Keywords
clinical decision support - drug–drug interaction - electronic health records - alert
fatigue - inpatient
Background and Significance
Background and Significance
A prolongation in the heart rate-corrected QT interval (QTc) above 500 milliseconds
(ms) or an increase of ≥60 milliseconds from baseline is a risk factor for ventricular
arrhythmias, particularly Torsade de Pointes (TdP), and sudden cardiac death.[1] Numerous medications and patient-specific factors have been associated with QTc
prolongation (QTc ≥450 in males or ≥460 in females) including female gender, age ≥65
years, cardiovascular history, liver or kidney failure, and electrolyte imbalances.[1]
[2]
[3] Therefore, in the hospital setting, it is not surprising to see patients with multiple
factors associated with QTc prolongation, especially in intensive care units (ICUs).
It has been shown that QTc prolongation was present at ICU admission in nearly 28%
of cardiac care unit patients, and that nearly 30% of those patients experience additional
QTc prolongation throughout their stay.[2] In particular, 57% of the patients who were admitted with QTc intervals ≥500 milliseconds
had an additional increase in QTc interval of at least 60 milliseconds.[2] Critically-ill patients with QTc prolongation have longer lengths of hospitalization
and a threefold increase in odds for mortality than those without prolongation.[4]
While severe complications from QTc prolongation are rare, monitoring of patients
with a prolonged QTc and minimization of risk factors are recommended in order to
decrease the risk of adverse outcomes.[1]
[5] The American Heart Association (AHA) and the American College of Cardiology Foundation
(ACCF) published a statement regarding the prevention of TdP in the hospital setting
in regards to the risk of QTc interval prolongation, appropriate and consistent electrocardiogram
(ECG) monitoring, and management of QTc interval prolongation including minimization
of offending drugs and replacement of electrolytes.[1] In addition to ECG monitoring, computerized alerts in the electronic health record
(EHR) for QTc interval prolonging drug–drug interactions (DDIs) provide additional
level of support for providers to mitigate risk factors for prolongation.
However, given the set-up with clinical decision support (CDS) DDI alerts in the EHR
provided by medication knowledge vendors, this type of CDS is prone to alert fatigue
and potentially causes providers to ignore more important QTc-related DDIs.[6]
[7]
[8] A 90% override rate for DDI alerts has been reported, and this has been the experience
at our institution despite ongoing work at optimizing and removing less severe DDI
pairs.[9]
[10] Recommendations to improve the usability of DDI CDS alerts include integration of
contextual information or modifying factors.[7]
[11] This includes patient-specific factors such as age, predisposing diseases, laboratory
results, medication dose, timing of co-administration with QT prolonging medications,
etc.[11] DDI alerts provided by medication knowledge vendors in EHRs typically are based
on an active interval between one medication co-ordered with another interacting medication
and therefore lack this key component of applying to a particular specific patient
context. EHRs provide the ability for an organization to develop custom alerts using
patient contextual information, and given the high frequency of QTc-related DDI alerts
and override rates at our organization, we focused our attention instead upon examining
opportunities to convert our medication knowledge vendor QTc DDI CDS to a more custom-based
approach.
Utilization of customized QTc DDI CDS has been previously described. A large university-associated,
tertiary care teaching institution implemented a CDS alerting tool for reducing the
risk of QTc prolongation in hospitalized cardiac care unit patients and showed that
implementing a validated risk score for QTc prolongation significantly decreased ordering
of non-cardiac QTc prolonging drugs and significantly reduced the risk of QTc prolongation.[12]
[13] The risk score was calculated based on factors including age, female gender, serum
potassium ≤3.5 mmol/L, admission QTc ≥450 milliseconds, diagnosis of acute myocardial
infarction, sepsis, or heart failure, and presence of a loop diuretic or one or two
medications known to prolong the QTc interval. An alert is generated to pharmacists
entering medications into the computer system if the risk score is ≥7, and pharmacists
had the option to contact the provider and intervene by recommending an alternative
agent with a lower risk of QTc prolongation.[12]
[13]
Several other studies since then have compared pre- and post-implementation of a QTc
CDS alert based on QTc >500 milliseconds and placement of medication orders with a
risk of prolonging the QTc interval.[14]
[15]
[16] Two studies found a significant reduction in ordering QTc prolonging medications
following implementation of the alert. One study found no difference in QTc prolonging
medications avoided, though there was a significant improvement in the action rate,
primarily in ECG monitoring. In all three studies, the medication knowledge vendor
traditional DDI alert was continued, and all three QTc CDS strategies primarily based
the alert on an elevated QTc interval without using additional patient risk factors
such as those examined by Tisdale and colleagues.
