ArticlePDF Available

# Estimating Ideal Body Weight – A New Formula

Authors:

## Abstract

A simple formula for estimating ideal body weight (IBW) in kilograms for both men and women is presented. The equation IBW = 22 x H2, where H is equal to patient height in meters, yields weight values midway within the range of weights obtained using published IBW formulae.
1082 Obesity Surgery, 15, 2005 © FD-Communications Inc.
Obesity Surgery, 15, 1082-1083
A simple formula for estimating ideal body weight
(IBW) in kilograms for both men and women is pre-
sented. The equation IBW = 22 x H2, where H is equal
to patient height in meters, yields weight values mid-
way within the range of weights obtained using pub-
lished IBW formulae.
Key words: Ideal body weight, obesity, drug dosage
Many medications are administered on the basis of
ideal body weight (IBW). This is especially impor-
tant in the morbidly obese patient because certain
classes of drugs with poor lipophilicity and narrow
therapeutic indexes, when administered on the basis
of total body weight (TBW), can lead to over-
dosage and drug toxicity.1For patients smaller than
IBW,simply scaling the dose of drug to TBW is
appropriate, because IBW and TBW approximate
each other. However, for morbidly obese patients
who are significantly larger than IBW, drug dosages
should be scaled to IBW, or IBW plus some fraction
of the difference between TBW and IBW.2How can
IBW be determined?
The concept of IBW was initially proposed by the
Metropolitan Insurance Company to describe a
range of weights associated with longevity for men
and women of different heights.3Although IBW
tables are available, few clinicians use them and
most rely on one of many complicated formulae to
estimate IBW.4-8 There is no absolute correct IBW
for any individual, and each IBW equation will give
a weight that differs for the same patient. IBW also
varies for different populations, and within the same
population at different times. For example, with
each new American generation, height-weight
tables have demonstrated a trend for adults to weigh
more than their predecessors while experiencing
similar or even greater longevity.3
We propose a simple approach to estimate IBW
based on the body mass index (BMI). BMI is calcu-
lated by dividing the patient's weight in kilograms
(kg) by the square of their height (H) in meters (m)
(BMI = kg / H2). A BMI between 20-25 is consid-
ered “normal” weight range. An equation, BMI =
IBW / H2or IBW = BMI x H2can be constructed to
reflect this. A similar concept was recently used to
estimate “normal weight” for fluid administration.9
Using a range of BMI values, we found that for
both men and women, IBW = 22 x H2yields weights
that fall midway within and “best fits” the range of
values obtained with accepted IBW formulae (Figure
1). We propose this formula as an extremely simple,
rapid, and reproducible means of estimating IBW.
References
1. Cheymol G. Effects of obesity on pharmacokinetics
implications for drug therapy. Clin Pharmacokinet
2000; 39: 215-31.
2. Bouillon T, Shafer SL. Does size matter? (Editorial).
Anesthesiology 1998; 89: 557-60.
3. Pai MP, Paloucek FP. The origin of the ‘ideal’ body
weight equations. Ann Pharmacother 2000; 34: 1066-9.
4. Broca PP. Memoires d'anthropologie. Paris 1871 / 1877.
5. Devine BJ. Gentamicin therapy. Drug Intell Clin
Pharm 1974; 8: 650-5.
6. Robinson JD, Lupkiewicz SM, Palenik L et al.
Brief Communication
Estimating Ideal Body Weight – A New Formula
Harry J. M. Lemmens, MD, PhD; Jay B. Brodsky, MD; Donald P. Bernstein, MD
Department of Anesthesia, Stanford University School of Medicine, Stanford, CA, USA
Reprint requests to: Jay Brodsky, MD, Department of Anesthesia,
H3580, Stanford University Medical Center, Stanford, CA, 94305,
USA. Fax: 650-725-8544; e-mail: Jbrodsky@stanford.edu
Delivered by Ingenta to: Stanford University Libraries (cid 5000191)
127.0.0.1
on: Thu, 11 Aug 2005 17:44:28
Obesity Surgery, 15, 2005 1083
Estimating Ideal Body Weight – A New Formula
Determination of ideal body weight for drug dosage
calculations. Am J Hosp Pharm 1983; 40: 1016-9.
