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

Validation of a clinical prediction model for falls in community-dwelling older adults with COPD: A preliminary analysis

BACKGROUND

People with chronic obstructive pulmonary disease (COPD) are at a higher risk of falls. This preliminary study aims to externally validate a previously developed clinical prediction model for falls in community-dwelling older adults with COPD.

METHODS

This was a secondary analysis of a 12-month prospective cohort study. Older adults (≥60 years) with COPD, who reported a fall in the past year and/or had balance concerns, were tracked for 12-month future falls. Baseline predictors included 12-month history of ≥2 falls, total chronic conditions, and Timed Up and Go Dual-Task (TUG-DT) test scores. Model performance was assessed for discrimination (c-statistic), calibration (E:O, CITL, and calibration slope), and clinical value (decision curve analysis).

RESULTS

The study included 89 participants (average age 73 ± 9 years; 83 females; FEV1%predicted = 47%). Of these, 35 (39%) reported ≥1 future fall, totaling 89 falls. The model demonstrated acceptable discrimination (c-statistic = 0.62, CI [0.51,0.72]), and calibration (E:O = 1, CITL = 0, and a calibration slope = 1). Decision curve analysis showed greater clinical value when using the prediction model compared to screening for fall history alone.

CONCLUSIONS

A 12-month history of ≥2 falls, higher total chronic conditions, and worse TUG-DT test scores, predicts falls in community-dwelling older adults with COPD. Larger studies are needed before clinical application.

Development of the ADFICE_IT clinical decision support system to assist deprescribing of fall-risk increasing drugs: A user-centered design approach

INTRODUCTION

Deprescribing fall-risk increasing drugs (FRIDs) is promising for reducing the risk of falling in older adults. Applying appropriate deprescribing in practice can be difficult due to the outcome uncertainties associated with stopping FRIDs. The ADFICE_IT intervention addresses this complexity with a clinical decision support system (CDSS) that facilitates optimum deprescribing of FRIDs by using a fall-risk prediction model, aggregation of deprescribing guidelines, and joint medication management.

METHODS

The development process of the CDSS is described in this paper. Development followed a user-centered design approach in which users and experts were involved throughout each phase. In phase I, a prototype of the CDSS was developed which involved a literature and systematic review, European survey (n = 581), and semi-structured interviews with clinicians (n = 19), as well as the aggregation and testing of deprescribing guidelines and the development of the fall-risk prediction model. In phase II, the feasibility of the CDSS was tested by means of two usability testing rounds with users (n = 11).

RESULTS

The final CDSS consists of five web pages. A connection between the Electronic Health Record allows for the retrieval of patient data into the CDSS. Key design requirements for the CDSS include easy-to-use features for fast-paced clinical environments, actionable deprescribing recommendations, information transparency, and visualization of the patient's fall-risk estimation. Key elements for the software include a modular architecture, open source, and good security.

CONCLUSION

The ADFICE_IT CDSS supports physicians in deprescribing FRIDs optimally to prevent falls in older patients. Due to continuous user and expert involvement, each new feedback round led to an improved version of the system. Currently, a cluster-randomized controlled trial with process evaluation at hospitals in the Netherlands is being conducted to test the effect of the CDSS on falls. The trial is registered with ClinicalTrials.gov (date; 7-7-2022, identifier: NCT05449470).

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.

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.

Effects of a clinical decision support system and patient portal for preventing medication-related falls in older fallers: Protocol of a cluster randomized controlled trial with embedded process and economic evaluations (ADFICE_IT)

BACKGROUND

Falls are the leading cause of injury-related mortality and hospitalization among adults aged ≥ 65 years. An important modifiable fall-risk factor is use of fall-risk increasing drugs (FRIDs). However, deprescribing is not always attempted or performed successfully. The ADFICE_IT trial evaluates the combined use of a clinical decision support system (CDSS) and a patient portal for optimizing the deprescribing of FRIDs in older fallers. The intervention aims to optimize and enhance shared decision making (SDM) and consequently prevent injurious falls and reduce healthcare-related costs.

