Algorithms for Prediction of Clinical Deterioration on the General Wards: A Scoping Review
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.
The early identification of clinical deterioration among adult hospitalized patients remains a challenge.1 Delayed identification is associated with increased morbidity and mortality, unplanned intensive care unit (ICU) admissions, prolonged hospitalization, and higher costs.2,3 Earlier detection of deterioration using predictive algorithms of vital sign monitoring might avoid these negative outcomes.4 In this scoping review, we summarize current algorithms and their evidence.
Vital signs provide the backbone for detecting clinical deterioration. Early warning scores (EWS) and outreach protocols were developed to bring structure to the assessment of vital signs. Most EWS claim to predict clinical end points such as unplanned ICU admission up to 24 hours in advance.5,6 Reviews of EWS showed a positive trend toward reduced length of stay and mortality. However, conclusions about general efficacy could not be generated because of case heterogeneity and methodologic shortcomings.4,7 Continuous automated vital sign monitoring of patients on the general ward can now be accomplished with wearable devices.8 The first reports on continuous monitoring showed earlier detection of deterioration but not improved clinical end points.4,9 Since then, different reports on continuous monitoring have shown positive effects but concluded that unprocessed monitoring data per se falls short of generating actionable alarms.4,10,11
Predictive algorithms, which often use artificial intelligence (AI), are increasingly employed to recognize complex patterns or abnormalities and support predictions of events in big data sets.12,13 Especially when combined with continuous vital sign monitoring, predictive algorithms have the potential to expedite detection of clinical deterioration and improve patient outcomes. Predictive algorithms using vital signs in the ICU have shown promising results.14 The impact of predictive algorithms on the general wards, however, is unclear.
The aims of our scoping review were to explore the extent and range of and evidence for predictive vital signs–based algorithms on the adult general ward; to describe the variety of these algorithms; and to categorize effects, facilitators, and barriers of their implementation.15
