Mean Expected Error in Prediction of Total Body Water: A True Accuracy Comparison between Bioimpedance Spectroscopy and Single Frequency Regression Equations

Development and validation of age-specific predictive equations for total energy expenditure and physical activity levels for older adults

BACKGROUND

Predicting energy requirements for older adults is compromised by the underpinning data being extrapolated from younger adults.

OBJECTIVES

To generate and validate new total energy expenditure (TEE) predictive equations specifically for older adults using readily available measures (age, weight, height) and to generate and test new physical activity level (PAL) values derived from 1) reference method of indirect calorimetry and 2) predictive equations in adults aged ≥65 y.

METHODS

TEE derived from "gold standard" methods from n = 1657 (n = 1019 females, age range 65-90 y), was used to generate PAL values. PAL ranged 1.28-2.05 for males and 1.26-2.06 for females. Physical activity (PA) coefficients were also estimated and categorized (inactive to very active) from population means. Nonlinear regression was used to develop prediction equations for estimating TEE. Double cross-validation in a randomized, sex-stratified, age-matched 50:50 split, and leave one out cross-validation were performed. Comparisons were made with existing equations.

RESULTS

Equations predicting TEE using the Institute of Medicine method are as follows: For males, TEE = -5680.17 - 17.50 × age (years) + PA coefficient × (6.96 × weight [kilograms] + 44.21 × height [centimeters]) + 1.13 × resting metabolic rate (RMR) (kilojoule/day). For females, TEE = -5290.72 - 8.38 × age (years) + PA coefficient × (9.77 × weight [kilograms] + 41.51 × height [centimeters]) + 1.05 × RMR (kilojoule/day), where PA coefficient values range from 1 (inactive) to 1.51 (highly active) in males and 1 to 1.44 in females respectively. Predictive performance for TEE from anthropometric variables and population mean PA was moderate with limits of agreement approximately ±30%. This improved to ±20% if PA was adjusted for activity category (inactive, low active, active, and very active). Where RMR was included as a predictor variable, the performance improved further to ±10% with a median absolute prediction error of approximately 4%.

CONCLUSIONS

These new TEE prediction equations require only simple anthropometric data and are accurate and reproducible at a group level while performing better than existing equations. Substantial individual variability in PAL in older adults is the major source of variation when applied at an individual level.

The Hydration Status of Adult Patients with Oropharyngeal Dysphagia and the Effect of Thickened Fluid Therapy on Fluid Intake and Hydration: Results of Two Parallel Systematic and Scoping Reviews

BACKGROUND

The effect of oropharyngeal dysphagia (OD) and thickened fluid (TF) therapy on hydration status has not been well defined in the literature. We aimed to assess the hydration status in patients with OD and the effect TF therapy has on it.

METHODS

Two literature reviews following PRISMA methodology (each one including a systematic and a scoping review) were performed: (R1) hydration status in adult patients with OD; (R2) effect of TF therapy on fluid intake and dehydration. Narrative and descriptive methods summarized both reviews. Quality assessment was assessed by Joanna Briggs Institute tools and GRADE.

RESULTS

(R1) Five out of twenty-two studies using analytical parameters or bioimpedance showed poorer hydration status among OD and 19-100% prevalence of dehydration; (R2) two high quality studies (total of 724 participants) showed positive effects of TF on hydration status. Among the articles included, nine out of ten studies that evaluated fluid intake reported a reduced TF intake below basal water requirements.

CONCLUSIONS

Dehydration is a highly prevalent complication in OD. There is scientific evidence on the positive effect of TF therapy on the hydration status of patients with OD. However, strict monitoring of fluid volume intake is essential due to the low consumption of TF in these patients.

Individualized body geometry correction factor (KB) for use when predicting body composition from bioimpedance spectroscopy

OBJECTIVE

Prediction of body composition from bioimpedance spectroscopy (BIS) measurements using mixture theory-based biophysical modelling invokes a factor (K_B) to account for differing body geometry (or proportions) between individuals. To date, a single constant value is commonly used. The aim of this study was to investigate variation in KB across individuals and to develop a procedure for estimating an individualized K_Bvalue.

APPROACH

Publicly available body dimension data, primarily from the garment industry, were used to calculate K_Bvalues for individuals of varying body sizes across the life-span. The 3-D surface relationship between weight, height and K_B, was determined and used to create look-up tables to enable estimation of K_Bin individuals based on height and weight. The utility of the proposed method was assessed by comparing body composition predictions from BIS using either a constant K_Bvalue or the individualized value.

