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The relationship between total anticholinergic burden (ACB) and early in-patient hospital mortality and length of stay in the oldest old aged 90 years and over admitted with an acute illness Andrew C. Kidd a,b,*, Patrick Musonda a, Roy L. Soiza c,d, Catherine Butchart c, Claire J. Lunt c, Yogish Pai e, Yasir Hameed f, Chris Fox a, John F. Potter a,b, Phyo Kyaw Myint c,d,** a
Norwich Medical School, Faculty of Medicine & Health Sciences, Chancellors Drive, University of East Anglia, Norwich NR4 7TJ, Norfolk Island Academic Department of Medicine for the Elderly, Norfolk and Norwich University Hospital, Colney Lane, Norwich NR4 7UY, Norfolk Island c Academic Department of Medicine for the Elderly, Woodend Hospital, Eday Road, Aberdeen AB15 6XS, Scotland, United Kingdom d School of Medicine & Dentistry, Division of Applied Health Sciences, University of Aberdeen, Aberdeen AB25 2ZD, United Kingdom e University Hospital of South Manchester, Manchester M23 9LT, United Kingdom f Norfolk & Waveney Mental Health Care Trust, Norwich NR6 5BE, United Kingdom b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 6 December 2013 Received in revised form 22 January 2014 Accepted 25 January 2014 Available online xxx
The use of prescription drugs in older people is high and many commonly prescribed drugs have anticholinergic effects. We examined the relationship between ACB on mortality and in-patient length of stay in the oldest old hospitalised population. This was a retrospective analysis of prospective audit using hospital audit data from acute medical admissions in three hospitals in England and Scotland. Baseline use of possible or definite anticholinergics was determined according to the Anticholinergic Cognitive Burden Scale. The main outcome measures were decline in-hospital mortality, early in-hospital mortality at 3- and 7-days and in-patient length of stay. A total of 419 patients (including 65 patients with known dementia) were included [median age = 92.9, inter-quartile range (IQR) 91.4–95.1 years]. 256 (61.1%) were taking anticholinergic medications. Younger age, greater number of pre-morbid conditions, ischemic heart disease, number of medications, higher urea and creatinine levels were significantly associated with higher total ACB burden on univariate regression analysis. There were no significant differences observed in terms of in-patient mortality, in-patient hospital mortality within 3and 7-days and likelihood of prolonged length of hospital stay between ACB categories. Compared to those without cardiovascular disease, patients with cardiovascular disease showed similar outcome regardless of ACB load (either =0 or >0 ACB). We found no association between ACB and early (within 3and 7-days) and in-patient mortality and hospital length of stay outcomes in this cohort of oldest old in the acute medical admission setting. ß 2014 Elsevier Ireland Ltd. All rights reserved.
Keywords: ACB Oldest old Mortality Length of stay
1. Introduction Medications with anticholinergic effects are routinely used in the treatment of many common conditions. Short term side effects of these medications are well documented in the literature and include peripheral effects such as dry mouth, constipation, urinary retention and blurred vision, and central nervous system
* Corresponding author at: Norwich Medical School, Faculty of Medicine & Health Sciences, Chancellors Drive, University of East Anglia, Norwich, NR4 7TJ, Norfolk, UK; Tel.: 0044 1603 456161. ** Co-corresponding author at: School of Medicine & Dentistry, Foresterhil, University of Aberdeen, Room 1.072, Polwarth Building, Aberdeen AB25 2ZD, Scotland, United Kingdom. Tel.: +44 1224 272000. E-mail addresses: [email protected] (A.C. Kidd), [email protected] (P.K. Myint).
effects such as confusion, attention deficits and hallucinations (Tune, 2001). Their side effects are thought to be enhanced in older age, aging being associated with a significant decrease in cholinergic neurons or receptors in the brain, compounded by the reduction in hepatic metabolism and renal excretion and the increase in blood–brain barrier permeability’ (Campbell, Boustani, & Lane, 2010). Further evidence suggests that their use is associated with cognitive impairment (Ancelin et al., 2006; Boustani, Campbell, & Munger, 2008; Carriere, Fourrier-Reglat, & Dartigues, 2009; Chew, Mulsant, & Pollock, 2005) and mortality (Fox et al., 2011). A systematic review of anticholinergic activity of medications and cognitive function found an association between acute cognitive impairment and anticholinergic potency of medications. This review developed the Anticholinergic Cognitive Burden Scale (ACB) (Campbell, Boustani, & Limbil, 2009), which identifies drugs
0167-4943/$ – see front matter ß 2014 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.archger.2014.01.006
Please cite this article in press as: Kidd, A.C., et al., The relationship between total anticholinergic burden (ACB) and early in-patient hospital mortality and length of stay in the oldest old aged 90 years and over admitted with an acute illness. Arch. Gerontol. Geriatr. (2014), http://dx.doi.org/10.1016/j.archger.2014.01.006
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with documented anticholinergic activity according to the serum anticholinergic assay or through consensus opinion. In addition to increasing the risk of cognitive impairment, exposure to anticholinergic medications has also been associated with impairments in physical function and incident frailty (Gnjidic et al., 2012; Han, Agostini, & Allore, 2008; Pasina et al., 2013). Moreover, a recent prospective study also found that the use of anticholinergic medications in older patients with stable cardiovascular disease was associated with an increased length of stay in hospital (Uusvaara, Pitkala, Kautiainen, Tilvis, & Strandberg, 2011). Drug consumption in older people is high and many commonly prescribed drugs have anticholinergic properties. A large prospective community-based epidemiological study across five centers in England and Wales of thirteen thousand random samples of people aged 65 years and older found that 48% were taking medications with possible or definite anticholinergic properties (Fox et al., 2011). In 2005, there were more than thirty six million Americans aged 65 and older with an estimated 20– 50% of those taking at least one medication with some anticholinergic activities (Carriere et al., 2009). Moreover, it is estimated that 30% of elderly residents in American nursing homes take more than two anticholinergic drugs and 5% take more than five (Blazer, Federspiel, Ray, & Schaffner, 1983; Feinberg, 1993). Current demographic trends suggest that in the future there will be a steady increase in the very elderly in Western societies (Zhao et al., 2010). This phenomenon is particularly seen in the oldest age-group and they are most likely to have adverse outcomes associated with drug use, including anticholinergic drugs. Few studies have investigated the possible association between use of anticholinergic drugs and mortality in the oldest old. In this study we examined the relationship between ACB on mortality and in-patient length of stay in the oldest old hospitalised population and determined whether ACB had an impact on patients with cardiovascular disease (CVD) compared to those without CVD.
