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(Chest. 2005;128:2108-2115.)
© 2005 American College of Chest Physicians

Mid-Arm Muscle Area Is a Better Predictor of Mortality Than Body Mass Index in COPD*

Juan José Soler-Cataluña, MD; Lourdes Sánchez-Sánchez, MD; Miguel Ángel Martínez-García, MD; Pilar Román Sánchez, MD; Emmanuel Salcedo, MD and Miriam Navarro, MD

* From the Unidad de Neumología (Drs. Soler-Cataluña and Martínez-García), Hospital General de Requena (Drs. Sánchez-Sánchez, Sánchez, Salcedo, and Navarro), Servicio de Medicina Interna, Requena, Valencia, Spain.

Correspondence to: Juan José Soler-Cataluña, MD, Unidad de Neumologyía, Servicio de Medicina Interna, Hospital General de Requena, Paraje Casablanca s/n, 46340 Requena, Valencia, Spain; e-mail: soler juacat{at}gva.es


   Abstract
TOP
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Background: A low body mass index (BMI) has been shown to be an independent indicator of poor prognosis in patients with COPD. However, some studies suggest that muscle mass depletion (MD) is the main factor responsible for the negative effects attributable to malnutrition.

Study objective: To evaluate the prognostic influence of MD estimated from anthropometric parameters.

Design and measurements: Mortality was studied in a prospective cohort of 96 male patients with COPD (average age, 69 ± 9 years; FEV1 percentage of predicted, 44 ± 18% [ ± SD]) followed up for 3 years, with an evaluation of the prognostic influence of the following anthropometric parameters: BMI, mid-arm muscle area (MAMA), and fat-free mass index. Other risk factors were also analyzed, such as age, comorbidity (Charlson index), basal dyspnea index, the St. George’s Respiratory Questionnaire score, the number of hospital admissions in the year prior to nutritional evaluation, the number of hospital admissions in the year immediately after nutritional evaluation (Hpost), spirometry, and blood gases.

Results: In the multivariate study, PaCO2 (p = 0.003; hazard ratio, 1.08), Hpost (p = 0.005, hazard ratio, 4.63), and a MAMA value less than or equal to percentile 25 of the reference value (p25) [p = 0.025; hazard ratio, 3.78] were found to be independent indicators of poor prognosis. Respiratory mortality after 12, 24, and 36 months in the patients with MAMA ≤ p25 was 12.1%, 31.4%, and 39.2%, respectively, vs 5.9%, 7.9%, and 13% in the group of patients without MD (p = 0.006). In normal-weight or overweight patients, MAMA ≤ p25 increased the risk of mortality 3.4-fold (p = 0.032).

Conclusions: MD is a better predictor of mortality than BMI in patients with COPD, fundamentally in normal-weight or overweight patients. The prognostic influence of MD can be estimated indirectly by determining the MAMA, an inexpensive, simple, and rapidly obtained anthropometric measure.

Key Words: anthropometric measurements • COPD • malnutrition • muscle mass • prognostic value


   Introduction
TOP
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
A low body mass index (BMI) has been shown to be an independent indicator of poor prognosis in patients with COPD.1234 This observation has renewed interest in the study of the nutritional aspects of COPD. In this context, a new severity classification has recently been proposed: the BODE index—(BMI, airflow obstruction, dyspnea, and exercise capacity)—which in addition to BMI considers the degree of bronchial obstruction, dyspnea, and exercise tolerance. This classification, proposed by Celli et al,5 stresses the multicomponent nature of COPD and addresses not only its pulmonary consequences but also the systemic manifestations of the disease. Among the latter, the authors point to the need to conduct an adequate nutritional study. However, the assessment of nutritional status based on body weight (or BMI) has limitations, since it affords no qualitative information on body composition, which is also altered in these patients.678

On studying the body composition of COPD patients, weight loss has been shown to be mainly attributable to muscle mass depletion (MD), unlike in starvation, where fat is the most affected body tissue compartment.678 Different studies have shown depletion of fat-free mass (FFM) to be associated with increased deterioration of skeletal muscle function (peripheral as well as respiratory),910 poorer exercise tolerance,1112 increased dyspnea,13 and poorer health-related quality of life.1415 Normal-weight or overweight patients also show MD.6 This is important since obesity is highly prevalent in developed countries. In a previous study,7 we found 62.7% of patients with a normal BMI and even 20.7% of overweight patients to have MD expressed as a low mid-arm muscle area (MAMA). Schols et al6 also reported a reduction in FFM without a decrease in body weight in 24 of 255 patients with COPD (9.4%) eligible for inclusion in a rehabilitation program. Such patients suffer from physical impairment to an even greater degree than underweight patients with relative preservation of FFM.

