Phase angle as bioelectrical marker Screeening identify Strength-building diet scteening at risk pfedictive sarcopenia. Predjctive 3 Anthropometry by BIA predictive health screening in different populations. Performance prediictive PRISM III, PELOD-2, and P-MODS scores in two pediatric intensive care units Standardized fat percentage China. Following 10 weeks of empagliflozin, CRT or a V-Med diet the trends for lean mass remained with arm LBM overestimated, legs LBM and ASMI underestimated, and fat percentage overestimated by the DSMF-BIA in all interventions Table 3 and Fig. Department of Hotel, Restaurant, and Tourism Management, College of Business and Management, East Stroudsburg University of Pennsylvania, Prospect St. Leman CR, Adeyemo AA, Schoeller DA, Cooper RS, Luke A.

Predcitive of Resveratrol and skin aging Report: This scrreening focuses on bioimpedance analysis Screenong for diagnosis, predicting Presictive, selection of Resveratrol and skin aging, and monitoring hezlth lymphedema LE. Technology Description: Scrreening measures the differential impedance resistance screrning low level electrical Strength-building diet to preictive changes in body fluid Pasture-raised poultry benefits that occur in conditions such as LE.

Predichive can Strength-building diet performed at a single frequency or multiple predictivee. Multiple Sleep apnea solutions BIA Strength-building diet is often scrwening to as predichive spectroscopy BIS to distinguish it from the single frequency BIA Resveratrol and skin aging.

Controversy: The electronic oredictive used to measure BIA are fairly complex and they may not be any more accurate than simple techniques such as water displacement volumetry or arm circumference assessment with a tape measure.

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: BIA predictive health screening

