Trigger alerts associated with laboratory abnormalities on identifying potentially preventable adverse drug events in the intensive care unit and general ward

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.

Interventions, Barriers, and Proposed Solutions Associated With the Implementation of a Protocol That Uses Clinical Decision Support and a Stress Biomarker Test to Identify ICU Patients at High-Risk for Drug Associated Acute Kidney Injury

BACKGROUND

Damage biomarkers are helpful in early identification of patients who are at risk of developing acute kidney injury (AKI). Investigations are ongoing to identify the optimal role of stress/damage biomarkers in clinical practice regarding AKI risk prediction, surveillance, diagnosis, and prognosis.

OBJECTIVE

To determine the impact of utilizing a clinical decision support system (CDSS) to guide stress biomarker testing in intensive care unit (ICU) patients at risk for drug-induced acute kidney injury (D-AKI).

METHODS

A protocol was designed utilizing a clinical decision support system (CDSS) alert to identify patients that were ordered 3 or more potentially nephrotoxic medications, suggesting risk for progressing to AKI from nephrotoxic burden. Once alerted to these high-risk patients, the pharmacist determined if action was needed by ordering a stress biomarker test, tissue inhibitor of metalloproteinase-2-insulin-like growth factor-binding protein 7 (TIMP-2•IGFBP7). If the biomarker test result was elevated, the pharmacist provided nephrotoxin stewardship recommendations to the team. Pharmacists recorded the response to the clinical decision support alert, ordering, and interpreting the TIMP-2•IGFBP7, and information regarding clinical interventions. An alert in conjunction with TIMP-2•IGFBP7 as a strategy for AKI risk prediction and stimulant for patient care management was assessed. In addition, barriers and solutions to protocol implementation were evaluated.

RESULTS

There were 394 total activities recorded by pharmacists for 345 unique patients. Ninety-three (93/394; 23.6%) actionable alerts resulted in a TIMP-2•IGFBP7 test being ordered. Thirty-one TIMP-2•IGFBP7 results were >0.3 (31/81; 38.3%), suggesting a high-risk of progression to AKI, which prompted 191 pharmacist/team interventions. On average, there were 1.64 interventions per patient in the low-risk patients, 3.43 in high-risk patients, and 3.75 in the highest-risk patients.

CONCLUSION AND RELEVANCE

Stress biomarkers can be used in conjunction with CDSS alerts to affect therapeutic decisions in ICU patients at high-risk for D-AKI.

Clinical validation of clinical decision support systems for medication review: A scoping review

The aim of this scoping review is to summarize approaches and outcomes of clinical validation studies of clinical decision support systems (CDSSs) to support (part of) a medication review. A literature search was conducted in Embase and Medline. In total, 30 articles validating a CDSS were ultimately included. Most of the studies focused on detection of adverse drug events, potentially inappropriate medications and drug‐related problems. We categorized the included articles in three groups: studies subjectively reviewing the clinical relevance of CDSS's output (21/30 studies) resulting in a positive predictive value (PPV) for clinical relevance of 4–80%; studies determining the relationship between alerts and actual events (10/30 studies) resulting in a PPV for actual events of 5–80%; and studies comparing output of CDSSs to chart/medication reviews in the whole study population (10/30 studies) resulting in a sensitivity of 28–85% and specificity of 42–75%. We found heterogeneity in the methods used and in the outcome measures. The validation studies did not report the use of a published CDSS validation strategy. To improve the effectiveness and uptake of CDSSs supporting a medication review, future research would benefit from a more systematic and comprehensive validation strategy.

Canned Comments in the Hospital Laboratory Information System Can Decrease Microbiology Requests

OBJECTIVES

Phone calls to the microbiology laboratory can be to clarify culture results and provide education, but those calls also interrupt laboratory workflow. We characterized calls that the laboratory received and developed targeted comments to educate providers.

METHODS

Calls were logged and characterized, and we developed comments to address common call subjects. We applied the new comments to cultures and logged calls over the same interval the subsequent year. Data before and after implementation were analyzed.

RESULTS

Call volume decreased from 496 calls to 419 calls after implementation. There was a significant difference in level of training among callers (P < .005), but the nature of the calls did not change. Laboratory response showed an increase in release of previously generated data (eg, suppressed susceptibility results). Comments specifically developed to address intrinsic antibiotic resistance and common susceptibility patterns did not decrease call volume.

CONCLUSIONS

Implementation of comments in the laboratory information system decreased call volume, but targeted comments were less effective than anticipated.

Association between neonatal intensive care unit medication safety practices, adverse events, and death

OBJECTIVE

Determine the associations between neonatal intensive care unit (NICU) medication safety practices, laboratory-based adverse events (lab-AEs), and death.

STUDY DESIGN

We combined data from a 2016 survey of Pediatrix NICUs on use of medication safety practices with 2014-2016 infant data. We grouped NICUs based on the number of safety practices used (≤5, 6-7, and 8-10) and evaluated the association between the number of safety practices used and lab-AEs and deaths using logistic regressions.

RESULTS

Of the 94 NICUs included, 17% used ≤5 medication safety practices, 51% used 6-7, and 32% used 8-10. NICUs with more safety practices did not have a difference in lab-AEs or death.

CONCLUSION

In this cohort, the use of more medication safety practices was not associated with fewer lab-AEs or decreased death.

Digital microbiology

BACKGROUND

Digitalization and artificial intelligence have an important impact on the way microbiology laboratories will work in the near future. Opportunities and challenges lie ahead to digitalize the microbiological workflows. Making efficient use of big data, machine learning, and artificial intelligence in clinical microbiology requires a profound understanding of data handling aspects.

