An Integrated Approach for Representing Knowledge on the Potential of Drugs to Cause Acute Kidney Injury.

INTRODUCTION AND OBJECTIVE: The recent rise in acute kidney injury (AKI) incidence, with approximately 30% attributed to potentially preventable adverse drug events (ADEs), poses challenges in evaluating drug-induced AKI due to polypharmacy and other risk factors. This study seeks to consolidate knowledge on the drugs with AKI potential from four distinct sources: (i) bio(medical) peer-reviewed journals; (ii) spontaneous reporting systems (SRS); (iii) drug information databases (DIDs); and (iv) NephroTox website. By harnessing the potential of these underutilised sources, our objective is to bridge gaps and enhance the understanding of drug-induced AKI. METHODS: By searching Medline, studies with lists of drugs with AKI potential established through consensus amongst medical experts were selected. A final list of 63 drugs was generated aggregating the original studies. For these 63 drugs, the AKI reporting odds ratios (RORs) using three SRS databases, the average frequency of ADEs from four different DIDs and the number of published studies identified via NephroTox was reported. RESULTS: Drugs belonging to the antivirals, antibacterials, and non-steroidal anti-inflammatory pharmacological classes exhibit substantial consensus on AKI potential, which was also reflected in strong ROR signals, frequent to very frequent AKI-related ADEs and a high number of published studies reporting adverse kidney events as identified via NephroTox. Renin-angiotensin aldosterone system inhibitors and diuretics also display comparable signal strengths, but this can be attributed to expected haemodynamic changes. More variability is noted for proton-pump inhibitors. CONCLUSIONS: By integrating four disjointed sources of knowledge, we have created a novel, comprehensive resource on drugs with AKI potential, contributing to kidney safety improvement efforts.

Efficiency of computerized clinical decision support systems involving anticoagulants: A flashmob study in Dutch hospital pharmacies.

AIMS: Computerized decision support systems (CDSSs) aim to prevent adverse drug events. However, these systems generate an overload of alerts that are not always clinically relevant. Anticoagulants are frequently involved in these alerts. The aim of this study was to investigate the efficiency of CDSS alerts on anticoagulants in Dutch hospital pharmacies. METHODS: A multicentre, single-day, cross-sectional study was conducted using a flashmob design in Dutch hospital pharmacies, which have CDSSs that operate on both a national medication surveillance database and on self-developed clinical rules. Hospital pharmacists and pharmacy technicians collected data on the number and type of alerts and time needed for assessing these alerts. The primary outcome was the CDSS efficiency on anticoagulants, defined as the percentage of alerts on anticoagulants that led to an intervention. Secondary outcomes where among other CDSSs efficiency related to any medications and the time expenditure. Descriptive data-analysis was used. RESULTS: Of the 69 hospital pharmacies invited, 42 (61%) participated. The efficiency of CDSS alerts on anticoagulants was 4.0% (interquartile range [IQR] 14.0%) for the national medication surveillance database alerts and 14.3% (IQR 40.0%) for alerts from clinical rules. For any medication, the efficiency was lower: 1.8% (IQR 7.5%) and 13.4% (IQR 21.5%) respectively. The median time for assessing the relevance of all alerts was 2 (IQR 1:21) h/day for pharmacists and 6 (IQR 5:01) h/day for pharmacy technicians. CONCLUSION: CDSS efficiency is generally low, both for anticoagulants and any medication, while the time investment is high. Optimization of CDSSs is needed.

Adoption of antithrombotic stewardship and utilization of clinical decision support systems-A questionnaire-based survey in Dutch hospitals.

Antithrombotics require careful monitoring to prevent adverse events. Safe use can be promoted through so-called antithrombotic stewardship. Clinical decision support systems (CDSSs) can be used to monitor safe use of antithrombotics, supporting antithrombotic stewardship efforts. Yet, previous research shows that despite these interventions, antithrombotics continue to cause harm. Insufficient adoption of antithrombotic stewardship and suboptimal use of CDSSs may provide and explanation. However, it is currently unknown to what extent hospitals adopted antithrombotic stewardship and utilize CDSSs to support safe use of antithrombotics. A semi-structured questionnaire-based survey was disseminated to 12 hospital pharmacists from different hospital types and regions in the Netherlands. The primary outcome was the degree of antithrombotic stewardship adoption, expressed as the number of tasks adopted per hospital and the degree of adoption per task. Secondary outcomes included characteristics of CDSS alerts used to monitor safe use of antithrombotics. All 12 hospital pharmacists completed the survey and report to have adopted antithrombotic stewardship in their hospital to a certain degree. The median adoption of tasks was two of five tasks (range 1-3). The tasks with the highest uptake were: drafting and maintenance of protocols (100%) and professional's education (58%), while care transition optimization (25%), medication reviews (8%) and patient counseling (8%) had the lowest uptake. All hospitals used a CDSS to monitor safe use of antithrombotics, mainly via basic alerts and less frequently via advanced alerts. The most frequently employed alerts were: identification of patients using a direct oral anticoagulant (DOAC) or a vitamin K antagonist (VKA) with one or more other antithrombotics (n = 6) and patients using a VKA to evaluate correct use (n = 6), both reflecting basic CDSS. All participating hospitals adopted antithrombotic stewardship, but the adopted tasks vary. CDSS alerts used are mainly basic in their logic.

The use of artificial intelligence to optimize medication alerts generated by clinical decision support systems: a scoping review.

OBJECTIVE: Current Clinical Decision Support Systems (CDSSs) generate medication alerts that are of limited clinical value, causing alert fatigue. Artificial Intelligence (AI)-based methods may help in optimizing medication alerts. Therefore, we conducted a scoping review on the current state of the use of AI to optimize medication alerts in a hospital setting. Specifically, we aimed to identify the applied AI methods used together with their performance measures and main outcome measures. MATERIALS AND METHODS: We searched Medline, Embase, and Cochrane Library database on May 25, 2023 for studies of any quantitative design, in which the use of AI-based methods was investigated to optimize medication alerts generated by CDSSs in a hospital setting. The screening process was supported by ASReview software. RESULTS: Out of 5625 citations screened for eligibility, 10 studies were included. Three studies (30%) reported on both statistical performance and clinical outcomes. The most often reported performance measure was positive predictive value ranging from 9% to 100%. Regarding main outcome measures, alerts optimized using AI-based methods resulted in a decreased alert burden, increased identification of inappropriate or atypical prescriptions, and enabled prediction of user responses. In only 2 studies the AI-based alerts were implemented in hospital practice, and none of the studies conducted external validation. DISCUSSION AND CONCLUSION: AI-based methods can be used to optimize medication alerts in a hospital setting. However, reporting on models' development and validation should be improved, and external validation and implementation in hospital practice should be encouraged.

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.

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

AIMS: Knowledge about adverse drug events caused by drug-drug interactions (DDI-ADEs) is limited. We aimed to provide detailed insights about DDI-ADEs related to three frequent, high-risk potential DDIs (pDDIs) in the critical care setting: pDDIs with international normalized ratio increase (INR+ ) potential, pDDIs with acute kidney injury (AKI) potential, and pDDIs with QTc prolongation potential. METHODS: We extracted routinely collected retrospective data from electronic health records of intensive care units (ICUs) patients (≥18 years), admitted to ten hospitals in the Netherlands between January 2010 and September 2019. We used computerized triggers (e-triggers) to preselect patients with potential DDI-ADEs. Between September 2020 and October 2021, clinical experts conducted a retrospective manual patient chart review on a subset of preselected patients, and assessed causality, severity, preventability, and contribution to ICU length of stay of DDI-ADEs using internationally prevailing standards. RESULTS: In total 85 422 patients with ≥1 pDDI were included. Of these patients, 32 820 (38.4%) have been exposed to one of the three pDDIs. In the exposed group, 1141 (3.5%) patients were preselected using e-triggers. Of 237 patients (21%) assessed, 155 (65.4%) experienced an actual DDI-ADE; 52.9% had severity level of serious or higher, 75.5% were preventable, and 19.3% contributed to a longer ICU length of stay. The positive predictive value was the highest for DDI-INR+ e-trigger (0.76), followed by DDI-AKI e-trigger (0.57). CONCLUSION: The highly preventable nature and severity of DDI-ADEs, calls for action to optimize ICU patient safety. Use of e-triggers proved to be a promising preselection strategy.

Acute kidney injury associated with nephrotoxic drugs in critically ill patients: a multicenter cohort study using electronic health record data.

Background: Nephrotoxic drugs frequently cause acute kidney injury (AKI) in adult intensive care unit (ICU) patients. However, there is a lack of large pharmaco-epidemiological studies investigating the associations between drugs and AKI. Importantly, AKI risk factors may also be indications or contraindications for drugs and thereby confound the associations. Here, we aimed to estimate the associations between commonly administered (potentially) nephrotoxic drug groups and AKI in adult ICU patients whilst adjusting for confounding. Methods: In this multicenter retrospective observational study, we included adult ICU admissions to 13 Dutch ICUs. We measured exposure to 44 predefined (potentially) nephrotoxic drug groups. The outcome was AKI during ICU admission. The association between each drug group and AKI was estimated using etiological cause-specific Cox proportional hazard models and adjusted for confounding. To facilitate an (independent) informed assessment of residual confounding, we manually identified drug group-specific confounders using a large drug knowledge database and existing literature. Results: We included 92 616 ICU admissions, of which 13 492 developed AKI (15%). We found 14 drug groups to be associated with a higher hazard of AKI after adjustment for confounding. These groups included established (e.g. aminoglycosides), less well established (e.g. opioids) and controversial (e.g. sympathomimetics with α- and ß-effect) drugs. Conclusions: The results confirm existing insights and provide new ones regarding drug associated AKI in adult ICU patients. These insights warrant caution and extra monitoring when prescribing nephrotoxic drugs in the ICU and indicate which drug groups require further investigation.

Drug-related causes attributed to acute kidney injury and their documentation in intensive care patients.

PURPOSE: To investigate drug-related causes attributed to acute kidney injury (DAKI) and their documentation in patients admitted to the Intensive Care Unit (ICU). METHODS: This study was conducted in an academic hospital in the Netherlands by reusing electronic health record (EHR) data of adult ICU admissions between November 2015 to January 2020. First, ICU admissions with acute kidney injury (AKI) stage 2 or 3 were identified. Subsequently, three modes of DAKI documentation in EHR were examined: diagnosis codes (structured data), allergy module (semi-structured data), and clinical notes (unstructured data). RESULTS: n total 8124 ICU admissions were included, with 542 (6.7%) ICU admissions experiencing AKI stage 2 or 3. The ICU physicians deemed 102 of these AKI cases (18.8%) to be drug-related. These DAKI cases were all documented in the clinical notes (100%), one in allergy module (1%) and none via diagnosis codes. The clinical notes required the highest time investment to analyze. CONCLUSIONS: Drug-related causes comprise a substantial part of AKI in the ICU patients. However, current unstructured DAKI documentation practice via clinical notes hampers our ability to gain better insights about DAKI occurrence. Therefore, both automating DAKI identification from the clinical notes and increasing structured DAKI documentation should be encouraged.

Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis: a systematic review.

OBJECTIVE: We conducted a systematic review to characterize and critically appraise developed prediction models based on structured electronic health record (EHR) data for adverse drug event (ADE) diagnosis and prognosis in adult hospitalized patients. MATERIALS AND METHODS: We searched the Embase and Medline databases (from January 1, 1999, to July 4, 2022) for articles utilizing structured EHR data to develop ADE prediction models for adult inpatients. For our systematic evidence synthesis and critical appraisal, we applied the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). RESULTS: Twenty-five articles were included. Studies often did not report crucial information such as patient characteristics or the method for handling missing data. In addition, studies frequently applied inappropriate methods, such as univariable screening for predictor selection. Furthermore, the majority of the studies utilized ADE labels that only described an adverse symptom while not assessing causality or utilizing a causal model. None of the models were externally validated. CONCLUSIONS: Several challenges should be addressed before the models can be widely implemented, including the adherence to reporting standards and the adoption of best practice methods for model development and validation. In addition, we propose a reorientation of the ADE prediction modeling domain to include causality as a fundamental challenge that needs to be addressed in future studies, either through acquiring ADE labels via formal causality assessments or the usage of adverse event labels in combination with causal prediction modeling.

Adverse drug event detection using natural language processing: A scoping review of supervised learning methods.

To reduce adverse drug events (ADEs), hospitals need a system to support them in monitoring ADE occurrence routinely, rapidly, and at scale. Natural language processing (NLP), a computerized approach to analyze text data, has shown promising results for the purpose of ADE detection in the context of pharmacovigilance. However, a detailed qualitative assessment and critical appraisal of NLP methods for ADE detection in the context of ADE monitoring in hospitals is lacking. Therefore, we have conducted a scoping review to close this knowledge gap, and to provide directions for future research and practice. We included articles where NLP was applied to detect ADEs in clinical narratives within electronic health records of inpatients. Quantitative and qualitative data items relating to NLP methods were extracted and critically appraised. Out of 1,065 articles screened for eligibility, 29 articles met the inclusion criteria. Most frequent tasks included named entity recognition (n = 17; 58.6%) and relation extraction/classification (n = 15; 51.7%). Clinical involvement was reported in nine studies (31%). Multiple NLP modelling approaches seem suitable, with Long Short Term Memory and Conditional Random Field methods most commonly used. Although reported overall performance of the systems was high, it provides an inflated impression given a steep drop in performance when predicting the ADE entity or ADE relation class. When annotating corpora, treating an ADE as a relation between a drug and non-drug entity seems the best practice. Future research should focus on semi-automated methods to reduce the manual annotation effort, and examine implementation of the NLP methods in practice.

The effect of structured medication review followed by face-to-face feedback to prescribers on adverse drug events recognition and prevention in older inpatients - a multicenter interrupted time series study.

BACKGROUND: The effectiveness of interventions to improve medication safety in older inpatients is unclear, given a paucity of properly designed intervention studies applying clinically relevant endpoints such as hospital-acquired preventable Adverse Drug Events (pADEs) and unrecognized Adverse Drug Events (uADEs). Therefore, we conducted a quality improvement study and used hospital-acquired pADEs and uADEs as main outcomes to assess the effect of an intervention aimed to improve medication safety in older inpatients. METHOD: The study followed an interrupted time series design and consisted of three equally spaced sampling points during baseline and during intervention measurements. Each sampling point included between 80 to 90 patients. A total of 500 inpatients ≥65 years and admitted to internal medicine wards of three Dutch hospitals were included. An expert team retrospectively identified and assessed ADEs via a structured patient chart review. The findings from baseline measurement and meetings with the internal medicine and hospital pharmacy staff were used to design the intervention. The intervention consisted of a structured medication review by hospital pharmacists, followed by face-to-face feedback to prescribers, on average 3 days per week. RESULTS: The rate of hospital-acquired pADEs per 100 hospitalizations was reduced by 50.6% (difference 16.8, 95% confidence interval (CI): 9.0 to 24.6, P <  0.001), serious hospital-acquired pADEs by 62.7% (difference 12.8, 95% CI: 6.4 to 19.2, P <  0.001), and uADEs by 51.8% (difference 11.2, 95% CI: 4.4 to 18.0, P <  0.001). Additional analyses confirmed the robustness of the intervention effect, but residual bias cannot be excluded. CONCLUSIONS: The intervention significantly decreased the overall and serious hospital-acquired pADE occurrence in older inpatients, and significantly improved overall ADE recognition by prescribers. TRIAL REGISTRATION: International Standard Randomized Controlled Trial Number Register, trial registration number: ISRCTN64974377 , registration date (date assigned): 07/02/2011.

