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  • 2015-2019  (5)
  • 2000-2004  (2)
  • 1
    Electronic Resource
    Electronic Resource
    Oxford, UK : Blackwell Publishing Ltd.
    Addiction 98 (2003), S. 0 
    ISSN: 1360-0443
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Medicine , Psychology
    Notes: Background  Biomedical markers may provide additive objective information in screening and confirmation of acute or recent consumption, intoxication, relapse, heavy drinking, hazardous/harmful use/abuse and dependence and alcohol use related organ dysfunction (alcohol use-related disorders: AUDs).Aims  To review the use of biomarkers in clinical practice to detect AUDs.Findings  About one-fifth of the patients seen in clinical practice have AUDs, which offer a variety of treatment options if diagnosed. The diagnosis of AUDs relies on clinical and alcohol-related history, physical examination, questionnaires and laboratory values. No clinical available laboratory test [e.g. for acute abuse: alcohol in blood or breath; for chronic alcohol abuse: γ-glutamyl transferase (GGT), mean corpuscular volume (MCV), carbohydrate-deficient transferrin (CDT)] is reliable enough on its own to support a diagnosis of alcohol dependence, harmful use or abuse. Sensitivities, specificities and the predictive values may vary considerably according to patient and control group characteristics (e.g. gender, age or related comorbidity). In patient groups with limited cooperation markers may be helpful when considering treatment options.Conclusions  More research is needed to determine the value of markers (single or combined, with questionnaires) in the context of clinical decision-making algorithms in defined settings and with defined dichotomous outcome variables.
    Type of Medium: Electronic Resource
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  • 2
    ISSN: 1432-1238
    Keywords: Dobutamine Indirect calorimetry Oxygen consumption Oxygen delivery Sepsis
    Source: Springer Online Journal Archives 1860-2000
    Topics: Medicine
    Notes: Abstract. Objective: Oxygen consumption (VO2) obtained from respiratory gases by indirect calorimetry (VO2,IC) with a metabolic monitor integrated in a ventilator were to be compared to VO2 obtained by the Fick principle (VO2,Fick ) in septic patients following an increase in oxygen delivery (DO2) induced by positive inotropic support. Design: Prospective clinical study. Setting: University Hospital, Surgical Intensive Care Unit (ICU). Patients: Thirty patients suffering from sepsis. Interventions: DO2 was increased by dobutamine infusion, starting with an initial dosage of 5 µg·kg·min, increased to a maximum of 10 µg·kg·min. Measurements and main results: Dobutamine infusion induced a dosage-related increase in DO2 (from 577±192 to 752±202 ml·min·m2, p〈0.01), which was associated with a statistically significant increase in VO2,IC (from 173±30 to 188±28 ml·min·m2, p〈0.01) and in VO2,Fick (from 140±25 to 156±24 ml·min·m2, p〈0.01). The comparison between VO2,IC and VO2,Fick revealed differences (bias and precision – 33±32 ml·min·m2). Conclusions: With a metabolic monitor integrated in a ventilator it was possible to carry out continuous monitoring of calorimetric data under clinical conditions. In contrast to previous studies using indirect calorimetry, this study showed a moderate correlation between VO2 and DO2 in septic patients using either method. The clinical relevance of this finding requires further investigation. Different factors (e. g. injectant temperature, pulmonary VO2) produced substantial differences between VO2,IC and VO2,Fick as previously shown.
    Type of Medium: Electronic Resource
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  • 3
    Publication Date: 2020-08-05
    Description: The problem of allocating operating rooms (OR) to surgical cases is a challenging task, involving both combinatorial aspects and uncertainty handling. In this article, we formulate this problem as a job shop scheduling problem, in which the job durations follow a lognormal distribution. We propose to use a cutting-plane approach to solve a robust version of this optimization problem. To this end, we develop an algorithm based on fixed-point iterations to solve the subproblems that identify worst-case scenarios and generate cut inequalities. The procedure is illustrated with numerical experiments based on real data from a major hospital in Berlin.
