Hospital falls: development of a predictive model for clinical practice

Hendrich, Ann; Nyhuis, Allen W.; Kippenbrock, Thomas; Soja, Mary E.

doi:10.1016/s0897-1897(95)80592-3

Cited by 202 publications

(183 citation statements)

References 10 publications

Supporting

Mentioning

166

Contrasting

Unclassified

Order By: Relevance

“…Over 15 scales attempt to identify patients at risk of falling. [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] The majority of these tools have not been validated, include only 1 population, or lack adequate sensitivity or specificity for clinical use. Three of these fall risk tools have been validated in multiple studies across the populations.…”

Section: Risk Stratificationmentioning

confidence: 99%

“…23 Common themes that indicate increased risk of patient falls between these 3 tools, and others, include assessment for previous falls or admission secondary to falling, presence of mental confusion or agitation, necessity for frequent toileting, and altered gait or mobility. 9,13,14,19,21,[25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41] One of the most common populations studied and cited as high risk of falls is the older adult (!65). 8,16,17,[27][28][29][30][42][43][44][45][46][47][48] Medications including benzodiazepines, anticonvulsants, antihypertensives, antidepressants, and sleep aids have also been targeted as high-risk medications.…”

Section: Risk Stratificationmentioning

confidence: 99%

See 1 more Smart Citation

Inpatient Falls

Cumbler

Simpson

Rosenthal

et al. 2013

The Neurohospitalist

View full text Add to dashboard Cite

In this 2 part series, analysis of the risk stratification tools that are available, definition for the scope of the problem, and potential solutions through a review of the literature are presented. A systematic review was used to identify articles for risk stratification and interventions. Three risk stratification systems are discussed, St Thomas's Risk Assessment Tool in Falling Elderly Inpatients, Morse Fall Scale, and the Hendrich Fall Risk Model. Of these scoring systems, the Hendrich Fall Risk Model is the easiest to use and score. Predominantly, multifactorial interventions are used to prevent patient falls. Education and rehabilitation are common themes in studies with statistically significant results. The second article presents a guide to implementing a quality improvement project around hospital falls. A 10-step approach to Plan-Do-Study-Act (PDSA) cycles is described. Specific examples of problems and analysis are easily applicable to any institution. Furthermore, the sustainability of interventions and targeting new areas for improvement is discussed. Although specific to falls in the hospitalized patient, the goal is to present a stepwise approach which is broadly applicable to other areas requiring quality improvement.

show abstract

Section: Risk Stratificationmentioning

confidence: 99%

Section: Risk Stratificationmentioning

confidence: 99%

Inpatient Falls

Cumbler

Simpson

Rosenthal

et al. 2013

The Neurohospitalist

View full text Add to dashboard Cite

show abstract

“…[2][3][4][5][6][7] Inpatient falls remain a main focus of patient safety and a measure of quality in this era of healthcare reform and quality improvement. 8 Inpatient fall rates per 1000 patientdays range from 1.4 to 18.2.…”

mentioning

confidence: 99%

Meta‐analysis: Multidisciplinary fall prevention strategies in the acute care inpatient population

DiBardino

Cohen

Didwania

2012

Journal of Hospital Medicine

View full text Add to dashboard Cite

BACKGROUND:Inpatient falls are common adverse events that lead to inpatient injury, increased length of stay, healthcare costs, litigation, and are a focus of patient safety and healthcare quality. Fall prevention methods are currently evolving to address the problem.PURPOSE:To examine the available data evaluating multidisciplinary fall prevention strategies in the acute inpatient setting.DATA SOURCES:A complete literature search of MEDLINE, CINAHL, EMBASE and the Cochrane Library through December 2011 was used. The bibliographies of all systematic reviews and meta‐analyses were hand searched.STUDY SELECTION:Only primary research studies relating to acute care inpatient hospital multidisciplinary fall prevention were included. Selected papers were assessed for quality by 2 authors using a 20‐point scale previously used in the fall literature.DATA EXTRACTION:Each selected study was carefully hand searched by 2 authors for the purposes of data extraction. Study results, in fall rate per 1000‐patient days, and the characteristics of the interventions used were extracted for analysis.DATA SYNTHESIS:Effect sizes (odds ratios) and 95% confidence intervals were derived for individual studies and then combined across research reports using a random‐effects meta‐analysis.CONCLUSIONS:Fall prevention strategies have a significant but small effect on fall rates despite the use of complex, multidisciplinary interventions. Additional randomized trials are needed to examine the possible benefits of multidisciplinary fall prevention strategies in the acute inpatient setting. Journal of Hospital Medicine 2012; © 2012 Society of Hospital Medicine

