Prediction of fall risk upon admission to an inpatient stroke

unit

Usual and standard care assessment yields a feasible, useful model.

Cristin McKenna MD, PhD

Kessler Institute for Rehabilitation, Outpatient Musculoskeletal and Neurologic Rehabilitation

Affiliations:

Professor, Physical Medicine and Rehabilitation, Rutgers-New Jersey Medical School

Research Scientist, Kessler Foundation Stroke Lab

cmckenna@kessler-rehab.com

Disclosures

Dr McKenna is an employee of Kessler Institute for

Rehabilitation, a Select Medical Corporation

Dr McKenna is on the speakers bureau for Merz,

and Avanir.

No products of those companies will be discussed

in this presentation.

Learning objectives

After the presentation attendees will be able to :

Identify fall prediction instruments used in clinical care and the limitations of these instruments.

Describe barriers to comparing fall risk across populations and institutions.

Make clinical observations regarding fall cause/effect and the limitations of clinical observation.

Identify hidden disabilities in stroke patients that contribute to falls and which impact rehabilitation outcomes

Scope of the problem… even

before they come to rehab

•Falls in outpatients

•Fall morbidity and mortality

•Fall as cause of hospitalization

Risk factors for falls

Clinical Assessment of Fall Risk

History

prior history of falls

Medications

Diuretics, sedatives, anti-hypertensives,

new medications

Physical Exam

gait, tone, cognition

Fall Prediction Instruments

The imperative for improvement

No Gold Standard

Many instruments exist

Need for disease specific instruments?

Definition of fall (poll)

1. lose one's balance and collapse

2. an event which results in a person coming to rest inadvertently on the ground or floor or other lower level

3. An unplanned descent to the floor (or extension of the floor, e.g., trash can or other equipment) with or without injury.

4. Unintentionally coming to the ground or some lower level and other than as a consequence of sustaining a violent blow, loss of consciousness, sudden onset of paralysis as in stroke or an epileptic seizure

5. any unintentional descent to the floor or other horizontal surface

Definition of Fall

seniors most frequently associated ‘‘loss of balance’’ with falling

When defining falls, health care providers mainly talked about the consequences of falling (injury, health, andanatomical landing point)

Our Definition: An unplanned descent with or without injury.

Safety & Quality Aspects

Risk identification

Prevention

Clinical Impact

Financial/Legal impact

How and where are falls and fall

risk data recorded?

Therapy notes

Nurse notes

Physician notes

Coding in ICD-9

Incident Reports

Team meetings

EMR systems

Clinical Fall Prediction Instruments Name Sensitivity (%) Specificity (%)

Conley scale 77 61

Hendrich Risk Model 46 71

Clinical judgment of RN in inpatient rehab setting

88 26

MacAvoy’s Fall Risk Assessment tool

43 70

Clinical Fall Prediction Instruments Name Sensitivity (%) Specificity (%)

Berg Balance Test 77 78

Elderly Fall Screening Test

93 38

Dynamic Gait Index 85 38

Timed Get Up and Go test

87 87

Sensitivity, Specificity, PPV, NPV

Specificity: The capacity of the test to correctly identify diseased individuals in a population “TRUE POSITIVES”. The greater the the sensitivity, the smaller the number of unidentified case “false negatives”.

Specificity: The capacity of the test to correctly exclude individuals who are free of the disease “TRUE NEGATIVES”. The greater the specificity, the fewer “false positives” will be included.

Positive Predictive Value: The probability of the disease being present,among those with positive diagnostic test results

Negative Predictive Value: The probability that the disease was absent, among those whose diagnostic test results were negative

Non-diseased

cases

Diseased

cases

Test result value

or

subjective judgement of likelihood that case is diseased

Threshold

Non-diseased

cases

Diseased

cases

Test result value

or

subjective judgement of likelihood that case is diseased

Fall Risk Assessments from

therapist and Nursing

Conley Score

Fall wristband

Berg Balance Test

Clinical judgement

Conley Score

Scoring: Score of 2 points or greater, or a fall during

hospitalization should initiate fall prevention

strategies

The Conley Scale is a 10-point scale that includes the

following:

