The Association Between Anticholinergic Burden and Treatment-Related Complications in Older Adults with Cancer.

Key Points

Background

In the USA, more than 50% of newly diagnosed cancers and about 70% of cancer-related deaths are in patients of ≥ 65 years [ 1 ]. It is expected that cancer incidence in older adults will represent nearly 60% of all cancer diagnoses by 2035 [ 2 ]. Of these patients, > 90% have one or more comorbidities [ 3 ]. As many comorbidities are treated with medication, polypharmacy is highly prevalent in this population and may be as high as 50–80% [ 3 – 5 ]. Older adults with cancer are at an even higher risk of polypharmacy due to additional drugs that are prescribed as part of cancer treatment or supportive care management [ 6 – 9 ]. It is known that polypharmacy is associated with various negative health outcomes, including adverse drug reactions, falls, hospitalization, complications, mortality, and decreased quality of life [ 10 , 11 – 16 ].

Although polypharmacy may negatively impact various outcomes in older adults with cancer, some medication groups may be related to negative outcomes in a more pronounced way. Medications with anticholinergic properties cause specific adverse drug reactions to which older adults are more prone. Examples are dry mouth, drowsiness, and constipation, but more severe adverse effects also include falls, delirium, and cognitive impairment [ 17 , 18 ].

Anticholinergic effects of medications can be expressed using the Anticholinergic Burden score (ACB score). The ACB score is the combined anticholinergic score of all medications of a patient. A limited number of studies indicate that older adults with cancer have a higher ACB score compared with patients without cancer due to anticholinergic medications that are part of anticancer treatment—e.g., dexamethasone—or to manage symptoms related to the cancer—e.g., opioid analgesics [ 19 , 20 ]. As a result, patients with cancer have a higher risk of anticholinergic adverse drug reactions, decreased functioning and quality of life, and increased mortality [ 19 – 21 ].

Some medications are well known for their anticholinergic properties, but other medications are not, although these also may exhibit—modest—anticholinergic effects. Cumulatively, these medications can lead to a high, but not recognized, ACB burden in older patients.

Whereas a few studies have demonstrated that ACB is associated with adverse health outcomes in older adults with cancer [ 19 – 21 ], it is unknown whether ACB affects treatment outcomes and other relevant outcomes of anticancer treatment in these patients. The primary objective of this study is to investigate the association between ACB score and treatment-related toxicity in older adults with cancer who receive chemotherapy and/or targeted therapy. Secondary objectives are the association of ACB-score and dose reductions or treatment discontinuation, emergency room (ER) visits, or hospitalization and functional decline.

Methods

Setting and Population

We used the data of the Triage of Elderly Needing Treatment (TENT) study, an ongoing prospective observational study that started in 2016 and includes patients aged ≥ 70 years who are candidates for intensive treatment in oncology, cardiovascular, thoracic, and orthopedic outpatient clinics. The design and rationale of the TENT study have been published previously [ 22 ]. The TENT study is approved by the medical ethics committee of the Leiden University Medical Center (ID number NL53575.058.15) and registered at the Dutch Trial Register (NL8107), and all participants provided written informed consent. The TENT study complies with the reporting guidelines according to Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) [ 23 ]. For the current study, patients were included if they received chemotherapy, targeted therapy, or a combination of these. Targeted therapies include monoclonal antibodies aimed at human epidermal growth factor receptor (HER2) inhibitors, epidermal growth factor receptor (EGFR) inhibitors, vascular endothelial growth factor receptor (VEGF) inhibitors, B-RAF kinase inhibitors, mitogen-activated protein kinase (MEK) inhibitors, tyrosine kinase inhibitors (TKIs), poly-ADP ribose polymerase (PARP) inhibitors, and cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors. Immunotherapy was not considered targeted therapy. Patients treated with chemoradiation were excluded.

