Dataset Information

Risk stratification to improve Pediatric Early Warning Systems: it is all about the context.

ABSTRACT: Early recognition of critically ill patients is of paramount importance to reduce pediatric mortality and morbidity. We created a risk stratification system combining vital parameters and predefined risk factors aimed at reducing the risk of unrecognized clinical deterioration compared with conventional Pediatric Early Warning Systems (PEWS). This single-center retrospective case cohort study included infants (gestational age ≥ 37 weeks) to adolescents (aged <18 years) with unplanned pediatric intensive care unit (PICU) admission between April 01, 2014, and February 28, 2018. The sensitivity in the 24 h prior to endpoint of the Pediatric Risk Evaluation and Stratification System (PRESS) was compared with that of the conventional PEWS and calculated as the proportion of study patients who received a high-risk score. Seventy-four PICU admissions were included. PRESS and PEWS sensitivities at 2 h prior to endpoint were 0.70 (95%CI 0.59 to 0.80) and 0.30 (95%CI 0.20 to 0.42) respectively (p < 0.001). Excluding patients with seizures, PRESS sensitivity increased to 0.75 (95%CI 0.64 to 0.85). Forty-nine patients (66%) scored positive on at least one high-risk factor, and "worried sign" was scored in 31 patients (42%).Conclusion: Risk stratification seems advantageous for a faster detection of clinical deterioration, providing opportunity for earlier intervention. What is Known: • Prompt detection of clinical deterioration is of essential importance to reduce morbidity and mortality. • Conventional Pediatric Early Warning Systems (PEWS) have limited sensitivity and a short window of detection of 1 to 2 h. What is New: • Risk stratification based on context factors allows earlier identification of patients at risk, well before deviation of vital signs. • Risk stratification combined with continuous monitoring of deteriorating trends in vital signs could lead to the development of next-generation warning systems achieving true patient safety.

SUBMITTER: Teheux L

PROVIDER: S-EPMC6733815 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Risk stratification to improve Pediatric Early Warning Systems: it is all about the context.

Teheux Lara L Verlaat Carin W CW Lemson Joris J Draaisma Jos M T JMT Fuijkschot Joris J

European journal of pediatrics 20190904 10

Early recognition of critically ill patients is of paramount importance to reduce pediatric mortality and morbidity. We created a risk stratification system combining vital parameters and predefined risk factors aimed at reducing the risk of unrecognized clinical deterioration compared with conventional Pediatric Early Warning Systems (PEWS). This single-center retrospective case cohort study included infants (gestational age ≥ 37 weeks) to adolescents (aged <18 years) with unplanned pediatric i ...[more]

PMID: 31485752

Similar Datasets

Project description:Background Sustainability, or continued use of evidence-based interventions for long-term patient benefit, is the least studied aspect of implementation science. In this study, we evaluate sustainability of a Pediatric Early Warning System (PEWS), an evidence-based intervention to improve early identification of clinical deterioration in hospitalized children, in low-resource settings using the Clinical Capacity for Sustainability Framework (CCS). Methods We conducted a secondary analysis of a qualitative study to identify barriers and enablers to PEWS implementation. Semi-structured interviews with PEWS implementation leaders and hospital directors at 5 Latin American pediatric oncology centers sustaining PEWS were conducted virtually in Spanish from June to August 2020. Interviews were recorded, professionally transcribed, and translated into English. Exploratory thematic content analysis yielded staff perceptions on PEWS sustainability. Coded segments were analyzed to identify participant perception about the current state and importance of sustaining PEWS, as well as sustainability successes and challenges. Identified sustainability determinants were mapped to the CCS to evaluate its applicability. Results We interviewed 71 staff including physicians (45%), nurses (45%), and administrators (10%). Participants emphasized the importance of sustaining PEWS for continued patient benefits. Identified sustainability determinants included supportive leadership encouraging ongoing interest in PEWS, beneficial patient outcomes enhancing perceived value of PEWS, integrating PEWS into the routine of patient care, ongoing staff turnover creating training challenges, adequate material resources to promote PEWS use, and the COVID-19 pandemic. While most identified factors mapped to the CCS, COVID-19 emerged as an additional external sustainability challenge. Together, these challenges resulted in multiple impacts on PEWS sustainment, ranging from a small reduction in PEWS quality to complete disruption of PEWS use and subsequent loss of benefits to patients. Participants described several innovative strategies to address identified challenges and promote PEWS sustainability. Conclusion This study describes clinician perspectives on sustainable implementation of evidence-based interventions in low-resource settings, including sustainability determinants and potential sustainability strategies. Identified factors mapped well to the CCS, however, external factors, such as the COVID pandemic, may additionally impact sustainability. This work highlights an urgent need for theoretically-driven, empirically-informed strategies to support sustainable implementation of evidence-based interventions in settings of all resource-levels.

