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Biomedical subjects

G De Nicolao

Publications and source records attributed to G De Nicolao.

16 recordsLinked to original sources
PubMed2002

Nonparametric AUC estimation in population studies with incomplete sampling: a Bayesian approach.

The estimation of the AUC in a population without frequent and/or fixed individual samplings is of interest because the number of plasma samples can often be limited due to technical, ethical and cost reasons. Non-linear mixed effect models can provide both population and individual estimates of AUC based on sparse sampling protocols; however, appropriate structural models for the description of the pharmacokinetics are required. Nonparametric solutions have also been proposed to estimate the population AUC and the associated error when particular sampling protocols are adopted. However, they do not estimate the individual AUCs and lack flexibility. Also a semiparametric method has been proposed for addressing the problem of sparse sampling in reasonably well designed studies. In this work, we propose and evaluate a nonparametric Bayesian scheme for AUC estimation in population studies with arbitrary sampling protocols. In the stochastic model representing the whole population, the individual plasma concentration curves and the "mean" population curve are described by random walk processes, allowing the application of the method to the reconstruction of any kind of "regular" curves. Population and individual AUC estimation are performed by numerically computing the posterior expectation through a Markov chain Monte Carlo algorithm.

Algorithms↗
PubMed2000

Bayesian analysis of blood glucose time series from diabetes home monitoring.

This paper describes the application of a novel Bayesian estimation technique to extract the structural components, i.e., trend and daily patterns, from blood glucose level time series coming from home monitoring of insulin dependent diabetes mellitus patients. The problem is formulated through a set of stochastic equations, and is solved in a Bayesian framework by using a Markov chain Monte Carlo technique. The potential of the method is illustrated by analyzing data coming from the home monitoring of a 14-year old male patient.

Adolescent↗
PubMed2000

Stimulated secretion of pituitary hormones in normal humans: a novel direct assessment from blood concentrations.

Gland responsiveness is usually assessed by administering suitable secretagogues and measuring the resulting hormone concentration in blood after the specific stimulus. Such response-to-stimulus tests are routinely conducted for the clinical diagnosis of pathologies involving the pituitary hormones growth hormone, prolactin, luteinizing hormone, follicle stimulating hormone, adrenocorticotropic hormone, and thyrotropin hormone. However, the current evaluation approaches, based on the maximum peak value or the (normalized) area under the curve, are inadequate under several respects. A more physiologically based index of responsiveness is the amount of released hormone. This is not directly accessible but is typically estimated by (computationally expensive) deconvolution analysis. The present work derives a simple formula yielding the amount of released hormone as a linear combination of blood concentrations through proper weights depending on hormone kinetics and sampling protocol. The weights are derived and reported for all six pituitary hormones and the more common sampling protocols. A validation study involving 174 test experiments has been carried out. The use of the formula shows excellent agreement with the cumulative secretion estimates obtained through deconvolution analysis.

Adolescent↗
PubMed2000

Abnormal LH pulsatility in women with hyperprolactinaemic amenorrhoea normalizes after bromocriptine treatment: deconvolution-based assessment.

OBJECTIVE: The present study examines the LH secretory process in hyperprolactinaemic women before, during and after bromocriptine therapy, using restrictive clinical selection criteria as well as improved methodological tools. PATIENTS AND DESIGN: Six women (aged 20-40 years) with microprolactinomas (mean +/- SE prolactin, PRL: 2478 +/- 427 mU/l, range: 1370-3800 mU/l) and four age- and sex-matched healthy controls were admitted to the study. After an overnight fast, all patients and controls had blood samples withdrawn at 10 minute intervals for 6 h (during saline infusion) from 0800 h to 1400 h to determine serum LH and PRL concentrations. After baseline evaluation, patients were treated with bromocriptine, which was started at a daily dose of 1.25 mg for 7 days; the dose was then increased to 2.5 mg daily for the next 7 days and subsequently to 2.5 mg twice daily. PRL levels were evaluated at weekly intervals after the beginning of bromocriptine therapy for the duration of the study. The 6 h pulsatility study was repeated on four patients during treatment at a time when PRL levels were decreased, although not normalized (PRL range: 450-1350 mU/l) and, on four patients, with the attainment of normal serum PRL levels (PRL < 450 mU/l) in the early follicular phase of the menstrual cycle (days 2-5). The LH instantaneous secretion rate was reconstructed by a nonparametric deconvolution method. In addition to pulse analysis made using the program DETECT, the evaluation of the secretion rate yielded the pulse frequency as well as the pulse amplitude distribution. RESULTS: Each time series was submitted to deconvolution analysis using a nonparametric method in order to estimate the instantaneous secretion rate (ISR). Hyperprolactinaemic patients had very few high-amplitude LH pulses above 0.2 IU/(l minutes) before treatment (average frequency: 0.83 +/- 0.40 pulses/6 h) and at the intermediate evaluation (0.25 +/- 0.25 pulses/6 h). In both cases, the pulse frequency was significantly lower than in controls (P < 0.05 and P < 0.01, respectively). When PRL was normalized, the number of high-amplitude LH pulses (4.25 +/- 1.03 pulses/6 h), became statistically different from the pulse number before (P < 0.01) and during (P < 0.01) therapy; in particular the pulse frequency after therapy rose to a level not statistically different from that in controls. CONCLUSION: The present study shows the presence of reduced LH pulsatility in hyperprolactinaemic women that recovers completely to within the physiological distribution when PRL levels are normalized by bromocriptine therapy.

