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. 2017 Dec 20;1(2):igx031. doi: 10.1093/geroni/igx031

Deprescribing in Older Nursing Home Patients: Focus on Innovative Composite Measures for Dosage Deintensification

Sherrie L Aspinall 1,2,3,✉, Joseph T Hanlon 2,3,4,5,6, Joshua D Niznik 2,3,5, Sydney P Springer 1,2,3, Carolyn T Thorpe 2,3
PMCID: PMC6176971  PMID: 30564752

Abstract

Deprescribing, which includes stopping or reducing the dosage of medications, is designed to improve safety and prevent adverse drug reactions in older patients. To date, there has been limited work on measuring decreases in dosage intensity, or deintensification, across therapeutic classes of medications. Given the ongoing focus on central nervous system (CNS) medications and the frequency with which providers encounter hypertension and diabetes in older nursing home patients, the objective of this expert review is to describe and critique innovative composite dosage intensity measures that have been, or could be, applied to quantify deintensification within three therapeutic medication targets commonly encountered in nursing home patients: CNS agents, antihypertensive therapy, and antidiabetic therapy and the extent to which they are associated with health outcomes. Composite measures for CNS medication intensity considered dividing a patient’s daily dose by defined daily dosage (DDD), or the minimum effective adult or geriatric daily dosage. In contrast, composite measures for antihypertensives used either DDD or maximum recommended daily dosage in the denominator. We were not able to identify any composite measure of intensity for antidiabetic classes. There was a paucity of interventional studies that showed reducing the dosage intensity resulted in improved health outcomes. In conclusion, we identified several innovative composite measures of dosage intensity for CNS and antihypertensive medications, and discussed possible approaches for developing an antidiabetic regimen composite measure. It is critical for future research to compare and contrast various measures and to determine their impact on important clinical outcomes.

Keywords: Deintensification, deprescribing, nursing homes, polypharmacy, end of life

Translational Significance

This expert review of innovative approaches to measuring dosage intensity for therapeutic classes of medications that are commonly encountered in nursing home patients helps to inform the emerging area of deprescribing in gerontology.

It is well known that polypharmacy is common in older nursing home residents (Maher, Hanlon, & Hajjar, 2014). Of concern is that polypharmacy is an important risk factor for adverse drug reactions and other negative health outcomes such as falls and other geriatric syndromes (Maher et al., 2014). To address this concern, in recent years there has been increased, explicit recognition of the need to incorporate “deprescribing” into the medication use process (Bain et al., 2008). Deprescribing is the “process of tapering or stopping drugs, aimed at minimizing polypharmacy and improving patient outcomes” (Scott et al., 2015). In order to guide the discontinuation of medications that may have otherwise been indicated in patients late in life, Holmes, Hayley, Alexander, & Sachs (2006) proposed a model that incorporates remaining life expectancy, time until benefit, goals of care, and treatment targets. For example, in patients with limited life expectancy and a goal of palliation, appropriate medications would include those with a short time until benefit and those that target symptom control (Holmes et al., 2006). All others should be evaluated for discontinuation. Many older nursing home patients have limited life expectancy, and medication classes that could be targeted include those most commonly prescribed in this setting in the United States and Canada, namely endocrine agents (e.g., antidiabetics), central nervous system agents (i.e., benzodiazepines, antidepressants, antiepileptics, antipsychotics, opioids), and cardiovascular medications (e.g., antihypertensives) (Jokanovic, Tan, Dooley, Kirkpatrick, & Bell, 2015). Once a decision is made to taper off or stop medications, a common way to measure this form of deprescribing is to examine the reduction in the percentage or number of medications (Iyer, Naganathan, McLachlan, & Le Couteur, 2008; Tjia, Velten, Parsons, Valluri, & Briesacher, 2013).

