Anticholinergic Burden and Hospital Delirium in an Appalachian University Hospital

Additionally, an ACB score was calculated at the time of discharge for comparison to the day of CAM administration. This was completed to assess if the anticholinergic burden had been reduced at the time of discharge. Tables 5 and illustrate these results. Patients who were delirium-positive and had not died before discharge totaled 36. In this group, the mean ACB on the date the CAM was 2.23, while the mean ACB score on the date of discharge was 2.50. There was no statistically significant change in ACB score by the date of discharge (p=0.738).

Discussion

Results from our study did not show an association between anticholinergic cognitive burden and the diagnosis of delirium. Delirium often has multiple precipitating factors and rendering an obvious solitary association is difficult to ascertain. The ACB scoring system was devised as a tool to help with accounting for anticholinergic effects of certain commonly used pharmaceutical agents. However, our study suggests that application of the tool would not benefit in guiding identification of the cause of delirium nor justify the reduction of a patient’s anticholinergic medications to treat their delirium. Critiques of using drug scales are that the cumulative exposure to anticholinergic medications are oversimplified into linear additive models and these scales do not necessarily consider the underlying patient characteristics including differences in pharmacodynamics, cholinergic reserve, and endogenous anticholinergic activity [21]. Within this population, comorbidity alone may present a unique challenge to identifying anticholinergic burden as a factor contributing to the presence of delirium. In reviewing the data, it was noted that many patients automatically scored higher on their ACB score based on their comorbidities. For example, many patients with diagnosed heart failure automatically had a score of two due to use of both metoprolol as well as furosemide. Additionally, digoxin and hydralazine were commonly present on their medication lists and will also raise the score. The degree of multimorbidity in this population may make it difficult to analyze a solitary contributing factor to the presence of delirium. West Virginia ranks third in the nation by definition for multiple chronic medical conditions. Multiple chronic conditions are defined as two or more of the following group (arthritis, asthma, cancer, COPD, coronary heart disease, diabetes, hepatitis, hypertension, stroke or weak or failing kidneys). This population notably does not reflect significant ethnic diversity as the 2013 WV DHHR data indicates the state is 96.7% Caucasian [20]. Additional limitations of this study include the small sample size of patients that were diagnosed with delirium. Forty patients who were diagnosed with delirium out of 174 total patient electronic medical records analyzed composes roughly 23% of the entire sample. The small sample size lead to a lower powered study and it is possible that a larger sample size would yield different results. Additionally, the small sample size made stratification for analyzing confounding variables challenging as there were not a sufficient number of patients with delirium to investigate other effects. Also, the anticholinergic usage was detected using medication orders rather than actual dispensing data, which could inform about whether the exposure was relatively new or cumulative over a prolonged period of time. One more complexity of this study that differs from other studies is that no cutoff for anticholinergic burden was selected, but rather the ACB scores were treated as a continuous numerical variable. Research groups that put an absolute cutoff for maximum anticholinergic value showed significant associations between anticholinergic burden and delirium. For example, Pascina and colleagues showed in 2012 a relationship between ACB scores and Short Blessed Test (SBT), which had previously been validated against the Mini-Mental State Exam (MMSE) by Carpenter et al in 2011 [22,23]. Future directions for similar studies ought to significantly increase sample size to better represent the population. Analysis of the ADS and ARS may prove to have different outcomes for example.

Conclusion

The growing population of elderly patients may necessitate refined tools for quick and early diagnosis of acute mental changes including those potentially caused by the anticholinergic effects of common medications. It is important to identify the acute negative impact that polypharmacy has on the aging brain especially amongst an aging and medically complex population. Therefore, there is value in identifying tools that allow clinicians to quickly determine whether an acutely delirious patient may benefit from reducing anticholinergic burden. The relationship between delirium, anticholinergic burden scores, and acetylcholine levels or serum anti-cholinergic levels should be investigated further. More studies are thus needed to identify such tools as well as establish therapeutic guidelines in dealing with delirium. Therefore, evidence-based tools such as the ACB need continued validation in order to reliably change practice.

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