The results of morbidity studies also appear to indicate that MD could have greater prognostic implications than low body weight or the depletion of any other body tissue compartment. In this sense, Marquis et al16 found muscle mass to be a better predictor of mortality than body weight in COPD patients. A mid-thigh muscle cross-sectional area of ≤ 70 cm2 was seen to increase the risk of death fourfold, independently of the influence of other prognostic variables. In this study,16 the authors measured muscle mass directly by CT. However, this technique is too costly for generalized use, and reference values are moreover lacking. Anthropometric measurements, however, are inexpensive, simple, and rapid to perform, and provide an indirect estimation of nutritional status and body composition, with correct interpretation requiring the use of reference values for the study population involved. In anthropometric assessment, the muscle compartment can be estimated indirectly by determining the FFM or by calculating the MAMA. To date, it has not been determined whether muscle mass measured from these anthropometric parameters has prognostic implications in COPD. The objectives of the present study were as follows: (1) to investigate whether MD, estimated from anthropometric parameters, is a predictor of mortality in patients with stable COPD; (2) to determine whether low MAMA or low FFM are better predictors of mortality than underweight status; and (3) to determine whether MD has prognostic implications in normal-weight or overweight patients.


   Materials and Methods
TOP
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Subjects
A prospective study was made of a cohort of 114 patients with stable COPD. The patients were recruited in the last trimester of the year 2000, with a subsequent follow-up period of 3 years. The diagnosis of COPD was based on a current or previous smoking history (> 20 packs-year), clinical assessment, and pulmonary function testing.17 The postbronchodilator FEV1, expressed as a percentage of the theoretical value, was used to classify the patients according to Global Initiative for Chronic Obstructive Lung Disease (GOLD) committee criteria.18 Patients exhibiting > 15% reversibility in the bronchodilator test were according to the latest excluded from the study, as were those subjects with a previous diagnosis of bronchial asthma, bronchiectasis, cystic fibrosis, upper airways obstruction, or bronchiolitis related to systemic disorders. Patients with concomitant diseases capable of altering nutritional status (heart failure, liver cirrhosis, decompensated diabetes, chronic renal failure, uncontrolled thyroid pathology, neoplasms, decompensated chronic cor pulmonale, sustained systemic steroid use) were also excluded. The patients were required to be in a stable phase of COPD, defined as the absence of disease exacerbations in the 2 months preceding the study.

Study Protocol
Clinical Assessment:
Patient age and sex were recorded, along with smoking history, comorbidity, dyspnea, health-related quality of life, the number of hospital admissions in the year prior to nutritional evaluation, and the number of hospital admissions in the year immediately after nutritional evaluation (Hpost). Comorbidity was evaluated by means of the Charlson index.19 The degree of dyspnea was in turn assessed according to the basal dyspnea index (BDI) of Mahler.20 St. George’s Respiratory Questionnaire (SGRQ) was used to analyze health-related quality of life. This questionnaire has been validated for Spain by Ferrer et al.21

Pulmonary Function Testing and Arterial Blood Gases:
Forced spirometry (Autospiro AS-600; Minato Medical Science; Osaka, Japan) was used to determine FEV1 and FVC following the specifications of the Spanish Society of Pneumology and Thoracic Surgery (Sociedad Española de Neumología y Cirugía Torácica).22 The results corresponding to FEV1 and FVC are expressed as percentages of the adult reference values.23 Blood gases were determined under resting conditions, according to the methodology recommended by the Sociedad Española de Neumología y Cirugía Torácica.24

