Dataset Information

High-Dose Ceftriaxone for Bacterial Meningitis and Optimization of Administration Scheme Based on Nomogram.

ABSTRACT: High dosages of ceftriaxone are used to treat central nervous system (CNS) infections. Dosage adaptation according to the glomerular filtration rate is currently not recommended. Ceftriaxone pharmacokinetics (PK) was investigated by a population approach in patients enrolled in a French multicenter prospective cohort study who received high-dose ceftriaxone for CNS infection as recommended by French guidelines (75 to 100?mg/kg of body weight/day without an upper limit). Only those with suspected bacterial meningitis were included in the PK analysis. A population model was developed using Pmetrics. Based on this model, a dosing nomogram was developed, using the estimated glomerular filtration rate (eGFR) and total body weight as covariates to determine the optimal dosage allowing achievement of targeted plasma trough concentrations. Efficacy and toxicity endpoints were based on previous reports, as follows: total plasma ceftriaxone concentrations of ?20?mg/liter in >90% of patients for efficacy and ?100?mg/liter in >90% of patients for toxicity. Based on 153 included patients, a two-compartment model including eGFR and total body weight as covariates was developed. The median value of the unbound fraction was 7.57%, and the median value of the cerebral spinal fluid (CSF)/plasma ratio was 14.39%. A nomogram was developed according to a twice-daily regimen. High-dose ceftriaxone administration schemes, used to treat meningitis, should be adapted to the eGFR and weight, especially to avoid underdosing using current guidelines. (This study has been registered at ClinicalTrials.gov under identifier NCT01745679.).

SUBMITTER: Gregoire M

PROVIDER: S-EPMC6709482 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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High-Dose Ceftriaxone for Bacterial Meningitis and Optimization of Administration Scheme Based on Nomogram.

Grégoire Matthieu M Dailly Eric E Le Turnier Paul P Garot Denis D Guimard Thomas T Bernard Louis L Tattevin Pierre P Vandamme Yves-Marie YM Hoff Jérôme J Lemaitre Florian F Verdier Marie-Clémence MC Deslandes Guillaume G Bellouard Ronan R Sébille Véronique V Chiffoleau Anne A Boutoille David D Navas Dominique D Asseray Nathalie N

Antimicrobial agents and chemotherapy 20190823 9

High dosages of ceftriaxone are used to treat central nervous system (CNS) infections. Dosage adaptation according to the glomerular filtration rate is currently not recommended. Ceftriaxone pharmacokinetics (PK) was investigated by a population approach in patients enrolled in a French multicenter prospective cohort study who received high-dose ceftriaxone for CNS infection as recommended by French guidelines (75 to 100 mg/kg of body weight/day without an upper limit). Only those with suspected ...[more]

PMID: 31235630

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Project description:Effective laboratory-based surveillance and public health response to bacterial meningitis depends on timely characterization of bacterial meningitis pathogens. Traditionally, characterizing bacterial meningitis pathogens such as Neisseria meningitidis (Nm) and Haemophilus influenzae (Hi) required several biochemical and molecular tests. Whole genome sequencing (WGS) has enabled the development of pipelines capable of characterizing the given pathogen with equivalent results to many of the traditional tests. Here, we present the Bacterial Meningitis Genomic Analysis Platform (BMGAP): a secure, web-accessible informatics platform that facilitates automated analysis of WGS data in public health laboratories. BMGAP is a pipeline comprised of several components, including both widely used, open-source third-party software and customized analysis modules for the specific target pathogens. BMGAP performs de novo draft genome assembly and identifies the bacterial species by whole-genome comparisons against a curated reference collection of 17 focal species including Nm, Hi, and other closely related species. Genomes identified as Nm or Hi undergo multi-locus sequence typing (MLST) and capsule characterization. Further typing information is captured from Nm genomes, such as peptides for the vaccine antigens FHbp, NadA, and NhbA. Assembled genomes are retained in the BMGAP database, serving as a repository for genomic comparisons. BMGAP's species identification and capsule characterization modules were validated using PCR and slide agglutination from 446 bacterial invasive isolates (273 Nm from nine different serogroups, 150 Hi from seven different serotypes, and 23 from nine other species) collected from 2017 to 2019 through surveillance programs. Among the validation isolates, BMGAP correctly identified the species for all 440 isolates (100% sensitivity and specificity) and accurately characterized all Nm serogroups (99% sensitivity and 98% specificity) and Hi serotypes (100% sensitivity and specificity). BMGAP provides an automated, multi-species analysis pipeline that can be extended to include additional analysis modules as needed. This provides easy-to-interpret and validated Nm and Hi genome analysis capacity to public health laboratories and collaborators. As the BMGAP database accumulates more genomic data, it grows as a valuable resource for rapid comparative genomic analyses during outbreak investigations.

