A retrospective cohort study of factors associated with severity of falls in hospital patients

Clinical characteristics and influencing factors of serious fall injuries among older inpatients: A secondary analysis of multicenter cross-sectional administrative data

BACKGROUND

Inpatient falls are common adverse events in hospitals, particularly among older adults. However, current research on falls in the older population is limited by small sample sizes and reliance on single-center designs without distinguishing the different levels of fall injuries.

OBJECTIVE

To examine the circumstances of falls in older inpatients and investigate the factors influencing serious fall injuries.

DESIGN

A secondary analysis of multicenter cross-sectional administrative data from the China National Database of Nursing Quality (CNDNQ).

SETTING(S)

67 hospitals in Hunan, China.

PARTICIPANTS

5143 fall incidents from 5048 older inpatients were included from January 2019 to December 2022.

METHODS

Descriptive statistics (means, standard deviations, frequencies, and percentages) were used to describe fall characteristics. Given the multilevel data structure, a Generalized Linear Mixed Model (GLMM) with a Logistic link function was employed, treating hospitals and wards as random effects. Univariate and multivariate analyses were conducted.

RESULTS

The prevalences of fall-related injuries and serious fall injuries in this study were 63.3 % and 25.0 %, respectively. Nurse-to-patient ratio at the time of fall ≤0.05 (aOR = 1.522, 95 % CI: 1.297-1.785), female gender (aOR = 1.304, 95 % CI: 1.137-1.497), a lengthy hospital stay (aOR = 1.009, 95 % CI: 1.001-1.018) were associated with an increased risk of serious fall injuries. Surgical (aOR = 0.655, 95 % CI: 0.482-0.889) and orthopedic wards (aOR = 0.649, 95 % CI: 0.449-0.939) exhibited lower risks of serious fall injuries than internal medicine wards. The period from 8:00 to 15:59 was associated with a higher risk of serious fall injuries compared to the period from 0:00 to 7:59 (aOR = 1.416, 95 % CI: 1.171-1.713). Compared to lying, standing (aOR = 2.594, 95 % CI: 1.733-3.883), getting in/out of bed (aOR = 1.403, 95 % CI: 1.009-1.951), walking or rehabilitating (aOR = 3.039, 95 % CI: 2.194-4.208), toileting (aOR = 1.411, 95 % CI: 1.038-1.917), and showering (aOR = 2.170, 95 % CI: 1.391-3.385) were associated with an increased risk of serious fall injuries.

CONCLUSIONS

Fall-related injuries and serious fall injuries are highly prevalent among older inpatients. The study provides insights for targeted fall prevention strategies, improving nursing management and patient safety.

REGISTRATION

Not registered.

Features of effective hospital fall prevention trials: an intervention component analysis

BACKGROUND

Falls in hospitals continue to burden patients, staff, and health systems. Prevention approaches are varied, as well as their success at preventing falls. Intervention component analysis (ICA) is useful in indicating important features associated with successful interventions in sets of trial with high heterogeneity.

METHODS

We conducted an ICA of systematically identified randomised controlled trials of interventions for preventing falls in older people in hospitals. Trial characteristics were extracted; inductive thematic analysis of published papers from included trials to seek triallists perspectives on drivers of success or failure of trials was conducted (ICA stage one) followed by a stratified thematic synthesis by trial outcomes, where trials were classified as positive or negative based on their falls rate or falls risk ratios (ICA stage two) and mapped to the presence of the theorised drivers of success or failure of the trials.

RESULTS

45 trials met the inclusion criteria. Inductive thematic analysis of 50 papers revealed three key drivers (themes), each with subthemes, of effective inpatient hospital fall prevention trials. Theme 1, integration with the local setting, was present in 79% of the positive trials and 67% of the negative trials (79% vs 62% engaging ward staff and 33% vs 43% engaging hospital management). Theme 2, responsive interventions, was present in 83% of the positive trials and 71% of the negative trials (29% vs 38% targeting patient risk assessments and 83% vs 57% tailoring to patient needs and abilities). Theme 3, patient and family involvement, featured in 83% of the positive trials and 52% of the negative trials (50% vs 19% through fall prevention awareness and 58% vs 48% through an active role in fall prevention).

