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Bioelectric impedance analysis for body composition measurement and other potential clinical applications in critical illness

Purpose of review Insight into body composition is of great value in the ICU. Bioelectric impedance analysis (BIA) is the most applicable bedside technique. However, bioimpedance has not been validated in the critically ill, and the interpretation of the measurements poses challenges. This review discusses the potential clinical applications of BIA and explores caveats and solutions to its use in the intensive care setting. Recent findings A correlation is repeatedly found between raw impedance parameters, fluid ratios, overhydration, and adverse outcome of critical illness. However, cut-off and reference values remain elusive. Experience with BIA-guided fluid management in the ICU is limited. BIA-derived muscle mass appears a promising biomarker for sarcopenia, correlating well with CT-analysis. Body cell mass and fat-free mass provide potential use in estimation of metabolic rate, protein requirements and pharmacokinetics. Several methods of reducing bias in BIA parameters in critical illness require validation. Summary There are currently too many uncertainties and discrepancies regarding interpretation of bioimpedance in critical illness, to justify therapeutic consequences. However, there are several promising areas of research, concerning some of the most urgent clinical problems in intensive care, emphasizing the need to evaluate further the use and interpretation of bioimpedance in the intensive care setting.


INTRODUCTION
Body composition describes the relative contribution of fat, muscle, bone and water to an individual's body volume. In the ICU, real-time knowledge of body composition is advantageous to the individualization and optimization of fluid balances, nutrition regimes and medication dosing. Several body composition techniques are available, based on assumptions of weight (hydrostatic weighing), water content (isotope dilution), volume (air displacement plethysmography), energy attenuation (Dual-Energy X-Ray Absorptiometry; DXA), and imaging techniques like computer tomography (CT) and MRI. Although extensively validated, all techniques have limitations when applied during critical illness, because of costs, impracticality or radiation exposure.
Bioelectric impedance analysis (BIA) is quick, noninvasive and relatively inexpensive, making it ideal for bedside use. However, BIA assumes static ratios, most notably a fixed hydration of tissues, which often do not apply to critically ill patients, making interpretation less straightforward. Nevertheless, it is worth exploring potential applications, as BIA currently seems the most feasible body composition measurement technique in the ICU.
Angles for future research will be indicated throughout this manuscript with an asterix ( Ã ) and are summarized in Table 1.

PRINCIPLES OF BIOIMPEDANCE ANALYSIS
Impedance is the vector analysis of resistance, the opposition to flow of a current, and reactance, the opposition to a current change because of a material's capacitance. When an electrical current is sent through the body, tissues present varying resistance levels. Electrolyte-rich body water is highly conductive; therefore, muscles, having a higher water content, will encounter less resistance than relatively anhydrous tissues, such as fat. Conversely, reactance increases proportionally to cell numbers and their integrity, because of membrane capacitance.
Single-frequency BIA devices (SF-BIA) use a single frequency (usually 50 kHz) to measure impedance. However, low-frequency currents will not penetrate cell membranes, and thus will only measure extracellular water (ECW) impedance. Total body water (TBW) is then estimated through proportional equations. High-frequency currents will go through cells. This impedance reflects combined ICW and ECW : TBW (Fig. 1).
Multifrequency BIA devices (MF-BIA), therefore, provide a more direct portrayal of water compartments, making them more reliable in case of altered hydration status or electrolyte imbalances. Bioimpedance spectroscopy (BIS) applies a more extensive frequency range than MF-BIA. The increase in information obtained from BIS potentially improves predictive power. However, it still requires extrapolation based on population references. Superiority of BIS to the SF-BIA and MF-BIA techniques has not been proven in nonhealthy populations [1][2][3]4 & ].

KEY POINTS
Knowledge of real-time body composition has a strong potential for prognostication, personalized nutrition, fluid therapy, and medication management.
Regular body composition measurements are feasible in the intensive care setting with bioelectrical impedance analysis.
Raw parameters and water parameters from multifrequency bioelectric impedance devices appear reliably interpretable in the critically ill population.
Derived bioelectric impedance body composition parameters rely on body water distribution assumptions, which might not be valid in the critically ill population.
There are currently no validated reference values for the critically ill population regarding derived bioelectric impedance body composition parameters. The phase angle (PhA) shows the relationship between reactance and resistance (Fig. 2).
The greater the number of cell membranes the signal has to pass through, the greater the reactance, and therefore, the PhA. Thus, a large PhA is consistent with a large body cell mass (BCM) relative to ECW, as seen in healthy individuals, whereas ICU patients tend to have a lower PhA. A PhA greater than 6 is assumed normal in health, although PhA varies with sex (men") and age (#because of loss of fat-free mass; FFM), and should ideally be related to a reference population, or converted to standardized PhA (SPhA) before comparing across populations [5,6]. PhA measured at 50 kHz is most frequently used, and most reference data are available for this frequency, as this is the frequency at which both resistance and maximum reactance are best measured [7,8].
Bio-electrical impedance vector analysis (BIVA) represents impedance as a vector of reactance and resistance in an x-y plot referring to reference population's tolerance ellipses (Fig. 3).
BIVA allows simultaneous interpretation of direction (phase) and length of the impedance vector; through which changes in tissue hydration and BCM can be appreciated, independent of regression equations, or body water. FIGURE 1. Low-frequency currents will not penetrate cell membranes, and as such will measure extracellular water impedance. High-frequency currents will go through cells, at which point the impedance reflects total body water (TBW).