7. Miller DR, Carlson JD, Loyd BJ et al. Determining
ideal body weight. (Letter). Am J Hosp Pharm 1983;
40: 1622.
8. Deitel M, Greenstein RJ. Recommendations for report-
ing weight loss (Editorial). Obes Surg 2003; 13: 159-60.
9. Kabon B, Nagele A, Reddy D et al. Obesity decreases
perioperative tissue oxygenation. Anesthesiology
2004; 100: 274-80.
(Received April 12, 2005; accepted May 20, 2005)
Figure 1. Estimated values for ideal body weight (IBW) for both males (A) and females (B) are shown using a variety of
published formulae. The equation IBW = 22 x H2yields the best fit for both men and women.
Height (cm)
A Males
IBW (kg) Formulae:
56.2 kg (60 inch) + 1.41 kg/inch (ref. 7)
50 kg (60 inch) + 2.3 kg/inch (ref. 5)
52 kg (60 inch) + 1.9 kg/inch (ref. 6)
height (cm) - 100 (ref. 4)
(1 inch = 2.54 cm)
A = 23 x Height (m)2
B = 22 x Height (m)2
A = 21 x Height (m)2
Ideal Body Weight (kg)
120
100
80
60
Height (cm)
B Females
IBW (kg) Formulae:
53.1 kg (60 inch) + 1.36 kg/inch (ref.7)
45.5 kg (60 inch) + 2.3 kg/inch (ref. 5)
49 kg (60 inch) + 1.7 kg/inch (ref. 6)
height (cm) - 105 (ref. 4)
(1 inch = 2.54 cm)
A = 23 x Height (m)2
B = 22 x Height (m)2
A = 21 x Height (m)2
Ideal Body Weight (kg)
100
90
80
70
60
50
Delivered by Ingenta to: Stanford University Libraries (cid 5000191)
127.0.0.1
on: Thu, 11 Aug 2005 17:44:28
... When evaluating energy, protein, fat, and carbohydrate intake, the intake per kilogram of ideal body weight (IBW) was calculated to consider the differences in body size. IBW was calculated as 22× the square of the participant height (m 2 ) [33]. Habitual alcohol intake was defined as alcohol consumption >20 g/day in the BDHQ analysis [34]. ...
Article
Full-text available
Smoking affects eating habits; however, few studies on smoking and the gut microbiota have reported the effects of diet in detail. This cross-sectional study aimed to determine the association between smoking and the gut microbiota, considering the impact of smoking on dietary intake. Dietary habits and the composition of the gut microbiota were assessed in 195 men with type 2 diabetes (164 non-current smokers and 31 current smokers) using a brief self-administered diet history questionnaire and 16S ribosomal RNA gene sequencing of fecal samples. The data were compared according to the current smoking status of the participants. Current smokers had high alcohol and sugar/sweetener intake and low fruit intake. The proportion of the Coprococcus genus was higher among current smokers. Multiple regression analysis adjusted for current smoking, age, exercise habits, alcohol intake, sugar and sweetener intake, and fruit intake showed that smoking was associated with the proportion of the Coprococcus genus. Current smoking was associated with both dietary intake and composition of the gut microbiota. Although dietary intake should be considered when investigating the association between smoking and the gut microbiota, the results suggest that the direct effect of smoking is more significant.
... In this study, total energy intake (kcal/day) was used to adjust for covariates. Because total energy intake (kcal/day) is influenced by body size, we also used the total energy intake per ideal body weight (kcal/ideal body weight/day) as a covariate [33]. ...