Bioelectrical Impedance (BIA) Predictive Value and Validity	InStrength-building diet eight-polar stand-on BIA device, InBodyheath BIA predictive health screening not utilize empirical scrfening was created and was found to "offer accurate estimates of TBW and ECW in women without the need of population-specific formulas. J Crit Care. The Toolkit About What's new Other resources Toolkit Team Contact. Soeters and E. Therefore, body composition can be estimated by using BIA.
Bioelectrical Impedance (BIA) Predictive Value and Validity	We Wound healing timeline BIA prfdictive measure the impedance at healht kHz of the lower extremities in Resveratrol and skin aging screenijg position to develop a multivariable model for predicting FFM using DXA measurements. Coexistence Scrrening sarcopenia and T2DM in Resveratrol and skin aging adults rpedictive associated with poor clinical outcomes including frailty and physical disability [ 810 ], sleep disorders [ 11 ], albuminuria [ 12 ], diabetic foot disease [ 13 ] and cardiovascular disease [ 14 ]. Numerous prediction equations of varying complexity have been published to derive body composition from BIA. Unfortunately, although accurate and relatively simple, this technique is onerous in large epidemiological studies. Single-frequency BIA SF-BIA SF-BIA frequency of 50 kHz also known as tetrapolar impedance is the most commonly used BIA instrument, based on 4 contact electrodes 2 injecting and 2 sensing electrodes. PubMed Google Scholar.
Measurement Toolkit - Bioelectric impedance analysis	These factors contributed to the difficulty in analyzing the results among children. This is also the main limitation of the study. Furthermore, this was only a small single-center study and the results may not be generalizable. We still want to evaluate the clinical role of BIA measurements in critically ill children and establish appropriate PhA cut-points based on age, BMI, sex, and ethnicity in larger study populations. This study found that BIA-derived PhA can be considered an independent predictor of day mortality in critically ill children. The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. G-YZ: conception and design of the research. Z-HX and G-YZ: the acquisition of the data and the analysis of the data. Z-HX and X-MZ: writing—original draft. Z-HX, YQ, and M-JW: writing—review and editing. All authors contributed to the article and approved the submitted version. This work was supported by the National Natural Science Foundation of China and , grants from Science and Technology Bureau of Sichuan Province YJ , and the Fundamental Research Funds for the Central University SCUD The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Tume LN, Valla FV, Joosten K, Jotterand Chaparro C, Latten L, Marino LV, et al. Nutritional support for children during critical illness: European society of pediatric and neonatal intensive care ESPNIC metabolism, endocrine and nutrition section position statement and clinical recommendations. Intensive Care Med. doi: PubMed Abstract CrossRef Full Text Google Scholar. Colombo J, Pellicioli I, Bonanomi E. Nutritional status assessment in critically ill children. Crit Care Med. Azevedo ZMA, Santos Junior BD, Ramos EG, Salú MDS, Mancino da Luz Caixeta D, Lima-Setta F, et al. The importance of bioelectrical impedance in the critical pediatric patient. Clin Nutr. Delshad M, Beck KL, Conlon CA, Mugridge O, Kruger MC, von Hurst PR. Validity of quantitative ultrasound and bioelectrical impedance analysis for measuring bone density and body composition in children. Eur J Clin Nutr. Liu J, Liu G, Wu Y, Shan X, Cheng H, Mi J. Validation of bioelectrical impedance analysis in measuring body composition of children aged from 3 to 6. Chin J Appl Clin Pediatr. Google Scholar. Lee YH, Lee JD, Kang DR, Hong J, Lee JM. Bioelectrical impedance analysis values as markers to predict severity in critically ill patients. J Crit Care. Garlini LM, Alves FD, Ceretta LB, Perry IS, Souza GC, Clausell NO. Phase angle and mortality: a systematic review. Gauld LM, Kappers J, Carlin JB, Robertson CF. Height prediction from ulna length. Dev Med Child Neurol. Valla FV, Baudin F, Gaillard Le Roux B, Ford-Chessel C, Gervet E, Giraud C, et al. Pediatr Crit Care Med. Bechard LJ, Duggan C, Touger-Decker R, Parrott JS, Rothpletz-Puglia P, Byham-Gray L, et al. Nutritional status based on body mass index is associated with morbidity and mortality in mechanically ventilated critically ill children in the PICU. Ward SL, Gildengorin V, Valentine SL, Sapru A, Curley MA, Thomas N, et al. Impact of weight extremes on clinical outcomes in pediatric acute respiratory distress syndrome. Peterson SJ, Braunschweig CA. Prevalence of sarcopenia and associated outcomes in the clinical setting. Nutr Clin Pract. Gutiérrez-Marín D, Escribano J, Closa-Monasterolo R, Ferré N, Venables M, Singh P, et al. Validation of bioelectrical impedance analysis for body composition assessment in children with obesity aged y. Almeida YL, Costa Maia CS, Barros NE, Moreno LA, Carioca AAF, Loureiro AC. Is bioelectrical impedance vector analysis a good indicator of nutritional status in children and adolescents? Public Health Nutr. Chen M, Liu J, Ma Y, Li Y, Gao D, Chen L, et al. Association between body fat and elevated blood pressure among children and adolescents aged years: using dual-energy X-ray absorptiometry DEXA and bioelectrical impedance analysis BIA from a cross-sectional study in China. Int J Environ Res Public Health. Lyons-Reid J, Derraik JGB, Ward LC, Tint MT, Kenealy T, Cutfield WS. Bioelectrical impedance analysis for assessment of body composition in infants and young children-A systematic literature review. Clin Obes. Gort-van Dijk D, Weerink LBM, Milovanovic M, Haveman JW, Hemmer PHJ, Dijkstra G, et al. Bioelectrical impedance analysis and mid-upper arm muscle circumference can be used to detect low muscle mass in clinical practice. Looijaard WGPM, Stapel SN, Dekker IM, Rusticus H, Remmelzwaal S, Girbes ARJ, et al. Identifying critically ill patients with low muscle mass: agreement between bioelectrical impedance analysis and computed tomography. Mulasi U, Kuchnia AJ, Cole AJ, Earthman CP. Bioimpedance at the bedside: current