Hot Keywords
Charcot-Marie-Tooth Disease Rare Diseases Genome Project DNA Damage Multiple myeloma Prostate Cancer Oncolytic Viruses Epilepsy Breast Cancer mitochondrial disease Long QT syndrome

J Transl Genet Genom 2022;6:375-87. 10.20517/jtgg.2022.12 © The Author(s) 2022.
Open Access Original Article

Development and early evaluation of clinical decision support for long QT syndrome population screening

1Sanford Imagenetics, Sanford Health, Sioux Falls, SD 57105, USA.

2College of Pharmacy & Allied Health Professions, South Dakota State University, Brookings, SD 57006, USA.

3Sanford School of Medicine, University of South Dakota, Vermillion, SD 57105, USA.

4Department of Pharmacy Practice, North Dakota State University, Fargo, ND 58108, USA.

Correspondence to: Jordan Baye, Sanford Imagenetics, Sanford Health, 1321 W 22nd St., Sioux Falls, SD 57105, USA. E-mail:

    Views:138 | Downloads:47 | Cited:0 | Comments:0 | :0
    Academic Editor: Sanjay Gupta | Copy Editor: Fangling Lan | Production Editor: Fangling Lan

    © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License (, which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.


    Aim: Long QT syndrome (LQTS) is an inherited condition that predisposes individuals to prolongation of the QT interval and increased risk for Torsade de Pointes. Pathogenic variants in three genes - KCNH2, KCNQ1 and SCN5A - are responsible for most cases of LQTS, and recent advances in genetic testing have improved knowledge of the disease, increased access to follow-up, and reduced adverse cardiovascular outcomes.

    Methods: Based around our preemptive genetic screening platform which includes the three long QT genes listed above, we developed and implemented a clinical decision support (CDS) module that alerts prescribers whenever a QT-prolonging medication is ordered for patients with a genetic predisposition to LQTS.

    Results: Of the 13,777 individuals screened, twenty-seven tested positive for a pathogenic or likely pathogenic variant of KCNH2, KCNQ1 or SCN5A. In a subsequent early evaluation of the CDS and clinical processes, the number of QT-prolonging medications in this cohort decreased by 20% and new QT-prolonging medications were avoided in approximately 1/3 of new prescription orders.

    Conclusions: While long-term evaluation is needed, early data support the benefit of utilizing CDS in expanded roles, such as drug-gene-disease interactions where rare genetic variants intersect with everyday prescribing.


    One out of 2000 individuals has an inherited risk for prolonged QT interval, known as Long QT syndrome (LQTS)[1]. Three genes - KCNH2, KCNQ1 and SCN5A - account for approximately 75% of genetically identifiable cases[2,3]. LQTS is classified into types based on a particular gene: Long QT Type 1 (LQT1; OMIM 192500) is associated with KCNQ1, LTQ2 (OMIM 613688) is associated with KCNH2, and LQT3 (OMIM 603830) is associated with SCN5A. SCN5A can also manifest as Brugada syndrome (OMIM 601144), which has a similar but unique clinical presentation and treatment options. Gain-of-function variants in SCN5A lead to more sodium influx into cardiomyocytes through aberrant channel gating and cause LQTS, a primary electric disease of the heart. Loss-of-function variants in SCN5A lead to lower expression of SCN5A or production of defective Nav1.5 proteins and cause Brugada syndrome (BrS), an electrical disease with minor structural changes in the heart[4]. LQTS is characterized by delayed repolarization of the ventricular myocardium, QT prolongation (QTc > 480 ms as the 50th percentile among LQTS cohorts), and increased risk for Torsades de Pointes (TdP)-mediated syncope, seizures, and sudden cardiac death (SCD) in an otherwise healthy young individual with a structurally normal heart[5]. Given the life-threatening nature of these outcomes, the detection of at-risk individuals is of great clinical value.

    The manifestations of LQTS are highly variable, even amongst family members with the same pathogenic variant, as a consequence of the low penetrance present in LQTS[6]. Therefore, management is focused on the prevention of syncope, cardiac arrest, and sudden death. Data suggests that medical management of patients with inherited risk for LQTS improves cardiac event outcomes. It is estimated that untreated patients have a mortality rate of about 20% within the first year[7]. However, patients receiving treatment and follow-up have demonstrated fewer breakthrough cardiac events and a lower risk of SCD in LQTS. Among treated LQTS patients, annualized rates of SCD range from 0.05% to 1.3%[8]. While there are many factors influencing these outcomes, early detection, referral to specialty care, and appropriate treatment likely play a major role in improving outcomes.

