Project Title

Comparative Analysis of EMR Fall Risk Calculator to Functional Impairments

Authors' Affiliations

Nitin Joshi, Department of Pharmaceutical Sciences, College of Pharmacy, East Tennessee State University, Johnson City, TN Nomathamsanqa Mgutshini, Department of Pharmaceutical Sciences, College of Pharmacy, East Tennessee State University, Johnson City, TN Regan Bell, Department of Pharmaceutical Sciences, College of Pharmacy, East Tennessee State University, Johnson City, TN Peter Panus, Department of Pharmaceutical Sciences, College of Pharmacy, East Tennessee State University, Johnson City, TN

Faculty Sponsor’s Department

Pharmaceutical Sciences

Type

Oral Competitive

Classification of First Author

Pharmacy Student

Project's Category

Healthcare and Medicine

Abstract Text

Introduction: The Centers for Disease Control and Prevention found that each year over three million people are treated for fall injuries, and of those three million, one in five falls causes serious injury. One clinical report stated only 37% of elderly patients are asked about falls in the primary care setting. The report found barriers to further fall-related care were due to the many factors that go into assessing if a patient is a fall-risk. Thus, assessing the fall risk for a large elderly population can be both challenging and time-consuming. The purpose of this study is to evaluate the effectiveness of assessing fall risk with the Theoretical Timed Up and Go (T-TUG), using Wave 1 of the Irish Longitudinal Database (TILDA). The validation was done by comparing the T-TUG results to that of the Impairments survey and activities of daily living (ADLs) found in the TILDA.

Methods: The data used in this study were obtained from Wave 1 of the TILDA. The TILDA is a survey-designed longitudinal study on aging done on a national scale in Ireland. Study participants who passed inclusion criteria were divided into those who had reported falling in the previous year (N=1221) and those who had not (N=4857). The T-TUG is a fall-risk calculator developed from the NSHAP database, with a multiple regression function using the Timed Up and Go as the dependent variable, and age, gender, body mass index, and over the counter and prescription drugs as the predictor variables. The NSHAP regression coefficients were combined with the TILDA participant parameters defined above to calculate new T-TUG scores for the TILDA cohort. Differentiation between the fall and no fall groups for the T-TUG, ADLs and Impairments survey were done using the Mann-Whitney U Test (p < 0.05). Receiver Operator Characteristics (ROC) curve analyses were done to identify cut-off points, sensitivities, and specificities differentiating the fall and no fall groups for these assessments.

Results: Mann-Whitney analysis demonstrated that the fall group scores were statistically different from the no fall group for all three assessments (p-value < 0.001). As determined by AUC, the ROC analysis indicated that the T-TUG (AUC=0.570, p

Conclusion: All assessments evaluated were effective at differentiating participants within this database reporting a fall within the last year from those who had not. Whereas the T-TUG and Impairments survey were equally effective at detecting true fallers and non-fallers, the ADLs were much more effective at detecting non-fallers. The T-TUG has the potential to be an EMR based fall risk calculator and could be invaluable as an institutional triage tool.

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Comparative Analysis of EMR Fall Risk Calculator to Functional Impairments

Introduction: The Centers for Disease Control and Prevention found that each year over three million people are treated for fall injuries, and of those three million, one in five falls causes serious injury. One clinical report stated only 37% of elderly patients are asked about falls in the primary care setting. The report found barriers to further fall-related care were due to the many factors that go into assessing if a patient is a fall-risk. Thus, assessing the fall risk for a large elderly population can be both challenging and time-consuming. The purpose of this study is to evaluate the effectiveness of assessing fall risk with the Theoretical Timed Up and Go (T-TUG), using Wave 1 of the Irish Longitudinal Database (TILDA). The validation was done by comparing the T-TUG results to that of the Impairments survey and activities of daily living (ADLs) found in the TILDA.

Methods: The data used in this study were obtained from Wave 1 of the TILDA. The TILDA is a survey-designed longitudinal study on aging done on a national scale in Ireland. Study participants who passed inclusion criteria were divided into those who had reported falling in the previous year (N=1221) and those who had not (N=4857). The T-TUG is a fall-risk calculator developed from the NSHAP database, with a multiple regression function using the Timed Up and Go as the dependent variable, and age, gender, body mass index, and over the counter and prescription drugs as the predictor variables. The NSHAP regression coefficients were combined with the TILDA participant parameters defined above to calculate new T-TUG scores for the TILDA cohort. Differentiation between the fall and no fall groups for the T-TUG, ADLs and Impairments survey were done using the Mann-Whitney U Test (p < 0.05). Receiver Operator Characteristics (ROC) curve analyses were done to identify cut-off points, sensitivities, and specificities differentiating the fall and no fall groups for these assessments.

Results: Mann-Whitney analysis demonstrated that the fall group scores were statistically different from the no fall group for all three assessments (p-value < 0.001). As determined by AUC, the ROC analysis indicated that the T-TUG (AUC=0.570, p

Conclusion: All assessments evaluated were effective at differentiating participants within this database reporting a fall within the last year from those who had not. Whereas the T-TUG and Impairments survey were equally effective at detecting true fallers and non-fallers, the ADLs were much more effective at detecting non-fallers. The T-TUG has the potential to be an EMR based fall risk calculator and could be invaluable as an institutional triage tool.

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