2. Methods We retrospectively analysed the data from a prospective observational outcome audit conducted in three UK centres: Norfolk and Norwich University Hospital (NNUH), Aberdeen Royal Infirmary (ARI) and Woodend Hospital, Aberdeen (WH). The NNUH had a catchment population of around 750,000 and the ARI and WH had a catchment population of around 500,000. All patients aged 90 years and over admitted to the acute medical assessment units (NNUH and ARI) or acute geriatric wards (WH) in a 3-month period from November 2008 were included. The detailed audit methodology and data collection methods have been described previously (Pai et al., 2011). A standardized proforma was used to record the data in all sites. For this study purpose we used demographic details (age, sex), place of residence prior to admission, chronic co-morbidities, drug and social history (including anticholinergic medications), baseline observations (temperature, heart rate, respiratory rate, systolic blood pressure, Glasgow Coma Scale) and investigations on admission (hemoglobin, white cell count, C-reactive protein, albumin, urea, creatinine and sodium) and physical functioning status depicted by mobility. Presence of co-morbidities was noted from the medical records such as correspondence (clinic letters), GP referral information and clinical history. Mobility was recorded as five categories: bedbound, transfer only, mobile 50 m. The patients were identified prospectively and the outcome data were collected at the time of discharge or in-patient death using computer-based discharge records and/or case notes.
Each participant’s total ACB was calculated using the Anticholinergic Cognitive Burden Scale (ACB) (Campbell et al., 2010). The scale was developed through a systematic review of the literature to identify drugs with documented anticholinergic activity. In their study content validity was tested by presenting the list to an expert interdisciplinary panel of geriatricians, pharmacists, geriatric psychiatrists, general physicians, specialist geriatric nurses and aging brain researchers, with any disagreements resolved by consensus. Medications on admission were identified to have absent, possible or definite anticholinergic properties based on the ACB (Campbell et al., 2010). Drugs with possible anticholinergic effects were defined as those with serum anticholinergic activity or in vitro affinity to muscarinic receptors but with no known clinically relevant negative cognitive effects (ACB score 1). Drugs with established and clinically relevant cognitive anticholinergic effects were considered to be definite anticholinergics (ACB score 2– 3) (see Appendix A for the list). This scale was validated in a large longitudinal study of participants enrolled in the Medical Research Council Cognitive Function and Aging Study (Fox et al., 2011). In that study total ACB was calculated using the formula (Total ACB = {[number of score 1 anti-cholinergic drugs] + [the number of score 2 anti-cholinergic drugs 2] + [the number score 3 anti-cholinergic drugs 3]}). Using the ACB, we categorized patients’ ACB status into 3 categories (no ACB = total score 0; low ACB = total score 1 and moderate to high = total score 2). We collected data on regular medications only and this was used to calculate the ACB score. This project was approved by hospital audit departments at NNUH, ARI and WH. Ethics committee approval was not required because only clinical teams who looked after the patients collected the data had access to patient identifiable information and those who were not involved with patient care did not have access to identifiable information in line with the National Information Governance Board (NIGB) guidance and Caldicott principles. Furthermore, data are presented in anonymised and aggregated fashion. The decision to publish was made retrospectively. Statistical analyses were performed using Stata software, version 12.1 MP (Stata Corporation, Texas, USA). Medians, percentages, odds ratios [OR] and their 95% confidence intervals [CI] are reported as appropriate. The characteristics of patients and outcomes defined as in-patient death, in-patient death within 3days, in-patient death within 7-days and hospital length of stay in those who died and in those discharged alive by ACB score category are presented descriptively. The ACB categories were defined as no, low and moderate-high (total ACB of 0, 1 and 2, respectively) as the number of patients with ACB score 2 are small. The odds ratios (95% CI) for unadjusted and adjusted models for the study outcomes were calculated with the reference category for all outcomes being an ACB score zero. The adjusted model included all variables with face validity based on clinical experience and all variables with a P-value of 0 in those with and without cardiovascular disease – composite of ischemic heart disease (IHD) and stroke – were further examined using those without CVD as the reference category to ascertain whether, given the same ACB score, people with CVD had a worse outcome compared to those without CVD. Again, the adjusted model included all the variables with a P-value of median – those who died
P-value
LOS > median – those discharged alive
P-value
Unadjusted With CVD =0 >0 ACB CVD
0.63 (0.22, 1.81) 0.69 (0.29, 1.62) 1.09 (0.28, 4.25)
0.391 0.392 0.899
n/a 1.45 (0.40, 5.25)
n/a 0.576
n/a 0.76 (0.23, 2.57) n/a
n/a 0.660 n/a
1.13 (0.37, 3.42) 0.72 (0.21, 2.43) 0.64 (0.12, 3.33)
0.835 0.600 0.598
6.57 (1.57, 27.5) 0.88 (0.19, 3.95) 0.13 (0.02, 1.07)
0.010 0.863 0.057
Adjusted With CVD =0 >0 ACB CVD
0.79 (0.26, 2.40) 0.75 (0.32, 1.78) 1.07 (0.27, 4.19)
0.672 0.514 0.926
n/a 1.43 (0.38, 5.40)
n/a 0.600
n/a 0.77 (0.23, 2.64) n/a
n/a 0.682 n/a
1.37 (0.41, 4.59) 0.78 (0.23, 2.67) 0.63 (0.12, 3.28)
0.609 0.696 0.5796
5.55 (1.21, 25.4) 0.64 (0.13, 4.05) 0.15 (0.02, 1.19)
0.027 0.575 0.072
Reference category is ‘No CVD’; n/a = not applicable due to no individuals for the category, ACB CVD = interaction estimates of ACB with CVD.