CONCLUSION

Tailored fall prevention approaches and involving patient and family in fall prevention through increasing awareness, in addition to integration with the local intervention setting, appear to play a role in impacting the effectiveness of fall prevention interventions. These theories should be considered in the design of future fall prevention programs and trials and require further evaluation in high quality trials.

The Minimum Number of Strides Required for Reliable Gait Measurements in Older Adult Fallers and Non-Fallers

While the value of walking gait metrics collected using pressure-sensing walkways has shown promise for fall risk assessment, there is no consensus on the minimum number of strides required to obtain reliable metrics. This study aimed to determine the minimum stride count required for reliable single-task (ST), dual-task (DT), and difference score (DS) measurements of the spatio-temporal parameters of gait in older adult fallers and non-fallers. Forty community-dwelling older adults (74.6 ± 3.5 years) performed 10 ST and 10 DT walking passes (~100 strides total) across a GAITRite™ pressure mat. Nine truncated datasets (1-9 passes) were created from the original for each walking condition to assess agreement using two-way random effects, absolute agreement, and single-rater intraclass correlations (ICCs). ICCs demonstrated that a minimum of one pass (~10 strides) is sufficient for reliable mean gait metrics for ST and DT conditions and 10-30 strides for DS, while 10-80 strides are needed for reliable gait variability measures, depending on the metric. This study provides stride count recommendations to ensure reliable gait measurement in older adult populations, highlighting that as few as 10-30 strides are necessary for mean metrics, while variability metrics may require up to 80 strides to ensure reliability.

Impact of Collaborative Nursing Care Delivery on Patient Safety Events in an Emergency Intensive Care Unit: A Retrospective Observational Study

OBJECTIVES

Patient safety events (PSEs) have detrimental consequences for patients and healthcare staff, highlighting the importance of prevention. Although evidence shows that nurse staffing affects PSEs, the role of an appropriate nursing care delivery system remains unclear. The current study aimed to investigate whether nursing care delivery systems could prevent PSEs.

METHODS

This retrospective study was conducted in Japan. The study examined the collaborative 4:2 nursing care delivery system in which 2 nurses are assigned to care for 4 patients, collaborating to perform tasks, and provide care. The cohort receiving care from a collaborative 4:2 nursing care delivery system was labeled the postintervention, whereas the cohort receiving care from a conventional individualized system, in which one nurse provides care for 2 patients, was labeled the preintervention. The primary outcome was the occurrence of PSEs.

RESULTS

The preintervention and postintervention comprised 561 and 401 patients, respectively, with the latter consisting of a younger and more critically ill population. The number of PSEs per 1000 patient-days was not significantly different between the 2 groups (10.3 [95% confidence interval, 7.1-13.5] versus 6.0 [95% confidence interval, 3.2-8.9], P = 0.058). Multiple logistic regression analysis showed that the collaborative 4:2 nursing care delivery system was significantly associated with PSEs (adjusted odds ratio, 0.53; 95% confidence interval, 0.29-0.95; P = 0.037).

CONCLUSIONS

These findings suggest that in an emergency intensive care unit, a collaborative nursing care delivery system was associated with a decrease in PSEs.

Validation of the Saga Fall Injury Risk Model

Background: Predicting fall injuries can mitigate the sequelae of falls and potentially utilize medical resources effectively. This study aimed to externally validate the accuracy of the Saga Fall Injury Risk Model (SFIRM), consisting of six factors including age, sex, emergency transport, medical referral letter, Bedriddenness Rank, and history of falls, assessed upon admission. Methods: This was a two-center, prospective, observational study. We included inpatients aged 20 years or older in two hospitals, an acute and a chronic care hospital, from October 2018 to September 2019. The predictive performance of the model was evaluated by calculating the area under the curve (AUC), 95% confidence interval (CI), and shrinkage coefficient of the entire study population. The minimum sample size of this study was 2,235 cases. Results: A total of 3,549 patients, with a median age of 78 years, were included in the analysis, and men accounted for 47.9% of all the patients. Among these, 35 (0.99%) had fall injuries. The performance of the SFIRM, as measured by the AUC, was 0.721 (95% CI: 0.662-0.781). The observed fall incidence closely aligned with the predicted incidence calculated using the SFIRM, with a shrinkage coefficient of 0.867. Conclusions: The external validation of the SFIRM in this two-center, prospective study showed good discrimination and calibration. This model can be easily applied upon admission and is valuable for fall injury prediction.