METHODS

A multicenter, cluster-randomized controlled trial with process evaluation will be conducted among hospitals in the Netherlands. We aim to include 856 individuals aged ≥ 65 years that visit the falls clinic due to a fall. The intervention comprises the combined use of a CDSS and a patient portal. The CDSS provides guideline-based advice with regard to deprescribing and an individual fall-risk estimation, as calculated by an embedded prediction model. The patient portal provides educational information and a summary of the patient's consultation. Hospitals in the control arm will provide care-as-usual. Fall-calendars will be used for measuring the time to first injurious fall (primary outcome) and secondary fall outcomes during one year. Other measurements will be conducted at baseline, 3, 6, and 12 months and include quality of life, cost-effectiveness, feasibility, and shared decision-making measures. Data will be analyzed according to the intention-to-treat principle. Difference in time to injurious fall between the intervention and control group will be analyzed using multilevel Cox regression.

DISCUSSION

The findings of this study will add valuable insights about how digital health informatics tools that target physicians and older adults can optimize deprescribing and support SDM. We expect the CDSS and patient portal to aid in deprescribing of FRIDs, resulting in a reduction in falls and related injuries.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05449470 (7-7-2022).

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.

Factors associated with falls in older adults: A secondary analysis of a 12-month randomized controlled trial

PURPOSE

While identifying older adults at risk for falls is important, fall prediction models have had limited success, in part because of a poor understanding of which physical function measures to include. The purpose of this secondary analysis was to determine physical function measures that are associated with future falls in older adults.

METHODS

In a 12-month trial comparing Vitamin D3 supplementation versus placebo on neuromuscular function, 124 older adults completed physical function measures at baseline, including the Short Physical Performance Battery (SPPB), Timed Up and Go, tests of leg strength and power, standing balance on a force plate with firm and foam surfaces, and walking over an instrumented walkway. Falls were recorded with monthly diaries over 12 months and categorized as no falls vs. one or more falls. Univariate and multivariable logistic regression adjusting for demographics, treatment assignment, depression, and prescription medications were conducted to examine the association between each physical function measure and future falls. Models were additionally adjusted for fall history.

RESULTS

61 participants sustained one or more falls. In univariate analysis, white race, depression, fall history, SPPB, and postural stability on foam were significantly associated with future falls. In multivariable analysis, fall history (OR (95% CI): 3.20 (1.42-7.43)), SPPB (0.80 (0.62-1.01)), and postural stability on foam (3.01 (1.18, 8.45)) were each significantly associated with future falls. After adjusting for fall history, only postural stability on foam was significantly associated with falls.

CONCLUSIONS

When developing fall prediction models, fall history, the SPPB, and postural stability when standing on foam should be considered.

Allopurinol attenuates repeated traumatic brain injury in old rats: A preliminary report

Traumatic brain injury (TBI) is an overlooked cause of morbidity, which was shown to accelerate inflammation, oxidative stress, and neuronal cell loss and is associated with spatial learning and memory impairments and some psychiatric disturbances in older adults. However, there is no effective treatment in order to offer a favorable outcome encompassing a good recovery after TBI in older adults. Hence, the present study aimed to investigate the histological and neurobehavioral effects of Allopurinol (ALL) in older rats that received repeated TBI (rTBI). For this purpose, a weight-drop rTBI model was used on old male Wistar rats. Rats received 5 repeated TBI/sham injuries 24 h apart and were treated with saline or Allopurinol 100 mg/kg, i.p. each time. They were randomly assigned to three groups: control group (no injury); rTBI group (received 5 rTBI and treated with saline); rTBI+ALL group (received 5 rTBI and treated with Allopurinol). Then, half of the animals from each group were sacrificed on day 6 and the remaining animals were assessed with Open field, Elevated plus maze and Morris Water Maze test. Basic neurological tasks were evaluated with neurological assessment protocol every other day until after the 19th day from the last injury. Brain sections were processed for neuronal cell count in the hippocampus (CA1), dentate gyrus (DG), and prefrontal cortex (PC). Also, an immunohistochemical assay was performed to determine NeuN, iNOS, and TNFα levels in the brain regions. The number of neurons was markedly reduced in CA1, GD, and PC in rats receiving saline compared to those receiving allopurinol treatment. Immunohistochemical analysis showed marked induction of iNOS and TNFα expression in the brain tissues which were reduced after allopurinol at 6 and 19 days post-injury. Also, ALL-treated rats demonstrated a remarkable induce in NeuN expression, indicating a reduction in rTBI-induced neuronal cell death. In neurobehavioral analyses, time spent in closed arms, in the corner of the open field, swimming latency, and distance were impaired in injured rats; however, all of them were significantly improved by allopurinol therapy. To sum up, this study demonstrated that ALL may mitigate rTBI-induced damage in aged rats, which suggests ALL as a potential therapeutic strategy for the treatment of recurrent TBI.