>0 in those with or without cardiovascular disease. Again, there were no statistically significant differences observed in terms of outcomes examined between those with or without CVD outcomes in this patient population. 4. Discussion In this study we found no evidence of an association between the use of drugs with anticholinergic properties and admission and early (within 3- and 7-days) in-patient mortality or length of hospital stay in the oldest old admitted to hospital with an acute illness. This study adds to the existing work evaluating anticholinergic exposure and mortality by providing some insight into ACB burden in the oldest old. The current literature examining the link between exposure to anticholinergic medications and mortality is inconclusive. A recent large prospective study by Fox et al. examined 13,004 participants (mean age of 75.2 years) of whom 48% were taking anticholinergic medications (Fox et al., 2011). They found that after adjusting for age, sex, baseline Mini Mental State Examination (MMSE) score, education, social class, number of anticholinergic medications and number of health conditions, taking anticholinergic medications was associated with increased mortality at 2 years. Mangoni, van Munster, Woodman, and de Rooij (2013) assessed possible
associations between anticholinergic drug exposure and serum anticholinergic activity and their capacities to predict all-cause mortality in 71 older hospitalised patients (mean age of 84 years) and found that the anticholinergic risk score, together with cognitive impairment, in-hospital delirium, place of residence and length of hospital stay, predicted 1-year all-cause mortality in this group. However, other studies suggest that exposure to anticholinergic medications and mortality are not associated. In their study, Wilson et al. (2012) investigated the association between increasing exposure to anticholinergic and sedative medications and mortality in 602 older adults (mean age of 85.7 years) living in Australian residential care facilities and found no significant association. In their study population 33.6% were exposed to anticholinergic medications. Likewise, a recent cohort study by Kumpula, Bell, Soini, & Pitka¨la¨ (2011) investigating the association between the use of anticholinergic drugs and mortality in 1004 patients from fifty three long term care wards in Finland found that higher anticholinergic loads were not associated with mortality. They found that 55% of their study population had mild or high anticholinergic loads. There is a dearth of information on the possible link between drug ACB and mortality in the acutely ill oldest old. Our study further advances understanding in this area. The median age of our
Please cite this article in press as: Kidd, A.C., et al., The relationship between total anticholinergic burden (ACB) and early in-patient hospital mortality and length of stay in the oldest old aged 90 years and over admitted with an acute illness. Arch. Gerontol. Geriatr. (2014), http://dx.doi.org/10.1016/j.archger.2014.01.006
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study population was significantly higher than other studies determining associations between anticholinergic medications and mortality [93.5 years (ACB score 0), 92.9 years (ACB score 1) and 92.5 (ACB score >2)]. Also, in our study the prevalence of patients taking anticholinergic medications was much higher than most other studies at 61.1%. We also found that older patients have lower ACB scores [93.4 years (ACB score 1) vs. 92.5 (ACB score >2), p 0.0018]. The association of higher ACB with relatively younger age, polypharmacy and higher co-morbidity is consistent with that found in another study (Lowry, Woodman, Soiza, & Mangoni, 2011). In their study, Fox et al. stated that their findings may reflect the prevalence of anticholinergic prescribing in disease states with significant mortality. We specifically examined this issue and found that the prevalence of IHD was significantly different between the ACB categories. We found no evidence to suggest that the presence of CVD (combined IHD and stroke) is associated with increased mortality or length of stay within the same ACB category (=0 or >0). This was also true in a recent prospective study by Uusvaara et al. which assessed the impact of anticholinergic medications on hospitalization and mortality in 400 older patients (aged 75–90 years) with stable cardiovascular disease (Uusvaara et al., 2011). The authors of this study concluded that the use of anticholinergic medications was associated with an increased number of hospital days but not with mortality, when adjusting for age, sex and Charlson Co-morbidity Index score (Uusvaara et al., 2011). In a study by Lo¨nnroos et al. who examined the relationship between Drug Burden Index (a measure of anticholinergic and sedative medications) and hospitalization in a population-based sample of 339 community-dwelling older Finns (mean age of) over a 1-year period (Lo¨nnroos et al., 2012). The authors found that exposure to Drug Burden Index medications was associated with a greater use of hospital days, but a cumulative dose-response relationship between Drug Burden Index and hospitalization was not observed. Similar to our findings, this study also had a high prevalence of anticholinergic medication usage in their study population at 74%. In our study, we determined in-patient mortality at 3-days and 7-days. In the studies that found an association between anticholinergic exposure and mortality, the length of follow-up times were considerably longer. To our knowledge this is the first study to examine the relationship between ACB and short-term mortality focusing on oldest old acutely unwell patients. The majority of studies to date investigated in the community setting (Fox et al., 2011; Lo¨nnroos et al., 2012; Uusvaara et al., 2011) or institutions (Kumpula et al., 2011; Wilson et al., 2012). One of the key strengths of this study was that the data came from three different sites, suggesting that the score could be used in different centers. Our study had a relatively large sample of patients whose data was collected prospectively. We used ACB Scale which has been externally validated by other studies (Campbell et al., 2010; Fox et al., 2011). Variables analysed were easily gathered, standard observations and biochemical and physiological markers which reflect acuteness of the illness condition for patients in hospital. We used objective data collected on standardized collection forms by fully qualified specialist trained investigators. Our study has limitations. There is the possibility of an element of bias in data collection in this study. We used health-care records for data collection rather than direct patient interview, and the data collected largely depended on the documentation made by nurses and front line doctors. Data were collected by two doctors in each center and inter-observer agreement was not specifically tested. Nevertheless, we collected objective measures which are not influenced by observer bias and used standardized methodology across all sites. Norwich medications were not obtained via a pharmacist-led medicines reconciliation process. Patients