    The key modalities for treatment of LQTS include pharmacotherapy with beta-blockers and procedural intervention via either implantable cardioverter-defibrillator (ICD) or left cardiac sympathetic denervation (LCSD). An expert consensus statement outlining how to diagnose and treat individuals with hereditary LQTS recommends beta-blockers first-line in patients with a diagnosis of LQTS who are asymptomatic with QTc > 470 ms and/or symptomatic for syncope or documented ventricular tachycardia/ventricular fibrillation (VT/VF)[9]. Beta-blockers are useful in patients with a diagnosis of LQTS who are asymptomatic with QTc ≤ 470 ms based on American College of Cardiology/American Heart Association Clinical Practice Guidelines. Recommended beta blockers per the ACC/AHA clinical guidelines include propranolol and nadolol[10]. ICD implantation is recommended in patients with a diagnosis of LQTS who are survivors of sudden cardiac arrest. ICD implantation may also be considered in patients with a diagnosis of LQTS who experience recurrent syncopal events while on beta-blocker therapy[9]. For individuals who are intolerant to or incompletely treated by beta blockers, LCSD has also shown benefits in reducing cardiac events[11-13]. Lifestyle modifications are often encouraged in addition to treatment. Awareness of potential triggers - strenuous exercise, emotional stress, auditory stimuli (for LQT2), and dietary monitoring - can help reduce the risk for cardiac events, though the overall frequency of routine life events precipitating arrhythmias appears to be relatively low[14]. Individuals with KCNH2 pathogenic variants should reduce exposure to loud, sudden noises such as alarm clocks.

    Another benefit of early detection is the ability to prevent medication administration of agents known to further prolong the QT interval. Drugs that cause further prolongation of the QT interval or provoke TdP should be avoided, if possible, for all individuals with LQTS. The website (CredibleMeds®) curates a complete and updated list of medications to avoid in individuals with LQTS, and maintains a list of medications to avoid in individuals with BrS. In situations where medications known to prolong the QT interval cannot be avoided, such as specific oncologic treatments, knowledge of the genetic predisposition can also be of great value in that additional monitoring could be utilized.

    Health care workers in all clinical settings routinely evaluate patients for multiple factors, including drug-drug interactions, drug-gene interactions, allergies, dosing recommendations based on age, organ function, or disease states as part of their routine workflows. This is largely facilitated by electronic medical record (EMR) functionality. As our understanding of an individual’s clinical picture expands to include genomic data such as variants which lead to prolonged QT, EMR functionality can be leveraged to facilitate provider use of this critical information in any clinical setting. Multiple medications cause QT prolongation making preemptive genomic screening for LQTS quite significant to clinical practice. The knowledge that a patient is predisposed to QT prolongation provides an opportunity at the time of prescribing to order additional tests or to change to alternate therapies as appropriate. Understanding that physician, advance practice provider, pharmacist, and nurse roles and availabilities vary across patient appointment types and clinical settings, we developed methodology that can be adapted to care across multiple points of patient contact.


    Implementation processes

    In 2018, Sanford Health launched a precision medicine program - Imagenetics - aimed at preemptive identification of genetic variants for medically actionable predispositions (MAP) and pharmacogenomic (PGx) variants with the Sanford Chip. Using a high-throughput single nucleotide polymorphism (SNP) genotyping platform with custom content (Infinium Global Screening Array, Illumina, San Diego, CA), results are reported for pathogenic and likely pathogenic variants detected in selected genes associated with predispositions to a variety of health conditions, including LQTS, BrS, cardiomyopathy, familial hypercholesterolemia, and several familial cancers[15,16]. Sanford Imagenetics also utilized a commercial vendor (Color Health, Burlingame, CA) for roughly 1000 patients in lieu of the screening array as a pilot project for next-generation sequencing for both PGx and disease predisposition testing. Eligible patients for the Sanford Chip or Color Health testing included patients 18 years or older who had a primary care provider within Sanford Health and were active users of our EMR patient portal. The test was offered preemptively as a screening tool within the context of outpatient primary care and not as a diagnostic test. The results are stored as discrete data within the laboratory information section of the EMR allowing for clinical decision support (CDS) to fire for MAP and PGx actionable results at the time of therapeutic choice (prescribing). All patient encounters - including initial genetic testing, primary care follow-up, genetic counselor appointments, and cardiology consults - were conducted in the outpatient setting.

