Predicting falls in community-dwelling older adults: a systematic review of prognostic models

A systematic review of fall prediction models for community-dwelling older adults: comparison between models based on research cohorts and models based on routinely collected data

BACKGROUND

Prediction models can identify fall-prone individuals. Prediction models can be based on either data from research cohorts (cohort-based) or routinely collected data (RCD-based). We review and compare cohort-based and RCD-based studies describing the development and/or validation of fall prediction models for community-dwelling older adults.

METHODS

Medline and Embase were searched via Ovid until January 2023. We included studies describing the development or validation of multivariable prediction models of falls in older adults (60+). Both risk of bias and reporting quality were assessed using the PROBAST and TRIPOD, respectively.

RESULTS

We included and reviewed 28 relevant studies, describing 30 prediction models (23 cohort-based and 7 RCD-based), and external validation of two existing models (one cohort-based and one RCD-based). The median sample sizes for cohort-based and RCD-based studies were 1365 [interquartile range (IQR) 426-2766] versus 90 441 (IQR 56 442-128 157), and the ranges of fall rates were 5.4% to 60.4% versus 1.6% to 13.1%, respectively. Discrimination performance was comparable between cohort-based and RCD-based models, with the respective area under the receiver operating characteristic curves ranging from 0.65 to 0.88 versus 0.71 to 0.81. The median number of predictors in cohort-based final models was 6 (IQR 5-11); for RCD-based models, it was 16 (IQR 11-26). All but one cohort-based model had high bias risks, primarily due to deficiencies in statistical analysis and outcome determination.

CONCLUSIONS

Cohort-based models to predict falls in older adults in the community are plentiful. RCD-based models are yet in their infancy but provide comparable predictive performance with no additional data collection efforts. Future studies should focus on methodological and reporting quality.

Difficulty resuming driving in acute acquired brain injury: Retrospective observational study using discriminant analysis

OBJECTIVES

We hypothesized that neuropsychological testing and history of falls would be associated with difficulty resume driving after acute acquired brain injury (ABI). This study aimed to analyze ABI facing difficulties in resuming driving in the acute phase.

METHODS

We retrospectively analyzed 63 patients receiving assistance in driving-resumption after ABI. Patients were categorized into two groups: driving-resumption-possible and driving-resumption-difficult. Discriminant analysis delineated characteristics of patients experiencing driving-resumption difficulty. Additionally, significant predictors were analyzed using ROC curves.

RESULTS

42 patients were able to resume driving, and 21 experienced difficulties in driving resumption. Factors predicting difficulty returning to driving were age, history of falls, TMT Part B, and ROCF. Furthermore, cut-off values for each were 72 years, 148 seconds for TMT Part B, and 29.5 points for ROCF.

CONCLUSIONS

Patients with advanced age, history of falls, delayed TMT Part B, and poor ROCF outcomes may face challenges in resuming driving after ABI. These factors may serve as a valuable metric to assess driving resumption difficulties after ABI.

Development and external validation of the eFalls tool: a multivariable prediction model for the risk of ED attendance or hospitalisation with a fall or fracture in older adults

BACKGROUND

Falls are common in older adults and can devastate personal independence through injury such as fracture and fear of future falls. Methods to identify people for falls prevention interventions are currently limited, with high risks of bias in published prediction models. We have developed and externally validated the eFalls prediction model using routinely collected primary care electronic health records (EHR) to predict risk of emergency department attendance/hospitalisation with fall or fracture within 1 year.

METHODS

Data comprised two independent, retrospective cohorts of adults aged ≥65 years: the population of Wales, from the Secure Anonymised Information Linkage Databank (model development); the population of Bradford and Airedale, England, from Connected Bradford (external validation). Predictors included electronic frailty index components, supplemented with variables informed by literature reviews and clinical expertise. Fall/fracture risk was modelled using multivariable logistic regression with a Least Absolute Shrinkage and Selection Operator penalty. Predictive performance was assessed through calibration, discrimination and clinical utility. Apparent, internal-external cross-validation and external validation performance were assessed across general practices and in clinically relevant subgroups.

