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Multisite Pragmatic Cluster-Randomized Controlled Trial of the CONCERN Early Warning System.
Rossetti, Sarah C; Dykes, Patricia C; Knaplund, Chris; Cho, Sandy; Withall, Jennifer; Lowenthal, Graham; Albers, David; Lee, Rachel; Jia, Haomiao; Bakken, Suzanne; Kang, Min-Jeoung; Chang, Frank Y; Zhou, Li; Bates, David W; Daramola, Temiloluwa; Liu, Fang; Schwartz-Dillard, Jessica; Tran, Mai; Abbas Bokhari, Syed Mohtashim; Thate, Jennifer; Cato, Kenrick D.
Afiliación
  • Rossetti SC; Columbia University Irving Medical Center, Department of Biomedical Informatics, New York, NY.
  • Dykes PC; Columbia University Irving Medical Center, School of Nursing, New York, NY.
  • Knaplund C; Brigham and Women's Hospital, Boston, MA.
  • Cho S; Harvard Medical School, Boston, MA.
  • Withall J; Columbia University Irving Medical Center, Department of Biomedical Informatics, New York, NY.
  • Lowenthal G; Newton Wellesley Hospital, Newton, MA.
  • Albers D; Columbia University Irving Medical Center, Department of Biomedical Informatics, New York, NY.
  • Lee R; Brigham and Women's Hospital, Boston, MA.
  • Jia H; Columbia University Irving Medical Center, Department of Biomedical Informatics, New York, NY.
  • Bakken S; University of Colorado, Anschutz Medical Campus, Department of Biomedical Informatics.
  • Kang MJ; Columbia University Irving Medical Center, Department of Biomedical Informatics, New York, NY.
  • Chang FY; Columbia University Irving Medical Center, School of Nursing, New York, NY.
  • Zhou L; Columbia University Irving Medical Center, Mailman School of Public Health, New York, NY.
  • Bates DW; Columbia University Irving Medical Center, Department of Biomedical Informatics, New York, NY.
  • Daramola T; Columbia University Irving Medical Center, School of Nursing, New York, NY.
  • Liu F; Brigham and Women's Hospital, Boston, MA.
  • Schwartz-Dillard J; Harvard Medical School, Boston, MA.
  • Tran M; Brigham and Women's Hospital, Boston, MA.
  • Abbas Bokhari SM; Brigham and Women's Hospital, Boston, MA.
  • Thate J; Harvard Medical School, Boston, MA.
  • Cato KD; Brigham and Women's Hospital, Boston, MA.
medRxiv ; 2024 Jun 04.
Article en En | MEDLINE | ID: mdl-38883706
ABSTRACT
Importance Late predictions of hospitalized patient deterioration, resulting from early warning systems (EWS) with limited data sources and/or a care team's lack of shared situational awareness, contribute to delays in clinical interventions. The COmmunicating Narrative Concerns Entered by RNs (CONCERN) Early Warning System (EWS) uses real-time nursing surveillance documentation patterns in its machine learning algorithm to identify patients' deterioration risk up to 42 hours earlier than other EWSs.

Objective:

To test our a priori hypothesis that patients with care teams informed by the CONCERN EWS intervention have a lower mortality rate and shorter length of stay (LOS) than the patients with teams not informed by CONCERN EWS.

Design:

One-year multisite, pragmatic controlled clinical trial with cluster-randomization of acute and intensive care units to intervention or usual-care groups.

Setting:

Two large U.S. health systems.

Participants:

Adult patients admitted to acute and intensive care units, excluding those on hospice/palliative/comfort care, or with Do Not Resuscitate/Do Not Intubate orders. Intervention The CONCERN EWS intervention calculates patient deterioration risk based on nurses' concern levels measured by surveillance documentation patterns, and it displays the categorical risk score (low, increased, high) in the electronic health record (EHR) for care team members. Main Outcomes and

Measures:

Primary

outcomes:

in-hospital mortality, LOS; survival analysis was used. Secondary

outcomes:

cardiopulmonary arrest, sepsis, unanticipated ICU transfers, 30-day hospital readmission.

Results:

A total of 60 893 hospital encounters (33 024 intervention and 27 869 usual-care) were included. Both groups had similar patient age, race, ethnicity, and illness severity distributions. Patients in the intervention group had a 35.6% decreased risk of death (adjusted hazard ratio [HR], 0.644; 95% confidence interval [CI], 0.532-0.778; P<.0001), 11.2% decreased LOS (adjusted incidence rate ratio, 0.914; 95% CI, 0.902-0.926; P<.0001), 7.5% decreased risk of sepsis (adjusted HR, 0.925; 95% CI, 0.861-0.993; P=.0317), and 24.9% increased risk of unanticipated ICU transfer (adjusted HR, 1.249; 95% CI, 1.093-1.426; P=.0011) compared with patients in the usual-care group. Conclusions and Relevance A hospital-wide EWS based on nursing surveillance patterns decreased in-hospital mortality, sepsis, and LOS when integrated into the care team's EHR workflow. Trial Registration ClinicalTrials.gov Identifier NCT03911687.
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Texto completo: 1 Banco de datos: MEDLINE Idioma: En Revista: MedRxiv Año: 2024 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Revista: MedRxiv Año: 2024 Tipo del documento: Article