Early warning systems and rapid response systems for the prevention of patient deterioration on acute adult hospital wards.
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ABSTRACT:
SUBMITTER: McGaughey J
PROVIDER: S-EPMC8608437 | biostudies-literature | 2021 Nov
REPOSITORIES: biostudies-literature
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Early warning systems and rapid response systems for the prevention of patient deterioration on acute adult hospital wards.
McGaughey Jennifer J Fergusson Dean A DA Van Bogaert Peter P Rose Louise L
The Cochrane database of systematic reviews 20211122
<h4>Background</h4>Early warning systems (EWS) and rapid response systems (RRS) have been implemented internationally in acute hospitals to facilitate early recognition, referral and response to patient deterioration as a solution to address suboptimal ward-based care. EWS and RRS facilitate healthcare decision-making using checklists and provide structure to organisational practices through governance and clinical audit. However, it is unclear whether these systems improve patient outcomes. Thi ...[more]
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Project description:IntroductionEarly warning scores (EWSs) are used extensively to identify patients at risk of deterioration in hospital. Previous systematic reviews suggest that studies which develop EWSs suffer methodological shortcomings and consequently may fail to perform well. The reviews have also identified that few validation studies exist to test whether the scores work in other settings. We will aim to systematically review papers describing the development or validation of EWSs, focusing on methodology, generalisability and reporting.MethodsWe will identify studies that describe the development or validation of EWSs for adult hospital inpatients. Each study will be assessed for risk of bias using the Prediction model Risk of Bias ASsessment Tool (PROBAST). Two reviewers will independently extract information. A narrative synthesis and descriptive statistics will be used to answer the main aims of the study which are to assess and critically appraise the methodological quality of the EWS, to describe the predictors included in the EWSs and to describe the reported performance of EWSs in external validation.Ethics and disseminationThis systematic review will only investigate published studies and therefore will not directly involve patient data. The review will help to establish whether EWSs are fit for purpose and make recommendations to improve the quality of future research in this area.Prospero registration numberCRD42017053324.
| S-EPMC5736035 | biostudies-literature
Project description:Background. Identification of patients at risk of deteriorating during their hospitalization is an important concern. However, many off-shelf scores have poor in-center performance. In this article, we report our experience developing, implementing, and evaluating an in-hospital score for deterioration. Methods. We abstracted 3 years of data (2014-2016) and identified patients on medical wards that died or were transferred to the intensive care unit. We developed a time-varying risk model and then implemented the model over a 10-week period to assess prospective predictive performance. We compared performance to our currently used tool, National Early Warning Score. In order to aid clinical decision making, we transformed the quantitative score into a three-level clinical decision support tool. Results. The developed risk score had an average area under the curve of 0.814 (95% confidence interval = 0.79-0.83) versus 0.740 (95% confidence interval = 0.72-0.76) for the National Early Warning Score. We found the proposed score was able to respond to acute clinical changes in patients' clinical status. Upon implementing the score, we were able to achieve the desired positive predictive value but needed to retune the thresholds to get the desired sensitivity. Discussion. This work illustrates the potential for academic medical centers to build, refine, and implement risk models that are targeted to their patient population and work flow.
