate of metabolism is related to underlying LBW, and this is therefore the recommended dosing scaler. This is supported by studies showing that the duration of action for both ester and aminosteroid NMBAs is significantly prolonged where TBW is used for dosing in obesity.24 In the presence of malnutrition, there is a risk of pseudocholinesterase deficiency and liver damage with the potential for prolonged neuromuscular block with succinylcholine.
There are few data on sugammadex in the obese. Chelation of a drug distributing largely within the extracellular compartment predicts that dosing by IBW is appropriate. A weight-based study of sugammadex demonstrated that dosing by IBW+40% (an ABW, accounting for some distribution to fatty tissues) achieved an optimal time to reversal.25 Importantly, there was no failure of reversal or re-paralysis when dosing at IBW, IBW+20%, IBW+40%, or TBW.
Local anaesthetics
The distribution and binding of local anaesthetics are affected by nutritional state; increased α-acid glycoprotein may reduce the free fraction of local anaesthetics and increase dose requirements for nerve blocks in obesity, and central neuraxial block can be unpredictable because of the physical effects of obesity on the epidural space. In general, dosing by LBW is recommended, although guidelines vary (see Supplementary Table 1).16
Volatile anaesthetic agents
The effect-site concentration of volatile agents depends on pulmonary uptake, fraction of inspired anaesthetic agent, and cardiac output. In the obese, functional residual capacity (FRC) is reduced, cardiac output is increased, and the poorly vascularised peripheral compartment is increased in size. Predictably, therefore, the increased pulmonary uptake (reduced FRC) is offset by prolonged equilibration (increased cardiac output). Moreover, the effects of an increased peripheral compartment are mitigated by its relatively poor vascularity. For prolonged procedures, lipid-soluble agents may accumulate and have a prolonged offset time, but in practical use and for short procedures, there is little difference between the obese and non-obese.26 Studies in malnutrition are limited.
Summary
Drug dosing at extremes of body weight is challenging, and traditional methods using body weight or derived measures may lead to under- or overdosing and toxicity. Drugs typically given using a fixed dosing regimen may require dose adjustment at extremes of weight to account for changes in size, composition, and concomitant pathology. However, in the absence of measures to quantify compartment size, composition, and metabolism, clinicians should rely on IBW, TBW, and ABW. In general, IBW is the most appropriate for calculating dose, as it approximates clearance and metabolism, reducing the potential for overdose and toxicity. However, where there is a narrow therapeutic index, drugs should be dosed according to renal and hepatic function, and adjusted according to therapeutic levels (e.g. gentamicin) or clinical effect (e.g. i.v. anaesthetics and remifentanil). This area is complicated by a lack of evidence, as many pharmacokinetic studies have specifically excluded those at high or low body weights.
Declaration of interest
J.P.T. is the current Editor-in-Chief of BJA Education. This paper was handled by other members of the editorial board; he had no part in the reviewing process or in the decision to accept it for publication. C.P.H. has no declaration of interest.
Biographies
Christopher Hebbes BSc, MMedSci (Med Ed), FRCA, FFICM is a specialty trainee and clinical lecturer in anaesthesia and critical care at the University of Leicester and University Hospitals of Leicester NHS Trust. He has a longstanding interest in pharmacology, completing a BSc in opioid pharmacology.
Jonathan Thompson BSc (Hons), MD, FRCA, FFICM is Honorary Professor of anaesthesia and critical care at the University of Leicester, and a consultant at Leicester Royal Infirmary. He is a former editor of the BJA, current editorial board member of the BJA and Perioperative Medicine, and current Editor-in-Chief of BJA Education. He is an expert advisor for the British National Formulary and a past member of the Pharmacology Subcommittee of the European Society of Anaesthesiologists.
Matrix codes: 1A02, 2A12, 3I00
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.bjae.2018.09.001.
MCQs
The associated MCQs (to support CME/CPD activity) will be accessible at www.bjaed.org/cme/home by subscribers to BJA Education.
Supplementary material
The following is the Supplementary data to this article:
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