Development and Validation of a Simplified Falls Assessment Tool in an Acute Care Setting

Yip, Wai Kin; Mordiffi, Siti Zubaidah; Wong, Hung Chew; Ang, Emily

doi:10.1097/ncq.0000000000000183

Cited by 9 publications

(9 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The predictive power of the HIIFRM found is this study was moderate, with AUC approaching 0.8 and similar to those obtained in studies on both general [ 6 , 17 ] and older acute care settings [ 18 , 29 ]. Interestingly, the performance of the HIIFRM resulted similar to that obtained by patient-specific tools assessed by the recent literature [ 5 , 6 , 30 , 31 ]. Along with predictive power, the identification of a tool with good specificity is of major importance to plan both screening procedures and sustainable prevention pathways.…”

Section: Discussionsupporting

confidence: 87%

“…The possibility of extending the use of HIIFRM to the rehabilitative settings too, would allow utilizing a unique fall risk assessment tool across all wards with high fall occurrence. This would be easy to implement in hospitals and should enhance compliance of nurses and other professionals [ 6 ].…”

Section: Discussionmentioning

confidence: 99%

“…Patients participating in rehabilitation may experience falls, as they are being encouraged to be more independent and mobile and may over-stimulate their balance systems [ 4 ]. The topic of fall prevention has been emerging in recent literature on neurological patients [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…Various clinical tools for the identification of subjects at risk of falling have been published in the past two decades, mainly suited for use in geriatric wards, such as the Tinetti Performance Oriented Mobility Assessment, the Morse Fall Scale, the Berg Balance Scale, the St Thomas Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY), the Conley scale and the Hendrich Fall Risk Model II (HIIFRM) [ 8 – 15 ]. Recently, there has been a spate of interest in falls risk assessment tools specific for stroke patients, such as the Stroke Assessment Fall Risk [ 5 ] and the 4-Item Falls Assessment Tool [ 6 ]. Nonetheless, no specific tools for a whole rehabilitation department have been provided.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Feasibility and predictive performance of the Hendrich Fall Risk Model II in a rehabilitation department: a prospective study

Campanini

Bargellini

Bassoli

et al. 2018

BMC Health Serv Res

View full text Add to dashboard Cite

BackgroundFalls 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.MethodsA 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).ResultsOne 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.ConclusionsThe 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.

show abstract

Section: Discussionsupporting

confidence: 87%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Feasibility and predictive performance of the Hendrich Fall Risk Model II in a rehabilitation department: a prospective study

Campanini

Bargellini

Bassoli

et al. 2018

BMC Health Serv Res

View full text Add to dashboard Cite

show abstract

“…Likewise, the number of abnormal laboratory results (labs) indicate the severity of patients’ health status, which influences how fast they will recover after surgery. Fall risk is measured by assessing the patient’s level of confusion, dizziness, altered elimination, and difficulty with mobility at admission [ 30 , 31 ]. Preoperative fall risks have been found to predict postoperative falls, functional decline, and surgical complications, all of which prolonged LOS [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

Factors associated with prolonged length of stay for elective hepatobiliary and neurosurgery patients: a retrospective medical record review

Lee

Tan

et al. 2018

BMC Health Serv Res

Self Cite

View full text Add to dashboard Cite

BackgroundPatients with prolonged length of hospital stay (LOS) not only increase their risks of nosocomial infections but also deny other patients access to inpatient care. Hepatobiliary (HPB) malignancies have some of highest incidences in East and Southeast Asia and the management of patients undergoing HPB surgeries have yet to be standardized. With improved neurosurgery techniques for intracranial aneurysms and tumors, neurosurgeries (NS) can be expected to increase. Elective surgeries account for far more operations than emergencies surgeries. Thus, with potentially increased numbers of elective HPB and NS, this study seeks to explore perioperative factors associated with prolonged LOS for these patients to improve safety and quality of practice.MethodsA retrospective cross-sectional medical record review study from January 2014 to January 2015 was conducted at a 1250-bed tertiary academic hospital in Singapore. All elective HPB and NS patients over 18 years old were included in the study except day and emergency surgeries, resulting in 150 and 166 patients respectively. Prolonged LOS was defined as above median LOS based on the complexity of the surgical procedure. The predictor variables were preoperative, intraoperative, and postoperative factors. Student’s t-test and stepwise logistic regression analyses were conducted to determine which factors were associated with prolonged LOS.ResultsFactors associated with prolonged LOS for the HPB sample were age and admission after 5 pm but for the NS sample, they were functional status, referral to occupational therapy, and the number of hospital-acquired infections.ConclusionOur findings indicate that preoperative factors had the greatest association with prolonged LOS for HPB and NS elective surgeries even after adjusting for surgical complexity, suggesting that patient safety and quality of care may be improved with better pre-surgery patient preparation and admission practices.Electronic supplementary materialThe online version of this article (10.1186/s12913-017-2817-8) contains supplementary material, which is available to authorized users.

show abstract

References

2021

Using Predictive Analytics to Improve Healthcare Outcomes

View full text Add to dashboard Cite

Development and Validation of a Simplified Falls Assessment Tool in an Acute Care Setting

Cited by 9 publications

References 28 publications

Feasibility and predictive performance of the Hendrich Fall Risk Model II in a rehabilitation department: a prospective study

Feasibility and predictive performance of the Hendrich Fall Risk Model II in a rehabilitation department: a prospective study

Factors associated with prolonged length of stay for elective hepatobiliary and neurosurgery patients: a retrospective medical record review

References

Contact Info

Product

Resources

About