Using rapid response system trigger clusters to characterize patterns of clinical deterioration among hospitalized adult patients.

BACKGROUND: Many rapid response system (RRS) events are activated using multiple triggers. However, the patterns in which multiple RRS triggers occur together to activate RRS events are unknown. The purpose of this study was to identify these patterns (RRS trigger clusters) and determine their association with outcomes among hospitalized adult patients. METHODS: RRS events among adult patients from January 2015 to December 2019 in the Get With The Guidelines- Resuscitation registry's MET module were examined (n = 134,406). Cluster analysis methods were performed to identify RRS trigger clusters. Pearson's chi-squared and ANOVA tests were used to examine differences in patient characteristics across RRS trigger clusters. Multilevel logistic regressions were used to examine the associations between RRS trigger clusters and outcomes. RESULTS: Six RRS trigger clusters were identified. Predominant RRS triggers for each cluster were: tachypnea, new onset difficulty in breathing, decreased oxygen saturation (Cluster 1); tachypnea, decreased oxygen saturation, staff concern (Cluster 2); respiratory depression, decreased oxygen saturation, mental status changes (Cluster 3); tachycardia, staff concern (Cluster 4); mental status changes (Cluster 5); hypotension, staff concern (Cluster 6). Significant differences in patient characteristics were observed across clusters. Patients in Clusters 3 and 6 had an increased likelihood of in-hospital cardiac arrest (p < 0.01). All clusters had an increased risk of mortality (p < 0.01). CONCLUSIONS: We discovered six novel RRS trigger clusters with differing relationships to adverse patient outcomes. RRS trigger clusters may prove crucial in clarifying the associations between RRS events and adverse outcomes and aiding in clinician decision-making during RRS events.

The associations between rapid response systems and their components with patient outcomes: A scoping review.

Background: While rapid response systems have been widely implemented, their impact on patient outcomes remains unclear. Further understanding of their components-including medical emergency team triggers, medical emergency team member composition, additional roles in patient care beyond responding to medical emergency team events, and their involvement in "Do-Not-Resuscitate" order placement-may elucidate the relationship between rapid response systems and outcomes. Objective: To explore how recent studies have examined rapid response system components in the context of relevant adverse patient outcomes, such as in-hospital cardiac arrests and hospital mortality. Design: Scoping review. Methods: PubMed, CINAHL, and Embase were searched for articles published between November 2014 and June 2022. Studies mainly focused on rapid response systems and associations with in-hospital cardiac arrests were considered. The following were extracted for analysis: study design, location, sample size, participant characteristics, system characteristics (including medical emergency team member composition, additional system roles outside of medical emergency team events), medical emergency team triggers, in-hospital cardiac arrests, and hospital mortality. Results: Thirty-four studies met inclusion criteria. While most studies described triggers used, few analyzed medical emergency team trigger associations with outcomes. Of those, medical emergency team triggers relating to respiratory abnormalities and use of multiple triggers to activate the medical emergency team were associated with adverse patient outcomes. Many studies described medical emergency team member composition, but the way composition was reported varied across studies. Of the seven studies with dedicated medical emergency team members, six found their systems were associated with decreased incidence of in-hospital cardiac arrests. Six of seven studies that described additional medical emergency team roles in educating staff in rapid response system use found their systems were associated with significant decreases in adverse patient outcomes. Four of five studies that described proactive rounding responsibilities reported found their systems were associated with significant decreases in adverse patient outcomes. Reporting of rapid response system involvement in "Do-Not-Resuscitate" order placement was variable across studies. Conclusions: Inconsistencies in describing rapid response system components and related data and outcomes highlights how these systems are complex to a degree not fully captured in existing literature. Further large-scale examination of these components across institutions is warranted. Development and use of robust and standardized metrics to track data related to rapid response system components and related outcomes are needed to optimize these systems and improve patient outcomes.

Using rapid response system trigger clusters to characterize patterns of clinical deterioration among hospitalized adult patients.