Factors Impacting Fall Severity in Hospitalized Patients: A Retrospective Cohort Study

Objectives: This retrospective case-controlled study aimed to evaluate the association between the severity of fall-related injuries and fall-risk-increasing drugs (FRIDs) in hospitalized patients. Methods: Data were collected from Changhua Christian Hospital, Taiwan, of all adult inpatients who experienced falls between January 2017 and December 2021, and were divided into two groups based on whether they sustained severe fall-related injuries. Retrospective data that may affect the severity of fall-related injuries and the use of FRIDs were investigated. Results: Among 1231 documented cases of falls, 26 patients sustained severe fall-related injuries. Older patients and those with osteoporosis were more susceptible to more severe injuries from a fall. The use of mobility aids and osteoporosis medications showed protective effects against fall injuries. No significant association was observed between fall-related injuries and comorbidities or FRIDs. Multivariate analysis confirmed the inverse correlation between the use of mobility aids, osteoporosis medications, and fall severity. Patients with osteoporosis exhibited significantly higher odds of sustaining more severe injuries with a fall (odds ratio = 3.02, 95% confidence interval: 1.21-7.53). Conclusions: This study highlights the importance of addressing risk factors associated with fall severity among hospitalized patients. Providing mobility aids to persons at greater risk.

Exploring nurses' clinical judgment concerning the relative importance of fall risk factors: A mixed method approach using the Q Methodology

BACKGROUND

Nurses are pivotal in averting patient falls through their assessment of cues presented by patients and their environments, rendering clinical judgments regarding the risk of falling, and implementing tailored interventions. Despite the intricate cognitive processes entailed in nurses' judgment, no prior studies have explored their approach to assessing the risk of falling.

OBJECTIVE

This study aimed to examine how nurses judge the risk of falling among patients with different conditions, whether there are differences in the importance of risk factors as judged by nurses, how they justify their judgments, and what attributes of the nurses influence their judgments.

DESIGN

A mixed method approach using the Q Methodology was employed.

SETTING(S)

Three public and private hospitals in Japan.

PARTICIPANTS

Eighteen nurses participated in the study.

METHODS

Participants were tasked with ranking 36 patient scenarios, each featuring a distinct set of fall risk factors. Subsequently, post-sorting interviews were conducted to gather insights into their typical approach to assessing fall risk and the rationale behind their ranking decisions. A by-person principal component factor extraction was employed to examine differences in the rankings of the scenarios. The interview data were analyzed descriptively to elucidate the reasons behind these discrepancies.

RESULTS

Nurses engage in complex cognitive manipulations when evaluating the risk of patient falls, drawing extensively from their wealth of experience while utilizing assessment tools to support their judgments. In essence, nurses identify patients' tendency to act alone without calling a nurse, impaired gait and cognition, sedative use, drains, and limited information sharing among healthcare professionals as key fall risks. In addition, nurses vary in the importance they attribute to certain risk factors, leading to the discrimination of three distinct judgment profiles. One group of nurses judges patients with cognitive impairment and acting alone as high risk. Another group of nurses considers patients with unstable gait and acting alone as high risk. The last group of nurses sees patients wearing slippers as high risk. The post-sorting interviews revealed that their judgments are closely related to the healthcare context and patient population.

CONCLUSIONS

Nurses operate within diverse contexts, wherein they interact with patients of varying characteristics, collaborate with professionals from diverse disciplines, and have access to varying levels of human and physical resources. This nuanced understanding empowers the formulation of judgments that are finely attuned to the specific context at hand.

