Potentially Inappropriate Prescribing and Falls-Risk Increasing Drugs in Fallers: A Systematic Review and Meta-Analysis

Overview and Study Objectives

• Background and Context: Medication-related harm is an international priority for patient safety. Falls are a common adverse outcome often linked to medication-related contributors. Beyond immediate injury, falls frequently lead to reduced mobility, loss of independence, premature admission into long-term care, and significant negative impacts on mental health (e.g., anxiety and post-traumatic stress disorder).

• Primary Objective: To systematically review and meta-analyze the prevalence and types of Potentially Inappropriate Prescribing (PIP) and Falls-Risk Increasing Drug (FRID) use in individuals who have already experienced a fall or fall-related event.

• Secondary Objectives:     * To identify the specific drug classes most frequently implicated in PIP among fallers.     * To determine whether the prevalence of PIP/FRID use changes following the occurrence of a fall.

• Significance of the Research: Falls function as "triggering events" that provide a clinical opportunity to optimize medication therapy. Understanding the scope of PIP allows for better targeting of interventions and deprescribing strategies to prevent recurrent falls and fractures.

Methodology of the Systematic Review

• Registration and Design: The review was preregistered on PROSPERO (CRD42023417534) and conducted following JBI (Joanna Briggs Institute) guidance and PRISMA 2020 reporting standards.

• Search Strategy: Conducted on July 5th, 2024, across multiple databases including MEDLINE (Ovid), EMBASE, CINAHL, and Google Scholar (via Harzing’s Publish or Perish). The search utilized keywords related to fall events, inappropriate prescribing, and FRIDs.

• Study Selection Criteria:     * Study Types: Observational studies (cohort, case-control, cross-sectional, before-after, quasi-experimental) and randomized trials.     * Population: Adults who had experienced a fall, fracture, or syncope. In studies with mixed populations (e.g., falls clinics), inclusion required that $\ge 70\%$ of participants had a fall/fracture or that data for the faller subgroup were separately reported.     * Case-Control Inclusion: Limited to studies assessing medication exposure within $90$ days prior to the fall.     * Exclusions: Systematic reviews, conference abstracts, protocols, commentaries, and case series. Studies focusing on single drug classes were also excluded.

• Outcome Definition: PIP was defined using validated tools (e.g., Beers criteria, STOPP/START), medication lists, or implicit clinical judgment. FRID use was categorized under psychotropic, cardiovascular, or "other" classes.

• Quality Assessment: Evaluated using the JBI Prevalence Critical Appraisal tool.

• Data Synthesis: Pooled prevalence and mean number of PIPs were calculated using random-effects meta-analysis via the metan package in Stata. Heterogeneity was assessed using the $I^2$ statistic and Cochran's $Q$ test.

Characteristics of Included Studies

• Volume and Distribution: The review included $50$ publications representing $46$ distinct studies published between 2007 and 2024.

• Geographic Scope: Studies spanned $21$ countries, including the USA ($n = 9$), Japan ($n = 5$), Sweden ($n = 4$), France ($n = 3$), UK ($n = 3$), and others such as Australia, Ireland, and Brazil.

• Study Settings:     * $33$ studies involved inpatients or Emergency Department attendees.     * $6$ studies targeted outpatient clinics.     * $5$ studies focused on community-dwelling individuals.     * Other settings included retirement facilities and nursing homes.

• Participant Demographics:     * Age: Average age was $\ge 65$ years across all studies, with $33$ studies having a mean/median age $\ge 80$ years.     * Gender: Females represented the majority of fallers in all but five studies (where males comprised $53.9\%$ to $62.1\%$ of the population).     * Sample Sizes: Ranged from a minimum of $29$ to a maximum of $1,678,037$ participants.

• Definitions of Falls: Varied across studies, utilizing free-text definitions based on literature, ICD (International Classification of Diseases) codes ($n = 12$), or orthopedic classification systems (e.g., AO/OTA fracture classification).

Definitions and Assessment Tools for PIP and FRIDs

• Prevalence Assessment Timeframes: $36$ studies measured PIP at a specific time point (most commonly at admission), while $10$ studies assessed prevalence over a period ranging from $3$ days to $12$ months pre-fall.

• Validated Tools Used:     * STOPP/START (Screening Tool of Older Persons' Prescriptions/Screening Tool to Alert to Right Treatment): Versions 1 and 2.     * Beers Criteria: Variously using the 2003, 2012, 2015, and 2019 updates by the American Geriatrics Society (AGS).     * STOPPFall: A specific tool focused on falls-risk increasing drugs.     * Drug Burden Index (DBI): Measures the cumulative exposure to anticholinergic and sedative medications.     * Anticholinergic Scales: Anticholinergic Drug Scale (ADS), Anticholinergic Cognitive Burden (ACB) score, and the Salahudeen extended Anticholinergic Risk Scale.     * Other tools: STEADI-Rx list, Medication Appropriateness Index (MAI), PRISCUS list (German-specific), and STOPP-J (Japanese adaptation).

Meta-Analysis Results: Prevalence of Inappropriate Prescribing

• Total Pooled Prevalence: Across $317,914$ participants in $43$ studies, the pooled estimate for PIP prevalence at the time of a fall was $68.6\%$ ($95\% \text{ CI } 66.1\%-71.2\%$, p < 0.001).