Clinically, it is not always possible to discontinue medications. In these cases, deprescribing as originally conceptualized also includes reducing dosage intensity, further referred to as deintensification, to improve safety and prevent adverse drug reactions (Woodward, 2003). Within a therapeutic class, dosage intensity can be standardized by equating the dose of an individual agent to a dose of a common medication (Davis, 1974). This approach has been used with typical antipsychotics (chlorpromazine equivalents), benzodiazepines (diazepam equivalents), and opioids (morphine equivalents) (Beers et al., 1988; Ray, Federspiel, & Schaffner, 1980; Won et al., 2004). Defined daily dosage (DDD), defined as “the assumed average maintenance dose per day for a drug used for its main indication in adults,” has also been used to standardize dosage intensity within a therapeutic class (WHO Collaborating Centre for Drug Statistics Methodology). It requires the use of a therapeutic class coding system named The Anatomical Therapeutic Chemical (ATC) Classification System, which is primarily used in European drug utilization studies, and it is based on use of a drug at the population level. Unfortunately, there has been limited work measuring reduction in dosage intensity of a regimen for conditions requiring multiple medications across therapeutic classes (e.g., hypertension, diabetes) or for multiple medications that share similar adverse effects (e.g., central nervous system agents [CNS]). Given the ongoing focus on CNS medications and the frequency with which providers encounter hypertension and diabetes in older nursing home patients, the objective of this expert review is to describe and critique innovative composite dosage intensity measures that have been, or could be, applied to quantify deintensification for CNS agents, antihypertensive therapy, and antidiabetic agents and the extent to which they are associated with health outcomes. We will not be covering anticholinergic medications as they have been comprehensively addressed in recent reviews (Gray and Hanlon 2016; O’Donnell, Gnjidic, Nahas, Bell, & Hilmer, 2017).

Composite Dosage Intensity Measures for CNS Medications

The use of multiple CNS medications is common and increases the risk of falls in older nursing home patients (Sterke, Verhagen, van Beeck, & van der Cammen, 2008). This increased risk is likely due to the shared adverse effects among these medications, including sedation, slowing of reaction time, and impaired balance. Clinically, it is not always possible to discontinue CNS medications, because they may be needed (e.g., opioid for cancer pain). However, deintensification of the overall CNS medication dosage may be feasible to reduce the risk of falls.

This deintensification may be captured by three composite dosage intensity measures for CNS medications (Taipale, Hartikainen, & Bell, 2010). The first employs a WHO assigned DDD measure for individual agents within specific CNS classes (i.e., antipsychotics, anxiolytics, hypnotics, or sedatives). The equation for the CNS DDD measure is:

CNS DDD Measure= Patient Daily Dose/DDD.

The results are then summed to create the total number of units, or dosage intensity, for the CNS medications of interest. One study of nursing home patients with dementia from the Netherlands showed a dose–response relationship between combinations of psychotropic drug DDDs and falls (Sterke et al., 2012) However, as mentioned previously, the DDD is based on medication use at the population level and may not adequately reflect the actual level of exposure.

The second is the Drug Burden Index (DBI), which is based on pharmacodynamic principles that consider both anticholinergic and CNS medications with sedative properties (e.g., opioids, antiepileptics, benzodiazepines, antidepressants, antipsychotics, etc) (Hilmer et al., 2009). The equation for the DBI is:

DBI=Daily Dose/(Daily Dose + Minimum Daily Dose).

Thus, the range of scores for each drug is between 0 and 1, and these are summed across drugs. A study of older nursing home residents in Australia found a nearly two-fold increase in the odds of falling in those with a higher DBI (i.e., ≥1) (Wilson et al., 2011). Depending on the CNS medications and outcomes of interest, it may not be appropriate to consider both anticholinergic and sedative properties, which is a limitation of the DBI.

The third is the CNS Burden Measure (CBM); it considers the daily dosage of antidepressants (i.e., tricyclic, selective serotonin, and serotonin norepinephrine reuptake inhibitors), antiepileptics, antipsychotics, benzodiazepine receptor agonists, and opioid analgesics. The equation for the CBM is:

CBM=CNS Drug1MEGDD1+ CNS Drug2MEGDD2+… + CNS DrugkMEGDDk,

“CNS Drug” is the daily dose of the central nervous system drug, and “MEGDD” is the minimum effective geriatric daily dose (http://www.pepper.pitt.edu/factsfindings.html). The result is the standardized daily dose (SDD) for all CNS medications. One study of U.S. nursing home residents with a history of falls found that those exposed to 3+ SDDs had a nearly two-fold increased risk of serious falls (Hanlon et al., 2017).

