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.

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.

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).

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.

This study aimed to prospectively validate an application that automates the detection of broad categories of hospital adverse events (AEs) extracted from a basic hospital information system, and to efficiently mobilize resources to reduce the level of acquired patient harm.

Data were collected from an internally designed software, extracting results from 14 triggers indicative of patient harm, querying clinical and administrative databases including all inpatient admissions (n = 8760) from October 2019 to June 2020. Representative samples of the triggered cases were clinically validated using chart review by a consensus expert panel. The positive predictive value (PPV) of each trigger was evaluated, and the detection sensitivity of the surveillance system was estimated relative to incidence ranges in the literature.

The system identified 394 AEs among 946 triggered cases, associated with 291 patients, yielding an overall PPV of 42%. Variability was observed among the trigger PPVs and among the estimated detection sensitivities across the harm categories, the highest being for the healthcare-associated infections. The median length of stay of patients with an AE showed to be significantly higher than the median for the overall patient population.

This application was able to identify AEs across a broad spectrum of harm categories, in a real-time manner, while reducing the use of resources required by other harm detection methods. Such a system could serve as a promising patient safety tool for AE surveillance, allowing for timely, targeted, and resource-efficient interventions, even for hospitals with limited resources.

There is a lack of research on adverse event (AE) detection in oncology patients, despite the propensity for iatrogenic harm. Two common methods include voluntary safety reporting (VSR) and chart review tools, such as the Institute for Healthcare Improvement's Global Trigger Tool (GTT). Our objective was to compare frequency and type of AEs detected by a modified GTT compared with VSR for identifying AEs in oncology patients in a larger clinical trial.

Patients across 6 oncology units (from July 1, 2013, through May 29, 2015) were randomly selected. Retrospective chart reviews were conducted by a team of nurses and physicians to identify AEs using the GTT. The VSR system was queried by the department of quality and safety of the hospital. Adverse event frequencies, type, and harm code for both methods were compared.

The modified GTT detected 0.90 AEs per patient (79 AEs in 88 patients; 95% [0.71-1.12] AEs per patient) that were predominantly medication AEs (53/79); more than half of the AEs caused harm to the patients (41/79, 52%), but only one quarter were preventable (21/79; 27%). The VSR detected 0.24 AEs per patient (21 AEs in 88 patients; 95% [0.15-0.37] AEs per patient), a large plurality of which were medication/intravenous related (8/21); more than half did not cause harm (70%). Only 2% of the AEs (2/100) were detected by both methods.

Neither the modified GTT nor the VSR system alone is sufficient for detecting AEs in oncology patient populations. Further studies exploring methods such as automated AE detection from electronic health records and leveraging patient-reported AEs are needed.

Pediatric hemato-oncological (HO) patients are highly susceptible to the occurrence of adverse events (AE), nevertheless few research has been done in this field. Our aim was to describe the incidence, type, severity and preventability of AE in these patients, including bone marrow transplant (BMT) patients, and to identify patient's risk factors for having an AE.

Retrospective cohort study. Children under 18yo hospitalized at the HO or BMT ward in 2016 were eligible for the study. Type of AE, severity and preventability were described as absolute and relative frequencies. Cumulative incidence of patients with at least one AE (CI_AE) and the rate of occurrence of all AE were calculated. Risk factors (sex, recovery probability, comorbidities and being a BMT patient) were analyzed using logistic regression.

114 patients were included, 58% were male, average age was 8.7yo and 25 were BMT patients. 44 had at least one AE, with CI_AE of 38.6% (95%CI 29.7-47.5). Overall rate of occurrence of AE was 2.5 cases per 100 patients-day (95%CI 2.15-2.98). For BMT and non-BMT patients they were 2.8 (95%CI 2.2-3.6) and 2.5 (95%CI 1.98-3.1) respectively. Healthcare related infection was the most frequent AE. Most AE were moderate and with high preventability. Being a BMT patient was the only independent factor associated with the occurrence of at least one AE (OR=11.5, p<0.001).

Our findings suggest that AE tend to be moderate and preventable in HO pediatric patients. BMT patients seem to be at greater risk of having an AE. Strategies focused on patient safety need to account for their specific characteristics.

The main objective was to assess the feasibility of the trigger tool method for the retrospective detection of adverse drug reactions (ADRs) in the Rennes University Hospital. The secondary objective was to describe the performance of the method in terms of positive predictive values (PPVs) and severity or preventability of ADRs.

