Kris M. Jamsen

Polypharmacy cut-points in older people with cancer: how many medications are too many?

PurposePolypharmacy is often defined as use of ‘five-or-more-medications’. However, the optimal polypharmacy cut-point for predicting clinically important adverse events in older people with cancer is unclear. The aim was to determine the sensitivities and specificities of a range of polypharmacy cut-points in relation to a variety of adverse events in older people with cancer.MethodsData on medication use, falls and frailty criteria were collected from 385 patients aged ≥70xa0years presenting to a medical oncology outpatient clinic. Receiver operating characteristic (ROC) curves were produced to examine sensitivities and specificities for varying definitions of polypharmacy in relation to exhaustion, falls, physical function, Karnofsky Performance Scale (KPS) and frailty. Sub-analyses were performed when stratifying by age, sex, comorbidity status and analgesic use.ResultsPatients had a mean age of 76.7xa0years. Using Youden’s index, the optimal polypharmacy cut-point was 6.5 medications for predicting frailty (specificity 67.0xa0%, sensitivity 70.0xa0%), physical function (80.2xa0%, 49.3xa0%) and KPS (69.8xa0%, 52.1xa0%), 5.5 for falls (59.2xa0%, 73.0xa0%) and 3.5 for exhaustion (43.4xa0%, 74.5xa0%). For polypharmacy defined as five-or-more-medications, the specificities and sensitivities were frailty (44.9xa0%, 77.5xa0%), physical function (58.0xa0%, 69.7xa0%), KPS (47.7xa0%, 69.4xa0%), falls (44.5xa0%, 75.7xa0%) and exhaustion (52.6xa0%, 64.1xa0%). The optimal polypharmacy cut-points were similar when the sample was stratified by age, sex, comorbidity status and analgesic use.ConclusionsOur results suggest that no single polypharmacy cut-point is optimal for predicting multiple adverse events in older people with cancer. In this population, the common definition of five-or-more-medications is reasonable for identifying ‘at-risk’ patients for medication review.

Effects of Changes in Number of Medications and Drug Burden Index Exposure on Transitions Between Frailty States and Death: The Concord Health and Ageing in Men Project Cohort Study

To investigate the effects of number of medications and Drug Burden Index (DBI) on transitions between frailty stages and death in community‐dwelling older men.

Comparative Plasma and Cerebrospinal Fluid Pharmacokinetics of Paracetamol After Intravenous and Oral Administration.

BACKGROUND: We compared plasma and cerebrospinal fluid (CSF) pharmacokinetics of paracetamol after intravenous (IV) and oral administration to determine dosing regimens that optimize CSF concentrations. METHODS: Twenty-one adult patients were assigned randomly to 1 g IV, 1 g oral or 1.5 g oral paracetamol. An IV cannula and lumbar intrathecal catheter were used to sample venous blood and CSF, respectively, over 6 hours. The plasma and CSF maximum concentrations (C max), times to maximum concentrations (T max), and area under the plasma and CSF concentration-time curves (AUCs) were calculated using noncompartmental techniques. Significance was defined by P < .0167 (Bonferroni correction for 3 comparisons for each parameter). Probability (X < Y) (p″) with Bonferroni corrected 95% confidence intervals (CIs) were calculated (CIs including 0.5 meet the null hypothesis). Results are presented as median (range) or p″ (CI). P values are listed as 1 g IV vs 1 g orally, 1 g IV vs 1.5 g orally and 1 g orally vs 1.5 g orally, respectively. RESULTS: Wide variation in measured paracetamol concentrations was observed, especially in the oral groups. The median plasma C max in the 1 g IV group was significantly greater than the oral groups. In contrast, the median CSF C max was not different between groups. The median plasma T max in the 1 g IV group was 105 and 75 minutes earlier than in the 1 and 1.5 g oral groups. The median CSF T max was not significantly different between groups. The median plasma AUC (total) was not significantly different between groups; however, in the first hour, the median plasma AUC was significantly greater in the IV group than in the oral groups. In the second hour, there was no difference between groups. The median CSF AUC (total) did not significantly differ between groups; however, in the first hour, the median CSF AUC was significantly greater in the IV compared with the orally groups. In the second hour, there was no difference between groups. Our analysis indicated that the median C max, T max, and AUC values lacked precision because of small sample sizes. CONCLUSIONS: Peak plasma concentrations were greater and reached earlier after IV than oral dosing. Evidence for differences in CSF C max and T max was lacking because of the small size of this study.