Adult↗
PubMed1999

LH and FSH secretory responses to GnRH in normal individuals: a non-parametric deconvolution approach.

OBJECTIVE: To reconstruct the instantaneous secretion rate (ISR) of LH and FSH after GnRH administration in normal volunteers using non-parametric deconvolution, and to derive a direct integration formula to evaluate the amount of LH and FSH secreted during the first 60 min after the stimulus. DESIGN AND METHODS: First, the deconvolution method was validated in vivo by reconstructing doses ranging from 7.5 IU to 75 IU injected in three healthy adult volunteers whose endogenous LH had previously been downregulated by pretreating them, 3-4 weeks earlier, with 3.75 mg GnRH agonist i.m. Then, 40 healthy adult male volunteers were tested with a single 100 microg GnRH bolus, administered at 0 min. LH and FSH concentrations were determined at -30, 0, 15, 30, 45, 60, 90, and 120 min. RESULTS AND CONCLUSIONS: The validation study, conducted over a 10-fold range of doses, demonstrated that non-parametric deconvolution provided a reasonably accurate estimate of the amount of hormone entering the circulation. Applying deconvolution to the LH and FSH responses to GnRH, the ISRs of both hormones were shown to have a similar pattern, with a clearly delimited pulse after the GnRH bolus. In conjunction with earlier analyses of estimates of GHRH-stimulated GH secretion, we conclude that secretagogues evoke discrete LH, FSH, and GH secretory bursts of about 60 min total duration, despite markedly unequal (glyco-)protein hormone half-lives (18-500 min). With respect to the assessment of total hormone release during the first 60 min after the stimulus, the integration formula provided a reliable approximation of the result obtained by deconvolution, and had a negligible dependence on the samples at times 90 and 120 min.

Adult↗
PubMed1998

Mining biomedical time series by combining structural analysis and temporal abstractions.

This paper describes the combination of Structural Time Series analysis and Temporal Abstractions for the interpretation of data coming from home monitoring of diabetic patients. Blood Glucose data are analyzed by a novel Bayesian technique for time series analysis. The results obtained are post-processed using Temporal Abstractions in order to extract knowledge that can be exploited "at the point of use" from physicians. The proposed data analysis procedure can be viewed as a Knowledge Discovery in Data Base process that is applied to time-varying data. The work here described is part of a Web-based telemedicine system for the management of Insulin Dependent Diabetes Mellitus patients, called T-IDDM.

Algorithms↗
PubMed1997

Non-parametric deconvolution provides an objective assessment of GH responsiveness to GH-releasing stimuli in normal subjects.

