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Predicting in-patient falls in a geriatric clinic

A clinical study combining assessment data and simple sensory gait measurements

Prädiktion von Stürzen stationärer Patienten in einer geriatrischen Klinik

Eine klinische Studie zur Kombination von Assessmentdaten und einfachen, sensorisch erfassten Gangparametern

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Abstract

Background

Falls are among the predominant causes for morbidity and mortality in elderly persons and occur most often in geriatric clinics. Despite several studies that have identified parameters associated with elderly patients’ fall risk, prediction models – e.g., based on geriatric assessment data – are currently not used on a regular basis. Furthermore, technical aids to objectively assess mobility-associated parameters are currently not used.

Objectives

To assess group differences in clinical as well as common geriatric assessment data and sensory gait measurements between fallers and non-fallers in a geriatric sample, and to derive and compare two prediction models based on assessment data alone (model #1) and added sensory measurement data (model #2).

Methods

For a sample of n=110 geriatric in-patients (81 women, 29 men) the following fall risk-associated assessments were performed: Timed ‘Up & Go’ (TUG) test, STRATIFY score and Barthel index. During the TUG test the subjects wore a triaxial accelerometer, and sensory gait parameters were extracted from the data recorded. Group differences between fallers (n=26) and non-fallers (n=84) were compared using Student’s t-test. Two classification tree prediction models were computed and compared.

Results

Significant differences between the two groups were found for the following parameters: time to complete the TUG test, transfer item (Barthel), recent falls (STRATIFY), pelvic sway while walking and step length. Prediction model #1 (using common assessment data only) showed a sensitivity of 38.5% and a specificity of 97.6%, prediction model #2 (assessment data plus sensory gait parameters) performed with 57.7% and 100%, respectively.

Discussion and conclusion

Significant differences between fallers and non-fallers among geriatric in-patients can be detected for several assessment subscores as well as parameters recorded by simple accelerometric measurements during a common mobility test. Existing geriatric assessment data may be used for falls prediction on a regular basis. Adding sensory data improves the specificity of our test markedly.

Zusammenfassung

Hintergrund

Sturzereignisse gehören zu den wichtigsten Ursachen für Morbidität und Mortalität im fortgeschrittenen Alter und treten besonders häufig in geriatrischen Kliniken auf. Obwohl einige Studien bereits Parameter identifiziert haben, welche mit dem Sturzrisiko assoziiert sind, werden zur Zeit Prädiktionsmodelle, die z. B. auf geriatrischen Assessmentdaten basieren, nicht regelhaft verwendet. Zudem werden in der Praxis kaum technische Hilfsmittel zur objektiven Messung von mobilitätsassoziierten Bewegungsparametern eingesetzt.

Zielsetzung

Ziel war die statistische Analyse von Unterschieden zwischen zwei Gruppen stationär-geriatrischer Patienten (Gestürzte und Nichtgestürzte) in Bezug auf gebräuchliche mobilitäts- bzw. sturzassoziierte geriatrische Assessmenttests sowie sensorisch erfasste Gangparameter. Auf der Basis der Ergebnisse sollten zwei statistische Prädiktionsmodelle berechnet und hinsichtlich ihrer Aussagekraft verglichen werden. Modell 1 verwendet nur die Assessmentdaten, Modell 2 zusätzlich technisch gemessene Gangparameter.

Methoden

Bei einer Stichprobe von n=110 stationär-geriatrischen Patienten (81 weiblich, 29 männlich) wurden folgende Assessmenttests durchgeführt: Timed ‚Up & Go‘ (TUG-)Test, STRATIFY-Score und Barthel-Index. Während des TUG wurden mit einem triaxialen Beschleunigungssensor Bewegungsdaten gemessen und Gangparameter berechnet. Die Mittelwertdifferenzen der einzelnen Parameter zwischen der Gruppe der Gestürzten (n=26) und Nichtgestürzten (n=84) wurden mit dem Student-t-Test verglichen. Zusätzlich wurden aus den Daten zwei Klassifikationsmodelle abgeleitet und miteinander verglichen.

Ergebnisse

Signifikante Gruppenunterschiede wurden für folgende Parameter gefunden: benötigte Zeitdauer zur Vollendung des TUG-Tests, Transfer (Barthel-Index), Sturz in den letzten zwei Monaten (STRATIFY-Score) und Schrittlänge sowie Schwankungsbreite des Beckens beim Gehen. Das Prädiktionsmodell 1 (nur Assessmentdaten) weist eine Sensitivität von 38,5% bei einer Spezifität von 97,6% auf, Modell 2 (zusätzlich Gangparameter) zeigt eine Sensitivität von 57,7% bei einer Spezifität von 100%.

Diskussion und Ausblick

Es zeigen sich signifikante Unterschiede zwischen gestürzten und nichtgestürzten stationär-geriatrischen Patienten sowohl bei Items gebräuchlicher geriatrischer Assessmenttests als auch bei sensorisch erfassten Gangparametern. Regulär erhobene Daten könnten für eine automatisierte Einschätzung des individuellen Sturzrisikos verwendet werden, wobei die prädiktive Aussagekraft durch das Einbeziehen einfach messbarer Gangparameter deutlich gesteigert wird.

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Conflicts of interest

The corresponding author states that there are no conflicts of interest.

Acknowledgements

The authors thank the staff of the physiotherapy department of the Braunschweig Medical Center, and especially Cornelia Kuehling, for the invaluable support in conducting the study. Furthermore, we thank Wolfram Ludwig for his support in the clinical measurements.

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Authors and Affiliations

  1. Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig , Institute of Technology and Hannover Medical School, Muehlenpfordtstraße 23, 38106 , Braunschweig, Deutschland

    M. Marschollek MD MSc, M. Gietzelt , K.H. Wolf  & R. Haux

  2. Department for Geriatric Medicine, Braunschweig Medical Center , Braunschweig, Deutschland

    G. Nemitz  & H. Meyer zu Schwabedissen

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  1. M. Marschollek MD MSc
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  2. G. Nemitz
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  3. M. Gietzelt
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  4. K.H. Wolf
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  5. H. Meyer zu Schwabedissen
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  6. R. Haux
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Correspondence to M. Marschollek MD MSc.

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Marschollek , M., Nemitz , G., Gietzelt , M. et al. Predicting in-patient falls in a geriatric clinic. Z Gerontol Geriat 42, 317–322 (2009). https://doi.org/10.1007/s00391-009-0035-7

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  • DOI: https://doi.org/10.1007/s00391-009-0035-7

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