At our health care organization, our goal was to implement a customized QTc CDS alert
strategy in our EHR for hospitalized patients aimed at providers with the following
objectives: minimize QTc prolongation, minimize exposure to QTc prolonging medications,
and decrease overall QTc-related alerts. To best accomplish this goal and related
outcomes, we examined a strategy that was based on a validated QTc risk scoring tool
and replaced our medication knowledge vendor alerts with custom QTc alerts. This study
will evaluate and describe the results and outcomes of our customized QTc CDS approach.
To our knowledge no other published research has examined an approach such as ours.
Methods
Design and Setting
This is a retrospective quasi-experimental unblinded study performed at a 1,000+ bed
tertiary academic medical center (Michigan Medicine) with three inpatient towers (adult,
cardiovascular and women's/pediatrics) and uses an Epic-based EHR (Epic Systems, Verona,
Wisconsin, United States; currently version May 2021). First Databank (FDB) (San Bruno,
California, United States), is the medication knowledge vendor integrated into our
EHR and provides content for medication warning data (e.g., drug dose checking, drug
allergy, DDI and drug–disease interactions, aged-based precautions and duplicate therapy).
Medication warnings/alerts (e.g., DDI alerts) trigger during signing of a new medication
order (e.g., typically by a provider) when there is either an interaction with an
active medication or with another new medication in the order entry activity. These
alerts additionally trigger during medication verification by the pharmacist. The
new or existing interacting medication may be removed or discontinued from the alert.
An acknowledgment/override reason if not taking action on the alert is optional. The
intervention described below received approval by our organization's Clinical Decision
Support Subcommittee and was deemed as exempt and not regulated status by the University
of Michigan Institutional Review Board.
Intervention
Prior to November 2019, all QTc-related DDI alerts were based on FDB content—contraindicated
and severe severity levels (traditional DDI alerting) ([Fig. 1]). In November 2019, a custom QTc CDS alert based on the QTc-interval prolonging
risk scoring tool by Tisdale et al was implemented for adult inpatients including
the emergency department ([Fig. 2]).[12]
[13] The alert was configured using Epic's best practice advisory (BPA) functionality.
Given that the Tisdale et al risk score tool has only been validated in adult inpatients,
traditional QTc DDI alerts continued for ambulatory/outpatient context orders, inpatients
<18 years old (Pediatrics). Note that traditional DDI alerts that involved a pharmacokinetic-type
interaction that could influence QTc interval (e.g., elevated levels of a drug augmenting
effects on the QTc through metabolic inhibition by another drug) continued in all
patients. The custom BPA triggers during signing of a medication order with known
or possible risk of TdP as defined by CredibleMeds (https://crediblemeds.org) and including the presence of other patient-specific risk factors as defined in
the risk scoring tool that would result in a high risk level (score ≥11) in inpatients
(≥18 years old—adults).[13]
[17] A risk score of 11 was selected by our Clinical Decision Support Committee and multidisciplinary
group of cardiology providers and pharmacists as it was felt that this threshold was
a good balance between too many alerts and clinical utility/patient safety based on
pilot BPA data. The alert allows the ordering clinician to remove the triggering medication
from order entry and/or place an order for an ECG. The user must select an acknowledgment
reason if continuing with the medication order and not placing an ECG order. See [Fig. 3] for an example of alert contents. A similar interruptive BPA (without the option
to place an ECG order) was also implemented in the pharmacist's verification screen
and will trigger upon medication order verification. Due to the inherent proarrhythmic
potential of Class IA, IC, and III anti-arrhythmic drugs, an additional custom BPA
was created to mimic the traditional DDI alert and in all patients ≥18 years old with
new or active antiarrhythmic medication orders and a QTc risk score <11. This antiarrhythmic
DDI BPA similarly triggers during signing or verifying the medication order ([Fig. 4]).
Fig. 1 Traditional DDI QTc alert. DDI, drug–drug interaction.
Fig. 2 Calculation of QTc prolongation risk score.a Active loop diuretic order (based on FDB pharmaceutical class) or any loop diuretic
received in last 12 hours.b Based on Epic's sepsis predictive scoring algorithm (risk score ≥6). FDB, First Databank.
Fig. 3 Custom QTc CDS alert. CDS, clinical decision support.
Fig. 4 Custom antiarrhythmic DDI alert in post-intervention adult patients (risk score<
11). DDI, drug–drug interaction.
Outcomes
Using QTc prolongation criteria adapted by Tisdale et al, the primary outcome for
this study was to compare the occurrence of QTc prolongation following the implementation
of a custom QTc CDS alert. QTc prolongation was defined as the following: (1) patients
with initial QTc interval <500 milliseconds following admission—either an increase
in the QTc ≥500 milliseconds or an increase in the QTc ≥60 milliseconds at any point
during hospitalization; (2) patients with an initial QTc interval ≥500 milliseconds—an
increase in the QTc ≥60 milliseconds from baseline during any point in the hospitalization.[10]
Secondary outcomes included the evaluation of the following measures between pre-
and post-implementation of the custom QTc CDS alert: (1) the number of QTc prolonging
medication orders broken down by total, pharmaceutical class, and number of patients
with one or two administrations of a QTc prolonging medications administered between
phases. (2) Number of QTc alerts and percentage overridden by ordering clinician.
The incidence of TdP between groups was also evaluated based on manual chart review.
Patients and Study Sample
Patients with the following characteristics were included: (1) ≥18 years of age, (2)
at least 2 QTc results following admission, and (3) greater than a 24-hour hospital
encounter. Patients with the following characteristics were excluded: (1) <18 years
old; (2) only one QTc result available following admission; (3) discharged within
24 hours of admission; or (4) receiving cardiac pacing. Data was collected over two
periods for the study: pre-intervention (prior to implementation of the QTc BPA) from
August to October, 2019, and post-intervention (following implementation of the custom
QTc BPA) from December to February, 2020. Since implementation of the BPA was in early
November 2019, that month was designated as a washout period. Given potential confounding
effects of the COVID-19 pandemic affecting our organization in March 2020, data collection
ceased at the end of February.
Data Collection and Statistical Methods
An SQL-based report (Oracle, Austin, Texas, United States) was used to extract necessary
information to evaluate study primary and secondary objectives for both pre- and post-intervention
periods from the EHR's relational database. Data reported here utilizes descriptive
statistics. For categorical variables, a p-value was calculated using a Chi-square test and a 2 × 2 contingency table. All p-values are two sided, and p ≤0.05 was interpreted to indicate statistical significance. Odds ratios were used
to examine the effect of the new alert.
Results
There were 1,871 patients in the pre-implementation group and 1,820 patients in the
post-implementation group after exclusions were applied ([Fig. 5]). The primary study end point was the occurrence of QTc prolongation pre- and post-implementation
of QTc-interval prolongation alert. In the pre-implementation group, 361 (19.3%) patients
developed QTc prolongation, and in the post-implementation group, 357 (19.6%) patients
developed QTc prolongation (OR: 1.02, 95% CI: 0.87–1.20, p = 0.81).
Fig. 5 Study patient population.
When evaluating QTc-related medication outcomes, all verified medication orders were
examined, and CredibleMeds (https://crediblemeds.org) was used to determine what medications were considered QTc prolonging ([Appendix A]). QTc prolonging medications were examined as a proportion of total medication orders
and as a proportion of the medication class. This was done to identify any trends
in ordering habits due to the new alert. There was a total of 144,300 medication orders
in the pre-implementation group, and 143,353 in the post-implementation group. When
examining QTc medication ordering out of total medication orders, there was a statistically
significant difference for total QTc prolonging medication orders and for QTc prolonging
antipsychotics, antiemetics, and methadone. When examining QTc prolonging medication
ordering out of the medication categories, there was a statistically significant difference
for QTc prolonging macrolides, antifungals, and antiemetics. [Tables 1] and [2] contain further details on QTc prolonging medication order outcomes.