RESULTS

Computed KB values were well fitted to height and weight by a 3-D surface (R2 = 0.988). Body composition was predicted more accurately compared to reference methods when using individualized K_Bthan a constant value in infants and children but improvement in prediction was less in adults particularly those with high body mass index.

SIGNIFICANCE

Prediction of body composition from BIS and mixture theory is improved by using an individualized body proportion factor in those of small body habitus, e.g. children. Improvement is small in adults or non-existent in those of large body size. Further improvements may be possible by incorporating a factor to account for trunk size, i.e., waist circumference.

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.

Validity of Bioimpedance Spectroscopy in the Assessment of Total Body Water and Body Composition in Wrestlers and Untrained Subjects

Bioimpedance spectroscopy (BIS) is an easy tool to assess hydration status and body composition. However, its validity in athletes remains controversial. We investigated the validity of BIS on total body water (TBW) and body composition estimation in Japanese wrestlers and untrained subjects. TBW of 49 young Japanese male subjects (31 untrained, 18 wrestlers) were assessed using the deuterium dilution method (DDM) and BIS. De Lorenzo’s and Moissl’s equations were employed in BIS for TBW estimation. To evaluate body composition, Siri’s 3-compartment model and published TBW/fat-free mass (FFM) ratio were applied in DDM and BIS, respectively. In untrained subjects, DDM and BIS with de Lorenzo’s equation showed consistent TBW estimates, whereas BIS with Moissl’s equation overestimated TBW (p < 0.001 vs. DDM). DDM and BIS with de Lorenzo’s equation estimated FFM and percent of fat mass consistently, whereas BIS with Moissl’s equation over-estimated and under-estimated them (p < 0.001 vs. DDM). In wrestlers, BIS with de Lorenzo’s and Moissl’s equations assessed TBW similarly with DDM. However, the Bland–Altman analysis revealed a proportional bias for TBW in BIS with de Lorenzo’s equation (r = 0.735, p < 0.001). Body composition assessed with BIS using both equations and DDM were not different. In conclusion, BIS with de Lorenzo’s equation accurately estimates the TBW and body composition in untrained subjects, whereas BIS with Moissl’s equation is more valid in wrestlers. Our results demonstrated the usefulness of BIS for assessing TBW and body composition in Japanese male wrestlers.

Comparison of segmental lean tissue mass in individuals with spinal cord injury measured by dual energy X-ray absorptiometry and predicted by bioimpedance spectroscopy

STUDY DESIGN

Observational.

OBJECTIVES

To compare two methods for predicting segmental (arms, legs, trunk) lean tissue mass (LTM: non-bone fat-free mass) from bioimpedance spectroscopy (BIS) against LTM measured from dual energy X-ray absorptiometry (DXA) in individuals with acute spinal cord injury (SCI).

SETTING

Austin Health Victorian Spinal Cord Service, Victoria, Australia.

METHODS

Fourteen participants (two female), within 8 weeks of traumatic SCI had BIS measured following an overnight fast and within 24 h of DXA scanning. Total body fat-free mass (FFM, body weight minus fat mass) and segmental LTM were predicted from BIS using manufacturer's proprietary software and a previously established SCI-specific prediction method. Appendicular LTM (ALM) was calculated from the sum of the LTM of the arms and legs. Agreement and strength of relationships with DXA for predicted LTM measures using both approaches were assessed using Lin's concordance coefficient and limits of agreement analysis (LOA).

RESULTS

The BIS proprietary method performed better than the SCI-specific prediction method in predicting DXA LTM, demonstrating substantial concordance for total body FFM (rc = 0.80), ALM (rc = 0.78), arm (rc = 0.76) and leg LTM (rc = 0.65) and a smaller bias and LOA for ALM (+0.8 vs. -3.4 kg; LOA -4.9-6.4 vs. -11.9-5.1 kg), arm (+0.02 vs. -0.3 kg; LOA -1.1-1.1 kg vs. -2.2-1.6 kg) and leg (+0.4 vs. -1.4 kg; LOA -2.0-2.8 vs. -5.6-2.8) LTM.

CONCLUSIONS

BIS can be used to accurately predict total body FFM, segmental LTM and ALM in individuals with acute SCI.