Data Collection

Baseline variables were collected between the time of diagnosis and the start of treatment and the follow-up was 12 months. Age, sex, World Health Organization Performance Score (WHO PS), comorbidities, and tumor- and treatment-related characteristics were retrieved from the digital patient files. Participants underwent geriatric screening before start of the treatment, which consisted of the Geriatric 8 questionnaire (G8) [ 24 ] and the Six-Item Cognitive Impairment Test (6CIT) [ 25 ]. If G8 or 6CIT indicated risk of frailty, participants underwent a Geriatric Assessment (GA). If both tools indicated no risk of frailty [ 22 ], a GA was performed by telephone. The cutoff scores to indicate risk of frailty were ≤ 14 for G8 and > 7 for 6CIT. Comorbidities were scored using the Charlson Comorbidity Index (CCI) [ 26 ].

ACB Score

Data on medication use were extracted from the digital patient files at baseline. The ACB score for each medication was assessed using the ACB Calculator [ 27 ], in which medications are classified according to their anticholinergic potency:

0: no anticholinergic potency

1: low anticholinergic potency

2: medium anticholinergic potency

3: high anticholinergic potency.

The ACB score for each patient was calculated by adding together the scores of each individual drug. A cumulative ACB score of 0–2 was considered low and an ACB score of ≥ 3 was considered high.

Various scales exist for measuring anticholinergic burden, which show only moderate concordance [ 28 ]. ACB Calculator was selected in this study as it combines the scores of two frequently used scales, the anticholinergic cognitive burden scale [ 29 ] and the German anticholinergic burden scale [ 30 ].

Geriatric Assessment

The somatic, functional, psychological, and social domains were assessed. The somatic domain included comorbidity (CCI ≥ 1), polypharmacy (presence of ≥ 5 medications for systemic use), and nutritional status. Nutritional status was assessed using the Mini Nutritional Assessment Short form with scores ranging from 0 to 14. Higher scores indicate better nutritional status and the cutoff for risk of malnutrition was < 12 points [ 31 ]. The functional domain consisted of falls within the past 6 months, institutionalization, and functional dependency (ADL and IADL). The Katz Index of Independence in Activities of Daily Living (ADL) [ 32 ] was used to assess functional status. Scores range from 0 to 6, with lower scores indicating dependence, and scores ≤ 2 indicating abnormal functional dependence. Urinary incontinence that can be managed by the patients with absorbent pads did not contribute to dependence. Independence in instrumental ADL was assessed using the Lawton Instrumental ADL (IADL) [ 33 ] with scores ranging from 0 to 5 for men and from 0 to 8 for women, with higher scores indicating independence. Abnormal dependence cutoff was ≤ 4 for men and ≤ 7 for women.

Dementia, history of delirium, and cognitive impairments (6CIT > 7) were the parameters of the psychological domain, and the social domain was considered abnormal for patients living alone. If one or more tests in a domain were scored as abnormal, the domain was considered abnormal. Participants who scored abnormally on two or more of four domains were classified as frail. Participants were contacted by telephone 6 and 12 months after initiation of treatment to obtain ADL and IADL questionnaires.

Outcomes

The primary endpoint was grade ≥ 3 toxicity, assessed within 3 months from the end of completion of the treatment for chemotherapy or until the end of follow-up for targeted therapy, as targeted therapy may lead to delayed toxicity occurring even after discontinuation of treatment [ 34 ]. Toxicity was assessed according to the National Cancer Institute Common Terminology Criteria for Adverse Events version 5.0 (NCI CTC-AE v5.0) [ 35 ], and grade ≥ 3 toxicity was considered severe toxicity. Secondary endpoints were functional decline and emergency room (ER) visits or hospitalizations within 12 months of follow-up. Functional decline was defined as decline in ADL (≥ 1 point increase or new institutionalization) or IADL (≥ 1 point decrease) within 12 months. Patients were followed up for 12 months after start of treatment or until death. Data on mortality were collected from electronic medical records or from municipal registries.

Arguably, death can be considered the ultimate form of functional failure. Therefore, we designed a composite endpoint consisting of either functional decline or mortality within 12 months [ 36 ].