Polypharmacy and medication regimen complexity as factors associated with staff informant rated quality of life in residents of aged care facilities: a cross-sectional study

PurposeThe purpose of this study is to investigate the association between polypharmacy with health-related quality of life (HRQoL) and medication regimen complexity with HRQoL in residential aged care facilities (RACFs).MethodsA cross-sectional study of 383 residents from six Australian RACFs was conducted. The primary exposures were polypharmacy (≥9 regular medications) and the validated Medication Regimen Complexity Index (MRCI). The outcome measure was staff informant rated quality of life assessed using the Quality of Life Alzheimer’s disease (QoL-AD) scale. Covariates included age, sex, Charlson’s comorbidity index, activities of daily living, and dementia severity. Logistic quantile regression was used to characterize the association between polypharmacy and QoL-AD (model 1) and MRCI and QoL-AD (model 2).ResultsThe median age of the 383 residents was 88xa0years and 297 (78xa0%) residents were female. In total, 63xa0% of residents were exposed to polypharmacy and the median MRCI score (range) was 43.5 (4–113). After adjusting for the covariates, polypharmacy was not associated with either higher or lower QoL-AD scores (estimate −0.02; 95xa0% confidence interval (CI) −0.165, 0.124; pu2009=u20090.78). Similarly, after adjusting for the covariates, MRCI was not associated with either higher or lower QoL-AD scores (estimate −0.0009, 95xa0% CI −0.005, 0.003; pu2009=u20090.63).ConclusionsThese findings suggest that polypharmacy and medication regimen complexity are not associated with staff informant rated HRQoL. Further research is needed to investigate how specific medication classes may impact change in quality of life over time.

Do statin users adhere to a healthy diet and lifestyle?: the Australian Diabetes, Obesity and Lifestyle Study

Background Lifestyle and dietary advice typically precedes or accompanies the prescription of statin medications. However, evidence for adherence to this advice is sparse. The objective was to compare saturated fat intake, exercise, alcohol consumption and smoking between statin users and non-users in Australia. Methods Data were analysed for 4614 participants aged ≥37 years in the Australian Diabetes, Obesity and Lifestyle study in 2011–2012. Statin use, smoking status and physical activity were self-reported. Saturated fat and alcohol intake were measured via a food frequency questionnaire. Multinomial logistic regression was used to compute adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between statin use and the four lifestyle factors. All models were adjusted for age, sex, education, number of general practitioner visits, body mass index, hypertension, diabetes and prior cardiovascular diseases. Results In total 1108 (24%) participants used a statin. Statin users were 29% less likely to be within the highest quartile versus the lowest quartile of daily saturated fat intake compared to non-users (OR 0.71, 95% CI 0.54–0.94). There were no statistically significant associations between statin use and smoking, physical activity or alcohol consumption. Conclusions Smoking status, alcohol consumption and exercise level did not differ between users and non-users of statins. However, statin users were less likely to consume high levels of saturated fat than non-users. We found no evidence that people took statins to compensate for a poor diet or lifestyle.

A systematic review of the statistical methods in prospective cohort studies investigating the effect of medications on cognition in older people.

BACKGROUNDnThere is increasing awareness that medications can contribute to cognitive decline. Prospective cohort studies are rich sources of clinical data. However, investigating the contribution of medications to cognitive decline is challenging because both medication exposure and cognitive impairment can be associated with attrition of study participants, and medication exposure status may change over time. The objective of this review was to investigate the statistical methods in prospective cohort studies assessing the effect of medications on cognition in older people.nnnMETHODSnA systematic literature search was conducted to identify prospective cohort studies of at least 12 months duration that investigated the effect of common medications or medication classes (anticholinergics, antihistamines, hypnotics, sedatives, opioids, statins, estrogens, testosterone, antipsychotics, anticonvulsants, antidepressants, anxiolytics, antiparkinson agents and bronchodilators) on cognition in people aged 65 years and older. Data extraction was performed independently by two investigators. A descriptive analysis of the statistical methods was performed.nnnRESULTSnA total of 44 articles were included in the review. The most common statistical methods were logistic regression (24.6% of all reported methods), Cox proportional hazards regression (22.8%), linear mixed-effects models (21.1%) and multiple linear regression (14.0%). The use of advanced techniques, most notably linear mixed-effects models, increased over time. Only 6 articles (13.6%) reported methods for addressing missing data.nnnCONCLUSIONSnA variety of statistical methods have been used for investigating the effect of medications on cognition in older people. While advanced techniques that are appropriate for the analysis of longitudinal data, most notably linear mixed-effects models, have increasingly been employed in recent years, there is an opportunity to implement alternative techniques in future studies that could address key research questions.