Background: Many rapid response system (RRS) events are activated using multiple triggers. However, the patterns in which RRS triggers co-occur to activate the medical emergency team (MET) to respond to RRS events is unknown. The purpose of this study was to identify and describe the patterns (RRS trigger clusters) in which RRS triggers co-occur when used to activate the MET and determine the association of these clusters with outcomes using a sample of hospitalized adult patients. Methods: RRS events among adult patients from January 2015 to December 2019 in the Get With The Guidelines- Resuscitation registry's MET module were examined (n=134,406). A combination of cluster analyses methods was performed to group patients into RRS trigger clusters based on the triggers used to activate their RRS events. Pearson's chi-squared and ANOVA tests were used to examine differences in patient characteristics across RRS trigger clusters. Multilevel logistic regression was used to examine the associations between RRS trigger clusters and outcomes following RRS events. Results: Six RRS trigger clusters were identified in the study sample. The RRS triggers that predominantly identified each cluster were as follows: tachypnea, new onset difficulty in breathing, and decreased oxygen saturation (Cluster 1); tachypnea, decreased oxygen saturation, and staff concern (Cluster 2); respiratory depression, decreased oxygen saturation, and mental status changes (Cluster 3); tachycardia and staff concern (Cluster 4); mental status changes (Cluster 5); hypotension and staff concern (Cluster 6). Significant differences in patient characteristics were observed across RRS trigger clusters. Patients in Clusters 3 and 6 were associated with an increased likelihood of in-hospital cardiac arrest (IHCA [p<0.01]), while Cluster 4 was associated with a decreased likelihood of IHCA (p<0.01). All clusters were associated with an increased risk of mortality (p<0.01). Conclusions: We discovered six novel RRS trigger clusters with differing relationships to adverse patient outcomes following RRS events. RRS trigger clusters may prove crucial in clarifying the associations between RRS events and adverse outcomes and may aid in clinician decision-making during RRS events.

Quantifying the labeling and the levels of plant cell wall precursors using ion chromatography tandem mass spectrometry.

The biosynthesis of cell wall polymers involves enormous fluxes through central metabolism that are not fully delineated and whose regulation is poorly understood. We have established and validated a liquid chromatography tandem mass spectrometry method using multiple reaction monitoring mode to separate and quantify the levels of plant cell wall precursors. Target analytes were identified by their parent/daughter ions and retention times. The method allows the quantification of precursors at low picomole quantities with linear responses up to the nanomole quantity range. When applying the technique to Arabidopsis (Arabidopsis thaliana) T87 cell cultures, 16 hexose-phosphates (hexose-Ps) and nucleotide-sugars (NDP-sugars) involved in cell wall biosynthesis were separately quantified. Using hexose-P and NDP-sugar standards, we have shown that hot water extraction allows good recovery of the target metabolites (over 86%). This method is applicable to quantifying the levels of hexose-Ps and NDP-sugars in different plant tissues, such as Arabidopsis T87 cells in culture and fenugreek (Trigonella foenum-graecum) endosperm tissue, showing higher levels of galacto-mannan precursors in fenugreek endosperm. In Arabidopsis cells incubated with [U-(13)C(Fru)]sucrose, the method was used to track the labeling pattern in cell wall precursors. As the fragmentation of hexose-Ps and NDP-sugars results in high yields of [PO(3)](-)/or [H(2)PO(4)](-) ions, mass isotopomers can be quantified directly from the intensity of selected tandem mass spectrometry transitions. The ability to directly measure (13)C labeling in cell wall precursors makes possible metabolic flux analysis of cell wall biosynthesis based on dynamic labeling experiments.

User Engagement With Smartphone Apps and Cardiovascular Disease Risk Factor Outcomes: Systematic Review.