OBJECTIVE

This review article summarizes the most important concepts of digital microbiology. The article gives microbiologists, clinicians and data scientists a viewpoint and practical examples along the diagnostic process.

SOURCES

We used peer-reviewed literature identified by a PubMed search for digitalization, machine learning, artificial intelligence and microbiology.

CONTENT

We describe the opportunities and challenges of digitalization in microbiological diagnostic processes with various examples. We also provide in this context key aspects of data structure and interoperability, as well as legal aspects. Finally, we outline the way for applications in a modern microbiology laboratory.

IMPLICATIONS

We predict that digitalization and the usage of machine learning will have a profound impact on the daily routine of laboratory staff. Along the analytical process, the most important steps should be identified, where digital technologies can be applied and provide a benefit. The education of all staff involved should be adapted to prepare for the advances in digital microbiology.

Effect of Best Practice Advisories on Sedation Protocol Compliance and Drug-Related Hazardous Condition Mitigation Among Critical Care Patients

OBJECTIVES

To determine whether best practice advisories improved sedation protocol compliance and could mitigate potential propofol-related hazardous conditions.

DESIGN

Retrospective observational cohort study.

SETTING

Two adult ICUs at two academic medical centers that share the same sedation protocol.

PATIENTS

Adults 18 years old or older admitted to the ICU between January 1, 2016, and January 31, 2018, who received a continuous infusion of propofol.

INTERVENTIONS

Two concurrent best practice advisories built in the electronic health record as a clinical decision support tool to enforce protocol compliance with triglyceride and lipase level monitoring and mitigate propofol-related hazardous conditions.

MEASUREMENTS AND MAIN RESULTS

The primary outcomes were baseline and day 3 compliance with triglyceride and lipase laboratory monitoring per protocol and time to discontinuation of propofol in the setting of triglyceride and/or lipase levels exceeding protocol cutoffs. A total of 1,394 patients were included in the study cohort (n = 700 in the pre-best practice advisory group; n = 694 in the post-best practice advisory group). In inverse probability weighted regression analyses, implementing the best practice advisory was associated with a 56.6% (95% CI, 52.6-60.9) absolute increase and a 173% relative increase (risk ratio, 2.73; 95% CI, 2.45-3.04) in baseline laboratory monitoring. The best practice advisory was associated with a 34.0% (95% CI, 20.9-47.1) absolute increase and a 74% (95% CI, 1.39-2.19) relative increase in day 3 laboratory monitoring after inverse probability weighted analyses. Among patients with laboratory values exceeding protocol cutoffs, implementation of the best practice advisory resulted in providers discontinuing propofol an average of 16.6 hours (95% CI, 4.8-28.3) sooner than pre-best practice advisory. Findings from alternate analyses using interrupted time series were consistent with the inverse probability weighted analyses.

CONCLUSIONS

Best practice advisories can be effectively used in ICUs to improve sedation protocol compliance and may mitigate potential propofol-related hazardous conditions. Best practice advisories should undergo continuous quality assurance and optimizations to maximize clinical utility and minimize alert fatigue.

Comprehensive analysis of rule formalisms to represent clinical guidelines: Selection criteria and case study on antibiotic clinical guidelines

BACKGROUND

The over-use of antibiotics in clinical domains is causing an alarming increase in bacterial resistance, thus endangering their effectiveness as regards the treatment of highly recurring severe infectious diseases. Whilst Clinical Guidelines (CGs) focus on the correct prescription of antibiotics in a narrative form, Clinical Decision Support Systems (CDSS) operationalize the knowledge contained in CGs in the form of rules at the point of care. Despite the efforts made to computerize CGs, there is still a gap between CGs and the myriad of rule technologies (based on different logic formalisms) that are available to implement CDSSs in real clinical settings.

OBJECTIVE

To helpCDSS designers to determine the most suitable rule-based technology (medical-oriented rules, production rules and semantic web rules) with which to model knowledge from CGs for the prescription of antibiotics. We propose a framework of criteria for this purpose that is extensible to more generic CGs.

MATERIALS AND METHODS

Our proposal is based on the identification of core technical requirements extracted from both literature and the analysis of CGs for antibiotics, establishing three dimensions for analysis: language expressivity, interoperability and industrial aspects. We present a case study regarding the John Hopkins Hospital (JHH) Antibiotic Guidelines for Urinary Tract Infection (UTI), a highly recurring hospital acquired infection. We have adopted our framework of criteria in order to analyse and implement these CGs using various rule technologies: HL7 Arden Syntax, general-purpose Production Rules System (Drools), HL7 standard Rule Interchange Format (RIF), Semantic Web Rule Language (SWRL) and SParql Inference Notation (SPIN) rule extensions (implementing our own ontology for UTI).

RESULTS

We have identified the main criteria required to attain a maintainable and cost-affordable computable knowledge representation for CGs. We have represented the JHH UTI CGs knowledge in a total of 12 Arden Syntax MLMs, 81 Drools rules and 154 ontology classes, properties and individuals. Our experiments confirm the relevance of the proposed set of criteria and show the level of compliance of the different rule technologies with the JHH UTI CGs knowledge representation.

CONCLUSIONS

The proposed framework of criteria may help clinical institutions to select the most suitable rule technology for the representation of CGs in general, and for the antibiotic prescription domain in particular, depicting the main aspects that lead to Computer Interpretable Guidelines (CIGs), such as Logic expressivity (Open/Closed World Assumption, Negation-As-Failure), Temporal Reasoning and Interoperability with existing HIS and clinical workflow. Future work will focus on providing clinicians with suggestions regarding new potential steps for CGs, considering process mining approaches and CGs Process Workflows, the use of HL7 FHIR for HIS interoperability and the representation of Knowledge-as- a-Service (KaaS).