Prescribed Doses of Opioids in Long-Term Care Facilities

To the Editor: Recent reports have highlighted a rapid increase in opioid use in long-term care. Pitkälä et al1 reported the prevalence of regular opioid use increased from 12% to 23% in Finnish nursing homes between 2003 and 2011. Hanlon et al2 reported that 65% of US nursing home hospice or palliative care residents with any pain used opioids. However, neither of these studies investigated opioid doses. This is important, because opioids are associated with a range of dose-dependent adverse drug events (ADEs), including sedation, orthostatic hypotension, dizziness, cognitive impairment, constipation, and falls.3 Australian guidelines recommend not exceeding a 24-hour oral morphine equivalent (MEQ) dose of 100 mg for noncancer pain.4,5 Older people, including those with dementia, may be particularly susceptible to opioid ADEs. However, there is a paucity of data on prescribed opioid doses in long-term care. For this reason, we investigated the median and range of doses of different opioids prescribed to residents with and without dementia in Australian long-term care facilities.

Drug Burden Index and change in cognition over time in community-dwelling older men: the CHAMP study

Abstract Objective: Anticholinergic and sedative medications are associated with acute cognitive impairment, but the long-term impact on change in cognition is unclear. This study investigated the effect of anticholinergic and sedative medications, quantified using the Drug Burden Index (DBI), on change in cognition over time in community-dwelling older men. Methods: This was a prospective cohort study of men aged ≥70 years in Sydney, Australia. DBI was assessed at baseline, 2, and 5 years. Cognitive performance was assessed using the Mini-Mental State Exam (MMSE) at each wave. Logistic quantile mixed-effects modelling was used to assess the adjusted effect of DBI on the median MMSE-time profile. Analyses were restricted to men with English-speaking backgrounds (nu2009=u20091059, 862, and 611 at baseline, 2, and 5 years). Results: Overall, 292 (27.7%), 258 (29.9%), and 189 (31.3%) men used anticholinergic or sedative medications at baseline, 2, and 5 years. There was a concave relationship between MMSE and time, where higher DBI corresponded to lower MMSE scores (coefficient: −0.161; 95% CI: −0.250 to −0.071) but not acceleration of declining MMSE over time. Conclusions: Exposure to anticholinergic and sedative medications is associated with a small impairment in cognitive performance but not decline in cognition over time. KEY MESSAGES Exposure to anticholinergic and sedative medications, quantified using the Drug Burden Index, is associated with small cross-sectional impairments in cognitive performance. There was no evidence that exposure to anticholinergic and sedative medications is associated with accelerating decline in cognitive performance over a 5-year follow-up. Older people taking anticholinergic and sedative medications may derive immediate but small benefits in cognitive performance from clinical medication reviews to minimize or cease prescribing of these medications.

Quantifying Uncertainty in the Ratio of Two Measured Variables: A Recap and Example

Estimating uncertainty in the ratio of 2 measured variables can be achieved via 2 seemingly different approaches: by determining the variance of the first-order Taylor approximation to the ratio, or by the so-called Propagation of Error approach. This Lesson Learned shows that the 2 approaches are mathematically equivalent, and provides an example of the approach.

Demographic, clinical and lifestyle factors associated with high-intensity statin therapy in Australia: the AusDiab study

PurposeClinical guidelines specify who should receive high-intensity statins; however, it is unclear how high-intensity statins are used in Australia. Our objective was to determine the demographic, clinical, and lifestyle factors associated with high-intensity statin therapy in Australia.MethodsData from the Australian Diabetes, Obesity and Lifestyle study collected in 2011–2012 were analyzed. High-, moderate-, and low-intensity statins were defined as use of statins at doses demonstrated to reduce low-density lipoprotein cholesterol levels by >u200950, 30–50, and <u200930%, respectively. Logistic regression was used to estimate adjusted odd ratios (ORs) and 95% confidence intervals (CIs) for factors associated with high- versus low-to-moderate-intensity statin therapy.ResultsOverall, 1108 (24%) study participants used a statin. Data on statin intensity were available for 1072 participants. The proportions of high-, moderate-, and low-intensity statin therapy were 32 (nu2009=u2009341), 65 (nu2009=u2009696), and 3% (nu2009=u200935), respectively. Overall, 51% of people with prior cardiovascular disease (CVD) used a high-intensity statin. In addition to prior CVD (ORu2009=u20093.34, 95% CIu2009=u20091.95–5.73), no (ORu2009=u20091.84, 95%CI 1.02–3.31) or insufficient physical activity (ORu2009=u20091.51, 95% CIu2009=u20091.01–2.25), obesity (ORu2009=u20091.87, 95% CIu2009=u20091.13–3.10), and consuming >u20092 alcoholic drinks daily (ORu2009=u20091.66, 95% CIu2009=u20091.08–2.55) were associated with high versus low-to-moderate-intensity statin therapy. Conversely, age 65–74 vs. <u200965xa0years was inversely associated with high-intensity statin therapy (ORu2009=u20090.62, 95% CIu2009=u20090.41–0.94).ConclusionsPrior CVD was the strongest factor associated with high-intensity statin therapy. Although the prevalence of CVD increases with age, older people were less likely to be treated with high-intensity statins.