Risk Stratification in the Emergency Department in Acutely Ill Older Patients (RiSEuP)

Risk Stratification in Elderly Patients in the Emergency Department

Procedures for identification of high-risk elderly patients in the emergency department are lacking.

We aim to identify early risk factors associated with an adverse outcome in elderly patients who visit the emergency department (ED). Second, we aim to find practical tools to identify those elderly patients who are at risk for an adverse outcome in an early stage (by applying and testing triage and risk stratification scores, clinical impression and laboratory results).

With the results of this study, we intend to develop a clinical prediction model to identify older emergency department patients with an increased risk of adverse outcomes.

Study Overview

• Status: Completed
• Conditions: Elderly Patiënts

Detailed Description

Background:

Elderly patients (≥65 years of age) constitute an increasing population in emergency departments (EDs) in many countries. These patients are largely different from younger patients and undoubtedly need different approaches in acute care. Commonly used triage systems are not validated in elderly patients. We hypothesize that this factor contributes to a lack of recognition of patients at risk for adverse events or death.

Aim of the study:

• At first, we will try to identify early risk factors associated with adverse outcome such as age, the premorbid state (comorbidity, living status and cognitive/functional state), medication use, vital signs, and number of previous visits to the ED.
• Secondly, we will investigate the discriminating power of several triage and risk stratification scores. In our study, we will retrieve and validate the following triage and risk stratification scores: The Manchester Triage System (MTS) triage score, the Acute Physiology and Chronic Health Evaluation II (APACHE II) score and the Identification of Seniors At Risk-Hospitalised Patients (ISAR-HP) score for all hospitalized patients. Furthermore, we will calculate four well-known disease specific stratification scores: The Glasgow-Blatchford Bleeding (GBS Score) for patients with an upper gastrointestinal bleeding, the Abbreviated Mortality ED Sepsis (abbMEDS) score and Sepsis-related Organ Failure Assessment (SOFA) score and the Confusion, Urea, Respiration, Blood pressure, Age >65 years (CURB-65) score.
• Thirdly, we will investigate the predictive value of the clinical impression of professionals (doctor/nurse) and the disease perception of patient/companion.
• Fourth, we will investigate the predictive value of laboratory tests (routine tests and biomarkers like lactate, N-terminal pro-B-type natriuretic peptide (NT-pro-BNP), high-sensitivity troponin (hs-TnT), procalcitonin (PCT) and d-dimer.
• At last, we intend to develop a clinical prediction model for short-term mortality and test the predictive ability of this model for the other adverse outcomes/endpoints. This model will be validated in an external cohort.

Study procedure:

The design is a multi-center prospective observational cohort study. The study will take place in Zuyderland MC in Heerlen and Maastricht UMC+ in the Netherlands. On presentation to the ED the patients will be given information about the study and informed consent will be signed. The patient or family member/companion, the nurse and the doctor will be asked to fill out a questionnaire in the ED (5 questions for patient/family and 9 questions for doctor or nurse). These questions will be used for evaluation of the clinical impression of the doctor/nurse and disease perception by the patient/companion. At the ED, 2 extra venous samples and one arterial blood gas sample will be taken in patients participating in the study in Zuyderland M.C. Blood gas analysis will take place immediately and the results will be presented to the attending doctor. Analysis of the biomarkers from the venous blood samples will take place after a few weeks and the results will be blinded in this study. Routine laboratory test will be analyzed in both hospitals. During the days and weeks after inclusion, data will be obtained from patients medical records. During the days and weeks after inclusion, data will be obtained from patients medical records. All patients will be followed up for 30 days and if possible for one year.

• Study Type: Observational
• Enrollment (Actual): 600

Contacts and Locations

Study Locations

• Netherlands
  • Limburg
    • Heerlen, Limburg, Netherlands, 6401 CX
      • Zuyderland Medisch Centrum
    • Maastricht, Limburg, Netherlands, 6202 AZ
      • Maastricht UMC+

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 65 years and older (Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

All patients ≥65 years of age who present to the ED for internal medicine or gastroenterology within the participating hospitals.

Description

Inclusion Criteria:

• Age ≥65 years
• Visit to the emergency department for the internist/gastroenterologist
• Informed consent

Exclusion Criteria:

• Earlier participation in study (patients can only be included once)
• No informed consent
• Inability to speak Dutch or English
• Admission to a ward of another specialty than internal medicine/gastroenterology

Study Plan

How is the study designed?

Design Details

• Observational Models: Cohort
• Time Perspectives: Prospective

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort
elderly patients who visit the ED; elderly patients (65 years and older) who present to the emergency department for internal medicine or gastroenterology

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
30-day all cause mortality	all cause mortality within 30 days (both in- and out-hopsital)	30 days

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
composite secondary outcome	Length of hospital stay longer than 1 week; ICU/MCU admission; all cause mortality within 30 days of presentation to the ED; Readmission within 30 days of discharge from the hospital; Discharge to another residence than previous address (nursing home/hospice)	30 days or 30 days after hospital discharge
all cause mortality after 1 year	all cause mortality after 1 year	1 year

Collaborators and Investigators

• Sponsor: Zuyderland Medisch Centrum
• Investigators: Principal Investigator: Jacqueline Buijs, dr., Zuyderland M.C.

Publications and helpful links

General Publications

• Zelis N, Hundscheid R, Buijs J, De Leeuw PW, Raijmakers MT, van Kuijk SM, Stassen PM. Value of biomarkers in predicting mortality in older medical emergency department patients: a Dutch prospective study. BMJ Open. 2021 Jan 31;11(1):e042989. doi: 10.1136/bmjopen-2020-042989.
• Zelis N, Huisman SE, Mauritz AN, Buijs J, de Leeuw PW, Stassen PM. Concerns of older patients and their caregivers in the emergency department. PLoS One. 2020 Jul 9;15(7):e0235708. doi: 10.1371/journal.pone.0235708. eCollection 2020.
• Zelis N, Buijs J, de Leeuw PW, van Kuijk SMJ, Stassen PM. 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). BMC Geriatr. 2019 Mar 4;19(1):65. doi: 10.1186/s12877-019-1078-2.

Study record dates

Study Major Dates

• Study Start (Actual): July 14, 2016
• Primary Completion (Actual): May 2, 2017
• Study Completion (Actual): July 6, 2019

Study Registration Dates

• First Submitted: September 20, 2016
• First Submitted That Met QC Criteria: October 25, 2016
• First Posted (Estimate): October 27, 2016

Study Record Updates

• Last Update Posted (Actual): July 9, 2019
• Last Update Submitted That Met QC Criteria: July 8, 2019
• Last Verified: July 1, 2019

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Pathologic Processes; Disease Attributes; Emergencies
• Other Study ID Numbers: NL55867.096.15

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: No