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- Pantazis, N, et al.
(författare)
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Determining the likely place of HIV acquisition for migrants in Europe combining subject-specific information and biomarkers data
- 2019
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Ingår i: Statistical methods in medical research. - : SAGE Publications. - 1477-0334 .- 0962-2802. ; 28:7, s. 1979-1997
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Tidskriftsartikel (refereegranskat)abstract
- In most HIV-positive individuals, infection time is only known to lie between the time an individual started being at risk for HIV and diagnosis time. However, a more accurate estimate of infection time is very important in certain cases. For example, one of the objectives of the Advancing Migrant Access to Health Services in Europe (aMASE) study was to determine if HIV-positive migrants, diagnosed in Europe, were infected pre- or post-migration. We propose a method to derive subject-specific estimates of unknown infection times using information from HIV biomarkers’ measurements, demographic, clinical, and behavioral data. We assume that CD4 cell count (CD4) and HIV-RNA viral load trends after HIV infection follow a bivariate linear mixed model. Using post-diagnosis CD4 and viral load measurements and applying the Bayes’ rule, we derived the posterior distribution of the HIV infection time, whereas the prior distribution was informed by AIDS status at diagnosis and behavioral data. Parameters of the CD4–viral load and time-to-AIDS models were estimated using data from a large study of individuals with known HIV infection times (CASCADE). Simulations showed substantial predictive ability (e.g. 84% of the infections were correctly classified as pre- or post-migration). Application to the aMASE study ( n = 2009) showed that 47% of African migrants and 67% to 72% of migrants from other regions were most likely infected post-migration. Applying a Bayesian method based on bivariate modeling of CD4 and viral load, and subject-specific information, we found that the majority of HIV-positive migrants in aMASE were most likely infected after their migration to Europe.
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| 2. |
- Taba, P., et al.
(författare)
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EAN consensus review on prevention, diagnosis and management of tick-borne encephalitis
- 2017
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Ingår i: European Journal of Neurology. - : WILEY. - 1351-5101 .- 1468-1331. ; 24:10, s. 1214-
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Forskningsöversikt (refereegranskat)abstract
- Background and purpose: Tick-borne encephalitis (TBE) is an infection of the central nervous system (CNS) caused by tick-borne encephalitis virus (TBEV) and transmitted by ticks, with a variety of clinical manifestations. The incidence of TBE in Europe is increasing due to an extended season of the infection and the enlargement of endemic areas. Our objectives are to provide recommendations on the prevention, diagnosis and management of TBE, based on evidence or consensus decisions. Methods: For systematic evaluation, the literature was searched from 1970 to 2015 (including early online publications of 2016), and recommendations were based on evidence or consensus decisions of the Task Force when evidence-based data were not available. Recommendations: Vaccination against TBE is recommended for all age groups above 1 year in highly endemic areas (amp;gt;= 5 cases/100 000/year), but also for individuals at risk in areas with a lower incidence. Travellers to endemic areas should be vaccinated if their visits will include extensive outdoor activities. Post-exposure prophylaxis after a tick bite is not recommended. A case of TBE is defined by the presence of clinical signs of meningitis, meningoencephalitis or meningoencephalomyelitis with cerebrospinal fluid (CSF) pleocytosis (amp;gt;5 x 10(6) cells/l) and the presence of specific TBEV serum immunoglobulin M (IgM) and IgG antibodies, CSF IgM antibodies or TBEV IgG seroconversion. TBEV-specific polymerase chain reaction in blood is diagnostic in the first viremic phase but it is not sensitive in the second phase of TBE with clinical manifestations of CNS inflammation. Lumbar puncture should be performed in all patients with suspected CNS infection unless there are contraindications. Imaging of the brain and spinal cord has a low sensitivity and a low specificity, but it is useful for differential diagnosis. No effective antiviral or immunomodulating therapy is available for TBE; therefore the treatment is symptomatic. Patients with a potentially life threatening meningoencephalitis or meningoencephalomyelitis should be admitted to an intensive care unit. In the case of brain oedema, analgosedation should be deepened; osmotherapy and corticosteroids are not routinely recommended. If intracranial pressure is increased, therapeutic hypothermia or decompressive craniectomy might be considered. Seizures should be treated as any other symptomatic epileptic seizures. Conclusions: Tick-borne encephalitis is a viral CNS infection that may result in long-term neurological sequelae. Since its incidence in Europe is increasing due to broadening of endemic areas and prolongation of the tick activity season, the health burden of TBE is enlarging. There is no effective antiviral treatment for TBE, but the disease may be effectively prevented by vaccination.
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| 6. |
- Germovsek, Eva, et al.
(författare)
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Development and Evaluation of a Gentamicin Pharmacokinetic Model That Facilitates Opportunistic Gentamicin Therapeutic Drug Monitoring in Neonates and Infants
- 2016
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Ingår i: Antimicrobial Agents and Chemotherapy. - 0066-4804 .- 1098-6596. ; 60:8, s. 4869-4877
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Tidskriftsartikel (refereegranskat)abstract
- Trough gentamicin therapeutic drug monitoring (TDM) is time-consuming, disruptive to neonatal clinical care, and a patient safety issue. Bayesian models could allow TDM to be performed opportunistically at the time of routine blood tests. This study aimed to develop and prospectively evaluate a new gentamicin model and a novel Bayesian computer tool (neoGent) for TDM use in neonatal intensive care. We also evaluated model performance for predicting peak concentrations and the area under the concentration-time curve from time 0 h to time t h (AUC(0-t)). A pharmacokinetic meta-analysis was performed on pooled data from three studies (1,325 concentrations from 205 patients). A 3-compartment model was used with the following covariates: allometric weight scaling, postmenstrual and postnatal age, and serum creatinine concentration. Final parameter estimates (standard errors) were as follows: clearance, 6.2 (0.3) liters/h/70 kg of body weight; central volume (V), 26.5 (0.6) liters/70 kg; intercompartmental disposition (Q), 2.2 (0.3) liters/h/70 kg; peripheral volume V2, 21.2 (1.5) liters/70 kg; intercompartmental disposition (Q2), 0.3 (0.05) liters/h/70 kg; peripheral volume V3, 148 (52.0) liters/70 kg. The model's ability to predict trough concentrations from an opportunistic sample was evaluated in a prospective observational cohort study that included data from 163 patients and 483 concentrations collected in five hospitals. Unbiased trough predictions were obtained; the median (95% confidence interval [CI]) prediction error was 0.0004 (-1.07, 0.84) mg/liter. Results also showed that peaks and AUC(0-t) values could be predicted (from one randomly selected sample) with little bias but relative imprecision, with median (95% CI) prediction errors being 0.16 (-4.76, 5.01) mg/liter and 10.8 (-24.9, 62.2) mg center dot h/liter, respectively. neoGent was implemented in R/NONMEM and in the freely available TDMx software.
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