History: On admission, history of falling in last 3 months (2

points)

Observation: Impaired judgment/lack of safety awareness (3

points)

Agitation (2 points)

Impaired gait, shuffle/wide base, unsteady walk (1 point)

Direct: Do you ever experience dizziness or vertigo? (1 point)

Questions:Do you ever wet or soil yourself on way to the

bathroom? (1 point)

Berg Balance

BERG Patient Name: ____________________________

BALANCE Rater Name: ____________________________

SCALE Date: ____________________________

Balance Item Score (0-4)

1. Sitting unsupported _______

2. Change of position: sitting to standing _______

3. Change of position” standing to sitting _______

4. Transfers _______

5. Standing unsupported _______

6. Standing with eyes closed _______

7. Standing with feet together _______

8. Tandem standing _______

9. Standing on one leg _______

10. Turning trunk (feet fixed) _______

11. Retrieving objects from floor _______

12. Turning 360 degrees _______

13. Stool stepping _______

14. Reaching forward while standing _______

Berg balance Scoring

TOTAL (0–56): _______

Interpretation

0–20, wheelchair bound

21–40, walking with assistance

41–56, independent

Role of Tone in Falls

Spasticity (Ashworth, pectoral muscle

reflex)

Paratonia (Kral’s modified test of

paratonia)

Parkinsonism (Pull Test)

Modified Ashworth Scale for

grading Spasticity Grade

Description

0 No increase in muscle tone

1 Slight increase in muscle tone, manifested by a catch and release, or by minimal resistance at the end of the range of motion when the affected part(s) is moved in flexion or extension

2 Slight increase in muscle tone, manifested by a catch, followed by minimal resistance throughout the remainder (less than half) of the range of movement (ROM)

3 More marked increase in muscle tone through most of ROM, but affected part(s) easily moved

4 Considerable increase in muscle tone, passive movement difficult

5 Affected part(s) rigid in flexion and extension

Cognitive Aspects of Fall Risk

Go/No Go Brain Systems 3-gesture test

Luria’s modified test of echopraxis

Aphasia

spatial neglect Physical exam, bedside pen and paper

screen

Dual task

Ecological validity

Science of Fall Prediction

Current methods:

fall wristband

Standardized instrument

Clinical Judgment

Are there separable items which can be used to predict fall risk?

Standardized Instruments: Why use them?

KEY ELEMENTS FOR ASSESSING RISK OF FALLS

AND MANAGING PATIENTS AT RISK

Step 1: Ideally for all patients but especially those with gait dysfunction

HPI: Inquire about falls in past year

And

History: Stroke, Age ≥ 65 years, Dementia, Vision deficit, Arthritis, arthralgia, Parkinsonism, Depression, Peripheral neuropathy, Polypharmacy, Use of cane or walker, Other condition w/ LE Restricted ADLs, sensorimotor loss

Physical Exam

Physiatric examination, emphasizing:

balance and gait

LE strength, sensation & coordination

mental status

In addition, may consider a

standardized assessment

Hidden Disabilities in stroke

No transfer records

Walk in the room

Which brain hemisphere?

Except when it’s not

How do each of these contribute

to fall risk?

•Aphasia

•Apraxia

•spatial neglect

•Anosagnosia

Rapid bedside assessment

Observe

Ask

Examine

Management

May address:

Underlying disorder

Adjustment of medication

Exercise program

Training in gait and balance

Training in assistive device

Assessment/modification of home environment

Fracture risk: BMD, Vit D

From research to real world

From lab to bedside.

The research presented today is

supported by Select Medical and

Kessler Foundation

Background of Current

Research

Falls in stroke patients represent a significant source of morbidity. Accurate identification of patients at high risk of falling may enable appropriate distribution of fall prevention resources which will in turn enhance care quality and in turn facilitate patient recovery.

Although this problem has been approached by other research groups, there is no single clinically accepted and widely used instrument, thus further work in this area is warranted

We have developed a model that correlates factors measurable within the first 72 hours of admission. We intend to use this set of correlations to develop a predictive model of falls

Objective

To develop a model to predict falls in the inpatient stroke patient population using data stemming from admission documentation and relevant to the rehabilitation population of patients. These factors were chosen because they are routinely collected data on every stroke inpatient.