Nutritional Assessment:
The following anthropometric parameters were recorded: body weight (in kilograms), height (meters), mid-arm circumference (MAC) [in centimeters], tricipital skin-fold thickness (millimeters), and bicipital, abdominal, and subscapular skin-fold thickness. Based on these measures, the following indexes and values were calculated: BMI, MAMA, fat mass, FFM, and the FFM index (FFMi). BMI was calculated as the weight/(height)2 ratio. MAMA was calculated by the following equation: (MAC [in centimeters] – 3.14 x tricipital skin fold thickness [in millimeters])2/(4 x 3.14).25 Fat mass was estimated using the tables of Durnin and Womersley.26 FFM was calculated by subtracting fat mass from body weight. FFMi was, in turn, calculated as FFM/(height)2.27 The fold-thickness measurements were made using a lipocalibrator (Holtain; Cambridge, UK), based on the usual method applied in nutrition studies.28 MAC was measured with a millimetered tape at the midpoint of the nondominant arm, between the olecranon and acromion. The corresponding percentiles were determined based on the tables developed by Ricart et al29 for working populations, and by Esquius et al30 for elderly populations. From the statistical perspective, values between percentiles 5 and 95 define normality. Nevertheless, in certain cases it is necessary to take into account that values outside the interquartile range (percentiles 25 and 75) can alert us to the existence of initial malnutrition or excess body weight.30 We have followed this latter approach. Low body weight was considered in the presence of BMI less than or equal to percentile 25 of the reference value (p25), and overweight in the presence of BMI > p75. The muscle depletion criteria used were MAMA ≤ p25 and FFMi ≤ 16 kg/m2. The value of FFMi ≤ 16 kg/m2 was taken from the literature.27

Statistical Analysis
Descriptive statistics were used to describe the study population at baseline. We first conducted univariate analyses based on the Cox proportional hazards model using each of the potential predictors of respiratory mortality as independent variables and survival status as the dependent variable.31 Survival curves for BMI, FFMi, and MAMA groups were plotted by the Kaplan-Meier product limit method and compared by the log-rank test.32 Independent variables associated to respiratory mortality with p < 0.15 in the univariate analysis were then incorporated to a forward stepwise multivariate analysis likewise based on the Cox proportional model.31 Prior to the multivariate analysis, an evaluation of the existence of confounding or interactive effects was made between variables and their possible colinearity. The Pearson correlation test (r) was used to study the association between quantitative parameters, with the Spearman ordinal correlation test (rs) for analyzing colinearity between qualitative variables. Among all the variables presenting r or rs >0.6, we selected for the multivariate model the parameter presenting the greatest significance in the prior univariate analysis. Nutritional parameters significantly related with prognosis were also controlled for confounding factors by using the Cox proportional model. All statistical analyses were performed using statistical software (SPSS for Windows, version 11.5; SPSS; Chicago, IL); p < 0.05 was considered significant.


   Results
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
Subject Characteristics
A total of 114 patients with a diagnosis of COPD were studied. Of these, 18 patients (15.8%) were excluded for different reasons: 7 patients (6.1%) with decompensated cor pulmonale, 3 patients (2.6%) with liver cirrhosis, 2 patients (1.7%) with neoplasms, 2 patients (1.7%) due to sustained oral corticosteroid use, 2 patients (1.7%) because of heart failure, 1 patient (0.9%) with chronic renal failure, and another patient (0.9%) due to malabsorption syndrome. A total of 96 patients were therefore finally included (all male; mean age, 69 ± 9 years [± SD]). Table 1 shows the baseline characteristics of these patients.


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  		Table 1. Baseline Characteristics of the Patients (n = 96)*

 
Univariate Survival Analysis
Table 2 shows the prognostic influence of the variables included in the univariate analysis. Figure 1 , in turn, shows the survival curves of the patients by BMI, MAMA, and FFMi groups. Eighteen patients (18.8%) presented BMI ≤ p25. The cumulative survival probabilities after 12, 24, and 36 months in this group were 83.3%, 70.7%, and 61.2%, respectively. The survival curve was significantly lower than in the case of overweight patients (p = 0.021). No significant differences were observed with respect to the group with normal body weight (Fig 1, top, A). Forty-four patients (45.8%) presented MAMA ≤ p25. Survival rates for these patients after 12, 24, and 36 months were 87.9%, 68.6%, and 60.8%, respectively. These figures were very similar to those recorded in the group with BMI ≤ p25. However, in this case the survival of patients with MD was lower than in the cases with normal MAMA values (p = 0.020) [Fig 1, center, B]. Only one patient with MAMA > p75 died in the course of the 3 years of follow-up. However, no statistically significant differences were reached vs the group with MAMA ≤ p25 (p = 0.098). Seventeen patients (17.7%) presented FFMi ≤ 16 kg/m2. No survival differences were recorded in relation to FFMi (Fig 1, bottom, C). Nevertheless, the patients with low FFMi values tended to exhibit poorer survival.