show abstract

“…Previous reports were screened to extract factors that have been associated with the occurrence of significant findings on CT brain scanning in patients with a history of suspected head trauma [5][6][7][8]. A total of 12 potential factors were identified: falls prior to the index fall; atrial fibrillation; signs of head, face or scalp trauma; new focal neurological signs; taking warfarin; international normalised ratio (INR) above therapeutic range, antecedent dementia, Glasgow coma score (GCS) ,14, history of alcohol abuse, vomiting, headache and previous stroke.…”

Section: Methodsmentioning

confidence: 99%

Development of a risk score to guide brain imaging in older patients admitted with falls and confusion

Brown¹,

Witham²,

George³

2011

BJR

View full text Add to dashboard Cite

Objectives: CT scanning of the brain is commonly performed in older people admitted to hospital with a fall, but the yield of positive findings is low. We used audit data to develop a risk-stratification score to guide more efficient use of CT scanning. Methods: 12 potential predictors of positive CT findings were derived from a literature review. Case notes of consecutive patients presenting with falls and confusion who had undergone brain imaging were reviewed as part of an ongoing audit. Correlation of each factor with positive CT findings was undertaken and a final risk score was developed. Receiver-operating characteristic analysis was undertaken, an optimum cut-off identified, and positive and negative predictive values were calculated. Results: 66 patients with a mean age of 74.8 years were included. 13 of the 66 (20%) brain imaging studies revealed a new pathology. Previous history of falls, atrial fibrillation, head or face trauma, focal neurological signs, warfarin use and a Glasgow coma score of ,14 were significant univariate positive predictors. Antecedent dementia was included as a negative predictor. The final weighted score (range -1 to 8 points) gave an area under the curve of 0.83 (95% confidence interval 0.70 to 0.96, p,0.001). When using a cut-off of 3 points, sensitivity for significant new pathology on brain imaging was 83%, specificity was 89%, positive predictive value was 63% and negative predictive value was 96%. Conclusion: A simple weighted risk score may be able to guide the need for brain imaging in older people presenting to hospital with falls. The score requires validation in a larger, prospectively collected cohort. Patients who fall and present to medical admissions units with confusion pose a diagnostic problem because of their inability to give a coherent history. A pre-existing diagnosis of dementia often further hampers the clinical assessment; because of these reasons, older people with falls and confusion often under go CT of the head. The diagnostic yield of these investigations is often low. A study of 294 patients with acute confusion found a diagnostic yield of only 14% if clinical suspicion was the sole reason for referral [1]. A large number of these investigations could therefore, in theory, be avoided, enabling better use of resources, reducing healthcare costs and minimising patient exposure to unnecessary radiation. To better target the use of brain imaging in this patient group, a risk scoring system is required. Such scoring systems have been developed for use in general [2] and paediatric patients [3] presenting with head trauma, but old age is usually included as part of the indication for scanning. As such, existing risk scoring systems may lack discrimination when used in older patients. Pre-existing falls risk scoring systems that are widely used, such as the Tinetti score [4], are mainly designed to calculate risk of falls rather than to help preselect patients that might have significant intracranial pathology as a cause (or result) of their fall.We the...

show abstract

Hospital falls: development of a predictive model for clinical practice

Cited by 202 publications

References 10 publications

Inpatient Falls

Inpatient Falls

Meta‐analysis: Multidisciplinary fall prevention strategies in the acute care inpatient population

Development of a risk score to guide brain imaging in older patients admitted with falls and confusion

Contact Info

Product

Resources

About