• Mean Number of PIPs: Pooled analysis of $23$ studies found an average of $2.21$ PIP occurrences per participant ($95\% \text{ CI } 1.98-2.45$).

• Heterogeneity Analysis:     * Extreme heterogeneity was observed ($I^2 = 99.5\%$     * Age Factor: Studies where participants had a mean/median age $\ge 85$ years showed a significantly higher prevalence of $83.5\%$ ($95\% \text{ CI } 76\%-91\%$     * Measurement Method Factor: Mean PIP was influenced by whether PIP was measured at a single time point (mean $2.12$) versus over a specific period (mean $2.77$), where $p = 0.034$ for between-group heterogeneity.

• Range of Findings: Reported prevalence in individual studies spanned from a low of $15\%$ to a high of $99\%$.

Analysis of Specific Drug Classes and Clinical Implications

• Most Common Drug Classes:     * Sedatives/Hypnotics: Reported in $13$ studies; prevalence ranged from $3.6\%$ to $36.5\%$. These drugs (e.g., benzodiazepines) impair balance and cognitive function.     * Opioids: Reported in $13$ studies; prevalence ranged from $8\%$ to $38.1\%$. Opioids cause sedation; age-related pharmacokinetic changes amplify these risks in older adults.     * Diuretics: Reported in $12$ studies; prevalence ranged from $12\%$ to $60.4\%$. These are high contributors to medication-induced orthostatic hypotension and volume depletion.     * Antidepressants: Reported in $12$ studies; prevalence ranged from $7.5\%$ to $56\%$.

• Clinical Implications: The prevalence of these drugs underscores the need for holistic assessments given that fallers often have multiple chronic conditions. While these medications may have been appropriate at the time of initial prescription, the occurrence of a fall alters the risk-benefit ratio.

Post-Fall Prescribing Patterns and Deprescribing

• Tracking Changes Post-Fall: $21$ studies assessed PIP prevalence after the fall event (at discharge or up to $12$ months later).

• Observations:     * Reductions: $9$ studies identified a decrease in PIP post-fall.     * Increases: $5$ studies identified an increase in PIP post-fall.     * Neutral/Mixed: $3$ studies found no change, and $5$ studies found mixed results across different follow-up periods or drug classes.

• Barriers to Deprescribing: Long-term use of medications like benzodiazepines often leads to dependency, making them difficult to stop. Current guidelines, such as those for benzodiazepines, opioids, and the developing diuretic guidelines, provide evidence-based frameworks for reducing medications where risks outweigh benefits.

Strengths, Limitations, and Research Implications

• Strengths: Inclusive approach toward various fall types, fracture contexts, and PIP definitions; preregistered protocol ensuring methodological rigor; inclusion of $12$ studies more than previous similar reviews.

• Limitations:     * Heterogeneity: Significant differences in study design, populations, and PIP criteria make direct comparisons difficult.     * Literature Focus: Only peer-reviewed literature was searched; grey literature was omitted.     * Data Gaps: Lack of standardized reporting at individual drug class levels prevented more granular statistical analysis for certain subclasses.

• Future Implications:     * Standardization: Researchers should adopt a standardized approach for reporting drug class prevalence.     * Intervention Targeting: Deprescribing interventions, as part of multifactorial strategies (World Guidelines for Fall Prevention), should focus on individuals taking FRIDs with high evidence of impact on fall risk.     * Medicine Review: Admission for a fall (and subsequent Comprehensive Geriatric Assessment) should be a mandatory prompt for medication optimized review to improve patient outcomes in the "oldest old" (aged $\ge 85$).

The article presents various graphs that summarize key findings regarding Potentially Inappropriate Prescribing (PIP) and Falls-Risk Increasing Drug (FRID) use among individuals who have experienced falls. Here's how to read the graphs: 1. PIP Prevalence Graph: This graph showcases the percentage of individuals with PIP at the time of their fall. The y-axis typically represents PIP prevalence (%), while the x-axis identifies different studies or categories. The pooled estimate line may indicate the average prevalence across studies. To interpret: look for the height of the bars or points to gauge how many fallers were prescribed inappropriate medications. 2. Drug Classes Graph: Graphs like this often highlight the different drug classes contributing to fall risk (e.g., sedatives, opioids). The y-axis might show prevalence by percentage for each drug class on the x-axis. You can read it by checking which classes have the highest bars to understand which medications are most associated with increased fall risk. 3. Post-Fall PIP Changes Graph: This graph illustrates changes in PIP prevalence after a fall event over time (e.g., at discharge versus 12 months later). It typically uses lines or bars for different time points. To read this, observe the trend: whether the percentage of individuals with PIP increases, decreases, or stays the same over time, which indicates the effectiveness of interventions. 4. Comparative Studies Graph: When comparing multiple studies, this graph uses side-by-side bars or clusters to show differences in PIP prevalence or drug use across different demographics or geographical locations. The height difference between the bars serves as an indicator of how prescribing practices might vary by study. For all graphs, pay attention to the legends, which explain the color codes or markers used, to understand the data accurately.