To the best of our knowledge, no randomized controlled studies have been published examining the impact of reducing these CNS medication measures on health outcomes in nursing home patients. However, a small before-after study from Australia examined the effect of pharmacist drug regimen review with recommendations to the prescriber and found that the DBI score decreased by 20% over time (Nishtala, Hilmer, McLachlan, & Hannan, 2009) The DBI was also used in a small, clustered, randomized controlled trial in 12 Australian self-care retirement villages. The intervention, performed by a geriatrician/clinical pharmacologist, consisted of a letter and phone call to prescribers; this resulted in a decrease in the DBI of 32% and 19% in the intervention and control groups, respectively (Gnjidic, Le Couteur, Abernethy, & Hilmer, 2010) Neither of these studies examined the impact of decreasing the DBI on health outcomes.

Composite Dosage Intensity Measures for Hypertension

The blood pressure goal that optimizes the balance between benefits and risks in older adults with hypertension is controversial. Expert consensus guidelines imply that less stringent management of hypertension (e.g., SBP <150 mmHg) provides an acceptable cardiovascular benefit, while minimizing medication burden and reducing the potential for adverse events (Aronow et al., 2011; James et al., 2014) However, a recent randomized controlled trial found that intensive hypertension management (i.e., SBP goal <120 mmHg) was associated with significantly lower rates of cardiovascular events and death, with no significant increase in the risk of adverse drug events among adults ≥75 years of age (Williamson et al., 2016). Treatment to this goal often requires multiple medications. Despite the potential for additional benefit associated with intensive management, the risk of falls due to orthostatic hypotension must also be considered, especially at the end of life (Lipsitz et al., 2015) Therefore, deintensification should be contemplated.

Several composite measures have been utilized in observational studies of the relationship between dosage intensity and outcomes or adverse events. The first approach was originally proposed by Wan et al. (2009) and defines regimen intensity by calculating “antihypertensive load.” This measure is defined by the sum of the ratios of prescribed daily doses divided by the maximum recommended daily doses for all antihypertensives a patient receives. The equation for antihypertensive load is:

Antihypertensive load=Σ Antihypertensive medications(prescribed daily dosemaximum daily dose) 

It provides a continuous measure that allows a comparison of overall treatment intensity between individuals. “Antihypertensive load” has been used in two cohort studies; both of which did not find an association between antihypertensive intensity and falls in one (Marcum et al., 2015) and urinary incontinence in the other (Peron et al., 2012). A major limitation with this composite measure is that it assumes a linear dose-response relationship for all antihypertensive medications, which is not true. It also does not account for differential effects on blood pressure among the therapeutic classes and that response may vary depending on the number of concomitant medications. A second method utilizing DDD as a composite measure has been applied in two studies that also sought to determine the relationship between antihypertensive intensity and falls. (Callisaya, Sharman, Close, Lord, & Srikanth, 2014; Tinetti et al., 2014) Investigators were able to make comparisons across classes based on the number of “units” of antihypertensive medication prescribed. In Tinetti et al., the total DDD for all antihypertensive medications, divided by the number of days the patient was under observation, was categorized as none (0–0.2), moderate (0.2–2.5), or high (>2.5) intensity based on the distribution of the total DDD in the study sample (Tinetti et al., 2014); whereas in Callisaya et al. (2014), DDD was used as a continuous variable and subsequently categorized as 0, 1–3, and >3. Both studies showed an increased risk of falls with greater antihypertensive DDDs (Callisaya et al., 2014; Tinetti et al., 2014) Similar to the previously described approaches, use of DDD allows for comparison across medication classes and is fairly easy to calculate. However, it has the same limitations discussed previously for the “antihypertensive load.”

To the best of our knowledge, only two randomized clinical trials have studied the effects of deprescribing blood pressure medications in the elderly, with neither using composite measures of dosage intensity across therapeutic classes. The DANTE Study Leiden assessed the effect of stopping antihypertensive medications in older adults with orthostatic hypotension and cognitive impairment and found that 50% of those assigned to the discontinuation group did not have orthostatic hypotension during follow-up (Moonen et al., 2016). The second study by Hajjar, Hart, Wan, & Novak (2013) examined safety and blood pressure changes associated with gradual, short-term withdrawal of antihypertensive therapy in older adults. No patients reported symptoms during withdrawal of their medications (e.g., headaches, dizziness), and the overall increase in blood pressure was 12/6 mmHg (95% CI 4/1, 21/11) (Hajjar et al., 2013). While this approach to deprescribing is easier to apply, it fails to account for the use of multiple medications across therapeutic classes and at various doses which would more accurately quantify the degree of deintensification of an antihypertensive regimen.