Article
Full-text available
Many beverages include bioactive components and energy but are frequently not considered in diet quality estimations. We examined the association of a healthy beverage score (HBS) with incident frailty in older adults from the Seniors-ENRICA-1 cohort. We used data from 1900 participants (mean ± SD age 68.7 ± 6.4 years, 51.7% women), recruited in 2008–2010 and followed-up until 2012 assessing food consumption at baseline with a validated diet history. The HBS was higher for increasing consumption of low fat milk, tea/coffee, lower consumption of whole milk, fruit juice, artificially sweetened beverages, sugar-sweetened beverages, and moderate intake of alcohol. Frailty was considered as having ≥3 criteria: exhaustion, low-physical activity, slow gait speed, weakness, and weight loss. We performed logistic regression analyses adjusted for potential confounders. During a 3.5 y mean follow-up, 136 new cases of frailty occurred. Compared to the lowest sex-specific HBS tertile, the fully adjusted odds ratio (95% confidence interval) of frailty was 0.59 (0.38, 0.92) in the intermediate tertile, and 0.52 (0.31, 0.88) in the highest tertile, p trend = 0.007. Results for slow gait speed were 0.79 (0.58, 1.07) and 0.71 (0.51–0.99), p trend = 0.033. Therefore, adherence to HBS was inversely associated with incident frailty and slow gait speed. HBS can help on the beverage quality evaluation, highlighting beverage importance as contributors to diet and to health.
... Body mass index (BMI) was obtained as follows: body weight (kg) divided by height squared (m 2 ). Ideal body weight (IBW) was determined as follows: IBW (kg) = 22 × (height [m]) 2 [24]. ...
Article
Full-text available
Household income was related to habitual dietary intake in general Japanese people. This cross-sectional study investigated the relationship between household income and habitual dietary intake in people with type 2 diabetes mellitus (T2DM). Household income was evaluated using a self-reported questionnaire and categorized into high and low household income. Nutritional status was assessed using a brief-type self-administered diet history questionnaire. Among 128 men and 73 women, the proportions of participants with low household income were 67.2% (n = 86/128) in men and 83.6% (n = 61/73) in women. Dietary fiber intake (11.3 ± 4.2 vs. 13.8 ± 6.0 g/day, p = 0.006) was lower, and dietary acid load, net endogenous aid production score (NEAP) (51.7 ± 10.5 vs. 46.8 ± 10.4 mEq/day, p = 0.014) and potential renal acid load score (PRAL) (9.5 ± 10.7 vs. 3.7 ± 14.1 mEq/day, p = 0.011) were higher in men with low household income than in those without. Multivariable linear regression analyses demonstrated that log (dietary fiber intake) in men with low household income was lower than that in those with high household income after adjusting for covariates (2.35 [2.26–2.44] vs. 2.52 [2.41–2.62], p = 0.010). Furthermore, NEAP (54.6 [51.7–57.4] vs. 45.8 [42.5–49.2], p <0.001) in men with low household income were higher than in those with high household income after adjusting for covariates. Contrastingly, household income was not related to diet quality in women. This study showed that household income was related to dietary fiber intake and dietary acid load in men but not in women.
... Body mass index (BMI) was evaluated by dividing body weight (kg) by height 2 (m 2 ). Ideal body weight (IBW) was calculated as 22 × (square of the participant's height [m 2 ]) (25). In addition, data on medications, including antidiabetic and antihypertensive drugs, were gathered from patients' records. ...
Article
Full-text available
Objectives Non-alcoholic fatty liver disease (NAFLD), which has a close relationship with type 2 diabetes (T2D), is related to salt intake in the general population. In contrast, the relationship between salt intake and the presence of NAFLD in patients with T2D has not been clarified. Methods Salt intake (g/day) was assessed using urinary sodium excretion, and a high salt intake was defined as an intake greater than the median amount of 9.5 g/day. Hepatic steatosis index (HSI) ≥ 36 points was used to diagnosed NAFLD. Odds ratios of high salt intake to the presence of NAFLD were evaluated by logistic regression analysis. Results The frequency of NAFLD was 36.5% in 310 patients with T2D (66.7 ± 10.7 years old and 148 men). The patients with high salt intake had a higher body mass index (25.0 ± 4.0 vs. 23.4 ± 3.8 kg/m ² , p < 0.001) than those with low salt intake. HSI in patients with high salt intake was higher than that in patients with low salt intake (36.2 ± 6.2 vs. 34.3 ± 5.5 points, p = 0.005). In addition, the presence of NALFD in patients with high salt intake was higher than that in patients with low salt intake (44.5% vs. 28.4%, p = 0.005). High salt intake was associated with the prevalence of NAFLD [adjusted odds ratio, 1.76 (95% confidence interval: 1.02–3.03), p = 0.043]. Conclusion This cross-sectional study revealed that salt intake is related to the prevalence of NAFLD in patients with T2D.