Using the Rennes University Hospital clinical data warehouse, pharmacovigilance experts performed a retrospective review of a random sample of 30 inpatient hospital medical records per month using the triggers "fall" and "delirium" to identify related ADRs among patients 65 years and older in 2018 in the geriatrics department. Using the Z test, we compared the proportion of medical records with a positive (identified) trigger related to an ADR, which were reviewed within 20 minutes using the reference of 50% reviewed within 20 minutes.

Among the 355 medical records reviewed, 222 had at least 1 trigger and 98 at least 1 related ADR. Among the 222 positive trigger medical records, 99.6% were reviewed in under 20 minutes (P < 0.001). The pharmacovigilance assessment took 3 months. The PPVs reached 53.9% (46.0%-61.7%) for falls and 21.0% (14.3%-27.5%) for delirium. Among the ADRs, 80% were serious and 53% were preventable.

Given the low PPV of the triggers used and the considerable need for technical and human resources, the trigger tool method cannot be used as a routine tool at the pharmacovigilance center. However, it could be implemented occasionally for specific purposes such as monitoring the impact of risk minimization measures to prevent ADRs.

Adverse drug events (ADEs) during hospitalization are common. Insulin-related events, specifically, are frequent and preventable. At a tertiary children's hospital, we sought to reduce insulin-related ADEs by decreasing the median event rate of hyper- and hypoglycemia over a 12-month period.

Using Lean 6 σ methodology, we instituted a house-wide process change from a single-order ordering process to a pro re nata (PRN) standing order process. The standardized process included parameters for administration and intervention, enabling physician and nursing providers to practice at top of licensure. Automated technology during dose calculation promoted patient safety during dual verification processes. Control charts tracked rates of insulin-related ADEs, defined as hyperglycemia (glucose level >250 mg/dL) or hypoglycemia (glucose level <65 mg/dL). Events were standardized according to use rates of insulin on each nursing unit. The rates of appropriately timed insulin doses (within 30 minutes of a blood sugar check) were assessed.

Baseline median house-wide frequencies of hyperglycemic and hypoglycemic episodes were 55 and 6.9 events (per 100 rapid-acting insulin days), respectively. The median time to insulin administration was 32 minutes. The implementation of the PRN process reduced the median frequencies of hyperglycemic and hypoglycemic episodes to 45 and 3.8 events, respectively. The median time to insulin administration decreased to 18 minutes.

A PRN ordering process and education decreased insulin-associated ADEs and the time to insulin dosing compared with single-entry processes. Engaging bedside providers was instrumental in reducing insulin-related ADEs. Strategies that decrease the time from patient assessment to drug administration should be studied for other high-risk drugs.

This guideline addresses the evaluation and management of well-appearing, term infants, 8 to 60 days of age, with fever ≥38.0°C. Exclusions are noted. After a commissioned evidence-based review by the Agency for Healthcare Research and Quality, an additional extensive and ongoing review of the literature, and supplemental data from published, peer-reviewed studies provided by active investigators, 21 key action statements were derived. For each key action statement, the quality of evidence and benefit-harm relationship were assessed and graded to determine the strength of recommendations. When appropriate, parents' values and preferences should be incorporated as part of shared decision-making. For diagnostic testing, the committee has attempted to develop numbers needed to test, and for antimicrobial administration, the committee provided numbers needed to treat. Three algorithms summarize the recommendations for infants 8 to 21 days of age, 22 to 28 days of age, and 29 to 60 days of age. The recommendations in this guideline do not indicate an exclusive course of treatment or serve as a standard of medical care. Variations, taking into account individual circumstances, may be appropriate.

To date, few studies have shown a significant association between off-label drug use and adverse drug reactions (ADRs). The main aims of this study is to evaluate the relationship between adverse drug reactions and unlicensed or off-label drugs in hospitalized children and to provide more information on prescribing practice, the amplitude, consequences of unlicensed or off-label drug use in pediatric inpatients.

In this multicenter prospective study started from 2013, we use the French summaries of product characteristics in Theriaque (a prescription products guide) as a primary reference source for determining pediatric drug labeling. The detection of ADRs is carried out spontaneously by health professionals and actively by research groups using a trigger tool and patients' electronic health records. The causality between suspected ADRs and medication is evaluated using the Naranjo and the French methods of imputability independently by pharmacovigilance center. All suspected ADRs are submitted for a second evaluation by an independent pharmacovigilance experts.

