Protein, malnutrition and wasting diseases

Handgrip strength cutoff values for undernutrition screening at hospital admission

European Journal of Clinical Nutrition volume 68, pages1315–1321(2014)Cite this article

Subjects

Abstract

Background/Objectives:

There is increasing evidence showing that handgrip strength (HGS) is an indicator of nutrition status and a promising undernutrition screening tool. However, HGS cutoff values for inpatient undernutrition screening remain to be studied. The present study aims to define gender- and age-specific HGS cutoff values for undernutrition screening of hospitalized patients at admission.

Subjects/Methods:

A cross-sectional study was conducted in a university hospital. Gender- and age-specific receiver operating characteristic curves were constructed to evaluate the performance of HGS for correctly screening undernourished patients on the basis of their classification by Patient-Generated Subjective Global Assessment. Sensitivity, specificity, areas under the curves (AUCs) and positive likelihood ratios (LRs) were calculated.

Results:

The study sample was composed of 712 participants between the ages of 18 and 91 years old, median (interquartile range) of 58 (22) years. For women, HGS cutoff values, sensitivities and specificities were as follows: 18–44 years, 20.2 kgf, 0.741 and 0.556; 45–64 years, 19.2 kgf, 0.795 and 0.522. For men, these values were as follows: 18–44 years, 41.7 kgf, 0.923 and 0.520; 45–64 years, 37.9 kgf, 0.817 and 0.402; ⩾65 years, 30.2 kgf, 0.736 and 0.567. The AUCs varied between 0.642 and 0.778 and LRs from 1.37 to 1.92.

Conclusions:

This study provides HGS cutoff values for men aged 18–91 years and for women aged 18–64 years.

Introduction

A large number of patients are undernourished when admitted to the hospital. Although this problem has been documented for a long time, undernutrition is estimated to range between 20 and 50%, depending on the patient's characteristics, clinical setting, method and criteria used for its assessment.1, 2, 3, 4 Undernutrition at hospital admission is associated with poor clinical progress and outcome, longer hospitalization, higher likelihood of hospital readmission, higher rates of mortality and increased costs.5, 6, 7, 8 Moreover, undernutrition status of a high proportion of patients worsens during hospital stay.1,9,10

As the wide spectrum of consequences are recognizably serious, nutrition societies have established guidelines for undernourishment documentation and screening.11, 12, 13, 14 However, this condition continues to be under-recognized and untreated in many hospitalized patients.3,11 Although undernutrition diagnosis cannot be established using a single parameter,11 the search for an indicator capable of screening undernourishment in a simple and quick manner is extremely relevant.

Undernourishment causes loss of muscular strength and muscular wasting,15, 16, 17 but muscle strength impairment occurs before changes in muscle structure and composition can be detected.15,17 Voluntary handgrip strength (HGS) is an indicator of overall body muscle strength18 and can identify effects of nutrition deprivation, before alterations in body composition parameters are identifiable.19,20

There is increasing evidence showing that HGS is an indicator of nutrition status11,19,21 and a promising undernutrition screening tool.22,23 In a first instance, the classical procedure would be to compare HGS with reference data for healthy individuals, but HGS reference data are only available for some populations, such as Spanish,24 Danish,25 German,26 Swedish27 and Brazilian.28,29 It was also previously shown that an entire sample of 314 hospitalized patients had Z-scores below −1.96 compared with Danish HGS reference values,23,25 suggesting that comparing HGS values with reference data is not the appropriate method for identifying undernourished hospitalized patients.

Through a receiver operating characteristic (ROC) analysis, HGS was found to discriminate undernutrition status evaluated with Patient-Generated Subjective Global Assessment (PG-SGA) in hospitalized patients.22 Within this study, HGS was a significant independent predictor of the PG-SGA score and category through a multivariable regression analysis.22 In another study, the lower HGS quartiles showed good sensitivity and specificity when compared with Nutritional Risk Screening (NRS-2002) for screening undernutrition in hospitalized patients.23 However, HGS quartiles have low practical applicability, and further research is needed to establish cutoff points, which have only been proposed for chronic hemodialysis outpatients.30

Also, HGS is considered as an outcome predictor, as reduced values are strongly correlated with increased postoperative complications, reduction in short- and long-term survival, longer hospital stay and a higher hospitalization rate.19,31, 32, 33 Moreover, HGS is the most frequently used indicator of muscle function for clinical purposes,19 as it has been shown to be a strong indicator of functional status.19,21,32 Dynamometers used to measure HGS are inexpensive, easy to use and portable. HGS measurements are also non-invasive, quick to perform, reliable, with low intra- and low between-observer variability and do not require specialized professionals.34 These characteristics make HGS an attractive method for undernutrition screening in clinical daily practice.

Moreover, height and hand anatomy are strongly associated with inpatient HGS and can modify the association between undernutrition and HGS and their possible influence should be studied.21

The present study aims to define gender- and age-specific HGS cutoff values for undernutrition screening of hospitalized patients at admission. It also intends to assess whether HGS adjustment for height and for hand anthropometric parameters improves HGS performance for undernutrition screening.

