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 1,741 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 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.

目的：本次范围审查的主要目的是识别和描述使用生命体征监测来预测普通病房临床恶化的最先进模型。次要目标是确定实施这些模型的促进因素、障碍和影响。

数据来源：截至 2020 年 11 月的 PubMed、Embase 和 CINAHL 数据库。

研究选择：我们选择的研究将基于生命体征的自动实时预测算法与当前的跟踪协议进行比较，以了解普通病房人群的临床恶化结果。

数据提取：研究特征、预测特征和障碍、促进因素和影响。

结果：我们确定了 1,741 篇出版物，其中 21 篇被纳入我们的审查。其中两项为临床试验，两项为前瞻性观察研究，其余 17 项为回顾性研究。所有研究都集中在住院的成年患者身上。报告的受试者工作特征曲线下面积在 0.65 和 0.95 之间，用于临床恶化的结果。阳性预测值和敏感性分别在 0.223 和 0.773 和 7.2% 到 84.0% 之间。输入变量差异很大，预测的终点定义不一致。我们确定了实施这些模型的 57 个促进因素和 48 个障碍。我们发现了 68 个报告的影响，其中 57 个是积极的。

结论：预测算法可以比传统方案更早、更准确地检测到普通病房的临床恶化，在最近的一项研究中，传统方案降低了死亡率。需要就输入变量、预测时间范围和端点定义达成共识，以更好地促进比较研究。