Methods

Retrospective chart analysis to

develop model

Prospective chart analysis to test

model

3112 consecutive stroke patients

undergoing inpatient acute

rehabilitation

Study Design

Pertinent data

Functional Independence Measure

(FIM)

Demographic information

Case mix group (CMG), which

incorporates the presence or

absence of comorbidities

Functional Independence Measure

(FIM)

18 Items

• Motor skills (13), Cognitive (5)

• Scale of 1 (total assist) to 7 (no assist)

• Ranges 13-91 Motor, 5-35 Cognitive

• Higher scores = Better function

• Range of total score: 18-126

FIM Subsets

•self-care (nutrition, hygiene, and

dressing)

• sphincter control, mobility (transfer to

bed, chair, and toilet)

• locomotion (walking and climbing stairs)

• communication (comprehension and

expression)

•cognitive function (social relations,

problem solving, and memory)

Results

Variables

Patients

who Fell

Patients who

did not fall Characteristic mean Std. Deviation mean Std. Deviation

age (years) 56.7 12.8 71.8 13.2

Female, n (%) 1409 (50.1) 0.5 136 (45) 0.5

AdmitFIMEating 3.6 1.6 3.7 1.8

AdmitFIMGrooming 2.8 1.3 3.0 1.5

AdmitFIMBathing 2.0 0.9 2.2 1.1

AdmitFIMDressingUpper 2.2 1.0 2.5 1.3

AdmitFIMDressingLower 1.6 0.8 1.9 1.0

AdmitFIMToileting 2.0 1.0 2.3 1.2

AdmitFIMBladderCtrl 2.3 1.5 2.6 1.7

AdmitFIMBowelCtrl 2.7 1.5 3.0 1.7

AdmitFIMBedTransfer 2.0 0.9 2.3 1.0

AdmitFIMToiletTransfer 2.0 0.9 2.2 1.1

AdmitFIMTubTransfer 1.8 1.0 2.1 1.1

AdmitFIMWalkWheelchair 1.2 0.5 1.5 0.9

AdmitFIMStairs 1.0 0.6 1.3 0.9

AdmitFIMComprehension 4.1 1.4 4.2 1.6

AdmitFIMExpression 3.9 1.7 4.0 1.8

AdmitFIMSocialInteraction 4.5 1.7 4.5 1.8

AdmitFIMProblemSolving 3.3 1.4 3.4 1.5

AdmitFIMMemory 3.7 1.5 3.7 1.6

LOS (days) 24.8 14.6 18.5 11.3

CMG 108.5 1.8 107.6 2.4

Tier 0.5 1.1 0.5 1.1

Results

• Gender M>F

• Age Younger>older

• Case mix group (CMG) and Tier assignment

Complexity level

Significant FIM scores

Admission FIM scores which contribute in a statistically

significant manner to the prediction:

• eating

• bed transfer

• stairs

• walking and wheelchair ability

• bowel accident frequency

Effect of FIM on falls

The admission FIM score subsets which contributed in a statistically significant manner to the fall-correlation model were eating, stairs, and toilet transfer.

The high performing to low performing comparison for eating revealed a hazard ratio of 1.15 hazard ratio0.016;

for stairs hazard ratio was 0.691, p-value 0.01;

for toileting hazard ration was 1.3, p-value 0.06

Factors Correlated with Falls

Gender was correlated with falls, with males being more likely to fall. The female vs. male hazard ratio was 0.727, p-value 0.04.

Older patients’ fall risk appeared to be lower than younger patients with patients in the group age 63 or less being more likely to fall than those older than 63. The age hazard ratio was 0.982, p-value 0.003.

