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ABSTRACT: Objective
To assess the predictive value of common measures validated to predict falls in other geriatric populations in patients presenting with suspected Normal Pressure Hydrocephalus (NPH).Methods
One hundred ninety-five patients over the age of 60 who received the Fall Risk Questionnaire were retrospectively recruited from the CSF Disorders clinic within the departments of Neurosurgery and Neurology. Multiple logistic regression was used to create a model to predict falls for patients with suspected NPH using common measures: Timed Up & Go, Dual Timed Up & Go, 10 Meter Walk, MiniBESTest, 6-Minute Walk, Lower Extremity Function (Mobility), Fall Risk Questionnaire, and Functional Activities Questionnaire.Results
The Fall Risk Questionnaire and age were shown to be the best predictors of falls. The model was 95.92% (Positive predictive value: 83.93%) sensitive and 47.92% specific (Negative predictive value: 77.78%).Conclusion
Patients presenting with suspected NPH are at an increased fall risk, 75% of the total patients and 89% of patients who responded to a temporary drain of CSF had at least one fall in the past 6 months. The Fall Risk Questionnaire and age were shown to be predictive of falls for patients with suspected NPH. The preliminary evidence indicates measures that have been validated to assess fall risk in other populations may not be valid for patients presenting with suspected NPH.
SUBMITTER: Davis A
PROVIDER: S-EPMC7869204 | biostudies-literature | 2021 Feb
REPOSITORIES: biostudies-literature
Davis Alexander A Luciano Mark M Moghekar Abhay A Yasar Sevil S
BMC neurology 20210208 1
<h4>Objective</h4>To assess the predictive value of common measures validated to predict falls in other geriatric populations in patients presenting with suspected Normal Pressure Hydrocephalus (NPH).<h4>Methods</h4>One hundred ninety-five patients over the age of 60 who received the Fall Risk Questionnaire were retrospectively recruited from the CSF Disorders clinic within the departments of Neurosurgery and Neurology. Multiple logistic regression was used to create a model to predict falls for ...[more]