Simplified Decision-Tree Algorithm to Predict Falls for Community-Dwelling Older Adults

The present study developed a simplified decision-tree algorithm for fall prediction with easily measurable predictors using data from a longitudinal cohort study: 2520 community-dwelling older adults aged 65 years or older participated. Fall history, age, sex, fear of falling, prescribed medication, knee osteoarthritis, lower limb pain, gait speed, and timed up and go test were assessed in the baseline survey as fall predictors. Moreover, recent falls were assessed in the follow-up survey. We created a fall-prediction algorithm using decision-tree analysis (C5.0) that included 14 nodes with six predictors, and the model could stratify the probabilities of fall incidence ranging from 30.4% to 71.9%. Additionally, the decision-tree model outperformed a logistic regression model with respect to the area under the curve (0.70 vs. 0.64), accuracy (0.65 vs. 0.62), sensitivity (0.62 vs. 0.50), positive predictive value (0.66 vs. 0.65), and negative predictive value (0.64 vs. 0.59). Our decision-tree model consists of common and easily measurable fall predictors, and its white-box algorithm can explain the reasons for risk stratification; therefore, it can be implemented in clinical practices. Our findings provide useful information for the early screening of fall risk and the promotion of timely strategies for fall prevention in community and clinical settings.

Clinical prediction models for hospital falls: a scoping review protocol

IntroductionFalls remain one of the most prevalent adverse events in hospitals and are associated with substantial negative health impacts and costs. Approaches to assess patients’ fall risk have been implemented in hospitals internationally, ranging from brief screening questions to multifactorial risk assessments and complex prediction models, despite a lack of clear evidence of effect in reducing falls in acute hospital environments. The increasing digitisation of hospital systems provides new opportunities to understand and predict falls using routinely recorded data, with potential to integrate fall prediction models into real-time or near-real-time computerised decision support for clinical teams seeking to mitigate fall risk. However, the use of non-traditional approaches to fall risk prediction, including machine learning using integrated electronic medical records, has not yet been reviewed relative to more traditional fall prediction models. This scoping review will summarise methodologies used to develop existing hospital fall prediction models, including reporting quality assessment.Methods and analysisThis scoping review will follow the Arksey and O’Malley framework and its recent advances, and will be reported using Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews recommendations. Four electronic databases (CINAHL via EBSCOhost, PubMed, IEEE Xplore and Embase) will be initially searched for studies up to 12 November 2020, and searches may be updated prior to final reporting. Additional studies will be identified by reference list review and citation analysis of included studies. No restriction will be placed on the date or language of identified studies. Screening of search results and extraction of data will be performed by two independent reviewers. Reporting quality will be assessed by the adherence to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis.Ethics and disseminationEthical approval is not required for this study. Findings will be disseminated through peer-reviewed publication and scientific conferences.

New Predictive Models for Falls in Acute Care Setting Classified Using An Activities of Daily Living Scale in Japan: A Retrospective Cohort Study

Background In several current fall prediction models, the reported predictors vary from one model to another. We developed and validated a new fall prediction model for patients admitted to an acute care hospital by identifying predictors of falls considering a combination of background factors and one crucial stratum. Methods We conducted a retrospective cohort study of patients admitted to Shizuoka General Hospital from April 2019 to September 2020, aged 20 years or older. We developed and validated a new fall prediction model by identifying predictors of falls stratified by essential activities of daily living (ADL) indicators and integrating these models. Results A total of 22,988 individuals were included in the analysis, with 653 (2.8 %) experiencing all falls and 400 (1.7 %) experiencing falls with medical resources during the study period. Multivariate analysis was performed after one stratification level, using bedridden rank (ability to move around in daily life) as a stratifying variable, a clinically important variable and highly correlated with 17 other variables. The results of multivariate analysis showed that the risk factors for falls (high risk) were age (high), sex (men), and ambulance transport (yes) for rank J (independence/autonomy); age (high),) and sex (men) for rank A (house-bound); There were no predictors for rank B (chair-bound); and there was ophthalmologic disease (no) for rank C (bed-bound). The c-index indicating the prediction model’s performance for falls within 28 days of hospitalisation was 0.705 (95 % CI, 0.664–0.746). Hosmer-Lemeshow goodness-of-fit statistics were significant (χ2 = 192.06; 8 degrees of freedom; p < 0.001). The c-index for the entire unstratified sample was 0.703 (95 % CI, 0.661–0.746), indicating that the predictive model stratified by bedriddenness rank was accurate (p < 0.001). Conclusion We identified predictors of falls using important ADLs (bedriddenness rank) and developed a more accurate prediction model in acute care hospital settings. This predictive model is an essential tool for fall prevention.

Wearable Feet Pressure Sensor for Human Gait and Falling Diagnosis

Human falls pose a serious threat to the person’s health, especially for the elderly and disease-impacted people. Early detection of involuntary human gait change can indicate a forthcoming fall. Therefore, human body fall warning can help avoid falls and their caused injuries for the skeleton and joints. A simple and easy-to-use fall detection system based on gait analysis can be very helpful, especially if sensors of this system are implemented inside the shoes without causing a sensible discomfort for the user. We created a methodology for the fall prediction using three specially designed Velostat®-based wearable feet sensors installed in the shoe lining. Measured pressure distribution of the feet allows the analysis of the gait by evaluating the main parameters: stepping rhythm, size of the step, weight distribution between heel and foot, and timing of the gait phases. The proposed method was evaluated by recording normal gait and simulated abnormal gait of subjects. The obtained results show the efficiency of the proposed method: the accuracy of abnormal gait detection reached up to 94%. In this way, it becomes possible to predict the fall in the early stage or avoid gait discoordination and warn the subject or helping companion person.

Early health technology assessment using the MAFEIP tool. A case study on a wearable device for fall prediction in elderly patients

By using a case-study on a fall-prediction device for elderly patients with orthostatic hypotension we aim to demonstrate how the MAFEIP tool, developed as part of the European Innovation Programme on Active and Healthy Ageing (EIP on AHA), can be used to inform manufacturers on their product development based on a cost-effectiveness criterion. Secondly, we critically appraise the tool and suggest further improvements that may be needed for a larger-scale adoption of MAFEIP within and beside the EIP on AHA initiative. The model was implemented using the MAFEIP tool. Within the tool one way sensitivity analyses were performed to assess the robustness of the model against the relative effectiveness of the fall-prevention device at different price levels. The MAFEIP tool was applied to a novel fall-prediction device and used to estimate the expected cost-effectiveness and perform threshold analysis. In our case study, the device produced estimated gains of 0.035 QALYs per patient and incremental costs of £ 518 (incremental cost-effectiveness ratio £14,719). Based on the one-way sensitivity analysis, the maximum achievable price at a willingness to pay threshold of £20,000 per QALY is estimated close to £900. The MAFEIP allows to quickly create early economic models, and to explore model uncertainty by performing deterministic sensitivity analysis for single parameters. However, the integration within the MAFEIP of common analytical tools such as probabilistic sensitivity analysis and Value of information would greatly contribute to its relevance for evaluating innovative technologies within and beside the EIP on AHA initiative.