OBJECTIVES: To investigate the relationship between polypharmacy and frailty. DESIGN: Longitudinal, observational cohort study. SETTING: Saarland, Germany. PARTICIPANTS: 3,058 community-dwelling adults aged between 57 and 84 years. MEASUREMENTS: Frailty was assessed according to the frailty phenotype, described by Fried et al. Polypharmacy and hyperpolypharmacy were defined as the concomitant use of five or more and 10 or more drugs, respectively. We assessed associations between polypharmacy and prevalent and incident frailty within 3 years of follow-up by logistic regression models controlled for multiple potential confounders including comorbidity. Additionally, cubic splines were used to assess dose-response associations. RESULTS: Polypharmacy was reported in 39.1% (n = 1,194), and hyperpolypharmacy in 8.9% (n = 273) of participants. Prevalent frailty was present in 271 (8.9%) participants; 186 (9.3%) of 1,998 non-frail participants with follow-up data became frail within 3 years. After adjustment, polypharmacy and hyperpolypharmacy were associated with prevalent frailty with adjusted odds ratios (95% confidence interval) of 2.30 (1.60-3.31) and 4.97 (2.97-8.32), respectively. Polypharmacy (odds ratio (OR) 1.51 (1.05-2.16)) and hyperpolypharmacy (OR 1.90 (1.10-3.28)) were also independent predictors of incident frailty. Furthermore, there was a moderate exposure-response relationship between the number of medicines and prevalent as well as incident frailty.CONCLUSION: Our study showed that polypharmacy is associated with frailty. Further research should address the potential benefit of reducing of inappropriate polypharmacy and better pharmacotherapeutic management for preventing medication-associated frailty. J Am Geriatr Soc 65:e27-e32, 2017.

BackgroundThe implementation of new technology can interrupt established workflows in health care settings. The Quality of Maternal Care (QUALMAT) project has introduced an electronic clinical decision support system (eCDSS) for antenatal care (ANC) and delivery in rural primary health care facilities in Africa.ObjectiveThis study was carried out to investigate the influence of the QUALMAT eCDSS on the workflow of health care workers in rural primary health care facilities in Ghana and Tanzania.DesignA direct observation, time-and-motion study on ANC processes was conducted using a structured data sheet with predefined major task categories. The duration and sequence of tasks performed during ANC visits were observed, and changes after the implementation of the eCDSS were analyzed.ResultsIn 24 QUALMAT study sites, 214 observations of ANC visits (144 in Ghana, 70 in Tanzania) were carried out at baseline and 148 observations (104 in Ghana, 44 in Tanzania) after the software was implemented in 12 of those sites. The median time spent combined for all centers in both countries to provide ANC at baseline was 6.5 min [interquartile range (IQR) =4.0–10.6]. Although the time spent on ANC increased in Tanzania and Ghana after the eCDSS implementation as compared to baseline, overall there was no significant increase in time used for ANC activities (0.51 min, p=0.06 in Ghana; and 0.54 min, p=0.26 in Tanzania) as compared to the control sites without the eCDSS. The percentage of medical history taking in women who had subsequent examinations increased after eCDSS implementation from 58.2% (39/67) to 95.3% (61/64) p<0.001 in Ghana but not in Tanzania [from 65.4% (17/26) to 71.4% (15/21) p=0.70].ConclusionsThe QUALMAT eCDSS does not increase the time needed for ANC but partly streamlined workflow at sites in Ghana, showing the potential of such a system to influence quality of care positively.

Increased healthcare utilization and costs as well as an increased probability for adverse events in individuals exposed to PIM demonstrate the health economic relevance of PIM prescriptions. Whether avoiding PIM listed on the PRISCUS list may potentially improve the quality and efficiency of healthcare is currently unknown.

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BackgroundCardiovascular disease is a leading cause of death in older people, and the impact of being exposed or not exposed to preventive cardiovascular medicines is accordingly high. Underutilization of beneficial drugs is common, but prevalence estimates differ across settings, knowledge on predictors is limited, and clinical consequences are rarely investigated.MethodsUsing data from a prospective population-based cohort study, we assessed the prevalence, determinants, and outcomes of medication underuse based on cardiovascular criteria from Screening Tool To Alert to Right Treatment (START).ResultsMedication underuse was present in 69.1% of 1454 included participants (mean age 71.1 ± 6.1 years) and was significantly associated with frailty (odds ratio: 2.11 [95% confidence interval: 1.24–3.63]), body mass index (1.03 [1.01–1.07] per kg/m2), and inversely with the number of prescribed drugs (0.84 [0.79–0.88] per drug). Using this information for adjustment in a follow-up evaluation (mean follow-up time 2.24 years) on cardiovascular and competing outcomes, we found no association of medication underuse with cardiovascular events (fatal and non-fatal) (hazard ratio: 1.00 [0.65–1.56]), but observed a significant association of medication underuse with competing deaths from non-cardiovascular causes (2.52 [1.01–6.30]).ConclusionMedication underuse was associated with frailty and adverse non-cardiovascular clinical outcomes. This may suggest that cardiovascular drugs were withheld because of serious co-morbidity or that concurrent illness can preclude benefit from cardiovascular prevention. In the latter case, adapted prescribing criteria should be developed and evaluated in those patients.