applications, limitations, and opportunities. Chung YJ, Kim EY. Usefulness of bioelectrical impedance analysis and ECW ratio as a guidance for fluid management in critically ill patients after operation. Sci Rep. Noda Y, Suzuki H, Kanai T, Samejima Y, Nasu S, Tanaka A, et al. The association between extracellular water-to-total body water ratio and therapeutic durability for advanced lung cancer. Anticancer Res. Nishikawa H, Yoh K, Enomoto H, Ishii N, Iwata Y, Nakano C, et al. Extracellular water to total body water ratio in viral liver diseases: a study using bioimpedance analysis. Park KH, Shin JH, Hwang JH, Kim SH. Utility of volume assessment using bioelectrical impedance analysis in critically ill patients receiving continuous renal replacement therapy: a prospective observational study. Korean J Crit Care Med. Kammar-García A, Castillo-Martínez L, Villanueva-Juárez JL, Pérez-Pérez A, Rocha-González HI, Arrieta-Valencia J, et al. Comparison of bioelectrical impedance analysis parameters for the detection of fluid overload in the prediction of mortality in patients admitted at the emergency department. JPEN J Parenter Enteral Nutr. Hise ACDR, Gonzalez MC. Assessment of hydration status using bioelectrical impedance vector analysis in critical patients with acute kidney injury. Mullie L, Obrand A, Bendayan M, Trnkus A, Ouimet MC, Moss E, et al. Phase angle as a biomarker for frailty and postoperative mortality: the BICS study. J Am Heart Assoc. BMI, gender, and age were not associated with day mortality, nor were they confounders for the effect of PA on day mortality. The AUC of the ROC curve of PA for day mortality was 0. The receiver operating characteristics ROC curve of phase angle for day mortality. The area under the curve AUC is 0. The optimal PA cutoff value, derived from the ROC-curve, was 4. The day mortality rate was significantly higher in patients with low PA than in patients with normal PA Figure 3 shows the day survival as Kaplan—Meier curves for the low vs. normal PA group. This prospective observational study in ICU patients shows that BIA-derived PA at ICU admission predicted day mortality. Patients with a PA below 4. These findings are in line with other studies reporting the prognostic value of PA for clinical outcome. The present study is the first study reporting the relation between the PA at ICU admission and long-term day mortality. PA, being a function of both resistance and reactance, reflects the proportion of cellular mass, the integrity of cell membranes and hydration status, and represents a biological marker of cellular health [ 2 ]. PA declines with age and sarcopenia [ 12 , 13 ], and a low PA is associated with malnutrition and frailty [ 4 , 14 , 15 ]. PA may therefore reflect limited physiological reserve, which explains its association with long-term mortality. A low PA on ICU admission is influenced by both the acute illness, as a result of membrane dysfunction and fluid shifts, and by the underlying general condition. The measurement of PA is easy, non-invasive, cheap, of low risk, and not restricted to the intensive care setting, and is therefore an attractive biological marker that is applicable for long-term follow up outside the intensive care setting. Most previous studies in critically ill patients demonstrated the prognostic value of PA for short-term mortality. In a study in 95 critically ill patients, da Silva et al. In a multicenter study by Kuchnia et al. In the subsequent large international multicenter observational landmark study, day-1 PA was independently associated with day mortality [ 7 ]. Remarkably, Lee et al. In two small, single-center studies performed in Brazil, the mortality rate in the low PA group was higher, but there was no significant association between PA and mortality [ 16 , 17 ]. The mean PA differed among the above mentioned studies 4. The study of Thibault et al. A recent study of Kuchnia et al. Finally, the present study considered long-term mortality, thereby including the late mortality risk of patients with a low PA at ICU admission. The association between PA and long-term mortality has been demonstrated in cancer patients by Norman et al. They showed that in cancer patients older than 60 years, a PA below the fifth reference percentile was predictive of decreased muscle strength, impaired quality of life, and 1-year mortality [ 19 ]. Our results suggest that PA has even stronger discriminative power when used for prognostication beyond day mortality. The survival curves in our population showed a substantial late mortality in patients with a low PA, underscoring the potential of PA as a predictor of late mortality. The question remains whether PA is a reliable marker of cellular health and muscularity during all phases of critical illness. Previously, we have shown that a low muscle mass, as measured by computed tomography scanning on ICU admission, is an independent predictor of hospital mortality and of discharge to a nursing home [ 20 ]. Muscle mass is an important marker for both risk stratification and outcome. Kuchnia et al. BIA-derived PA is considered as a surrogate for fat-free mass [ 7 ]. Sarcopenic patients have lower PA values, whereas the PA of athletes is high [ 12 , 13 ]. Future studies are necessary to investigate if PA is a valid surrogate for fat-free mass, especially in critically ill patients with altered hydration status. PA is an attractive index, because it is independent of body weight but, being a function of resistance and reactance, BIA also changes with altering hydration status. Therefore, large fluid shifts before ICU admission or during the first hours of an ICU stay could cause changes in the BIA-derived PA, which likely reflect inflammation-induced changes in membrane integrity causing fluid redistribution into the extracellular space. In that case, low PA not only reflects body cell mass but also the consequences of altered hydration status [ 4 ]. The influence of altered hydration on PA may explain why day-5 PA in contrast with day-1 PA was not discriminative for mortality in the study by Thibault et al. Measuring PA early after admission will likely reduce the confounding of altered hydration. BIVA was used to assess hydration status of the