John W Devlin’s research while affiliated with University of Massachusetts Boston and other places

We report results from a survey of members of the Society of Critical Care Medicine to assess ICU clinicians’ perceptions of artificial airway safety practices and unplanned extubation (UE) prevention. The survey was distributed between January and February 2024 and received 518 responses (68.5% response rate), with 87.5% from adult ICUs and 12.5% from Pediatric ICUs. Only 48% of adult ICU respondents tracked UE, compared with 73% tracking pressure injuries. Most respondents did not consider UE a “never event,” with over half viewing it as unavoidable. In adult ICUs, delirium was ranked as the highest UE risk factor, and commercial securement devices were the primary endotracheal tube securement method (75.2%). Significant variations were observed in artificial airway management practices and responsibility assignments across ICU settings. The results highlight substantial disparities in airway safety management beliefs and practices, underscoring the need for standardized, evidence-based guidelines.

Background Fluid overload (FO) in the intensive care unit (ICU) is common, serious, and may be preventable. Intravenous medications (including administered volume) are a primary cause for FO but are challenging to evaluate as a FO predictor given the high frequency and time‐dependency of their use and other factors affecting FO. We sought to employ unsupervised machine learning methods to uncover medication administration patterns correlating with FO. Methods This retrospective cohort study included 927 adults admitted to an ICU for ≥72 h. FO was defined as a positive fluid balance ≥7% of admission body weight. After reviewing medication administration record data in 3‐h periods, medication exposure was categorized into clusters using principal component analysis (PCA) and Restricted Boltzmann Machine (RBM). Medication regimens of patients with and without FO were compared within clusters to assess their temporal association with FO. Results FO occurred in 127 (13.7%) of 927 included patients. Patients received a median (interquartile range) of 31(13–65) discrete intravenous medication administrations over the 72‐h period. Across all 47,803 intravenous medication administrations, 10 unique medication clusters, containing 121 to 130 medications per cluster, were identified. The mean number of Cluster 7 medications administered was significantly greater in the FO cohort compared with patients without FO (25.6 vs.10.9, p < 0.0001). A total of 51 (40.2%) of 127 unique Cluster 7 medications were administered in more than five different 3‐h periods during the 72‐h study window. The most common Cluster 7 medications included continuous infusions, antibiotics, and sedatives/analgesics. Addition of Cluster 7 medications to an FO prediction model including the Acute Physiologic and Chronic Health Evaluation (APACHE) II score and receipt of diuretics improved model predictiveness from an Area Under the Receiver Operation Characteristic (AUROC) curve of 0.719 to 0.741 ( p = 0.027). Conclusions Using machine learning approaches, a unique medication cluster was strongly associated with FO. Incorporation of this cluster improved the ability to predict FO compared to traditional prediction models. Integration of this approach into real‐time clinical applications may improve early detection of FO to facilitate timely intervention.

Objectives To summarize the delirium treatment trial literature, identify the unique challenges in delirium treatment trials, and formulate recommendations to address each in older adults. Design A 39-member interprofessional and international expert working group of clinicians (physicians, nurses, and pharmacists) and nonclinicians (biostatisticians, epidemiologists, and trial methodologists) was convened. Four expert panels were assembled to explore key subtopics (pharmacological/nonpharmacologic treatment, methodological challenges, and novel research designs). Methods To provide background and context, a review of delirium treatment randomized controlled trials (RCTs) published between 2003 and 2023 was conducted and evidence gaps were identified. The four panels addressed the identified subtopics. For each subtopic, research challenges were identified and recommendations to address each were proposed through virtual discussion before a live, full-day, and in-person conference. General agreement was reached for each proposed recommendation across the entire working group via moderated conference discussion. Recommendations were synthesized across panels and iteratively discussed through rounds of virtual meetings and draft reviews. Results We identified key evidence gaps through a systematic literature review, yielding 43 RCTs of delirium treatments. From this review, eight unique challenges for delirium treatment trials were identified, and recommendations to address each were made based on panel input. The recommendations start with design of interventions that consider the multifactorial nature of delirium, include both pharmacological and nonpharmacologic approaches, and target pathophysiologic pathways where possible. Selecting appropriate at-risk patients with moderate vulnerability to delirium may maximize effectiveness. Targeting patients with at least moderate delirium severity and duration will include those most likely to experience adverse outcomes. Delirium severity should be the primary outcome of choice; measurement of short- and long-term clinical outcomes will maximize clinical relevance. Finally, plans for handling informative censoring and missing data are key. Conclusions By addressing key delirium treatment challenges and research gaps, our recommendations may serve as a roadmap for advancing delirium treatment research in older adults.