Participants and methods

Study population and design

A cross-sectional study was conducted in a university hospital between July 2011 and December 2013. A consecutive sampling approach was used. From the daily list of inpatients admitted to each ward, those who met the inclusion criteria were invited to participate in the study.

Patients were eligible to participate in the study if they were ⩾18 years old, white, with an expected length of hospital stay >24 h, conscious, cooperative and able to provide written informed consent. Patients incapable of performing the HGS technique, defined as an inability to understand verbal instructions or having a condition in which the patient could not perform the technique correctly (namely pain), as well as patients with critical illness, defined as failure of at least one vital organ35 and admitted to intensive care units, were excluded from the study. Pregnant women, individuals in isolation, those who were admitted for procedures that could put them in a critical situation and those with hemodynamic instability at the time of evaluation were also excluded. Therefore, inpatients from angiology and vascular surgery; cardiology; digestive, non-digestive and hepatobiliary surgeries; endocrinology; gastroenterology; internal medicine; nephrology; orthopedics; otolaryngology; and urology wards were considered as eligible for this study.

To have a representative sample of the hospitalized patients who met the inclusion criteria, data collection took place, until the number of patients corresponded to twice the total number of beds of all the selected wards (n=739). For ethical and reliability reasons, patients positively screened for cognitive impairment (n=13) and also patients with missing data (n=14) were excluded from data analysis. The final study sample was composed of 712 participants.

This research was conducted according to the guidelines established by the Declaration of Helsinki and approved by the Institutional Review Board and the Ethics Committee of Centro Hospitalar do Porto. Written informed consent was obtained from all study participants.

Data collection

Demographic data and clinical history were obtained by reviewing the patient clinical file. The remaining data were collected using a structured questionnaire within 72 h of admission to the hospital.

Cognitive impairment was assessed using the abbreviated mental test.36 The education level was evaluated by the number of years of school completed, and the following classes were subsequently created: 0–4, 5–12 and >12 years. Disease severity was assessed using the Charlson comorbidity index.37 Functional activity during the month before hospital admission was categorized in four classes: ‘normal with no limitations’ (score=0); ‘not normal, but able to be up and about with fairly normal activities’ (score=1); ‘not feeling up to the most things, but in bed or chair less than half the day’ (score=2); ‘able to do little activity and spend most of the day in bed or chair’ (score=3); and ‘primarily bedridden, rarely out of bed’ (score=4).12 Patient undernutrition status was evaluated with PG-SGA,12 whereas undernutrition risk was assessed with NRS-200213 and Malnutrition Universal Screening Tool (MUST).14

Non-dominant HGS was measured in kgf with a calibrated Jamar Hydraulic Hand dynamometer (Sammons Preston, Bolingbrook, IL, USA), recommended by the American Society of Hand Therapists.38 Measurements were obtained with patients on a chair or on a bed, with the shoulder adducted and neutrally rotated, elbow flexed to an angle of 90º and the forearm and wrist in the neutral position.39 Elbows were unsupported during HGS measurements. Patients were instructed to exert their maximum strength; however, no verbal encouragement was given. Each participant performed three measurements with a 1-min pause between measurements, and the maximum value was chosen as the HGS value.40 When the individual was unable to perform the measurement with the non-dominant hand, the dominant hand was used (n=60).

Data for non-dominant hand length, palm width and wrist circumference were collected. For patients who had HGS measurement in the dominant hand, hand length, palm width and wrist circumference data were obtained in the dominant hand. Predicted HGS was also calculated according to the National Isometric Muscle Strength Database Consortium regression equations.41

Standing height,42 body weight,42 mid-upper arm circumference,42,43 triceps skinfold thickness,42,43 half-span,44 hand length,42 palm width45 and wrist circumference42 were measured according to standardized procedures. Standing height (cm), half-span, mid-upper arm circumference (cm) and wrist circumference data were obtained with a metal tape (Rosscraft, Innovations Incorporated, Surrey, Canada) with a 0.1-cm resolution, and a headboard was also used for measuring standing height. Hand length and palm width were measured using a small bone calliper with a 0.1-cm resolution (Kennon Instruments, Vignola, Italy).42 Body weight (kg) was measured with a calibrated portable beam scale with a 0.5-kg resolution. Triceps skinfold thickness (mm) was measured with a Harpenden calliper (Baty International, Burgess Hill, UK) with a 0.2-mm resolution.

