Hypoglycemia adverse events in PICUs and cardiac ICUs: differentiating preventable and nonpreventable events*

An Initiative to Reduce Insulin-Related Adverse Drug Events in a Children's Hospital

OBJECTIVES

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Trigger Tool-Based Automated Adverse Event Detection in Electronic Health Records: Systematic Review

BACKGROUND

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.

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Monitoring the harm associated with use of anticoagulants in pediatric populations through trigger-based automated adverse-event detection

BACKGROUND

The safety profile of anticoagulants, which are being used with increasing frequency in pediatric populations, is not well studied. Automatic triggers built into electronic health record systems (EHR) have been shown to be an effective way to monitor for and identify medication errors. Anticoagulant-associated adverse events were examined through the use of an anticoagulant trigger panel.

METHODS

In a retrospective, five-year (September 2007-September 2012) observational study, four automated triggers were used to detect anticoagulant-related adverse events: activated partial thromboplastin time (aPTT) > 100 seconds in patients on an unfractionated heparin (UFH) infusion, International Normalized Ratio (INR) > 4, anti-factor Xa (anti-FXa) >1.5U/mL for patients on enoxaparin, and the documented use of protamine.

RESULTS

For the 1,664 triggers evaluated, 12 were associated with the aPTT trigger, only 1 of which was preventable. Receiver operator characteristic curve analysis indicated that increasing the aPTT trigger > 140 seconds would optimize sensitivity and specificity. The INR trigger identified four outpatients with adverse events. No adverse events were associated with the anti-FXa trigger. The protamine trigger identified 12 adverse events and was associated with more severe events. Minimal overlap was found with protamine and aPTT triggers.

CONCLUSION

Laboratory- and medication-based triggers can be effective monitoring tools for anticoagulants. For patients receiving a UFH infusion, an aPTT cutoff value of > 140 seconds is more precise. We also found that protamine use as a trigger adds value to a trigger-based anticoagulant monitoring system. Continued improvement in the logic algorithms associated with the EHR-based trigger tool will allow expanded use of this tool in a clinical manner.