RESULTS

The model's discrimination performance (c-statistic) was 0.72 (95% confidence interval, CI: 0.68 to 0.76) on internal-external cross-validation and 0.82 (95% CI: 0.80 to 0.83) on external validation. Calibration was variable across practices, with some over-prediction in the validation population (calibration-in-the-large, -0.87; 95% CI: -0.96 to -0.78). Clinical utility on external validation was improved after recalibration.

CONCLUSION

The eFalls prediction model shows good performance and could support proactive stratification for falls prevention services if appropriately embedded into primary care EHR systems.

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 ADFICE_IT Models for Predicting Falls and Recurrent Falls in Geriatric Outpatients

OBJECTIVES

Before being used in clinical practice, a prediction model should be tested in patients whose data were not used in model development. Previously, we developed the ADFICE_IT models for predicting any fall and recurrent falls, referred as Any_fall and Recur_fall. In this study, we externally validated the models and compared their clinical value to a practical screening strategy where patients are screened for falls history alone.

DESIGN

Retrospective, combined analysis of 2 prospective cohorts.

SETTING AND PARTICIPANTS

Data were included of 1125 patients (aged ≥65 years) who visited the geriatrics department or the emergency department.

METHODS

We evaluated the models' discrimination using the C-statistic. Models were updated using logistic regression if calibration intercept or slope values deviated significantly from their ideal values. Decision curve analysis was applied to compare the models' clinical value (ie, net benefit) against that of falls history for different decision thresholds.

RESULTS

During the 1-year follow-up, 428 participants (42.7%) endured 1 or more falls, and 224 participants (23.1%) endured a recurrent fall (≥2 falls). C-statistic values were 0.66 (95% CI 0.63-0.69) and 0.69 (95% CI 0.65-0.72) for the Any_fall and Recur_fall models, respectively. Any_fall overestimated the fall risk and we therefore updated only its intercept whereas Recur_fall showed good calibration and required no update. Compared with falls history, Any_fall and Recur_fall showed greater net benefit for decision thresholds of 35% to 60% and 15% to 45%, respectively.

CONCLUSIONS AND IMPLICATIONS

The models performed similarly in this data set of geriatric outpatients as in the development sample. This suggests that fall-risk assessment tools that were developed in community-dwelling older adults may perform well in geriatric outpatients. We found that in geriatric outpatients the models have greater clinical value across a wide range of decision thresholds compared with screening for falls history alone.

Developing a multivariate prediction model of falls among older community-dwelling adults using measures of neuromuscular control and proprioceptive acuity: A pilot study

OBJECTIVE

To examine whether measures of neuromuscular control and proprioceptive acuity were predictive of falls in an older community-dwelling population and to develop a multivariate prediction model.

METHODS

Fifty-eight adults aged above 60 living independently in the community were recruited for a prospective falls study. On entry, they undertook a Sensory Organisation Test (SOT) and an Active Movement Extent Discrimination Assessment (AMEDA) and completed a short fall risk questionnaire. Participants were monitored for falls over the subsequent 12 months. Prior to analysis, falls were classified into three categories based on the difficulty of the activity being undertaken and the demands of the environment in which the fall occurred. Logistic regression was used to predict the probability of a fall.

RESULTS

For falls occurring under the least challenging circumstances, the model fitted using the AMEDA score and two of the questions from the fall risk questionnaire, related to balance and confidence, achieved a specificity of 87% and sensitivity of 83%. Falls occurring in more challenging circumstances could not be predicted with any accuracy based on the variables recorded at inception.

CONCLUSIONS

This study highlights the importance of considering the heterogeneous nature of falls. Poorer proprioceptive acuity appears to play a role in falls occurring where neither the environment nor the activity is challenging, but not in falls occurring in other circumstances. Falls in the least-challenging circumstances affected 15% of participants, but this group was considerably more likely to have multiple falls, increasing their vulnerability to adverse consequences.

Predicting mortality risk after a fall in older adults using health care spending patterns: a population-based cohort study

OBJECTIVE

To develop a prognostic model of 1-year mortality for individuals aged 65+ presenting at the emergency department (ED) with a fall based on health care spending patterns to guide clinical decision-making.

DESIGN

Population-based cohort study (n = 35,997) included with a fall in 2013 and followed 1 year.