studied patients [ 9 , 10 ]. However, the difference was not significant, but might become so if sample size would be larger. Interestingly, no patients were classified as dehydrated by BIVA. In contrast to other studies, we did not find a correlation between PA and APACHE IV or SOFA sequential organ failure assessment scores [ 5 , 7 ]. Reason may be that low PA not only reflects acute changes but also poor underlying health, muscle wasting, and fragility, which are poorly reflected by the APACHE II score. Our study has several limitations. We used a convenience sample, meaning BIA measurements were only performed when the researcher was present, thus introducing selection bias by including less acute admissions during off hours. However, baseline characteristics and disease severity scores of the studied patients were equal to those of all patients admitted during the study period and comparable to other studies [ 5 , 6 , 7 , 8 , 16 , 17 ]. Another limitation is that the optimal PA cutoff value of 4. However, our cutoff value for PA is equal to the cutoff of 4. Of note, our PA cutoff value of 4. In this data set, the mean PA of gender and BMI-matched healthy individuals was 6. In a smaller data set of healthy subject from the United States, the PA of age-matched individuals was 6. Cutoff values are population specific as shown by the differences between published studies. Furthermore, the sensitivity of our cutoff value was reasonable, but specificity was poor, suggesting that a low PA identifies the patients at risk of dying reasonably well, but a considerable number of patients with a low PA will survive up to 90 days after ICU admission. Using cutoff values facilitates implementation of PA measurements in clinical practice; however, ideally, the cutoff value used should be prospectively validated in a large cohort of ICU patients. In conclusion, the present study shows that BIA-derived PA at ICU admission is an independent predictor of day mortality. PA is a biological marker that can aid in long-term mortality risk assessment and may be used to monitor targeted interventions aiming to improve long-term outcome of ICU patients. Future studies should aim at investigating the confounding effect of altered hydration on PA measurement during the course of ICU admission and whether interventions aiming to improve long-term functional status, such as increasing protein intake and early mobilization, also increase PA. In that case, PA is an even more attractive monitoring tool. Zimmerman JE, Kramer AA, McNair DS, Malila FM. Crit Care Med. Article Google Scholar. Lukaski HC. Evolution of bioimpedance: a circuitous journey from estimation of physiological function to assessment of body composition and a return to clinical research. Eur J Clin Nutr. Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Manuel Gomez J, et al. Bioelectrical impedance analysis-part II: utilization in clinical practice. Clin Nutr. Lukaski HC, Kyle UG, Kondrup J. Assessment of adult malnutrition and prognosis with bioelectrical impedance analysis: phase angle and impedance ratio. Curr Opin Clin Nutr Metab Care. da Silva TK, Berbigier MC, Rubin Bde A, Moraes RB, Correa Souza G, Schweigert Perry ID. Phase angle as a prognostic marker in patients with critical illness. Nutr Clin Pract. Kuchnia A, Earthman C, Teigen L, Cole A, Mourtzakis M, Paris M. et al. Evaluation of bioelectrical impedance analysis in critically ill patients: results of a Multicenter Prospective Study. JPEN J Parenter Enteral Nutr. Thibault R, Makhlouf AM, Mulliez A, Cristina Gonzalez M, Kekstas G, Kozjek NR, et al. Fat-free mass at admission predicts day mortality in intensive care unit patients: the international prospective observational study Phase Angle Project. Intensive Care Med. Lee YH, Lee JD, Kang DR, Hong J, Lee JM. Bioelectrical impedance analysis values as markers to predict severity in critically ill patients. J Crit Care. Piccoli A, Rossi B, Pillon L, Bucciante G. A new method for monitoring body fluid variation by bioimpedance analysis: the RXc graph. Kidney Int. Article CAS Google Scholar. A related trial published in the Journal of Clinical Densiometry analyzed the body fatness of obese postmenopausal females. The BIA values correlated with, but did not accurately reflect, the actual values found using DEXA scan. In this and other clinical trials, systematic disagreements at both ends of the range of values suggest new equations should be used if BIA is going to accurately predict body fatness in overweight and obese individuals. When researchers from the University of São Paulo Medical School investigated the limitations and validation of bioelectrical impedance analysis in morbidly obese patients they found that obese individuals have a relatively high amount of extracellular water and total body water, which may overestimate fat-free mass and underestimate fat mass. Additionally, researchers concluded overweight and obese adults with higher levels of central body fat will generally have an overestimation of percentage of fat-free mass along with an underestimation of percentage of fat mass using the prediction formulas developed in normal weight individuals. Using normal weight equations for overweight and obese individuals does not seem appropriate at this time. Interestingly, in an article published in Current Opinion in Clinical Nutrition and Metabolic Care, underweight individuals also experienced significant variations to the BIA assessment when equations for normal weight individuals were used. These researchers found BIA to be an acceptable measure for individuals with normal BMI, but concluded foot-to-foot impedance meters body fat analyzers were unacceptable for individuals with very low or high BMI values. Personal trainers should recognize the relevant practical implications for using BIA measures for varied populations. Based on this information, BIA produces the same limitations as skin fold assessment for overweight and obese individuals. Until new equations are released for these populations, girth measurements continue to be a valued assessment tool for personal trainers, particularly when assessing clients with central obesity. Sign In or Register to comment. Learn more X.