Variable selection and importance in presence of high collinearity: an application to the prediction of lean body mass from multi-frequency bioelectrical impedance

In prediction problems both response and covariates may have high correlation with a second group of influential regressors, that can be considered as background variables. An important challenge is to perform variable selection and importance assessment among the covariates in the presence of these variables. A clinical example is the prediction of the lean body mass (response) from bioimpedance (covariates), where anthropometric measures play the role of background variables. We introduce a reduced dataset in which the variables are defined as the residuals with respect to the background, and perform variable selection and importance assessment both in linear and random forest models. Using a clinical dataset of multi-frequency bioimpedance, we show the effectiveness of this method to select the most relevant predictors of the lean body mass beyond anthropometry.

An evaluation of phase angle, bioelectrical impedance vector analysis and impedance ratio for the assessment of disease status in children with nephrotic syndrome

Background

Oedema, characterized by accumulation of extracellular water (ECW), is one of the major clinical manifestations in children suffering from nephrotic syndrome (NS).

The lack of a simple, inexpensive and harmless method for assessing ECW may be solved by the use of the bioelectrical impedance analysis (BIA) technique.

The aims of this study were to examine whether phase angle (PA), bioelectrical impedance vector analysis (BIVA) and the impedance ratio (IR) reflect change in disease status in children with NS.

Methods

Eight children (age range: 2–10 years) with active NS (ANS group) were enrolled. In five of these (ANS* subgroup), impedance was also measured at remission (NSR group). Thirty-eight healthy children (age range: 2–10 years) were included as healthy controls (HC group). Whole-body impedance was measured with a bioimpedance spectroscopy device (Xitron 4200) with surface electrodes placed on the wrist and ankle.

Results

Values of PA, BIVA and IR were found to be significantly lower (p-value range < 0.001 to < 0.01) in the ANS patients compared to the HC and NSR groups. No significant differences were observed between the NSR and HC groups.

Conclusion

The studied parameters can be used to assess change in disease status in NS patients. Data were consistent with NS being associated with expansion of ECW.

Electronic supplementary material

The online version of this article (10.1186/s12882-019-1511-y) contains supplementary material, which is available to authorized users.

Bioimpedance Method for Human Body Hydration Assessment

A high-precision wearable bioimpedance sensor developed at Imec was extensively tested. Unlike known bioimpedance sensors on the market, the new device enables hydration shift measurement in a single person, with no need for averaging over a population. For reaching this target, a method for hydration monitoring in case of altered hydration is tested. An assessment of fluid shift with sensitivity of about 700 ml has been demonstrated, which is comparable with the capabilities of known methods because of the device accuracy, immunity to electrode-skin impedance variation, and due to establishing the impedance baseline prior to fluid shift.

Classification of Hydration in Clinical Conditions: Indirect and Direct Approaches Using Bioimpedance

Although the need to assess hydration is well recognized, laboratory tests and clinical impressions are impractical and lack sensitivity, respectively, to be clinically meaningful. Different approaches use bioelectrical impedance measurements to overcome some of these limitations and aid in the classification of hydration status. One indirect approach utilizes single or multiple frequency bioimpedance in regression equations and theoretical models, respectively, with anthropometric measurements to predict fluid volumes (bioelectrical impedance spectroscopy-BIS) and estimate fluid overload based on the deviation of calculated to reference extracellular fluid volume. Alternatively, bioimpedance vector analysis (BIVA) uses direct phase-sensitive measurements of resistance and reactance, measured at 50 kHz, normalized for standing height, then plotted on a bivariate graph, resulting in a vector with length related to fluid content, and direction with phase angle that indexes hydration status. Comparison with healthy population norms enables BIVA to classify (normal, under-, and over-) and rank (change relative to pre-treatment) hydration independent of body weight. Each approach has wide-ranging uses in evaluation and management of clinical groups with over-hydration with an evolving emphasis on prognosis. This review discusses the advantages and limitations of BIS and BIVA for hydration assessment with comments on future applications.

Model of fluid and solute shifts during hemodialysis with active transport of sodium and potassium

Background

Mathematical models are useful tools to predict fluid shifts between body compartments in patients undergoing hemodialysis (HD). The ability of a model to accurately describe the transport of water between cells and interstitium (J_v,ISIC), and the consequent changes in intracellular volume (ICV), is important for a complete assessment of fluid distribution and plasma refilling. In this study, we propose a model describing transport of fluid in the three main body compartments (intracellular, interstitial and vascular), complemented by transport mechanisms for proteins and small solutes.

