Clinical trial NCT05039047: External Validation of Two Prediction Models for Independent Gait After Stroke
Official title: External Validation of Two Prediction Models for Independent Gait After Stroke
Study type: OBSERVATIONAL
Overall status: TERMINATED
Conditions: Stroke
Keywords: Gait
Prognosis
External validation
EPOS model
TWIST model
Falls
Lead sponsor: Luzerner Kantonsspital
Sponsor class: OTHER
First posted: 2021-09-09
Enrollment: 149
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Admitted to a hospital with an acute stroke

Brief summary: Regaining independent gait is one of the main goals in stroke rehabilitation and early prediction of gait outcome is important to guide discharge planning at acute stroke units, design rehabilitation and inform patients and relatives. In the last decade, two easy-to-apply prediction models for gait were developed: the EPOS model in the Netherlands and the TWIST model in New Zealand. Although the models' performance in the development cohorts was good, this does not automatically mean that the models are ready for application in clinical practice, as it is unknown whether their performance is also good in an independent cohort from a different country and with different patient characteristics. Such external validation is an essential step towards clinical implementation of prediction models.

A mobility-related problem is the occurrence of falls after stroke. Walking is among the Top 3 activities during which stroke patients fall, with the other two activities being transferring or sitting in a wheelchair. Especially soft tissue injuries after a fall are common and in 1-15% of the patients, the fall results in a fracture. Apart from the costs that arise from these injuries, falls have a negative impact on the patient's physical functioning and psychological status, with an increased dependency and fear of falling, resulting in a reduced quality of life.

A systematic review found 12 studies that developed fall risk prediction models for either inpatient rehabilitation stroke patients or those living in the community. Important predictors for falls are the presence of hemi-inattention, fall history and balance deficits. However, none of the models had an acceptable performance and predictors were not always captured by a validated assessment, which is an important prerequisite for an unbiased prediction model.

The primary aim of this study is to externally validate the EPOS and TWIST models for independent gait after stroke in a heterogeneous sample of subjects admitted to the hospital with an acute stroke. It is hypothesized that the performance of both models in this independent cohort will be lower than in the development cohorts, but still be adequate.

The secondary objective is to investigate the occurrence and predictability of falls within the first six months after stroke and its relationship with the prognosis for independent gait within this sample.


Eligibility criteria: Inclusion criteria:

* First-ever or recurrent stroke as confirmed by computerized tomography and/ or magnetic resonance imaging
* Not able to walk independently within the first 72 hours after stroke (Functional Ambulation Categories \<4)
* Age ≥18 year
* Written informed consent

Exclusion criterion:

• Not able to walk independently before hospital admission (Functional Ambulation Categories \<4)

Primary outcomes:
measure: Functional Ambulation Categories (0-5 points, higher scores being better)
timeFrame: 6 weeks poststroke
measure: Functional Ambulation Categories (0-5 points, higher scores being better)
timeFrame: 12 weeks poststroke
measure: Functional Ambulation Categories (0-5 points, higher scores being better)
timeFrame: 26 weeks poststroke
Secondary outcomes:
measure: Falls
timeFrame: 6 weeks poststroke
measure: Falls
timeFrame: 12 weeks poststroke
measure: Falls
timeFrame: 26 weeks poststroke
Other outcomes:
measure: National Institutes of Health Stroke Scale (0-42 points, lower scores being better)
timeFrame: Hospital admission, usually within 24 hours poststroke
measure: Trunk Control Test (0-100 points, higher scores being better)
timeFrame: within 72 hours poststroke
measure: Motricity Index - lower extremity subscale (0-100 points, higher scores being better)
timeFrame: within 72 hours poststroke
measure: Medical Research Grading - hip extension (0-5 points, higher scores being better)
timeFrame: within 72 hours poststroke
measure: Modified Rankin Scale (0-5 points, lower scores being better)
timeFrame: 6 weeks poststroke
measure: Modified Rankin Scale (0-5 points, lower scores being better)
timeFrame: 12 weeks poststroke
measure: Modified Rankin Scale (0-5 points, lower scores being better)
timeFrame: 26 weeks poststroke
Locations:
city: Lucerne
country: Switzerland
facility: Luzerner Kantonsspital
Study officials:
affiliation: Luzerner Kantonsspital
name: Janne M. Veerbeek, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Veerbeek JM, Van Wegen EE, Harmeling-Van der Wel BC, Kwakkel G; EPOS Investigators. Is accurate prediction of gait in nonambulatory stroke patients possible within 72 hours poststroke? The EPOS study. Neurorehabil Neural Repair. 2011 Mar-Apr;25(3):268-74. doi: 10.1177/1545968310384271. Epub 2010 Dec 26.
citation: Smith MC, Barber PA, Stinear CM. The TWIST Algorithm Predicts Time to Walking Independently After Stroke. Neurorehabil Neural Repair. 2017 Oct-Nov;31(10-11):955-964. doi: 10.1177/1545968317736820. Epub 2017 Nov 1.
citation: Steyerberg EW, Moons KG, van der Windt DA, Hayden JA, Perel P, Schroter S, Riley RD, Hemingway H, Altman DG; PROGRESS Group. Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med. 2013;10(2):e1001381. doi: 10.1371/journal.pmed.1001381. Epub 2013 Feb 5.
citation: Moons KGM, Wolff RF, Riley RD, Whiting PF, Westwood M, Collins GS, Reitsma JB, Kleijnen J, Mallett S. PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration. Ann Intern Med. 2019 Jan 1;170(1):W1-W33. doi: 10.7326/M18-1377.
citation: Moons KG, Kengne AP, Grobbee DE, Royston P, Vergouwe Y, Altman DG, Woodward M. Risk prediction models: II. External validation, model updating, and impact assessment. Heart. 2012 May;98(9):691-8. doi: 10.1136/heartjnl-2011-301247. Epub 2012 Mar 7.
citation: Lamb SE, Jorstad-Stein EC, Hauer K, Becker C; Prevention of Falls Network Europe and Outcomes Consensus Group. Development of a common outcome data set for fall injury prevention trials: the Prevention of Falls Network Europe consensus. J Am Geriatr Soc. 2005 Sep;53(9):1618-22. doi: 10.1111/j.1532-5415.2005.53455.x.
citation: Batchelor FA, Mackintosh SF, Said CM, Hill KD. Falls after stroke. Int J Stroke. 2012 Aug;7(6):482-90. doi: 10.1111/j.1747-4949.2012.00796.x. Epub 2012 Apr 12.
citation: Walsh M, Galvin R, Horgan NF. Fall-related experiences of stroke survivors: a meta-ethnography. Disabil Rehabil. 2017 Apr;39(7):631-640. doi: 10.3109/09638288.2016.1160445. Epub 2016 Mar 23.
citation: Walsh ME, Galvin R, Boland F, Williams D, Harbison JA, Murphy S, Collins R, Crowe M, McCabe DJH, Horgan F. Validation of two risk-prediction models for recurrent falls in the first year after stroke: a prospective cohort study. Age Ageing. 2017 Jul 1;46(4):642-648. doi: 10.1093/ageing/afw255.