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Patient Safety Tip
of the Week
February 16, 2021
New Methods for QTc
Monitoring
Torsade
de pointes (TdP) is a form of ventricular
tachycardia, often fatal, in which the QRS complexes become “twisted” (changing
in amplitude and morphology) but is best known for its occurrence in patients
with long QT intervals. (See our earlier columns on the several methods of
measuring the QT interval and criteria for QTc prolongation). Though cases of
the long QT interval syndrome (LQTS) may be congenital, many are acquired and
due to a variety of drugs that we prescribe. The syndrome is more common in females
and many have a genetic predisposition. Underlying heart disease, electrolyte
abnormalities (eg. hypokalemia, hypomagnesemia,
hypocalcemia), renal or hepatic impairment, and bradycardia may be
precipitating factors. This list of medications that may prolong the QT
interval is substantial and continues to grow. For a full list of drugs
that commonly cause prolongation of the QT interval and may lead to Torsade de
Pointes, go to the CredibleMeds®
website. That extremely valuable site provides
frequent updates when new information becomes available about drugs that may
prolong the QT interval.
Particularly
when we start patients on a medication known to increase the QTc, we would like
to be able to monitor trends in the QTc. But that has been impractical and
costly in most situations. The need for a convenient and
inexpensive way to monitor the QTc was highlighted when some began hyping
chloroquine or hydroxychloroquine plus azithromycin for COVID-19 (see our April
7, 2020 Patient Safety Tip of the Week “Patient Safety Tidbits for
the COVID-19 Pandemic”).
Researchers
at the Mayo Clinic (Giudicessi 2021) have
now developed an artificial
intelligence (AI)-enabled 12-lead electrocardiogram (ECG) algorithm to
determine the QTc, and then prospectively test this algorithm on tracings
acquired from a smartphone-enabled mobile ECG device.
They
used data from over 1.6 million 12-lead ECG’s to derive and validate a deep
neural network (DNN) to predict the QTc interval. They then prospectively
tested the ability of this DNN to detect clinically relevant QTc prolongation (e.g. QTc ≥ 500 ms) on 686
genetic heart disease patients (50% with LQTS) with QTc values obtained from
both a 12-lead ECG and a prototype mobile ECG device equivalent to a well-known
commercially-available mobile ECG device (the AliveCor
KardiaMobile 6L). When applied to mECG
tracings, the DNN's ability to detect a QTc value ≥ 500 ms yielded an area under the curve, sensitivity, and
specificity of 0.97, 80.0%, and 94.4%, respectively. The negative predictive
value was 99.2% for detecting a QTc value ≥ 500 ms.
Giudicessi and
colleagues note that studies from their institution and others have
demonstrated that ~1% of all individuals who receive an inpatient or outpatient
12-lead ECG have a QTc ≥ 500 ms and that when
this QTc threshold is met or exceeded, there is a 2- to 4-fold increased risk
of death. They note that the identification of substantial QTc prolongation
provides an important opportunity to identify vulnerable, at-risk hosts and make
potentially lifesaving change(s) (i.e. initiation of
β-blockers, discontinuation of QTc-prolonging medications, or correction
of hypokalemia and hypomagnesemia) needed to mitigate the risk of TdP and sudden cardiac death.
Giudicessi et al.
discuss the potential applications of an AI-enabled mobile ECG device approach
to QTc assessment and monitoring. That could include universal screening for the
early detection of congenital LQTS, plus monitoring patient prescribed QTc
prolonging drugs.
Our June 25, 2019 Patient Safety Tip of the
Week “Found Dead in a Bed – Part 2”
noted many of the QTc prolonging drugs commonly prescribed and also mentioned
the importance of combinations of such drugs.
While the Giudicessi
study focused on specific QTc intervals, don’t forget
that trends in the QTc interval may also be important. In our June 10, 2014 Patient Safety Tip of the Week “Another
Clinical Decision Support Tool to Avoid Torsade de Pointes” we discussed a study by Tisdale et al. (Tisdale 2014) which demonstrated that use of CDSS (clinical
decision support systems) and computerized alerts can reduce the risk of QT
interval prolongation. Their system would trigger an alert when the QTc interval was >500 ms
or there was an increase in QTc of ≥60 ms from
baseline. It would be important to see how the metrics of the Giudicessi tool stack up when evaluating change in QTc
from baseline.