... Body weight (kg) and appendicular muscle mass (kg) were measured, and the body mass index (BMI, kg/m 2 ) and skeletal muscle mass index (SMI, kg/m 2 ) were calculated, which were defined, respectively, as body weight (kg) ÷ height-squared (m²) and appendicular muscle mass (kg) ÷ height-squared (m 2 ). Next, ideal body weight (IBW) was estimated using the following formula: 22 × height-squared (m²) (26). Furthermore, definition of SMI decrease (kg/m²/year), the rate of SMI decrease (%) were follows; [baseline SMI (kg/m²)follow-up SMI (kg/m²)] ÷ follow-up duration (year) and [SMI decrease (kg/m²/year) ÷ baseline SMI (kg/m²)] ×100. ...
Article
Full-text available
Background and Aims Maintenance of muscle mass is important for sarcopenia prevention. However, the effect of eating speed, especially fast, normal, or slow speed, on muscle mass changes remains unclear. Therefore, the purpose of this prospective study was to investigate the effect of eating speed on muscle mass changes in patients with type 2 diabetes (T2DM). Methods This study included 284 patients with T2DM. Based on a self–reported questionnaire, participants were classified into three groups: fast–, normal–, and slow–speed eating. Muscle mass was assessed using a multifrequency impedance body composition analyzer, and skeletal muscle mass (SMI) decrease (kg/m ² /year) was defined as [baseline SMI (kg/m ² )–follow–up SMI (kg/m ² )] ÷ follow–up duration (year). The rate of SMI decrease (%) was defined as [SMI decrease (kg/m ² /year) ÷ baseline SMI (kg/m ² )] × 100. Results The proportions of patients with fast–, normal–, and slow–speed eating were, respectively, 50.5%, 42.9%, and 6.6% among those aged <65 years and 40.4%, 38.3%, and 21.3% among those aged ≥65 years. In patients aged ≥65 years, the rate of SMI decrease in the normal (0.85 [95% confidence interval, CI: −0.66 to 2.35]) and slow (0.93 [95% CI −0.61 to 2.46]) speed eating groups was higher than that in the fast speed eating group (−1.08 [95% CI −2.52 to 0.36]). On the contrary, there was no difference in the rate of SMI decrease among the groups in patients aged <65 years. Compared with slow speed eating, the adjusted odds ratios of incident muscle loss [defined as rate of SMI decrease (%) ≥0.5%] due to fast– and normal–speed eating were 0.42 (95% CI 0.18 to 0.98) and 0.82 (95% CI 0.36 to 2.03), respectively. Conclusion Slow–speed eating is associated with a higher risk of muscle mass loss in older patients with T2DM.
... Body mass index (BMI, kg/m 2 ) was calculated by dividing body weight in kilogram (kg) by height in meters squared (m 2 ). Ideal body weight (IBW) was regarded as height in meters squared multiplied by 22 (29). Percent body fat mass (%) was calculated as (body fat mass [kg]/body weight [kg]) × 100. ...
Article
Full-text available
Objectives To investigate the relationship between dietary fiber intake and skeletal muscle mass, body fat mass, and muscle-to-fat ratio (MFR) among men and women with type 2 diabetes (T2D). Methods This cross-sectional study involved 260 men and 200 women with T2D. Percent skeletal muscle mass (%) or percent body fat mass (%) was calculated as (appendicular muscle mass [kg] or body fat mass [kg]/body weight [kg]) × 100. MFR was calculated as appendicular muscle mass divided by body fat mass. Information about dietary fiber intake (g/day) was obtained from a brief-type self-administered diet history questionnaire. Results Dietary fiber intake was correlated with percent body fat mass ( r = –0.163, p = 0.021), percent skeletal muscle mass ( r = 0.176, p = 0.013), and MFR ( r = 0.157, p = 0.026) in women. However, dietary fiber intake was not correlated with percent body fat mass ( r = –0.100, p = 0.108), percent skeletal muscle mass ( r = 0.055, p = 0.376), and MFR ( r = 0.065, p = 0.295) in men. After adjusting for covariates, dietary fiber intake was correlated with percent body fat mass (β = 0.229, p = 0.009), percent skeletal muscle mass (β = 0.364, p < 0.001), and MFR (β = 0.245, p = 0.006) in women. Further, dietary fiber intake was related to percent skeletal muscle mass (β = 0.221, p = 0.008) and tended to be correlated with percent body fat mass (β = 0.148, p = 0.071) in men. Conclusion Dietary fiber intake was correlated with skeletal muscle mass, body fat mass, and MFR among women with T2D.