Methods

The model was applied to data from 23 patients who underwent standard HD. The substances described in the baseline model were: water, proteins, Na, K, and urea. Small solutes were described with two-compartment kinetics between intracellular and extracellular compartments. Solute transport across the cell membrane took place via passive diffusion and, for Na and K, through the ATPase pump, characterized by the maximum transport rate, Jp_MAX. From the data we estimated Jp_MAX and two other parameters linked to transcapillary transport of fluid and protein: the capillary filtration coefficient Lp and its large pores fraction α_LP. In an Expanded model one more generic solute was included to evaluate the impact of the number of substances appearing in the equation describing J_v,ISIC.

Results

In the baseline model, median values (interquartile range) of estimated parameters were: Lp: 11.63 (7.9, 14.2) mL/min/mmHg, α_LP: 0.056 (0.050, 0.058), and Jp_MAX: 5.52 (3.75, 7.54) mmol/min. These values were significantly different from those obtained by the Expanded model: Lp: 8.14 (6.29, 10.01) mL/min/mmHg, α_LP: 0.046 (0.038, 0.052), and Jp_MAX: 16.7 (11.9, 25.2) mmol/min. The relative RMSE (root mean squared error)averaged between all simulated quantities compared to data was 3.9 (3.1, 5.6) %.

Conclusions

The model was able to accurately reproduce most of the changes observed in HD by tuning only three parameters. While the drop in ICV was overestimated by the model, the difference between simulations and data was less than the measurement error. The biggest change in the estimated parameters in the Expanded model was a marked increase of Jp_MAX indicating that this parameter is highly sensitive to the number of species modeled, and that the value of Jp_MAX should be interpreted only in relation to this factor.

Bioelectrical impedance analysis for body composition assessment: reflections on accuracy, clinical utility, and standardisation

Bioelectrical impedance analysis is an extremely popular method for assessment of body composition. Despite its wide-spread use over the past thirty years, its accuracy and clinical value is still questioned. Most frequently, criticisms focus on its purported poor absolute accuracy and that different impedance analysers or prediction equations fail to measure body composition identically. This perspective review highlights that the magnitude of errors associated with impedance methods are not dissimilar to those observed for so-called gold standard methods. It is argued that the focus on statistically significant but small differences between methods can obscure operational equivalence and that such differences may be of minor clinical significance. Finally, the need for better standardization of protocols and the need for consensus on what is a minimal clinically important difference between methods is highlighted.

Volume Estimates in Chronic Hemodialysis Patients by the Watson Equation and Bioimpedance Spectroscopy and the Impact on the Kt/Vurea calculation

Background

Accurate assessment of total body water (TBW) is essential for the evaluation of dialysis adequacy (Kt/V_urea). The Watson formula, which is recommended for the calculation of TBW, was derived in healthy volunteers thereby leading to potentially inaccurate TBW estimates in maintenance hemodialysis recipients. Bioimpedance spectroscopy (BIS) may be a robust alternative for the measurement of TBW in hemodialysis recipients.

Objectives

The primary objective of this study was to evaluate the accuracy of Watson formula-derived TBW estimates as compared with TBW measured with BIS. Second, we aimed to identify the anthropometric characteristics that are most likely to generate inaccuracy when using the Watson formula to calculate TBW. Finally, we derived novel anthropometric equations for the more accurate estimation of TBW.

Design and Setting

This was a cross-sectional study of prevalent in-center HD patients at St Michael's Hospital.

Patients

One hundred eighty-four hemodialysis patients (109 men and 75 women) were evaluated in this study.

Measurements

Anthropometric measurements including weight, height, waist circumference, midarm circumference, and 4-site skinfold (biceps, triceps, subscapular, and suprailiac) thickness were measured; fat mass was measured using the formula by Durnin and Womersley. We measured TBW by BIS using the Body Composition Monitor (Fresenius Medical Care, Bad Homburg, Germany).

Methods

We used the Bland-Altman method to calculate the difference between the TBW derived from the Watson method and the BIS. To derive new equations for TBW estimation, Pearson's correlation coefficients between BIS-TBW (the reference test) and other variables were examined. We used the least squares regression analysis to develop parsimonious equations to predict TBW.

Results

TBW values based on the Watson method had a high correlation with BIS-TBW (correlation coefficients = 0.87 and P < .001). Despite the high correlation, the Watson formula overestimated TBW by 5.1 (4.5-5.8) liters and 3.8 (3.0-4.5) liters, in men and women, respectively. Higher fat mass and waist circumference (general and abdominal obesity) were correlated with the greater TBW overestimation by the Watson formula. We created separate equations for men and women based on weight and waist circumference.