| S-EPMC6956604 | biostudies-literature
Project description:ObjectiveTo provide an overview and critical appraisal of early warning scores for adult hospital patients.DesignSystematic review.Data sourcesMedline, CINAHL, PsycInfo, and Embase until June 2019.Eligibility criteria for study selectionStudies describing the development or external validation of an early warning score for adult hospital inpatients.Results13 171 references were screened and 95 articles were included in the review. 11 studies were development only, 23 were development and external validation, and 61 were external validation only. Most early warning scores were developed for use in the United States (n=13/34, 38%) and the United Kingdom (n=10/34, 29%). Death was the most frequent prediction outcome for development studies (n=10/23, 44%) and validation studies (n=66/84, 79%), with different time horizons (the most frequent was 24 hours). The most common predictors were respiratory rate (n=30/34, 88%), heart rate (n=28/34, 83%), oxygen saturation, temperature, and systolic blood pressure (all n=24/34, 71%). Age (n=13/34, 38%) and sex (n=3/34, 9%) were less frequently included. Key details of the analysis populations were often not reported in development studies (n=12/29, 41%) or validation studies (n=33/84, 39%). Small sample sizes and insufficient numbers of event patients were common in model development and external validation studies. Missing data were often discarded, with just one study using multiple imputation. Only nine of the early warning scores that were developed were presented in sufficient detail to allow individualised risk prediction. Internal validation was carried out in 19 studies, but recommended approaches such as bootstrapping or cross validation were rarely used (n=4/19, 22%). Model performance was frequently assessed using discrimination (development n=18/22, 82%; validation n=69/84, 82%), while calibration was seldom assessed (validation n=13/84, 15%). All included studies were rated at high risk of bias.ConclusionsEarly warning scores are widely used prediction models that are often mandated in daily clinical practice to identify early clinical deterioration in hospital patients. However, many early warning scores in clinical use were found to have methodological weaknesses. Early warning scores might not perform as well as expected and therefore they could have a detrimental effect on patient care. Future work should focus on following recommended approaches for developing and evaluating early warning scores, and investigating the impact and safety of using these scores in clinical practice.Systematic review registrationPROSPERO CRD42017053324.
| S-EPMC7238890 | biostudies-literature
Project description:BackgroundTimely identification of patients at a high risk of clinical deterioration is key to prioritizing care, allocating resources effectively, and preventing adverse outcomes. Vital signs-based, aggregate-weighted early warning systems are commonly used to predict the risk of outcomes related to cardiorespiratory instability and sepsis, which are strong predictors of poor outcomes and mortality. Machine learning models, which can incorporate trends and capture relationships among parameters that aggregate-weighted models cannot, have recently been showing promising results.ObjectiveThis study aimed to identify, summarize, and evaluate the available research, current state of utility, and challenges with machine learning-based early warning systems using vital signs to predict the risk of physiological deterioration in acutely ill patients, across acute and ambulatory care settings.MethodsPubMed, CINAHL, Cochrane Library, Web of Science, Embase, and Google Scholar were searched for peer-reviewed, original studies with keywords related to "vital signs," "clinical deterioration," and "machine learning." Included studies used patient vital signs along with demographics and described a machine learning model for predicting an outcome in acute and ambulatory care settings. Data were extracted following PRISMA, TRIPOD, and Cochrane Collaboration guidelines.ResultsWe identified 24 peer-reviewed studies from 417 articles for inclusion; 23 studies were retrospective, while 1 was prospective in nature. Care settings included general wards, intensive care units, emergency departments, step-down units, medical assessment units, postanesthetic wards, and home care. Machine learning models including logistic regression, tree-based methods, kernel-based methods, and neural networks were most commonly used to predict the risk of deterioration. The area under the curve for models ranged from 0.57 to 0.97.ConclusionsIn studies that compared performance, reported results suggest that machine learning-based early warning systems can achieve greater accuracy than aggregate-weighted early warning systems but several areas for further research were identified. While these models have the potential to provide clinical decision support, there is a need for standardized outcome measures to allow for rigorous evaluation of performance across models. Further research needs to address the interpretability of model outputs by clinicians, clinical efficacy of these systems through prospective study design, and their potential impact in different clinical settings.
| S-EPMC7892287 | biostudies-literature
Project description:Infections caused by multidrug-resistant pathogens and their inpatient (nosocomial) and outpatient spread, pose increasing challenges to our healthcare system. Control strategies are derived from vertical (individual, pathogen-specific) and horizontal (general, pathogen-unspecific) preventive measures. The discussion of a competing "either or" has not proven to be effective in the past. In addition, the problem of multidrug-resistant pathogens cannot be solved solely in inpatient health care: the goals are a seamless communication and transparent cooperation between all actors in the healthcare system. Nowadays, modern molecular methods for pathogen typing are available, with the help of which transmission pathways can be clarified and hospital hygiene measures can be efficiently controlled. These procedures will be used increasingly more in the future to combat the problem of multidrug resistance across interfaces within regional network structures.
