Comparison of different methods for hemodialysis evaluation by means of ROC curves: from artificial intelligence to current methods

Fernández, Elmer; Valtuille, Rodolfo; Presedo, J.; Willshaw, Peter

doi:10.5414/cnp64205

Cited by 16 publications

(13 citation statements)

References 5 publications

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“…In each step sensitivity (Se) and specificity (Sp) were calculated according to the BCM classification. The Se-Sp pair achieving the smaller distance to the optimal point (Sp = 100%, Se = 100%) was chosen as the optimal cut-off for this variable [ 13 ].…”

Section: Methodsmentioning

confidence: 99%

Nutritional Markers and Body Composition in Hemodialysis Patients

Valtuille¹,

Casos²,

Fernández

et al. 2015

International Scholarly Research Notices

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The aims of this study were to analyse body composition, to detect the presence of undernutrition, and to establish a relationship between undernutrition and the biological markers routinely used as indicators of nutritional status in hemodialysis (HD) patients (pts). We used a body composition monitor (BCM) that expresses body weight in terms of lean tissue mass (LTM) and fat tissue mass (FTM) independent of hydration status. From nine HD units, 934 pts were included. Undernutrition was defined as having a lean tissue index (LTI = LTM/height2) below the 10th percentile of a reference population. Biochemical markers and parameters delivered by BCM were used to compare low LTI and normal LTI groups. Undernutrition prevalence was 58.8% of the population studied. Low LTI pts were older, were significantly more frequently overhydrated, and had been on HD for a longer period of time than the normal LTI group. FTI (FTI = FTM/ height2) was significantly higher in low LTI pts and increased according to BMI. LTI was not influenced by different BMI levels. Albumin and C-reactive protein correlated inversely (r = −0.28). However neither of them was statistically different when considering undernourished and normal LTI pts. Our BCM study was able to show a high prevalence of undernutrition, as expressed by low LTI. In our study, BMI and other common markers, such as albumin, failed to predict malnutrition as determined by BCM.

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Section: Methodsmentioning

confidence: 99%

Nutritional Markers and Body Composition in Hemodialysis Patients

Valtuille¹,

Casos²,

Fernández

et al. 2015

International Scholarly Research Notices

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“…[10][11][12]14 In this context, we demonstrate that the PLS-based model can provide a reliable estimate of the equilibrated urea, which in turn could be used in dose adequacy evaluation.…”

Section: Discussionmentioning

confidence: 99%

Partial Least Squares Regression: A Valuable Method for Modeling Molecular Behavior in Hemodialysis

Fernández

Valtuille²,

Willshaw

et al. 2008

Ann Biomed Eng

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The aim of this work was to use the Partial Least Squares Regression (PLS) technique to fit simple models for the interpretation of an underlying complex process. In this study, the technique was used to build a statistical model for molecular kinetic data obtained from hemodialyzed patients. By using PLS we derived statistical linear models for the prediction of the equilibrated urea concentration which would be reached 30-60 min after the end of the dialysis session. Models with an average relative prediction error (RPE) of less than 0.05% were achieved. The model predictive accuracy was evaluated in a cross-center study yielding an RPE < 3%. The chosen model was robust to variations such as sampling extraction time demonstrating a high capacity for modeling kinetics. It also was found to be useful for bedside monitoring. Finally, the PLS technique allowed identification of the most important co-variables in the model and of those patients with outlier patterns in their molecular dynamics.

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“…The dataset used for modeling consisted of 766,000 records, each representing a dialysis session recorded in the Spanish NephroCare centers. NN proved to be a better predictor than a standard recommendation from the guidelines regarding the urea removal ratio, postdialysis BUN, or Kt/V [19]. ML algorithms were used to predict low blood pressure [20], blood volume [21,22], or TBW [23].…”

Section: Biomed Research Internationalmentioning

confidence: 99%

Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review

Burlacu

Iftene

Jugrin

et al. 2020

BioMed Research International

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Background. The purpose of this review is to depict current research and impact of artificial intelligence/machine learning (AI/ML) algorithms on dialysis and kidney transplantation. Published studies were presented from two points of view: What medical aspects were covered? What AI/ML algorithms have been used? Methods. We searched four electronic databases or studies that used AI/ML in hemodialysis (HD), peritoneal dialysis (PD), and kidney transplantation (KT). Sixty-nine studies were split into three categories: AI/ML and HD, PD, and KT, respectively. We identified 43 trials in the first group, 8 in the second, and 18 in the third. Then, studies were classified according to the type of algorithm. Results. AI and HD trials covered: (a) dialysis service management, (b) dialysis procedure, (c) anemia management, (d) hormonal/dietary issues, and (e) arteriovenous fistula assessment. PD studies were divided into (a) peritoneal technique issues, (b) infections, and (c) cardiovascular event prediction. AI in transplantation studies were allocated into (a) management systems (ML used as pretransplant organ-matching tools), (b) predicting graft rejection, (c) tacrolimus therapy modulation, and (d) dietary issues. Conclusions. Although guidelines are reluctant to recommend AI implementation in daily practice, there is plenty of evidence that AI/ML algorithms can predict better than nephrologists: volumes, Kt/V, and hypotension or cardiovascular events during dialysis. Altogether, these trials report a robust impact of AI/ML on quality of life and survival in G5D/T patients. In the coming years, one would probably witness the emergence of AI/ML devices that facilitate the management of dialysis patients, thus increasing the quality of life and survival.

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Comparison of different methods for hemodialysis evaluation by means of ROC curves: from artificial intelligence to current methods

Cited by 16 publications

References 5 publications

Nutritional Markers and Body Composition in Hemodialysis Patients

Nutritional Markers and Body Composition in Hemodialysis Patients

Partial Least Squares Regression: A Valuable Method for Modeling Molecular Behavior in Hemodialysis

Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review

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