For our best knowledge, EREMI is the first large multicenter prospective and objective study in France with an active ADRs monitoring and independent ADRs validation. This study identifies the risk factors that could be used to adjust preventive actions in children's care, guides future research in the field and increases the awareness of physicians in off-label drug use and in detecting and declaring ADRs. As data are obtained through extraction of information from hospital database and medical records, there is likely to be some under-reporting of items or missing data. In this study the field specialists detect all adverse events, experts in pharmacovigilance centers assess them and finally only the ADRs assessed by the independent committee are confirmed. Although we recruit a high number of patients, this observational study is subject to different confounders.

To assess risk factors for electronic prescription errors in a PICU.

A database of electronic prescriptions issued by a computerized physician order entry with clinical decision support system was analyzed to identify risk factors for prescription errors.

Of 6,250 prescriptions, 101 were associated with errors (1.6%). The error rate was twice as high in patients older than 12 years than in patients children 6-12 and 0-6 years old (2.4% vs 1.3% and 1.2%, respectively, p < 0.05). Compared with patients without errors, patients with errors had a significantly higher score on the Pediatric Index of Mortality 2 (-3.7 vs -4.5; p = 0.05), longer PICU stay (6 vs 3.1 d; p < 0.0001), and higher number of prescriptions per patient (40.8 vs. 15.7; p < 0.0001). In addition, patients with errors were more likely to have a neurologic main admission diagnosis (p = 0.008) and less likely to have a cardiologic diagnosis (p = 0.03) than patients without errors.

Our findings suggest that older patient age and greater disease severity are risk factors for electronic prescription errors.

To evaluate a modified Global Trigger Tool (GTT) method with manual review of automatic triggered records to measure adverse events.

A cross-sectional study was performed using the original GTT method as gold standard compared to a modified GTT method.

Medium size hospital trust in Northern Norway.

One thousand two hundred thirty-three records selected between March and December 2013.

Records with triggers, adverse events and number of adverse events identified. Recall (sensitivity), precision (positive predictive value), specificity and Cohen's kappa with 95 % confidence interval were calculated.

Both methods identified 35 adverse events per 1000 patient days. The modified GTT method with manual review of 658 automatic triggered records identified adverse events (n = 214) in 189 records and the original GTT method identified adverse events (n = 216) in 186 records. One hundred and ten identical records were identified with adverse events by both methods. Recall, precision, specificity and reliability for records identified with adverse events were respectively 0.59, 0.58, 0.92 and 0.51 for the modified GTT method. The total manual review time in the modified GTT method was 23 h while the manual review time using the original GTT method was 411 h.

The modified GTT method is as good as the original GTT method that complies with the GTTs aim monitoring the rate of adverse events. Resources saved by using the modified GTT method enable for increasing the sample size. The automatic trigger identification system may be developed to assess triggers in real-time to mitigate risk of adverse events.

The continued digitization and maturation of health care information technology has made access to real-time data easier and feasible for more health care organizations. With this increased availability, the promise of using data to algorithmically detect health care-related events in real-time has become more of a reality. However, as more researchers and clinicians utilize real-time data delivery capabilities, it has become apparent that simply gaining access to the data is not a panacea, and some unique data challenges have emerged to the forefront in the process.

The aim of this viewpoint was to highlight some of the challenges that are germane to real-time processing of health care system-generated data and the accurate interpretation of the results.

Distinct challenges related to the use and processing of real-time data for safety event detection were compiled and reported by several informatics and clinical experts at a quaternary pediatric academic institution. The challenges were collated from the experiences of the researchers implementing real-time event detection on more than half a dozen distinct projects. The challenges have been presented in a challenge category-specific challenge-example format.

In total, 8 major types of challenge categories were reported, with 13 specific challenges and 9 specific examples detailed to provide a context for the challenges. The examples reported are anchored to a specific project using medication order, medication administration record, and smart infusion pump data to detect discrepancies and errors between the 3 datasets.

The use of real-time data to drive safety event detection and clinical decision support is extremely powerful, but it presents its own set of challenges that include data quality and technical complexity. These challenges must be recognized and accommodated for if the full promise of accurate, real-time safety event clinical decision support is to be realized.

Adverse events in health care entail substantial burdens to health care systems, institutions, and patients. Retrospective trigger tools are often manually applied to detect AEs, although automated approaches using electronic health records may offer real-time adverse event detection, allowing timely corrective interventions.

The aim of this systematic review was to describe current study methods and challenges regarding the use of automatic trigger tool-based adverse event detection methods in electronic health records. In addition, we aimed to appraise the applied studies' designs and to synthesize estimates of adverse event prevalence and diagnostic test accuracy of automatic detection methods using manual trigger tool as a reference standard.