OBJECTIVE: Deconvolution analysis has been proposed as an effective method for analysing the physiology of GH secretion. In the literature, it has been applied to spontaneous secretion data characterized by long and uniform sampling paradigms. In the present study we investigated the applicability of non-parametric deconvolution to the analysis of response-to-stimuli (RTS) data characterized by infrequent and non-uniform sampling. PATIENTS: Thirty-six healthy adult male volunteers (age range 24-37 years) were randomly subdivided into two groups (group I, n = 30; group II, n = 6). DESIGN: Subjects of group I were tested with a single 1 microgram/kg body weight GH-releasing hormone (GHRH) bolus, administered at 0 minutes. Subjects of group II were tested, in random order, with a 4- or 5-day interval, with (1) two consecutive 1 microgram/kg body weight GHRH boluses at 0 and 120 minutes and (2) two consecutive 1 microgram/kg body weight hexarelin boluses, administered at 0 and 120 minutes. MEASUREMENTS: GH levels were determined at 0, 15, 30, 45, 60, 90 and 120 minutes (group I) and -30, 0, 15, 30, 45, 60, 120, 135, 150, 165, 180 and 240 minutes (group II). A numerically efficient regularization-based non-parametric deconvolution algorithm incorporating non-negativity constraints was used to estimate the time profile of the instantaneous secretion rate (ISR). Confidence limits allowing for both measurement error and kinetic model uncertainty were computed using a Monte-Carlo procedure. In order to validate the deconvolution method, a simulated benchmark problem was set up. RESULTS: The analysis of the benchmark problem showed that the proposed method is capable of providing an accurate reconstruction of the ISR (as measured by the root mean square (RMS) error). Moreover, it appeared that reliable confidence limits cannot be obtained unless the kinetic model uncertainty is taken into account. The analysis of the data showed a clear rise in the ISR subsequent to the first bolus (either GHRH or hexarelin), with most of the response occurring within 60 minutes of the stimulus. In group I, it was also seen that discarding the samples collected at times 90 and 120 minutes only marginally affected the estimate of the cumulated ISR over 0-60 minutes (the variation was always less than 3%). The analysis of GH responsiveness to repeated stimuli (group II) showed that the amount of hormone secreted after the second bolus was clearly reduced in comparison with the elicited by the first stimulus, most of the response occurring within 60 minutes of the injection. The amount of GH secreted after the second stimulus ranged from 13 to 36% (GHRH 17-36%; hexarelin 13-36%) of the overall amount of hormone secreted after time 0 minutes. CONCLUSIONS: Even with relatively few samples, non-parametric deconvolution of response-to-stimulus data is capable of providing a reliable, smooth and non-negative estimate of the GH instantaneous secretion rate that offers a realistic representation of the GH secretory dynamics. The non-parametric approach compares favourably with respect to discrete deconvolution methods, that yield discontinuous instantaneous secretion rates profiles, and parametric methods that would require more stringent assumptions on the shape of the instantaneous secretion rate. When assessing confidence limits it is essential to take into account both measurement error and kinetic model uncertainty. Using deconvolution in normal subjects, the estimated instantaneous secretion rate between 0 and 60 minutes is scarcely affected by samples taken after time 60 minutes. Since most of the secretory response takes place during this time interval, there is motivation for investigating the use of shorter sampling protocols in conjunction with deconvolution analysis. Although pulse detection and the assessment of the shape of spontaneous pulses have not been investigated, it could be interesting to apply non-parametric deconvolution to spontaneous sec

Adult↗
PubMed1995

WENDEC: a deconvolution program for processing hormone time-series.

The estimation of the glandular secretory rate from time-series of hormone concentration in plasma can be formulated as a deconvolution problem. In particular, the paper addresses the analysis of frequently sampled data collected in order to study spontaneous pulsatile secretion. Standard deconvolution methods do not allow for the non-negativity constraint and the presence of high-frequency components in the secretory rate. In order to overcome the intrinsic ill-conditioning of the problem, the maximum entropy method is used to obtain a probabilistic representation of the prior knowledge concerning the unknown secretory signal, thus leading to a White Exponential Noise (WEN) model. The deconvolution problem is then posed within a Bayesian framework and solved by means of Maximum-A-Posteriori estimation. The program that implements the algorithm handles non-negativity constraints, provides confidence intervals, and is computationally and memory efficient.

Algorithms↗
PubMed1995

Adaptive controllers for intelligent monitoring.

The project we describe here is aimed at assisting out-patients affected by Insulin Dependent Diabetes Mellitus. Our approach exploits the usual scheme of diabetic patients management, based on (i) a periodic evaluation of patients' metabolic control performed by the physician, and (ii) patient-tailored tables for self-adjustments of insulin dosages. Following this scheme we have defined a system built on a two-level architecture. The High Level Module exploits both medical knowledge and clinical information in order to assess an insulin protocol, defined in terms of insulin timing, type, and total amount. The High Level Module exchanges information with the Low Level Module in order to define the control actions to be taken at the low level, as well as to periodically evaluate protocol adequacy on the basis of patient data. The goal of the Low Level Module, whose characteristics can be adaptively modified by the High Level Module, is to suggest the next insulin dosage, depending on the actual blood glucose measurement and a certain pre-defined insulin delivery protocol. The Level Control Module is based on an adaptive controller, consisting of a Fuzzy Set Controller and an ARX (Autoregressive eXogenous input) Model. The scheme here presented may be conveniently viewed in a telemedicine context, in which the low level controller is implemented on a portable device communicating to the high level controller, implemented on a remote computer. A preliminary assessment has been performed, analyzing a data set of 60 patients provided by the American Association of Artificial Intelligence, Artificial Intelligence in Medicine Subgroup, and the implementation of the system is currently in progress.