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admitted to surgical or orthopedic specialties were not included in the study. Although this number may be small in comparison with acute medical admissions, nonetheless this could underestimate the actual magnitude of all hospital admissions in this age group and may have an impact on mortality and length of stay figures. Moreover, we could not determine the length of ACB exposure to allow Cox regression analysis. A recent study found an association between ACB and the risk of developing cognitive impairment in those exposed to anticholinergic medications greater than 90 days, which could be relevant to this sample (Cai, Campbell, Khan, Callahan, & Boustani, 2013). Furthermore, it is possible that some important predictors of outcome were not measured in the study or that the study was under-powered to detect some important effects in included measures. The outcome analyses were based on dichotomised predictor variables. Length of stay is a problematic outcome as it is prone to bias from early mortality. Although this was adjusted by describing length of stay in survivors and non-survivors, the creation of these subgroups meant that the power to capture a true difference was substantially diminished. The study was conducted in the winter months for a relatively short time-frame, we are not able to assess the presence or absence of seasonal variation in determinants of outcomes. We only used the ACB score, so it is possible other measures of ACB may have yielded different results. Moreover, in previous studies prevalence of ACB drug use varies between studies also in terms of anticholinergic scale used. The Drug Burden Index includes also sedative drugs and our scoring system is not restricted to anticholinergic component of the Drug Burden Index. In summary, we found no association between anticholinergic medications on admission and early and in-patient mortality, nor hospital length of stay in patients aged 90 years in acute medical admission settings in this series. It is, however, important to note that our study is limited by several factors discussed above. With the increasing age of the world population, particularly in Western countries, further research into the adverse effects of medication use on the mortality and length of hospital stay of older individuals is needed. Larger studies are required to examine the dose-response relationship between anticholinergic medications and both immediate and longer term outcomes in wide ranging aspects relevant in oldest old including cognition and functional limitation. Author contribution Dr A.C. Kidd and all co-authors had full access to all of the anonymised data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Andrew C. Kidd: study design, interpretation, writing and manuscript preparation. Patrick Musonda: data preparation and manuscript preparation. Roy L. Soiza: audit design, manuscript preparation and editing. Catherine Butchart, Claire J. Lunt and Yogish Pai: audit design, data collection and manuscript preparation. Yasir Hameed: data collection and manuscript preparation. Chris Fox: manuscript preparation and editing. John F. Potter: manuscript preparation and editing. Phyo Kyaw Myint: study design, analysis plan, interpretation, manuscript preparation and editing. Conflicts of interest statement There are no conflicts of interest. Funding No funding was received for this paper.
Please cite this article in press as: Kidd, A.C., et al., The relationship between total anticholinergic burden (ACB) and early in-patient hospital mortality and length of stay in the oldest old aged 90 years and over admitted with an acute illness. Arch. Gerontol. Geriatr. (2014), http://dx.doi.org/10.1016/j.archger.2014.01.006
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Appendix B (Continued )
Acknowledgements
ACB score 0 (%)
ACB score 1 (%)
N = 163
N = 256
Respiratory Acute bronchitis Aspiration pneumonia Bilateral pneumonia COPD LRTI Pleural effusion Community acquired pneumonia PE Tuberculosis
27 (16.6%)
32 (12.5%)
0.285a
GI Diarrhea Ampulla tumor pancreas Cholangitis Constipation Duodenal ulcer Ulcerative colitis Iron deficiency anemia Gastroenteritis Mallory Weiss tear Oseophagitis Upper GI bleed Biliary sepsis
11 (6.75%)
13 (5.08%)
0.491a
Nephrology UTI Acute-on-chronic renal failure Rhabdomyolosis
27 (16.6%)
33 (12.9%)
0.336a
Other Bilateral leg cellulitis Diabetes Digoxin toxicity Epistaxis Hypoglycaemia DVT Leg ulcers Mechanical fall Anemia
41 (25.2%)
69 (27.0%)
0.732a
We thank Dr Nishant Gautham who contributed in data collection of the initial audit at one of the sites.
P-value
VT
Appendix A Drugs with established and clinically relevant cognitive anticholinergic effects.a Score 3 (High)
Score 2 (Medium)
Score 1 (Low)
Atropine Amitriptyline Chlorpheniramine Chlorpromazine Clemastine Clomipramine Clozapine Darifenacin Desipramine Dicyclomine Dimenhydrinate (with cinnarizine = Arlevert) Doxepin Flavoxate Hydroxyzine Hyoscine hydrobromide Imipramine Nortriptyline Olanzapine Orphenadrine Oxybutynin Paroxetine Paroxetine Perphenazine Procyclidine Promazine
Amantadine Carbamazepine Cyproheptadine Methotrimeprazine Oxcarbazepine Pethidine
Alimemazine Alverine Amisulpride Atenolol Bupropion HCl Captopril Chlorthalidone Cimetidine Codeine Colchicine Diazepam Digoxin Dipyridamole Disopyramide Fentanyl Furosemide Flupenthixol Fluvoxamine Haloperidol Hydralazine Hydrocortisone Isosorbide Mono/Di Loperamide Metoprolol Morphine Nifedipine Pericyazine Prednisone Prochlorperazine Quinidine Ranitidine Risperidone Theophylline Trazodone Triamterene
Promethazine Propentheline Quetiapine Thioridazine Tolterodine Trifluoperazine Trihexyphenidyl Trimipramine a
To calculate the Anticholinergic Cognitive Burden score for a patient, identify medications the patient is taking and add the total points for each medication.
Appendix B Diagnosis on discharge of users and non-users of anticholinergic drugs.
Cardiology ACS LVF AF Angina Acute pericarditis Bradycardia Atrial flutter CCF MVR Cardiac syncope Complete heart block SVT Pulmonary edema
ACB score 0 (%)
ACB score 1 (%)
N = 163
N = 256
34 (20.9%)
60 (23.4)
P-value
0.593a
a
P-value calculated using binomial exact methods.