METHODS

Health care spending indicators (dynamical indicators of resilience, DIORs) 2 years before admission were evaluated as potential predictors, along with age, sex and other clinical and sociodemographic covariates. Multivariable logistic regression models were developed and internally validated (10-fold cross-validation). Performance was assessed via discrimination (area under the receiver operating characteristic curve, AUC), Brier scores, calibration and decision curve analysis.

RESULTS

The AUC of age and sex for mortality was 72.5% [95% confidence interval 71.8 to 73.2]. The best model included age, sex, number of medications and health care spending DIORs. It exhibited high discrimination (AUC: 81.1 [80.5 to 81.6]), good calibration and potential clinical benefit for various threshold probabilities. Overall, health care spending patterns improved predictive accuracy the most while also exhibiting superior performance and clinical benefit.

CONCLUSIONS

Patterns of health care spending have the potential to significantly improve assessments on who is at high risk of dying following admission to the ED with a fall. The proposed methodology can assist in predicting the prognosis of fallers, emphasising the added predictive value of longitudinal health-related information next to clinical and sociodemographic predictors.

Prognostic models of quality of life after total knee replacement: A systematic review

OBJECTIVE

To systematically review and critically appraise prognostic models for quality of life (QOL) in patients with total knee replacement (TKA).

METHODS

Subjects were TKA recipients recruited from inpatient postoperative settings. Searches were made on June 2022 and updated on April 2023. Databases included PubMed.gov, CINAHL, The Cochrane Library, Web of Science. Two authors performed all review stages independently. Risk of bias assessments on participants, predictors, outcomes and analysis methods followed the Prediction study Risk Of Bias ASsessment Tool (PROBAST).

RESULTS

After screening 2204 studies, 9 were eligible for inclusion. Twelve prognostic models were reported, of which 10 models were developed from data without validation and 2 were both developed and validated. The most frequently applied predictor was the pre-TKA QOL score. Discriminatory measures were reported for 9 (75.0%) models with areas under the curve values of 0.66-0.95. All models showed a high risk of bias, mostly due to limitations in statistical methods and outcome assessments.

CONCLUSION

Several prognostic models have been developed for QOL in patients with TKA, but all models show a high risk of bias and are unreliable in clinical practice. Future, prognostic models overcoming the risk of bias identified in this study are needed.

Falls and fracture risk screening in primary care: update and validation of a postal screening tool for community dwelling older adults recruited to UK Prevention of Falls Injury Trial (PreFIT)

BACKGROUND

Postal screening has not previously been validated as a method for identifying fall and fracture risk in community-dwelling populations. We examined prognostic performance of a postal risk screener used in the UK Prevention of Falls Injury Trial (PreFIT; ISRCTN71002650), to predict any fall, recurrent falls, and fractures over 12 months. We tested whether adding variables would improve screener performance.

METHODS

Nine thousand eight hundred and eight community-dwelling participants, aged 70 years and older, and 63 general practices in the UK National Health Service (NHS) were included in a large, pragmatic cluster randomised trial comparing screen and treat fall prevention interventions. The short postal screener was sent to all participants in the trial intervention arms as an A4 sheet to be completed and returned to the GP (n = 6,580). The postal screener items were embedded in the baseline pre-randomisation postal questionnaire for all arms of the trial (n = 9,808). We assessed discrimination and calibration using area under the curve (AUC). We identified additional predictors using data from the control arm and applied these coefficients to internal validation models in the intervention arm participants. We used logistic regression to identify additional predictor variables.

FINDINGS

A total of 10,743 falls and 307 fractures were reported over 12 months. Over one third of participants 3,349/8,136 (41%) fell at least once over 12 month follow up. Response to the postal screener was high (5,779/6,580; 88%). Prediction models showed similar discriminatory ability in both control and intervention arms, with discrimination values for any fall AUC 0.67 (95% CI 0.65 to 0.68), and recurrent falls (AUC 0.71; 95% CI 0.69, 0.72) but poorer discrimination for fractures (AUC 0.60; 95% CI 0.56, 0.64). Additional predictor variables improved prediction of falls but had modest effect on fracture, where AUC rose to 0.71 (95% CI 0.67 to 0.74). Calibration slopes were very close to 1.