    Since our platform captures information about both PGx and genetic predisposition to disease, we identified opportunities to leverage the capabilities of both areas. LQTS, in particular, was identified as an area where CDS could be used as an active alert system with the EMR. Therefore, we implemented a system of CDS alerts to notify prescribers if a high-risk medication was ordered for individuals with a predisposition to either LQTS or BrS. Our team elected to utilize both and as clinical references for CDS. The PGx team reviews these lists at scheduled intervals as well as subscribes to updates to ensure our lists remain up to date. Provider and pharmacy education was provided on instances when CDS would generate, which includes an alert specifically triggered upon a pharmacist opening a chart encounter. Additional pharmacy education was provided on specific inpatient interventions with templated documentation to guide possible interventions to address additional risk factors for QT prolongation, such as electrolyte disturbances.

    Given the significant impact certain medications may have on a patient’s QT interval, it is vital that real time decision support is available to both prescribers and pharmacists involved in the care of the patient. Due to the severity of the consequences of a prolonged QT interval, including the risk of death, CDS must be actionable (real-time and highly reliable). Frontline staff would be alerted when prescribing a medication with the potential to prolong the QT interval in patients who tested positive for genetic variants associated with LQTS shown in Figure 1, top panel. Our initial approach consisted of utilizing medications curated at CredibleMeds® to review for QT prolongation potential; however, after working with our genetics team, we later also added medications curated at for individuals who may also be at risk for BrS shown in Figure 1, center panel. Additionally, an advisory based on the genetic variants regardless of medication ordering was created to facilitate pharmacists to conduct a comprehensive review shown in Figure 1, bottom panel. The comprehensive review is supported through a pharmacist intervention workflow which prompts a review of patient characteristics that may prolong the QT interval, such as medications, electrolyte abnormalities, history of cardiac abnormalities, blood glucose, gastrointestinal distress, and vital signs. In addition, a pharmacist on the PGx team will review all patients with a positive variant and provides medication recommendations to the primary care provider in patients not admitted to the hospital.

    Figure 1. CDS Alerts developed for Long QT (LQTS)and Brugada Syndrome (BrS). Top panel: Alert encountered by frontline staff when placing an order for a medication which may prolong QT interval. Center panel: Alert encountered by frontline staff when a potential medication could precipitate a cardiac rhythm abnormality. Bottom panel: Alert generated upon pharmacist entry into a patient’s chart in which a patient has a genetic variant for LQTS or BrS

    CredibleMeds® (Available from: and (Available from: were chosen as primary medication references, given the comprehensive material and well-curated information. Our standard process is to review medications that would trigger these alerts quarterly and as needed with any updates to the lists or additional data available. At the time of writing, we have made twelve revisions since the implementation of these interruptive alerts in July 2019. A recent update included collaboration between the PGx and research pharmacy teams to include investigational medications that have warnings about QT prolongation. The CDS triggers on all medications are listed as medications to be avoided in patients with LQTS.

    These alerts were approved by interdisciplinary committees (Clinical Decision Support Committee and Pharmacy Optimization Committee) prior to implementation. Education was provided to frontline staff via newsletters. Additionally, education was provided to inpatient pharmacy staff across the Sanford Health institution regarding the disease state as well as modifiable risk factors and treatment. Staff development was presented by a member of the PGx team, and references were made available via the internal website to be available in real time for pharmacy staff.

    Cohort evaluation

    Following implementation and clinical utilization, we sought to complete a retrospective periodic evaluation of the LQTS CDS alert system. The study was reviewed and approved by the Sanford Health Institutional Review Board (study #00002071). The primary objective of our review was to describe basic demographics and clinical processes related to LQTS or BrS in a cohort of patients undergoing preemptive genetic screening. Secondary objectives were (1) To determine whether receipt of genetic results impacted the number of QT-prolonging medications in individuals with pathogenic or likely pathogenic variants for LQTS or BrS; and (2) to assess whether CDS impacted medication adjustment and future selection of medications. Data was collected from existing records in our EMR from the earliest available electronic records (approximately 2010) through October 31, 2021. All individuals with a pathogenic or likely pathogenic variant of KCNH2, KCNQ1 or SCN5A were included in the evaluation cohort. Due to the low prevalence of positive genetic variants in our population, the demographics reported were constrained to age and sex.

