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Using rapid response system trigger clusters to characterize patterns of clinical deterioration among hospitalized adult patients.
Piasecki, Rebecca J; Hunt, Elizabeth A; Perrin, Nancy; Spaulding, Erin M; Winters, Bradford; Samuel, Laura; Davidson, Patricia M; Chandra Strobos, Nisha; Churpek, Matthew; Himmelfarb, Cheryl R.
Afiliação
  • Piasecki RJ; Johns Hopkins University, 3400 N. Charles St., Baltimore, MD 21218, United States. Electronic address: piasec15@msu.edu.
  • Hunt EA; Johns Hopkins University, 3400 N. Charles St., Baltimore, MD 21218, United States.
  • Perrin N; Johns Hopkins University, 3400 N. Charles St., Baltimore, MD 21218, United States.
  • Spaulding EM; Johns Hopkins University, 3400 N. Charles St., Baltimore, MD 21218, United States.
  • Winters B; Johns Hopkins University, 3400 N. Charles St., Baltimore, MD 21218, United States.
  • Samuel L; Johns Hopkins University, 3400 N. Charles St., Baltimore, MD 21218, United States.
  • Davidson PM; University of Wollongong Australia, Northfields Ave., Wollongong, NSW 2522, Australia.
  • Chandra Strobos N; Johns Hopkins University, 3400 N. Charles St., Baltimore, MD 21218, United States.
  • Churpek M; University of Wisconsin-Madison, Union South, 1308 W. Dayton St., Madison, WI 53715, United States.
  • Himmelfarb CR; Johns Hopkins University, 3400 N. Charles St., Baltimore, MD 21218, United States.
Resuscitation ; 194: 110041, 2024 Jan.
Article em En | MEDLINE | ID: mdl-37952578
ABSTRACT

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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Equipe de Respostas Rápidas de Hospitais / Deterioração Clínica Limite: Adult / Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Equipe de Respostas Rápidas de Hospitais / Deterioração Clínica Limite: Adult / Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article