FIGURE 2.
When an electric current passes a cell membrane, reactance causes a time delay, creating a phase shift between voltage and current. The phase angle describes this difference between the voltage and the current. A high-phase angle is, therefore, consistent with large quantities of intact cell membranes and body cell mass.

DERIVED PARAMETERS
Reactance, resistance, impedance and PhA, are often referred to as 'raw' BIA parameters, that is, not reliant upon empirical modeling. BIA defines the water volumes using impedance and body height, upon which other body composition parameters are based. Earlier BIA devices regarded the body as one cylinder and extrapolated impedance measured on one side of the body. However, this simplification overlooks possible asymmetry and the proportional difference between the trunk and the limbs. Segmental BIA (SM-BIA) devices consider the body as five separate cylinders and use electrodes on all limbs, improving accuracy (Fig. 4).
Various body composition parameters are derived from thereon, using regression analyses with multiple variables obtained through reference measurements. Figure 5 provides an overview of the relationship between several frequently used parameters. SM-BIA can provide additional values, such as  the appendicular skeletal muscle mass (ASMM), the sum of the four limbs' muscle masses.

OUTCOME PREDICTION WITH BIOELECTRIC IMPEDANCE ANALYSIS
Several raw and BIA-derived body composition parameters have been validated as mortality and morbidity predictors in various patient groups and are now being researched as predictors of critical illness outcome [9,10].

Raw parameters
Diminished cell count, membrane integrity and altered hydration status in critical illness can lead to changes in reactance and resistance, thereby decreasing PhA compared with healthy individuals [11,12]. Decreased PhA at ICU admission has been associated with hospital, 28-day, 90-day and 12month mortality [6,13 -16]. Concordantly, PhA improved over the first 5 days of ICU stay in ICU survivors, while decreasing significantly in nonsurvivors [17]. Furthermore, negative correlations have been observed between admission PhA and the length of ward stay, ICU stay and hospital stay, mechanical ventilation duration the APACHE-II score, and recently with the severity of disease of However, the cut-off values for the predictive value of PhA vary across these studies. The heterogeneity of the ICU populations studied might in part explain these discrepancies. A study comparing sepsis patients with other critically ill patients found that PhA was negatively correlated with the APACHE-II score only in the nonsepsis group [21]. Additionally, in addition to the acute changes because of the current illness, PhA inherently also reflects poor underlying health, muscle wasting and frailty, which are independently associated with outcome.
One study using Segmental Multifrequency BIA (SMMF-BIA) found that impedance, reactance and PhA showed more predictive power for mortality than the SAPS, APACHE-II and SOFA severity scoring systems. Similarly, the landmark Phase Angle Project showed that a combined multivariable score improved the discriminative power in predicting mortality, compared with PhA alone [22] Ã .

Hydration parameters
Overhydration in ICU patients is positively correlated with adverse outcomes but current methods to assess volume status [in-bed weighing, cumulative fluid balance (CFB), central venous pressure] have their limitations. Marked BIVA-OH on the first 5 days after ICU admission was shown in ICU and 60-day nonsurvivors [19 & ,23,24]. Notably, BIVA predicted mortality better than CFB [23].
Fluid distribution can also be assessed by BIAderived ECW/TBW ratio. A healthy ECW/TBW ratio varies slightly between sources and device manufacturers but ranges from 0.36 to 0.40. An ECW/TBW ratio of more than 0.40 is considered indicative of overhydration of the extracellular compartment Ã . ECW/TBW-ratio is higher among ICU nonsurvivors and correlates with a longer mechanical ventilation duration [25]. Slobod et al. found that a SF-BIA ECW/ TBW-ratio greater than 0.39 on ICU-day 1, associated with an increased number of ventilation days, independent of the APACHE-II score [26 & ]. In CRRT ICU patients, a cut-off for SMMF-BIA ECW/TBWratio of 0.413 predicted 28-day mortality, with 71.4% sensitivity and 70.6% specificity [27].
On the basis of the assumption that excess volume accumulates primarily as ECW, the quantity of overhydration can be calculated as the difference between expected ECW, based on the euvolemic ECW/TBW ratio, and the measured ECW [28,29]. On ICU days 1 and 3, BIS-OH (>1l) associated significantly with hospital mortality in 140 ICU patients with 23 nonsurvivors. Day 3 volume status correlated with the duration of ventilation and ICU stay. More ICU-free and ventilator-free days were observed among patients with normal hydration status on day 3 (OH <1 to 1 l) [30 & ]. We showed increased SMMF-BIA-OH, and ECW/TBW ratio were associated with mortality in COVID-19 [20 & ].