    We used a combination of automated data query and manual chart review to collect data. Demographics and genetic testing results were assessed descriptively. In order to assess medication changes pre- and post-genetic testing, we reviewed the active medication list immediately prior to genetic testing results (i.e., pre-genetic testing) and compared those against the medication list following the first consult visit. Any medications determined to be QT-prolonging (per or CredibleMeds) at both time points were counted. A t-test was used to compare the average number of QT-prolonging medications pre- and post-testing. Consult visits, procedural records, and electrocardiogram (ECG) data were collected and analyzed using descriptive statistics.

    To evaluate the impact of CDS on prescribing practices, we compiled a list of all Long QT alerts that were triggered from the time of implementation; this list contained the alert type, patient identifier, provider name, and action taken. Using this list, we reviewed all patient encounters - clinic or hospital visit -- during which a Long QT alert was triggered. If by the close of the encounter, no new QT-prolonging medications were ordered, it was inferred the alert was accepted by the provider. If a QT-prolonging medication was newly ordered or reordered (i.e., refilled), the alert was deemed rejected by the provider. Some orders were particularly difficult to quantify. For instance, the EMR does not record the medication name on the CDS alert report, and an additional manual chart review was needed to exactly determine which medications caused the alert to fire. Additionally, given the routine use of order sets (which could include multiple QT-prolonging medications) in the inpatient encounters, there were some instances in which multiple concurrent medications could trigger a CDS alert. In the event that a CDS alert could not be associated with a specific medication, it was classified as unknown. Descriptive statistics were used to assess this data.


    Our initial EMR query yielded a total of 13,777 patients who had received preemptive genetic screening that included medically actionable predispositions. Of these, a cohort of 27 individuals (1 in 510) were found to have a pathogenic or likely pathogenic variant of KCNH2, KCNQ1 or SCN5A. The cohort was predominantly female (81.5%) with an average age of 54. The median duration of observation for this study was 19 months (range 2.9-38.6 months). Basic demographic data and a list of the genes for the entire cohort can be found in Table 1. Of the 27 individuals included in this review, 51% (n = 14) were found to have a variant in KCNQ1; all but two KCNQ1 variants were deemed pathogenic. SCN5A and KCNH2 accounted for 37% (n = 10) and 11.1% (n = 3) of the discovered variants, respectively. Due to the relatively high frequency of pathogenic variants in this population, an additional review was performed to determine whether individuals with positive variants were related. We found one pair of second-degree relatives within the KCNQ1 variant group and another pair of first-degree relatives within the SCN5A variant group. To the best of our knowledge, no other individuals in the study were related.

    Table 1

    Pathogenicity and Frequency of LQTS and BrS variants encountered in preemptive screening population

    GenePositional changeProtein changePathogenicityN (%)
    KCNH2c.98A>CN33TLikely pathogenic (LQT2)2 (7.4)
    c.2145G>AA715=Likely pathogenic (LQT2)1 (3.7)
    KCNQ1c.477+5G>AIntron variantPathogenic (LQT1)3 (11.1)
    c.535G>A G179RPathogenic (LQT1)1 (3.7)
    c.1552C>TR391*Pathogenic (LTQ1)4 (14.8)
    c.1588C>TQ403*Pathogenic (LQT1)3 (11.1)
    c.1792_1793del K598fsLikely pathogenic (LQT1)2 (7.4)
    c.1893dupR632fsPathogenic (LQT1)1 (3.7)
    SCN5Ac.673C>TR225WPathogenic (BrS or LQT3)1 (3.7)
    c.2582_2583delTTF861fsPathogenic (BrS)1 (3.7)
    c.3911C>TT1304MLikely pathogenic (LQT3)7 (25.9)
    c.3926G>AR1309HLikely pathogenic (BrS)1 (3.7)

    Within the study cohort, 24 individuals received ECG monitoring secondary to the genetic testing result. On ECG, the median QTc was 460 ms (range: 400-512 ms), with sixteen individuals at or above 450 ms. Two individuals were noted to have QTc above 500 ms. Post-testing ECG was compared against baseline ECG; no statistical difference was noted (data not shown). Six patients were subsequently diagnosed with clinical LQTS: three were started on beta-blocker therapy at the time of cardiology consult, two have received no beta-blockers to date, and one had a previous intolerance to beta-blocker therapy. After developing significant QT prolongation on the stress test, this individual was subsequently started on a beta-blocker.