PubMed, EMBASE, CINAHL, and the Cochrane Library were queried. We included observational studies, applying trigger tools in acute care settings, and excluded studies using nonhospital and outpatient settings. Eligible articles were divided into diagnostic test accuracy studies and prevalence studies. We derived the study prevalence and estimates for the positive predictive value. We assessed bias risks and applicability concerns using Quality Assessment tool for Diagnostic Accuracy Studies-2 (QUADAS-2) for diagnostic test accuracy studies and an in-house developed tool for prevalence studies.

A total of 11 studies met all criteria: 2 concerned diagnostic test accuracy and 9 prevalence. We judged several studies to be at high bias risks for their automated detection method, definition of outcomes, and type of statistical analyses. Across all the 11 studies, adverse event prevalence ranged from 0% to 17.9%, with a median of 0.8%. The positive predictive value of all triggers to detect adverse events ranged from 0% to 100% across studies, with a median of 40%. Some triggers had wide ranging positive predictive value values: (1) in 6 studies, hypoglycemia had a positive predictive value ranging from 15.8% to 60%; (2) in 5 studies, naloxone had a positive predictive value ranging from 20% to 91%; (3) in 4 studies, flumazenil had a positive predictive value ranging from 38.9% to 83.3%; and (4) in 4 studies, protamine had a positive predictive value ranging from 0% to 60%. We were unable to determine the adverse event prevalence, positive predictive value, preventability, and severity in 40.4%, 10.5%, 71.1%, and 68.4% of the studies, respectively. These studies did not report the overall number of records analyzed, triggers, or adverse events; or the studies did not conduct the analysis.

We observed broad interstudy variation in reported adverse event prevalence and positive predictive value. The lack of sufficiently described methods led to difficulties regarding interpretation. To improve quality, we see the need for a set of recommendations to endorse optimal use of research designs and adequate reporting of future adverse event detection studies.

Underreporting of adverse events by physicians is a barrier to improving patient safety. In an effort to increase resident and medical student (hereafter "trainee") reporting of adverse events, trainees developed and led a monthly conference during which they reviewed adverse event reports (AERs), identified system vulnerabilities, and designed solutions to those vulnerabilities.

Monthly conferences over the 22-month study period were led by pediatric trainees and attended by fellow trainees, departmental leadership, and members of the hospital's quality improvement team. Trainees selected which AERs to review, with a focus on common near misses. Discussions were directed toward the development of potential solutions to issues identified in the reports. Trainee submissions of AERs were tracked monthly.

The mean number of AERs submitted by trainees increased from 6.7 per month during the baseline period to 14.1 during the study period (P < .001). The average percent of reports submitted by trainees increased from a baseline of 27.6% to 46.1% during the study period (P = .0059). There was no significant increase in reporting by any other group (attending, nursing, or pharmacy). Multiple meaningful solutions to identified system vulnerabilities were developed with trainee input.

Trainee-led monthly adverse event review conferences sustainably increased trainee reporting of adverse events. These conferences had the additional benefit of having trainees use their unique perspective as frontline providers to identify important system vulnerabilities and develop innovative solutions.

From 2009 through 2012, the Adventist Health System Patient Safety Organization (AHS PSO) used the Global Trigger Tool method for harm identification and demonstrated harm reduction. Although the awareness of harm demonstrated opportunities for improvement across the system, leaders determined that the human and fiscal resources required to continue with a retrospective manual harm identification process were unsustainable. In addition, there was growing concern that the identification of harm after the patient's discharge did not allow for intervention during the hospital stay. Therefore, the AHS PSO decided to seek an alternative method for patient harm identification.

The AHS PSO and another PSO jointly developed a novel automated all-cause harm trigger identification system that allowed for real-time bedside intervention, real-time trend analysis affecting patient safety, and continued learning about harm measurement. A sociotechnical approach of people, process, and technology was used at two pilot hospitals sharing the same electronic health record platform. Automated positive harm triggers and work-flow models were developed and evaluated.

Combined data from the two hospitals in a period of 11 consecutive months indicated (1) a total of 2,696 harms (combined hospital-acquired and outside-acquired); (2) that hypoglycemia (blood glucose ≤ 40 mg/dL) was the most frequently identified harm; (3) 256 harms related to the Patient Safety Indicator 90 (PSI 90) Composite descriptions versus 77 harms reported to regulatory harm reduction programs; and (4) that almost one third (32%) of total harms were classified as outside-acquired.

The automated harm trigger system revealed not only more harm but a broader scope of harm and led to a deeper understanding of patient safety vulnerabilities.