STUDY REGISTRATION

Not registered.

Development and validation of a machine learning-based fall-related injury risk prediction model using nationwide claims database in Korean community-dwelling older population

BACKGROUND

Falls impact over 25% of older adults annually, making fall prevention a critical public health focus. We aimed to develop and validate a machine learning-based prediction model for serious fall-related injuries (FRIs) among community-dwelling older adults, incorporating various medication factors.

METHODS

Utilizing annual national patient sample data, we segmented outpatient older adults without FRIs in the preceding three months into development and validation cohorts based on data from 2018 and 2019, respectively. The outcome of interest was serious FRIs, which we defined operationally as incidents necessitating an emergency department visit or hospital admission, identified by the diagnostic codes of injuries that are likely associated with falls. We developed four machine-learning models (light gradient boosting machine, Catboost, eXtreme Gradient Boosting, and Random forest), along with a logistic regression model as a reference.

RESULTS

In both cohorts, FRIs leading to hospitalization/emergency department visits occurred in approximately 2% of patients. After selecting features from initial set of 187, we retained 26, with 15 of them being medication-related. Catboost emerged as the top model, with area under the receiver operating characteristic of 0.700, along with sensitivity and specificity rates around 65%. The high-risk group showed more than threefold greater risk of FRIs than the low-risk group, and model interpretations aligned with clinical intuition.

CONCLUSION

We developed and validated an explainable machine-learning model for predicting serious FRIs in community-dwelling older adults. With prospective validation, this model could facilitate targeted fall prevention strategies in primary care or community-pharmacy settings.

Validation of the stratify scale for the prediction of falls among hospitalized adults in a tertiary hospital in Colombia: a retrospective cohort study

The STRATIFY scale has been implemented as a preventive strategy for predicting the risk of accidental falls among hospitalized adults. However, there is still uncertainty about its accuracy. This study aimed to perform an external validation of the STRATIFY fall prediction scale in hospitalized adults in one tertiary care hospital in Bogotá, Colombia. The study was a retrospective cohort of adult hospitalized patients in a high-level complexity care hospital. The sample selected included admitted patients (age ≥ 18), consecutively by the institution between 2018 and 2020, with an evaluation of the fall risk measured by the STRATIFY score given to each at the time of hospital admission. For assessing the scale's feasibility, its discriminative capability was obtained by calculating sensitivity, specificity, likelihood ratios, predictive values, and area under the ROC curve. The evaluation included 93,347 patient hospital records (mean 56.9 years, 50.2% women). The overall sensitivity score was 0.672 [IC 95% 0.612-0.723], the specificity score was 0.612 [IC 95% 0.605-0.615], and the positive likelihood ratio was 1.73 [IC 95% 1.589-1.891]. The area under the ROC curve was 0.69 [IC 95% 0.66-0.72]. Subgroups of age obtained similar results. Applying the STRATIFY scale at hospital admission resulted in a lower performance of the tool-predict falls in hospitalized patients. It is necessary to implement an individual evaluation of the risk factors for falls in order to structure appropriate care plans to prevent and improve hospital safety.

Maturity degree assessment of hospital ward system using integrated fuzzy AHP-TOPSIS model

BACKGROUND

The hospital ward system is the core service unit of a hospital and an important aspect of hospital management. The maturity of the hospital ward system represents the level of development and improvement in ward management and services. In order to improve the quality of hospital services, it is significant to assess the maturity of the ward system. Although various assessment methods have been proposed in existing studies, there are some issues such as a single-dimensional factor system, subjectivity in qualitative factor values, and insufficient objectivity in the evaluation methods.

METHODS

Therefore, based on the data collection of the factors used in the existing literature, this study made correlation analysis, determined the similarity of factors, and established a maturity assessment factor system satisfying distinctiveness and comprehensiveness. Furthermore, an evaluation method for the weights of each factor was proposed based on the fuzzy analytic hierarchy process, while taking account of the ambiguity of expert information. In addition, through the technique for order of preference by similarity to ideal solution method, an objective evaluation method for the maturity of the hospital ward system was established. Finally, a case study involving 6 hospitals in a specific location was conducted.