Computer Simulation↗
PubMed1995

Deconvolution of infrequently sampled data for the estimation of growth hormone secretion.

In this paper, the deconvolution of infrequently and nonuniformly sampled data is addressed. A nonparametric technique is worked out that provides a smooth estimate of the unknown input signal and takes into account nonnegativity constraints. In spite of the size of the problem, efficient algorithms for solving the constrained optimization problem and computing confidence intervals are proposed. The new technique is used to estimate growth hormone (GH) secretion after repeated GH-releasing hormone (GHRH) administration from samples of blood concentration.

Adult↗
PubMed1993

Linear and nonlinear techniques for the deconvolution of hormone time-series.

Pulsatile hormone secretion is usually investigated by measuring hormone concentration in samples of peripheral plasma. In this paper, the deconvolution of hormone time-series to reconstruct the instantaneous secretion rate of glands is considered. Various techniques are discussed and compared in order to overcome the ill-conditioning of the problem and reduce the computational burden. In particular, linear techniques based on least squares, maximum a posteriori (MAP) estimation, and Wiener filtering are compared. A new nonlinear MAP estimator that keeps into account the non-Gaussian distribution of the unknown signal is worked out and shown to yield the best results. The performances of the algorithms are tested on simulated time-series as well as on series of Luteinizing Hormone (LH).

Algorithms↗
PubMed1990

The relationship between rate of venous sampling and visible frequency of hormone pulses.

In this paper, a stochastic model of episodic hormone secretion is used to quantify the effect of the sampling rate on the frequency of pulses that can be detected by objective computer methods in time series of plasma hormone concentrations. Occurrence times of secretion pulses are modeled as recurrent events, with interpulse intervals described by Erlang distributions. In this way, a variety of secretion patterns, ranging from Poisson events to periodic pulses, can be studied. The notion of visible and invisible pulses is introduced and the relationship between true pulses frequency and mean visible pulse frequency is analytically derived. It is shown that a given visible pulse frequency can correspond to two distinct true frequencies. In order to compensate for the 'invisibility error', an algorithm based on the analysis of the original series and its undersampled subsets is proposed and the derived computer program is tested on simulated and clinical data.

Algorithms↗
PubMed1988

Evaluation of pulse-detection algorithms by computer simulation of hormone secretion.

A versatile method is presented for generating synthetic hormonal time series, containing peaks at known locations, to be used to objectively evaluate both the false-negative (F-) and false-positive (F+) statistical error rates of computerized pulse-detection algorithms. Synthetic data are generated by assuming hormone secretion to occur as a succession of instantaneous release pulses, distributed as Poisson events, separated by quiescent intervals. The pulses are convolved to simulate cumulation of consecutive events and clearance of the hormone. Randomly generated errors, corresponding in magnitude to typical experimental measurement error, are then added to the convolved series. The choice of different values for simulation parameters (e.g., frequency and amplitude of pulses) allows one to emulate some typical physiological patterns of hormone secretion for luteinizing hormone, growth hormone, and thyrotropin or other hormones. Various subsets can be extracted from a simulated time series to study the effect of sampling frequency on the detection of pulses. We show that in sampled series the "observable frequency" of pulses is less than the true nominal frequency. Methods for evaluating pulse-detection algorithms and expressing the results are presented. Simulations of LH secretion were analyzed with the program DETECT. We show that minimizing F+ error rates only might lead to excessively high F- rates. A proper choice of sampling frequency and program probability levels can be made to provide acceptable F+ and F- error rates for various patterns of hormone secretion.

Algorithms↗
PubMedDate not supplied

CURT: a randomization test for statistical comparison between experimental curves.

In this paper, a new nonparametric method for testing the difference between two groups of time series is considered. A difference index between the two groups, based on the mathematical notion of norm, is introduced. Then, the statistical significance of the observed difference is assessed by means of a randomization procedure. The percentages of alpha and beta errors are evaluated by means of computer simulation. The method is also applied to a set of experimental data and the results are compared with those obtained by means of Student's t-test.

Animals↗