Quite frankly, we see this new tool being even
more valuable in the inpatient setting. There are a number of reasons why this syndrome
is more likely to both occur and result in death in hospitalized patients. So,
for those patients not being monitored in ICU settings or via remote
monitoring, wearing a watch or wearable device capable of trending the QTc
interval in real time could help identify patients at risk for Torsade.
Hospitalized patients have a whole host of other factors that may help
precipitate malignant arrhythmias in vulnerable patients. They tend to have
underlying heart disease, electrolyte abnormalities (eg.
hypokalemia, hypomagnesemia, hypocalcemia), COPD, renal or hepatic impairment,
and bradycardia, all of which may be precipitating factors. More importantly,
hospitalized patients may have the sorts of conditions for which we prescribe
the drugs that are primarily responsible for prolonging the QT interval (eg. haloperidol, antiarrhythmic agents, etc.). And many of
those drugs are given intravenously and in high doses in the hospital as
compared to the outpatient arena. Rapid intravenous infusion of such drugs may
be more likely to precipitate TdP than slow infusion.
This work by Giudicessi
and colleagues at the Mayo Clinic may be a real game changer! Mobile ECG
devices such as the AliveCor KardiaMobile
6L are relatively inexpensive and easy to use. And, as the quality of ECG
tracings from smartwatches has improved, we anticipate the smartwatch may
ultimately be a most valuable tool for monitoring QTc intervals in at-risk
patients.
Artificial intelligence
(AI) and neural networks are being used with increasing frequency in medicine.
The same Mayo Clinic researchers also recently published a study (Bos 2021) in which AI-ECG was found to distinguish
patients with electrocardiographically “concealed” LQTS from those discharged
without a diagnosis of LQTS. About 40% of patients with genetically confirmed
LQTS have a normal corrected QT (QTc) at rest. The neural network they developed
provided a nearly 80% accurate pregenetic test anticipation of LQTS. The
authors suggest this model may aid in the detection of LQTS in patients
presenting to an arrhythmia clinic and, with validation, may be the stepping stone to similar tools to be developed for use in
the general population.
We refer you back to our June 25, 2019 Patient
Safety Tip of the Week “Found Dead in a Bed – Part 2” and
our other columns on torsade (listed below) to see what your hospital or
healthcare organization should be doing to reduce the risk you’ll find a
patient “dead in a bed” from torsade de pointes.
Some
of our prior columns on QT interval prolongation and Torsade de Pointes:
June 29, 2010 “Torsade de Pointes: Are Your Patients At Risk?”
February 5, 2013 “Antidepressants and QT Interval Prolongation”
April 9, 2013 “Mayo Clinic System Alerts for QT Interval
Prolongation”
June 10, 2014 “Another Clinical Decision Support Tool to
Avoid Torsade de Pointes”
April 2015 “Anesthesia and QTc Prolongation”
October 10, 2017 “More
on Torsade de Pointes”
June
25, 2019 “Found Dead in a Bed – Part 2”
April
7, 2020 “Patient Safety Tidbits for
the COVID-19 Pandemic”
References:
CredibleMeds®
website
Giudicessi JR,
Schram M, Bos JM, et al. Artificial Intelligence-Enabled Assessment of the
Heart Rate Corrected QT Interval Using a Mobile Electrocardiogram Device.
Circulation 2021; Originally published 1 Feb 2021
https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.120.050231
Tisdale JE, Jaynes HA, Kingery J, et al. Effectiveness
of a Clinical Decision Support System for Reducing the Risk of QT Interval
Prolongation in Hospitalized Patients. Circulation: Cardiovascular Quality and
Outcomes 2014; 7(3): 381-390 Published online before print May 6, 2014
https://www.ahajournals.org/doi/full/10.1161/CIRCOUTCOMES.113.000651
Bos JM, Attia ZI, Albert DE, Noseworthy PA,
Friedman PA, Ackerman MJ. Use of Artificial Intelligence and Deep Neural
Networks in Evaluation of Patients With
Electrocardiographically Concealed Long QT Syndrome From the Surface 12-Lead Electrocardiogram.
JAMA Cardiol 2021; Published online February 10, 2021
https://jamanetwork.com/journals/jamacardiology/fullarticle/2776241
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