For bedridden patients or those with conditions limiting their ability to stand, hand length was used as an alternative measurement of height (n=359).46 Compared with measured height, height predicted from hand length has shown mean differences ⩽−0.6 (s.d.: 4.4) cm.46 When hand length was impossible to obtain, height was estimated from half-span (n=2).44 Differences between height obtained from half-span and measured height ⩽6.8 (s.d.: 4.0) cm were previously described.44 For participants on dialytic therapies, dry body weight registered in the clinical file was used or, when unavailable, was referred by the patient (n=24). When it was not possible to weigh a patient, body weight predicted from height and mid-upper arm circumference47 was used as a surrogate measurement (n=166). Weight was predicted from regression equations developed to estimate body mass index (BMI) with a coefficient of determination equal to 0.76.47

Data were collected by two previously trained nutritionists. The intra- and inter-observer technical errors of measurement were obtained for all anthropometric measurements, respectively, in 17 and 18 individuals. Intra-observer error varied between 0.2 and 0.6%, and inter-observer error varied between 0 and 1.4%, which is considered acceptable for trained anthropometrists.48

Statistical analysis

The Kolmogorov–Smirnov test was used to evaluate the normality of variables' distribution. Continuous variables are reported as the mean and s.d. or as median and interquartile range (IQR) according to data distribution. Categorical variables are reported as frequencies.

Because of non-parametric distribution of HGS, this variable was compared between genders and age groups using Mann–Whitney and Kruskal–Wallis tests, respectively. Differences between measured and predicted HGS were compared using the Wilcoxon test. Baseline characteristics of the participants were compared according to PG-SGA nutrition status (not undernourished versus moderate or suspected and severe undernutrition), using the Mann–Whitney test (or the independent samples t-test) for continuous variables and the Pearson χ2-test for categorical variables. The level of agreement between PG-SGA, NRS-2002 and MUST was assessed by percentage agreement and by kappa coefficient.

Gender- and age-specific ROC curves were constructed to evaluate the performance of HGS for correctly screening undernourished patients on the basis of their classification by PS-SGA, NRS-2002 and MUST. Sensitivity and specificity were calculated for a range of HGS cutoff values. By plotting the ROC curve (sensitivity against 1−specificity), the point at which most individuals are correctly classified and a minimum of individuals are incorrectly classified can be identified.49

The areas under the curves (AUCs) for the ROC curves and their 95% confidence intervals (CIs) were also calculated. The AUCs should be >0.5, as AUC=0.5 indicates that the screening test is no better than chance.50 To construct ROC curves, individuals with moderate or suspected undernutrition and with severe undernutrition according to PG-SGA were grouped in one single category. Likewise, individuals with medium and high undernutrition risk according to MUST were grouped in single category. Positive likelihood ratios (LRs) for describing the performance of HGS for undernutrition screening were also calculated. An LR of >1 indicates a change in disease probability.49

Gender- and age-specific ROC curves were constructed to evaluate the performance of HGS adjusted for height (HGS/height), for hand length (HGS/hand length), for palm width (HGS/palm width) and for wrist circumference (HGS/wrist circumference), for correctly screening undernourished patients on the basis of their classification by PS-SGA. Sensitivities, specificities, AUCs (95% CIs) and LRs were calculated.

Results were considered as significant when P<0.05. All statistical analyses were carried out using the Software Package for Social Sciences (SPSS) for Windows, version 21.0 (SPSS, Inc., an IBM Company, Chicago, IL, USA).

Results

The study sample was composed of 712 participants between the ages of 18 and 91 years, with a median (IQR) of 58 (22) years. HGS varied broadly between 1 and 35.1 kgf for women with a median (IQR) equal to 16 (9.6) kgf. For men, HGS values varied further between 1 and 61 kgf, with a median (IQR) of 32 (12) kgf. Men had significantly higher HGS values compared with women (P<0.001), and HGS decreased significantly as age increased (P<0.001 for women and men) (Figure 1).

Figure 1
figure1

HGS according to gender, age and nutrition status by PG-SGAa. aP<0.001 for differences between genders (the Mann–Whitney test) and age groups (the Kruskal–Wallis test).

Full size image

Predicted HGS was significantly higher compared with measured HGS for both genders (P<0.001). For men, predicted HGS values varied from 35.6 to 51.2 kgf, with median (IQR) equal to 41.7 (3.8) kgf, whereas for women values ranged from 18 to 33.6 kgf and median (IQR) was equal to 26 (4) kgf.

Participants' characteristics according to PG-SGA nutrition status are presented in Table 1. A high proportion of participants was found to be undernourished. Undernourished patients were older, presented lower abbreviated mental test score and a decline of functional activity. They were also lighter, shorter and presented lower BMI and HGS values.

Table 1 Sample characteristics according to Patient-Generated Subjective Global Assessment nutrition status
Full size table

According to NRS-2002 and MUST, respectively, 36.4 and 39.5% of the participants were nutritionally at risk. Regarding nutrition status classification, the PG-SGA agreed with NRS-2002 and MUST, respectively, in 80.1% (kappa=0.60) and 76.7% (kappa=0.53) of the patients. The best agreement was obtained between NRS-2002 and MUST (84.3%, kappa=0.68). For undernourishment identification, PG-SGA and NRS-2002 identified the same 32%, whereas using PG-SGA and MUST this proportion was 31.9%. The NRS-2002 and MUST identified the same 30.3% undernourished participants.