    Following the identification of the study cohort, we completed a review of new consults, procedures, and medications specifically ordered in response to pathogenic/likely pathogenic results. A consult flow diagram is displayed in Figure 2. Nearly all patients (25 of 27, 92.6%) received a follow-up consultation visit with a genetic counselor to review the results and address questions. Patients were further referred to cardiology, and as of the time of data collection, 24 (89%) patients had either been seen by a cardiologist or had an appointment pending. Only one patient had previously established care with a cardiologist, and prior to testing, there was no documented history of LTQS or BrS. The median time from discovery of a pathogenic variant to cardiologist follow-up was 38 days (range: 14-109 days). There have been no subsequent referrals to medical genetics for follow-up.

    Figure 2. Patient referrals and consults following receipt of pathogenic or likely pathogenic results.

    During follow-up evaluation, additional testing was ordered on most patients [Table 2]. The most common were echocardiogram, stress tests, and Holter monitoring, accounting for 52%, 22%, and 22% of patients, respectively. No deaths, ventricular arrhythmias, or significant cardiovascular events have been reported in any patients in the study cohort since receiving genetic testing results. Notably, upon a detailed review of consult notes, ECG results, and diagnostic codes, none of the patients carrying an SCN5A pathogenic/likely pathogenic variant were found to have BrS. However, three individuals in the study carry an SCN5A variant associated with BrS. Two variants (c.2582_2583delTT and c.3926G>A; 2 patients) are associated solely with BrS, and one variant (c.673C>T; 1 patient) has evidence linking it to either BrS or LQTS.

    Table 2

    Description of additional care received following receipt of genetic results

    Modality of careEvents n (%)
    Provider consultations
           Genetic counselor25 (92.6%)
           Cardiology24 (89%)
           Medical genetics0 (0%)
    Additional testing
           Echocardiogram14 (52%)
           Stress test6 (22%)
           Zio patch/holter monitor6 (22%)
           Tilt table procedure1 (4%)
           Calcium score2 (7%)
    Medications changes
           Discontinued 1+ QT-prolonging medication6 (22%)
           Addition of beta-blocker2 (7%)

    Medication changes were relatively uncommon following receipt and evaluation (through cardiology consultation) of genetic testing results. The key modality of LQTS treatment - beta-blockers - were added for two patients. Considering that two other patients were previously prescribed a beta-blocker, a total of 4 (15%) patients for whom beta-blockers were clinically indicated were taking a beta-blocker by the end of the present evaluation period. However, there have been several QT-prolonging medications (per criteria) that were discontinued following genetic testing; a total of eight medications (tizanidine, sertraline x2, fluconazole, amiodarone, esomeprazole, omeprazole, hydroxychloroquine) were discontinued from six patients. The mean number of QT-prolonging medications decreased from 1.48 to 1.19 [P = 0.0352, Figure 3].

    Figure 3. Average number of QT-prolonging medications pre- and post- LQTS genetic testing.

    CDS acceptance

    Long QT CDS alerts were implemented in July 2019; since then, 264 unique alerts have been triggered for 22 patients. Ninety-eight patient encounters were identified, during which at least one CDS alert was fired. Alerts were accepted in 30% (29 of 98) of encounters, and 91% (n = 89) of alerts occurred in the outpatient setting (typically primary care). Among all encounters, there was a total of 138 medications that fired a Long QT alert. The median number of alerts per encounter was 2 (range: 1-14); however, among inpatient-only encounters, the median increased to 5 (range 2-14). The rationale for acceptance or rejection of the alert was not possible in most cases due to the scope of this preliminary review. However, anecdotally there were multiple instances in which “new” QT-prolonging medications led to increased monitoring (e.g., ECG) or discussion with the cardiology team.

    Medications linked to LQTS alerts were categorized by class and this data is summarized in Table 3. Amongst all medication orders associated with a LQTS alert (n = 138), 29% of medications were ultimately not ordered, which was classified as an “Accepted” alert. Conversely, medications that were ordered despite a LQTS alert (i.e., “Rejected”) accounted for 71% of all alerts. Numerically, there appeared to be no difference between prescriber acceptance rates in the outpatient versus inpatient procedural setting. In the outpatient setting, refill medication orders tended to be rejected at a higher frequency (43%) than new medication orders (27%). There were a few notable outliers in prescriber actions, namely a high rejection rate for mental health medication refills in the outpatient setting and a high rejection rate for antiemetics in the inpatient procedural setting. Alert acceptance rates were highest among acid suppression and weight loss medications in the outpatient setting - both ≥ 40%.