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  		Table 2. Predictors of Mortality: Univariate Analysis

 


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  		Figure 1. Kaplan-Meier survival curves in a patient with COPD by BMI (top, A), MAMA (center, B), and FFMi (bottom, C). NS = not significant.

 
Normal-Weight or Overweight Patients and MD
Nineteen patient with normal body weight (59.4%) and 9 overweight subjects (19.6%) presented MAMA ≤ p25. Both subgroups showed a poorer prognosis, with a 3.39-fold (95% confidence interval [CI], 1.11 to 10.38) greater mortality risk (p = 0.032) [Fig 2 ]. Only two patients (3.9%) with MAMA > p25 showed low body weight values.



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  		Figure 2. Kaplan-Meier survival curves by MAMA in COPD patients with BMI > p25.

 
Colinearity, Interaction, and Confusion Between Variables
Significant colinearity was observed among the three nutritional parameters (BMI, MAMA, and FFMi). BMI presented important correlation to both MAMA (r = 0.69, p < 0.0001) and FFMi (r = 0.89, p < 0.0001). MAMA and FFMi likewise showed good correlation (r = 0.73, p < 0.0001). On jointly evaluating these parameters with the Cox multivariate model in terms of both absolute and dichotomic values, the only predictor showing statistically significant differences was found to be MAMA (Table 3 ). Colinearity was also observed between BDI and global SGRQ (r = – 0.71, p < 0.0001) and between FEV1 percentage of predicted and FVC percentage of predicted (r = 0.71, p < 0.0001). A weak correlation was likewise identified between MAMA level and Hpost (r = – 0.30, p = 0.003).


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  		Table 3. Predictors of Mortality, Including Only Nutritional Parameters: Multivariate Analysis

 
No significant interaction was recorded between any of the variables studied. The only parameters seen to behave as confounding factors on MAMA ≤ p25 were Hpost and FEV1 percentage of predicted. After fitting for Hpost, MAMA ≤ p25 was seen to be an independent prognostic factor (odds ratio, 2.83; 95% CI, 1.01 to 7.97; p = 0.048). However, on controlling for FEV1 percentage of predicted, the prognostic influence of MAMA ≤ p25 failed to yield statistically significant differences (odds ratio, 2.45; 95% CI, 0.84 to 7.09; p = 0.099) [Table 4 ]. In patients with FEV1 > 50% of predicted, the presence of MD exerted no prognostic influence (p = 0.616). However, among the cases with FEV1 < 50% of predicted (GOLD stages III and IV), those presenting MAMA ≤ p25 showed a trend toward poorer survival (p = 0.058) [Fig 3 ].


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  		Table 4. Prognostic Effect of Muscle Depletion (MAMA ≤ p25) After Controlling for Confounding Factors

 


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  		Figure 3. Kaplan-Meier survival curves by MAMA in COPD patients with FEV1 < 50% of predicted.

 
Multivariate Survival Analysis
Age, BDI, global SGRQ, comorbidity index, number of hospital admissions in the year prior to nutritional evaluation, Hpost, FEV1 percentage of predicted, PaO2/fraction of inspired oxygen, PaCO2, and MAMA were the variables included in the Cox multiple regression test. In this predictive model, MAMA values were analyzed as dichotomic variables (≤ p25 vs > p25). In the final regression equation, the variables found to be independent indicators of poor prognosis were high PaCO2, Hpost, and MAMA ≤ p25 (Table 5 ).


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  		Table 5. Predictors of Mortality: Stepwise Multivariate Analysis

 

   Discussion
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
The present study confirms previous data on the effect of muscle mass on mortality in patients with COPD. In stable patients, MD is associated to an increased risk of death. In addition, as a remarkable finding, we show that the prognostic influence of muscle mass can be assessed by determining the MAMA, an inexpensive, simple, and rapidly obtained anthropometric measure. MAMA ≤ p25 was found to be a poor prognosis marker, exerting an influence in our series superior to that of other anthropometric parameters such as BMI or FFMi. In underweight patients, the presence of muscle depletion did not contribute significant prognostic information. However, those subjects with normal body weight or overweight status who presented MAMA ≤ p25 had a poorer prognosis.