RESULTS

There are a total of 16 factors used to assess the maturity of the hospital ward system, among which the most important is Doctor service. The maturity degree of the 6 researched hospitals are 0.4517, 0.0035, 0.4254, 0.8681, 0.6636, 0.1586, and the maturity degree of the 6 researched hospitals are II, I, II, IV, III, I.

CONCLUSION

The factor system constructed in this study effectively reflected the importance of human-related factors in the development process of ward system. The evaluation results were consistent with the actual situation, demonstrating the effectiveness of the proposed method.

What Tools Can We Use to Screen for Fall Risk in Older Patients with Mild Cognitive Impairment? Findings from the MEMENTO Cohort

OBJECTIVES

Identifying risk factors for falls can improve outcomes in older patients without cognitive decline. Yet this has not been demonstrated in older people with mild cognitive impairment (MCI). We therefore sought to better identify risk factors for falls in this particular group.

DESIGN

The analysis was conducted on the MEMENTO cohort, which is a large, French, prospective cohort.

SETTING AND PARTICIPANTS

We included older people (>65 years old) with MCI (defined from neuropsychological scores) and a Short Physical Performance Battery (SPPB) score at baseline.

METHODS

Fallers were defined as participants having fallen at least once during the study's 2-year follow-up period. We compared clinical, neuropsychological, and biological data at baseline in fallers vs nonfallers. Additional analyses were performed on the following subgroups: women, men, people aged ≥75 years.

RESULTS

Of the 1416 people included in our study, 194 (13.5%) fell at least once. A bivariate analysis showed that fallers were older, predominantly women, less independent in activities of daily living, and more apathetic. Fallers performed less well in executive function, balance, and gait tests. In a multivariable analysis, only age, gender, the number of limitations in instrumental activities of daily living, and living alone were significantly associated with falls. In a multivariable analysis of the subgroup of oldest patients and of the subgroup of men, executive function was significantly worse in fallers than in nonfallers.

CONCLUSION AND IMPLICATIONS

Our results demonstrate that easily attainable risk factors can be used to identify individuals with MCI with a higher risk of falls and for whom prevention could be beneficial. Future studies are needed to further evaluate the role of mild executive dysfunction in certain subgroups, such as men and oldest patients.

Patient falls while under supervision: trends from incident reporting

In one large UK hospital trust, data from the electronic incident reporting system were studied to determine the number of falls within one division taking place while patients were under enhanced supervision. This supervision was commonly carried out by registered nurses or healthcare assistants. It was noted that, despite increased supervision, patients were still falling and when they did the degree of harm they suffered was often greater than for those patients not under supervision. It was also noted that more male patients fell under supervision than female patients, although the reasons for this were not clear, suggesting an area for further research. A large number of patients fell while in the bathroom, where they were often left alone for periods of time. This shows an increasing need to find a balance between maintaining patient dignity and ensuring patient safety.

Preventing Patient Falls Overnight Using Video Monitoring: A Clinical Evaluation

Inpatient falls are devastating for patients and their families and an ongoing problem for healthcare providers worldwide. Inpatient falls overnight are particularly difficult to predict and prevent. The aim of this cohort study was to evaluate effectiveness of overnight portable video monitoring as an adjunct falls prevention strategy for high falls risk patients in inpatient clinical units. Over three months, three clinical inpatient wards were provided with baby monitor equipment to facilitate portable video monitoring. Portable video monitoring registers were completed nightly and nursing staff were invited to complete surveys (n = 31) to assess their experiences of using portable video monitoring. A total of 494 episodes of portable video monitoring were recorded over the three-month period, with clinical areas reporting a total of four inpatient falls from monitoring participants (0.8% of total portable video monitoring episodes). Overall, there was a statistically significant reduction in total inpatient falls overnight on the target wards. Surveyed nursing staff reported feeling better equipped to prevent falls and indicated they would like to continue using portable monitoring as a falls prevention strategy. This study provides evidence to support the use of portable video monitoring as an effective falls prevention strategy in the hospital environment.