scholarly journals POPULATION- SPECIFIC FALL RISK ASSESSMENT TOOLS FOR ADULT INPATIENTS: A SYSTEMISED LITERATURE REVIEW

2020 ◽  
pp. 62-63
Author(s):  
Priya Padmanabhan ◽  
Salumon Chandrasekaran

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.

Author(s):  
Insook Cho ◽  
Eun-Hee Boo ◽  
Eunja Chung ◽  
David W. Bates ◽  
Patricia Dykes

BACKGROUND Electronic medical records (EMRs) contain a considerable amount of information about patients. The rapid adoption of EMRs and the integration of nursing data into clinical repositories have made large quantities of clinical data available for both clinical practice and research. OBJECTIVE In this study, we aimed to investigate whether readily available longitudinal EMR data including nursing records could be utilized to compute the risk of inpatient falls and to assess their accuracy compared with existing fall risk assessment tools. METHODS We used 2 study cohorts from 2 tertiary hospitals, located near Seoul, South Korea, with different EMR systems. The modeling cohort included 14,307 admissions (122,179 hospital days), and the validation cohort comprised 21,172 admissions (175,592 hospital days) from each of 6 nursing units. A probabilistic Bayesian network model was used, and patient data were divided into windows with a length of 24 hours. In addition, data on existing fall risk assessment tools, nursing processes, Korean Patient Classification System groups, and medications and administration data were used as model parameters. Model evaluation metrics were averaged using 10-fold cross-validation. RESULTS The initial model showed an error rate of 11.7% and a spherical payoff of 0.91 with a c-statistic of 0.96, which represent far superior performance compared with that for the existing fall risk assessment tool (c-statistic=0.69). The cross-site validation revealed an error rate of 4.87% and a spherical payoff of 0.96 with a c-statistic of 0.99 compared with a c-statistic of 0.65 for the existing fall risk assessment tool. The calibration curves for the model displayed more reliable results than those for the fall risk assessment tools alone. In addition, nursing intervention data showed potential contributions to reducing the variance in the fall rate as did the risk factors of individual patients. CONCLUSIONS A risk prediction model that considers longitudinal EMR data including nursing interventions can improve the ability to identify individual patients likely to fall.


2021 ◽  
Vol 11 (2) ◽  
pp. 430-443
Author(s):  
Veronica Strini ◽  
Roberta Schiavolin ◽  
Angela Prendin

Background: Falls are recognized globally as a major public health problem. Although the elderly are the most affected population, it should be noted that the pediatric population is also very susceptible to the risk of falling. The fall risk approach is the assessment tool. There are different types of tools used in both clinical and territorial settings. Material and methods: In the month of January 2021, a literature search was undertaken of MEDLINE, CINHAL and The Cochrane Database, adopting as limits: last 10 years, abstract available, and English and Italian language. The search terms used were “Accidental Falls” AND “Risk Assessment” and “Fall Risk Assessment Tool” or “Fall Risk Assessment Tools”. Results: From the 115 selected articles, 38 different fall risk assessment tools were identified, divided into two groups: the first with the main tools present in the literature, and the second represented by tools of some specific areas, of lesser use and with less supporting literature. Most of these articles are prospective cohort or cross-sectional studies. All articles focus on presenting, creating or validating fall risk assessment tools. Conclusion: Due to the multidimensional nature of falling risk, there is no “ideal” tool that can be used in any context or that performs a perfect risk assessment. For this reason, a simultaneous application of multiple tools is recommended, and a direct and in-depth analysis by the healthcare professional is essential.


Author(s):  
Indri Hapsari Susilowati ◽  
Susiana Nugraha ◽  
Sabarinah Sabarinah ◽  
Bonardo Prayogo Hasiholan ◽  
Supa Pengpid ◽  
...  