Appendix A
QTc Prolonging and non-QTc prolonging medications
|
Medication class
|
QTc Prolonging[a]
|
Non-QTc prolonging
|
|
Antiarrhythmics
|
Amiodarone, disopyramide, dofetilide, dronedarone, flecainide, ibutilide, and quinidine
|
Adenosine, lidocaine, mexiletine, and propafenone
|
|
Fluoroquinolones
|
Ciprofloxacin, levofloxacin, and moxifloxacin
|
N/A
|
|
Macrolides
|
Azithromycin, erythromycin, and clarithromycin
|
Fidaxomicin
|
|
Azole-antifungals
|
Fluconazole
|
Isavuconazonium, itraconazole, posaconazole, and voriconazole
|
|
Antipsychotics
|
Aripiprazole, asenapine, chlorpromazine, clozapine, haloperidol, lurasidone, paliperidone,
quetiapine, risperidone, ziprasidone
|
Brexipiprazole, fluphenazine, and olanzapine
|
|
Antiemetics
|
Granisetron, ondansetron, and promethazine
|
Aprepitant, fosaprepitant, meclizine, prochlorperazine, scopolamine, and trimethobenzamide
|
a CredibleMeds was used to determine QTc prolonging medications.
Table 1
QTc Prolonging medication orders verified for study patients out of total orders for
all medications[a]
|
Pre-implementation (%) n = 144,300 total orders
|
Post-implementation (%) n = 143,354 total orders
|
Odds ratio [95% CI]
|
p-Value (Chi-square)
|
|
Total orders for QTc prolonging meds
|
7,921 (5.5)
|
7,566 (5.3)
|
0.96
[0.93–0.99]
|
0.01
|
|
QTc Antiarrhythmic
|
1,193 (0.8)
|
1,266 (0.9)
|
1.07
[0.99–1.16]
|
0.10
|
|
QTc Fluoroquinolone
|
252 (0.2)
|
231 (0.2)
|
0.92
[0.77–1.10]
|
0.38
|
|
QTc Macrolide
|
171 (0.1)
|
144 (0.1)
|
0.85
[0.68–1.06]
|
0.14
|
|
QTc Azole antifungal
|
282 (0.2)
|
259 (0.2)
|
0.92
[0.78–1.09]
|
0.36
|
|
QTc Antipsychotic
|
579 (0.4)
|
793 (0.6)
|
1.38
[1.24–1.54]
|
<0.001
|
|
QTc Antiemetic
|
3,675 (2.6)
|
3,247 (2.3)
|
0.89
[0.85–0.93]
|
<0.001
|
|
Methadone
|
101 (0.1)
|
42 (< 0.1)
|
0.42
[0.29–0.60]
|
<0.001
|
a CredibleMeds was used to determine QTc prolonging medications.
Table 2
QTc Prolonging medication orders verified for study patients out of total orders within
each medication category[a]
|
Pre-Implementation (%)
|
Post-Implementation (%)
|
Odds ratio
[95% CI]
|
p-Value
(Chi-square)
|
|
Total antiarrhythmic
|
1,350
|
1,424
|
|
|
|
QTc Antiarrhythmic
|
1,193 (88.4)
|
1,266 (88.9)
|
1.06
[0.83–1.33]
|
0.66
|
|
Total fluoroquinolone
|
252
|
231
|
|
|
|
QTc Fluoroquinolone[b]
|
252 (100)
|
231 (100)
|
–
|
–
|
|
Total macrolide
|
174
|
154
|
|
|
|
QTc Macrolide
|
171 (98.3)
|
144 (93.5)
|
0.25
[0.07–0.94]
|
0.03
|
|
Total antifungal
|
337
|
363
|
|
|
|
QTc Azole antifungal
|
282 (83.7)
|
259 (71.4)
|
0.49
[0.34–0.70]
|
<0.001
|
|
Total antipsychotic
|
914
|
1,263
|
|
|
|
QTc Antipsychotic
|
579 (63.4)
|
793 (62.8)
|
0.98
[0.82–1.17]
|
0.79
|
|
Total antiemetic
|
6,456
|
6,064
|
|
|
|
QTc Antiemetic
|
3,675 (56.9)
|
3,247 (53.6)
|
0.87
[0.81–0.94]
|
<0.001
|
a CredibleMeds was used to determine QTc prolonging medications.
b There were no non-QTc prolonging fluoroquinolones ordered during the study period.
In the pre-implementation group, there were 1,112 (59.4%) patients that were administered
at least one QTc prolonging agent and 468 (25%) patients that were administered at
least two QTc prolonging agents. In the post-implementation group, there were 1,076
(59.1%) patients that were administered at least one QTc prolonging agent and 435
(23.9%) patients who were administered at least two QTc prolonging agents. The odds
of a patient receiving at least one QTc prolonging agent post-implementation of the
alert was 0.99 (95% CI: 0.87–1.13, p = 0.85) and the odds of a patient receiving at least two QTc prolonging agents post-implementation
of the alert was 0.94 (95% CI: 0.81–1.09, p = 0.43).
When examining alert-related outcomes, the odds ratio of an action taken post-implementation
compared with pre-implementation was 18.90 (95% CI: 14.03–25.47, p <0.001). When excluding the additional antiarrhythmic DDI alert, the odds ratio of
an action taken was 20.34 (95% CI: 15.09–27.43, p <0.001). [Table 3] contains QTc alert-related outcomes on order entry, and [Table 4] contains the rationale if no action was taken.
Table 3
QTc-Prolongation alert-related outcomes
|
Pre-implementation
(%)
|
Post-implementation
(%)
|
Odds ratio
[95% CI]
|
p-Value
(Chi-square)
|
|
Order entry alert[a]
|
2,404
|
2,430
|
|
|
|
Action taken[c]
|
48 (2)
|
712 (29.3)
|
20.34
[15.09–27.43]
|
<0.001
|
|
No action taken[d]
|
2,356 (98)
|
1,718 (70.7)
|
0.05
[0.04–0.07]
|
<0.001
|
|
Additional antiarrhythmic DDI alert[b]
|
–
|
199
|
|
|
|
Action taken
|
–
|
19 (9.6)
|
|
|
|
No action taken
|
–
|
180 (90.5)
|
|
|
|
Total QTc alerts
|
2,404
|
2,629
|
|
|
|
Action taken
|
48 (2)
|
731 (27.8)
|
18.90
[14.03–25.47]
|
<0.001
|
|
No action taken
|
2,356 (98)
|
1,898 (72.2)
|
0.05
[0.04–0.07]
|
<0.001
|
Abbreviation: DDI, drug–drug interaction.
a Traditional knowledge vendor DDI alert vs. custom QTc-prolongation alert.
b Traditional knowledge vendor DDI alert for antiarrhythmic medications in patients
low or medium risk.
c Action taken by ordering clinician: (1) cancelled alert and exited order entry; (2)
removed interacting medication from the alert (or discontinued active interacting
medication—in traditional/pre-intervention alert); (3) ordered ECG from the custom
QTc alert.
d No action taken by ordering clinician: overrode the alert and did not take any of
the above actions 1 to 3.
Table 4
Documented reasons why no action was taken
|
Total
|
Percentage
|
|
Pre-implementation alert[a]
|
220
|
|
|
Aware of dose; monitoring for signs of toxicity
|
77
|
35
|
|
Assessed benefit is greater than risk
|
70
|
31.8
|
|
Monitoring for signs of drug interaction/ordered combination
|
43
|
19.6
|
|
Patient has previously tolerated this drug/dose
|
28
|
12.7
|
|
Patient stabilized on drug combination; monitoring effects
|
1
|
0.5
|
|
Intolerance not an allergy
|
1
|
0.5
|
|
Post-implementation custom alert
|
1,718
|
|
|
Monitoring for signs of drug interaction/ordered combination
|
1,021
|
59.4
|
|
Assessed benefit is greater than risk
|
456
|
26.5
|
|
Patient stabilized on drug combination; monitoring effects
|
140
|
8.2
|
|
Other (document as comments)
|
101
|
5.9
|
|
Post-implementation additional DDI alert
|
180
|
|
|
Will take recommended follow-up action
|
121
|
67.2
|
|
Benefit outweighs risk
|
38
|
21.1
|
|
Does not meet criteria
|
12
|
6.7
|
|
Other (document as comments)
|
7
|
3.9
|
|
See comments
|
2
|
1.1
|
Abbreviation: DDI, drug–drug interaction.
a Documentation was optional for the pre-implementation alert. The same override reasons
were used for both pre- and post-implementation alerts.