Composite Dosage Intensity Measures for Type 2 Diabetes Mellitus

Diabetes in older adults has been linked to higher mortality, increased institutionalization, and a reduction in functional status and activities of daily living (Kirkman et al., 2012). However, management of diabetes in this population is difficult as they are a frequent cause of adverse drug reactions in older adults (Kirkman et al., 2012). Thus, it is not surprising that there are conflicting recommendations regarding the glycated hemoglobin (i.e., A1c) goal in older adults. The American Geriatric Society (AGS) and the American Diabetes Association (ADA) developed a consensus report in 2012 that recommended higher A1c goals for those with more comorbid conditions and shorter estimated life expectancy (e.g., <8.5% for those in long-term care or with end-stage illnesses) (Kirkman et al., 2012). A reason for liberalizing A1c goals in older adults is the increased risk of hypoglycemia. In addition, a U-shaped relationship between mortality and A1c has been reported, with higher mortality rates seen in those with an A1c <6.0% and >8.5–11%. (Hamada and Gulliford, 2016; Huang, Liu, Moffet, John, & Karter, 2011) Several studies have also shown little cardiovascular and microvascular benefit in those with multiple comorbidities when stringent A1c goals are achieved. (ADVANCE Collaborative Group, 2008; Duckworth et al., 2009; Greenfield et al., 2009; Gerstein et al., 2008).

However, little research has been published on deintensification of antidiabetic regimens and associated outcomes, and we did not identify any studies that used a composite measure of dosage intensity across antidiabetic medication classes. Goals of deintensification in older patients with diabetes include simplification of the medication regimen, reduction of hypoglycemic episodes, and avoidance of symptomatic hyperglycemia. Insulin dose reductions, or simplification of an insulin regimen, can be an ideal approach to deintensification (Munshi et al., 2016; Sjöblom et al., 2008). In a small study involving adults ≥65 years of age with type 2 diabetes (n = 65), insulin injections were reduced by changing mixed or meal-time insulin to once daily morning injections of insulin glargine or transitioning to a non-insulin regimen altogether (Munshi et al., 2016). The mean duration (in minutes over 5 days) of hypoglycemia decreased at 5 and 8 months, and the mean HbA1c overall remained unchanged (7.7% at baseline, 7.5% at 5 months, and 7.7% at 8 months). (Munshi et al. 2016) In a small study of nursing home patients in Sweden (N = 32) with tight glycemic control (i.e., HgA1c <6%), the mean HbA1c rose from 5.2% at baseline to 5.8% at 3 and 6 months after discontinuing oral medications and insulin, in those receiving ≤20 units daily, or cutting the insulin dose by 50% in those receiving >20 units daily. There was also a reduction in hypoglycemic events. (Sjöblom et al., 2008) Finally, a study by Sussman et al. (2015) reviewed those patients who had their diabetes regimen deintensified (i.e., medication discontinued or dose decreased [for non-insulin drugs]) and found that only 0.8% had a follow-up A1c >7.5%, supporting the low risk of rebound hyperglycemia upon medication discontinuation in those with already low A1cs.

Although the outcomes of these studies related to glucose control and adverse events are encouraging, and illustrate a few ways to stop or deintensify therapy (Munshi et al., 2016; Sjöblom et al., 2008; Sussman et al., 2015) they are not composite measures for dosage deintensification. This is important because most patients fail to maintain their goal A1c over time with monotherapy and require the addition of one or more antidiabetic medications. Therefore, more accurately measuring deintensification requires a method that can incorporate medications from different classes and various doses. Unfortunately, no composite measures have been published; however, developing one is possible. As described for antihypertensives (Wan et al., 2009), one option could be the creation of an “antidiabetic load” for non-insulin medications (i.e., sum the ratios of prescribed daily doses divided by maximum recommended daily doses for all antidiabetic agents a patient receives) that could be used as a continuous measure. However, there are limitations. Based on a systematic review and meta-analysis of oral antidiabetic drugs, there is not a linear relationship between dose and change in A1c (Sherifali, Nerenberg, Pullenayegum, Cheng, & Gerstein, 2010). In addition, the degree of A1c lowering is not the same across all classes of antidiabetic drugs and varies with monotherapy versus combination therapy (Inzucchi et al., 2015; Maruthur et al., 2016; Qaseem, Barry, Humphrey, Forciea, 2017). Finally, the degree of lowering is dependent on the baseline A1c, with a greater reduction occurring in those with higher baseline values, and duration of therapy (Inzucchi et al., 2015; Sherifali et al., 2010).

