Validity of Tools for Assessing the Risk of Falls in Patients

Majkusová, Kamila; Jarošová, Darja

doi:10.15452/cejnm.2017.08.0021

Cited by 5 publications

(5 citation statements)

References 31 publications

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“…Falls occur as a consequence of a constellation of underlying multiple factors and accurate quantification of every factor is a tenuous and challenging endeavor. This was previously also highlighted in a review article by Majkusova et al 51 . For example factors like "unsteadiness" or "dizziness" are inherently subjective; thus, evaluation of these factors varies greatly among different tools.…”

Section: Discussionsupporting

confidence: 60%

Fall risk question-based tools for fall screening in community-dwelling older adults: a systematic review of the literature

Argyrou,

Dionyssiotis,

Galanos

et al. 2023

JFSF

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Section: Discussionsupporting

confidence: 60%

Fall risk question-based tools for fall screening in community-dwelling older adults: a systematic review of the literature

Argyrou,

Dionyssiotis,

Galanos

et al. 2023

JFSF

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“…The fall risk assessment is generally carried out by nurses during the initial assessment (Majkusová & Jarošová, 2017;Yazdani & Hall, 2017). Accurate identication of risk factors and risk status enables the care providers to implement targeted prevention strategies.…”

Section: Resultsmentioning

confidence: 99%

“…Majkusová & Jarošová, 2017;Murray, Vess, & Edlund, 2016). In medical-surgical settings, IV therapy and presence of secondary diagnosis are common among patients.…”

mentioning

confidence: 99%

Population- Specific Fall Risk Assessment Tools for Adult Inpatients: A Systemised Literature Review

Padmanabhan

Chandrasekaran

2020

ijsr

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Fall is one of the most commonly reported adverse events from the hospitals and around one-third of them result in injury. A carefully tailored fall reduction program begins with the identification of the “at-risk” population. Commonly used adult fall risk assessment tools do not take into consideration the risk factors of some of the vulnerable patient populations. This paper provides a systemised literature review of the need and availability of population-specific risk assessment tools. One of the most commonly used tools - Morse Fall Scale- does not assess the effect of certain medications and population-specific risk factors. The Cleveland Clinic – Capone- Albert (CC-CA) Fall Risk Score is a tool that is specifically developed for cancer patients. Similarly, Obstetric Fall Risk Assessment Tool (OFRAS) helps in identifying the fall risk factors in perinatal women. Usage of such population-specific tools help in focused identification of risks, distinct implementation of interventions and thus, results in reducing the incidents of falls and injuries thereof.

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“…Traditional models overlook other significant fall risk factors that we identified in our ML models, such as diastolic blood pressure and respiratory rate, which are measurements easily obtained from EHR data without interrupting the clinical workflow. The sensitivity of these tools is inconsistent across the literature, ranging from 33.33% to 95% [ 58 ]. Using the MDS data set, the study by Marier et al [ 53 ] examined the use of MDS in tandem with EHR data, as the latter incorporates more frequent clinical measurements that may indicate changes in an individual’s health status, thus potentially providing improved risk assessment [ 51 ].…”

Section: Discussionmentioning

confidence: 99%

Predicting Falls in Long-term Care Facilities: Machine Learning Study

Thapa¹,

Garikipati²,

Shokouhi³

et al. 2022

JMIR Aging

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Background Short-term fall prediction models that use electronic health records (EHRs) may enable the implementation of dynamic care practices that specifically address changes in individualized fall risk within senior care facilities. Objective The aim of this study is to implement machine learning (ML) algorithms that use EHR data to predict a 3-month fall risk in residents from a variety of senior care facilities providing different levels of care. Methods This retrospective study obtained EHR data (2007-2021) from Juniper Communities’ proprietary database of 2785 individuals primarily residing in skilled nursing facilities, independent living facilities, and assisted living facilities across the United States. We assessed the performance of 3 ML-based fall prediction models and the Juniper Communities’ fall risk assessment. Additional analyses were conducted to examine how changes in the input features, training data sets, and prediction windows affected the performance of these models. Results The Extreme Gradient Boosting model exhibited the highest performance, with an area under the receiver operating characteristic curve of 0.846 (95% CI 0.794-0.894), specificity of 0.848, diagnostic odds ratio of 13.40, and sensitivity of 0.706, while achieving the best trade-off in balancing true positive and negative rates. The number of active medications was the most significant feature associated with fall risk, followed by a resident’s number of active diseases and several variables associated with vital signs, including diastolic blood pressure and changes in weight and respiratory rates. The combination of vital signs with traditional risk factors as input features achieved higher prediction accuracy than using either group of features alone. Conclusions This study shows that the Extreme Gradient Boosting technique can use a large number of features from EHR data to make short-term fall predictions with a better performance than that of conventional fall risk assessments and other ML models. The integration of routinely collected EHR data, particularly vital signs, into fall prediction models may generate more accurate fall risk surveillance than models without vital signs. Our data support the use of ML models for dynamic, cost-effective, and automated fall predictions in different types of senior care facilities.

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Validity of Tools for Assessing the Risk of Falls in Patients

Cited by 5 publications

References 31 publications

Fall risk question-based tools for fall screening in community-dwelling older adults: a systematic review of the literature

Fall risk question-based tools for fall screening in community-dwelling older adults: a systematic review of the literature

Population- Specific Fall Risk Assessment Tools for Adult Inpatients: A Systemised Literature Review

Predicting Falls in Long-term Care Facilities: Machine Learning Study

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