Trigger tools are as effective as non-targeted chart review for adverse drug event detection in intensive care units

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.

Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method

BACKGROUND

Big data tools provide opportunities to monitor adverse events (patient harm associated with medical care) (AEs) in the unstructured text of electronic health care records (EHRs). Writers may explicitly state an apparent association between treatment and adverse outcome ("attributed") or state the simple treatment and outcome without an association ("unattributed"). Many methods for finding AEs in text rely on predefining possible AEs before searching for prespecified words and phrases or manual labeling (standardization) by investigators. We developed a method to identify possible AEs, even if unknown or unattributed, without any prespecifications or standardization of notes. Our method was inspired by word-frequency analysis methods used to uncover the true authorship of disputed works credited to William Shakespeare. We chose two use cases, "transfusion" and "time-based." Transfusion was chosen because new transfusion AE types were becoming recognized during the study data period; therefore, we anticipated an opportunity to find unattributed potential AEs (PAEs) in the notes. With the time-based case, we wanted to simulate near real-time surveillance. We chose time periods in the hope of detecting PAEs due to contaminated heparin from mid-2007 to mid-2008 that were announced in early 2008. We hypothesized that the prevalence of contaminated heparin may have been widespread enough to manifest in EHRs through symptoms related to heparin AEs, independent of clinicians' documentation of attributed AEs.

OBJECTIVE

We aimed to develop a new method to identify attributed and unattributed PAEs using the unstructured text of EHRs.

METHODS

We used EHRs for adult critical care admissions at a major teaching hospital (2001-2012). For each case, we formed a group of interest and a comparison group. We concatenated the text notes for each admission into one document sorted by date, and deleted replicate sentences and lists. We identified statistically significant words in the group of interest versus the comparison group. Documents in the group of interest were filtered to those words, followed by topic modeling on the filtered documents to produce topics. For each topic, the three documents with the maximum topic scores were manually reviewed to identify PAEs.

RESULTS

Topics centered around medical conditions that were unique to or more common in the group of interest, including PAEs. In each use case, most PAEs were unattributed in the notes. Among the transfusion PAEs was unattributed evidence of transfusion-associated cardiac overload and transfusion-related acute lung injury. Some of the PAEs from mid-2007 to mid-2008 were increased unattributed events consistent with AEs related to heparin contamination.

CONCLUSIONS

The Shakespeare method could be a useful supplement to AE reporting and surveillance of structured EHR data. Future improvements should include automation of the manual review process.

A mixed methods study to effectively utilize trigger tools in the ICU

PURPOSE

This study aimed to create a trigger tool for our intensive care units (ICUs) to support our departmental quality improvement efforts.

METHODS

We compiled an initial list of triggers used in an ICU setting through literature review. We used a modified Delphi method to develop a unique set of triggers. An expert panel was selected for multidisciplinary and multi-site representation from four adult medical-surgical ICUs of a Canadian city. Respondents ranked triggers on a Likert scale based on its likelihood of being associated with adverse event (sensitivity to harm), and likelihood of being associated with suboptimal ICU processes (specificity for internal recommendations).

OUTCOMES

Our literature search yielded 10 articles and 59 triggers. Completion of the rating process resulted in 12 items that achieved consensus. Triggers included specific clinical, hospital-acquired infection, medication related, and procedural occurrences. One additional trigger (cardiopulmonary arrest) which consistently held high scores but did not achieve multidisciplinary consensus, was also included.

CONCLUSIONS

We used the modified Delphi process to derive consensus-selected triggers to identify ICU specific adverse events with opportunity for improvement in local care. This methodology can be adopted by other centers looking to introduce trigger tools in a manner selective to their practice needs.

Challenges to the consolidation of pharmacovigilance practices in Brazil: limitations of the hospital pharmacist

Aims:

The aim of this study was to present the needs of hospital pharmacists in pharmacovigilance practices.

Methods:

This study has a cross-sectional design and was carried out with hospital pharmacists in Brazil. The sample was obtained by voluntary recruitment. Pharmacists who worked at Brazilian hospitals and were registered in their respective regulatory councils were invited to participate in the present study. A personalized questionnaire was developed by the authors and was electronically filled out by the respondents on the platform ‘Google forms’. The questionnaire was nationally available on the digital platform of the Pharmacy Federal Council, the Brazilian Society of Hospital Pharmacy and Health Services, four Pharmacy regional councils and the social network farmacêuticoclínico^®. Quantitative variables were analyzed by mean and standard deviation. The qualitative variables were analyzed by means of absolute and relative frequency. Difficulties related to pharmacovigilance activities are presented in an Ishikawa diagram in the Supplemental Material online.

Results:

Of the 27 federative units of Brazil, we obtained answers from pharmacists located in 85.2% (n = 23) of them. Among the pharmacovigilance practices developed by Brazilian pharmacists, the adverse drug reaction investigation (55.4%) and notification activities (47.0%) were worthy of note. Numerous difficulties were reported by the pharmacists, highlighting the difficulty in monitoring the medication and imputation of causality (27.7%). After categorizing the difficulties reported, it was observed that the category ‘people involved’ (45.1%) stood out from the others.

Conclusion:

This study pointed out numerous challenges to pharmacovigilance practices involving pharmacists in Brazil. It is believed that the correction of certain difficulties may impact on the better consolidation of pharmacovigilance activities in the country. However, regulatory agencies at all hierarchical levels of pharmacovigilance must work together to make it possible.

Plain language Summary

Challenges to the consolidation of pharmacovigilance

This is a study that seeks to present the needs of Brazilian hospital pharmacists in relation to pharmacovigilance activities. Through online interviews, pharmacists answered a questionnaire, presented the pharmacovigilance activities they develop and expressed their anxieties and difficulties for the development of these activities. With this study, it was concluded that numerous activities of active search, investigation and notification of adverse drug reaction are developed by Brazilian pharmacists. However, each pharmacist performs a different method of pharmacovigilance. In addition, it was observed that among the interviewees there was a perception of insufficient professional training and a shortage of professionals to assist in pharmacovigilance activities. These were the main difficulties reported. Therefore, the search for models or agile solutions to solve problems involving adverse drug reactions seems necessary for a better consolidation of pharmacovigilance services in Brazil.