Statistical Analysis

Data on patient-, tumor-, and treatment-related characteristics were summarized as median and interquartile range (IQR) for continuous variables and numbers (percentage) for categorical variables. Differences between ACB score and patient characteristics were analyzed using chi-squared tests for categorical values or Mann–Whitney U test for continuous variables, as these were nonnormally distributed. Associations between ACB score and primary and secondary outcomes were analyzed using univariable and multivariable logistic regression models in a stepwise approach. Potential confounding variables were predefined by creating a directed acyclic graph (Supplementary Fig. 1 ) [ 37 ] and frailty and treatment intention were added on the basis of their known impact on toxicity. A direct acyclic graph (DAG), also known as a causal Bayesian network, shows the causal paths between exposure, outcome, and variables and is used for identifying potential confounders in a transparent way. A DAG forces one to consider the causal paths instead of relying on statistical methods only and prevents unnecessary variables in the multivariable model. The potential confounders included in the models were age, sex, comorbidities, frailty, and treatment intention. The outcome functional decline was performed in the “while alive” population [ 36 ].

Analyses were conducted using complete-case data; records with missing outcome values were excluded on a per-variable basis. A p -value of < 0.05 was considered statistically significant. All analyses were performed using IBM SPSS version 28.

Results

Baseline Characteristics

A total of 434 patients were included with a median age of 75.4 years (IQR 6) and 55.8% male. Treatment intention was curative for 63.4% of the patients; 87.7% received chemotherapy, 5.6% targeted therapy, and 6.8% a combination of chemotherapy and targeted therapy. CCI was 0 or 1 for 37.8% and 25.3% of the patients, respectively, and polypharmacy was present in 50.0% of the patients. Nearly all (96.3%) participants were ADL independent at the start of treatment and 70.7% were IADL independent. Frailty was present in 58.1% of patients. ACB score was high for 14.5% of patients. As presented in Table 1 , among patients with a higher ACB score, CCI ( p = 0.015), history of delirium ( p = 0.007), falls in last 6 months ( p = 0.005), polypharmacy ( p < 0.001), and frailty ( p = 0.004) were significantly more prevalent. The most frequently used medications with anticholinergic properties were pantoprazole (30.0%), omeprazole (20.5%), and metformin (15.9%), as presented in Table 2 .

Toxicity

Grade ≥ 3 toxicity occurred in 50.2% of all patients and more frequently in patients with a high compared with a low ACB score (65.6% versus 48.2%, p = 0.013). Table 3 presents the results of the uni- and multivariable analysis of the association between grade ≥ 3 toxicity and ACB score and potential confounders. ACB score was associated with grade ≥ 3 toxicity in the crude model (OR 2.04, 95% CI 1.16–3.60) and in all adjusted models. In the multivariable model, ACB and frailty were associated with grade ≥ 3 toxicity. Overall results on grade ≥ 3 toxicity across ACB groups are presented in Supplementary Table 1 . Supplementary Tables 2 A–D present regression models adjusted for all potential confounders.

Logistic regression analysis of ACB score, frailty, and their interaction on toxicity revealed no significant results, demonstrating no interaction between these variables (Supplementary Table 3 ).

Secondary Outcomes

After 12 months of follow-up, 112 people had died and survival status was unknown for 3 patients. Functional decline—established in patients still alive—after 12 months occurred in 61.0% of patients with a high ACB score and in 36.6% in patients with a low ACB score (OR 2.71, p = 0.004), and ER visit or hospitalization occurred in 48.3% versus 32.1% (OR 1.98, p = 0.018). The results for the univariable analyses are shown in Fig. 1 A and B. ACB score, sex, and treatment intention were associated with functional decline in both univariable and multivariable analysis (Supplementary Tables 4 A–E). ACB score was also associated with ER visits or hospitalization in the univariable and multivariable analysis, but the other variables were not (Supplementary Tables 5 A–E).

The multinomial regression analysis with mortality as a competing risk for functional decline showed that patients with a high ACB score had a significantly higher risk of functional decline (OR 2.13, p = 0.015). The composite endpoint of functional decline or mortality occurred in 70.4% of patients with a high ACB score and in 55.5% with a low ACB score (OR 1.90, p = 0.043). In the adjusted model, only a palliative treatment intention was associated with the composite outcome (Supplementary Tables 6 A–E).

