Early prediction of hospital admission for emergency department patients
Patients presenting at the emergency department (ED) are at risk for hospital admission, functional decline and mortality, with older patients having even higher risks. The current model was developed to assess potential differences in independent predictors between age groups. 

The model is developed as part of the APOP study (acronym for: Acutely Presenting Older Patient).

The model contains two separate equations, one for patients aged <70 years, and the other for patients ≥70 years. The latter model did not use age, gender, type of specialist and heart rate as individual predictors. 

The model predicts a patients' individual probability of hospital admission. 

Please note that the model is lacking proper external validation and might not provide accurate predictions. Therefore, outcomes should be interpreted with care before implementing the model in clinical practice. 
Research authors: Jacinta A. Lucke, Jelle de Gelder, Fleur Clarijs, Christian Heringhaus, Anton J.M. de Craen, Anne J. Fogteloo, Gerard J. Blauw, Bas de Groot, Simon P. Mooijaart
Details Formula Study characteristics Files & References
Model author
Model ID
Revision date
MeSH terms
  • Emergency Department
  • Elderly
  • Hospital Readmission
  • Model type
    Custom model (Conditional)
    Condition Formula

    Additional information

    In 2012, there were 27862 visits to the LUMC ED, of which 21287 were included in this analysis . The 6575 excluded patients were due to ED use for logistical reasons or arrival during CPR (n=1486), patients aged Baseline characteristics of the study population were stratified by age group. The distribution of demographics and clinical characteristics by age group was similar within the derivation and validation cohorts. In the derivation cohort, 2014 (23.1%) younger patients and 898 (43.2%) older patients were admitted to the hospital. In the validation cohort, 2030 (24.1%) younger patients and 919 (44.4%) older patients were admitted.

    Additional study characteristics shown here are of the older patients used for the derivation of the model. 

    Study Population

    Total population size: 2079
    Males: {{ model.numberOfMales }}
    Females: {{ model.numberOfFemales }}

    Continuous characteristics

    Name Mean SD Unit
    Systolic bloodpressure 145 27.3 mm HG
    Temperature 36.9 1.0 degrees Celsius
    Respiratory rate 18.7 5.5 breaths per minute
    Heart rate 84 20 Beats per minute
    Name LL Q1 Median Q3 UL Unit
    Age 73.9 78.1 83.6 Years
    O2 saturation 98 98 100 %

    Categorical characteristics

    Name Subset / Group Nr. of patients
    Triage Category <10 min 657
    <1hour 943
    <2hours 472
    <4hours 7
    Arrival mode Self-referral 467
    Ambulance/other institution 596
    Referred by GP/specialist 1016
    Type of specialist Medicine 1251
    Surgery 828
    Revisit to the ED No 1832
    Yes (visit <30 days) 247
    Chief complaint Minor trauma 621
    Major trauma 32
    Chest pain 302
    Dyspnoea 221
    Syncope 118
    Psychiatric complaints 34
    Malaise 337
    Abdominal pain 183
    Other 177
    Phlebotomised blood sample No 473
    Yes 1606
    Early prediction of hospital admission for emergency department patients
    Refer to Intended Use for instructions before use
    Evidencio B.V., Irenesingel 19, 7481 GJ, Haaksbergen, the Netherlands

    Related files

    Notes are only visible in the result download and will not be saved by Evidencio

    The predicted probability of hospital admission is: %

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    Outcome stratification

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    Conditional information

    Result interpretation

    The models created in this study indicate that predictors of hospital admission from the ED are similar for younger and older patients, but differ in their prognostic capabilities. The overall prognostic  ability of the models was greater for the patients under 70, but the model for older patients is better at identifying the group of patients very likely to be admitted.

    These results constitute preparatory work towards creating a screening instrument that could adequately predict hospital admission, particularly for older adults.

    Note: It is uncertain whether the outcomes of this model are clinically useful in other hospitals since the model has not been externally validated. The admission rate in the patients used to develop the model may be different in other hospitals. 


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    Calculations alone should never dictate patient care, and are no substitute for professional judgement. See our full disclaimer.

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