Introduction: One of the causes of disability among elderly is falling. The ability to predict the risk of falls among this group is important so that the appropriate treatment can be provided to reduce the risk. The objective of this study was to compare the Stopping Elderly Accidents, Deaths, & Injuries (STEADI) Initiative from the Centers for Disease Control and Prevention (CDC) and The Johns Hopkins Fall Risk Assessment Tool (JHFRAT) from the Johns Hopkins University. Methods: This study used the STEADI tool, JHFRAT, Activities-Specific Balance Confidence Scale (ABC), and The Geriatric Depression Scale (GDS). The study areas were in community and elderly home in both public and private sectors and the samples were 427 after cleaning. Results: The results for the STEADI and JHFRAT tools were similar where the respondents at highest risk of falling among women (STEADI: 49%; JHFRAT: 3.4%), in Bandung area (63.5%; 5.4%), in private homes (63.3%; 4.4%), non-schools (54.6%; 6.2%), aged 80 or older (64.8%; 6.7%) and not working (48.9%;3.3%). The regression analysis indicated that there was a significant relationship between the risk factors for falls in the elderly determined by the JHFRAT and STEADI tools: namely, region, type of home, age, disease history, total GDS and ABC averages. Conclusion: Despite the similarity in the risk factors obtained through these assessments, there was a significant difference between the results for the STEADI tool and the JHFRAT. The test strength was 43%. However, STEADI is more sensitive to detect fall risk smong elderly than JHFRATKeywords: Activities-Specific Balance Confidence scale, elderly, fall risk,The Johns Hopkins Fall Risk Assessment Tool, the Stopping Elderly Accidents, Deaths, & Injuries


2021 ◽  
Author(s):  
Kesetebirhan Delele Yirdaw ◽  
Justin Mandala

Abstract Background There are a number of risk factors being used to identify undiagnosed HIV infected adults. As the number of undiagnosed people gets lesser and lesser, it is important to know if existing risk factors and risk assessment tools are valid for use. In this study, we validate existing HIV risk assessment tools and see if they are worth using for HIV case finding among adults who remain undiagnosed. Methods The Tanzania and Zambia Population-Based HIV Impact Assessment (PHIA) household surveys were conducted during 2016. We used adult interview and HIV datasets to assess validity of different HIV risk assessment tools. We first included 12 risk factors (being divorced, separated or widowed (DSW); having an HIV+ spouse; having one of the following within 12 months of the survey: paid work, slept away from home for at least a month, had multiple sexual partners, paid for sex, had sexually transmitted infection (STI), being a tuberculosis (TB) suspect, being very sick for at least 3 months; had ever sold sex; diagnosed with cervical cancer; and had TB disease into a risk assessment tool and assessed its validity by comparing it against HIV test result. Sensitivity, specificity and predictive value of the tool were assessed against the HIV test result. A receiver operator characteristic (ROC) analysis was conducted to determine a suitable cut-off score in order to have a tool with better sensitivity, specificity, and PPV. ROC comparison statistics was used to statistically test equality between AUC (area under the curve) of the different scores. ROC comparison statistics was also used to determine which risk assessment tool was better compared to the tool that contained all risk factors. Results Of 14,820 study participants, 57.8% were men, and had a median age of 30 (IQR: 21-24). HIV prevalence was 2.3% (95% confidence interval (CI): 2.0-2.6). For the tool containing all risk factors, HIV prevalence was 1.0% when none of the risk factors were positive (Score 0) compared to 3.2% when at least one factor (Score ≥1) was present and 8.0% when ≥4 risk factors were present. Sensitivity, specificity, PPV, and NPV were 82.3% (78.6%-85.9%), 41.9% (41.1%-42.7%), 3.2% (2.8%-3.6%), and 99.0% (98.8%-99.3%), respectively. The use of a tool containing conventional risk factors (all except those related with working and sleeping away) was found to have higher AUC compared to the use of all risk factors (p value <0.001), with corresponding sensitivity, specificity, PPV, and NPV of 63.5% (58.9%-68.1%), 66.2% (65.5%-67.0%), 4.2% (3.6%-4.8%), and 98.7% (98.5%-98.9%), respectively. Conclusion Use of a screening tool containing conventional risk factors improved HIV testing yield compared to doing universal testing. Prioritizing people who fulfil multiple risk factors should be explored further to improve HIV testing yield.


2019 ◽  
Vol 32 (7) ◽  
pp. 1279-1287 ◽  
Author(s):  
Nermien Naim Adly ◽  
Wafaa Mostafa Abd-El-Gawad ◽  
Rania Mohammed Abou-Hashem

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