Limitations

The main limitation of our study was the lack of an external validation for our novel estimating equation. Furthermore, though BIS has been validated against traditional reference standards, our assumption that it represents the "gold standard" for body compartment assessment may be flawed.

Conclusions

The Watson formula generally overestimates TBW in chronic dialysis recipients, particularly in patients with the highest waist circumference. Widespread reliance on the Watson formula for derivation of TBW may lead to the underestimation of Kt/V_urea..

Chiarello P. Estimation of Body Composition and Water Data Depends on the Bioelectrical Impedance Device

Overweight, obese and chronic kidney disease patients have an altered and negative body composition being its assessment important. Bioelectrical impedance analysis is an easy-to-operate and low-cost method for this purpose. This study aimed to compare and correlate data from single- and multi-frequency bioelectrical impedance spectroscopy applied in subjects with different body sizes, adiposity, and hydration status. It was a cross-sectional study with 386 non-chronic kidney disease volunteers (body mass index from 17 to 40 kg/m2), 30 patients in peritoneal dialysis, and 95 in hemodialysis. Bioelectrical impedance, body composition, and body water data were assessed with single- and multi-frequency bioelectrical impedance spectroscopy. Differences (95% confidence interval) and agreements (Bland-Atman analyze) between devices were evaluated. The intraclass correlation coefficient was used to measure the strength of agreement and Pearson's correlation to measure the association. Regression analyze was performed to test the association between device difference with body mass index and overhydration. The limits of agreement between devices were very large. Fat mass showed the greatest difference and the lowest intraclass and Pearson's correlation coefficients. Pearson's correlation varied from moderate to strong and the intraclass correlation coefficient from weak to substantial. The difference between devices were greater as body mass index increased and was worse in the extremes of water imbalance. In conclusion, data obtained with single- and multi-frequency bioelectrical impedance spectroscopy were highly correlated with poor agreement; the devices cannot be used interchangeably and the agreement between the devices was worse as body mass index and fat mass increased and in the extremes of overhydration.

Quantification of whole-body and segmental skeletal muscle mass using phase-sensitive 8-electrode medical bioelectrical impedance devices

Background/Objectives:

Bioelectrical impedance analysis (BIA) provides noninvasive measures of skeletal muscle mass (SMM) and visceral adipose tissue (VAT). This study (i) analyzes the impact of conventional wrist-ankle vs segmental technology and standing vs supine position on BIA equations and (ii) compares BIA validation against magnetic resonance imaging (MRI) and dual X-ray absorptiometry (DXA).

Subjects/Methods:

One hundred and thirty-six healthy Caucasian adults (70 men, 66 women; age 40±12 years) were measured by a phase-sensitive multifrequency BIA (seca medical body composition analyzers 515 and 525). Multiple stepwise regression analysis was used to generate prediction equations. Accuracy was tested vs MRI or DXA in an independent multiethnic population.

Results:

Variance explained by segmental BIA equations ranged between 97% for total SMM_MRI, 91–94% for limb SMM_MRI and 80–81% for VAT with no differences between supine and standing position. When compared with segmental measurements using conventional wrist-ankle technology. the relationship between measured and predicted SMM was slightly deteriorated (r=0.98 vs r=0.99, P<0.05). Although BIA results correctly identified ethnic differences in muscularity and visceral adiposity, the comparison of bias revealed some ethnical effects on the accuracy of BIA equations. The differences between LST_DXA and SMM_MRI at the arms and legs were sizeable and increased with increasing body mass index.

Conclusions:

A high accuracy of phase-sensitive BIA was observed with no difference in goodness of fit between different positions but an improved prediction with segmental compared with conventional wrist-ankle measurement. A correction factor for certain ethnicities may be required. When compared with DXA MRI-based BIA equations are more accurate for predicting muscle mass.