... Data included demographics (age, gender) and type of BS [i.e., adjustable gastric banding (AGB), Roux-en-Y gastric bypass (RYGB), and sleeve gastrectomy (LSG)]. For the anthropometric outcomes, we collected information on weight and height and computed the BMI, BMI change, EW, WL, EWL%, and total weight loss percentage (TWL%) using formulas provided in previous studies [29,30]. The percentages of patients achieving TWL% of 0-4.9%, ≥ 5%, ≥ 10%, or ≥ 15%, and those who gained weight (non-responders) while on liraglutide were also computed. ...
Article
Full-text available
Background No study appraised the effectiveness and safety of liraglutide in managing inadequate weight loss or weight regain (IWL/ WR) after primary versus revisional bariatric surgery (BS). Methods Retrospective study of all eligible adults who completed liraglutide 3 mg therapy for IWL/WR after primary or revisional BS at our institution between May 2016 and June 2019 (N = 145; 119 primary, 82%; 26 revisional, 18%). Changes in anthropometric and cardiometabolic parameters were assessed before the start of liraglutide and at 6 and 12 months after treatment. Results The mean age was 43.32 ± 10.49 years, and 83% were females. Patients received liraglutide at a mean of 54.10 ± 31.75 months after their BS, for WR (74.3%) or IWL (25.6%). Liraglutide significantly reduced weight and BMI among primary and revisional patients (P < 0.0001 for all) and was equally effective in these reductions for both groups. Primary patients achieved total weight loss percentage (TWL%) of 5.97% and 6.93% at 6 and 12 months. Additionally, 52.3% and 60% of the patients lost ≥ 5% of their total weight (TW) at 6 and 12 months after primary BS. Revisional patients achieved TWL% of 6.41% and 4.99% at 6 and 12 months, and 60% and 48% of patients lost ≥ 5% TW at the two time points. Liraglutide did not improve cardiometabolic outcome for primary patients; for revisional patients, only the systolic blood pressure decreased after treatment. Liraglutide was well tolerated, and the most common side effect was nausea. Conclusions Liraglutide is useful as an adjunct weight loss medication for patients achieving unsatisfactory outcomes with BS. Graphical abstract
Article
Purpose Plasma daptomycin has not been fully characterized in diabetic and obese patients. This study aimed to evaluate the associations of plasma daptomycin with glycation of serum albumin and obesity. Methods Infectious patients (n = 70) receiving intravenous daptomycin were enrolled. The plasma concentration of total and free daptomycin were determined using liquid chromatograph-tandem mass spectrometer. The associations of the plasma concentrations of daptomycin with clinical factors including serum albumin fractionations and physical status (obese including overweight, body mass index ≥ 25) were investigated. Daptomycin doses were adjusted using total body-weight. Results The serum albumin level was positively and negatively correlated with the plasma concentration of total daptomycin and its free fraction proportion, respectively. The serum non-glycated albumin was negatively correlated with the free fraction proportion. The dose-normalized plasma concentration of total daptomycin was higher in the obese patients than in non-obese patients when the body-weight was corrected with total and adjusted values. For the dose adjustment with lean body-weight, no difference was observed in the dose-normalized plasma concentration of total daptomycin between the physical statuses. For each body-weight correction method, physical status did not affect the dose-normalized plasma concentration of free daptomycin. Conclusion The glycation of serum albumin and obesity did not associate with dose-normalized plasma free daptomycin. In obese patients, daptomycin dosage adjustment with total body-weight and adjusted body-weight may lead to an apparent excessive exposure resulting in overdosage compared to lean body-weight.