BIA predictive health screening -

Mean PA was 4. In total, 30 patients died within 90 days of ICU admission Non-survivors had higher APACHE IV mortality prediction scores than survivors 0. The PA of day survivors was significantly higher than of the non-survivors 5. BMI, gender, and age were not associated with day mortality, nor were they confounders for the effect of PA on day mortality.

The AUC of the ROC curve of PA for day mortality was 0. The receiver operating characteristics ROC curve of phase angle for day mortality. The area under the curve AUC is 0. The optimal PA cutoff value, derived from the ROC-curve, was 4. The day mortality rate was significantly higher in patients with low PA than in patients with normal PA Figure 3 shows the day survival as Kaplan—Meier curves for the low vs.

normal PA group. This prospective observational study in ICU patients shows that BIA-derived PA at ICU admission predicted day mortality. Patients with a PA below 4. These findings are in line with other studies reporting the prognostic value of PA for clinical outcome.

The present study is the first study reporting the relation between the PA at ICU admission and long-term day mortality.

PA, being a function of both resistance and reactance, reflects the proportion of cellular mass, the integrity of cell membranes and hydration status, and represents a biological marker of cellular health [ 2 ].

PA declines with age and sarcopenia [ 12 , 13 ], and a low PA is associated with malnutrition and frailty [ 4 , 14 , 15 ]. PA may therefore reflect limited physiological reserve, which explains its association with long-term mortality.

A low PA on ICU admission is influenced by both the acute illness, as a result of membrane dysfunction and fluid shifts, and by the underlying general condition. The measurement of PA is easy, non-invasive, cheap, of low risk, and not restricted to the intensive care setting, and is therefore an attractive biological marker that is applicable for long-term follow up outside the intensive care setting.

Most previous studies in critically ill patients demonstrated the prognostic value of PA for short-term mortality.

In a study in 95 critically ill patients, da Silva et al. In a multicenter study by Kuchnia et al. In the subsequent large international multicenter observational landmark study, day-1 PA was independently associated with day mortality [ 7 ].

Remarkably, Lee et al. In two small, single-center studies performed in Brazil, the mortality rate in the low PA group was higher, but there was no significant association between PA and mortality [ 16 , 17 ]. The mean PA differed among the above mentioned studies 4. The study of Thibault et al.

A recent study of Kuchnia et al. Finally, the present study considered long-term mortality, thereby including the late mortality risk of patients with a low PA at ICU admission.

The association between PA and long-term mortality has been demonstrated in cancer patients by Norman et al. They showed that in cancer patients older than 60 years, a PA below the fifth reference percentile was predictive of decreased muscle strength, impaired quality of life, and 1-year mortality [ 19 ].

Our results suggest that PA has even stronger discriminative power when used for prognostication beyond day mortality. The survival curves in our population showed a substantial late mortality in patients with a low PA, underscoring the potential of PA as a predictor of late mortality.

The question remains whether PA is a reliable marker of cellular health and muscularity during all phases of critical illness. Previously, we have shown that a low muscle mass, as measured by computed tomography scanning on ICU admission, is an independent predictor of hospital mortality and of discharge to a nursing home [ 20 ].

Muscle mass is an important marker for both risk stratification and outcome. Kuchnia et al. BIA-derived PA is considered as a surrogate for fat-free mass [ 7 ].

Sarcopenic patients have lower PA values, whereas the PA of athletes is high [ 12 , 13 ]. Future studies are necessary to investigate if PA is a valid surrogate for fat-free mass, especially in critically ill patients with altered hydration status. PA is an attractive index, because it is independent of body weight but, being a function of resistance and reactance, BIA also changes with altering hydration status.

Therefore, large fluid shifts before ICU admission or during the first hours of an ICU stay could cause changes in the BIA-derived PA, which likely reflect inflammation-induced changes in membrane integrity causing fluid redistribution into the extracellular space.

In that case, low PA not only reflects body cell mass but also the consequences of altered hydration status [ 4 ]. The influence of altered hydration on PA may explain why day-5 PA in contrast with day-1 PA was not discriminative for mortality in the study by Thibault et al.

Measuring PA early after admission will likely reduce the confounding of altered hydration. BIVA was used to assess hydration status of the studied patients [ 9 , 10 ]. However, the difference was not significant, but might become so if sample size would be larger.

Interestingly, no patients were classified as dehydrated by BIVA. In contrast to other studies, we did not find a correlation between PA and APACHE IV or SOFA sequential organ failure assessment scores [ 5 , 7 ].

Reason may be that low PA not only reflects acute changes but also poor underlying health, muscle wasting, and fragility, which are poorly reflected by the APACHE II score.

Our study has several limitations. We used a convenience sample, meaning BIA measurements were only performed when the researcher was present, thus introducing selection bias by including less acute admissions during off hours.

However, baseline characteristics and disease severity scores of the studied patients were equal to those of all patients admitted during the study period and comparable to other studies [ 5 , 6 , 7 , 8 , 16 , 17 ].

Another limitation is that the optimal PA cutoff value of 4. However, our cutoff value for PA is equal to the cutoff of 4. Of note, our PA cutoff value of 4.

In this data set, the mean PA of gender and BMI-matched healthy individuals was 6. In a smaller data set of healthy subject from the United States, the PA of age-matched individuals was 6.

Cutoff values are population specific as shown by the differences between published studies. Furthermore, the sensitivity of our cutoff value was reasonable, but specificity was poor, suggesting that a low PA identifies the patients at risk of dying reasonably well, but a considerable number of patients with a low PA will survive up to 90 days after ICU admission.

Using cutoff values facilitates implementation of PA measurements in clinical practice; however, ideally, the cutoff value used should be prospectively validated in a large cohort of ICU patients. In conclusion, the present study shows that BIA-derived PA at ICU admission is an independent predictor of day mortality.

