Development and validation of early warning score system: A systematic literature review

Fu, Li-heng; Schwartz, James H.; Moy, Amanda J.; Knaplund, Christopher; Kang, Min-Jeoung; Schnock, Kumiko O.; García, José Pérez; Jia, Haomiao; Dykes, Patricia C.; Cato, Kenrick; Albers, David J.; Rossetti, Sarah Collins

doi:10.1016/j.jbi.2020.103410

Cited by 64 publications

(60 citation statements)

References 76 publications

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“…Evidence suggests that f R is an important marker of clinical deterioration for a variety of pathological conditions in both adults [4,[108][109][110] and children [111]. Indeed, f R is a fundamental variable included in the majority of prognostic scores developed for the prediction of different outcomes, including intensive care unit (ICU) admission and mortality [4,109,110]. As such, f R contributes to the computation of the most accurate prognostic scores developed so far, such as the National Early Warning Score (NEWS) and the Modified Early Warning Score (MEWS) [4].…”

Section: Current Evidencementioning

confidence: 99%

“…However, despite the clinical relevance of f R , this vital sign is often under-recorded [29,37,120,121] or not measured accurately [38,59,60,[122][123][124]. This may impair the efficacy of early warning scores [118,120,121], which also suffer from other methodological issues [109,110]. Therefore, it is imperative to improve the accuracy and frequency of f R monitoring throughout the healthcare chain (pre-hospital, hospital, and post-hospital).…”

Section: Current Evidencementioning

confidence: 99%

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The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise

Nicolò

Massaroni

Schena

et al. 2020

Sensors

234

137

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Respiratory rate is a fundamental vital sign that is sensitive to different pathological conditions (e.g., adverse cardiac events, pneumonia, and clinical deterioration) and stressors, including emotional stress, cognitive load, heat, cold, physical effort, and exercise-induced fatigue. The sensitivity of respiratory rate to these conditions is superior compared to that of most of the other vital signs, and the abundance of suitable technological solutions measuring respiratory rate has important implications for healthcare, occupational settings, and sport. However, respiratory rate is still too often not routinely monitored in these fields of use. This review presents a multidisciplinary approach to respiratory monitoring, with the aim to improve the development and efficacy of respiratory monitoring services. We have identified thirteen monitoring goals where the use of the respiratory rate is invaluable, and for each of them we have described suitable sensors and techniques to monitor respiratory rate in specific measurement scenarios. We have also provided a physiological rationale corroborating the importance of respiratory rate monitoring and an original multidisciplinary framework for the development of respiratory monitoring services. This review is expected to advance the field of respiratory monitoring and favor synergies between different disciplines to accomplish this goal.

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Section: Current Evidencementioning

confidence: 99%

Section: Current Evidencementioning

confidence: 99%

The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise

Nicolò

Massaroni

Schena

et al. 2020

Sensors

234

137

View full text Add to dashboard Cite

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“…Thus, we compared PICTURE's performance to published results for machine learning-based early warning systems using standard criteria to show our model has comparable performance. We compared PICTURE to the following models: one by Alvarez et al [13], the electronic cardiac arrest risk triage (eCART) [26], eCART random forest [9], [14], and the Advanced Alert Monitor [10], which were highlighted in recent literature reviews [1], [16]. Because we could only compare our performance indicators to published performance indicators, this does not allow us to definitively determine which score performs the best.…”

Section: Comparing To Other Early Warning Systemsmentioning

confidence: 99%

“…Electronic health record (EHR) data has many characteristics (e.g. collection of data for certain treatments) that may bias early warning system development [16] and the missingness pattern may capture these characteristics. Specifically, tree-based machine learning methods have the potential to learn the missingness pattern when using methods such as mean or extreme value imputation.…”

Section: Introductionmentioning

confidence: 99%

Demonstrating the consequences of learning missingness patterns in early warning systems for preventative health care: A novel simulation and solution

Gillies

Taylor²,

Cummings³

et al. 2020

Journal of Biomedical Informatics

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“…Clinical and physiological measurement data have limited predictive power for early detection of deterioration in hospitalized patients, 1 prompting the search for additional, complementary predictive data sources. Clinicians are continuously engaged in complex decision making and pattern matching, informed by their knowledge and expertise, to drive skilled judgments (eg, prediction of patient trajectories) and subsequent actions (eg, increasing the frequency and type of clinical assessments needed).…”

Section: Introductionmentioning

confidence: 99%

Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework

Rossetti

Knaplund

Albers

et al. 2021

Journal of the American Medical Informatics Association

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Objective There are signals of clinicians’ expert and knowledge-driven behaviors within clinical information systems (CIS) that can be exploited to support clinical prediction. Describe development of the Healthcare Process Modeling Framework to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals). Materials and Methods We employed an iterative framework development approach that combined data-driven modeling and simulation testing to define and refine a process for phenotyping clinician behaviors. Our framework was developed and evaluated based on the Communicating Narrative Concerns Entered by Registered Nurses (CONCERN) predictive model to detect and leverage signals of clinician expertise for prediction of patient trajectories. Results Seven themes—identified during development and simulation testing of the CONCERN model—informed framework development. The HPM-ExpertSignals conceptual framework includes a 3-step modeling technique: (1) identify patterns of clinical behaviors from user interaction with CIS; (2) interpret patterns as proxies of an individual’s decisions, knowledge, and expertise; and (3) use patterns in predictive models for associations with outcomes. The CONCERN model differentiated at risk patients earlier than other early warning scores, lending confidence to the HPM-ExpertSignals framework. Discussion The HPM-ExpertSignals framework moves beyond transactional data analytics to model clinical knowledge, decision making, and CIS interactions, which can support predictive modeling with a focus on the rapid and frequent patient surveillance cycle. Conclusions We propose this framework as an approach to embed clinicians’ knowledge-driven behaviors in predictions and inferences to facilitate capture of healthcare processes that are activated independently, and sometimes well before, physiological changes are apparent.

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Development and validation of early warning score system: A systematic literature review

Cited by 64 publications

References 76 publications

The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise

The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise

Demonstrating the consequences of learning missingness patterns in early warning systems for preventative health care: A novel simulation and solution

Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework

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