Gender- and age-specific ROC curves and HGS cutoff values at which most individuals are correctly classified and a minimum of individuals are incorrectly classified, the calculated sensitivity, specificity, LR and also AUCs and 95% CIs for undernutrition evaluated by PG-SGA are presented in Figure 2 and Table 2. For all age-specific ROC curves, LRs were >1, AUCs were >0.5 and statistically significant. HGS sensitivity values varied between 0.736 and 0.923, and specificity values varied between 0.402 and 0.567. However, for the ⩾65-year-old women, the AUC (0.608) was not statistically significant. HGS sensitivity and specificity values were equal to 0.373 and 0.824, respectively, and LR was 2.12.

Figure 2
figure2

Gender- and age-specific ROC curves of HGS for undernutrition screening on the basis of PG-SGA classification (not undernourished versus moderate or suspected and severe undernutrition).

Full size image
Table 2 Gender- and age-specific handgrip strength cutoffs and diagnostic values for undernutrition screening on the basis of undernutrition status classification by Patient-Generated Subjective Global Assessmenta
Full size table

Gender- and age-specific ROC curves of HGS adjusted for height, hand length, palm width or wrist circumference, on the basis of undernutrition classification by PS-SGA, did not lead to substantial improvements in diagnostic values. Adjustment for height produced AUCs between 0.605 and 0.783, sensitivities ranged from 0.622 to 0.885, specificities between 0.353 and 0.657 and LRs from 1.28 to 2.21. Adjustment for hand length resulted in AUCs of between 0.615 and 0.770, sensitivities varied from 0.519 to 0.885, whereas specificities ranged from 0.412 to 0.730 and LRs varied between 1.36 and 2.28. The adjustment of HGS for palm width resulted in AUCs of between 0.610 and 0.755, sensitivities between 0.531 and 0.897, specificities between 0.324 and 0.700 and LRs between 1.33 and 2.44. Finally, AUCs for HGS values adjusted for wrist circumference varied from 0.600 to 0.770, sensitivities varied from 0.390 to 0.923, whereas specificities ranged from 0.370 to 0.794 and LRs varied from 1.28 to 1.89.

For undernutrition risk assessment by NRS-2002 and by MUST, HGS resulted in lower AUCs, sensitivities, specificities and LRs for almost all gender- and age-specific ROC curves, compared with HGS AUCs, sensitivities, specificities and LRs for undernutrition identification evaluated by PG-SGA (data not shown).

Discussion

This study intended to explore the performance of HGS for undernutrition screening at hospital admission using nutrition status classification by PS-SGA as the reference method. Despite the well-described association between nutrition status and HGS,11,19,21, 22, 23 as far as we are concerned, HGS cutoff values for inpatient undernutrition screening at hospital admission have yet to be defined.

For men aged 18–91 years and for women aged 18–64 years, results are promising given that sensitivities were acceptable (0.736–0.923), the AUCs were significant (P<0.05) and moderate (0.642–0.778) and LRs>1 (1.37–1.92). However, HGS cutoff sensitivity for women aged 65–91 years was low (0.373). The high proportion of false negatives in this age group does not allow the definition of an HGS cutoff value for undernutrition screening of ⩾65-year-old women. Moreover, the specificities obtained (0.402–0.567), especially for the 45- to 64-year-old men, should be acknowledged, as it corresponds to a high proportion of false positives.

Notwithstanding the present study sample having different characteristics, HGS cutoff sensitivities and specificities for men aged 18–91 years and for women under 65 years old are similar to those previously reported for undernutrition screening of 436 Brazilian chronic hemodialysis patients on the basis of the malnutrition-inflammation score classification.30 These HGS cutoffs were 23.4 kgf for women (sensitivity: 0.87; specificity: 0.43) and 28.3 kgf for men (sensitivity: 0.70; specificity: 0.66).30 In a previous study conducted among 314 Portuguese hospital inpatients, HGS sensitivities and specificities compared with NRS-2002 varied, respectively, between 0.68 and 0.92 and 0.31 and 0.70. However, these diagnostic values were determined on the basis of HGS quartiles and HGS cutoff values for undernutrition screening were not explored.23

It was also recently shown that HGS was a predictor of nutrition status evaluated with PG-SGA in 217 Australian hospital patients.22 A large computed AUC (0.776) was displayed, but neither HGS cutoff values and respective sensitivities or specificities were determined, highlighting the relevance of the current data as a step forward in the investigation of the association between HGS and nutrition status.

It is also noteworthy that, despite the lower HGS cutoffs specificity values obtained in the current study for men aged 18–91 years and women under 65 years compared with PG-SGA, HGS cutoff sensitivity values were higher compared with those previously obtained with MUST and NRS-2002 for undernutrition risk assessment compared with SGA.51 These findings were also shared by the current study, which found lower AUCs, sensitivities, specificities and LRs for HGS with nutrition evaluation using NRS-2002 and MUST.