Background Adverse events have been associated with unplanned intensive care unit (ICU) transfers in adults. Objective To examine trends in unplanned ICU transfers in pediatrics resulting from adverse events. Design, Setting, Patients Retrospective observational study of pediatric and cardiac ICU transfers from acute care units during a 2-year period in a tertiary care children's hospital. Methods Transfers were identified via electronic health record query and investigated for adverse events. Predefined adverse events included ICU transfers within 12 hours of admission to an acute care unit, readmissions to an ICU within 24 hours, and cardiopulmonary arrest on an acute care unit. Other adverse events examined were not predefined. Adverse events were evaluated for preventability and categorized by type, diagnosis, time of day and weekday versus weekend occurrence, and level of associated patient harm. Results There were 1,008 ICU transfers during the study period; 67% were unplanned. Of the unplanned transfers, 32% were attributed to adverse events, 35% of which were preventable. Unplanned transfers associated with a high rate of preventable adverse events included readmission to an ICU within 24 hours (58%, p = 0.002) and ICU transfer within 12 hours of acute care admission (34%). Conclusions We observed a high rate of preventable adverse events associated with unplanned pediatric ICU transfers, many of which were due to inappropriate triage. Readmission to an ICU within 24 hours of transfer to an acute care unit was significantly associated with preventability.

Evaluate the ability of vital sign data versus a commercially available acuity score adapted for children (pediatric Rothman Index) to predict need for critical intervention in hospitalized pediatric patients to form the foundation for an automated early warning system.

Retrospective review of electronic medical record data.

Academic children's hospital.

A total of 220 hospitalized children 6.7 ± 6.7 years old experiencing a cardiopulmonary arrest (condition A) and/or requiring urgent intervention with transfer (condition C) to the ICU between January 2006 and July 2011.

None.

Physiologic data 24 hours preceding the event were extracted from the electronic medical record. Vital sign predictors were constructed using combinations of age-adjusted abnormalities in heart rate, systolic and diastolic blood pressures, respiratory rate, and peripheral oxygen saturation to predict impending deterioration. Sensitivity and specificity were determined for vital sign-based predictors by using 1:1 age-matched and sex-matched non-ICU control patients. Sensitivity and specificity for a model consisting of any two vital sign measurements simultaneously outside of age-adjusted normal ranges for condition A, condition C, and condition A or C were 64% and 54%, 57% and 53%, and 59% and 54%, respectively. The pediatric Rothman Index (added to the electronic medical record in April 2009) was evaluated in a subset of these patients (n = 131) and 16,138 hospitalized unmatched non-ICU control patients for the ability to predict condition A or C, and receiver operating characteristic curves were generated. Sensitivity and specificity for a pediatric Rothman Index cutoff of 40 for condition A, condition C, and condition A or C were 56% and 99%, 13% and 99%, and 28% and 99%, respectively.

A model consisting of simultaneous vital sign abnormalities and the pediatric Rothman Index predict condition A or C in the 24-hour period prior to the event. Vital sign only prediction models have higher sensitivity than the pediatric Rothman Index but are associated with a high false-positive rate. The high specificity of the pediatric Rothman Index merits prospective evaluation as an electronic adjunct to human-triggered early warning systems.

To investigate the use of a trigger tool for the detection of adverse drug events (ADE) in a pediatric hospital specializing in oncology, hematology, and other catastrophic diseases.

A medication-based trigger tool package analyzed electronic health records from February 2009 to February 2013. Chart review determined whether an ADE precipitated the trigger. Severity was assigned to ADEs, and preventability was assessed. Preventable ADEs were compared with the hospital's electronic voluntary event reporting system to identify whether these ADEs had been previously identified. The positive predictive values (PPVs) of the entire trigger tool and individual triggers were calculated to assess their accuracy to detect ADEs.

Trigger occurrences (n = 706) were detected in 390 patients from 6 medication triggers, 33 of which were ADEs (overall PPV = 16%). Hyaluronidase had the greatest PPV (60%). Most ADEs were category E harm (temporary harm) per the National Coordinating Council for Medication Error Reporting and Prevention index. One event was category H harm (intervention to sustain life). Naloxone was associated with the most grade 4 ADEs per the Common Terminology Criteria for Adverse Events v4.03. Twenty-one (64%) ADEs were preventable, 3 of which were submitted via the voluntary reporting system.

Most of the medication-based triggers yielded low PPVs. Refining the triggers based on patients' characteristics and medication usage patterns could increase the PPVs and make them more useful for quality improvement. To efficiently detect ADEs, triggers must be revised to reflect specialized pediatric patient populations such as hematology and oncology patients.