Project description:IntroductionThere is a global effort to address the school dropout phenomenon. The urgency to act on it comes from the harmful evidence that school dropout has on societal and individual levels. Early Warning Systems (EWS) for school dropout at-risk student identification have been developed to anticipate and help schools have a better chance of acting on it. However, several studies point to a doubt that Correct EWS may come too late because they use only publicly available and general student and school information. We hypothesize that having a tool to assess more subjective and inter-relational factors would help anticipate where and when to act to prevent school dropout. This study aimed to develop a multidimensional measure for assessing relational factors for predicting school dropout (SD) risk in the Brazilian context.MethodsWe performed several procedures, including (a) the specialized literature review, (b) the item development of the Relational Factors for the Risk of School Dropout Scale (IAFREE in Portuguese), (c) the content validity analysis, (d) a pilot study, and (e) the administration of the IAFREE to a large Brazilian sample of high school and middle school students (N = 15,924).ResultsAfter the theoretical steps, we found content validity for five relational dimensions for SD (Student-School, Student-School Professionals, Student-Family, Student-Community, and Student-Student) that include 12 facets of risk factors. At the empirical stage, confirmatory analysis corroborated the proposed theoretical model with 12 first-order risk factors and 5 s-order dimensions (36 items). Further, through the Item Response Theory analysis, we assessed the individual item parameters of the items, providing a brief measure without losing psychometric quality (IAFREE-12).DiscussionWe discuss how this model may fill gaps in Correct EWS models and how to advance it. The IAFREE is a good measure for scholars investigating the risk of SD. These results are important for implementing an early warning system for SD that looks into the complexity of the school dropout phenomenon.

Project description:IntroductionSeveral versions of Early Warning Systems (EWS) are used in obstetrics to detect and treat early clinical deterioration to avert morbidity and mortality. EWS can potentially be useful to improve the quality of care and reduce the risk of maternal mortality in resource-limited settings. We conducted a systematic literature review of published obstetric early warning systems, define their predictive accuracy for morbidity and mortality, and their effectiveness in triggering corrective actions and improving health outcomes.MethodsWe systematically searched for primary research articles on obstetric EWS published in peer-reviewed journals between January 1997 and March 2018 in Medline, CINAHL, SCOPUS, Science Direct, and Science Citation Index. We also searched reference lists of relevant articles and websites of professional societies. We included studies that assessed the predictive accuracy of EWS to detect clinical deterioration, or/and their effectiveness in improving clinical outcomes in obstetric inpatients. We excluded studies with a paediatric or non-obstetric adult population. Cross-sectional and qualitative studies were also excluded. We performed a narrative synthesis since the outcomes reported were heterogeneous.ResultsA total of 381 papers were identified, 17 of which met the inclusion criteria. Eleven of the included studies evaluated the predictive accuracy of EWS for obstetric morbidity and mortality, 5 studies assessed the effectiveness of EWS in improving clinical outcomes, while one study addressed both. Sixteen published EWS versions were reviewed, 14 of which included five basic clinical observations (pulse rate, respiratory rate, temperature, blood pressure, and consciousness level). The obstetric EWS identified had very high median (inter-quartile range) sensitivity-89% (72% to 97%) and specificity-85% (67% to 98%) but low median (inter-quartile range) positive predictive values-41% (25% to 74%) for predicting morbidity or ICU admission. Obstetric EWS had a very high accuracy in predicting death (AUROC >0.80) among critically ill obstetric patients. Obstetric EWS improves the frequency of routine vital sign observation, reduces the interval between the recording of specifically defined abnormal clinical observations and corrective clinical actions, and can potentially reduce the severity of obstetric morbidity.ConclusionObstetric EWS are effective in predicting severe morbidity (in general obstetric population) and mortality (in critically ill obstetric patients). EWS can contribute to improved quality of care, prevent progressive obstetric morbidity and improve health outcomes. There is limited evidence of the effectiveness of EWS in reducing maternal death across all settings. Clinical parameters in most obstetric EWS versions are routinely collected in resource-limited settings, therefore implementing EWS may be feasible in such settings.

Dataset Information

Risk stratification to improve Pediatric Early Warning Systems: it is all about the context.

Publications

Risk stratification to improve Pediatric Early Warning Systems: it is all about the context.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets