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.

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ObjectiveBenzodiazepines and “Z-drug” GABA-receptor modulators (BDZ) are among the most frequently used drugs in hospitals. Adverse drug events (ADE) associated with BDZ can be the result of preventable medication errors (ME) related to dosing, drug interactions and comorbidities. The present study evaluated inpatient use of BDZ and related ME and ADE.MethodsWe conducted an observational study within a pharmacoepidemiological database derived from the clinical information system of a tertiary care hospital. We developed algorithms that identified dosing errors and interacting comedication for all administered BDZ. Associated ADE and risk factors were validated in medical records.ResultsAmong 53,081 patients contributing 495,813 patient-days BDZ were administered to 25,626 patients (48.3%) on 115,150 patient-days (23.2%). We identified 3,372 patient-days (2.9%) with comedication that inhibits BDZ metabolism, and 1,197 (1.0%) with lorazepam administration in severe renal impairment. After validation we classified 134, 56, 12, and 3 cases involving lorazepam, zolpidem, midazolam and triazolam, respectively, as clinically relevant ME. Among those there were 23 cases with associated adverse drug events, including severe CNS-depression, falls with subsequent injuries and severe dyspnea. Causality for BDZ was formally assessed as ‘possible’ or ‘probable’ in 20 of those cases. Four cases with ME and associated severe ADE required administration of the BDZ antagonist flumazenil.ConclusionsBDZ use was remarkably high in the studied setting, frequently involved potential ME related to dosing, co-medication and comorbidities, and rarely cases with associated ADE. We propose the implementation of automated ME screening and validation for the prevention of BDZ-related ADE.

To develop and internally validate prediction models for medication-related risks arising from overuse, misuse, and underuse that utilize clinical context information and are suitable for routine risk assessment in claims data (i.e., medication-based models predicting the risk for hospital admission apparent in routine claims data or MEDI-RADAR). Methods: Based on nationwide claims from healthinsured persons in Germany between 2010 and 2012, we drew a random sample of people aged 65 years (N ¼ 22,500 randomly allocated to training set, N ¼ 7500 to validation set). Individual duration of drug supply was estimated from prescription patterns to yield timevarying drug exposure windows. Together with concurrent medical conditions (ICD-10 diagnoses), exposure to the STOPP/START (screening tool of older persons' potentially inappropriate prescriptions/screening tool to alert doctors to the right treatment) criteria was derived. These were tested as time-dependent covariates together with time-constant covariates (patient demographics, baseline comorbidities) in regularized Cox regression models. Results: STOPP/START variables were iteratively refined and selected by regularization to include 2 up to 11 START variables and 8 up to 31 STOPP variables in parsimonious and liberal selections in the prediction modeling. The models discriminated well between patients with and without allcause hospitalizations, potentially drug-induced hospitalizations, and mortality (parsimonious model c-indices with 95% confidence intervals: 0.63 [0.62e0.64], 0.67 [0.65e0.68], and 0.78 [0.76e0.80]). Conclusions: The STOPP/START criteria proved to efficiently predict medication-related risk in models possessing good performance. Timely detection of such risks by routine monitoring in claims data can support tailored interventions targeting these modifiable risk factors. Their impact on older peoples' medication safety and effectiveness can now be explored in future implementation studies.

WHAT IS KNOWN AND OBJECTIVES Paracetamol is a frequently used antipyretic and analgesic drug, but also a dose-dependent hepatotoxin. Unintentional paracetamol overdosing is a common medication error in hospitals. The present study aimed at (i) analysis of unintentional paracetamol overdosing in hospitalized patients; (ii) development, implementation and outcome analysis of an alert algorithm for the prevention of relevant paracetamol overdosing. METHODS All patients who received paracetamol in a Swiss tertiary care hospital during 2011 to 2014 were analysed to detect cases of paracetamol overdosing in a local pharmacoepidemiological database. In 2014, an automated algorithm screened the hospital's electronic prescribing system for patients at risk of overdosing, followed by expert validation. When imminent relevant overdosing was confirmed, alerts were issued to prescribers. Relevance was defined as prescriptions that permitted repeated daily paracetamol exposure of 5 g. RESULTS AND DISCUSSION From 2011 to 2013, relevant overdosing occurred in 11 patients (5-8 g/day for 3 to 5 days), which corresponds to 0·4 % of all patients exposed to any paracetamol overdosing (mean n = 988 per year). In 2014, alerts were issued by experts in 23 cases with subsequent changes to prescriptions in 21 (91·3 %) thereof. Although the occurrence of any paracetamol overdosing declined only marginally in 2014 (n = 914), no relevant overdosing occurred anymore. WHAT IS NEW AND CONCLUSION Unintentional paracetamol overdosing was frequent but only a small fraction thereof was deemed relevant. This proof of concept study analysed local hospital data and developed a preventive system combining sensitive automated detection with subsequent specific expert validation. The resulting alerts achieved high compliance and prevented relevant paracetamol overdosing. SUMMARYWhat is known and objectives: Paracetamol is a frequently used antipyretic and analgesic drug, but also a dose-dependent hepatotoxin. Unintentional paracetamol overdosing is a common medication error in hospitals. The present study aimed at 1)Analysis of unintentional paracetamol overdosing in hospitalized patients; 2)Development, implementation and outcome of an alert algorithm for the prevention of considerable paracetamol overdosing. Methods:All patients who received paracetamol in a Swiss tertiary care hospital during 2011 to 2014 were analysed to detect cases of paracetamol overdosing in a local pharmacoepidemiological database. In 2014 an algorithm screened the hospital's electronic prescribing system for patients at risk of considerable overdosing, followed by expert validation. When such overdosing was confirmed as imminent, alerts were issued to prescribers. Considerable overdosing was defined as prescriptions that permitted repeated daily paracetamol exposure of ≥ 5 g. Results and What is new and Conclusion:This proof of concept demonstrates that analyzing local hospital data allows the development of alert algorithms that successfully prevent relevant medi...