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Study protocol for a multicentre prospective cohort study to identify predictors of adverse outcome in older medical emergency department patients (the Risk Stratification in the Emergency Department in Acutely Ill Older Patients (RISE UP) study). | LitMetric

AI Article Synopsis

  • The RISE UP study aims to improve the prediction of adverse outcomes in older patients (≥65 years) visiting emergency departments by identifying various risk factors and creating a practical prediction model.
  • This prospective observational study will gather data from multiple centers in the Netherlands, assessing patient characteristics, vital signs, and laboratory results to evaluate factors predicting 30-day mortality and other health outcomes.
  • The study's main focus is on 30-day all-cause mortality, with follow-ups planned for at least 30 days and up to one year, alongside secondary outcomes like hospital stay length and readmission rates.

Article Abstract

Background: Older patients (≥65 years old) experience high rates of adverse outcomes after an emergency department (ED) visit. Reliable tools to predict adverse outcomes in this population are lacking. This manuscript comprises a study protocol for the Risk Stratification in the Emergency Department in Acutely Ill Older Patients (RISE UP) study that aims to identify predictors of adverse outcome (including triage- and risk stratification scores) and intends to design a feasible prediction model for older patients that can be used in the ED.

Methods: The RISE UP study is a prospective observational multicentre cohort study in older (≥65 years of age) ED patients treated by internists or gastroenterologists in Zuyderland Medical Centre and Maastricht University Medical Centre+ in the Netherlands. After obtaining informed consent, patients characteristics, vital signs, functional status and routine laboratory tests will be retrieved. In addition, disease perception questionnaires will be filled out by patients or their caregivers and clinical impression questionnaires by nurses and physicians. Moreover, both arterial and venous blood samples will be taken in order to determine additional biomarkers. The discriminatory value of triage- and risk stratification scores, clinical impression scores and laboratory tests will be evaluated. Univariable logistic regression will be used to identify predictors of adverse outcomes. With these data we intend to develop a clinical prediction model for 30-day mortality using multivariable logistic regression. This model will be validated in an external cohort. Our primary endpoint is 30-day all-cause mortality. The secondary (composite) endpoint consist of 30-day mortality, length of hospital stay, admission to intensive- or medium care units, readmission and loss of independent living. Patients will be followed up for at least 30 days and, if possible, for one year.

Discussion: In this study, we will retrieve a broad range of data concerning adverse outcomes in older patients visiting the ED with medical problems. We intend to develop a clinical tool for identification of older patients at risk of adverse outcomes that is feasible for use in the ED, in order to improve clinical decision making and medical care.

Trial Registration: Retrospectively registered on clinicaltrials.gov ( NCT02946398 ; 9/20/2016).

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6399878PMC
http://dx.doi.org/10.1186/s12877-019-1078-2DOI Listing

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