Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Piasecki RJ

PROVIDER: S-EPMC9934794 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Publications

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

Piasecki Rebecca J RJ Hunt Elizabeth A EA Perrin Nancy N Spaulding Erin M EM Winters Bradford B Samuel Laura L Davidson Patricia M PM Strobos Nisha Chandra NC Churpek Matthew M Himmelfarb Cheryl R CR

medRxiv : the preprint server for health sciences 20230208

<h4>Background</h4>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.<h4>Met ...[more]

PMID: 36798369

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Project description:BackgroundPatients with hematologic malignancies who are admitted to hospital are at increased risk of deterioration and death. Rapid response systems (RRSs) respond to hospitalized patients who clinically deteriorate. We sought to describe the characteristics and outcomes of hematologic oncology inpatients requiring rapid response system (RRS) activation, and to determine the prognostic accuracy of the SIRS and qSOFA criteria for in-hospital mortality of hematologic oncology patients with suspected infection.MethodsWe used registry data from two hospitals within The Ottawa Hospital network, between 2012 and 2016. Consecutive hematologic oncology inpatients who experienced activation of the RRS were included in the study. Data was gathered at the time of RRS activation and assessment. The primary outcome was in-hospital mortality. Logistical regression was used to evaluate for predictors of in-hospital mortality.ResultsWe included 401 patients during the study period. In-hospital mortality for all included patients was 41.9% (168 patients), and 145 patients (45%) were admitted to ICU following RRS activation. Among patients with suspected infection at the time of RRS activation, Systemic Inflammatory Response Syndrome (SIRS) criteria had a sensitivity of 86.9% (95% CI 80.9-91.6) and a specificity of 38.2% (95% CI 31.9-44.8) for predicting in-hospital mortality, while Quick Sequential Organ Failure Assessment (qSOFA) criteria had a sensitivity of 61.9% (95% CI 54.1-69.3) and a specificity of 91.4% (95% CI 87.1-94.7). Factors associated with increased in-hospital mortality included transfer to ICU after RRS activation (adjusted odds ratio [OR] 3.56, 95% CI 2.12-5.97) and a higher number of RRS activations (OR 2.45, 95% CI 1.63-3.69). Factors associated with improved survival included active malignancy treatment at the time of RRS activation (OR 0.54, 95% CI 0.34-0.86) and longer hospital length of stay (OR 0.78, 95% CI 0.70-0.87).ConclusionsHematologic oncology inpatients requiring RRS activation have high rates of subsequent ICU admission and mortality. ICU admission and higher number of RRS activations are associated with increased risk of death, while active cancer treatment and longer hospital stay are associated with lower risk of mortality. Clinicians should consider these factors in risk-stratifying these patients during RRS assessment.

Project description:Understanding archaeological leather degradation helps inform economies, crafts, and technologies of historic communities. However, archaeological leather is at high risk of degradation due to deterioration and changes within the burial conditions. This research applied non-destructive FTIR-ATR to experimentally buried vegetable-tanned leather and archaeological leather excavated at the Roman site of Vindolanda, UK to explore survival, destruction, and preservation processes of tanned leather. Analyses focused on observing and monitoring changes in chemical functional groups related to leather tannins, collagen and lipid components following burial. FTIR-ATR results highlighted rapid changes following experimental burial in wet soil, tentatively associated with early onset microbial activity, which targeted readily available lipids but not tightly bound collagen. Prior to burial, differences in structural composition were present in leather spectra based on manufacture; however, following burial in wet soil, FTIR-ATR spectra indicated de-tanning occurs rapidly, especially in waterlogged conditions, with archaeological leather becoming more uniform and similar to untanned leather. Therefore, the comparison of FTIR-ATR results from archaeological leather to experimentally buried leather samples was informative for showing the destructive de-tanning in waterlogged environments. The comparison of FTIR-ATR data from modern unburied leather cannot be compared against archaeological samples. Importantly, despite de-tanning occurring soon after burial, the vegetable-tanning method promoted long-term preservation of leather in wet soil. The observed changes could not be directly associated with the proportion of condensed to hydrolysable tannin, suggesting alternate variables impacted the preservation. Furthermore, mineral components introduced into the leather through the animal skin, tannin material and/or tannin liquid are suggested to contribute to these changes. Crucially a high degree of heterogeneity in error results within the experimentally buried sample material underlined that any changes in collagen ratios cannot be overinterpreted and must be considered within the context of larger datasets.

Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

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