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The relationship between total anticholinergic burden (ACB) and early in-patient hospital mortality and length of stay in the oldest old aged 90 years and over admitted with an acute illness Andrew C. Kidd a,b,*, Patrick Musonda a, Roy L. Soiza c,d, Catherine Butchart c, Claire J. Lunt c, Yogish Pai e, Yasir Hameed f, Chris Fox a, John F. Potter a,b, Phyo Kyaw Myint c,d,** a
Norwich Medical School, Faculty of Medicine & Health Sciences, Chancellors Drive, University of East Anglia, Norwich NR4 7TJ, Norfolk Island Academic Department of Medicine for the Elderly, Norfolk and Norwich University Hospital, Colney Lane, Norwich NR4 7UY, Norfolk Island c Academic Department of Medicine for the Elderly, Woodend Hospital, Eday Road, Aberdeen AB15 6XS, Scotland, United Kingdom d School of Medicine & Dentistry, Division of Applied Health Sciences, University of Aberdeen, Aberdeen AB25 2ZD, United Kingdom e University Hospital of South Manchester, Manchester M23 9LT, United Kingdom f Norfolk & Waveney Mental Health Care Trust, Norwich NR6 5BE, United Kingdom b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 6 December 2013 Received in revised form 22 January 2014 Accepted 25 January 2014 Available online xxx
The use of prescription drugs in older people is high and many commonly prescribed drugs have anticholinergic effects. We examined the relationship between ACB on mortality and in-patient length of stay in the oldest old hospitalised population. This was a retrospective analysis of prospective audit using hospital audit data from acute medical admissions in three hospitals in England and Scotland. Baseline use of possible or definite anticholinergics was determined according to the Anticholinergic Cognitive Burden Scale. The main outcome measures were decline in-hospital mortality, early in-hospital mortality at 3- and 7-days and in-patient length of stay. A total of 419 patients (including 65 patients with known dementia) were included [median age = 92.9, inter-quartile range (IQR) 91.4–95.1 years]. 256 (61.1%) were taking anticholinergic medications. Younger age, greater number of pre-morbid conditions, ischemic heart disease, number of medications, higher urea and creatinine levels were significantly associated with higher total ACB burden on univariate regression analysis. There were no significant differences observed in terms of in-patient mortality, in-patient hospital mortality within 3and 7-days and likelihood of prolonged length of hospital stay between ACB categories. Compared to those without cardiovascular disease, patients with cardiovascular disease showed similar outcome regardless of ACB load (either =0 or >0 ACB). We found no association between ACB and early (within 3and 7-days) and in-patient mortality and hospital length of stay outcomes in this cohort of oldest old in the acute medical admission setting. ß 2014 Elsevier Ireland Ltd. All rights reserved.
Keywords: ACB Oldest old Mortality Length of stay
1. Introduction Medications with anticholinergic effects are routinely used in the treatment of many common conditions. Short term side effects of these medications are well documented in the literature and include peripheral effects such as dry mouth, constipation, urinary retention and blurred vision, and central nervous system
* Corresponding author at: Norwich Medical School, Faculty of Medicine & Health Sciences, Chancellors Drive, University of East Anglia, Norwich, NR4 7TJ, Norfolk, UK; Tel.: 0044 1603 456161. ** Co-corresponding author at: School of Medicine & Dentistry, Foresterhil, University of Aberdeen, Room 1.072, Polwarth Building, Aberdeen AB25 2ZD, Scotland, United Kingdom. Tel.: +44 1224 272000. E-mail addresses: [email protected] (A.C. Kidd), [email protected] (P.K. Myint).
effects such as confusion, attention deficits and hallucinations (Tune, 2001). Their side effects are thought to be enhanced in older age, aging being associated with a significant decrease in cholinergic neurons or receptors in the brain, compounded by the reduction in hepatic metabolism and renal excretion and the increase in blood–brain barrier permeability’ (Campbell, Boustani, & Lane, 2010). Further evidence suggests that their use is associated with cognitive impairment (Ancelin et al., 2006; Boustani, Campbell, & Munger, 2008; Carriere, Fourrier-Reglat, & Dartigues, 2009; Chew, Mulsant, & Pollock, 2005) and mortality (Fox et al., 2011). A systematic review of anticholinergic activity of medications and cognitive function found an association between acute cognitive impairment and anticholinergic potency of medications. This review developed the Anticholinergic Cognitive Burden Scale (ACB) (Campbell, Boustani, & Limbil, 2009), which identifies drugs
0167-4943/$ – see front matter ß 2014 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.archger.2014.01.006
Please cite this article in press as: Kidd, A.C., et al., The relationship between total anticholinergic burden (ACB) and early in-patient hospital mortality and length of stay in the oldest old aged 90 years and over admitted with an acute illness. Arch. Gerontol. Geriatr. (2014), http://dx.doi.org/10.1016/j.archger.2014.01.006
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with documented anticholinergic activity according to the serum anticholinergic assay or through consensus opinion. In addition to increasing the risk of cognitive impairment, exposure to anticholinergic medications has also been associated with impairments in physical function and incident frailty (Gnjidic et al., 2012; Han, Agostini, & Allore, 2008; Pasina et al., 2013). Moreover, a recent prospective study also found that the use of anticholinergic medications in older patients with stable cardiovascular disease was associated with an increased length of stay in hospital (Uusvaara, Pitkala, Kautiainen, Tilvis, & Strandberg, 2011). Drug consumption in older people is high and many commonly prescribed drugs have anticholinergic properties. A large prospective community-based epidemiological study across five centers in England and Wales of thirteen thousand random samples of people aged 65 years and older found that 48% were taking medications with possible or definite anticholinergic properties (Fox et al., 2011). In 2005, there were more than thirty six million Americans aged 65 and older with an estimated 20– 50% of those taking at least one medication with some anticholinergic activities (Carriere et al., 2009). Moreover, it is estimated that 30% of elderly residents in American nursing homes take more than two anticholinergic drugs and 5% take more than five (Blazer, Federspiel, Ray, & Schaffner, 1983; Feinberg, 1993). Current demographic trends suggest that in the future there will be a steady increase in the very elderly in Western societies (Zhao et al., 2010). This phenomenon is particularly seen in the oldest age-group and they are most likely to have adverse outcomes associated with drug use, including anticholinergic drugs. Few studies have investigated the possible association between use of anticholinergic drugs and mortality in the oldest old. In this study we examined the relationship between ACB on mortality and in-patient length of stay in the oldest old hospitalised population and determined whether ACB had an impact on patients with cardiovascular disease (CVD) compared to those without CVD.