Another possibility would be to create a SDD based on linking the drug and dose with an expected decrease in A1c as has been done with the statins and their effect on low-density lipoprotein cholesterol (LDL). LaCroix et al. (2008) conducted a study examining exposure to statins on the outcome of frailty. Based on clinical trial data, statin daily doses were set to that required to reduce LDL cholesterol by 37%. Therefore, one unit of equivalent dose was 10 mg for atorvastatin, 80 mg for fluvastatin, 40 mg for lovastatin, 40 mg of pravastatin, 20 mg of simvastatin, and 5 mg for rosuvastatin. A similar approach was utilized by other studies examining the impact of statins on mobility (Gray et al., 2011; Lo-Ciganic et al., 2015). But, given the multiple factors that can influence the A1c level, it is challenging to compare the efficacy of individual antidiabetic agents and doses. Rather, ranges are usually given for the mean A1c reduction by class of drugs (Inzucchi et al., 2015; VA DoD Clinical Practice Guideline for the Management of Diabetes Mellitus in Primary Care, 2017). Therefore, categories could be created based on the degree of A1c lowering, and each patient’s regimen would be classified as “high-intensity” (i.e., receiving insulin ± other antidiabetic medications), “moderate-intensity” (e.g., receiving metformin or sulfonylurea ± other non-insulin antidiabetic agents), or “low-intensity” (e.g., DPP-4 inhibitors) (Maruthur et al., 2016; Sherifali et al., 2010).

For insulin, the intensity of regimens could be compared by creating an SDD using the patient’s total daily dose of insulin expressed in units or units/kg as the numerator and the usual starting dose in patients with Type 2 diabetes (i.e., 0.1–0.2 units/kg/day or 10 units/day) as the denominator (Inzucchi et al., 2015). Including insulin in the potential composite measures discussed previously is complicated, except for categorizing it as high-intensity, because there is theoretically neither a maximum dose nor maximum reduction in A1c level.

Integration and Conclusion

This article provided an expert review of measuring dosage intensity of a regimen both for conditions requiring multiple medications across therapeutic classes (e.g., hypertension, diabetes) and for multiple medications that share similar adverse effects (e.g., CNS agents). Most published approaches involved the summation of a patient’s daily dose for each medication divided by either DDDs or minimum, or maximum, effective daily dosage. A novel approach was presented that involved anchoring antidiabetic intensity on the percent of A1c lowering by class of agents and using this to create a categorical variable for low, medium, and high intensity.

However, there are issues that have yet to be addressed in the existing research. As discussed, there are no studies that created a composite measure for the use of multiple antidiabetic drug classes. In addition, there is only one randomized controlled trial that evaluated the effect of reducing dosage intensity across therapeutic classes; the study examined the association between a lower anticholinergic drug scale and cognition (Kersten et al., 2013). These issues should be addressed in the future, both conceptually and experimentally. As a first step, we plan to create a composite dosage measure for antidiabetic agents in our current Veterans Affairs Health Services Research and Development funded grant (IIR 14–306) entitled, “De-intensifying unnecessary medications in VA CLC residents nearing end of life.” Randomized controlled trials are needed to assess the impact of reducing dosage intensity on positive health outcomes (e.g., improved quality of life, fewer falls, decreased hypoglycemic episodes), while accounting for potential risks (e.g., depression, uncontrolled hypertension, symptomatic hyperglycemia) in vulnerable older nursing home patients.

In conclusion, we identified several innovative composite measures of dosage intensity for CNS and antihypertensive medications and discussed possible approaches for developing a composite measure for antidiabetic agents. Currently, it is unclear which composite dosage intensity measures are the most useful, but those incorporating DDD are probably less clinically relevant. Given differences in the effect of medications from the same therapeutic class on specific outcomes (e.g., metformin versus DPP-4 inhibitors on A1c), one measure is unlikely to fit all disease states and medications. Therefore, it is critical for future research to compare and contrast various measures and to determine their impact on important clinical outcomes.

Funding

This work was supported by grants from the National Institute on Aging (T32-AG021885, P30 AG024827), Agency for Health Research and Quality (R18-HS023779), and VA Health Services Research (IIR 12–379, IIR 14–297, IIR 14–306, IIR 15–115).

Conflict of Interest

None reported.

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