CONCLUSION

A short fall risk postal screener was acceptable for use in primary care but fall prediction was limited, although consistent with other tools. Fracture and fall prediction were only partially reliant on fall risk although were improved with the additional variables.

Development and external validation of a risk prediction model for falls in patients with an indication for antihypertensive treatment: retrospective cohort study

OBJECTIVE

To develop and externally validate the STRAtifying Treatments In the multi-morbid Frail elderlY (STRATIFY)-Falls clinical prediction model to identify the risk of hospital admission or death from a fall in patients with an indication for antihypertensive treatment.

DESIGN

Retrospective cohort study.

SETTING

Primary care data from electronic health records contained within the UK Clinical Practice Research Datalink (CPRD).

PARTICIPANTS

Patients aged 40 years or older with at least one blood pressure measurement between 130 mm Hg and 179 mm Hg.

MAIN OUTCOME MEASURE

First serious fall, defined as hospital admission or death with a primary diagnosis of a fall within 10 years of the index date (12 months after cohort entry). Model development was conducted using a Fine-Gray approach in data from CPRD GOLD, accounting for the competing risk of death from other causes, with subsequent recalibration at one, five, and 10 years using pseudo values. External validation was conducted using data from CPRD Aurum, with performance assessed through calibration curves and the observed to expected ratio, C statistic, and D statistic, pooled across general practices, and clinical utility using decision curve analysis at thresholds around 10%.

RESULTS

Analysis included 1 772 600 patients (experiencing 62 691 serious falls) from CPRD GOLD used in model development, and 3 805 366 (experiencing 206 956 serious falls) from CPRD Aurum in the external validation. The final model consisted of 24 predictors, including age, sex, ethnicity, alcohol consumption, living in an area of high social deprivation, a history of falls, multiple sclerosis, and prescriptions of antihypertensives, antidepressants, hypnotics, and anxiolytics. Upon external validation, the recalibrated model showed good discrimination, with pooled C statistics of 0.833 (95% confidence interval 0.831 to 0.835) and 0.843 (0.841 to 0.844) at five and 10 years, respectively. Original model calibration was poor on visual inspection and although this was improved with recalibration, under-prediction of risk remained (observed to expected ratio at 10 years 1.839, 95% confidence interval 1.811 to 1.865). Nevertheless, decision curve analysis suggests potential clinical utility, with net benefit larger than other strategies.

CONCLUSIONS

This prediction model uses commonly recorded clinical characteristics and distinguishes well between patients at high and low risk of falls in the next 1-10 years. Although miscalibration was evident on external validation, the model still had potential clinical utility around risk thresholds of 10% and so could be useful in routine clinical practice to help identify those at high risk of falls who might benefit from closer monitoring or early intervention to prevent future falls. Further studies are needed to explore the appropriate thresholds that maximise the model's clinical utility and cost effectiveness.

Limits of stability and falls during a multicomponent exercise program in faller older adults: A retrospective cohort study

BACKGROUND/OBJETIVES

Multicomponent exercise programs have been demonstrated to prevent falls in older adults. However, the underlying responsible mechanisms are not clear. We aimed to analyze the association between changes in the limits of stability (LOS) as a relevant balance component, and falls occurrence during a multicomponent physical exercise program.

METHODS

Retrospective study, including ninety-one participants who had experienced a fall in the previous year, and were attended in a falls unit. All of them were included in a twice-a-week multicomponent exercise program during 16 weeks. Pre- and post-program measurements were collected for leg press, gait speed, the short physical performance battery (SPPB), and LOS (point of excursion [POE] and maximal excursion [MEX]) with posturography. Falls occurrence was assessed between the beginning and the completion of the exercise program (16 week).

RESULTS

The mean age was 77.2 years, and 72 were female. Thirty-two participants fell at least once during the exercise period. The global baseline POE was 47.6 %, and the MEX was 64.7 %, and there were no differences between fallers and nonfallers. Nonfallers presented greater improvements in POE (6.3 % versus 1.3 %; p < .05) and MEX (9.2 % versus 3.0 %; p < .01) than fallers. The POE and MEX were independently associated with a reduced probability of having had a fall, OR: 0.95 (95 % CI: 0.91 to 0.99) and 0.94 (95 % CI: 0.90 to 0.99), respectively. Changes in SPPB results or leg press strength were not associated with decreased falls. Adjusted probability of fall occurrence decreased by 5 % and 6 % per 1 % improvement in absolute values in POE and MEX, respectively.