Three previous studies had shown that HGS was not a good indicator of patient undernutrition status.52, 53, 54 Nevertheless, some differences related to study design may explain these discrepancies. Garcia et al.52 studied the association between HGS and nutrition status on a smaller sample (n=118) that may not allow to show statistical significant differences in HGS between undernourished and not undernourished patients, nor to compare subgroups and identify significant associations using a multivariable linear regression model. Haverkort et al.53 also evaluated the accuracy of HGS in diagnosing undernutrition defined by involuntary weight loss and BMI. These criteria are different from those contained in PG-SGA that includes weight loss, food intake, symptoms, activities and function and a physical examination (body fat and muscle stores, fluid accumulation).12 Therefore, the association between HGS and PG-SGA is expected to be different from the association of HGS with weight loss and BMI. Finally, only 5.8% of the participants were found to be undernourished. As the predictive value of a test is, among other factors, influenced by the prevalence of the disease in the population being tested,49 the low proportion of undernourished participants may have limited the ability to explore HGS capacity for undernutrition identification. In contrast, a very high proportion of participants included in Pham et al.54 research were undernourished (77.7%). However, this study was conducted in an area characterized by endemic undernutrition, and it is therefore possible that the association between HGS and undernutrition for these patients is different from the association between these two variables in individuals from developed countries.

In this sample of hospitalized patients, HGS cutoff values were lower than those previously reported for healthy populations24, 25, 26, 27,29 and below the values published by the National Isometric Muscle Strength Database Consortium41 for healthy adults. These results suggest that HGS normative data may not be discriminative for undernutrition screening in the hospital setting and not useful for the routine identification of undernourished patients.

It would be ideal to have a test that is both highly sensitive and highly specific. Unfortunately, this is not possible because for a given test, sensitivity can be increased only at the expense of specificity.49 A sensitive test should be chosen to avoid the non-identification of undernutrition, which can compromise patient's clinical course and result. Specific tests are useful to confirm a diagnosis that has been suggested by other data.49 Thereby, for a screening tool, its sensitivity is more important compared with its specificity. Nevertheless, high proportions of false positives are undesirable in clinical practice procedures.

Given the well-known effect of age on HGS, the low-specificity values here obtained for men aged 18–91 years and women under 65 years may be the result of groups having a wide range age. However, it was not possible to establish narrow age groups given the low sample size obtained when sample was split in more age groups. Further studies should be conducted in larger samples to allow the definition of age groups of narrow amplitude. Moreover, HGS cutoff values for undernutrition screening of ⩾65-year-old women should be tested in larger samples as a high proportion of false negatives was found in this group. The possibility that the low specificities obtained for men aged 18–91 years and for women under 65 years and the low sensitivity obtained for ⩾65-year-old women may be due to uncontrolled confounding factors cannot be ruled out. It is usually not possible to control for all the confounding variables that characterize older inpatients, with many comorbidities. In future studies, a larger sample will also allow to establish subgroups of patients and to improve the understanding of the role of undernutrition in HGS impairment, considering factors such as disease, comorbidity index and education level. The present study results are therefore the first step and the HGS ability to screen undernutrition at hospital admission should be further validated in different samples.

As HGS reflects early nutrition deprivation, before changes in muscle mass or body weight can be detected19,20 the possibility cannot be ruled out that some of the patients with low HGS are developing undernutrition but were not identified as undernourished according to PG-SGA. This phenomenon can, at least in part, be explained by the high false-positive rates, and therefore low specificities were obtained for men aged 18–91 years and for women aged 18–64 years. Given that HGS is able to detect early nutrition changes, the possibility that this parameter is a better undernutrition measure compared with PG-SGA cannot be excluded. Moreover, a stratified analysis by hand dominance would be helpful but within this sample would be statistically unpowered.

Compared with measured height, the previously reported mean differences for height predicted from half-span are considerably higher than for height predicted from hand length and they are clinically relevant.44,46 Nevertheless, as height predicted from half-span was used only in two patients, this is unlikely to have biased study results.

The implications of the exclusion criteria on the external validity of the study results should also be acknowledged. HGS technique requires patients to be conscious and cooperative, and patients unable to provide written informed consent were excluded, which limits the generalization of the results for the hospitalized patients who are in this situation.

Despite the limitations, this study has several strengths. The current study was composed by a large number of inpatients within a wide age range (18–91 years old) from a variety of hospital wards and therefore ensuring a wide spectrum of diagnoses and relevant pathologies. The present HGS values replicate previous study findings,55,56 strengthening the external validity of the present study results. Moreover, a large proportion of undernourished patients compose the study sample, therefore empowering statistical analysis.

Also, several adjustments were made in an attempt to control for possible confounding factors of the association between HGS and nutrition status, considering the ratio of HGS/anthropometrical indicator instead of the HGS value. However, most gender- and age-specific groups did not benefit from these adjustments, given that for some groups the number of false-positive patients decreased, whereas the rate of false positives increased for other patients.

Moreover, in this study PG-SGA was used for undernutrition assessment, whereas NRS-2002 and MUST were applied for undernutrition screening, following recommendations released by different nutrition societies. The American Society for Parenteral and Enteral Nutrition recommends PG-SGA for nutritional assessment,57 whereas for nutritional screening the European Society for Clinical Nutrition and Metabolism6 recommends NRS-2002 and MUST and the British Association for Parenteral and Enteral Nutrition14 recommends MUST.