CMG which incorporates the presence or absence of co morbidities was also correlated with falls. Increasing burden of CMG had a hazard ratio of 1.3, p-value of 0.0001

Select Medical Innovative Fall

Predictor for Stroke Inpatients

SeMIFall

Sensitivity (60%)

Specificity (58%)

Positive predictive value (13.8%)

Negative predictive value (93.9%)

Patients identified to be high fall risk

who fell

(true positives)

75

Patients identified to be high fall risk

who didn’t fall (false positives)

467

Falls that happened in

patients who were not identified as

high fall risk (false negatives)

50

Patients who did not fall and who were correctly

identified to be not at risk for falling (true negatives)

657

What is SeMIFall?

The SeMIFall is a novel fall prediction

instrument based on subsets of FIM,

demographics, and medical stratification

systems such as case mix groups and

tiers. Further work in this area will seek

additional factors to improve the

sensitivity and specificity of this test.

Implications for Practice

Our results suggest that stroke patients

who fall during their inpatient stay can be

identified within the first 72 hours of

admission to acute rehabilitation.

This instrument uses no additional time

beyond routine clinical assessments

Prediction of fall risk upon admission to an

inpatient stroke unit: usual and standard care

assessment yields a feasible, useful model.

Cristin McKenna MD, PhD1, Joan Alverzo PhD, CRRN1, Jeffrey Yangang Zhang PhD2, Peii Chen PhD2, Pasquale G. Frisina, PhD1, Annie Kutlik1, Mooyeon Oh-Park MD2, Mahesh Lellella MBBS2, A.M. Barrett, MD2

1.Select Medical Corporation - Kessler Institute for Rehabilitation, 1199 Pleasant Valley Way, West Orange NJ 07052

2.Kessler Foundation, 1199 Pleasant Valley Way, West Orange NJ 07052

Types of falls (poll)

accidental falls

anticipated physiologic falls

unanticipated physiologic falls

extrinsic

Hospital-Acquired Conditions Inpatient Prospective Payment System (IPPS) Fiscal Year (FY) 2009 Final

Rule, CMS included 10 categories of conditions that were selected for the

HAC payment provision.

The 10 categories of HACs include:

1. Foreign Object Retained After Surgery

2. Air Embolism

3. Blood Incompatibility

4. Stage III and IV Pressure Ulcers

5. Falls and Trauma 1. Fractures

2. Dislocations

3. Intracranial Injuries

4. Crushing Injuries

5. Burns

6. Electric Shock

6. Manifestations of Poor Glycemic Control

7. Catheter-Associated Urinary Tract Infection (UTI)

8. Vascular Catheter-Associated Infection

9. Surgical Site Infection Following: CABG, Bariatric Surgery, Orthopedic Procedures

10. Deep Vein Thrombosis (DVT)/Pulmonary Embolism (PE):

Total Knee Replacement, Hip Replacement

https://www.cms.gov/hospitalacqcond/06_hospital-acquired_conditions.asp

Affected Hospitals

The Present on Admission (POA) Indicator requirement and Hospital-Acquired Conditions (HAC) payment provision only apply to Inpatient Prospective Payment Systems (IPPS) Hospitals.

At this time, the following hospitals are exempt from the POA Indicator and HAC:

Critical Access Hospitals (CAHs)

Long-term Care Hospitals (LTCHs)

Maryland Waiver Hospitals

Cancer Hospitals

Children's Inpatient Facilities

Rural Health Clinics

Federally Qualified Health Centers

Religious Non-Medical Health Care Institutions

Inpatient Psychiatric Hospitals

Inpatient Rehabilitation Facilities

Veterans Administration/Depart of Defense Hospitals

Which types of falls are we held

responsible for?

Community fall risk

CMS perspective on falls

Unintended consequences

Fall Risk Perspectives

Within disease

Within institution

Individual

Summary

Clinical: How to identify and manage

fall risk in your patients

Research: Impact of prediction of fall

risk on function

Standardized Scales

Hidden disabilities

Alarm Fatigue

Alarm Fatigue

Relationship to falls

Avoiding adverse event

Improving outcomes

Personalized therapy plan

Helpful resources

http://www.rehabmeasures.org

http://www.aan.com/go/practice/guideli

nes

http://www.strokecenter.org/profession

als/

http://www.cdc.gov/HomeandRecreatio

nalSafety/Falls/index.html

REFERENCES

Aizen, E., I. Shugaev, et al. (2007). "Risk factors and characteristics of falls during inpatient rehabilitation of elderly patients."

