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Objectives For the measurement of patient-reported outcomes, such as (health-related) quality of life, often many measurement instruments exist that intend to measure the same construct. To facilitate instrument selection, our aim was to develop a highly sensitive search filter for finding studies on measurement properties of measurement instruments in PubMed and a more precise search filter that needs less abstracts to be screened, but at a higher risk of missing relevant studies. Methods A random sample of 10,000 PubMed records (01-01-1990 to 31-12-2006) was used as a gold standard. Studies on measurement properties were identified using an exclusion filter and hand searching. Search terms were selected from the relevant records in the gold standard as well as from 100 systematic reviews of measurement properties and combined based on sensitivity and precision. The performance of the filters was tested in the gold standard as well as in two validation sets, by calculating sensitivity, precision, specificity, and number needed to read. Results We identified 116 studies on measurement properties in the gold standard. The sensitive search filter was able to retrieve 113 of these 116 studies (sensitivity 97.4%, precision 4.4%). The precise search filter had a sensitivity of 93.1% and a precision of 9.4%. Both filters performed very well in the validation sets. Conclusion The use of these search filters will contribute to evidence-based selection of measurement instruments in all medical fields.

The analysis of covariance (ANCOVA) has notably proven to be an effective tool in a broad range of scientific applications. Despite the well-documented literature about its principal uses and statistical properties, the corresponding power analysis for the general linear hypothesis tests of treatment differences remains a less discussed issue. The frequently recommended procedure is a direct application of the ANOVA formula in combination with a reduced degrees of freedom and a correlation-adjusted variance. This article aims to explicate the conceptual problems and practical limitations of the common method. An exact approach is proposed for power and sample size calculations in ANCOVA with random assignment and multinormal covariates. Both theoretical examination and numerical simulation are presented to justify the advantages of the suggested technique over the current formula. The improved solution is illustrated with an example regarding the comparative effectiveness of interventions. In order to facilitate the application of the described power and sample size calculations, accompanying computer programs are also presented.

BackgroundHospitalization offers smokers an opportunity to quit smoking. Starting cessation treatment in hospital is effective, but sustaining treatment after discharge is a challenge. Automated telephone calls with interactive voice response (IVR) technology could support treatment continuance after discharge.ObjectiveTo assess smokers’ use of and satisfaction with an IVR-facilitated intervention and to test the relationship between intervention dose and smoking cessation.DesignAnalysis of pooled quantitative and qualitative data from the intervention groups of two similar randomized controlled trials with 6-month follow-up.ParticipantsA total of 878 smokers admitted to three hospitals. All received cessation counseling in hospital and planned to stop smoking after discharge.InterventionAfter discharge, participants received free cessation medication and five automated IVR calls over 3 months. Calls delivered messages promoting smoking cessation and medication adherence, offered medication refills, and triaged smokers to additional telephone counseling.Main MeasuresNumber of IVR calls answered, patient satisfaction, biochemically validated tobacco abstinence 6 months after discharge.Key ResultsParticipants answered a median of three of five IVR calls; 70% rated the calls as helpful, citing the social support, access to counseling and medication, and reminders to quit as positive factors. Older smokers (OR 1.36, 95% CI 1.20–1.54 per decade) and smokers hospitalized for a smoking-related disease (OR 1.65, 95% CI 1.21–2.23) completed more calls. Smokers who completed more calls had higher quit rates at 6-month follow-up (OR 1.49, 95% CI 1.30–1.70, for each additional call) after multivariable adjustment for age, sex, education, discharge diagnosis, nicotine dependence, duration of medication use, and perceived importance of and confidence in quitting.ConclusionsAutomated IVR calls to support smoking cessation after hospital discharge were viewed favorably by patients. Higher IVR utilization was associated with higher odds of tobacco abstinence at 6-month follow-up. IVR technology offers health care systems a potentially scalable means of sustaining tobacco cessation interventions after hospital discharge.Clinical Trial Registration: ClinicalTrials.gov Identifiers NCT01177176, NCT01714323.

Clinical documentation has undergone a change due to the usage of electronic health records. The core element is to capture clinical findings and document therapy electronically. Health care personnel spend a significant portion of their time on the computer. Alternatives to self-typing, such as speech recognition, are currently believed to increase documentation efficiency and quality, as well as satisfaction of health professionals while accomplishing clinical documentation, but few studies in this area have been published to date. This study describes the effects of using a Web-based medical speech recognition system for clinical documentation in a university hospital on (1) documentation speed, (2) document length, and (3) physician satisfaction. Reports of 28 physicians were randomized to be created with (intervention) or without (control) the assistance of a Web-based system of medical automatic speech recognition (ASR) in the German language. The documentation was entered into a browser's text area and the time to complete the documentation including all necessary corrections, correction effort, number of characters, and mood of participant were stored in a database. The underlying time comprised text entering, text correction, and finalization of the documentation event. Participants self-assessed their moods on a scale of 1-3 (1=good, 2=moderate, 3=bad). Statistical analysis was done using permutation tests. The number of clinical reports eligible for further analysis stood at 1455. Out of 1455 reports, 718 (49.35%) were assisted by ASR and 737 (50.65%) were not assisted by ASR. Average documentation speed without ASR was 173 (SD 101) characters per minute, while it was 217 (SD 120) characters per minute using ASR. The overall increase in documentation speed through Web-based ASR assistance was 26% (P=.04). Participants documented an average of 356 (SD 388) characters per report when not assisted by ASR and 649 (SD 561) characters per report when assisted by ASR. Participants' average mood rating was 1.3 (SD 0.6) using ASR assistance compared to 1.6 (SD 0.7) without ASR assistance (P<.001). We conclude that medical documentation with the assistance of Web-based speech recognition leads to an increase in documentation speed, document length, and participant mood when compared to self-typing. Speech recognition is a meaningful and effective tool for the clinical documentation process.

