Factors Associated with Functional Performance among Patients on Hemodialysis in Taiwan

Lai, Yi-Chun; Wang, Chun-Yeh; Moi, Sin-Hua; Wu, Chien-Hsing; Yang, Chih‐Jen; Chen, Jin‐Bor

doi:10.1159/000486233

Cited by 9 publications

(15 citation statements)

References 16 publications

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“…Hence, these patients had better physical function. Higher Karnofsky performance status also implies better survival in hemodialysis patients [ 31 ]. In a previous study, PFOS was postively correlated with cholesterol level, and negatively correlated with Karnofsky performance status [ 15 , 32 , 33 ].…”

Section: Discussionmentioning

confidence: 99%

Dialysis Membranes Influence Perfluorochemical Concentrations and Liver Function in Patients on Hemodialysis

Liu

Chan

Lai

et al. 2018

IJERPH

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Introduction: Perfluoro-octanesulfonate (PFOS) and perfluoro-octanoic acid (PFOA) are two toxic perfluorochemicals (PFCs) commonly used as surfactants. PFCs are difficult to be eliminated from the body. We investigated the influence of different dialysis membranes on the concentrations of PFCs in patients under hemodialysis. Method: We enrolled 98 patients. Of these, 58 patients used hydrophobic polysulfone (PS) dialysis membranes, and the other 40 had hydrophilic membranes made by poly-methyl methacrylate (PMMA) or cellulose triacetate (CTA). Liquid chromatography tandem mass spectrometry coupled was used with isotope dilution to quantify PFOA and PFOS. Results: The predialysis concentrations of PFOA and PFOS in patients with hydrophobic PS dialysis membranes were 0.50 and 15.77 ng/mL, respectively, lower than the concentrations of 0.81 and 22.70 ng/mL, respectively, in those who used hydrophilic membranes (such as CTA or PMMA). Older patients have higher PFOS and poorer body function, with lower Karnofsky Performance Status Scale (KPSS) scores. The demographic data of the two groups were similar. However, patients with hydrophobic PS dialysis membranes had lower predialysis aspartate transaminase (AST) (p = 0.036), lower glucose levels (p = 0.017), and better body function (nonsignificantly higher KPSS scores, p = 0.091) compared with patients who used other membranes. These differences may be associated with the effects of different membranes, because PFOA positively correlated with AST, while PFOS negatively correlated with body function. Conclusions: This is the first study comparing PFC levels in uremic patients with different dialysis membrane. PS membrane may provide better clearance of PFCs and may, therefore, be beneficial for patients.

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Section: Discussionmentioning

confidence: 99%

Dialysis Membranes Influence Perfluorochemical Concentrations and Liver Function in Patients on Hemodialysis

Liu

Chan

Lai

et al. 2018

IJERPH

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“…The parameters included hemoglobin (Hb), albumin, blood urea nitrogen (BUN), creatinine (Cr), Ca, phosphate (P), sodium (Na), potassium (K), ferritin, and intact parathyroid hormone (iPTH) levels; the fractional removal of urea per dialysis treatment (Kt/V); urea reduction ratio (URR); normalized protein catabolic rate (nPCR); and cardiothoracic ratio estimated by the chest x-ray examination. Detailed information about the measurements such as Kt/V urea, URR, nPCR, and cardiothoracic ratio have been described in our previous article [23].…”

Section: Laboratory Datamentioning

confidence: 99%

Deep Learning for Intradialytic Hypotension Prediction in Hemodialysis Patients

Chen

Moi

et al. 2020

IEEE Access

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Intradialytic hypotension is a common problem during hemodialysis treatment. Despite several clinical variables have been authenticated for associations during dialysis session, the interaction effects between variables has not yet been presented. Our study aimed to investigate clinical factors associated with intradialytic hypotension by deep learning. A total of 279 participants with 780 hemodialysis sessions on an outpatient in a hospital-facilitated hemodialysis center were enrolled in March 2018. Associations between clinical factors and intradialytic hypotension were determined using linear regression method and deep neural network. A full-adjusted model indicated that intradialytic hypotension is positively associated with body mass index (Beta = 0.17, p = 0.028), hypertension comorbidity (Beta = 0.17, p = 0.008), and ultrafiltration amount (Beta = 0.31, p < 0.001), and is inversely associated with the ultrafiltration rate in a hemodialysis session (Beta = −0.30, p = 0.001). The 4-factor locus obtained by the deep neural network reached the maximum performance metrics evaluation (accuracy = 64.97± 0.94; true positive rate = 87.97 ± 2.73; positive predictive value = 66.74 ± 0.98; Matthews correlation coefficient = 0.19 ± 0.03). The prediction model obtained by the deep learning scheme could be a potential tool for the management of intradialytic hypotension. INDEX TERMS Hemodialysis, deep learning, intradialytic hypotension.

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“…Cardiac function was evaluated using Pulsed Doppler echocardiography. On the other hand, physical functional performance status was measured using the Karnofsky Performance Status scoring system [24].…”

Section: Informative Data Collectionmentioning

confidence: 99%

Clinical Factors for Sudden Unexpected Death in Patients Undergoing Peritoneal Dialysis

Zhang¹,

Lee²,

Chang³

et al. 2020

Preprint

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Background: Sudden unexpected death (SUD) accounts for a significant proportion of overall mortality in patients undergoing peritoneal dialysis (PD).This study aimed to investigate the SUD clinical profiles in patients undergoing PD.Methods: Medical records from January 2009 to December 2018 were retrospectively reviewed in a hospital-facilitated PD center in Taiwan. Demographic data, laboratory parameters, comorbidities, drug history, physical performance status, cardiac function parameters, and peritoneal transport category were abstracted. Cox proportional hazard regression was used to determine hazard ratio(HR) in SUD clinical profiles in patients undergoing PD. Results: Based on 28 patients undergoing PD with SUD, 60 controls were matched for date of death on a 2:1 ratio for comparison. The incidence of diabetes and the prevalence of low physical performance status, analgesic drug use, concordant comorbidity number, mental health and chronic pain were higher in patients undergoing PD with SUD than in controls. In the cardiac function analysis, the QTc interval on the electrocardiogram was longer (467 vs. 453 ms, P=0.010) in patients undergoing PD with SUD than in controls. At 3 months before death, patients who experienced SUD demonstrated progressive lower serum potassium concentrations. Cox proportional hazard regression analysis revealed that diabetes [adjusted HR, 4.73; 95% confidence interval (CI), 2.75–8.14; P<0.001)] and mental health and chronic pain (adjusted HR, 2.07; 95% CI, 1.06–4.05; P=0.033) remain significant predictors for SUD in patients undergoing PD.Conclusions: Diabetes mellitus, mental health and chronic pain, incidence of hypokalemia before death are significant clinical factors for SUD in patients undergoing PD.

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Factors Associated with Functional Performance among Patients on Hemodialysis in Taiwan

Cited by 9 publications

References 16 publications

Dialysis Membranes Influence Perfluorochemical Concentrations and Liver Function in Patients on Hemodialysis

Dialysis Membranes Influence Perfluorochemical Concentrations and Liver Function in Patients on Hemodialysis

Deep Learning for Intradialytic Hypotension Prediction in Hemodialysis Patients

Clinical Factors for Sudden Unexpected Death in Patients Undergoing Peritoneal Dialysis

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