    Table 3

    Prescriber action taken on medications following LQTS BPA

    Medication classAcceptedRejected - newRejected - refill
    Outpatient (n = 98)29 (30%)27 (27%)42 (43%)
           Acid suppression/GI866
           Mental health5412
           Weight loss313
    Inpatient procedural orders (n = 40)11 (28%)29 (73%)-
           Acid suppression/GI12-
    All orders (n = 138)40 (29%)98 (71%)


    In the present manuscript, we describe the implementation process and early evaluation of CDS for three genes predisposing patients to LQTS. This system is designed to notify physicians and advanced practice providers in the event they attempt to prescribe a medication likely to prolong the QT interval. Since implementing our preemptive genetic screening platform in 2016 and the CDS process in 2019, we have identified 27 patients with pathogenic or likely pathogenic variants of KCNH2, KCNQ1 or SCN5A. Notably, the overall prevalence of a pathogenic or likely pathogenic variant within our genetic screening population was 1:510, which is substantially more common than previous reports. The majority of patients with a genetic variant have received additional monitoring and follow-up by a genetic counselor and a cardiologist, and a diagnosis of clinical LQTS has been made for six of the patients. To date, no significant cardiovascular events or deaths have been reported.

    Pharmacotherapy plays a vital role in both treatment and avoidance events in patients with LQTS or BrS. Beta-blockers, which are considered the mainstay of preventative therapy, were prescribed for 4 of 6 patients diagnosed with clinical LQTS. Avoidance of events is a far more difficult metric to quantify, though our early evaluation has shown that CDS alerts prevent some new QT-prolonging medications and decrease the overall number of active QT-prolonging medications. The CDS was triggered during 98 unique patient care visits (among 22 of the 27 patients) and at least one QT-prolonging medication (new or continuing) was avoided in nearly one third of those visits. Twenty-nine percent of all medications which received a CDS alert were accepted, with the highest acceptance rate in acid suppressing and weight loss medications. In addition, the total number of QT-prolonging medications among all members of the LQTS cohort decreased from 1.48 to 1.19, which was statistically significant. We infer from these data that the interventions - genetic screening, consultative follow-up, and CDS - have a positive short-term impact on medications implicated in LQTS prevention.

    There are several limitations that should be addressed with both our implementation and assessment processes. First, the small population and relatively short length of follow-up for this cohort limit the ability to assess major clinical outcomes. Furthermore, the lack of a true comparator group and the retrospective nature of our evaluation permit only associative findings. While we have demonstrated a trend toward avoidance of QT-prolonging medications, the clinical relevance of these numbers is difficult to assess. Our findings should, therefore, be considered hypothesis generating. A more formal evaluation is anticipated as the number of patients in this cohort increases over time. Second, CDS processes are inherently difficult to evaluate unless designed specifically for research evaluation. Since our CDS was designed with the clinical user (i.e., prescriber) in mind, the alerts do not directly associate with some important data fields used in research. For instance, we could not identify some medications flagged by the alerts if the prescriber did not sign the order, which would be the preferred outcome of medication avoidance. It was also difficult to associate alerts with medications when multiple medications were ordered at the same time, such as on order sets or inpatient medication protocols. We were able to manually associate most alerts with medications due to strong clinical documentation, but the scope of future assessment may make this untenable. Finally, the consultative processes and the CDS, while part of an overarching preemptive genetic screening platform, are separate functions and can independently impact medication use and clinical outcomes. Generally, medications used for treatment (e.g., the addition of beta-blockers) came about through consultation and specialist assessment and medication avoidance (e.g., withholding medications after an alert is triggered) resulted from the CDS. However, parsing out the clinical impact of these different functions will involve a more nuanced evaluation. The authors acknowledge the confounding factors in the present assessment, though the overall implementation of preemptive genetic screening for LQTS genes with CDS support does appear to identify and decrease the use of potential interacting medications.

    While some institutions have implemented systems designed to alert providers when patients have documented a history of QT prolongation, to our knowledge, no other published literature describes CDS for QT prolongation that directly includes genetic variants of KCNH2, KCNQ1 or SCN5A[17,18]. Previously published studies examining prescribers’ responses to QT prolongation included QTc > 500 ms and QT-prolonging medications. An antiemetic decision tool alert for postoperative nausea and vomiting described by Hymel and Davies advised prescribers to “Avoid in Congenital Long QT Syndrome” but did not trigger based on genes that put a patient at increased risk for congenital LTQS (cLQTS)[19]. In 2016, Schwartz and Woosley suggested that future CDS for LQTS should consider including genetic and clinical predictors of LQTS[20]. A 12-year-old child with cLQTS was prescribed azithromycin and died as a result of TdP leading to AV-block associated with QT prolongation[21].