Although undernutrition screening is recommended by world nutrition societies, this situation is often under recognized,3,11 which may occur because assessment and screening methods are time-consuming, require several measurements, including weighing the patient, in addition to technical expertise. HGS measurement does not require specialized professionals, it is quick and easy to use and therefore an attractive procedure for undernutrition screening in clinical daily practice. For these reasons, the present study is innovative and clinically relevant. HGS cutoff values for undernutrition screening at hospital admission for men aged 18–91 years and for women aged 18–64 years were defined. Despite being promising, these HGS cutoff values should be tested in other samples and validated in future studies.

References

  1. 1

    Rasmussen HH, Holst M, Kondrup J . Measuring nutritional risk in hospitals. Clin Epidemiol 2010; 2: 209–216.

  2. 2

    Barker LA, Gout BS, Crowe TC . Hospital malnutrition: prevalence, identification and impact on patients and the healthcare system. Int J Environ Res Public Health 2011; 8: 514–527.

  3. 3

    Tappenden KA, Quatrara B, Parkhurst ML, Malone AM, Fanjiang G, Ziegler TR . Critical role of nutrition in improving quality of care: an interdisciplinary call to action to address adult hospital malnutrition. J Acad Nutr Diet 2013; 113: 1219–1237.

  4. 4

    Norman K, Pichard C, Lochs H, Pirlich M . Prognostic impact of disease-related malnutrition. Clin Nutr 2008; 27: 5–15.

  5. 5

    Sorensen J, Kondrup J, Prokopowicz J, Schiesser M, Krahenbuhl L, Meier R et al. EuroOOPS: an international, multicentre study to implement nutritional risk screening and evaluate clinical outcome. Clin Nutr 2008; 27: 340–349.

  6. 6

    Kondrup J, Allison SP, Elia M, Vellas B, Plauth M . ESPEN guidelines for nutrition screening 2002. Clin Nutr 2003; 22: 415–421.

  7. 7

    Lim SL, Ong KC, Chan YH, Loke WC, Ferguson M, Daniels L . Malnutrition and its impact on cost of hospitalization, length of stay, readmission and 3-year mortality. Clin Nutr 2012; 31: 345–350.

  8. 8

    van Bokhorst-de van der Schueren MA, Guaitoli PR, Jansma EP, de Vet HC . Nutrition screening tools: Does one size fit all? A systematic review of screening tools for the hospital setting. Clin Nutr 2014; 33: 39–58.

  9. 9

    Leandro-Merhi VA, de Aquino JL, Sales Chagas JF . Nutrition status and risk factors associated with length of hospital stay for surgical patients. JPEN J Parenter Enteral Nutr 2011; 35: 241–248.

  10. 10

    Hiesmayr M, Schindler K, Pernicka E, Schuh C, Schoeniger-Hekele A, Bauer P et al. Decreased food intake is a risk factor for mortality in hospitalised patients: the NutritionDay survey 2006. Clin Nutr 2009; 28: 484–491.

  11. 11

    White JV, Guenter P, Jensen G, Malone A, Schofield M . Consensus statement: Academy of Nutrition and Dietetics and American Society for Parenteral and Enteral Nutrition: characteristics recommended for the identification and documentation of adult malnutrition (undernutrition). JPEN J Parenter Enteral Nutr 2012; 36: 275–283.

  12. 12

    Ottery FD . Patient-Generated Subjective Global Assessment. In: McCallum PD, Polisena CG (eds) The Clinical Guide to Oncology Nutrition. The American Dietetic Association: Chicago, 2000, pp 11–23.

  13. 13

    Kondrup J, Rasmussen HH, Hamberg O, Stanga Z . Nutritional risk screening (NRS 2002): a new method based on an analysis of controlled clinical trials. Clin Nutr 2003; 22: 321–336.

  14. 14

    Elia M . The ‘MUST’ Report. Nutritional Screening of Adults: A Multidisciplinary Responsability. Development and Use of the ‘Malnutrition Universal Screening Tool’ (‘MUST’) for Adults. Malnutrition Advisory Group (MAG), a Standing Committee of the British Association of Parenteral and Enteral Nutrition (BAPEN): Redditch, 2003.

  15. 15

    Lopes J, Russell DM, Whitwell J, Jeejeebhoy KN . Skeletal muscle function in malnutrition. Am J Clin Nutr 1982; 36: 602–610.

  16. 16

    Humphreys J, de la Maza P, Hirsch S, Barrera G, Gattas V, Bunout D . Muscle strength as a predictor of loss of functional status in hospitalized patients. Nutrition 2002; 18: 616–620.

  17. 17

    Russell DM, Leiter LA, Whitwell J, Marliss EB, Jeejeebhoy KN . Skeletal muscle function during hypocaloric diets and fasting: a comparison with standard nutritional assessment parameters. Am J Clin Nutr 1983; 37: 133–138.