2. Methods We retrospectively analysed the data from a prospective observational outcome audit conducted in three UK centres: Norfolk and Norwich University Hospital (NNUH), Aberdeen Royal Infirmary (ARI) and Woodend Hospital, Aberdeen (WH). The NNUH had a catchment population of around 750,000 and the ARI and WH had a catchment population of around 500,000. All patients aged 90 years and over admitted to the acute medical assessment units (NNUH and ARI) or acute geriatric wards (WH) in a 3-month period from November 2008 were included. The detailed audit methodology and data collection methods have been described previously (Pai et al., 2011). A standardized proforma was used to record the data in all sites. For this study purpose we used demographic details (age, sex), place of residence prior to admission, chronic co-morbidities, drug and social history (including anticholinergic medications), baseline observations (temperature, heart rate, respiratory rate, systolic blood pressure, Glasgow Coma Scale) and investigations on admission (hemoglobin, white cell count, C-reactive protein, albumin, urea, creatinine and sodium) and physical functioning status depicted by mobility. Presence of co-morbidities was noted from the medical records such as correspondence (clinic letters), GP referral information and clinical history. Mobility was recorded as five categories: bedbound, transfer only, mobile 50 m. The patients were identified prospectively and the outcome data were collected at the time of discharge or in-patient death using computer-based discharge records and/or case notes.
Each participant’s total ACB was calculated using the Anticholinergic Cognitive Burden Scale (ACB) (Campbell et al., 2010). The scale was developed through a systematic review of the literature to identify drugs with documented anticholinergic activity. In their study content validity was tested by presenting the list to an expert interdisciplinary panel of geriatricians, pharmacists, geriatric psychiatrists, general physicians, specialist geriatric nurses and aging brain researchers, with any disagreements resolved by consensus. Medications on admission were identified to have absent, possible or definite anticholinergic properties based on the ACB (Campbell et al., 2010). Drugs with possible anticholinergic effects were defined as those with serum anticholinergic activity or in vitro affinity to muscarinic receptors but with no known clinically relevant negative cognitive effects (ACB score 1). Drugs with established and clinically relevant cognitive anticholinergic effects were considered to be definite anticholinergics (ACB score 2– 3) (see Appendix A for the list). This scale was validated in a large longitudinal study of participants enrolled in the Medical Research Council Cognitive Function and Aging Study (Fox et al., 2011). In that study total ACB was calculated using the formula (Total ACB = {[number of score 1 anti-cholinergic drugs] + [the number of score 2 anti-cholinergic drugs 2] + [the number score 3 anti-cholinergic drugs 3]}). Using the ACB, we categorized patients’ ACB status into 3 categories (no ACB = total score 0; low ACB = total score 1 and moderate to high = total score 2). We collected data on regular medications only and this was used to calculate the ACB score. This project was approved by hospital audit departments at NNUH, ARI and WH. Ethics committee approval was not required because only clinical teams who looked after the patients collected the data had access to patient identifiable information and those who were not involved with patient care did not have access to identifiable information in line with the National Information Governance Board (NIGB) guidance and Caldicott principles. Furthermore, data are presented in anonymised and aggregated fashion. The decision to publish was made retrospectively. Statistical analyses were performed using Stata software, version 12.1 MP (Stata Corporation, Texas, USA). Medians, percentages, odds ratios [OR] and their 95% confidence intervals [CI] are reported as appropriate. The characteristics of patients and outcomes defined as in-patient death, in-patient death within 3days, in-patient death within 7-days and hospital length of stay in those who died and in those discharged alive by ACB score category are presented descriptively. The ACB categories were defined as no, low and moderate-high (total ACB of 0, 1 and 2, respectively) as the number of patients with ACB score 2 are small. The odds ratios (95% CI) for unadjusted and adjusted models for the study outcomes were calculated with the reference category for all outcomes being an ACB score zero. The adjusted model included all variables with face validity based on clinical experience and all variables with a P-value of 0 in those with and without cardiovascular disease – composite of ischemic heart disease (IHD) and stroke – were further examined using those without CVD as the reference category to ascertain whether, given the same ACB score, people with CVD had a worse outcome compared to those without CVD. Again, the adjusted model included all the variables with a P-value of median – those who died
P-value
LOS > median – those discharged alive
P-value
Unadjusted With CVD =0 >0 ACB CVD
0.63 (0.22, 1.81) 0.69 (0.29, 1.62) 1.09 (0.28, 4.25)
0.391 0.392 0.899
n/a 1.45 (0.40, 5.25)
n/a 0.576
n/a 0.76 (0.23, 2.57) n/a
n/a 0.660 n/a
1.13 (0.37, 3.42) 0.72 (0.21, 2.43) 0.64 (0.12, 3.33)
0.835 0.600 0.598
6.57 (1.57, 27.5) 0.88 (0.19, 3.95) 0.13 (0.02, 1.07)
0.010 0.863 0.057
Adjusted With CVD =0 >0 ACB CVD
0.79 (0.26, 2.40) 0.75 (0.32, 1.78) 1.07 (0.27, 4.19)
0.672 0.514 0.926
n/a 1.43 (0.38, 5.40)
n/a 0.600
n/a 0.77 (0.23, 2.64) n/a
n/a 0.682 n/a
1.37 (0.41, 4.59) 0.78 (0.23, 2.67) 0.63 (0.12, 3.28)
0.609 0.696 0.5796
5.55 (1.21, 25.4) 0.64 (0.13, 4.05) 0.15 (0.02, 1.19)
0.027 0.575 0.072
Reference category is ‘No CVD’; n/a = not applicable due to no individuals for the category, ACB CVD = interaction estimates of ACB with CVD.