BACKGROUND: The use of mobile health (mHealth) interventions, including smartphone apps, for the prevention of cardiovascular disease (CVD) has demonstrated mixed results for obesity, hypercholesterolemia, diabetes, and hypertension management. A major factor attributing to the variation in mHealth study results may be mHealth user engagement. OBJECTIVE: This systematic review aims to determine if user engagement with smartphone apps for the prevention and management of CVD is associated with improved CVD health behavior change and risk factor outcomes. METHODS: We conducted a comprehensive search of PubMed, CINAHL, and Embase databases from 2007 to 2020. Studies were eligible if they assessed whether user engagement with a smartphone app used by an individual to manage his or her CVD risk factors was associated with the CVD health behavior change or risk factor outcomes. For eligible studies, data were extracted on study and sample characteristics, intervention description, app user engagement measures, and the relationship between app user engagement and the CVD risk factor outcomes. App user engagement was operationalized as general usage (eg, number of log-ins or usage days per week) or self-monitoring within the app (eg, total number of entries made in the app). The quality of the studies was assessed. RESULTS: Of the 24 included studies, 17 used a randomized controlled trial design, 4 used a retrospective analysis, and 3 used a single-arm pre- and posttest design. Sample sizes ranged from 55 to 324,649 adults, with 19 studies recruiting participants from a community setting. Most of the studies assessed weight loss interventions, with 6 addressing additional CVD risk factors, including diabetes, sleep, stress, and alcohol consumption. Most of the studies that assessed the relationship between user engagement and reduction in weight (9/13, 69%), BMI (3/4, 75%), body fat percentage (1/2, 50%), waist circumference (2/3, 67%), and hemoglobin A1c (3/5, 60%) found statistically significant results, indicating that greater app user engagement was associated with better outcomes. Of 5 studies, 3 (60%) found a statistically significant relationship between higher user engagement and an increase in objectively measured physical activity. The studies assessing the relationship between user engagement and dietary and diabetes self-care behaviors, blood pressure, and lipid panel components did not find statistically significant results. CONCLUSIONS: Increased app user engagement for prevention and management of CVD may be associated with improved weight and BMI; however, only a few studies assessed other outcomes, limiting the evidence beyond this. Additional studies are needed to assess user engagement with smartphone apps targeting other important CVD risk factors, including dietary behaviors, hypercholesterolemia, diabetes, and hypertension. Further research is needed to assess mHealth user engagement in both inpatient and outpatient settings to determine the effect of integrating mHealth interventions into the existing clinical workflow and on CVD outcomes.

Aligning community-engaged research competencies with online training resources across the Clinical and Translational Science Award Consortium.

INTRODUCTION: The extent to which Clinical and Translational Science Award (CTSA) programs offer publicly accessible online resources for training in community-engaged research (CEnR) core competencies is unknown. This study cataloged publicly accessible online CEnR resources from CTSAs and mapped resources to CEnR core competency domains. METHODS: Following a search and review of the current literature regarding CEnR competencies, CEnR core competency domains were identified and defined. A systematic review of publicly accessible online CEnR resources from all 64 current CTSAs was conducted between July 2018 and May 2019. Resource content was independently reviewed by two reviewers and scored for the inclusion of each CEnR core competency domain. Domain scores across all resources were assessed using descriptive statistics. RESULTS: Eight CEnR core competency domains were identified. Overall, 214 CEnR resources publicly accessible online from 35 CTSAs were eligible for review. Scoring discrepancies for at least one domain within a resource initially occurred in 51% of resources. "CEnR methods" (50.5%) and "Knowledge and relationships with communities" (40.2%) were the most frequently addressed domains, while "CEnR program evaluation" (12.1%) and "Dissemination and advocacy" (11.2%) were the least frequently addressed domains. Additionally, challenges were noted in navigating CTSA websites to access CEnR resources, and CEnR competency nomenclature was not standardized. CONCLUSIONS: Our findings guide CEnR stakeholders to identify publicly accessible online resources and gaps to address in CEnR resource development. Standardized nomenclature for CEnR competency is needed for effective CEnR resource classification. Uniform organization of CTSA websites may maximize navigability.