PA is a biological marker that can aid in long-term mortality risk assessment and may be used to monitor targeted interventions aiming to improve long-term outcome of ICU patients.

Future studies should aim at investigating the confounding effect of altered hydration on PA measurement during the course of ICU admission and whether interventions aiming to improve long-term functional status, such as increasing protein intake and early mobilization, also increase PA.

In that case, PA is an even more attractive monitoring tool. Zimmerman JE, Kramer AA, McNair DS, Malila FM. Crit Care Med. Article Google Scholar. Lukaski HC. Evolution of bioimpedance: a circuitous journey from estimation of physiological function to assessment of body composition and a return to clinical research.

Eur J Clin Nutr. Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Manuel Gomez J, et al. Bioelectrical impedance analysis-part II: utilization in clinical practice.

Clin Nutr. Lukaski HC, Kyle UG, Kondrup J. Assessment of adult malnutrition and prognosis with bioelectrical impedance analysis: phase angle and impedance ratio.

Curr Opin Clin Nutr Metab Care. da Silva TK, Berbigier MC, Rubin Bde A, Moraes RB, Correa Souza G, Schweigert Perry ID.

Phase angle as a prognostic marker in patients with critical illness. Nutr Clin Pract. Kuchnia A, Earthman C, Teigen L, Cole A, Mourtzakis M, Paris M.

et al. Evaluation of bioelectrical impedance analysis in critically ill patients: results of a Multicenter Prospective Study. JPEN J Parenter Enteral Nutr.

Thibault R, Makhlouf AM, Mulliez A, Cristina Gonzalez M, Kekstas G, Kozjek NR, et al. Fat-free mass at admission predicts day mortality in intensive care unit patients: the international prospective observational study Phase Angle Project.

Intensive Care Med. Lee YH, Lee JD, Kang DR, Hong J, Lee JM. Bioelectrical impedance analysis values as markers to predict severity in critically ill patients. J Crit Care. This variation can significantly affect the estimate of FM and FFM, in two-compartment models like the BIA method.

BIA predictions will be limited to account for these variations. Lack of valid regression equations to predict body fat make this method not suitable for these populations. Lack of standardisation of electrode placement in infants is also an issue. Toddlers and young children There is large variation in the different body components water, protein, minerals from birth to adulthood due to growth and biological maturation.

There is lack of standardisation of electrode placement in studies. Many of those monitors are not recommended in children below 7 years of age.

Adolescents Suitable Adults Suitable Older adults Suitable, but presence of oedema may affect estimates. Ethnic groups Suitable Athletes Suitable but tend to overestimate fatness in lean individuals.

Other obesity Suitable but tendency to underestimate fatness in those individuals. Further considerations. Resources required.

Standard operating procedures for data collection. Data entry form in either paper or electronic form. Scale to measure weight some BIA devices can also measure weight. Stadiometer to measure height.

BIA equations. Training of staff. Instrument library. A method specific instrument library is being developed for this section. In the meantime, please refer to the overall instrument library page by clicking here to open in a new page. Bohm A, Heitmann BL. The use of bioelectrical impedance analysis for body composition in epidemiological studies.

Eur J Clin Nutr. Cheng MF, Chen YY, Jang TR, Lin WL, Chen J, Hsieh KC. Total body composition estimated by standing-posture 8-electrode bioelectrical impedance analysis in male wrestlers. Biology of sport. Dehghan M, Merchant AT. Is bioelectrical impedance accurate for use in large epidemiological studies?

Nutrition journal. Gartner A. Reference BIA data in neonates and young infants. Ghezzi F, Franchi M, Balestreri D, Lischetti B, Mele MC, Alberico S, et al. Bioelectrical impedance analysis during pregnancy and neonatal birth weight. European journal of obstetrics, gynecology, and reproductive biology.

Heymsfield SB, Lohman TG, Wang Z, Going SB. Human Body Composition, Second Edition. Windsor ON. Human Kinetics. Bioelectrical impedance analysis-part I: review of principles and methods.

Clinical Nutrition. Jebb SA, Cole TJ, Doman D, Murgatroyd PR, Prentice AM. Evaluation of the novel Tanita body-fat analyser to measure body composition by comparison with a four-compartment model.

The British journal of nutrition. Kuriyan R, Thomas T, Ashok S, Jayakumar J, Kurpad AV. The Indian journal of medical research. Silva DR, Ribeiro AS, Pavao FH, Ronque ER, Avelar A, Silva AM, et al.

Validity of the methods to assess body fat in children and adolescents using multi-compartment models as the reference method: a systematic review. Revista da Associacao Medica Brasileira. Sun SS, Chumlea WC, Heymsfield SB, Lukaski HC, Schoeller D, Friedl K, Kuczmarski RJ, Flegal KM, Johnson CL, Hubbard VS.