    Language: English
    Type: reportzib , doc-type:preprint
    Format: application/pdf
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  • 4
    Publication Date: 2020-08-05
    Description: We propose an algorithm to approximate the distribution of the completion time (makespan) and the tardiness costs of a project, when durations are lognormally distributed. This problem arises naturally for the optimization of surgery scheduling, where it is very common to assume lognormal procedure times. We present an analogous of Clark's formulas to compute the moments of the maximum of a set of lognormal variables. Then, we use moment matching formulas to approximate the earliest starting time of each activity of the project by a shifted lognormal variable. This approach can be seen as a lognormal variant of a state-of-the-art method used for the statistical static timing analysis (SSTA) of digital circuits. We carried out numerical experiments with instances based on real data from the application to surgery scheduling. We obtained very promising results, especially for the approximation of the mean overtime in operating rooms, for which our algorithm yields results of a similar quality to Monte-Carlo simulations requiring an amount of computing time several orders of magnitude larger.
    Language: English
    Type: reportzib , doc-type:preprint
    Format: application/pdf
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  • 5
    Publication Date: 2021-01-22
    Description: The problem of allocating operating rooms (OR) to surgical cases is a challenging task, involving both combinatorial aspects and uncertainty handling. We formulate this problem as a parallel machines scheduling problem, in which job durations follow a lognormal distribution, and a fixed assignment of jobs to machines must be computed. We propose a cutting-plane approach to solve the robust counterpart of this optimization problem. To this end, we develop an algorithm based on fixed-point iterations that identifies worst-case scenarios and generates cut inequalities. The main result of this article uses Hilbert's projective geometry to prove the convergence of this procedure under mild conditions. We also propose two exact solution methods for a similar problem, but with a polyhedral uncertainty set, for which only approximation approaches were known. Our model can be extended to balance the load over several planning periods in a rolling horizon. We present extensive numerical experiments for instances based on real data from a major hospital in Berlin. In particular, we find that: (i) our approach performs well compared to a previous model that ignored the distribution of case durations; (ii) compared to an alternative stochastic programming approach, robust optimization yields solutions that are more robust against uncertainty, at a small price in terms of average cost; (iii) the \emph{longest expected processing time first} (LEPT) heuristic performs well and efficiently protects against extreme scenarios, but only if a good prediction model for the durations is available. Finally, we draw a number of managerial implications from these observations.
    Language: English
    Type: article , doc-type:article
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  • 6
    Publication Date: 2020-08-05
    Description: The problem of allocating operating rooms (OR) to surgical cases is a challenging task, involving both combinatorial aspects and uncertainty handling. We formulate this problem as a parallel machines scheduling problem, in which job durations follow a lognormal distribution, and a fixed assignment of jobs to machines must be computed. We propose a cutting-plane approach to solve the robust counterpart of this optimization problem. To this end, we develop an algorithm based on fixed-point iterations that identifies worst-case scenarios and generates cut inequalities. The main result of this article uses Hilbert's projective geometry to prove the convergence of this procedure under mild conditions. We also propose two exact solution methods for a similar problem, but with a polyhedral uncertainty set, for which only approximation approaches were known. Our model can be extended to balance the load over several planning periods in a rolling horizon. We present extensive numerical experiments for instances based on real data from a major hospital in Berlin. In particular, we find that: (i) our approach performs well compared to a previous model that ignored the distribution of case durations; (ii) compared to an alternative stochastic programming approach, robust optimization yields solutions that are more robust against uncertainty, at a small price in terms of average cost; (iii) the \emph{longest expected processing time first} (LEPT) heuristic performs well and efficiently protects against extreme scenarios, but only if a good prediction model for the durations is available. Finally, we draw a number of managerial implications from these observations.
    Language: English
    Type: reportzib , doc-type:preprint
    Format: application/pdf
    Format: application/pdf
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  • 7
    Publication Date: 2023-07-17
    Language: English
    Type: article , doc-type:article
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