Discussion

The primary goal of this study was to investigate the association between ACB and the occurrence of grade ≥ 3 toxicity in patients ≥ 70 years who were treated for cancer with chemotherapy, targeted therapy, or a combination of these. A higher anticholinergic burden was associated with poorer outcomes, as grade ≥ 3 toxicity occurred more frequently in patients with a high ACB score in the univariable analysis. Furthermore, patients with a high ACB score also showed more functional decline and ER visits or hospitalizations. The multivariable model revealed that both ACB score and frailty were associated with grade ≥ 3 toxicity. A high ACB score was associated with the composite outcome of functional decline or mortality in the univariable analysis, as was palliative treatment intention. In the multivariable model, only an association with palliative treatment intention remained. Patients receiving palliative treatment are more likely to die within 12 months. As the proportion of patients who died were a large part of all patients with this outcome, the association with the outcome is according to expectation.

The negative effects of anticholinergic medications are known in the general older population and associations with poor health outcomes have been established in various patient groups. This study analyzed their associations with grade ≥ 3 toxicity, functional decline, and ER visits or hospitalization in older adults treated for cancer. This is in line with a Brazilian study in patients with multiple myeloma, which showed that anticholinergic drug use was associated with reduced functioning scores [ 38 ].

Besides the effects of the medication itself, the use of anticholinergic medications may also reflect a poorer condition or deteriorating clinical status of the patient. Indeed, prescription of medication with anticholinergic properties may be increased in patients with comorbidities associated with frailty. ACB is associated with frailty, as patients with multiple morbidities and a poorer physical condition tend to use more medications and hence show a higher anticholinergic burden. At the same time, frail patients are more prone to anticholinergic effects of medications.

As a result, the seemingly plausible option to reduce the anticholinergic burden prior to start of anticancer treatment is not a means to reduce toxicity. Yet, the ACB score can provide a simple way as an additional means for clinicians to become aware of potentially frail and vulnerable patients on the basis of the association between frailty and ACB. Both oncologists and pharmacists usually have the medication overview readily available. The ACB score can be calculated quickly using an online tool such as http://www.acbcalc.com . In addition, a high ACB score is associated with functional decline, and clinicians could utilize that information to support decisions on geriatric assessment, treatment of cancer in older adults, and geriatric co-management.

When discontinuation or substitution of drugs with anticholinergic properties is considered, other activities should be taken into consideration as well. Metformin, for example, exhibits minor anticholinergic effects and may be discontinued when sufficient glucose regulation can be attained without this drug [ 39 ]. Yet, metformin also may have antiproliferative properties in certain types of cancer [ 40 , 41 ] and careful consideration of the potential benefits and adverse effects is necessary.

Frailty is a well-known risk factor for chemotherapy toxicity and poor outcomes in older adults with cancer, and a comprehensive geriatric assessment may be used to identify patients prone to those outcomes and improve outcomes. This study shows that both anticholinergic burden and frailty contribute to toxicity independently, and somewhat unexpected, no interaction between ACB score and frailty was identified for their risk of toxicity. In frail older adults, anticholinergic burden should be treated as a potentially reversible contributor to frailty, delirium risk, falls, and functional decline—not just as a medication side-effect score [ 42 – 44 ].

The question arises of whether anticholinergic burden is an independent risk factor for treatment toxicity or whether its effect is mediated by frailty. The relationship between anticholinergic burden and frailty is likely bidirectional. Frail patients are more likely to use multiple medications, including drugs with anticholinergic properties, while the anticholinergic effects themselves may further contribute to frailty through their impact on cognition, mobility, muscle strength, and overall functional status. Proposed mechanisms include central and peripheral cholinergic dysfunction, sedation, orthostatic hypotension, impaired balance, and negative effects on physical functioning and nutritional status. This study shows that both anticholinergic burden and frailty contribute to toxicity independently, and somewhat unexpected, no interaction between ACB score and frailty was identified for their risk of toxicity.

