Adverse drug events caused by three high-risk drug-drug interactions in patients admitted to intensive care units: A multicentre retrospective observational study

Pharmacotherapy assessment and adverse drug reactions in older patients admitted to intensive care

INTRODUCTION

Older patients are more susceptible to medication use, and physiological changes resulting from aging and organic dysfunctions presented by critically ill patients may alter the pharmacokinetic or pharmacodynamic behavior. Thus, critically ill older people present greater vulnerability to the occurrence of pharmacotherapeutic problems.

OBJECTIVE

To evaluate pharmacotherapy and the development of potential adverse drug reactions (ADRs) in older patients admitted to an intensive care unit (ICU).

METHOD

A cohort study was conducted in an ICU for adults of a Brazilian University Hospital during a 12-month period. The patients' pharmacotherapy was evaluated daily, considering the occurrence of ADRs and drug-drug interactions (DDIs), the use of potentially inappropriate medications (PIMs) for older people, and the pharmacotherapy anticholinergic burden (ACB). A trigger tool was used for active search of ADRs, with subsequent causality evaluation. PIM use was evaluated by means of the Beers criteria and the STOPP/START criteria. The ABC scale was employed to estimate ACB. The Micromedex® and Drugs.com® medication databases were employed to evaluate the DDIs.

RESULTS

The sample of this study consisted of 41 patients, with a mean age of 66.8 years old (±5.2). The 22 triggers used assisted in identifying 15 potential ADRs, and 26.8% of the patients developed them. The mean estimated ACB score was 3.0 (±1.8), and the patients used 3.1 (±1.4) and 3.3 (±1.6) PIMs according to the Beers and the STOPP criteria, respectively. A total of 672 DDIs were identified, with a mean of 16.8 (±9.5) DDIs/patient during ICU hospitalization. Our findings show an association between occurrence of ADRs in the ICU and polypharmacy (p=.03) and DDIs (p=.007), corroborating efforts for rational medication use as a preventive strategy.

CONCLUSIONS

Using tools to evaluate the pharmacotherapy for older people in intensive care can assist in the recognition and prevention of pharmacotherapeutic problems, with emphasis on the identification of ADRs through the observation of triggers and subsequent causality analysis.

Drug-Drug Interactions and Their Association with Adverse Health Outcomes in the Older Community-Dwelling Population: A Prospective Cohort Study

BACKGROUND

Evidence on associations between drug-drug interactions (DDIs) and health outcomes in the older community-dwelling population is limited.

OBJECTIVE

We estimate potentially clinically important DDI prevalence and examine the association between DDIs and (1) adverse drug events (ADEs), (2) emergency hospital attendance and (3) health-related quality of life (HRQoL) in an older community-dwelling population in Ireland.

METHODS

This is a prospective cohort study of community-dwelling older adults (N = 904) aged ≥ 70 years from 15 general practices in Ireland recruited in 2010 (wave-1) and followed-up over 2 years (wave-2; 2012-2013), with linked national pharmacy claims data. Individuals dispensed two or more drugs (wave-1: N = 842; wave-2: N = 763) were included. DDI prevalence at baseline, follow-up and 6 months prior to each health outcome was estimated. Multi-level regression was used to model the association between DDI-exposure and health outcomes at follow-up. DDI prevalence, adjusted incidence-rate ratios (aIRR), adjusted odds ratios (aOR), β coefficients and robust standard error (RSE) from multi-level regression analyses, and 95% confidence intervals (CIs) are reported.

RESULTS

At wave-1, n = 196 (23.3% [95% CI 20.5-26.3]), individuals were potentially exposed to ≥ 1 DDI, increasing to n = 345 (45.2% [41.7-48.9]) at wave-2. At 2-year follow-up, the median number of ADEs was 3 (interquartile range [IQR 2-5]); 229 (30.1%) had ≥ 1 emergency hospital attendance, and the mean EQ-5D was 0.74 (± 0.23). Evidence for the association between DDI-exposure and emergency hospital attendance at follow-up was lacking (aOR = 1.38 [0.42-4.53]). DDI-exposure was associated with an increasing number of ADEs (aIRR = 1.26 [1.03-1.55]), and decreasing EQ-5D utility (β = - 0.07, [-0.11 to -0.04], RSE = 0.02). Aspirin-warfarin, clarithromycin-prednisolone, amiodarone-furosemide, clarithromycin-salbutamol, rosuvastatin-warfarin, amiodarone-bisoprolol, and aspirin-nicorandil were common DDIs 6 months preceding these health outcomes.

CONCLUSIONS

We found a two-fold increase in DDI prevalence between wave 1 and 2. DDI exposure was associated with increasing ADEs and declining HRQoL at 2-year follow-up. Common DDIs involved anticoagulants, cardiovascular and antimicrobial drugs, which should be targeted for medicine optimisation.

The effect of computerised decision support alerts tailored to intensive care on the administration of high-risk drug combinations, and their monitoring: a cluster randomised stepped-wedge trial

BACKGROUND

Drug-drug interactions (DDIs) can harm patients admitted to the intensive care unit (ICU). Yet, clinical decision support systems (CDSSs) aimed at helping physicians prevent DDIs are plagued by low-yield alerts, causing alert fatigue and compromising patient safety. The aim of this multicentre study was to evaluate the effect of tailoring potential DDI alerts to the ICU setting on the frequency of administered high-risk drug combinations.

METHODS

We implemented a cluster randomised stepped-wedge trial in nine ICUs in the Netherlands. Five ICUs already used potential DDI alerts. Patients aged 18 years or older admitted to the ICU with at least two drugs administered were included. Our intervention was an adapted CDSS, only providing alerts for potential DDIs considered as high risk. The intervention was delivered at the ICU level and targeted physicians. We hypothesised that showing only relevant alerts would improve CDSS effectiveness and lead to a decreased number of administered high-risk drug combinations. The order in which the intervention was implemented in the ICUs was randomised by an independent researcher. The primary outcome was the number of administered high-risk drug combinations per 1000 drug administrations per patient and was assessed in all included patients. This trial was registered in the Netherlands Trial Register (identifier NL6762) on Nov 26, 2018, and is now closed.

FINDINGS

In total, 10 423 patients admitted to the ICU between Sept 1, 2018, and Sept 1, 2019, were assessed and 9887 patients were included. The mean number of administered high-risk drug combinations per 1000 drug administrations per patient was 26·2 (SD 53·4) in the intervention group (n=5534), compared with 35·6 (65·0) in the control group (n=4353). Tailoring potential DDI alerts to the ICU led to a 12% decrease (95% CI 5-18%; p=0·0008) in the number of administered high-risk drug combinations per 1000 drug administrations per patient, after adjusting for clustering and prognostic factors.

INTERPRETATION

This cluster randomised stepped-wedge trial showed that tailoring potential DDI alerts to the ICU setting significantly reduced the number of administered high-risk drug combinations. Our list of high-risk drug combinations can be used in other ICUs, and our strategy of tailoring alerts based on clinical relevance could be applied to other clinical settings.

FUNDING

ZonMw.