BACKGROUND: Approximately 18% to 25% of patients with alcohol use disorders admitted to the hospital develop alcohol withdrawal syndrome (AWS). Symptom-triggered dosing of benzodiazepines (BZDs) seems to lead to shorter courses of treatment, lower cumulative BZD dose, and more rapid control of symptoms in non-critically ill patients. This study compares the outcomes of critically ill patients with AWS when treated using a protocolized, symptom-triggered, dose escalation approach versus a nonprotocolized approach. METHODS: This is a retrospective pre-post study of patients 18 years or older with AWS admitted to an intensive care unit (ICU). The preintervention cohort (PRE) was admitted between February 2008 and February 2010. The postintervention cohort (POST) was admitted between February 2012 and January 2013. The PRE patients were treated by physician preference and compared with POST patients who were given escalating doses of BZDs and/or phenobarbital according to an AWS protocol, titrating to light sedations (Richmond Agitation Sedation Scale score of 0 to -2). RESULTS: There were 135 episodes of AWS in 132 critically ill patients. POST patients (n = 75) were younger (50.7 [13.8] years vs. 55.7 [8.7] years, p = 0.03) than PRE patients (n = 60). Sequential Organ Failure Assessment (SOFA) scores were higher in the PRE group (6.1 [3.7] vs. 3.9 [2.9], p = 0.0004). There was a significant decrease in mean ICU length of stay from 9.6 (10.5) days to 5.2 (6.4) days (p = 0.0004) in the POST group. The POST group also had significantly fewer ventilator days (5.6 [13.9] days vs. 1.31 [5.6] days, p < 0.0001) as well as a significant decrease in BZD use (319 [1,084] mg vs. 93 [171] mg, p = 0.002). There were significant differences between the two cohorts with respect to the need for continuous sedation (p < 0.001), duration of sedation (p < 0.001), and intubation secondary to AWS (p < 0.001). In all of these outcomes, the POST cohort had a notably lower frequency of occurrence. CONCLUSION: A protocolized treatment approach of AWS in critically ill patients involving symptom-triggered, dose escalations of diazepam and phenobarbital may lead to a decreased ICU length of stay, decreased time spent on mechanical ventilation, and decreased BZD requirements. LEVEL OF EVIDENCE: Epidemiologic study, level III; therapeutic study, level IV.

As digital mental health interventions (DMHIs) proliferate, there is a growing need to understand the complexities of moving these tools from concept and design to service-ready products. We highlight five case studies from a center that specializes in the design and evaluation of digital mental health interventions to illustrate pragmatic approaches to the development of digital mental health interventions, and to make transparent some of the key decision points researchers encounter along the design-to-product pipeline. Case studies cover different key points in the design process and focus on partnership building, understanding the problem or opportunity, prototyping the product or service, and testing the product or service. We illustrate lessons learned and offer a series of questions researchers can use to navigate key decision points in the digital mental health intervention (DMHI) development process.

The Long-read RNA-Seq Genome Annotation Assessment Project Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. Using different protocols and sequencing platforms, the consortium generated over 427 million long-read sequences from complementary DNA and direct RNA datasets, encompassing human, mouse and manatee species. Developers utilized these data to address challenges in transcript isoform detection, quantification and de novo transcript detection. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. Incorporating additional orthogonal data and replicate samples is advised when aiming to detect rare and novel transcripts or using reference-free approaches. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.