Incorrect application of the KDIGO acute kidney injury staging criteria.

Background: Recent research demonstrated substantial heterogeneity in the Kidney Disease: Improving Global Outcomes (KDIGO) acute kidney injury (AKI) diagnosis and staging criteria implementations in clinical research. Here we report an additional issue in the implementation of the criteria: the incorrect description and application of a stage 3 serum creatinine (SCr) criterion. Instead of an increase in SCr to or beyond 4.0 mg/dL, studies apparently interpreted this criterion as an increase in SCr by 4.0 mg/dL. Methods: Using a sample of 8124 consecutive intensive care unit (ICU) admissions, we illustrate the implications of such incorrect application. The AKI stage distributions associated with the correct and incorrect stage 3 SCr criterion implementations were compared, both with and without the stage 3 renal replacement therapy (RRT) criterion. In addition, we compared chronic kidney disease presence, ICU mortality rates and hospital mortality rates associated with each of the AKI stages and the misclassified cases. Results: Where incorrect implementation of the SCr stage 3 criterion showed a stage 3 AKI rate of 29%, correct implementation revealed a rate of 34%, mainly due to shifts from stage 1 to stage 3. Without the stage 3 RRT criterion, the stage 3 AKI rates were 9% and 19% after incorrect and correct implementation, respectively. The ICU and hospital mortality rates in cases misclassified as stage 1 or 2 were similar to those in cases correctly classified as stage 1 instead of stage 3. Conclusions: While incorrect implementation of the SCr stage 3 criterion has significant consequences for AKI severity epidemiology, consequences for clinical decision making may be less severe. We urge researchers and clinicians to verify their implementation of the AKI staging criteria.

Heterogeneity in the Identification of Potential Drug-Drug Interactions in the Intensive Care Unit: A Systematic Review, Critical Appraisal, and Reporting Recommendations.

Patients admitted to the intensive care unit (ICU) are frequently exposed to potential drug-drug interactions (pDDIs). However, reported frequencies of pDDIs in the ICU vary widely between studies. This can be partly explained by significant variation in their methodological approach. Insight into methodological choices affecting pDDI frequency would allow for improved comparison and synthesis of reported pDDI frequencies. This study aimed to evaluate the association between methodological choices and pDDI frequency and formulate reporting recommendations for pDDI frequency studies in the ICU. The MEDLINE database was searched to identify papers reporting pDDI frequency in ICU patients. For each paper, the pDDI frequency and methodological choices such as pDDI definition and pDDI knowledge base were extracted, and the risk of bias was assessed. Each paper was categorized as reporting a low, medium, or high pDDI frequency. We sought associations between methodological choices and pDDI frequency group. Based on this comparison, reporting recommendations were formulated. Analysis of methodological choices showed significant heterogeneity between studies, and 65% of the studies had a medium to high risk of bias. High risk of bias, small sample size, and use of drug prescriptions instead of administrations were related to a higher pDDI frequency. The findings of this review may support researchers in designing a reliable methodology assessing pDDI frequency in ICU patients. The reporting recommendations may contribute to standardization, comparison, and synthesis of pDDI frequency studies, ultimately improving knowledge about pDDIs in and outside the ICU setting.

[Drug-related health damage]. / Gezondheidsschade door geneesmiddelen.

Gurwitz and colleagues showed that a complex intervention, aimed at a reduction of drug-related adverse events and medication errors immediately after hospital discharge, did not result in a significant outcome difference between the intervention and control groups. We feel that the intervention lacked standardization, that a better outcome might have been achieved by intervening prior to hospital discharge, that more details about the nature of observed medication errors and acceptance of the intervenor recommendations should have been reported. Also, the number of unpreventable adverse drug events was higher in the intervention (n = 37) than in the control group (n = 27), suggesting a Hawthorne effect. The small number of adverse drug events detected overall points to a low sensitivity of the detection method used. We recommend that future studies be designed differently, including a stronger focus on physician-pharmacist collaboration, patient participation and improved communication between the hospital and general practice.