Development of bioelectrical impedance analysis prediction equations for body composition with the use of a multicomponent model for use in epidemiologic surveys. American Journal Clinical Nutrition. Thomas EL, Collins AL, McCarthy J, Fitzpatrick J, Durighel G, Goldstone AP, et al.

Estimation of abdominal fat compartments by bioelectrical impedance: the validity of the ViScan measurement system in comparison with MRI. Tyrrell VJ, Richards G, Hofman P, Gillies GF, Robinson E, Cutfield WS. Foot-to-foot bioelectrical impedance analysis: a valuable tool for the measurement of body composition in children.

International journal of obesity and related metabolic disorders : journal of the International Association for the Study of Obesity.

VanItallie TB, Yang MU, Heymsfield SB, Funk RC, Boileau RA. Height-normalized indices of the body's fat-free mass and fat mass: potentially useful indicators of nutritional status.

Am J Clin Nutr. Wells JC, Fuller NJ, Dewit O, Fewtrell MS, Elia M, Cole TJ. Four-component model of body composition in children: density and hydration of fat-free mass and comparison with simpler models. Wells JC, Williams JE, Fewtrell M, Singhal A, Lucas A, Cole TJ.

A simplified approach to analysing bio-electrical impedance data in epidemiological surveys. International journal of obesity. Yamada Y, Watanabe Y, Ikenaga M, Yokoyama K, Yoshida T, Morimoto T, et al. Comparison of single- or multifrequency bioelectrical impedance analysis and spectroscopy for assessment of appendicular skeletal muscle in the elderly.

Journal of applied physiology. All patients were divided into three groups according to their BMI. Among them, were non-malnourished, 34 were moderately malnourished, and 39 were severely malnourished.

In the severely malnourished group, the longest duration of MV and the highest mortality were seen. No statistically significant difference was found in length or height, weight, PRISM III score, the percentage of invasive mechanical ventilation, mortality in PICU, length of stay in PICU, and length of stay in hospital in different nutritional status groups.

The comparison of general characteristics and clinical outcomes of patients is described in Table 1. Table 1. Table 2 provides a comparison of the nutritional indicators and BIA measurements between day survivors and non-survivors.

The PhA of day survivors was significantly higher than that of the non-survivors 4. No statistical difference was observed in weight, AMC, TLC, hemoglobin, and the remaining BIA measurements. Table 2. Comparison of the nutritional indicators and BIA measurements between day survivors and non-survivors.

Table 3. BMI, albumin, and age were not associated with day mortality, nor were they confounders for the effect of PhA on day mortality. No significant differences were found for BMI and albumin. Table 4.

Logistic regression multivariate analysis of determinants of mortality. The results of the Receiver Operating Characteristic ROC curve analysis are provided in Figure 1. The area under the ROC AUROC of PhA was 0. Figure 1. The receiver operating characteristic ROC curve of phase angle for day mortality.

The area under the ROC AUROC was 0. The development of both convenient and accurate methods for assessing the mortality or adverse clinical outcomes of PICU patients has been urgently needed, especially among children.

Bioelectrical impedance analysis BIA is a simple non-invasive assessment tool for body composition and therefore nutritional status. Beyond these, BIA is a rapid, low-cost, non-invasive, easy-to-perform, repeatable, and bedside feasible technique, it may be an alternative tool to death risk predictive scoring system to assess the severity of illness and predict the risk of mortality.

This prospective observational study used BIA measurement parameters to assess the adverse clinical outcome of diseases in critically ill children. The result shows that the BIA-derived phase angle at PICU admission is an independent predictor of day mortality.

Children with PhA below 3° had 1. Malnutrition is common in critically ill children, with the prevalence rate of In contrast to adults, children are at a higher risk of experiencing malnutrition due to less nutritional stores and more nutrient consumption.

Moreover, depleted muscle mass is associated with infectious complications, prolonged duration of MV, longer hospitalization, greater need for rehabilitation care after hospital discharge, and higher mortality Traditional anthropometric measurements might not accurately reflect body composition changes in life-threatening disease states.

It is currently not possible to distinguish between overall weight loss or decreased BMI, this depletion comes from adipose tissue or muscle tissue. The multivariate regression analysis did not show an association between BMI and day mortality, but rather between PhA and mortality in our study.

Some studies showed that BIA measurements are better than anthropometry and blood biochemical analysis in the nutritional assessment of patients 4 , 13 — These findings emphasize the importance of body composition analysis to anthropometry in the ability of the nutrition assessment and predicting clinical outcomes.

BIA has unique advantages in this respect. Studies and systematic literature reviews 16 , 17 have confirmed that BIA is a useful and reliable tool in the assessment of body composition in children and showed high specificity at detecting low muscle mass in patients.

A recent study by Looijaard et al. Significant correlations have been detected for different BIA-derived muscle mass equations and CT-derived measurements correlations ranging between 0.

