Health, Functional and Nutritional Determinants of Falls Experienced in the Previous Year-A Cross-Sectional Study in a Geriatric Ward

A novel multistep approach to standardize the reported risk factors for in-hospital falls: a proof-of-concept study

Background

Uncertainty and inconsistency in terminology regarding the risk factors (RFs) for in-hospital falls are present in the literature.

Objective

(1) To perform a literature review to identify the fall RFs among hospitalized adults; (2) to link the found RFs to the corresponding categories of international health classifications to reduce the heterogeneity of their definitions; (3) to perform a meta-analysis on the risk categories to identify the significant RFs; (4) to refine the final list of significant categories to avoid redundancies.

Methods

Four databases were investigated. We included observational studies assessing patients who had experienced in-hospital falls. Two independent reviewers performed the inclusion and extrapolation process and evaluated the methodological quality of the included studies. RFs were grouped into categories according to three health classifications (ICF, ICD-10, and ATC). Meta-analyses were performed to obtain an overall pooled odds ratio for each RF. Finally, protective RFs or redundant RFs across different classifications were excluded.

Results

Thirty-six articles were included in the meta-analysis. One thousand one hundred and eleven RFs were identified; 616 were linked to ICF classification, 450 to ICD-10, and 260 to ATC. The meta-analyses and subsequent refinement of the categories yielded 53 significant RFs. Overall, the initial number of RFs was reduced by about 21 times.

Conclusion

We identified 53 significant RF categories for in-hospital falls. These results provide proof of concept of the feasibility and validity of the proposed methodology. The list of significant RFs can be used as a template to build more accurate measurement instruments to predict in-hospital falls.

The risk of falls among the aging population: A systematic review and meta-analysis

Aim

This study aims to clarify the risk factors for falls to prevent severe consequences in older adults.

Methods

We searched the PubMed, Web of Science, Embase, and Google Scholar databases using the terms "risk factors" OR "predicting factors" OR "predictor" AND "fall" OR "drop" to identify all relevant studies and compare their results. The study participants were divided into two groups, the "fall group" and the "control group", and differences in demographic characteristics, lifestyles, and comorbidities were compared.

Results

We included 34 articles in the analysis and analyzed 22 factors. Older age, lower education level, polypharmacy, malnutrition, living alone, living in an urban area, smoking, and alcohol consumption increased the risk of falls in the aging population. Additionally, comorbidities such as cardiac disease, hypertension, diabetes, stroke, frailty, previous history of falls, depression, Parkinson's disease, and pain increased the risk of falls.

Conclusion

Demographic characteristics, comorbidities, and lifestyle factors can influence the risk of falls and should be taken into consideration.

Falls at the Geriatric Hospital Ward in the Context of Risk Factors of Falling Detected in a Comprehensive Geriatric Assessment

It is only by knowing the most common causes of falls in the hospital that appropriate and targeted fall prevention measures can be implemented. This study aimed to assess the frequency of falls in a hospital geriatrics ward and the circumstances in which they occurred and evaluate the parameters of the comprehensive geriatric assessment (CGA) correlating with falls. We considered medical, functional, and nutritional factors associated with falls and built multivariable logistic regression analysis models. A total of 416 (median age 82 (IQR 77-86) years, 77.4% women) hospitalizations in the geriatrics ward were analyzed within 8 months. We compared the results of a CGA (including health, psycho-physical abilities, nutritional status, risk of falls, frailty syndrome, etc.) in patients who fell and did not fall. Fourteen falls (3.3% of patients) were registered; the rate was 4.4 falls per 1000 patient days. They most often occurred in the patient's room while changing position. Falls happened more frequently among people who were more disabled, had multimorbidity, were taking more medications (certain classes of drugs in particular), had Parkinson's disease and diabetes, reported falls in the last year, and were diagnosed with orthostatic hypotension. Logistic regression determined the significant independent association between in-hospital falls and a history of falls in the previous 12 months, orthostatic hypotension, Parkinson's disease, and taking statins, benzodiazepines, and insulin. Analysis of the registered falls that occurred in the hospital ward allowed for an analysis of the circumstances in which they occurred and helped to identify people at high risk of falling in a hospital, which can guide appropriate intervention and act as an indicator of good hospital care.

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

BACKGROUND

The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications.

OBJECTIVES

This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature.

METHODS

We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care-acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture.

RESULTS

Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing.

CONCLUSION

This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.

The impact of urinary incontinence on falls: A systematic review and meta-analysis

OBJECTIVE

Previous studies on the association between urinary incontinence (UI) and falls have reported conflicting results. We, therefore, aimed to evaluate and clarify this association through a systematic review and meta-analysis of relevant studies.

METHODS

We performed a literature search for relevant studies in databases including PubMed and EMBASE from inception up to December 13, 2020, using several search terms related to UI and falls. Based on the data reported in these studies, we calculated the pooled odds ratios (ORs) for falls and the corresponding 95% confidence intervals (CIs) using the Mantel-Haenszel method.

RESULTS

This meta-analysis included 38 articles and a total of 230,129 participants. UI was significantly associated with falls (OR, 1.62; 95% CI, 1.45-1.83). Subgroup analyses based on the age and sex of the participants revealed a significant association between UI and falls in older (≥65 years) participants (OR, 1.59; 95% CI, 1.31-1.93), and in both men (OR, 1.88; 95% CI, 1.57-2.25) and women (OR, 1.41; 95% CI, 1.29-1.54). Subgroup analysis based on the definition of falls revealed a significant association between UI and falls (≥1 fall event) (OR, 1.61; 95% CI, 1.42-1.82) and recurrent falls (≥2 fall events) (OR, 1.63; 95% CI, 1.49-1.78). According to the UI type, a significant association between UI and falls was observed in patients with urgency UI (OR, 1.76; 95% CI, 1.15-1.70) and those with stress UI (OR, 1.73; 95% CI, 1.39-2.15).

CONCLUSIONS

This meta-analysis, which was based on evidence from a review of the published literature, clearly demonstrated that UI is an important risk factor for falls in both general and older populations.