Predicting sequelae and death after bacterial meningitis in childhood: A systematic review of prognostic studies

Background: Bacterial meningitis (BM) is a severe infection responsible for high mortality and disabling sequelae. Early identification of patients at high risk of these outcomes is necessary to prevent their occurrence by adequate treatment as much as possible. For this reason, several prognostic models have been developed. The objective of this study is to summarize the evidence regarding prognostic factors predicting death or sequelae due to BM in children 0-18 years of age. Methods: A search in MEDLINE and EMBASE was conducted to identify prognostic studies on risk factors for mortality and sequelae after BM in children. Selection of abstracts, full-text articles and assessment of methodological quality using the QUIPS checklist was performed by two reviewers independently. Data on prognostic factors per outcome were summarized. Results: Of the 31 studies identified, 15 were of moderate to high quality. Due to substantial heterogeneity in study characteristics and evaluated prognostic factors, no quantitative analysis was performed. Prognostic factors found to be statistically significant in more than one study of moderate or high quality are: complaints > 48 hours before admission, coma/impaired consciousness, (prolonged duration of) seizures, (prolonged) fever, shock, peripheral circulatory failure, respiratory distress, absence of petechiae, causative pathogen Streptococcus pneumoniae, young age, male gender, several cerebrospinal fluid (CSF) parameters and white blood cell (WBC) count. Conclusions: Although several important prognostic factors for the prediction of mortality or sequelae after BM were identified, the inability to perform a pooled analysis makes the exact (independent) predictive value of these factors uncertain. This emphasizes the need for additional well-conducted prognostic studie

Neural networks for genetic epidemiology: past, present, and future

During the past two decades, the field of human genetics has experienced an information explosion. The completion of the human genome project and the development of high throughput SNP technologies have created a wealth of data; however, the analysis and interpretation of these data have created a research bottleneck. While technology facilitates the measurement of hundreds or thousands of genes, statistical and computational methodologies are lacking for the analysis of these data. New statistical methods and variable selection strategies must be explored for identifying disease susceptibility genes for common, complex diseases. Neural networks (NN) are a class of pattern recognition methods that have been successfully implemented for data mining and prediction in a variety of fields. The application of NN for statistical genetics studies is an active area of research. Neural networks have been applied in both linkage and association analysis for the identification of disease susceptibility genes

Lens stem cells may reside outside the lens capsule: an hypothesis

In this paper, we consider the ocular lens in the context of contemporary developments in biological ideas. We attempt to reconcile lens biology with stem cell concepts and a dearth of lens tumors

Search for neutral MSSM Higgs bosons decaying to a pair of tau leptons in pp collisions

Peer reviewe

Measurement of top quark–antiquark pair production in association with a W or Z boson in pp collisions at √s=8 TeV

Peer reviewe

Searches for electroweak production of charginos, neutralinos, and sleptons decaying to leptons and W, Z, and Higgs bosons in pp collisions at 8 TeV

Peer reviewe

Study of hadronic event-shape variables in multijet final states in pp collisions at √s=7 TeV

Peer reviewe

Measurement of prompt J/ψ pair production in pp collisions at √s = 7 Tev

Peer reviewe