Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
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The call comes in: Mass shooting in Oak Creek. Not sure about the number of victims, maybe 10-12. Trauma alert: first victim, multiple gunshot wounds; neck, back, arms. ⋯ Next, we find out: - four dead at the scene; not sure if shooter is dead. This article is protected by copyright. All rights reserved.
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Predictive analytics in emergency care has mostly been limited to the use of clinical decision rules (CDR) in the form of simple heuristics and scoring systems. In the development of CDRs, limitations in analytic methods and concerns with usability have generally constrained models to a pre-selected small set of variables judged to be clinically relevant and to rules that are easily calculated. Furthermore, CDRs frequently suffer from questions of generalizability, take years to develop, and lack the ability to be updated as new information becomes available. Newer analytic and machine learning techniques capable of harnessing the large number of variables that are already available through electronic health records may better predict patient outcomes and facilitate automation and deployment within clinical decision support systems. In this proof-of-concept study, a local, big data driven, machine learning approach is compared to existing CDRs and traditional analytic methods using the prediction of sepsis in-hospital mortality as the use case. ⋯ In this proof-of-concept study, a local big data driven, machine learning approach outperformed existing CDRs as well as traditional analytic techniques for predicting in-hospital mortality of ED patients with sepsis. Future research should prospectively evaluate the effectiveness of this approach and whether it translates into improved clinical outcomes for high-risk sepsis patients. The methods developed serve as an example of a new model for predictive analytics in emergency care that can be automated, applied to other clinical outcomes of interest, and deployed in EHRs to enable locally relevant clinical predictions. This article is protected by copyright. All rights reserved.
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Therapeutic hypothermia has been shown to improve neurologic outcome and survival in out-of-hospital cardiac arrest (OHCA) following return of spontaneous circulation (ROSC), and current guidelines recommend therapeutic hypothermia for all comatose survivors of OHCA. However, recommendations for nonshockable rhythms are not as strongly supported. Our study aims to provide further evidence on the use of therapeutic hypothermia in nonshockable rhythms. ⋯ Based on this retrospective study, therapeutic hypothermia is not associated with improved survival in patients with OHCA secondary to nonshockable rhythms. Given the limitations of our study, further prospective trials to assess the effect of therapeutic hypothermia for OHCA with nonshockable rhythms are warranted.
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Diagnostic imaging is integral to the evaluation of many emergency department (ED) patients. However, relatively little effort has been devoted to patient-centered outcomes research (PCOR) in emergency diagnostic imaging. ⋯ The authors discuss applicable research methods and approaches such as shared decision-making that could facilitate better integration of patient-centered outcomes and patient-reported outcomes into decisions regarding emergency diagnostic imaging. Finally, based on a modified Delphi process involving members of the PCOR work group, prioritized research questions are proposed to advance the science of patient-centered outcomes in ED diagnostic imaging.