| S-EPMC7571528 | biostudies-literature
Project description:ObjectivesTo determine whether time-series analysis and Shannon information entropy of facial expressions predict acute clinical deterioration in patients on general hospital wards.DesignPost hoc analysis of a prospective observational feasibility study (Visual Early Warning Score study).SettingGeneral ward patients in a community hospital.PatientsThirty-four patients at risk of clinical deterioration.InterventionsA 3-minute video (153,000 frames) for each of the patients enrolled into the Visual Early Warning Score study database was analyzed by a trained psychologist for facial expressions measured as action units using the Facial Action Coding System.Measurements and main resultsThree-thousand six-hundred eighty-eight action unit were analyzed over the 34 3-minute study periods. The action unit time variables considered were onset, apex, offset, and total time duration. A generalized linear regression model and time-series analyses were performed. Shannon information entropy (Hn) and diversity (Dn) were calculated from the frequency and repertoire of facial expressions. Patients subsequently admitted to critical care displayed a reduced frequency rate (95% CI moving average of the mean: 9.5-10.9 vs 26.1-28.9 in those not admitted), a higher Shannon information entropy (0.30 ± 0.06 vs 0.26 ± 0.05; p = 0.019) and diversity index (1.36 ± 0.08 vs 1.30 ± 0.07; p = 0.020) and a prolonged action unit reaction time (23.5 vs 9.4 s) compared with patients not admitted to ICU. The number of action unit identified per window within the time-series analysis predicted admission to critical care with an area under the curve of 0.88. The area under the curve for National Early Warning Score alone, Hn alone, National Early Warning Score plus Hn, and National Early Warning Score plus Hn plus Dn were 0.53, 0.75, 0.76, and 0.81, respectively.ConclusionsPatients who will be admitted to intensive care have a decrease in the number of facial expressions per unit of time and an increase in their diversity.
| S-EPMC7259563 | biostudies-literature
Project description:ObjectivesPrevious studies have looked at National Early Warning Score performance in predicting in-hospital deterioration and death, but data are lacking with respect to patient outcomes following implementation of National Early Warning Score. We sought to determine the effectiveness of National Early Warning Score implementation on predicting and preventing patient deterioration in a clinical setting.DesignRetrospective cohort study.SettingTertiary care academic facility and a community hospital.PatientsPatients 18 years old or older hospitalized from March 1, 2014, to February 28, 2015, during preimplementation of National Early Warning Score to August 1, 2015, to July 31, 2016, after National Early Warning Score was implemented.InterventionsImplementation of National Early Warning Score within the electronic health record and associated best practice alert.Measurements and main resultsIn this study of 85,322 patients (42,402 patients pre-National Early Warning Score and 42,920 patients post-National Early Warning Score implementation), the primary outcome of rate of ICU transfer or death did not change after National Early Warning Score implementation, with adjusted hazard ratio of 0.94 (0.84-1.05) and 0.90 (0.77-1.05) at our academic and community hospital, respectively. In total, 175,357 best practice advisories fired during the study period, with the best practice advisory performing better at the community hospital than the academic at predicting an event within 12 hours 7.4% versus 2.2% of the time, respectively. Retraining National Early Warning Score with newly generated hospital-specific coefficients improved model performance.ConclusionsAt both our academic and community hospital, National Early Warning Score had poor performance characteristics and was generally ignored by frontline nursing staff. As a result, National Early Warning Score implementation had no appreciable impact on defined clinical outcomes. Refitting of the model using site-specific data improved performance and supports validating predictive models on local data.
| S-EPMC6298839 | biostudies-literature
Project description:AimWe conducted a systematic review and meta-analysis to answer the question: Does the implementation of Paediatric Early Warning Systems (PEWS) in the hospital setting reduce mortality, cardiopulmonary arrests, unplanned codes and critical deterioration events among children, as compared to usual care without PEWS?MethodsWe conducted a comprehensive search using Medline, EMBASE, Cochrane Central Register of Controlled Trials, Cumulative Index to Nursing and Allied Health Literature and Web of Science. We included studies published between January 2006 and April 2022 on children <18 years old performed in inpatient units and emergency departments, and compared patient populations with PEWS to those without PEWS. We excluded studies without a comparator, case control studies, systematic reviews, and studies published in non-English languages. We employed a random effects meta-analysis and synthesised the risk and rate ratios from individual studies. We used the Scottish Intercollegiate Guidelines Network (SIGN) to appraise the risk of bias.ResultsAmong 911 articles screened, 15 were included for descriptive analysis. Fourteen of the 15 studies were pre- versus post-implementation studies and one was a multi-centre cluster randomised controlled trial (RCT). Among 10 studies (580,604 hospital admissions) analysed for mortality, we found an increased risk (pooled RR 1.18, 95% CI 1.01-1.38, p = 0.036) in the group without PEWS compared to the group with PEWS. The sensitivity analysis performed without the RCT (436,065 hospital admissions) showed a non-significant relationship (pooled RR 1.17, 95% CI 0.98-1.40, p = 0.087). Among four studies (168,544 hospital admissions) analysed for unplanned code events, there was an increased risk in the group without PEWS (pooled RR 1.73, 95%CI 1.01-2.96, p = 0.046) There were no differences in the rate of cardiopulmonary arrests or critical deterioration events between groups. Our findings were limited by potential confounders and imprecision among included studies.ConclusionsHealthcare systems that implemented PEWS were associated with reduced mortality and code rates. We recognise that these gains vary depending on resource availability and efferent response systems.PROSPERO registration: CRD42021269579.
| S-EPMC9253845 | biostudies-literature
Project description:ObjectiveWe aimed to develop a model for accurate prediction of general care inpatient deterioration.Materials and methodsTraining and internal validation datasets were built using 2-year data from a quaternary hospital in the Midwest. Model training used gradient boosting and feature engineering (clinically relevant interactions, time-series information) to predict general care inpatient deterioration (resuscitation call, intensive care unit transfer, or rapid response team call) in 24 hours. Data from a tertiary care hospital in the Southwest were used for external validation. C-statistic, sensitivity, positive predictive value, and alert rate were calculated for different cutoffs and compared with the National Early Warning Score. Sensitivity analysis evaluated prediction of intensive care unit transfer or resuscitation call.ResultsTraining, internal validation, and external validation datasets included 24 500, 25 784 and 53 956 hospitalizations, respectively. The Mayo Clinic Early Warning Score (MC-EWS) demonstrated excellent discrimination in both the internal and external validation datasets (C-statistic = 0.913, 0.937, respectively), and results were consistent in the sensitivity analysis (C-statistic = 0.932 in external validation). At a sensitivity of 73%, MC-EWS would generate 0.7 alerts per day per 10 patients, 45% less than the National Early Warning Score.DiscussionLow alert rates are important for implementation of an alert system. Other early warning scores developed for the general care ward have achieved lower discrimination overall compared with MC-EWS, likely because MC-EWS includes both nursing assessments and extensive feature engineering.ConclusionsMC-EWS achieved superior prediction of general care inpatient deterioration using sophisticated feature engineering and a machine learning approach, reducing alert rate.
| S-EPMC8661441 | biostudies-literature
Project description:BackgroundCommunication between providers and patients' families is an integral part of clinical care. Family concern is a validated component of Pediatric Early Warning Systems (PEWS); however, little is known about the impact of PEWS on provider-family communication.MethodsSemi-structured interviews were conducted with 83 ward and Pediatric Intensive Care Unit (PICU) providers involved in the care of patients with deterioration at two pediatric oncology hospitals of different resource levels: St. Jude Children's Research Hospital (n = 42) in the United States and Unidad Nacional de Oncología Pediátrica (UNOP, n = 41) in Guatemala. Interviews were conducted in the participants' native language (English or Spanish), transcribed, and translated into English. Transcripts were coded by two researchers and analyzed for thematic content surrounding family communication and concern.ResultsAll participants recognized patients' families as a valuable part of the care team, particularly during events requiring escalation of care. Perceived barriers to communication included limited time spent at the bedside, and, at UNOP, language and literacy challenges which occasionally limited providers' ability to assess family concern and involve families in patient care. Despite these barriers, providers perceived PEWS improved communication by facilitating more interaction with families, allowing for relationship-building, anticipatory guidance, and destigmatization of the PICU. PEWS assessments also allowed families to contribute to identification of deterioration.ConclusionsPEWS improve the quality of communication between providers and families by providing more opportunities for interaction, building relationships, and trust. These findings further support the use of PEWS in the care of children with cancer in hospitals of all resource-levels.
| S-EPMC9939098 | biostudies-literature