Systematic review of prognostic models in traumatic brain injury

BACKGROUND: Traumatic brain injury (TBI) is a leading cause of death and disability world-wide. The ability to accurately predict patient outcome after TBI has an important role in clinical practice and research. Prognostic models are statistical models that combine two or more items of patient data to predict clinical outcome. They may improve predictions in TBI patients. Multiple prognostic models for TBI have accumulated for decades but none of them is widely used in clinical practice. The objective of this systematic review is to critically assess existing prognostic models for TBI METHODS: Studies that combine at least two variables to predict any outcome in patients with TBI were searched in PUBMED and EMBASE. Two reviewers independently examined titles, abstracts and assessed whether each met the pre-defined inclusion criteria. RESULTS: A total of 53 reports including 102 models were identified. Almost half (47%) were derived from adult patients. Three quarters of the models included less than 500 patients. Most of the models (93%) were from high income countries populations. Logistic regression was the most common analytical strategy to derived models (47%). In relation to the quality of the derivation models (n:66), only 15% reported less than 10% pf loss to follow-up, 68% did not justify the rationale to include the predictors, 11% conducted an external validation and only 19% of the logistic models presented the results in a clinically user-friendly way CONCLUSION: Prognostic models are frequently published but they are developed from small samples of patients, their methodological quality is poor and they are rarely validated on external populations. Furthermore, they are not clinically practical as they are not presented to physicians in a user-friendly way. Finally because only a few are developed using populations from low and middle income countries, where most of trauma occurs, the generalizability to these setting is limited

Hand sanitisers for reducing illness absences in primary school children in New Zealand: a cluster randomised controlled trial study protocol

<p>Abstract</p> <p>Background</p> <p>New Zealand has relatively high rates of morbidity and mortality from infectious disease compared with other OECD countries, with infectious disease being more prevalent in children compared with others in the population. Consequences of infectious disease in children may have significant economic and social impact beyond the direct effects of the disease on the health of the child; including absence from school, transmission of infectious disease to other pupils, staff, and family members, and time off work for parents/guardians. Reduction of the transmission of infectious disease between children at schools could be an effective way of reducing the community incidence of infectious disease. Alcohol based no-rinse hand sanitisers provide an alternative hand cleaning technology, for which there is some evidence that they may be effective in achieving this. However, very few studies have investigated the effectiveness of hand sanitisers, and importantly, the potential wider economic implications of this intervention have not been established.</p> <p>Aims</p> <p>The primary objective of this trial is to establish if the provision of hand sanitisers in primary schools in the South Island of New Zealand, in addition to an education session on hand hygiene, reduces the incidence rate of absence episodes due to illness in children. In addition, the trial will establish the cost-effectiveness and conduct a cost-benefit analysis of the intervention in this setting.</p> <p>Methods/Design</p> <p>A cluster randomised controlled trial will be undertaken to establish the effectiveness and cost-effectiveness of hand sanitisers. Sixty-eight primary schools will be recruited from three regions in the South Island of New Zealand. The schools will be randomised, within region, to receive hand sanitisers and an education session on hand hygiene, or an education session on hand hygiene alone. Fifty pupils from each school in years 1 to 6 (generally aged from 5 to 11 years) will be randomly selected for detailed follow-up about their illness absences, providing a total of 3400 pupils. In addition, absence information will be collected on all children from the school rolls. Investigators not involved in the running of the trial, outcome assessors, and the statistician will be blinded to the group allocation until the analysis is completed.</p> <p>Trial registration</p> <p>ACTRN12609000478213</p

A comparative analysis of multi-level computer-assisted decision making systems for traumatic injuries

<p>Abstract</p> <p>Background</p> <p>This paper focuses on the creation of a predictive computer-assisted decision making system for traumatic injury using machine learning algorithms. Trauma experts must make several difficult decisions based on a large number of patient attributes, usually in a short period of time. The aim is to compare the existing machine learning methods available for medical informatics, and develop reliable, rule-based computer-assisted decision-making systems that provide recommendations for the course of treatment for new patients, based on previously seen cases in trauma databases. Datasets of traumatic brain injury (TBI) patients are used to train and test the decision making algorithm. The work is also applicable to patients with traumatic pelvic injuries.</p> <p>Methods</p> <p>Decision-making rules are created by processing patterns discovered in the datasets, using machine learning techniques. More specifically, CART and C4.5 are used, as they provide grammatical expressions of knowledge extracted by applying logical operations to the available features. The resulting rule sets are tested against other machine learning methods, including AdaBoost and SVM. The rule creation algorithm is applied to multiple datasets, both with and without prior filtering to discover significant variables. This filtering is performed via logistic regression prior to the rule discovery process.</p> <p>Results</p> <p>For survival prediction using all variables, CART outperformed the other machine learning methods. When using only significant variables, neural networks performed best. A reliable rule-base was generated using combined C4.5/CART. The average predictive rule performance was 82% when using all variables, and approximately 84% when using significant variables only. The average performance of the combined C4.5 and CART system using significant variables was 89.7% in predicting the exact outcome (home or rehabilitation), and 93.1% in predicting the ICU length of stay for airlifted TBI patients.</p> <p>Conclusion</p> <p>This study creates an efficient computer-aided rule-based system that can be employed in decision making in TBI cases. The rule-bases apply methods that combine CART and C4.5 with logistic regression to improve rule performance and quality. For final outcome prediction for TBI cases, the resulting rule-bases outperform systems that utilize all available variables.</p

Novel Approach Identifies SNPs in SLC2A10 and KCNK9 with Evidence for Parent-of-Origin Effect on Body Mass Index

Marja-Liisa Lokki työryhmien Generation Scotland Consortium, LifeLines Cohort Study ja GIANT Consortium jäsenPeer reviewe