Clinically relevant potential drug-drug interactions in intensive care patients: A large retrospective observational multicenter study.

PURPOSE: Potential drug-drug interactions (pDDIs) may harm patients admitted to the Intensive Care Unit (ICU). Due to the patient's critical condition and continuous monitoring on the ICU, not all pDDIs are clinically relevant. Clinical decision support systems (CDSSs) warning for irrelevant pDDIs could result in alert fatigue and overlooking important signals. Therefore, our aim was to describe the frequency of clinically relevant pDDIs (crpDDIs) to enable tailoring of CDSSs to the ICU setting. MATERIALS & METHODS: In this multicenter retrospective observational study, we used medication administration data to identify pDDIs in ICU admissions from 13 ICUs. Clinical relevance was based on a Delphi study in which intensivists and hospital pharmacists assessed the clinical relevance of pDDIs for the ICU setting. RESULTS: The mean number of pDDIs per 1000 medication administrations was 70.1, dropping to 31.0 when considering only crpDDIs. Of 103,871 ICU patients, 38% was exposed to a crpDDI. The most frequently occurring crpDDIs involve QT-prolonging agents, digoxin, or NSAIDs. CONCLUSIONS: Considering clinical relevance of pDDIs in the ICU setting is important, as only half of the detected pDDIs were crpDDIs. Therefore, tailoring CDSSs to the ICU may reduce alert fatigue and improve medication safety in ICU patients.

Improving medication safety in the Intensive Care by identifying relevant drug-drug interactions - Results of a multicenter Delphi study.

PURPOSE: Drug-drug interactions (DDIs) may cause adverse outcomes in patients admitted to the Intensive Care Unit (ICU). Computerized decision support systems (CDSSs) may help prevent DDIs by timely showing relevant warning alerts, but knowledge on which DDIs are clinically relevant in the ICU setting is limited. Therefore, the purpose of this study was to identify DDIs relevant for the ICU. MATERIALS AND METHODS: We conducted a modified Delphi procedure with a Dutch multidisciplinary expert panel consisting of intensivists and hospital pharmacists to assess the clinical relevance of DDIs for the ICU. The procedure consisted of two rounds, each included a questionnaire followed by a live consensus meeting. RESULTS: In total the clinical relevance of 148 DDIs was assessed, of which agreement regarding the relevance was reached for 139 DDIs (94%). Of these 139 DDIs, 53 (38%) were considered not clinically relevant for the ICU setting. CONCLUSIONS: A list of clinically relevant DDIs for the ICU setting was established on a national level. The clinical value of CDSSs for medication safety could be improved by focusing on the identified clinically relevant DDIs, thereby avoiding alert fatigue.

Interruptions during intravenous medication administration: A multicentre observational study.

AIMS: The aim of this study was to determine the frequency and cause of interruptions during intravenous medication administration, which factors are associated with interruptions and to what extent interruptions influence protocol compliance. BACKGROUND: Hospital nurses are frequently interrupted during medication administration, which contributes to the occurrence of administration errors. Errors with intravenous medication are especially worrisome, given their immediate therapeutic effects. However, knowledge about the extent and type of interruptions during intravenous medication administration is limited. DESIGN: Multicentre observational study. METHODS: Data were collected during two national evaluation studies (2011 - 2012 & 2015 - 2016). Nurses were directly observed during intravenous medication administration. An interruption was defined as a situation where a break during the administration was needed or where a nurse was distracted but could process without a break. Interruptions were categorized according to source and cause. Multilevel logistic regression analyses were conducted to assess the associations between explanatory variables and interruptions or complete protocol compliance. RESULTS: In total, 2,526 intravenous medication administration processes were observed. During 291 (12%) observations, nurses were interrupted 321 times. Most interruptions were externally initiated by other nurses (19%) or patients (19%). Less interruptions occurred during the evening (odds ratio: 0.23 [95% confidence interval: 0.08-0.62]). Do-not-disturb vests were worn by 61 (2%) nurses. No significant association was found between being interrupted and complete protocol compliance. CONCLUSION: An interruption occurred in every eight observed intravenous medication administration, mainly caused by other nurses or patients. One needs to consider critically which strategies effectively improve safety during the high-risk nursing-task of intravenous medication administration.