CONCLUSIONS

Improvements in LOS after a multicomponent physical exercise program in older adults with previous falls may be associated with a decreased occurrence of falls.

Antihypertensive Deprescribing in Older Adults: a Practical Guide

PURPOSE OF REVIEW

To summarise evidence on both appropriate and inappropriate antihypertensive drug withdrawal.

RECENT FINDINGS

Deprescribing should be attempted in the following steps: (1) identify patients with several comorbidities and significant functional decline, i.e. people at higher risk for negative outcomes related to polypharmacy and lower blood pressure; (2) check blood pressure; (3) identify candidate drugs for deprescribing; (4) withdraw medications at 4-week intervals; (5) monitor blood pressure and check for adverse events. Although evidence is accumulating regarding short-term outcomes of antihypertensive deprescribing, long-term effects remain unclear. The limited evidence for antihypertensive deprescribing means that it should not be routinely attempted, unless in response to specific adverse events or following discussions between physicians and patients about the uncertain benefits and harms of the treatment.

PERSPECTIVES

Clinical controlled trials are needed to examine the long-term effects of deprescribing in older subjects, especially in those with comorbidities, and significant functional decline.

Association Between Low Serum Phosphate Level and Risk of Falls in Hospitalized Patients Over 50 Years of Age: A Retrospective Observational Cohort Study

Purpose

Falls are the leading cause of injury among hospitalized patients, particularly among older patients. We investigated the association between serum phosphate (s-phosphate) levels and the risk of in-hospital falls.

Patients and Methods

This retrospective observational cohort study included all patients aged over 50 years who were admitted to Yongin Severance Hospital in South Korea between January 2018 and March 2021. Demographic, anthropometric, and biochemical parameters were recorded on admission. S-phosphate levels were classified into three groups: below normal (<2.8 mg/dL), normal (2.8–4.4 mg/dL), and above normal (≥4.5 mg/dL). The normal group was further stratified into tertiles (2.8–3.2, 3.3–3.7, and 3.8–4.4 mg/dL). The incidence of in-hospital falls was compared between the five groups. Logistic regression analyses were performed to assess the association between s-phosphate levels and the incidence of falls during the hospital stay, with clinical factors included as covariates in the multivariable models.

Results

A total of 15,485 patients (female: 52.1%) with a median age of 70.0 years (interquartile range: 60.0–79.0 years) were included in the analysis, of whom 295 (1.9%) experienced a fall during the hospital stay. The incidence of falls was significantly higher among patients with lower s-phosphate levels, and this relationship also applied among patients with s-phosphate levels within the normal range as well. The association between lower s-phosphate levels and increased risk of falls remained significant in the adjusted analyses.

Conclusion

A lower s-phosphate level on admission was independently associated with an increased risk of in-hospital falls. Further studies are needed to determine whether the s-phosphate level on admission could improve prediction of the risk of in-hospital falls.

A systematic review of chiropractic care for fall prevention: rationale, state of the evidence, and recommendations for future research

BACKGROUND

Falls in older adults are a significant and growing public health concern. There are multiple risk factors associated with falls that may be addressed within the scope of chiropractic training and licensure. Few attempts have been made to summarize existing evidence on multimodal chiropractic care and fall risk mitigation. Therefore, the broad purpose of this review was to summarize this research to date. BODY: Systematic review was conducted following PRISMA guidelines. Databases searched included PubMed, Embase, Cochrane Library, PEDro, and Index of Chiropractic Literature. Eligible study designs included randomized controlled trials (RCT), prospective non-randomized controlled, observational, and cross-over studies in which multimodal chiropractic care was the primary intervention and changes in gait, balance and/or falls were outcomes. Risk of bias was also assessed using the 8-item Cochrane Collaboration Tool. The original search yielded 889 articles; 21 met final eligibility including 10 RCTs. One study directly measured the frequency of falls (underpowered secondary outcome) while most studies assessed short-term measurements of gait and balance. The overall methodological quality of identified studies and findings were mixed, limiting interpretation regarding the potential impact of chiropractic care on fall risk to qualitative synthesis.

