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Drugs as fall-related cause by view of physicians and clinical pharmacists (N = 280)
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This study aimed to analyze the effect of fall risk-increasing drugs (FRIDs) and drug-related factors relative to falls through clinical pharmacy service in hospitalized patients, focusing on the relevance of clinical pharmacist evaluation in the context of physician assessment. A prospective study of inpatient falls was conducted in 2017 retrievin...
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Citations
... Risk factors for falls are multifactorial and may include age, gender, or length of hospital stay [2]. It is well established that medication use is a risk factor for falls [4][5][6]. Fall-riskincreasing drugs (FRIDs) are widely prescribed for older people [6][7][8][9][10]. A strategy to avoid and reduce FRID use represents an essential component of a multifactorial falls risk management approach and should be implemented in routine practice in healthcare settings [6,20,21] to reduce the incidence of falls [6,[10][11][12][13][14][15][16][17][18][19]22]. ...
Background
Falls are a significant public health problem and constitute a major cause of injuries and mortality. Risk factors for falls are multifactorial and include medication use.
Aim
To develop and investigate the content validity of the Medication-Related fall (MRF) screening and scoring tool.
Method
The MRF tool was developed from clinical practice guidelines addressing medication-related problems, and additional medications identified by specialist pharmacists across a region of the United Kingdom (Northern Ireland). Medication classes were categorised according to their ‘potential to cause falls’ as: high-risk (three points), moderate-risk (two points) or low-risk (one point). The overall medication-related falls risk for the patient was determined by summing the scores for all medications. The MRF was validated using Delphi consensus methodology, whereby three iterative rounds of surveys were conducted using SurveyMonkey ® . Twenty-two experts from 10 countries determined their agreement with the falls risk associated with each medication on a 5-point Likert scale. Only medications with at least 75% of respondents agreeing or strongly agreeing were retained in the next round.
Results
Consensus was reached for 19 medications/medication classes to be included in the final version of the MRF tool; ten were classified as high-risk, eight as moderate-risk and one as low-risk.
Conclusion
The MRF tool is simple and has the potential to be integrated into medicines optimisation to reduce falls risk and negative fall-related outcomes. The score from the MRF tool can be used as a clinical parameter to assess the need for medication review and clinical interventions.
... Table 5 The most commonly used fall risk increasing drugs (Category A) having very common (≥1/10) or common (≥1/100) frequency of ADEs connected to fall risk according to statutory summary of product characteristics (SmPCs) [46,47]. Furthermore, the previous study from Czech Republic (Maly et al. [48]) classified drugs that affected to the nervous system (antipsychotics, antidepressants, analgesics) and to the cardiovascular system (diuretics, beta blocking agents, agents acting on the renin-angiotensin system) as the most frequently used fall risk-increasing drugs in hospitals. Thus, many studies have reached similar conclusions, although the fall-increasing medicines have been presented slightly in different order. ...
... According to our findings, the fall risk assessment tool presented in this study could be helpful to prevent medication-related falls and increase quality of geriatric care in health care institutions. This is supported by the findings of previous studies that have reported the medication use as a remarkable but modifiable fall risk factor [48,50]. Therefore, it is reasonable to implement a strategy to avoid use of fall risk-increasing drugs in the routine practice in geriatric care units. ...
Background
Falls are common undesirable events for older adults in institutions. Even though the patient’s fall risk may be scored on admission, the medication-induced fall risk may be ignored. This study developed a preliminary categorization of fall-risk-increasing drugs (FRIDs) to be added as a risk factor to the existing fall risk assessment tool routinely used in geriatric care units.
Methods
Medication use data of older adults who had experienced at least one fall during a hospital ward or a nursing home stay within a 2-year study period were retrospectively collected from patient records. Medicines used were classified into three risk categories (high, moderate and none) according to the fall risk information in statutory summaries of product characteristics (SmPCs). The fall risk categorization incorporated the relative frequency of such adverse drug effects (ADEs) in SmPCs that were known to be connected to fall risk (sedation, orthostatic hypotension, syncope, dizziness, drowsiness, changes in blood pressure or impaired balance). Also, distribution of fall risk scores assessed on admission without considering medications was counted.
