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G Backus

Publications and source records attributed to G Backus.

9 recordsLinked to original sources
PubMed2001

The influence of different glucose concentrations in haemodialysis solutions on metabolism and blood pressure stability in diabetic patients.

In recent years the percentage of diabetic patients on haemodialysis has increased. Considering the high frequency of intradialytic hypotensive and hypoglycaemic episodes experienced by these patients, it was the aim of the present study to evaluate the influence of different dialysate glucose concentrations (5.5 mmol/L or 11 mmol/L) on blood pressure and glycaemic regulation, using special dialysis equipment - the GENIUS System. This cross-over, prospective and randomised study, total duration 14 weeks, included 20 diabetic patients on maintenance haemodialysis. Group 1: 9 patients dialysed using dialysate with a glucose concentration of 5.5 mmol/L and after 7 weeks switched to dialysate with a glucose concentration of 11 mmol/L. Group 2: vice versa. Results show a statistically higher number of patients with hypoglycaemic and hypotensive episodes using dialysate with a 5.5 mmol/L glucose concentration. Also, mean serum glucose values were higher during haemodialysis sessions with a glucose dialysate concentration of 11 mmol/L. There were no statistical differences between the groups in laboratory values, HbA1C, insulin doses or in anthropometric parameters. Our results suggest that fewer diabetic patients undergoing haemodialysis using a higher dialysate glucose concentration of 11 mmol/L have hypoglycaemic and hypotensive episodes. Since this dialysate glucose concentration had no influence on lipid or hepatic metabolism, anthropometric parameters and especially HbA/1C values in this short-term study, the long term examination of its effects is warranted.

Aged↗
PubMed1998

Long-term experience with an ultrapure individual dialysis fluid with a batch type machine.

BACKGROUND: This paper discusses long-term experience with a specific type of dialysis equipment which has been used more than 15 years without variation. The system was designed to allow easy individualization of dialysis fluid composition and to deliver dialysate of the highest hygienic quality. METHODS: Data from 399 patients covering the period from 1971 onwards were analysed retrospectively. Survival probabilities were estimated by the Kaplan-Meier method and the median number of days in hospital was calculated. Additional data collected from patient subgroups included serum albumin level, erythropoietin requirement and antihypertensive treatments. Kt/V and PCR from one subgroup were computed using the formulae of Daugirdas and Depner. RESULTS: The estimated survival probability after 5 years for all patients was 59.1% (95% CI: 52.6-65.6%). The main risk factors from the available covariables were age and IDDM. The cumulative incidence of carpal tunnel syndrome after 10 years of dialysis was estimated as 7% (95% CI: 0-14%). Data from the subgroups revealed that 82% of the patients had serum albumin levels >4.0 g/dl, 65% of the patients received no antihypertensive drugs and 39% received erythropoietin (37 +/- 28 units/kg bw/week) to correct dialysis anaemia (haemoglobin level = 98 +/- 8 g/l). Average Kt/V was 1.21 +/- 0.17, PCR was 1.10 +/- 0.22 g/kg/day. CONCLUSIONS: The setup described permits individualized therapy of high quality. The high serum albumin values and our very low incidence of carpal tunnel syndrome underline the importance of water and dialysate quality.

Adult↗
PubMed1987

Isotropic probability measures in infinite-dimensional spaces.

Every isotropic probability measure on the space R(infinity) of real sequences x = (x(1), x(2),...) is a convex combination of the measure concentrated at 0 and a member of I(0)(R(infinity)), the set of all isotropic probability measures p(infinity) on R(infinity) with p(infinity)({0}) = 0. Each p(infinity) [unk] I(0)(R(infinity)) is completely determined by any one of its finite-dimensional marginal distributions p(n). Each p(n) has a density function f(n) with dp(n)(x(1),..., x(n)) = dx(1)... dx(n)f(n)(x(1) (2) +... + x(n) (2)). Each f(n) is completely monotone in 0 < xi < infinity (hence analytic in the right complex xi half-plane), and pi(n/2)Gamma(n/2)(-1) (0) (infinity)dxi xi(n/2-1)f(n)(xi) = 1. Every f that satisfies these two conditions is f(n) for a unique p(infinity) [unk] I(0)(R(infinity)). Hence the equation pi(xi) (infinity)dzeta f(2)(zeta) = (0) (infinity)dmu (t)e(-txi) defines a bijection between I(0)(R(infinity)) and the set of all probability measures mu on 0 </= t < infinity. If p(infinity) [unk] I(0)(R(infinity)) then p(infinity)({x: Sigma(i=1) (infinity)x(i) (2) < infinity}) = 0, so p(infinity) is not a "softened" or "fuzzy" version of the inequality Sigma(i=1) (infinity)x(i) (2) </= 1. If the prior information in a linear inverse problem consists of this inequality and nothing else, stochastic inversion and Bayesian inference are both unsuitable inversion techniques.

Journal Article↗
PubMed1970

Inference from inadequate and inaccurate data, I.

Having measured D numerical properties of a physical object E which requires many more than D parameters for its complete specification, an observer seeks to estimate P other numerical properties of E. This paper describes how he can proceed when E is adequately described by one member m(E) of a Hilbert space [unk] of possible models of E, when he believes that the Hilbert norm of m(E) is very likely rather smaller than some known number M, and (except for section 6) when all the observed and sought-after properties of E are continuous linear functionals on [unk]. Section 6 treats Frechet-differentiable non-linear functionals. A later paper will reduce unbounded functionals on arbitrary topological linear spaces to the present case.

Journal Article↗
PubMed1970

Inference from Inadequate and Inaccurate Data, II.

Having measured D numerical properties of a physical object E which requires many more than D parameters for its complete specification, an observer seeks to estimate P other numerical properties of E. A previous paper discussed how he can proceed when E is adequately described by one member m(E) of a Hilbert space [unk] of possible models of E and when all the observed and sought-for properties of E are Fréchet differentiable functionals on [unk]. The present paper describes a technique often available for reducing to this tractable case problems in which the functionals are discontinuous and are defined only on parts of [unk], and [unk] is an arbitrary real linear space. Applications include geophysical inverse problems and numerical differentiation, integration, interpolation, and extrapolation.

Journal Article↗
PubMed1970

Inference from Inadequate and Inaccurate Data, III.

Having measured D numerical properties of a physical object E which requires many more than D parameters for its complete description, we want to estimate P other numerical properties of E. Continuing the discussion in papers I(1) and II,(2) the present paper gives estimates when we believe it likely that we can guess an upper bound M on the Hilbert norm not of h(E), the model representing E in some Hilbert space, but of the orthogonal projection of h(E) onto a sufficiently large subspace. In addition, the present paper simplifies the notation of I, and makes explicit the application of Bayesian subjective probability when there are errors in the data and we want to find the joint probability distribution of more than one prediction.

Journal Article↗