Aims The aim of the present study was to conduct a meta‐analysis of controlled trials assessing the impact of pharmaceutical care interventions (e.g. medication reviews) on medication underuse in older patients (≥65 years). Methods The databases MEDLINE and EMBASE were searched for controlled studies, and data on interventions, patient characteristics and exposure, and outcome assessment were extracted. Risk of bias was assessed using the Cochrane Collaboration's ‘risk of bias’ table. Results from reported outcomes were synthesized in multivariate random effects meta‐analysis, subgroup meta‐analysis and meta‐regression. Results From 954 identified articles, nine controlled studies, mainly comprising a medication review, were included (2542 patients). These interventions were associated with significant reductions in the mean number of omitted drugs per patient (estimate from six studies with 1469 patients: – 0.44; 95% confidence interval –0.61, –0.26) and the proportion of patients with ≥1 omitted drugs (odds ratio from eight studies with 1833 patients: 0.29; 95% confidence interval 0.13, 0.63). The only significant influential factor for improving success was the utilization of explicit screening instruments when conducting a medication review (P = 0.033). Conclusion Pharmaceutical care interventions, including medication reviews, can significantly reduce medication underuse in older people. The use of explicit screening instruments alone or in combination with implicit reasoning is strongly recommendable for clinical practice.

Background:Whether arrhythmia risks will increase if drugs with electrocardiographic (ECG) QT-prolonging properties are combined is generally supposed but not well studied. Based on available evidence, the Arizona Center for Education and Research on Therapeutics (AZCERT) classification defines the risk of QT prolongation for exposure to single drugs. We aimed to investigate how combining AZCERT drug categories impacts QT duration and how relative drug exposure affects the extent of pharmacodynamic drug–drug interactions.Methods:In a cohort of 2558 psychiatric inpatients and outpatients, we modeled whether AZCERT class and number of coprescribed QT-prolonging drugs correlates with observed rate-corrected QT duration (QTc) while also considering age, sex, inpatient status, and other QTc-prolonging risk factors. We concurrently considered administered drug doses and pharmacokinetic interactions modulating drug clearance to calculate individual weights of relative exposure with AZCERT drugs. Because QTc duration is concentration-dependent, we estimated individual drug exposure with these drugs and included this information as weights in weighted regression analyses.Results:Drugs attributing a ‘known’ risk for clinical consequences were associated with the largest QTc prolongations. However, the presence of at least two versus one QTc-prolonging drug yielded nonsignificant prolongations [exposure-weighted parameter estimates with 95% confidence intervals for ‘known’ risk drugs + 0.93 ms (–8.88;10.75)]. Estimates for the ‘conditional’ risk class increased upon refinement with relative drug exposure and co-administration of a ‘known’ risk drug as a further risk factor.Conclusions:These observations indicate that indiscriminate combinations of QTc-prolonging drugs do not necessarily result in additive QTc prolongation and suggest that QT prolongation caused by drug combinations strongly depends on the nature of the combination partners and individual drug exposure. Concurrently, it stresses the value of the AZCERT classification also for the risk prediction of combination therapies with QT-prolonging drugs.

Objective Hip fractures are among the most frequently occurring fragility fractures in older adults, associated with a loss of quality of life, high mortality, and high use of healthcare resources. The aim was to apply the superlearner method to predict osteoporotic hip fractures using administrative claims data and to compare its performance to established methods. Methods We devided claims data of 288,086 individuals aged 65 years and older without care level into a training (80%) and a validation set (20%). Subsequently, we trained a superlearner algorithm that considered both regression and machine learning algorithms (e.g., support vector machines, RUSBoost) on a large set of clinical risk factors. Mean squared error and measures of discrimination and calibration were employed to assess prediction performance. Results All algorithms used in the analysis showed similar performance with an AUC ranging from 0.66 to 0.72 in the training and 0.65 to 0.70 in the validation set. Superlearner showed good discrimination in the training set but poorer discrimination and calibration in the validation set. Conclusions The superlearner achieved similar predictive performance compared to the individual algorithms included. Nevertheless, in the presence of non-linearity and complex interactions,