    In addition to genetics and medications, hypokalemia, hypomagnesemia, hypocalcemia, hypothyroidism, hypothermia, and extreme bradycardia are factors for LQTS potentially leading to TdP. An ideal alert would include multiple clinical factors including the most recent ECG data. Balancing the complexity of building an alert with succinct but informative CDS, our alerts inform prescribers of the risk for prolonged QT due to genetic and medication factors, and they allow pharmacists reviewing the medication to select to review most recent ECG results and/or open a QT prolongation intervention to address additional modifiable risk factors. This approach allows for pharmacists to further investigate other factors that could contribute to prolonged QT and make recommendations accordingly.

    This manuscript provides a roadmap for the implementation of genetic screening CDS to address complex drug-gene-disease interactions. Our preemptive genetic screening platform tests for both an 11-gene PGx panel as well as a MAP portion including 57 genes curated from the American College of Medical Genetics (ACMG) list of secondary findings. Some of the recommendations for positive MAP variants have significant overlap with pharmacy. CACNA1S and RYR1 are included in the ACMG list of secondary findings[22]. Clinical Pharmacogenetics Implementation Consortium (CPIC) published guidelines for CACNA1S and RYR1 variants associated with Malignant Hyperthermia Syndrome, stating certain anesthetics are contraindicated in such patients. Sanford Imagenetics deploys alerts based on these findings; a description of this process has been published elsewhere[23]. Pharmacists can help with therapeutic recommendations for patients with familial hypercholesterolemia genes of APOB and LDLR. KCNH2, KCNQ1, or SCN5A are also included in the ACMG list, though no current associated CPIC guidelines exist to date. Identifying risk factors for QT prolongation is important given the potential severity of adverse events.

    Finally, it should be noted that our approach utilizes clinical pharmacists with specialty training in pharmacogenomics to review PGx results, make medication recommendations, and assist prescribers when questions arise. Many health systems do not employ PGx pharmacists, and while we strongly endorse the use of pharmacists for PGx and drug-gene-disease interactions, medical geneticists and genetic counselors can be used in this capacity. Pharmacists and providers should be aware of the risk of QT-prolonging medications in general, but a heightened awareness should be employed for a patient with a genetic predisposition to QT prolongation. Having CDS alerts in place are a patient safety measure to help alert providers and pharmacists to the potential for additive risk.



    The authors would like to thank Chad Larson for his work building, testing, and deploying LQTS CDS alerts.

    Authors’ contributions

    Designed study: Baye J, Massmann A, Petry N, Van Heukelom J, Schultz A, Hajek C

    Completed data collection: Baye J, Massmann A, Petry N, Schultz A, Van Heukelom J

    Analyzed and interpreted data: Baye J, Massmann A, Petry N, Van Heukelom J

    Wrote manuscript: Baye J, Massmann A, Petry N, Van Heukelom J, De Berg K, Schultz A, Hajek C

    Availability of data and materials

    Not applicable.

    Financial support and sponsorship


    Conflicts of interest

    All authors declared that there are no conflicts of interest.

    Ethical approval and consent to participate

    The study was reviewed and approved by the Sanford Health Institutional Review Board (study #00002071). Waiver of consent was granted for this study.

    Consent for publication

    Not applicable.


    © The Author(s) 2022.


    • 1. Schwartz PJ, Stramba-Badiale M, Crotti L, et al. Prevalence of the congenital long-QT syndrome. Circulation 2009;120:1761-7.

      DOIPubMed PMC
    • 2. Nakano Y, Shimizu W. Genetics of long-QT syndrome. J Hum Genet 2016;61:51-5.

    • 3. Giudicessi JR, Ackerman MJ. Determinants of incomplete penetrance and variable expressivity in heritable cardiac arrhythmia syndromes. Transl Res 2013;161:1-14.

      DOIPubMed PMC
    • 4. Wilde AAM, Amin AS. Clinical spectrum of SCN5A mutations: long QT syndrome, brugada syndrome, and cardiomyopathy. JACC Clin Electrophysiol 2018;4:569-79.

    • 5. Schwartz PJ, Crotti L, Insolia R. Long-QT syndrome: from genetics to management. Circ Arrhythm Electrophysiol 2012;5:868-77.