Earlier studies1234 have shown that a low body weight (determined from BMI) constitutes an independent poor prognosis indicator, and that weight loss implies an increased mortality risk. Nevertheless, some authors81516 suggest that the true factor underlying the negative effects attributed to denutrition is MD, not body weight loss. The present study confirms this idea, since the presence of MAMA ≤ p25 increased the mortality risk 3.6-fold. Moreover, MD was seen to exert a negative prognostic influence independently of other classical prognostic variables such as age, respiratory failure, hospitalizations, and comorbidity. In patients with mild-to-moderate COPD, the presence of MD did not exert a negative prognostic influence. However, among the individuals with FEV1 < 50%, those presenting MAMA ≤ p25 showed a trend toward increased mortality risk (p = 0.058) [Fig 3]. Only 66 of our cases (68.7%) presented FEV1 < 50% of predicted. Possibly as a result of the limited sample size involved, no statistically significant differences were observed. Nevertheless, the observed tendency suggests that muscle depletion does have prognostic importance in seriously ill patients. Recently, Marquis et al16 obtained similar results in a series of 142 patients with stable COPD subjected to mid-thigh muscle cross-sectional area measurements by CT. In this sense, an area of ≤ 70 cm2 was seen to increase the mortality risk fourfold independently of other prognostic variables. In this case, the authors did find the effect of MD in seriously ill patients to be independent of FEV1.

In both their study and in our own, the direct and indirect measures of muscle mass yielded a better estimate of mortality risk than a low body weight. On analyzing the BMI-based survival curve in our study, we found no statistically significant differences between low-weight patients and those with normal body weight. In contrast, survival among patients with MAMA ≤ p25 was significantly lower than in patients with normal MAMA values, thus suggesting a greater prognostic discriminatory capacity on the part of this latter parameter. The multivariate analysis performed with the three nutritional parameters studied (BMI, MAMA, and FFMi) likewise confirms the superiority of MAMA, since this was the only variable to exert a significant prognostic influence (Table 3).

BMI may be valid as an indirect nutritional marker; however, in countries where obesity is prevalent, BMI may mask situations of MD. Almost 50% of our patients were overweight, and in 20% of these cases MD was observed. Among the patients with normal body weight, almost 60% showed MAMA ≤ p25. On analyzing these latter two subgroups combined, the patients with MD had a poorer prognosis, with a 3.4-fold greater mortality risk (p = 0.032) [Fig 2]. We have found no studies in the literature describing this interesting phenomenon. Nevertheless, an earlier study3 of morbidity attributable to malnutrition in COPD also found patients with normal BMI and FFM depletion to present poorer exercise tolerance.

Marquis et al16 measured muscle mass directly by CT, assessing the proximal muscles of the lower limbs, which are particularly affected in COPD patients.8 However, this technique is too costly for generalized use, and reference values are moreover lacking. The authors therefore attempted to estimate the muscle mass of the thigh based on anthropometric parameters, although the correlation was not satisfactory. Anthropometric equations predict muscle mass of both the thigh and arm by assuming a series of principles that do not always apply to all patients. Heymsfield et al,33 using CT, showed a 20 to 25% overestimation in arm muscle area. From 10 to 15% of this overestimation is attributable to adoption of a circular shape for the muscle compartment. Moreover, calculation of the circumference of the arm, and posteriorly of MAMA, is based on the assumption that measurement of the subcutaneous fatty layer represents a constant fraction of total body fat. This is not completely true in the case of elderly patients, where fat is mainly found in central and internal locations of the body. For this reason, fatty tissue mass is usually underestimated, and FFM tends to be overestimated.34 Despite these imprecisions, our study shows the usefulness of anthropometric measurements in assessing the prognostic influence of MD in patients with COPD. The difference may lie in the use of reference values (both younger and elderly populations), which allows adjustment of the results to the normal values of our study population, thereby helping to balance the initial imprecisions of the anthropometric measurements. In the Canadian series,16 the anthropometric measurements were not adjusted to any reference value. Nevertheless, thigh circumference was found to be an inverse predictive factor of patient mortality in the univariate analysis.

