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Feasibility and predictive performance of the Hendrich Fall Risk Model II in a rehabilitation department: a
Isabella Campanini1,2, Stefano Mastrangelo3, Annalisa Bargellini4
1Department of Rehabilitation, LAM-Motion Analysis Laboratory, AUSL of Reggio Emilia, S. Sebastiano Hospital, Via Mandriolo Superiore 11, 42015, Correggio, RE, Italy. isabella.campanini@ausl.re.it.
Background:
Falls are a common adverse event in both elderly inpatients and patients admitted to rehabilitation units. The Hendrich Fall Risk Model II (HIIFRM) has been already tested in all hospital wards with high fall rates, with the exception of the rehabilitation setting. This study's aim is to address the feasibility and predictive performances of HIIFRM in a hospital rehabilitation department.
Methods:
A 6 months prospective study in a Italian rehabilitation department with patients from orthopaedic, pulmonary, and neurological rehabilitation wards. All admitted patients were enrolled and assessed within 24 h of admission by means of the HIIFRM. The occurrence of falls was checked and recorded daily. HIIFRM feasibility was assessed as the percentage of successful administrations at admission. HIIFRM predictive performance was determined in terms of area under the Receiver Operating Characteristic (ROC) curve (AUC), best cutoff, sensitivity, specificity, positive and negative predictive values, along with their asymptotic 95% confidence intervals (95% CI).
Results:
One hundred ninety-one patents were admitted. HIIFRM was feasible in 147 cases (77%), 11 of which suffered a fall (7.5%). Failures in administration were mainly due to bedridden patients (e.g. minimally conscious state, vegetative state). AUC was 0.779(0.685-0.873). The original HIIFRM cutoff of 5 led to a sensitivity of 100% with a mere specificity of 49%(40-57%), thus suggesting using higher cutoffs. Moreover, the median score for non-fallers at rehabilitation units was higher than that reported in literature for geriatric non fallers. The best trade-off between sensitivity and specificity was obtained by using a cutoff of 8. This lead to sensitivity = 73%(46-99%), specificity = 72%(65-80%), positive predictive value = 17% and negative predictive value = 97%. These results support the use of the HIIFRM as a predictive tool.
Conclusions:
The HIIFRM showed satisfactory feasibility and predictive performances in rehabilitation wards. Based on both available literature and these results, the prediction of falls among all hospital wards, with high risk of falling, could be achieved by means of a unique tool and two different cutoffs: a standard cutoff of 5 in geriatric wards and an adjusted higher cutoff in rehabilitation units, with predictive performances similar to those of the best-preforming pathology specific tools for fall-risk assessment.
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