Article
Objective Resting energy expenditure (REE) declines with age in healthy subjects, independent of the age-related decrease in lean body mass. Our study evaluates whether this holds true in critically ill medical patients. Moreover, we assessed how measured REE compares to energy requirements calculated by prediction equations in different age groups. Materials and Methods In this retrospective cohort study, 200 critically ill medical patients with need for mechanical ventilation underwent indirect calorimetry within 72 hours of admission after an overnight fast to determine REE. REE was adjusted for body weight (REEaBW). Patients were divided into age quartiles (I: 18-35, n=21; II: 36-52, n=43; III 53-69, n=93; IV=70-86 years, n=43). Sex, SAPS II score, temperature at time of measurement, height, weight and BMI were assessed. We calculated energy requirements by Harris Benedict and Mifflin-St.-Jeor equations. Kruskal-Wallis-Test was used for group comparisons. Parameters that were significant in univariate regression entered the multivariate regression model. Results REE (p=0.009) and REEaBW (p<0.001) decline with age in our study population. Multivariate regression reveals age (R=- 4.97 (95% CI -8.30–1.64), p=0.004) and body temperature (R= 94.30 (95%CI 50.75-137.84, p<0.001)) as independent predictors for REE. Likewise, age (R=-0.11 (95%CI -0.15- -0.06), p<0.001) and body temperature (R=1.02 (95%CI 0.37-1.67), p=0.002) were independent predictors for REEaBW. Prediction equations underestimated energy needs in all groups. Conclusion REE and REEaBW decrease with age in critically ill medical patients. Age and body temperature are independent predictors of both REE and REEaBW. Prediction equations underestimate energy requirements in critically ill medical patients. (256 words)
Chapter
The anesthetic management of obese patients differs from that of normal-weight patients. Obesity alters anatomy and physiology and is associated with numerous medical comorbidities. Obstructive sleep apnea (OSA) and obesity hypoventilation syndrome are common and fall under the umbrella of sleep-disordered breathing. OSA should be identified preoperatively and treated with continuous positive airway pressure. Morbidly obese (MO) patients should never be allowed to lie flat for induction of anesthesia. Obese patients should be positioned in the “ramped” or “head elevated laryngoscopy position.” Inadequate arm support can result in brachial plexus injury. The lateral position requires larger axillary rolls and beanbags. Obesity is a factor for a difficult airway but not a single study has demonstrated a direct relationship between increasing weight with increasing tracheal intubation difficulty. There is a subset of patients that often present with challenging airways. This group consists of MO men, often with OSA, who have thick necks and high Mallampati scores. Emergence from anesthesia should also be in an upper-body elevated position. MO patients can maintain adequate oxygenation during one-lung ventilation, but arterial oxygen tension remains significantly lower than in normal-weight patients. Postthoracotomy pain control in MO patients should include thoracic epidural or paravertebral regional anesthesia. Systemic opioids, in general, should be limited or avoided. This chapter considers the anesthetic management of the obese patient undergoing thoracic procedures with an emphasis on patients with sleep-disordered breathing.
Article
Full-text available
To provide a historical perspective on the origin and similarity of the "ideal" body weight (IBW) equations, and clarify the terms ideal and lean body weight (LBW). Primary and review literature were identified using MEDLINE (1966-November 1999) and International Pharmaceutical Abstracts (1970-November 1999) pertaining to ideal and lean weight, height-weight tables, and obesity. In addition, textbooks and relevant reference lists were reviewed. All articles identified through the data sources were evaluated. Information deemed to be relevant to the objectives of the review were included. Height-weight tables were generated to provide a means of comparing a population with respect to their relative weight. The weight data were found to correlate with mortality and resulted in the use of the terms desirable or ideal to describe these weights. Over the years, IBW was interpreted to represent a "fat-free" weight and thus was used as a surrogate for LBW. In addition, the pharmacokinetics of certain drugs were found to correlate with IBW and resulted in the use of IBW equations published by Devine. These equations were consistent with an old rule that was developed from height-weight tables to estimate IBW. Efforts to improve the IBW equations through regression analyses of height-weight data resulted in equations similar to those published by Devine. The similarity between the IBW equations was a result of the general agreement among the various height-weight tables from which they were derived. Therefore, any one of these equations may be used to estimate IBW.