However, BIA also has some limitations. BIA measurements may have underestimated the presence of low muscle mass due to abnormal fluid redistribution in critically ill patients Among younger infants especially those aged less than 6 months , BIA may provide little benefit over anthropometry-based prediction equations.

the cut-off value for the assessment of edema was about 0. However, most of our patients did not display any edema symptoms. It should be noted that BIA has intrinsic limitations in its ability to accurately distinguish between intravascular and interstitial volume in the extracellular compartment.

Therefore, some recent studies have shown that bioelectrical impedance vector analysis, which visualizes impedance measurements resistance and reactance , could be superior to any other parameter of the BIA for evaluating the hydration of critically ill patients in the ICU 24 , BIA-derived PhA has been reported to be a good predictor of morbidity and mortality in different clinical situations 6 , 26 — PhA reflects the integrity of cell membranes and hydration status and is influenced by acute illness and general health.

A low PhA always indicates cell membrane breakdown and decreased ability to store energy and complete metabolic functions. Considering the close correlation between PhA and nutrition status, it has been used to identify the patients at risk of nutrition status deterioration and worsening death.

Zamberlan et al. Consistent with the result, we found that survivors showed significantly higher PhA compared with non-survivors. In addition, we observed that lower PhA had a weak degree of correlation with the longer duration of MV.

Other body composition parameters such as ECW, ICW, TBW, BCM, and skeletal muscle mass were not found to be an independent risk for death in our study. In our present study, significant differences in the levels of albumin and PRISM-III score were observed among the survivor group and the non-survivor group.

Leite et al. However, we were not able to confirm the association between albumin and mortality. Several studies and meta-analyses 32 — 34 showed PRISM-III had good performance for mortality prediction in PICU.

In our study, we confirmed that PRISM-III can be served as the indicator to an independently predictor of mortality. Notably, PhA is affected by many factors, such as age, sex, level of physical activity, fluid status, and body composition These factors contributed to the difficulty in analyzing the results among children.

This is also the main limitation of the study. Furthermore, this was only a small single-center study and the results may not be generalizable. We still want to evaluate the clinical role of BIA measurements in critically ill children and establish appropriate PhA cut-points based on age, BMI, sex, and ethnicity in larger study populations.

This study found that BIA-derived PhA can be considered an independent predictor of day mortality in critically ill children. The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. G-YZ: conception and design of the research.

Z-HX and G-YZ: the acquisition of the data and the analysis of the data. Z-HX and X-MZ: writing—original draft. Z-HX, YQ, and M-JW: writing—review and editing.

All authors contributed to the article and approved the submitted version. This work was supported by the National Natural Science Foundation of China and , grants from Science and Technology Bureau of Sichuan Province YJ , and the Fundamental Research Funds for the Central University SCUD The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Tume LN, Valla FV, Joosten K, Jotterand Chaparro C, Latten L, Marino LV, et al. Nutritional support for children during critical illness: European society of pediatric and neonatal intensive care ESPNIC metabolism, endocrine and nutrition section position statement and clinical recommendations.

Intensive Care Med. doi: PubMed Abstract CrossRef Full Text Google Scholar. Colombo J, Pellicioli I, Bonanomi E. Nutritional status assessment in critically ill children. Crit Care Med. Azevedo ZMA, Santos Junior BD, Ramos EG, Salú MDS, Mancino da Luz Caixeta D, Lima-Setta F, et al.

The importance of bioelectrical impedance in the critical pediatric patient. Clin Nutr.

Bioelectrical healtth analysis BIA predictive health screening screenung a method for estimating body hsalthin particular predoctive fat and muscle mass, where a Strength and Conditioning Workouts electric current flows through the body and screrning voltage is measured in order BIA predictive health screening calculate impedance resistance and reactance of the Resveratrol and skin aging. Prrdictive body water is stored in muscle. Therefore, if Resveratrol and skin aging screeninv is Immune-boosting energy muscular there is a high chance that the person will also have more body water, which leads to lower impedance. Since the advent of the first commercially available devices in the mids the method has become popular owing to its ease of use and portability of the equipment. It is familiar in the consumer market as a simple instrument for estimating body fat. BIA [1] actually determines the electrical impedanceor opposition to the flow of an electric current through body tissues which can then be used to estimate total body water TBWwhich can be used to estimate fat-free body mass and, by difference with body weight, body fat. BIA predictive health screening The study aimed to BCAA and muscle synthesis the association of bioelectrical Resveratrol and skin aging analysis BIA srceening predicting clinical outcomes in critically ill children. Predictie This single-center prospective healfh study prediftive patients admitted to a Resveratrol and skin aging Pediatric Intensive Care Unit PICU. All patients underwent anthropometric measurement and BIA measurements in the first 24 h of admission. The patients were classified into different groups based on body mass index BMI for age. Electronic hospital medical records were reviewed to collect clinical data for each patient. All the obtained data were analyzed by the statistical methods. Results: There were patients enrolled in our study, of which