CONCLUSION

Little high-quality research has been published to inform how multimodal chiropractic care can best address and positively influence fall prevention. We propose strategies for building an evidence base to inform the role of multimodal chiropractic care in fall prevention and outline recommendations for future research to fill current evidence gaps.

External Validation of a Prediction Model for Falls in Older People Based on Electronic Health Records in Primary Care

OBJECTIVE

Early identification of older people at risk of falling is the cornerstone of fall prevention. Many fall prediction tools exist but their external validity is lacking. External validation is a prerequisite before application in clinical practice. Models developed with electronic health record (EHR) data are especially challenging because of the uncontrolled nature of routinely collected data. We aimed to externally validate our previously developed and published prediction model for falls, using a large cohort of community-dwelling older people derived from primary care EHR data.

DESIGN

Retrospective analysis of a prospective cohort drawn from EHR data.

SETTING AND PARTICIPANTS

Pseudonymized EHR data were collected from individuals aged ≥65 years, who were enlisted in any of the participating 59 general practices between 2015 and 2020 in the Netherlands.

METHODS

Ten predictors were defined and obtained using the same methods as in the development study. The outcome was 1-year fall and was obtained from free text. Both reproducibility and transportability were evaluated. Model performance was assessed in terms of discrimination using the area under the receiver operating characteristic curve (ROC-AUC), and in terms of calibration, using calibration-in-the-large, calibration slope and calibration plots.

RESULTS

Among 39,342 older people, 5124 (13.4%) fell in the 1-year follow-up. The characteristics of the validation and the development cohorts were similar. ROC-AUCs of the validation and development cohort were 0.690 and 0.705, respectively. Calibration-in-the-large and calibration slope were 0.012 and 0.878, respectively. Calibration plots revealed overprediction for high-risk groups in a small number of individuals.

CONCLUSIONS AND IMPLICATIONS

Our previously developed prediction model for falls demonstrated good external validity by reproducing its predictive performance in the validation cohort. The implementation of this model in the primary care setting could be considered after impact assessment.

Predicting falls in older adults: an umbrella review of instruments assessing gait, balance, and functional mobility

Background

To review the validated instruments that assess gait, balance, and functional mobility to predict falls in older adults across different settings.

Methods

Umbrella review of narrative- and systematic reviews with or without meta-analyses of all study types. Reviews that focused on older adults in any settings and included validated instruments assessing gait, balance, and functional mobility were included. Medical and allied health professional databases (MEDLINE, PsychINFO, Embase, and Cochrane) were searched from inception to April 2022. Two reviewers undertook title, abstract, and full text screening independently. Review quality was assessed through the Risk of Bias Assessment Tool for Systematic Reviews (ROBIS). Data extraction was completed in duplicate using a standardised spreadsheet and a narrative synthesis presented for each assessment tool.

Results

Among 2736 articles initially identified, 31 reviews were included; 11 were meta-analyses. Reviews were primarily of low quality, thus at high risk of potential bias. The most frequently reported assessments were: Timed Up and Go, Berg Balance Scale, gait speed, dual task assessments, single leg stance, functional Reach Test, tandem gait and stance and the chair stand test. Findings on the predictive ability of these tests were inconsistent across the reviews.

Conclusions

In conclusion, we found that no single gait, balance or functional mobility assessment in isolation can be used to predict fall risk in older adults with high certainty. Moderate evidence suggests gait speed can be useful in predicting falls and might be included as part of a comprehensive evaluation for older adults.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-022-03271-5.

Development of the ADFICE_IT models for predicting falls and recurrent falls in community-dwelling older adults: pooled analyses of European cohorts with special attention to medication

Background

Use of fall prevention strategies requires detection of high-risk patients. Our goal was to develop prediction models for falls and recurrent falls in community-dwelling older adults and to improve upon previous models by using a large, pooled sample and by considering a wide range of candidate predictors, including medications.