Article
Full-text available
Article
Obesity is a worldwide problem, with major health, social and economic implications. The adaptation of drug dosages to obese patients is a subject of concern, particularly for drugs with a narrow therapeutic index. The main factors that affect the tissue distribution of drugs are body composition, regional blood flow and the affinity of the drug for plasma proteins and/or tissue components. Obese people have larger absolute lean body masses as well as fat masses than non-obese individuals of the same age, gender and height. However, the percentage of fat per kg of total bodyweight (TBW) is markedly increased, whereas that of lean tissue is reduced. Cardiac performance and adipose tissue blood flow may be altered in obesity. There is uncertainty about the binding of drugs to plasma proteins in obese patients. Some data suggest that the activities of hepatic cytochrome P450 isoforms are altered, but no clear overview of drug hepatic metabolism in obesity is currently available. Pharmacokinetic studies provide differing data on renal function in obese patients. This review analyses recent publications on several classes of drugs: antibacterials, anticancer drugs, psychotropic drugs, anticonvulsants, general anaesthetics, opioid analgesics, neuromuscular blockers, β-blockers and drugs commonly used in the management of obesity. Pharmacokinetic studies in obesity show that the behaviour of molecules with weak or moderate lipophilicity (e.g. lithium and vecuronium) is generally rather predictable, as these drugs are distributed mainly in lean tissues. The dosage of these drugs should be based on the ideal bodyweight (IBW). However, some of these drugs (e.g. antibacterials and some anticancer drugs) are partly distributed in adipose tissues, and their dosage is based on IBW plus a percentage of the patient’s excess bodyweight. There is no systematic relationship between the degree of lipophilicity of markedly lipophilic drugs (e.g. remifentanil and some β-blockers) and their distribution in obese individuals. The distribution of a drug between fat and lean tissues may influence its pharmacokinetics in obese patients. Thus, the loading dose should be adjusted to the TBW or IBW, according to data from studies carried out in obese individuals. Adjustment of the maintenance dosage depends on the observed modifications in clearance. Our present knowledge of the influence of obesity on drug pharmacokinetics is limited. Drugs with a small therapeutic index should be used prudently and the dosage adjusted with the help of drug plasma concentrations.
Article
Formulas for ideal body weight (IBW) in men and women were derived from the Metropolitan Life Insurance Company height and weight tables. Regression determinations of median weight versus height were performed for men and women. A program for a minicomputer was developed to generate plots for small, medium, and large frame sizes and for subjects of all frame sizes. Equations for ideal body weight were derived from the resulting data. For men of all frame sizes, IBW = 51.65 kg + 1.85 kg/inch of height >5 feet. For women of all frame sizes, IBW = 48.67 kg + 1.65 kg/inch of height > 5 feet. More accurate estimates of IBW by frame size can be obtained using equations derived from the plots for men and women of each frame size. Estimates of IBW obtained by the widely used empirical method probably contain only minor errors. However, formulas derived from actual height and weight data should be used in pharmacokinetic determination of dosage regimens for some drugs.
Article
Obesity is a worldwide problem, with major health, social and economic implications. The adaptation of drug dosages to obese patients is a subject of concern, particularly for drugs with a narrow therapeutic index. The main factors that affect the tissue distribution of drugs are body composition, regional blood flow and the affinity of the drug for plasma proteins and/or tissue components. Obese people have larger absolute lean body masses as well as fat masses than non-obese individuals of the same age, gender and height. However, the percentage of fat per kg of total bodyweight (TBW) is markedly increased, whereas that chrome P450 isoforms are altered, but no clear overview of drug hepatic metabolism in obesity is currently available. Pharmacokinetic studies provide differing data on renal function in obese patients. This review analyses recent publications on several classes of drugs: antibacterials, anticancer drugs, psychotropic drugs, anticonvulsants, general anaesthetics, opioid analgesics, neuromuscular blockers, beta-blockers and drugs commonly used in the management of obesity. Pharmacokinetic studies in obesity show that the behaviour of molecules with weak or moderate lipophilicity (e.g. lithium and vecuronium) is generally rather predictable, as these drugs are distributed mainly in lean tissues. The dosage of these drugs should be based on the ideal bodyweight (IBW). However, some of these drugs (e.g. antibacterials and some anticancer drugs) are partly distributed in adipose tissues, and their dosage is based on IBW plus a percentage of the patient's excess bodyweight. There is no systematic relationship between the degree of lipophilicity of markedly lipophilic drugs (e.g. remifentanil and some beta-blockers) and their distribution in obese individuals. The distribution of a drug between fat and lean tissues may influence its pharmacokinetics in obese patients. Thus, the loading dose should be adjusted to the TBW or IBW, according to data from studies carried out in obese individuals. Adjustment of the maintenance dosage depends on the observed modifications in clearance. Our present knowledge of the influence of obesity on drug pharmacokinetics is limited. Drugs with a small therapeutic index should be used prudently and the dosage adjusted with the help of drug plasma concentrations.