  18. 18

    Rantanen T, Volpato S, Ferrucci L, Heikkinen E, Fried LP, Guralnik JM . Handgrip strength and cause-specific and total mortality in older disabled women: exploring the mechanism. J Am Geriatr Soc 2003; 51: 636–641.

  19. 19

    Norman K, Stobaus N, Gonzalez MC, Schulzke JD, Pirlich M . Hand grip strength: outcome predictor and marker of nutritional status. Clin Nutr 2011; 30: 135–142.

  20. 20

    Windsor JA, Hill GL . Grip strength: a measure of the proportion of protein loss in surgical patients. Br J Surg 1988; 75: 880–882.

  21. 21

    Guerra RS, Fonseca I, Pichel F, Restivo MT, Amaral TF . Handgrip strength and associated factors in hospitalized patients. JPEN J Parenter Enteral Nutr 2013; doi:10.1177/0148607113514113.

  22. 22

    Flood A, Chung A, Parker H, Kearns V, O'Sullivan TA . The use of hand grip strength as a predictor of nutrition status in hospital patients. Clin Nutr 2014; 33: 106–114.

  23. 23

    Matos LC, Tavares MM, Amaral TF . Handgrip strength as a hospital admission nutritional risk screening method. Eur J Clin Nutr 2007; 61: 1128–1135.

  24. 24

    Luna-Heredia E, Martin-Pena G, Ruiz-Galiana J . Handgrip dynamometry in healthy adults. Clin Nutr 2005; 24: 250–258.

  25. 25

    Frederiksen H, Hjelmborg J, Mortensen J, McGue M, Vaupel JW, Christensen K . Age trajectories of grip strength: cross-sectional and longitudinal data among 8,342 Danes aged 46 to 102. Ann Epidemiol 2006; 16: 554–562.

  26. 26

    Gunther CM, Burger A, Rickert M, Crispin A, Schulz CU . Grip strength in healthy caucasian adults: reference values. J Hand Surg Am 2008; 33: 558–565.

  27. 27

    Peolsson A, Hedlund R, Oberg B . Intra- and inter-tester reliability and reference values for hand strength. J Rehabil Med 2001; 33: 36–41.

  28. 28

    Budziareck MB, Pureza Duarte RR, Barbosa-Silva MC . Reference values and determinants for handgrip strength in healthy subjects. Clin Nutr 2008; 27: 357–362.

  29. 29

    Schlussel MM, dos Anjos LA, de Vasconcellos MT, Kac G . Reference values of handgrip dynamometry of healthy adults: a population-based study. Clin Nutr 2008; 27: 601–607.

  30. 30

    Silva LF, Matos CM, Lopes GB, Martins MT, Martins MS, Arias LU et al. Handgrip strength as a simple indicator of possible malnutrition and inflammation in men and women on maintenance hemodialysis. J Ren Nutr 2011; 21: 235–245.

  31. 31

    Mendes J, Azevedo A, Amaral TF . Handgrip strength at admission and time to discharge in medical and surgical inpatients. JPEN J Parenter Enteral Nutr 2014; 38: 481–488.

  32. 32

    Kilgour RD, Vigano A, Trutschnigg B, Lucar E, Borod M, Morais JA . Handgrip strength predicts survival and is associated with markers of clinical and functional outcomes in advanced cancer patients. Support Care Cancer 2013; 21: 3261–3270.

  33. 33

    Mahalakshmi VN, Ananthakrishnan N, Kate V, Sahai A, Trakroo M . Handgrip strength and endurance as a predictor of postoperative morbidity in surgical patients: can it serve as a simple bedside test? Int Surg 2004; 89: 115–121.

  34. 34

    Bohannon RW, Schaubert KL . Test-retest reliability of grip-strength measures obtained over a 12-week interval from community-dwelling elders. J Hand Ther 2005; 18: 426–427.

  35. 35

    ASPEN Board of Directors and the Clinical Guidelines Task Force. Guidelines for the use of parenteral and enteral nutrition in adult and pediatric patients. JPEN J Parenter Enteral Nutr 2002; 26: 1SA–138SA.

  36. 36

    Hodkinson HM . Evaluation of a mental test score for assessment of mental impairment in the elderly. Age Ageing 1972; 1: 233–238.

  37. 37

    Charlson ME, Pompei P, Ales KL, MacKenzie CR . A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987; 40: 373–383.

  38. 38

    Fess EE . Grip Strength, 2nd edn. American Society of Hand Therapists: Chicago, 1992.

  39. 39

    Fess EE, Moran CA . Clinical Assessment Recommendations. American Society of Hand Therapists: Chicago, 1981.

  40. 40

    Vaz M, Thangam S, Prabhu A, Shetty PS . Maximal voluntary contraction as a functional indicator of adult chronic undernutrition. Br J Nutr 1996; 76: 9–15.

  41. 41

    Muscular weakness assessment: use of normal isometric strength data. The National Isometric Muscle Strength (NIMS) Database Consortium. Arch Phys Med Rehabil 1996; 77: 1251–1255.

