Algorithms for Prediction of Clinical Deterioration on the General Wards: A Scoping Review
SourceJournal of Hospital Medicine, 16, 10, (2021), pp. 612-619
Article / Letter to editor
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Journal of Hospital Medicine
SubjectRadboudumc 10: Reconstructive and regenerative medicine RIHS: Radboud Institute for Health Sciences; Radboudumc 16: Vascular damage RIHS: Radboud Institute for Health Sciences; Radboudumc 18: Healthcare improvement science RIHS: Radboud Institute for Health Sciences
OBJECTIVE: The primary objective of this scoping review was to identify and describe state-of-the-art models that use vital sign monitoring to predict clinical deterioration on the general ward. The secondary objective was to identify facilitators, barriers, and effects of implementing these models. DATA SOURCES: PubMed, Embase, and CINAHL databases until November 2020. STUDY SELECTION: We selected studies that compared vital signs-based automated real-time predictive algorithms to current track-and-trace protocols in regard to the outcome of clinical deterioration in a general ward population. DATA EXTRACTION: Study characteristics, predictive characteristics and barriers, facilitators, and effects. RESULTS: We identified 1741 publications, 21 of which were included in our review. Two of the these were clinical trials, 2 were prospective observational studies, and the remaining 17 were retrospective studies. All of the studies focused on hospitalized adult patients. The reported area under the receiver operating characteristic curves ranged between 0.65 and 0.95 for the outcome of clinical deterioration. Positive predictive value and sensitivity ranged between 0.223 and 0.773 and from 7.2% to 84.0%, respectively. Input variables differed widely, and predicted endpoints were inconsistently defined. We identified 57 facilitators and 48 barriers to the implementation of these models. We found 68 reported effects, 57 of which were positive. CONCLUSION: Predictive algorithms can detect clinical deterioration on the general ward earlier and more accurately than conventional protocols, which in one recent study led to lower mortality. Consensus is needed on input variables, predictive time horizons, and definitions of endpoints to better facilitate comparative research.
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