Muscle mass
Determining muscle mass is essential in distinguishing the sarcopenic, from the nonsarcopenic obese, as the former are at higher risk of adverse outcome in the ICU. Furthermore, rapid wasting of muscle mass is a major clinical conundrum, as it is a strong independent predictor for morbidity, mortality, physical functioning and quality of life. PhA is often considered a proxy for LBM. Indeed, two studies found that low BIA/BIS-PhA corresponded to low CT-muscle mass (CT-MM) and muscle density in the critically ill ]. Another group calculated SMM, ASMM and total muscle mass based on raw SF-BIA measurements, using three different equations, and found that although the BIA and CT measurements correlated significantly, the agreement was low, with increasing overestimation of muscle mass by BIA at higher CT-MM. However, BIA did correctly identify patients with low CT-MM [31 && ]. Therefore, BIA might be clinically useful to identify sarcopenic patients at risk for adverse outcome. However, there was a time difference between the BIA and CT evaluation in these studies, potentially inducing bias. Furthermore, increased muscle mass in ICU patients should not be interpreted as muscle mass of good quality, as intramuscular edema will be classified as muscle mass by both BIA and CT analysis. However, a recent pilotstudy comparing CT-MM at ICU admission and BIS-FFM adjusted for overhydration, using an algorithm developed for dialysis patients, found significant correlations and good agreements between the two tech-

NUTRITION MANAGEMENT
Critically ill patients are at increased risk of malnutrition. Several BIA parameters can potentially provide information on nutrition status and requirements.

Body cell mass
BCM is the metabolically active part of FFM, in contrast to bone and ECW. As such, a decrease of BCM resulting from critical illness is a marker for malnutrition. Logically, increased ECW is associated with a lower BCM/FFM-ratio. A study comparing BIA measurements before and after hemodialysis in AKI patients (mean weight loss 3.8 kg), suggested hydration shifts have little effect on the BCM measurement, theoretically making it more reliable in critically ill patients [34,35] Ã .

Raw parameters
PhA inherently reflects BCM. In 89 ICU patients, a PhA less than less than 5.58 showed an accuracy of 79% in identifying patients at high nutrition risk (NUTRIC score !5) [19 & ]. In renal replacement therapy patients, a PhA cut-off of 4.68 has been shown to predict malnutrition, defined by protein-energy wasting [36,37]. A study comparing the accuracy of BIVA, versus the definition according to ESPEN in hospitalized patients, in predicting malnutrition, found that BIVA might be the superior method [38].

Fat-free mass
Assessment of muscularity by BIA is recommended by the Global Leadership Initiative on Malnutrition (GLIM) [39]. A prospective study among 60 ventilated ICU patients found that a cumulative energydeficit during ICU stay was independently associated with loss of BIS-FFM between inclusion and ICU discharge, as well as with ICU-acquired weakness [40 && ]. In a retrospective post hoc analysis, including this study, these associations disappeared [41 & ]. However, raw parameters remained related to muscle weakness [41 & ] Ã . FFM is closely related to energy expenditure, and some BIA devices offer options to estimate basal metabolic rate (BMR), using FFM-based equations (e.g. Cunningham, or Katch-McArdle). However, based upon derived FFM, these calculations are subject to caveats and have proven to be inferior to indirect calorimetry in several populations, albeit still more accurate than weight-based equations [42,43].
Potentially, BIA-FFM could facilitate targeted protein dosing. Protein targets are usually set to measured actual weight or calculated FFM Ã . However, these methods do not incorporate changes in body composition and weight gain because of overhydration, as such masking the decrease of FFM during ICU stay.

FLUID MANAGEMENT
BIA is commonly used in dialysis patients to guide fluid management by calculating dry weight goals [11,44]. Likewise, in critical illness PhA, ECW/TBW ratio and overhydration could be used to monitor the effect of fluid management strategies. A prospective, clinician-blinded study was conducted to assess the feasibility and validity of BIVA as a measure of hydration in critically ill patients. The study showed that clinicians blinded to the BIVA results, achieved a mean CFB that was concordant with the prior BIVA classification (i.e. positive for patients' BIVA classified as dehydrated, negative for overhydrated patients and neutral for normally hydrated patients), proving feasibility [45]. Moreover, directional BIVA changes correlated with directional changes in fluid balance. However, the study showed that vector length increased in parallel with 2.4-l fluid loss, suggesting BIVA might be insensitive to smaller changes [45]. The effect of BIVA/BIA-guided fluid management on patientcentered outcomes has not yet been researched Ã .

