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Table 3 Characteristics of predictors used in the final models of included studies

From: The use of predictive fall models for older adults receiving aged care, using routinely collected electronic health record data: a systematic review

Predictors

Volrathongchai et al. (2005) [31]

Marier et al. (2016) [30]

Kuspinar et al. (2019) [29]

Lo et al. (2019) [32]

1: MDS AssessmentsA

2: MDS Assessments & EMR onlyA

3: MDS assessments & EMR duplicatesA

4: MDS assessments & EMR Only & EMR DuplicatesA

1: Combined (OASIS & MACH)

2: Oasis Model

3: MACH Model

Demographics

 Age

     

X

   

 Sex

     

X

   

Assessments

 Cognitive Performance Scale

     

X

   

 Activities of Daily Living Hierarchy

     

X

   

 Worsening of Activities of Daily Living Status

     

X

   

 Pain Scale

     

X

   

 Managing Medication

     

X

   

 Missouri Alliance for Home Care Fall Risk Assessment (MACH)

      

X b

 

X b

 Outcome and Assessment Information Set (OASIS-C) – 46 Itemsc

      

Xc

Xc

 

 Unstable Health Patterns

     

X

   

Fall History

 Fall in Last 30 Days

X

X

X

X

X

    

 Fall in 31–180 Days

X

X

X

X

X

    

Medication

 Anticoagulant

 

X

X

X

X

    

 Anticonvulsant

  

X

 

X

    

 Antihypertensive (Alpha II Agonist)

  

X

 

X

    

 Antihypertensive (Alpha-Adregen Blocker)

  

X

 

X

    

 Antipsychotic (last 7 days)

X

        

 Antipsychotic

 

X

X

X

X

    

 Anxiolytic

   

X

X

    

 Antidepressant

   

X

X

    

 Diuretic

 

X

X

X

X

    

 Hypnotic

 

X

X

X

X

    

 Opioid Analgesic

  

X

 

X

    

 Psychotropic

  

X

 

X

    

Health Conditions

 Anaemia

 

X

X

X

X

    

 Alzheimer's Disease

 

X

X

X

X

    

 Atrial Fibrillation

 

X

X

X

X

    

 Behavioural Problems

   

X

X

    

 Cognitive Impairment

 

X

X

X

X

    

 Depression

 

X

X

X

X

    

 Diagnosis Causing Imbalance

 

X

X

X

X

    

 Hearing loss

X

        

 Hemiplegia or Hemiparesis

X

        

 Incontinence

   

X

X

X

   

 Mental Instability

 

X

X

X

X

    

 Malnutrition

 

X

X

X

X

    

 Osteoporosis

 

X

X

X

X

    

 Pain

 

X

X

X

X

    

 Parkinson’s Disease

     

X

   

 Vision poor

 

X

X

X

X

    

 Urinary Tract Infection

   

X

X

    

Physical abilities

 Ambulation

   

X

X

    

 Imbalance

 

X

X

X

X

    

 Mode of Expression: Writing

X

        

 Mobility in Bed

     

X

   

 Primary Mode of Locomotion

     

X

   

 Restricted Lower Range of Motion

 

X

X

X

X

    

 Use of Walking Aids

 

X

X

X

X

    

 Unsteady Gait

     

X

   

 Wandering

   

X

X

    

 Wheelchair Use

         

Environmental factors

 Admission from Transfer

 

X

X

X

X

    

 Week after Admission

   

X

X

    

 Week after Room Change

  

X

 

X

    

 Total Number of Variables

6

21

27

29

35

13

300 + 10bc

300c

10b

  1. MDS Minimum Data Set, EMR Electronic Medical record, OASIS Outcome and Assessment information Set, MACH-10 Missouri Alliance for Home Care fall risk assessment
  2. a Models also contained the covariates: days since admission, days since admission squared, interactions between each risk factor and days since admission and duration of time that each resident exhibits a particular risk profile
  3. b MACH scale includes the following ten binary variables: Age 65 + , Diagnosis (three or more co-existing), Prior history of falls within 3 months, Incontinence, Visual impairment, Impaired functional mobility, Environmental hazards, Polypharmacy (four or more prescriptions – any type), Pain affecting level of function, and Cognitive impairment
  4. c OASIS contained 46 items of the 115, chosen based on literature and association with falls. These were used this to create 300 estimates. Example items included 2 + hospitalisations in the past year, shortness of breath, ability to hear and 2 + falls with an injury in the past year
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BMC Geriatrics

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