Use of medications with anticholinergic properties is common, and in various populations of older adults, a high ACB score is present in 12–36% [ 20 , 45 – 48 ], which is comparable to the prevalence of 11–46% in patients with cancer [ 19 , 20 ]. This study found a prevalence of 14% for a high ACB score, which is in the lower end of the expected range. One possible explanation may be that physicians in the Netherlands tend to be more conservative in prescribing medications than their peers in other countries [ 49 ]. In addition, this study used the common cutoff of ACB ≥ 3 for a high ACB score, whereas some other investigators used a lower threshold of ACB ≥ 2 [ 20 ], which would result in a larger proportion of patients in the higher ACB group.

It is to be expected that patients with cancer generally have a higher anticholinergic burden compared with patients without cancer.

Disease progression and increase in cancer-related symptoms are associated with an increase in the use of anticholinergic medications as described by other investigators. Lee et al. found an increase in ACB score in patients with stage IV solid cancers during hospitalization, which was associated with higher in-hospital mortality [ 21 ]. Several medications with anticholinergic properties are commonly used in patients with cancer, for example, dexamethasone in chemotherapy treatment regimen or antihistamines to reduce infusion-related reactions. In addition, medications frequently used in symptom management of patients in palliative care show anticholinergic effects [ 50 ], such as haloperidol or quetiapine for the management of delirium or opioid analgesics for the management of pain. However, most of such changes occur after the diagnosis of cancer when treatment starts and symptoms increase.

Strengths and Weaknesses

A strength of this study is that the study population was recruited from several hospitals. The study population is a representative reflection of the general population of older adults with cancer in the Netherlands. Another strength is that participants were contacted by telephone for follow-up questionnaires if they were still alive, leading to a low percentage of loss to follow-up at 12 months. A weakness is that the reason for ER visits or hospitalizations is not registered. These endpoints can be the result of toxicity, but may also be due to disease progression, which is unrelated to the ACB. Furthermore, time-to-event analysis of ER-visits or hospitalizations was not possible due to the nature of the data. Data on medication use were extracted from medical records, but dispensing data from the pharmacy were not included. Therefore, nonadherence to medication is not accounted for and may attenuate a possible association between ACB score and outcomes.

Future Research

More research is needed on the effects of anticholinergic medication in older, especially frail adults who receive treatment for cancer to establish to what extent a decrease in ACB improves treatment tolerance and possibly prevents functional decline. Ideally, a comprehensive medication review before the start of cancer treatment should be performed with specific attention for decreasing the ACB. In addition, grade ≥ 2 toxicity may have serious impact on the quality of life in older adults and may lead to preliminary treatment discontinuation. The combined effects of ACB and grade ≥ 2 toxicities are worth investigating as well.

Conclusions

Older adults with cancer frequently use medications with anticholinergic properties. A high ACB is associated with poorer outcomes, such as grade ≥ 3 toxicity, functional decline, and ER visits or hospitalization. A high ACB score has an independent effect on the risk for toxicity, additional to the known risk of frailty.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 1264 KB)

Funding

No funding was received to assist with the preparation of this manuscript.

Declarations

Conflict of Interest

The authors have nothing to declare.

Ethics Approval

The study protocol was approved by the appropriate institutional review board. The original study from which the data were extracted, was approved by an independent ethics committees and was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

Consent to Participate

All participant provided written informed consent.

Consent for Publication

Not applicable.

Author Contributions

EB: conceptualization, data curation, formal analysis, methodology, project administration, and writing—original draft; ST: conceptualization, data curation, methodology, and writing—review and editing; LV: conceptualization, methodology, and writing—review and editing; SM: writing—review and editing; AudB: data collection and writing—review and editing JB: data collection and writing—review and editing; YvH: writing—review and editing; FvdB: conceptualization, methodology, and writing—review and editing; and JP: conceptualization, methodology, and writing—review and editing. All authors read and approved the final manuscript and agree to be accountable for the work.

Data Availability

The data that support the findings of this study are available from the corresponding author (EB) upon reasonable request.

Code Availability

Not applicable.

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Associated Data

Supplementary Materials

Supplementary file1 (PDF 1264 KB)

Data Availability Statement

The data that support the findings of this study are available from the corresponding author (EB) upon reasonable request.