A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases.

Modern high-throughput experiments provide us with numerous potential associations between genes and diseases. Experimental validation of all the discovered associations, let alone all the possible interactions between them, is time-consuming and expensive. To facilitate the discovery of causative genes, various approaches for prioritization of genes according to their relevance for a given disease have been developed. In this article, we explain the gene prioritization problem and provide an overview of computational tools for gene prioritization. Among about a hundred of published gene prioritization tools, we select and briefly describe 14 most up-to-date and user-friendly. Also, we discuss the advantages and disadvantages of existing tools, challenges of their validation, and the directions for future research.

Data-Driven Assessment of Potentially Inappropriate Medication in the Elderly.

Multimorbid patients taking polypharmacy represent a growing population at high risk for inappropriate prescribing. Various lists for identifying potentially inappropriate medication are spread across scientific journals and difficult to access. To address this ongoing need, a new database named PIMBase is developed which integrates these well-known lists and unifies their rating scales. The analysis of the pharmacovigilance data reveals the benefits of combining the lists. PIMBase is meant to be a web-based system and starting point for the data-driven assessment of polypharmacy to identify inappropriate medication and to improve the quality of prescribing. PIMBase is available at https://pimbase.kalis-amts.de.

Effects of computerized decision support system implementations on patient outcomes in inpatient care: a systematic review.

Objectives: To systematically classify the clinical impact of computerized clinical decision support systems (CDSSs) in inpatient care. Materials and Methods: Medline, Cochrane Trials, and Cochrane Reviews were searched for CDSS studies that assessed patient outcomes in inpatient settings. For each study, 2 physicians independently mapped patient outcome effects to a predefined medical effect score to assess the clinical impact of reported outcome effects. Disagreements were measured by using weighted kappa and solved by consensus. An example set of promising disease entities was generated based on medical effect scores and risk of bias assessment. To summarize technical characteristics of the systems, reported input variables and algorithm types were extracted as well. Results: Seventy studies were included. Five (7%) reported reduced mortality, 16 (23%) reduced life-threatening events, and 28 (40%) reduced non-life-threatening events, 20 (29%) had no significant impact on patient outcomes, and 1 showed a negative effect (weighted κ: 0.72, P < .001). Six of 24 disease entity settings showed high effect scores with medium or low risk of bias: blood glucose management, blood transfusion management, physiologic deterioration prevention, pressure ulcer prevention, acute kidney injury prevention, and venous thromboembolism prophylaxis. Most of the implemented algorithms (72%) were rule-based. Reported input variables are shared as standardized models on a metadata repository. Discussion and Conclusion: Most of the included CDSS studies were associated with positive patient outcomes effects but with substantial differences regarding the clinical impact. A subset of 6 disease entities could be filtered in which CDSS should be given special consideration at sites where computer-assisted decision-making is deemed to be underutilized. Registration number on PROSPERO: CRD42016049946.

Medical Effect of Venous Thromboembolism Prophylaxis Systems and Common Input Categories: Preliminary Findings from a Systematic Review.

Computerized Clinical Decision Support Systems (CDSS) are implemented in hospitals to improve prevention of Venous Thromboembolisms (VTE). A physician-driven review was conducted to assess extent of patient outcome effects of recently published CDSS studies. To facilitate future re-implementations within existing hospital information systems, input variables of included systems were extracted, standardized and annotated with semantic codes. Item category coverages of the different systems were then compared. 73% of studies showed positive medical effect. Of these, 53% showed strong positive medical effect by reducing incidence of deep vein thromboses and pulmonary embolisms. Outcome-improving systems tend to cover more item categories. A broad set of clinically relevant input variables should be taken into account or reused from the electronic health record if considering CDSS implementation. Input data models are provided for download in different standardized formats. Site-specific organizational factors that determine how systems are introduced, implemented and tested are also crucial for success.

Data in longitudinal randomised controlled trials in cancer pain: is there any loss of the information available in the data? Results of a systematic literature review and guideline for reporting.

BACKGROUND: Given the prevalence of untreated pain among cancer patients, there have been calls for more and better research in the domain. Increasingly, calls for less waste and more optimal use of trial data collected are being made. Waste of data includes non-optimal statistical analysis and non-presentation of interpretable effect size as a measure of effectiveness of an intervention which also enable comparisons across studies. METHODS: We reviewed the recent literature on randomised trials on longitudinal cancer pain to identify sources of loss of data information by collecting material on the nature of outcomes collected, analysed, the method of analysis and what was presented as a result of the trial. Illustrated with real data, we propose some guidelines on how to adequately analyse longitudinal data and report the results using mixed models. RESULTS: We identified some major source of data information loss, one of which is the transformation of a continuous pain outcome. Not adjusting for the collected outcome baseline value is moreover a source of bias. Multiple testing by analysing the data cross-sectionnally at each time-point leads to loss of information and power. Finally, effect sizes reflecting the effectiveness of the intervention were never reported. CONCLUSIONS: We identified several sources of information loss in the way longitudinal trials on pain were analysed and reported. However these problems could be easily solved by using regression methods like mixed models and presenting regression parameters to provide a concrete quantitative effect of the intervention.

Longitudinal randomised controlled trials in rehabilitation post-stroke: a systematic review on the quality of reporting and use of baseline outcome values.

BACKGROUND: The World Health Organisation stresses the need to collect high quality longitudinal data on rehabilitation and to improve the comparability between studies. This implies using all the information available and transparent reporting. We therefore investigated the quality of reported or planned randomised controlled trials on rehabilitation post-stroke with a repeated measure of physical functioning, provided recommendations on the presentation of results using regression parameters, and focused on the difficulties of adjustment for baseline outcome measures. METHODS: We performed a systematic review of the literature from 2011 to 2013 and collected information on the way data was analysed. Moreover we described various approaches to analyse the data using mixed models illustrated with real data. RESULTS: Eighty-four eligible studies were identified of which 61% (51/84) failed to analyse the data longitudinally. Moreover, for 30% (25/83) the method for adjustment for baseline is not known or not existent. Using real data we were able to show how much difference in results an adjustment for baseline data can make. We showed how to provide interpretable intervention effects using regression coefficients while making use of all the information available in the data. CONCLUSIONS: Our review showed that improvements were needed in the analysis of longitudinal trials in rehabilitation post-stroke in order to maximise the use of collected data and improve comparability between studies. Reporting fully the method used (including baseline adjustment) and using methods like mixed models could easily achieve this.