Background Lorazepam is one of the preferred agents used for intravenous treatment of status epilepticus (SE). We combined data from two pediatric clinical trials to characterize the population pharmacokinetics of intravenous lorazepam in infants and children aged 3 months to 17 years with active SE or a history of SE. Methods We developed a population pharmacokinetic model for lorazepam using the NONMEM software. We then assessed exploratory exposure–response relationships using the overall efficacy and safety study endpoints, and performed dosing simulations. Results A total of 145 patients contributed 439 pharmacokinetic samples. The median (range) age and dose were 5.4 years (0.3–17.8) and 0.10 mg/kg (0.02–0.18), respectively. A two-compartment pharmacokinetic model with allometric scaling described the data well. In addition to total body weight (WT), younger age was associated with slightly higher weight-normalized clearance (CL). The following relationships characterized the typical values for the central compartment volume ( V 1), CL, peripheral compartment volume ( V 2), and intercompartmental CL ( Q ), using individual subject WT (kg) and age (years): V 1 (L) = 0.879*WT; CL (L/h) = 0.115*(Age/4.7) 0.133 *WT 0.75 ; V 2 (L) = 0.542* V 1; Q (L/h) = 1.45*WT 0.75 . No pharmacokinetic parameters were associated with clinical outcomes. Simulations suggest uniform pediatric dosing (0.1 mg/kg, to a maximum of 4 mg) can be used to achieve concentrations of 50–100 ng/mL in children with SE, which have been previously associated with effective seizure control. Conclusions The population pharmacokinetics of lorazepam were successfully described using a sparse sampling approach and a two-compartment model in pediatric patients with active SE.

Integration of genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) studies is needed to improve our understanding of the biological mechanisms underlying GWAS hits, and our ability to identify therapeutic targets. Gene-level association methods such as PrediXcan can prioritize candidate targets. However, limited eQTL sample sizes and absence of relevant developmental and disease context restrict our ability to detect associations. Here we propose an efficient statistical method (MultiXcan) that leverages the substantial sharing of eQTLs across tissues and contexts to improve our ability to identify potential target genes. MultiXcan integrates evidence across multiple panels using multivariate regression, which naturally takes into account the correlation structure. We apply our method to simulated and real traits from the UK Biobank and show that, in realistic settings, we can detect a larger set of significantly associated genes than using each panel separately. To improve applicability, we developed a summary result-based extension called S-MultiXcan, which we show yields highly concordant results with the individual level version when LD is well matched. Our multivariate model-based approach allowed us to use the individual level results as a gold standard to calibrate and develop a robust implementation of the summary-based extension. Results from our analysis as well as software and necessary resources to apply our method are publicly available.

In recent years, the explosion of genomic data and bioinformatic tools has been accompanied by a growing conversation around reproducibility of results and usability of software. However, the actual state of the body of bioinformatics software remains largely unknown. The purpose of this paper is to investigate the state of source code in the bioinformatics community, specifically looking at relationships between code properties, development activity, developer communities, and software impact. To investigate these issues, we curated a list of 1,720 bioinformatics repositories on GitHub through their mention in peer-reviewed bioinformatics articles. Additionally, we included 23 high-profile repositories identified by their popularity in an online bioinformatics forum. We analyzed repository metadata, source code, development activity, and team dynamics using data made available publicly through the GitHub API, as well as article metadata. We found key relationships within our dataset, including: certain scientific topics are associated with more active code development and higher community interest in the repository; most of the code in the main dataset is written in dynamically typed languages, while most of the code in the high-profile set is statically typed; developer team size is associated with community engagement and high-profile repositories have larger teams; the proportion of female contributors decreases for high-profile repositories and with seniority level in author lists; and, multiple measures of project impact are associated with the simple variable of whether the code was modified at all after paper publication. In addition to providing the first large-scale analysis of bioinformatics code to our knowledge, our work will enable future analysis through publicly available data, code, and methods. Code to generate the dataset and reproduce the analysis is provided under the MIT license at https://github.com/pamelarussell/github-bioinformatics. Data are available at https://doi.org/10.17605/OSF.IO/UWHX8.