For alerts on pharmacist order verification, there were 1,912 total alerts in the
pre-implementation group and 3,333 alerts in the post-implementation group. The median
was used to investigate the large increase in alert counts during verification. In
the pre-implementation phase, each unique alert was triggered for a median of 1.5
times, and in the post-implementation phase, each unique alert was triggered for a
median of three times.
The ratio of QTc prolongation alerts to QTc prolonging medication orders was 0.30
in the pre-implementation phase and 0.35 in the post-implementation phase. When excluding
the antiarrhythmic DDI alert, the ratio of QTc prolonging alerts to QTc prolongation
medication orders in the post-implementation phase is 0.32.
Lastly, a chart review was done to see if any patient experienced TdP during the study
period. No patients in the pre-implementation and one patient in the post-implementation
phase experience TdP during hospitalization. Prior to the episode of TdP, ECG readings
showed no evidence of QTc prolongation, and Torsade was precipitated by R on T phenomenon.
The cardiology consult notes state Torsade was possibly precipitated by ischemia.
Discussion
This study reports that the implementation of a customized alert based on a validated
scoring tool did not result in a difference in QTc prolongation rates. There were
significant differences in QTc prolonging medication orders as well as actions taken
on alerts. Previous traditional vendor-based QTc alerts utilized at our health system
were limited in scope and only examined DDIs that could potentially prolong the QTc
interval. Use of a customized alert that is based on a validated risk score such as
demonstrated by Tisdale et al provides a more precise method of examining patient-specific
QTc prolongation risk factors that potentially provides outcome benefits.[13]
Tisdale et al demonstrated a statistically significant difference in QTc prolongation.[13] However, our study did not demonstrate a significant difference in rates of QTc
prolongation. This could be attributed to institutional differences, such as differences
in electrolyte replacement or sepsis protocols, and there may be further value in
customizing our alert by taking into account these conditions. A study by Chernoby
et al also implemented Tisdale et al's risk score in a customized alert but did not
report the incidence of QTc prolongation.[14] When compared with Tisdale et al's study, we did not limit our intervention exclusively
to the cardiac care unit and expanded our intervention across the entire adult hospital
population. Tisdale's risk score for QTc prolongation was developed and validated
in cardiac care units within the same institution.[12] While their study showed a statistically significant difference in rates of QTc
prolongation post-implementation, the applicability of the risk score may be limited
when implementing it in other institutions and patient populations. Other variables
or risk factors may contribute to the development of QTc prolongation, and certain
risk factors may have a different weight for a non-cardiac care unit patient population.
When examining the ordering rates of medications, our study was able to show significant
reductions in overall QTc prolonging medication orders as well as certain non-cardiac
medication categories which includes antiemetics, antipsychotics, and certain antibiotic
classes. This is similar to other studies that have CDS for QTc prolonging medications.
Sorita et al implemented a custom QTc prolonging medication alert that resulted in
statistically significant reductions of antiemetic, antiarrhythmic, antipsychotic,
immunosuppressant, and antibiotic medication classes.[15] Tisdale et al additionally demonstrated statistically significant reductions in
the ordering rate for non-cardiac QTc prolonging agents.[13] In our study, there were decreased odds of ordering a QTc prolonging antiemetic
when compared with total antiemetic orders which was mostly attributed to the decreased
use of ondansetron and the increased use of other antiemetic agents and trimethobenzamide.
This aligns with institutional guidelines and order sets which often have trimethobenzamide
as a second line antiemetic agent to ondansetron. There was also a statistically significant
increase in the odds of ordering a QTc prolonging antipsychotics when compared with
total medication orders. However, there was a large increase in total antipsychotic
orders as well as QTc prolonging antipsychotic orders in the post-implementation phase,
so the increased odds ratio may not be directly attributed to the QTc prolongation
alert. There was also a statistically significant decrease in the odds of QTc prolonging
macrolides and QTc prolonging antifungals when compared with their respective drug
categories which may indicate a shift in prescribing patterns due to the custom QTc
prolongation alert.