  42. 42

    Marfell-Jones M, Olds T, Stewart A, Carter L . International Standards for Anthropometric Assessment. International Standards for Anthropometric Assessment: Potchefstroom: South Africa, 2006.

  43. 43

    Lee RD, Nieman DC . Assessment of the hospitalized patient. In: Lee RD, Nieman DC (eds). Nutritional Assessment, 4th edn. McGraw Hill Higher Education: Boston, 2007, pp 226–231.

  44. 44

    Kwok T, Whitelaw MN . The use of armspan in nutritional assessment of the elderly. J Am Geriatr Soc 1991; 39: 492–496.

  45. 45

    Chroni E, Paschalis C, Arvaniti C, Zotou K, Nikolakopoulou A, Papapetropoulos T . Carpal tunnel syndrome and hand configuration. Muscle Nerve 2001; 24: 1607–1611.

  46. 46

    Guerra RS, Fonseca I, Pichel F, Restivo MT, Amaral TF . Hand length as an alternative measurement of height. Eur J Clin Nutr 2014; 68: 229–233.

  47. 47

    Powell-Tuck J, Hennessy EM . A comparison of mid upper arm circumference, body mass index and weight loss as indices of undernutrition in acutely hospitalized patients. Clin Nutr 2003; 22: 307–312.

  48. 48

    Pederson D, Gore C . Anthropometry Measurement Error. University of New South Wales Press: Sydney, Australia, 1996.

  49. 49

    Fletcher RH, Fletcher SW . Diagnosis. In: Fletcher RW, Fletcher SW (eds). Clinical Epidemiology: The Essentials, 4th edn. Lippincott, Williams & Wilkins: Philadelphia, 2005, pp 35–58.

  50. 50

    Hanley JA, McNeil BJ . The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982; 143: 29–36.

  51. 51

    Kyle UG, Kossovsky MP, Karsegard VL, Pichard C . Comparison of tools for nutritional assessment and screening at hospital admission: a population study. Clin Nutr 2006; 25: 409–417.

  52. 52

    Garcia MF, Meireles MS, Führ LM, Donini AB, Wazlawik E . Relationship between hand grip strength and nutritional assessment methods used of hospitalized patients. Rev Nutr 2013; 26: 49–57.

  53. 53

    Haverkort EB, Binnekade JM, de Haan RJ, van Bokhorst-de van der Schueren MA . Handgrip strength by dynamometry does not identify malnutrition in individual preoperative outpatients. Clin Nutr 2012; 31: 647–651.

  54. 54

    Pham NV, Cox-Reijven PL, Wodzig WK, Greve JW, Soeters PB . SGA and measures for muscle mass and strength in surgical Vietnamese patients. Nutrition 2007; 23: 283–291.

  55. 55

    Mendes J, Alves P, Amaral TF . Comparison of nutritional status assessment parameters in predicting length of hospital stay in cancer patients. Clin Nutr 2014; 33: 466–470.

  56. 56

    Norman K, Stobaus N, Reiss J, Schulzke J, Valentini L, Pirlich M . Effect of sexual dimorphism on muscle strength in cachexia. J Cachexia Sarcopenia Muscle 2012; 3: 111–116.

  57. 57

    Mueller C, Compher C, Ellen DM . A.S.P.E.N. clinical guidelines: Nutrition screening, assessment, and intervention in adults. JPEN J Parenter Enteral Nutr 2011; 35: 16–24.

Download references

Acknowledgements

We thank Centro Hospitalar do Porto and all ward directors for facilitating the data collection. RSG as a Ph.D. student is receiving a scholarship from FCT – Fundação para a Ciência e a Tecnologia under the project (SFRH/BD/61656/2009).

Author information

Affiliations

  1. Departamento de Bioquímica, Faculdade de Medicina da Universidade do Porto, Porto, Portugal
    • R S Guerra
  2. Unidade de Integração de Sistemas e Processos Automatizados, Instituto de Engenharia Mecânica (UISPA-IDMEC), Faculdade de Engenharia da Universidade do Porto, Porto, Portugal
    • R S Guerra
    • , M T Restivo
    •  & T F Amaral
  3. Unidade de Nutrição, Centro Hospitalar do Porto, Porto, Portugal
    • R S Guerra
    • , I Fonseca
    •  & F Pichel
  4. Faculdade de Ciências da Nutrição e Alimentação da Universidade do Porto, Porto, Portugal
    • T F Amaral
Authors
  1. Search for R S Guerra in:
  2. Search for I Fonseca in:
  3. Search for F Pichel in:
  4. Search for M T Restivo in:
  5. Search for T F Amaral in:

Corresponding author

Correspondence to R S Guerra.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Guerra, R., Fonseca, I., Pichel, F. et al. Handgrip strength cutoff values for undernutrition screening at hospital admission. Eur J Clin Nutr 68, 1315–1321 (2014) doi:10.1038/ejcn.2014.226

Download citation

Further reading