An individual patient data meta-analysis on factors associated with adverse drug events in surgical and non-surgical inpatients.

AIM: The incidence of adverse drug events (ADEs) in surgical and non-surgical patients may differ. This individual patient data meta-analysis (IPDMA) identifies patient characteristics and types of medication most associated with patients experiencing ADEs and suggests target areas for reducing harm and implementing focused interventions. METHODS: Authors of eligible studies on preventable ADEs (pADEs) were approached for collaboration. For assessment of differences among (non-)surgical patients and identification of associated factors descriptive statistics, Pearson chi-square, Poisson and logistic regression analyses were performed. For identification of high risk drugs (HRDs), a model was developed based on frequency, severity and preventability of medication related to ADEs. RESULTS: Included were 5367 patients from four studies. Patients aged ≥ 77 years experienced more ADEs and pADEs compared with patients aged ≤ 52 years (odds ratios (OR) 2.12 (95% CI 1.70, 2.65) and 2.55 (95% CI 1.70, 3.84), respectively, both P < 0.05). Polypharmacy on admission also increased the risk of ADEs (OR 1.21 (95% CI 1.03, 1.44), P < 0.05) and pADEs (OR 1.85 (95% CI 1.34, 2.56), P < 0.05). pADEs were associated with more severe harm than non-preventable ADEs (54% vs. 32%, P < 0.05). The top five HRDs were antibiotics, sedatives, anticoagulants, diuretics and antihypertensives. Events associated with HRDs included diarrhoea or constipation, abnormal liver function test and central nervous system events. Most pADEs resulted from prescribing errors (90%). CONCLUSION: Elderly patients with polypharmacy on admission and receiving antibiotics, sedatives, anticoagulants, diuretics or antihypertensives were more prone to experiencing ADEs. Efficiency in prevention of ADEs may be improved by targeted vigilance systems for alertness of physicians and pharmacists.

Adverse drug events in older hospitalized patients: results and reliability of a comprehensive and structured identification strategy.

BACKGROUND: Older patients are at high risk for experiencing Adverse Drug Events (ADEs) during hospitalization. To be able to reduce ADEs in these vulnerable patients, hospitals first need to measure the occurrence of ADEs, especially those that are preventable. However, data on preventable ADEs (pADEs) occurring during hospitalization in older patients are scarce, and no 'gold standard' for the identification of ADEs exists. METHODOLOGY: The study was conducted in three hospitals in the Netherlands in 2007. ADEs were retrospectively identified by a team of experts using a comprehensive and structured patient chart review (PCR) combined with a trigger-tool as an aid. This ADE identification strategy was applied to a cohort of 250 older hospitalized patients. To estimate the intra- and inter-rater reliabilities, Cohen's kappa values were calculated. PRINCIPAL FINDINGS: In total, 118 ADEs were detected which occurred in 62 patients. This ADE yield was 1.1 to 2.7 times higher in comparison to other ADE studies in older hospitalized patients. Of the 118 ADEs, 83 (70.3%) were pADEs; 51 pADEs (43.2% of all ADEs identified) caused serious patient harm. Patient harm caused by ADEs resulted in various events. The overall intra-rater agreement of the developed strategy was substantial (κ = 0.74); the overall inter-rater agreement was only fair (κ = 0.24). CONCLUSIONS/SIGNIFICANCE: The ADE identification strategy provided a detailed insight into the scope of ADEs occurring in older hospitalized patients, and showed that the majority of (serious) ADEs can be prevented. Several strategy related aspects, as well as setting/study specific aspects, may have contributed to the results gained. These aspects should be considered whenever ADE measurements need to be conducted. The results regarding pADEs can be used to design tailored interventions to effectively reduce harm caused by medication errors. Improvement of the inter-rater reliability of a PCR remains challenging.