Archives of Gerontology and Geriatrics 44(1): 1-12.

Ashburn, A., D. Hyndman, et al. (2008). "Predicting people with stroke at risk of falls." Age and Ageing 37(3): 270-276.

Buella, C. J., E. Martin, et al. (2008). "Validation of an adapted Falls Efficacy Scale in older rehabilitation patients." Archives of

Physical Medicine and Rehabilitation 89(2): 291-296.

Centers for Disease Control and Prevention, National Center for Injury Prevention and Control. Web–based Injury Statistics

Query and Reporting System (WISQARS). November 30, 2010.

Divani, A. A., G. Vazquez, et al. (2009). "Risk Factors Associated With Injury Attributable to Falling Among Elderly Population

With History of Stroke." Stroke 40(10): 3286-3292.

Englander, F., T. J. Hodson, et al. (1996). "Economic dimensions of slip and fall injuries." Journal of Forensic Sciences 41(5):

733-746.

Forster, A. and J. Young (1995). "Incidence and consequences of falls due to stroke – a systematic Inquiry." British Medical

Journal 311(6997): 83-86.

Gilewski, M. J., P. Roberts, et al. (2007). "Discriminating high fall risk on an inpatient rehabilitation unit." Rehabilitation

Nursing 32(6): 234-240.

Gillen, R., H. Tennen, et al. (2005). "Unilateral spatial neglect: Relation to rehabilitation outcomes in patients with right

hemisphere stroke." Archives of Physical Medicine and Rehabilitation 86(4): 763-767.

Granek, E., S. P. Baker, et al. (1987). " Medications and diagnoses in relation to falls in a long-term care." Journal of the

American Geriatrics Society 35(6): 503-511.

Jorgensen, L., T. Engstad, et al. (2002). "Higher incidence of falls in long-term stroke survivors than in population controls -

Depressive symptoms predict falls after stroke." Stroke 33(2): 542-547.

Kalra, L., I. Perez, et al. (1997). "The influence of visual neglect on stroke rehabilitation." Stroke 28(7): 1386-1391.

Kim, E. A. N., S. Z. Mordiffi, et al. (2007). "Evaluation of three fall-risk assessment tools in an acute care setting." Journal of

Advanced Nursing 60(4): 427-435.

Lee, J. E. and D. S. Stokic (2008). "Risk factors for falls during inpatient rehabilitation." American Journal of Physical Medicine

& Rehabilitation 87(5): 341-350.

Lovallo, C., S. Rolandi, et al. (2010). "Accidental falls in hospital inpatients: evaluation of sensitivity and specificity of two risk

assessment tools." Journal of Advanced Nursing 66(3): 690-696.

Mackintosh, S. F., K. D. Hill, et al. (2006). "Balance score and a history of falls in hospital predict recurrent falls in the 6

months following stroke rehabilitation." Archives of Physical Medicine and Rehabilitation 87(12): 1583-1589.

Maeda, N., J. Kato, et al. (2009). "Predicting the Probability for Fall Incidence in Stroke Patients Using the Berg Balance

Scale." Journal of International Medical Research 37(3): 697-704.

Nyberg, L. and Y. Gustafson (1995). "Patient falls in stroke rehabilitation – a challenge to rehabilitation strategies." Stroke

26(5): 838-842.

Nyberg, L. and Y. Gustafson (1997). "Fall prediction index for patients in stroke rehabilitation." Stroke 28(4): 716-721.

Paolucci, S., G. Antonucci, et al. (2001). "The role of unilateral spatial neglect in rehabilitation of right brain-damaged ischemic stroke

patients: A matched comparison." Archives of Physical Medicine and Rehabilitation 82(6): 743-749.

Paolucci, S., G. Antonucci, et al. (1996). "Predicting stroke inpatient rehabilitation outcome: The prominent role of neuropsychological

disorders." European Neurology 36(6): 385-390.

