Ann L Hendrich1, Angelo Bufalino2, Clariecia Groves3. 1. Ascension, St. Louis, MO, United States; Building Age-Friendly Healthcare Systems, The John A. Hartford Foundation, United States. Electronic address: alhendrich@ahiofindiana.com. 2. Clinical Quality & Advanced Analytics, Ascension Data Sciences Institute, Ascension, St. Louis, MO, United States. 3. Ascension, St. Louis, MO, United States.
Abstract
AIM: To validate the psychometrics of the Hendrich II Fall Risk Model (HIIFRM) and identify the prevalence of intrinsic fall risk factors in a diverse, multisite population. BACKGROUND: Injurious inpatient falls are common events, and hospitals have implemented programs to achieve "zero" inpatient falls. METHODS: Retrospective analysis of patient data from electronic health records at nine hospitals that are part of Ascension. Participants were adult inpatients (N = 214,358) consecutively admitted to the study hospitals from January 2016 through December 2018. Fall risk was assessed using the HIIFRM on admission and one time or more per nursing shift. RESULTS: Overall fall rate was 0.29%. At the standard threshold of HIIFRM score ≥ 5, 492 falls and 76,800 non-falls were identified (fall rate 0.36%; HIIFRM specificity 64.07%, sensitivity 78.72%). Area under the receiver operating characteristic curve was 0.765 (standard error 0.008; 95% confidence interval 0.748, 0.781; p < 0.001), indicating moderate accuracy of the HIIFRM to predict falls. At a lower cut-off score of ≥4, an additional 74 falls could have been identified, with an improvement in sensitivity (90.56%) and reduction in specificity (44.43%). CONCLUSION: Analysis of this very large inpatient sample confirmed the strong psychometric characteristics of the HIIFRM. The study also identified a large number of inpatients with multiple fall risk factors (n = 77,292), which are typically not actively managed during hospitalization, leaving patients at risk in the hospital and after discharge. This finding represents an opportunity to reduce injurious falls through the active management of modifiable risk factors.
AIM: To validate the psychometrics of the Hendrich II Fall Risk Model (HIIFRM) and identify the prevalence of intrinsic fall risk factors in a diverse, multisite population. BACKGROUND: Injurious inpatient falls are common events, and hospitals have implemented programs to achieve "zero" inpatient falls. METHODS: Retrospective analysis of patient data from electronic health records at nine hospitals that are part of Ascension. Participants were adult inpatients (N = 214,358) consecutively admitted to the study hospitals from January 2016 through December 2018. Fall risk was assessed using the HIIFRM on admission and one time or more per nursing shift. RESULTS: Overall fall rate was 0.29%. At the standard threshold of HIIFRM score ≥ 5, 492 falls and 76,800 non-falls were identified (fall rate 0.36%; HIIFRM specificity 64.07%, sensitivity 78.72%). Area under the receiver operating characteristic curve was 0.765 (standard error 0.008; 95% confidence interval 0.748, 0.781; p < 0.001), indicating moderate accuracy of the HIIFRM to predict falls. At a lower cut-off score of ≥4, an additional 74 falls could have been identified, with an improvement in sensitivity (90.56%) and reduction in specificity (44.43%). CONCLUSION: Analysis of this very large inpatient sample confirmed the strong psychometric characteristics of the HIIFRM. The study also identified a large number of inpatients with multiple fall risk factors (n = 77,292), which are typically not actively managed during hospitalization, leaving patients at risk in the hospital and after discharge. This finding represents an opportunity to reduce injurious falls through the active management of modifiable risk factors.
Authors: Isabella Campanini; Annalisa Bargellini; Stefano Mastrangelo; Francesco Lombardi; Stefano Tolomelli; Mirco Lusuardi; Andrea Merlo Journal: Int J Environ Res Public Health Date: 2021-02-04 Impact factor: 3.390
Authors: Nasir Wabe; Joyce Siette; Karla L Seaman; Amy D Nguyen; Magdalena Z Raban; Jacqueline C T Close; Stephen R Lord; Johanna I Westbrook Journal: BMC Geriatr Date: 2022-04-01 Impact factor: 3.921