>0 in those with or without cardiovascular disease. Again, there were no statistically significant differences observed in terms of outcomes examined between those with or without CVD outcomes in this patient population. 4. Discussion In this study we found no evidence of an association between the use of drugs with anticholinergic properties and admission and early (within 3- and 7-days) in-patient mortality or length of hospital stay in the oldest old admitted to hospital with an acute illness. This study adds to the existing work evaluating anticholinergic exposure and mortality by providing some insight into ACB burden in the oldest old. The current literature examining the link between exposure to anticholinergic medications and mortality is inconclusive. A recent large prospective study by Fox et al. examined 13,004 participants (mean age of 75.2 years) of whom 48% were taking anticholinergic medications (Fox et al., 2011). They found that after adjusting for age, sex, baseline Mini Mental State Examination (MMSE) score, education, social class, number of anticholinergic medications and number of health conditions, taking anticholinergic medications was associated with increased mortality at 2 years. Mangoni, van Munster, Woodman, and de Rooij (2013) assessed possible
associations between anticholinergic drug exposure and serum anticholinergic activity and their capacities to predict all-cause mortality in 71 older hospitalised patients (mean age of 84 years) and found that the anticholinergic risk score, together with cognitive impairment, in-hospital delirium, place of residence and length of hospital stay, predicted 1-year all-cause mortality in this group. However, other studies suggest that exposure to anticholinergic medications and mortality are not associated. In their study, Wilson et al. (2012) investigated the association between increasing exposure to anticholinergic and sedative medications and mortality in 602 older adults (mean age of 85.7 years) living in Australian residential care facilities and found no significant association. In their study population 33.6% were exposed to anticholinergic medications. Likewise, a recent cohort study by Kumpula, Bell, Soini, & Pitka¨la¨ (2011) investigating the association between the use of anticholinergic drugs and mortality in 1004 patients from fifty three long term care wards in Finland found that higher anticholinergic loads were not associated with mortality. They found that 55% of their study population had mild or high anticholinergic loads. There is a dearth of information on the possible link between drug ACB and mortality in the acutely ill oldest old. Our study further advances understanding in this area. The median age of our
Please cite this article in press as: Kidd, A.C., et al., The relationship between total anticholinergic burden (ACB) and early in-patient hospital mortality and length of stay in the oldest old aged 90 years and over admitted with an acute illness. Arch. Gerontol. Geriatr. (2014), http://dx.doi.org/10.1016/j.archger.2014.01.006
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study population was significantly higher than other studies determining associations between anticholinergic medications and mortality [93.5 years (ACB score 0), 92.9 years (ACB score 1) and 92.5 (ACB score >2)]. Also, in our study the prevalence of patients taking anticholinergic medications was much higher than most other studies at 61.1%. We also found that older patients have lower ACB scores [93.4 years (ACB score 1) vs. 92.5 (ACB score >2), p 0.0018]. The association of higher ACB with relatively younger age, polypharmacy and higher co-morbidity is consistent with that found in another study (Lowry, Woodman, Soiza, & Mangoni, 2011). In their study, Fox et al. stated that their findings may reflect the prevalence of anticholinergic prescribing in disease states with significant mortality. We specifically examined this issue and found that the prevalence of IHD was significantly different between the ACB categories. We found no evidence to suggest that the presence of CVD (combined IHD and stroke) is associated with increased mortality or length of stay within the same ACB category (=0 or >0). This was also true in a recent prospective study by Uusvaara et al. which assessed the impact of anticholinergic medications on hospitalization and mortality in 400 older patients (aged 75–90 years) with stable cardiovascular disease (Uusvaara et al., 2011). The authors of this study concluded that the use of anticholinergic medications was associated with an increased number of hospital days but not with mortality, when adjusting for age, sex and Charlson Co-morbidity Index score (Uusvaara et al., 2011). In a study by Lo¨nnroos et al. who examined the relationship between Drug Burden Index (a measure of anticholinergic and sedative medications) and hospitalization in a population-based sample of 339 community-dwelling older Finns (mean age of) over a 1-year period (Lo¨nnroos et al., 2012). The authors found that exposure to Drug Burden Index medications was associated with a greater use of hospital days, but a cumulative dose-response relationship between Drug Burden Index and hospitalization was not observed. Similar to our findings, this study also had a high prevalence of anticholinergic medication usage in their study population at 74%. In our study, we determined in-patient mortality at 3-days and 7-days. In the studies that found an association between anticholinergic exposure and mortality, the length of follow-up times were considerably longer. To our knowledge this is the first study to examine the relationship between ACB and short-term mortality focusing on oldest old acutely unwell patients. The majority of studies to date investigated in the community setting (Fox et al., 2011; Lo¨nnroos et al., 2012; Uusvaara et al., 2011) or institutions (Kumpula et al., 2011; Wilson et al., 2012). One of the key strengths of this study was that the data came from three different sites, suggesting that the score could be used in different centers. Our study had a relatively large sample of patients whose data was collected prospectively. We used ACB Scale which has been externally validated by other studies (Campbell et al., 2010; Fox et al., 2011). Variables analysed were easily gathered, standard observations and biochemical and physiological markers which reflect acuteness of the illness condition for patients in hospital. We used objective data collected on standardized collection forms by fully qualified specialist trained investigators. Our study has limitations. There is the possibility of an element of bias in data collection in this study. We used health-care records for data collection rather than direct patient interview, and the data collected largely depended on the documentation made by nurses and front line doctors. Data were collected by two doctors in each center and inter-observer agreement was not specifically tested. Nevertheless, we collected objective measures which are not influenced by observer bias and used standardized methodology across all sites. Norwich medications were not obtained via a pharmacist-led medicines reconciliation process. Patients