Petitpierre, N. J., A. Trombetti, et al. (2010). "The FIM (R) instrument to identify patients at risk of falling in geriatric wards: a 10-year

retrospective study." Age and Ageing 39(3): 326-331.

Ramnemark, A., L. Nyberg, et al. (1998). "Fractures after stroke." Osteoporosis International 8(1): 92-95.

Rapport, L. J., J. S. Webster, et al. (1993). "Predictors of falls among right-hemisphere stroke patients in the rehabilitation setting."

Archives of Physical Medicine and Rehabilitation 74(6): 621-626.

Rizzo, J. A., R. Friedkin, et al. (1998). "Health care utilization and costs in a Medicare population by fall status." Medical Care 36(8):

1174-1188.

Robertson, M. C., N. Devlin, et al. (2001). "Economic evaluation of a community based exercise programme to prevent falls." Journal

of Epidemiology and Community Health 55(8): 600-606.

Ruchinskas, R. (2003). "Clinical prediction of falls in the elderly." American Journal of Physical Medicine & Rehabilitation 82(4): 273-

278.

Saverino A., E. Benevolo, et al. (2006). “Falls in a rehabilitation setting: functional independence and fall risk.” Eura Medicophys

42(3): 179-184.

Stevens, J. A., P. S. Corso, et al. (2006). "The costs of fatal and non-fatal falls among older adults." Injury Prevention 12(5): 290-295.

Stone, S. P., P. W. Halligan, et al. (1993). "The incidence of neglect phenomena and related disorders in patients with an acute right

or left-hemisphere stroke." Age and Ageing 22(1): 46-52.

Suzuki, T., S. Sonoda, et al. (2005). "Incidence and consequence of falls in inpatient rehabilitation of stroke patients." Experimental

Aging Research 31(4): 457-469.

Teasell, R., M. McRae, et al. (2002). "The incidence and consequences of falls in stroke patients during inpatient rehabilitation:

Factors associated with high risk." Archives of Physical Medicine and Rehabilitation 83(3): 329-333.

Tinetti, M. E. and M. Speechley (1989). "Prevention of falls among the elderly." New England Journal of Medicine 320(16): 1055-1059.

Tsur, A. and Z. Segal (2010). "Falls in Stroke Patients: Risk Factors and Risk Management." Israel Medical Association Journal 12(4):

216-219.

Tutuarima, J. A., R. J. Dehaan, et al. (1993). "Number of nursing staff and falls – a case-control study on falls by stroke patients in

acute-care settings." Journal of Advanced Nursing 18(7): 1101-1105.

Tutuarima, J. A., J. H. P. vanderMeulen, et al. (1997). "Risk factors for falls of hospitalized stroke patients." Stroke 28(2): 297-301.

Ugur, C., D. Gucuyener, et al. (2000). "Characteristics of falling in patients with stroke." Journal of Neurology Neurosurgery and

Psychiatry 69(5): 649-651.

Vassallo, M., R. Stockdale, et al. (2005). "A comparative study of the use of four fall risk assessment tools on acute medical wards."

Journal of the American Geriatrics Society 53(6): 1034-1038.

Vlahov, D., A. H. Myers, et al. (1990). "Epidemiology of falls among patients in a rehabilitation hospital." Archives of Physical Medicine

and Rehabilitation 71(1): 8-12.

Wagner, L.M., V.L. Phillips, et al. (2009). “Falls among community-residing stroke survivors following inpatient rehabilitation: a

descriptive analysis of longitudinal data.” BMC Geriatrics Oct 14; 9: 46.

Webster, J. S., L. A. Roades, et al. (1995). " Rightward orienting bias, wheelchair maneuvering and fall risk." Archives of Physical

Medicine and Rehabilitation 76(10): 924-928.

Cristin McKenna MD, PhD cmckenna@kessler-rehab.com

Kessler Institute for Rehabilitation, Outpatient

Musculoskeletal and Neurologic Rehabilitation

Professor, Physical Medicine and Rehabilitation

Rutgers-New Jersey Medical School

Research Scientist, Kessler Foundation Stroke Lab

What to do ?!?!

Open floor for

discussion