Methods

Harmonized data from 2 Dutch (LASA, B-PROOF) and 1 German cohort (ActiFE Ulm) of adults aged ≥65 years were used to fit 2 logistic regression models: one for predicting any fall and another for predicting recurrent falls over 1 year. Model generalizability was assessed using internal–external cross-validation.

Results

Data of 5 722 participants were included in the analyses, of whom 1 868 (34.7%) endured at least 1 fall and 702 (13.8%) endured a recurrent fall. Positive predictors for any fall were: educational status, depression, verbal fluency, functional limitations, falls history, and use of antiepileptics and drugs for urinary frequency and incontinence; negative predictors were: body mass index (BMI), grip strength, systolic blood pressure, and smoking. Positive predictors for recurrent falls were: educational status, visual impairment, functional limitations, urinary incontinence, falls history, and use of anti-Parkinson drugs, antihistamines, and drugs for urinary frequency and incontinence; BMI was a negative predictor. The average C-statistic value was 0.65 for the model for any fall and 0.70 for the model for recurrent falls.

Conclusion

Compared with previous models, the model for recurrent falls performed favorably while the model for any fall performed similarly. Validation and optimization of the models in other populations are warranted.

New horizons in evidence-based care for older people: individual participant data meta-analysis

Evidence-based decisions on clinical and cost-effectiveness of interventions are ideally informed by meta-analyses of intervention trial data. However, when undertaken, such meta-analyses in ageing research have typically been conducted using standard methods whereby summary (aggregate) data are extracted from published trial reports. Although meta-analysis of aggregate data can provide useful insights into the average effect of interventions within a selected trial population, it has limitations regarding robust conclusions on which subgroups of people stand to gain the greatest benefit from an intervention or are at risk of experiencing harm. Future evidence synthesis using individual participant data from ageing research trials for meta-analysis could transform understanding of the effectiveness of interventions for older people, supporting evidence-based and sustainable commissioning. A major advantage of individual participant data meta-analysis (IPDMA) is that it enables examination of characteristics that predict treatment effects, such as frailty, disability, cognitive impairment, ethnicity, gender and other wider determinants of health. Key challenges of IPDMA relate to the complexity and resources needed for obtaining, managing and preparing datasets, requiring a meticulous approach involving experienced researchers, frequently with expertise in designing and analysing clinical trials. In anticipation of future IPDMA work in ageing research, we are establishing an international Ageing Research Trialists collective, to bring together trialists with a common focus on transforming care for older people as a shared ambition across nations.

Predicting falls within 3 months of emergency department discharge among community-dwelling older adults using self-report tools versus a brief functional assessment

BACKGROUND

Identifying older adults with risk for falls prior to discharge home from the Emergency Department (ED) could help direct fall prevention interventions, yet ED-based tools to assist risk stratification are under-developed. The aim of this study was to assess the performance of self-report and functional assessments to predict falls in the 3 months post-ED discharge for older adults.

METHODS

A prospective cohort of community-dwelling adults age 60 years and older were recruited from one urban ED (N = 134). Participants completed: a single item screen for mobility (SIS-M), the 12-item Stay Independent Questionnaire (SIQ-12), and the Timed Up and Go test (TUG). Falls were defined through self-report of any fall at 1- and 3-months and medical record review for fall-related injury 3-months post-discharge. We developed a hybrid-convolutional recurrent neural network (HCRNN) model of gait and balance characteristics using truncal 3-axis accelerometry collected during the TUG. Internal validation was conducted using bootstrap resampling with 1000 iterations for SIS-M, FRQ, and GUG and leave-one-out for the HCRNN. We compared performance of M-SIS, FRQ, TUG time, and HCRNN by calculating the area under the receiver operating characteristic area under the curves (AUCs).

RESULTS

14 (10.4%) of participants met our primary outcome of a fall or fall-related injury within 3-months. The SIS-M had an AUC of 0.42 [95% confidence interval (CI) 0.19-0.65]. The SIQ-12 score had an AUC of 0.64 [95% confidence interval (CI) 0.49-0.80]. The TUG had an AUC of 0.48 (95% CI 0.29-0.68). The HCRNN model using generated accelerometer features collected during the TUG had an AUC of 0.99 (95% CI 0.98-1.00).