Muscle mass can be estimated from anthropometric parameters in two ways: by calculating whole-body FFM, or by calculating arm muscle area. The two parameters are not interchangeable. FFM results by subtracting the total body fat weight from total body weight, the former in turn being calculated from different skin-fold thicknesses. MAMA, in contrast, only informs of the muscle mass of the arm. On comparing both measures in our study (in both absolute values and in percentiles), MAMA was always found to be superior to FFM as a mortality predictor. Recently, Engelen et al,35 using dual radiograph absorptiometry, found limb FFM to relate better to skeletal muscle function than whole-body FFM. This study could at least partially explain why in our series MAMA was seen to be a better predictor of mortality than whole-body FFM.

In addition to the anthropometric parameters, other techniques are able to evaluate body composition (bioelectric impedance, densitometry, dual radiograph absorptiometry). These techniques assess whole-body FFM as an indirect marker of muscle mass. In general, they offer superior accuracy and greater reproducibility than anthropometric parameters.27 However, their application to clinical practice is limited for several reasons. On one hand, these techniques are expensive (and therefore not widely available), while on the other no reference test serving as a "gold standard" has been established. Lastly, most of these techniques lack reference values, particularly for the elderly population. In Spain, reference values have been established for different anthropometric measurements in both the younger and elderly populations (including BMI and MAMA, but not FFM).2930 These reference values allow adequate adjustment of the results obtained, with improved definition of normality and abnormality.

The other two variables included in the final predictive model were PaCO2 and the number of hospital admissions following nutritional assessment. Different references are found in the literature regarding the adverse prognostic influence of hypercapnia in COPD.3637 However, there are very few data on the influence of hospital admissions, as an expression of severe exacerbation, on the survival of these patients. In recent years, a series of studies383940 have reported a marked increase in mortality following an admission to hospital. Most of these authors point to the baseline severity of the disease as the main factor determining mortality in such cases: more serious disease leads to more hospitalizations, and thus to increased mortality. However, our own results showed hospital admission to be a prognostic factor independently of the influence of other baseline severity parameters. The present study was not designed to specifically evaluate this parameter; however, further research is thus required.

Our study has a series of limitations that deserve comment. Firstly, the sample size was small. Studies involving more extensive patient series are needed. Secondly, the nutritional evaluation methodology used (anthropometric measurements) is less sensitive than other tests that offer increased accuracy (bioelectric impedance, dual radiograph absorptiometry).27 In our opinion, future research should be based on these latter techniques, although they would have to become less costly and more accessible in order to allow application to routine clinical practice. Moreover, these techniques first should be applied to the general population in order to define the opportune reference values. Lastly, initial collection of the anthropometric data were cross-sectional in our series. We have no longitudinal data to inform us of the time-dependent change in muscle mass and its relation to prognosis, or of the influence of treatment. New studies in this direction are needed.

In conclusion, our findings stress the prognostic influence of MD in patients with stable COPD, and point to the need to consider body composition instead of resorting to simple nutritional parameters such as BMI, particularly in normal-weight or overweight patients, since an important proportion of these subjects present muscle depletion with prognostic repercussions. The usefulness of anthropometric parameters is reinforced by our data, particularly the muscle area of the nondominant arm (MAMA), which constitutes an inexpensive, simple, and rapidly obtained anthropometric measure.


   Footnotes
 
Abbreviations: BDI = basal dyspnea index; BMI = body mass index; CI = confidence interval; FFM = fat-free mass; FFMi = fat-free mass index; GOLD = Global Initiative for Chronic Obstructive Lung Disease; Hpost = number of hospital admissions in the year immediately after nutritional evaluation; MAC = mid-arm circumference; MAMA = mid-arm muscle area; p25 = percentile 25 of the reference value; MD = muscle mass depletion; SGRQ = St. George’s Respiratory Questionnaire

This work was performed at Hospital General de Requena, Unidad de Neumología, Servicio de Medicina Interna, Valencia, Spain.

Received for publication October 18, 2004. Accepted for publication February 22, 2005.


   References
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

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