Article
Obesity is an important risk factor for surgical site infections. The incidence of surgical wound infections is directly related to tissue perfusion and oxygenation. Fat tissue mass expands without a concomitant increase in blood flow per cell, which might result in a relative hypoperfusion with decreased tissue oxygenation. Consequently, the authors tested the hypotheses that perioperative tissue oxygen tension is reduced in obese surgical patients. Furthermore, they compared the effect of supplemental oxygen administration on tissue oxygenation in obese and nonobese patients. Forty-six patients undergoing major abdominal surgery were assigned to one of two groups according to their body mass index: body mass index less than 30 kg/m2 (nonobese) or 30 kg/m2 or greater (obese). Intraoperative oxygen administration was adjusted to arterial oxygen tensions of approximately 150 mmHg and approximately 300 mmHg in random order. Anesthesia technique and perioperative fluid management were standardized. Subcutaneous tissue oxygen tension was measured with a polarographic electrode positioned within a subcutaneous tonometer in the lateral upper arm during surgery, in the recovery room, and on the first postoperative day. Postoperative tissue oxygen was also measured adjacent to the wound. Data were compared with unpaired two-tailed t tests and Wilcoxon rank sum test; P < 0.05 was considered statistically significant. Intraoperative subcutaneous tissue oxygen tension was significantly less in the obese patients at baseline (36 vs. 57 mmHg; P = 0.002) and with supplemental oxygen administration (47 vs. 76 mmHg; P = 0.014). Immediate postoperative tissue oxygen tension was also significantly less in subcutaneous tissue of the upper arm (43 vs. 54 mmHg; P = 0.011) as well as near the incision (42 vs. 62 mmHg; P = 0.012) in obese patients. In contrast, tissue oxygen tension was comparable in each group on the first postoperative morning. Wound and tissue hypoxia were common in obese patients in the perioperative period and most pronounced during surgery. Even with supplemental oxygen tissue, oxygen tension in obese patients was reduced to levels that are associated with a substantial increase in infection risk.
Determining ideal body weight. (Letter)
• Miller Dr
• Carlson
• Jd
• Loyd
• Bj
Miller DR, Carlson JD, Loyd BJ et al. Determining ideal body weight. (Letter). Am J Hosp Pharm 1983; 40: 1622.
Brief Communication Estimating Ideal Body Weight – A New Formula Fax: 650-725-8544; e-mail: Jbrodsky@stanford 1083 Estimating Ideal Body Weight – A New Formula Determination of ideal body weight for drug dosage calculations
• Robinson Jd Lupkiewicz
• Sm
• Palenik
Robinson JD, Lupkiewicz SM, Palenik L et al. Brief Communication Estimating Ideal Body Weight – A New Formula Harry J. M. Lemmens, MD, PhD; Jay B. Brodsky, MD; Donald P. Bernstein, MD Department of Anesthesia, Stanford University School of Medicine, Stanford, CA, USA Reprint requests to: Jay Brodsky, MD, Department of Anesthesia, H3580, Stanford University Medical Center, Stanford, CA, 94305, USA. Fax: 650-725-8544; e-mail: Jbrodsky@stanford.edu Obesity Surgery, 15, 2005 1083 Estimating Ideal Body Weight – A New Formula Determination of ideal body weight for drug dosage calculations. Am J Hosp Pharm 1983; 40: 1016-9.