CONCLUSION

We found that self-report and functional assessments lack sufficient accuracy to be used in isolation in the ED. A neural network model using accelerometer features could be a promising modality but research is needed to externally validate these findings.

The roles of lower-limb joint proprioception in postural control during gait

The objective of the present study was to investigate the roles of lower-limb joint proprioception in postural control during gait. Seventy-two healthy adults including 36 younger and 36 older adults participated in two experimental sessions, i.e., lower-limb joint proprioception assessment session and gait assessment session. Lower-limb joint proprioception was assessed by joint position sense errors measured at the ankle, knee and hip of the dominant side. Postural control during gait was characterized by step length, step width and local dynamic stability. Results showed that hip proprioception contributed the most to postural control during gait among the lower-limb joint proprioception components, and that mechanisms for the hip proprioception effects were different between age groups. These findings highlighted the importance of incorporating hip proprioception enhancement exercises in postural control training programs, and the necessity of considering age-related differences in the effects of hip proprioception when designing these exercises.

Development and internal validation of a risk prediction model for falls among older people using primary care electronic health records

Background

Currently used prediction tools have limited ability to identify community-dwelling older people at high risk for falls. Prediction models utilizing electronic health records (EHRs) provide opportunities but up to now showed limited clinical value as risk stratification tool, because of among others the underestimation of falls prevalence. The aim of this study was to develop a fall prediction model for community-dwelling older people using a combination of structured data and free text of primary care EHRs and to internally validate its predictive performance.

Methods

We used EHR data of individuals aged 65 or older. Age, sex, history of falls, medications, and medical conditions were included as potential predictors. Falls were ascertained from the free text. We employed the Bootstrap-enhanced penalized logistic regression with the least absolute shrinkage and selection operator to develop the prediction model. We used 10-fold cross-validation to internally validate the prediction strategy. Model performance was assessed in terms of discrimination and calibration.

Results

Data of 36 470 eligible participants were extracted from the data set. The number of participants who fell at least once was 4 778 (13.1%). The final prediction model included age, sex, history of falls, 2 medications, and 5 medical conditions. The model had a median area under the receiver operating curve of 0.705 (interquartile range 0.700–0.714).

Conclusions

Our prediction model to identify older people at high risk for falls achieved fair discrimination and had reasonable calibration. It can be applied in clinical practice as it relies on routinely collected variables and does not require mobility assessment tests.

Development of a multivariable prognostic PREdiction model for 1-year risk of FALLing in a cohort of community-dwelling older adults aged 75 years and above (PREFALL)

Background

Falls are the leading cause of fatal and non-fatal injuries in older adults, and attention to falls prevention is imperative. Prognostic models identifying high-risk individuals could guide fall-preventive interventions in the rapidly growing older population. We aimed to develop a prognostic prediction model on falls rate in community-dwelling older adults.

Methods

Design: prospective cohort study with 12 months follow-up and participants recruited from June 14, 2018, to July 18, 2019.

Setting: general population.

Subjects: community-dwelling older adults aged 75+ years, without dementia or acute illness, and able to stand unsupported for one minute.

Outcome: fall rate for 12 months.

Statistical methods: candidate predictors were physical and cognitive tests along with self-report questionnaires. We developed a Poisson model using least absolute shrinkage and selection operator penalization, leave-one-out cross-validation, and bootstrap resampling with 1000 iterations.

Results

Sample size at study start and end was 241 and 198 (82%), respectively. The number of fallers was 87 (36%), and the fall rate was 0.94 falls per person-year. Predictors included in the final model were educational level, dizziness, alcohol consumption, prior falls, self-perceived falls risk, disability, and depressive symptoms. Mean absolute error (95% CI) was 0.88 falls (0.71–1.16).

Conclusion

We developed a falls prediction model for community-dwelling older adults in a general population setting. The model was developed by selecting predictors from among physical and cognitive tests along with self-report questionnaires. The final model included only the questionnaire-based predictors, and its predictions had an average imprecision of less than one fall, thereby making it appropriate for clinical practice. Future external validation is needed.

Trial registration

Clinicaltrials.gov (NCT03608709).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-021-02346-z.