GLOMERULAR FILTRATION RATE AND PHARMACOKINETICS
Adequate dosing of renally excreted drugs is challenging in critically ill patients because of changes in kidney function. Most equations to estimate glomerular filtration rate are based on serum creatinine measurement. However, significant limitations arise when these formulas are applied to patients with altered body composition, like low muscle mass [9,[10][11][12][13]. A Dutch group recently developed and Ideal body weight (IBW) cannot replace measured body weight as BIA input parameter [49].
The Biasioli equation to calculate total body weight is based upon height but not weight and can be used to avoid the need for weighing [9,50 & ].
BIA is not validated in patients undergoing large and swift hydration shifts Changes in TBW determine changes in phase angle (PhA) during ICU days 1-3, suggesting that overhydration (OH) significantly influences PhA [50 & ].
BIA might be most reliable at ICU admission (before fluid resuscitation) or after ICU discharge when hydration status has stabilized [14].
Altered BIA raw parameters because of hydration shifts do not devaluate their prognostic value [50 & ].
Overhydration distorts the normal distribution of water in the intracellular and extracellular space that is used to obtain derived BIA parameters and BIVA Interstitial edema is interpreted by BIVA as a state of OH, even if there is a state of relative intravascular hypovolemia [24].
Derived values might be recalculated to a normalized ECW/TBW ratio, analogous to dialysis BIA software [20 & ].
Ascites, pleural effusion and urine retention theoretically influence BIA measurements Segmental BIA can distinguish apparent trunk OH because of peritoneal dialysate, without influencing the extremities' measurements [52].
In cirrhosis patients, PhA is positively correlated with CT-derived MM, irrespective of ascites' presence [53].
Changing tissue electrolyte concentrations might influence raw BIA parameters In chronic kidney disease patients, a 20% increase in Naþ as measured by 23Na-MRI, leads BIS to overestimate ECW by 1.2-2.4 l because of lower extracellular resistance [54]. BIA also provides interesting theoretical ways for pharmacokinetic characterization and medication dosing through real-time appreciation of the changing body composition and volumes of distribution [47]. However, no recent attempts for predictive pharmacokinetic models using BIA in the ICU have been published Ã .

CAVEATS
One main drawback of BIA is the incorporation of reference population values, for all but the raw parameters, which might not apply to the individual patient or population. Although they are validated against standard methods (usually MRI and DXA), the exact equations used by BIA software are rarely released by manufacturers, impairing judgment of applicability [48] Ã . Several other caveats impair routine use of BIA in the ICU, such as use of inexact input parameters, the lack of ICU reference and cutoff values, and the possible bias introduced by a rapidly changing clinical status. Evidence regarding other considerations to use and interpretation of BIA in the ICU setting are summarized in Table 2.

CONCLUSION
There are several promising areas of BIA research concerning some of the most urgent clinical problems in intensive care. A correlation is repeatedly found between raw impedance parameter, fluid ratios, overhydration and adverse outcomes in critical illness. BIA-derived muscle mass appears a promising biomarker for sarcopenia, as it correlates well with CTanalysis. BCM and fat-free mass provide potential use in estimation of metabolic rate, glomerular filtration rate, protein needs and pharmacokinetics. Contrastingly, experience with BIA-guided fluid management is still limited and suggested methods of reducing bias in BIA-measurements in the critically ill require validation. There are currently too many uncertainties and discrepancies regarding the interpretation of BIA measurements in critical illness to justify large therapeutic consequences, emphasizing the need for further evaluation of the use and interpretation of bioelectric impedance in the ICU setting. The first prospective study comparing BIA-PhA and CT measurements of the muscle compartment. It demonstrates that BIA can identify the same population with low SMA. The correlation between PhA and skeletal muscle density, and the finding that low PhA, as defined by nutritional risk-related cut-off points and mortality-related cut-off points, corresponded to low CT-derived muscle area and CT-derived muscle density are new and provide future research perspectives. 32.

&&
Lambell KJ, Earthman CP, Tierney AC, et al. How does muscularity assessed by bedside methods compare to computed tomography muscle area at intensive care unit admission? A pilot prospective cross-sectional study. J Hum Nutr Diet 2020; 34:345-355. In this pilot study, a unique BIS-derived FFM variable using an equation that accounts for fluid overload was significantly correlated to CT muscle area and was able to correctly classify all the participants with low CT muscle area at ICU admission. 33.