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admitted to surgical or orthopedic specialties were not included in the study. Although this number may be small in comparison with acute medical admissions, nonetheless this could underestimate the actual magnitude of all hospital admissions in this age group and may have an impact on mortality and length of stay figures. Moreover, we could not determine the length of ACB exposure to allow Cox regression analysis. A recent study found an association between ACB and the risk of developing cognitive impairment in those exposed to anticholinergic medications greater than 90 days, which could be relevant to this sample (Cai, Campbell, Khan, Callahan, & Boustani, 2013). Furthermore, it is possible that some important predictors of outcome were not measured in the study or that the study was under-powered to detect some important effects in included measures. The outcome analyses were based on dichotomised predictor variables. Length of stay is a problematic outcome as it is prone to bias from early mortality. Although this was adjusted by describing length of stay in survivors and non-survivors, the creation of these subgroups meant that the power to capture a true difference was substantially diminished. The study was conducted in the winter months for a relatively short time-frame, we are not able to assess the presence or absence of seasonal variation in determinants of outcomes. We only used the ACB score, so it is possible other measures of ACB may have yielded different results. Moreover, in previous studies prevalence of ACB drug use varies between studies also in terms of anticholinergic scale used. The Drug Burden Index includes also sedative drugs and our scoring system is not restricted to anticholinergic component of the Drug Burden Index. In summary, we found no association between anticholinergic medications on admission and early and in-patient mortality, nor hospital length of stay in patients aged 90 years in acute medical admission settings in this series. It is, however, important to note that our study is limited by several factors discussed above. With the increasing age of the world population, particularly in Western countries, further research into the adverse effects of medication use on the mortality and length of hospital stay of older individuals is needed. Larger studies are required to examine the dose-response relationship between anticholinergic medications and both immediate and longer term outcomes in wide ranging aspects relevant in oldest old including cognition and functional limitation. Author contribution Dr A.C. Kidd and all co-authors had full access to all of the anonymised data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Andrew C. Kidd: study design, interpretation, writing and manuscript preparation. Patrick Musonda: data preparation and manuscript preparation. Roy L. Soiza: audit design, manuscript preparation and editing. Catherine Butchart, Claire J. Lunt and Yogish Pai: audit design, data collection and manuscript preparation. Yasir Hameed: data collection and manuscript preparation. Chris Fox: manuscript preparation and editing. John F. Potter: manuscript preparation and editing. Phyo Kyaw Myint: study design, analysis plan, interpretation, manuscript preparation and editing. Conflicts of interest statement There are no conflicts of interest. Funding No funding was received for this paper.
Please cite this article in press as: Kidd, A.C., et al., The relationship between total anticholinergic burden (ACB) and early in-patient hospital mortality and length of stay in the oldest old aged 90 years and over admitted with an acute illness. Arch. Gerontol. Geriatr. (2014), http://dx.doi.org/10.1016/j.archger.2014.01.006
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Appendix B (Continued )
Acknowledgements
ACB score 0 (%)
ACB score 1 (%)
N = 163
N = 256
Respiratory Acute bronchitis Aspiration pneumonia Bilateral pneumonia COPD LRTI Pleural effusion Community acquired pneumonia PE Tuberculosis
27 (16.6%)
32 (12.5%)
0.285a
GI Diarrhea Ampulla tumor pancreas Cholangitis Constipation Duodenal ulcer Ulcerative colitis Iron deficiency anemia Gastroenteritis Mallory Weiss tear Oseophagitis Upper GI bleed Biliary sepsis
11 (6.75%)
13 (5.08%)
0.491a
Nephrology UTI Acute-on-chronic renal failure Rhabdomyolosis
27 (16.6%)
33 (12.9%)
0.336a
Other Bilateral leg cellulitis Diabetes Digoxin toxicity Epistaxis Hypoglycaemia DVT Leg ulcers Mechanical fall Anemia
41 (25.2%)
69 (27.0%)
0.732a
We thank Dr Nishant Gautham who contributed in data collection of the initial audit at one of the sites.
P-value
VT
Appendix A Drugs with established and clinically relevant cognitive anticholinergic effects.a Score 3 (High)
Score 2 (Medium)
Score 1 (Low)
Atropine Amitriptyline Chlorpheniramine Chlorpromazine Clemastine Clomipramine Clozapine Darifenacin Desipramine Dicyclomine Dimenhydrinate (with cinnarizine = Arlevert) Doxepin Flavoxate Hydroxyzine Hyoscine hydrobromide Imipramine Nortriptyline Olanzapine Orphenadrine Oxybutynin Paroxetine Paroxetine Perphenazine Procyclidine Promazine
Amantadine Carbamazepine Cyproheptadine Methotrimeprazine Oxcarbazepine Pethidine
Alimemazine Alverine Amisulpride Atenolol Bupropion HCl Captopril Chlorthalidone Cimetidine Codeine Colchicine Diazepam Digoxin Dipyridamole Disopyramide Fentanyl Furosemide Flupenthixol Fluvoxamine Haloperidol Hydralazine Hydrocortisone Isosorbide Mono/Di Loperamide Metoprolol Morphine Nifedipine Pericyazine Prednisone Prochlorperazine Quinidine Ranitidine Risperidone Theophylline Trazodone Triamterene
Promethazine Propentheline Quetiapine Thioridazine Tolterodine Trifluoperazine Trihexyphenidyl Trimipramine a
To calculate the Anticholinergic Cognitive Burden score for a patient, identify medications the patient is taking and add the total points for each medication.
Appendix B Diagnosis on discharge of users and non-users of anticholinergic drugs.
Cardiology ACS LVF AF Angina Acute pericarditis Bradycardia Atrial flutter CCF MVR Cardiac syncope Complete heart block SVT Pulmonary edema
ACB score 0 (%)
ACB score 1 (%)
N = 163
N = 256
34 (20.9%)
60 (23.4)
P-value
0.593a
a
P-value calculated using binomial exact methods.
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