A Handheld and Textile-Enabled Bioimpedance System for Ubiquitous Body Composition Analysis. An Initial Functional Validation

In recent years, many efforts have been made to promote a healthcare paradigm shift from the traditional reactive hospital-centered healthcare approach towards a proactive, patient-oriented, and self-managed approach that could improve service quality and help reduce costs while contributing to sustainability. Managing and caring for patients with chronic diseases accounts over 75% of healthcare costs in developed countries. One of the most resource demanding diseases is chronic kidney disease (CKD), which often leads to a gradual and irreparable loss of renal function, with up to 12% of the population showing signs of different stages of this disease. Peritoneal dialysis and home haemodialysis are life-saving home-based renal replacement treatments that, compared to conventional in-center hemodialysis, provide similar long-term patient survival, less restrictions of life-style, such as a more flexible diet, and better flexibility in terms of treatment options and locations. Bioimpedance has been largely used clinically for decades in nutrition for assessing body fluid distributions. Moreover, bioimpedance methods are used to assess the overhydratation state of CKD patients, allowing clinicians to estimate the amount of fluid that should be removed by ultrafiltration. In this work, the initial validation of a handheld bioimpedance system for the assessment of body fluid status that could be used to assist the patient in home-based CKD treatments is presented. The body fluid monitoring system comprises a custom-made handheld tetrapolar bioimpedance spectrometer and a textile-based electrode garment for total body fluid assessment. The system performance was evaluated against the same measurements acquired using a commercial bioimpedance spectrometer for medical use on several voluntary subjects. The analysis of the measurement results and the comparison of the fluid estimations indicated that both devices are equivalent from a measurement performance perspective, allowing for its use on ubiquitous e-healthcare dialysis solutions.

Slightly superior performance of bioimpedance spectroscopy over single frequency regression equations for assessment of total body water

Electrical bioimpedance has been used for several decades to assess body fluid distribution and body composition by using single frequency and bioimpedance spectroscopic (BIS) techniques. It remains uncertain whether BIS methods have better performance compare to single frequency regression equations. In this work the performance of two BIS methods and four different 50 kHz single frequency prediction equations was studied in a data set of wrist-to-ankle tetrapolar BIS measurements (5-1000 kHz) together with reference values of total body water obtained by tritium dilution in 92 patients. Data were compared using regression techniques and Bland-Altman plots. The results of this study showed that all methods produced similarly high correlation and concordance coefficients, indicating good accuracy as a method. Limits of agreement analysis indicated that the population level performance of Sun's prediction equations was very similar to the performance of both BIS methods. However, BIS methods in practice have slightly better predictive performance than the single-frequency equations as judged by higher correlation and the limits of agreement from the Bland-Altman analysis. In any case, the authors believe that an accurate evaluation of performance of the methods cannot be done as long as the evaluation is done using Bland-Altman analysis, the commonly accepted technique for this kind of performance comparisons.

Detection and Classification of Measurement Errors in Bioimpedance Spectroscopy

Bioimpedance spectroscopy (BIS) measurement errors may be caused by parasitic stray capacitance, impedance mismatch, cross-talking or their very likely combination. An accurate detection and identification is of extreme importance for further analysis because in some cases and for some applications, certain measurement artifacts can be corrected, minimized or even avoided. In this paper we present a robust method to detect the presence of measurement artifacts and identify what kind of measurement error is present in BIS measurements. The method is based on supervised machine learning and uses a novel set of generalist features for measurement characterization in different immittance planes. Experimental validation has been carried out using a database of complex spectra BIS measurements obtained from different BIS applications and containing six different types of errors, as well as error-free measurements. The method obtained a low classification error (0.33%) and has shown good generalization. Since both the features and the classification schema are relatively simple, the implementation of this pre-processing task in the current hardware of bioimpedance spectrometers is possible.

A method to adapt thoracic impedance based on chest geometry and composition to assess congestion in heart failure patients

Multi-frequency trans-thoracic bioimpedance (TTI) could be used to track fluid changes and congestion of the lungs, however, patient specific characteristics may impact the measurements. We investigated the effects of thoracic geometry and composition on measurements of TTI and developed an equation to calculate a personalized fluid index. Simulations of TTI measurements for varying levels of chest circumference, fat and muscle proportion were used to derive parameters for a model predicting expected values of TTI. This model was then adapted to measurements from a control group of 36 healthy volunteers to predict TTI and lung fluids (fluid index). Twenty heart failure (HF) patients treated for acute HF were then used to compare the changes in the personalized fluid index to symptoms of HF and predicted TTI to measurements at hospital discharge. All the derived body characteristics affected the TTI measurements in healthy volunteers and together the model predicted the measured TTI with 8.9% mean absolute error. In HF patients the estimated TTI correlated well with the discharged TTI (r=0.73,p <0.001) and the personalized fluid index followed changes in symptom levels during treatment. However, 37% (n=7) of the patients were discharged well below the model expected value. Accounting for chest geometry and composition might help in interpreting TTI measurements.