Implementation of the custom QTc prolongation alert resulted in a statistically significant
increase in the odds that there was an action taken on the alert at the time of order
entry when compared with the traditional vendor-based DDI alert. This may have indicated
a potential increase in the quality of the alert, showing the direct impact that the
alert had on the patients it fired for. Our customized alert was able to incorporate
more patient-specific information, communicate the criteria of the scoring tool and
reason the alert fired, and provide users to the ability to order an ECG directly
within the alert. When an action was not taken, ordering providers had to give a reason
why they did not take an action. The reasons for overriding the alert were tailored
specifically to this customize alert rather than a general override message and allowed
more specific information gathering on ordering habits. A future area we are investigating
to further enhance the utility of the BPA is to include alternative agents which can
be ordered directly from the alert, particularly for antiemetics with less potential
to prolong the QTc to facilitate ease of ordering.
While this study showed an increase in the odds of an action taken to reduce QTc prolongation
medication prescribing post-implementation of the custom QTc prolongation alert, there
was a net increase in total alerts. The additional increase in alert volume is attributed
to the additional antiarrhythmic DDI alert for less than high-risk patients (risk
score <11), which was continued post-implementation of the custom QTc prolongation
alert. Current efforts are being made to examine the necessity of the antiarrhythmic
DDI alert as well as incorporating it within the custom QTc prolongation alert, adjusting
the firing threshold, or turning it off. A large increase in order verification alerts
was also noticed in the study. The median times a unique alert fired increased from
1.5 to 3 and may indicate that a pharmacist or multiple pharmacists were viewing the
alert multiple times. This may be attributed to the visually distinct design of the
custom QTc prolongation alert. There was one person in the study who experienced TdP,
and as noted, it does not appear to be drug induced.
Several limitations should be noted in our study. Patients with prolonged QRS intervals
were not excluded. The custom alert fired regardless of the duration of the QRS interval.
It should be noted that in Tisdale et al, patients with prolonged QRS intervals also
were not excluded in the creation and validation of the risk score. There is a potential
limitation of the risk score as patients with prolonged QRS intervals may have inaccurate
QT intervals. Other limitations of the study include the short time period of the
study, difficulty and inability to easily capture actions or pharmacists interventions
made outside of the alert time frame given that pharmacist–provider communication
is frequently not documented, and the potential for temporal bias due to the study
design.
Conclusion
In summary, we implemented a customized QTc CDS alert strategy in our EHR for hospitalized
patients aimed at providers. We were able to decrease patient exposure to QTc prolonging
medications while not increasing the rate of QTc prolongation. Our results illustrate
the benefit of using a validated risk score with a higher quality customized CDS approach
compared with a traditional vendor-based strategy which further resulted in a significantly
improved alert acceptance and action rate. Further research is needed to confirm if
an approach such as ours can decrease QTc prolongation rates as well as the applicability
of the scoring tool for non-cardiac care patients.
Clinical Relevance Statement
Clinical Relevance Statement
Drug–drug CDS within the EHR are typically based on data provided by medication knowledge
vendors and are not patient-specific which can promote alert fatigue and negate their
relevance and cause suboptimal outcomes through ignoring clinically important alerts.
QTc prolonging medications are frequently prescribed and have numerous drug–drug interactions,
particularly based on the presence of patient-specific risk factors. This research
provides a pragmatic evaluation where replacing non-patient-specific CDS with a more
patient-focused and information-rich approach can improve behavior around CDS alerts
and medication prescribing related to QTc prolonging medications.
Multiple Choice Questions
Multiple Choice Questions
-
You are tasked with implementing a clinical decision tool to enhance medication dose
checking across a health system. Which of the below options would you pick to ensure
patient safety and reduce alert fatigue?
-
Commercial database driven alerts that cannot be modified once implemented.
-
Customized, rule-based alerts that can utilize patient-specific information.
-
Non-interruptive in basket message notifying users of potential inappropriate medication
doses.
-
None of the above.
Correct Answer: The correct answer is option b. Clinical decision support based on patient-specific
information provides the best opportunity to maximize effectiveness, alert relevance,
and minimize alert fatigue.
-
Which of the following were risk factors for developing QTc prolongation based on
a validated QTc prolongation risk scoring tool?
Correct Answer: The correct answer is option e. All the risk factors indicated have been found to
be significantly associated with QTc-risk prolongation based on the work by Tisdale
et al.[9]
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