Results
The fall-experienced patients (n = 188, 128 from the hospital and 60 from nursing home records) used altogether 1748 medicaments, including 216 different active substances. Of the active substances, 102 (47%) were categorized as high risk (category A) for increasing fall risk. Fall-experienced patients (n = 188) received a mean of 3.8 category A medicines (n = 710), 53% (n = 375) of which affected the nervous and 40% (n = 281) the cardiovascular system. Without considering medication-related fall risk, 53% (n = 100) of the patients were scored having a high fall risk (3 or 4 risk scores).
Conclusion
It was possible to develop a preliminary categorization of FRIDs basing on their adverse drug effect profile in SmPCs and frequency of use in older patients who had experienced at least one documented fall in a geriatric care unit. Even though more than half of the fall-experienced study participants had high fall risk scores on admission, their fall risk might have been underestimated as use of high fall risk medicines was common, even concomitant use. Further studies are needed to develop the FRID categorization and assess its impact on fall risk.
... It has been estimated that at least 5% of all hospital admissions are linked to medicines with almost half being preventable (142) (143). Work has been undertaken to mitigate this, including identifying the most likely medicines to cause admission (144), and developing tools to identify patients at risk, such as PREVENT (145) (151) and particularly falls-riskinducing drugs (152). This work also supports other literature in recognising that older people are more prone to falls and there is a need for clinicians to know more about medicines that may induce falls, such as anticholinergic medications (153). ...
This thesis describes the author’s publication history from 2001 to 2019, and relates this to their key career milestones from registration as a pharmacist in 1991. From a career output of over 80 items published in a variety of media, eleven key publications form the basis of four publication themes, which the author has related to the concept of medicines optimisation. An exemplar case is used to illustrate these publication themes, arranged into four chapters: a) improving the patient experience and supporting medication adherence b) providing safe care: medication review, polypharmacy and deprescribing c) making medicines optimisation part of routine practice through clinical education, and d) supporting safe practice through professional and personal development of healthcare staff. Following Chapter 1 (introduction), the second chapter discusses the author’s contribution to the medication adherence agenda which closely relates to their outputs encouraging the development of pharmacists’ consultation skills, particularly with patients who have a learning disability. The third chapter discusses the author’s published outputs in the areas of medication review, polypharmacy and deprescribing, the success of which they outline as contingent on the improved communication skills and person-centred approach described in Chapter 2. Chapters four and five discuss the author’s wide-ranging contribution as a clinical educator with a focus on developing others, which the author contends is an essential underpinning of the mission to deliver the benefits of medicines optimisation. The exemplar case from the introduction is briefly revisited to illustrate that the author’s publications directly relate to the challenges of the patient’s medication regime which in turn relate to three of the four Royal Pharmaceutical Society principles of medicines optimisation. The conclusion of this thesis includes a summary of the methodologies used in the key publications, and summarises the author’s belief that their career activity, leading to their publications, broadly align to the concept of medicines optimisation. Moreover, a recommendation can be made that education of all stakeholders should be explicitly mentioned in any future refinements of its definition.
Introduction
Falls are a significant public health problem and constitute a major cause of injuries and mortality. Risk factors for falls are multifactorial and include medication use.
Aim
To develop a medication-related fall (MRF) screening and scoring tool and to determine its content validity.
Methods
The MRF tool was developed from clinical practice guidelines addressing medication-related problems and additional medications identified by specialist pharmacists across a region of the United Kingdom (Northern Ireland (NI)). Medication classes were categorised according to their ‘potential to cause falls’ as: high-risk (three points), moderate-risk (two points) or low-risk (one point). The overall medication-related falls risk for the patient was determined by summing the scores for all medications. The MRF was validated using Delphi consensus methodology, whereby three iterative rounds of surveys were conducted using SurveyMonkey®. Twenty-two experts from 10 countries determined their agreement with the falls risk associated with each medication on a 5-point Likert scale. Only medications with at least 75% of respondents agreeing or strongly agreeing were retained in the next round.
Results
Consensus was reached for 19 medications/medication classes to be included in the final version of the MRF tool; ten were classified as high-risk, eight as moderate-risk and one as low-risk.
Conclusion
The MRF tool is simple and has the potential to be integrated into medicines optimisation to reduce falls risk and negative fall-related outcomes. The score from the MRF tool can be used to as a clinical parameter to assess the need for medication review and clinical interventions.