      DOIPubMed PMC
    • 6. Priori SG, Napolitano C, Schwartz PJ. Low penetrance in the long-QT syndrome: clinical impact. Circulation 1999;99:529-33.

    • 7. Schwartz PJ. Idiopathic long QT syndrome: progress and questions. Am Heart J 1985;109:399-411.

    • 8. Rohatgi RK, Sugrue A, Bos JM, et al. Contemporary outcomes in patients with long QT syndrome. J Am Coll Cardiol 2017;70:453-62.

    • 9. Priori SG, Wilde AA, Horie M, et al. HRS/EHRA/APHRS expert consensus statement on the diagnosis and management of patients with inherited primary arrhythmia syndromes: document endorsed by HRS, EHRA, and APHRS in May 2013 and by ACCF, AHA, PACES, and AEPC in June 2013. Heart Rhythm 2013;10:1932-63.

    • 10. Al-Khatib SM, Stevenson WG, Ackerman MJ, et al. 2017 AHA/ACC/HRS guideline for management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: a report of the American college of cardiology/American heart association task force on clinical practice guidelines and the heart rhythm society. J Am Coll Cardiol 2018;72:e91-e220.

    • 11. Schwartz PJ, Priori SG, Cerrone M, et al. Left cardiac sympathetic denervation in the management of high-risk patients affected by the long-QT syndrome. Circulation 2004;109:1826-33.

    • 12. Schwartz PJ, Ackerman MJ. Cardiac sympathetic denervation in the prevention of genetically mediated life-threatening ventricular arrhythmias. Eur Heart J 2022;43:2096-102.

    • 13. Dusi V, Pugliese L, De Ferrari GM, et al. Left cardiac sympathetic denervation for long QT Syndrome: 50 Years’ experience provides guidance for management. JACC Clin Electrophysiol 2022;8:281-94.

    • 14. Johnson JN, Ackerman MJ. Competitive sports participation in athletes with congenital long QT syndrome. JAMA 2012;308:764-5.

    • 15. Christensen KD, Bell M, Zawatsky CLB, et al. Precision population medicine in primary care: the sanford chip experience. Front Genet 2021;12:626845.

      DOIPubMed PMC
    • 16. Natasha Petry, Baye J, Aifaoui A, et al. Implementation of wide-scale pharmacogenetic testing in primary care. Pharmacogenomics 2019;20:903-13.

    • 17. Sorita A, Bos JM, Morlan BW, Tarrell RF, Ackerman MJ, Caraballo PJ. Impact of clinical decision support preventing the use of QT-prolonging medications for patients at risk for torsade de pointes. J Am Med Inform Assoc 2015;22:e21-7.

      DOIPubMed PMC
    • 18. Trinkley KE, Pell JM, Martinez DD, Maude NR, Hale G, Rosenberg MA. Assessing prescriber behavior with a clinical decision support tool to prevent drug-induced long QT syndrome. Appl Clin Inform 2021;12:190-7.

      DOIPubMed PMC
    • 19. Hymel N, Davies M. Evidence-based antiemetic decision tool for management of postoperative nausea and vomiting in patients at high risk of QT prolongation and patients receiving neurotransmitter-modulating medications. AANA J 2020;88:312-8.

    • 20. Schwartz PJ, Woosley RL. Predicting the unpredictable: drug-induced QT prolongation and torsades de pointes. J Am Coll Cardiol 2016;67:1639-50.

    • 21. Grissinger M. Selected medication safety risks that can easily fall off the radar screen. P T 2018;43:521-67.

    • 22. Miller DT, Lee K, Chung WK, et al. ACMG SF v3.0 list for reporting of secondary findings in clinical exome and genome sequencing: a policy statement of the American College of Medical Genetics and Genomics (ACMG). Genet Med 2021;23:1381-90.

    • 23. Baye JF, Petry NJ, Jacobson SL, et al. Malignant hyperthermia susceptibility: utilization of genetic results in an electronic medical record to increase safety. Pharmacogenomics 2020;21:1207-15.


    Cite This Article

    Baye J, Massmann A, Petry N, Van Heukelom J, De Berg K, Schultz A, Hajek C. Development and early evaluation of clinical decision support for long QT syndrome population screening. J Transl Genet Genom 2022;6:375-87.




    Comments must be written in English. Spam, offensive content, impersonation, and private information will not be permitted. If any comment is reported and identified as inappropriate content by OAE staff, the comment will be removed without notice. If you have any queries or need any help, please contact us at

    © 2016-2022 OAE Publishing Inc., except certain content provided by third parties