Project description:ObjectivesBacterial meningitis remains an important cause of morbidity and mortality worldwide. Its epidemiological characteristics, however, are changing due to new vaccines and secular trends. Conjugate vaccines against Haemophilus influenzae type b and Streptococcus pneumoniae (10-valent) were introduced in 1986 and 2010 in Finland. We assessed the disease burden and long-term trends of five common causes of bacterial meningitis in a population-based observational study.MethodsA case was defined as isolation of S. pneumoniae, Neisseria meningitidis, Streptococcus agalactiae, Listeria monocytogenes or H. influenzae from cerebrospinal fluid and reported to national, population-based laboratory surveillance system during 1995-2014. We evaluated changes in incidence rates (Poisson or negative binomial regression), case fatality proportions (?2) and age distribution of cases (Wilcoxon rank-sum).ResultsDuring 1995-2014, S. pneumoniae and N. meningitidis accounted for 78% of the total 1361 reported bacterial meningitis cases. H. influenzae accounted for 4% of cases (92% of isolates were non-type b). During the study period, the overall rate of bacterial meningitis per 1?00?000 person-years decreased from 1.88 cases in 1995 to 0.70 cases in 2014 (4% annual decline (95% CI 3% to 5%). This was primarily due to a 9% annual reduction in rates of N. meningitidis (95%?CI 7% to 10%) and 2% decrease in S. pneumoniae (95%?CI 1% to 4%). The median age of cases increased from 31 years in 1995-2004 to 43 years in 2005-2014 (p=0.0004). Overall case fatality proportion (10%) did not change from 2004 to 2009 to 2010-2014.ConclusionsSubstantial decreases in bacterial meningitis were associated with infant conjugate vaccination against pneumococcal meningitis and secular trend in meningococcal meningitis in the absence of vaccination programme. Ongoing epidemiological surveillance is needed to identify trends, evaluate serotype distribution, assess vaccine impact and develop future vaccination strategies.

Project description:Epidemiological time series forecasting plays an important role in health public systems, due to its ability to allow managers to develop strategic planning to avoid possible epidemics. In this paper, a hybrid learning framework is developed to forecast multi-step-ahead (one, two and three-month-ahead) meningitis cases in four states of Brazil. First, the proposed approach applies an ensemble empirical mode decomposition (EEMD) to decompose the data into intrinsic mode functions and residual components. Then, each component is used as the input of five different forecasting models, and, from there, forecasted results are obtained. Finally, all combinations of models and components are developed, and for each case, the forecasted results are weighted integrated (WI) to formulate a heterogeneous ensemble forecaster for the monthly meningitis cases. In the final stage, a multi-objective optimization (MOO) using the Non-Dominated Sorting Genetic Algorithm - version II is employed to find a set of candidates' weights, and then the Technique for Order of Preference by similarity to Ideal Solution (TOPSIS) is applied to choose the adequate set of weights. Next, the most adequate model is the one with the best generalization capacity out-of-sample in terms of performance criteria including mean absolute error (MAE), relative root mean squared error (RRMSE) and symmetric mean absolute percentage error (sMAPE). By using MOO, the intention is to enhance the performance of the forecasting models by improving simultaneously their accuracy and stability measures. To access the model's performance, comparisons based on metrics are conducted with: (i) EEMD, heterogeneous ensemble integrated by direct strategy, or simple sum; (ii) EEMD, homogeneous ensemble of components WI; (iii) models without signal decomposition. At this stage, MAE, RRMSE, sMAPE criteria and Diebold-Mariano statistical test are adopted. In all twelve scenarios, the proposed framework was able to perform more accurate and stable forecasts, which showed, on 89.17% of the cases, that the errors of the proposed approach are statistically lower than other approaches. These results showed that combining EEMD, heterogeneous ensemble and WI with weights obtained by optimization can develop precise and stable forecasts. The modelling developed in this paper is promising and can be used by managers to support decision making.

Project description:BackgroundThe differential diagnosis between tuberculous meningitis (TBM) and bacterial meningitis (BM) remains challenging in clinical practice. This study aimed to establish a diagnostic model that could accurately distinguish TBM from BM.MethodsPatients with TBM or BM were recruited between January 2017 and January 2021 at Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort). The detection for indicators involved in cerebrospinal fluid (CSF) and T-SPOT assay were performed simultaneously. Multivariate logistic regression was used to create a diagnostic model.ResultsA total of 174 patients (76 TBM and 98 BM) and another 105 cases (39 TBM and 66 BM) were enrolled from Qiaokou cohort and Caidian cohort, respectively. Significantly higher level of CSF lymphocyte proportion while significantly lower levels of CSF chlorine, nucleated cell count, and neutrophil proportion were observed in TBM group when comparing with those in BM group. However, receiver operating characteristic (ROC) curve analysis showed that the areas under the ROC curve (AUCs) produced by these indicators were all under 0.8. Meanwhile, tuberculosis-specific antigen/phytohemagglutinin (TBAg/PHA) ratio yielded an AUC of 0.889 (95% CI, 0.840-0.938) in distinguishing TBM from BM, with a sensitivity of 68.42% (95% CI, 57.30%-77.77%) and a specificity of 92.86% (95% CI, 85.98%-96.50%) when a cutoff value of 0.163 was used. Consequently, we successfully established a diagnostic model based on the combination of TBAg/PHA ratio, CSF chlorine, CSF nucleated cell count, and CSF lymphocyte proportion for discrimination between TBM and BM. The established model showed good performance in differentiating TBM from BM (AUC: 0.949; 95% CI, 0.921-0.978), with 81.58% (95% CI, 71.42%-88.70%) sensitivity and 91.84% (95% CI, 84.71%-95.81%) specificity. The performance of the diagnostic model obtained in Qiaokou cohort was further validated in Caidian cohort. The diagnostic model in Caidian cohort produced an AUC of 0.923 (95% CI, 0.867-0.980) with 79.49% (95% CI, 64.47%-89.22%) sensitivity and 90.91% (95% CI, 81.55%-95.77%) specificity.ConclusionsThe diagnostic model established based on the combination of four indicators had excellent utility in the discrimination between TBM and BM.

Dataset Information

High-Dose Ceftriaxone for Bacterial Meningitis and Optimization of Administration Scheme Based on Nomogram.

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High-Dose Ceftriaxone for Bacterial Meningitis and Optimization of Administration Scheme Based on Nomogram.

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