Drugs associated with a risk of supraventricular tachycardia: analysis using the OpenVigil database

Abstract

Objective

The OpenVigil database can be used to assess medications that may cause supraventricular tachycardia (SVT) and to produce a reference for their safe use in clinical settings.

Methods

We analyzed first-quarter data from 2004 to 2023, obtained by searching the OpenVigil database using the keyword “supraventricular tachycardia.” Trade names and generic names were obtained by querying the RxNav database, and the proportions were summarized. The proportionate reporting ratio (PRR), reporting odds ratio, and chi-square values were also summarized. We created Asahi diagrams and set the screening criteria to drug events ≥30, PRR >2, and chi-square >4. Outcomes were evaluated using the Side Effect Resource database, several scientific literature databases, and the Hangzhou Yiyao Rational Medication System.

Results

A total of 2435 distinct medications were found to induce SVT between the first quarter of 2004 and 2023, leading to 22,375 documented adverse events related to SVT. Further investigation revealed that salbutamol, paroxetine, formoterol, paclitaxel, venlafaxine, and theophylline were most likely to cause SVT.

Conclusion

We conducted signal mining of adverse drug events using the OpenVigil database and evaluated the six drugs most likely to cause SVT. The results of this research can serve as a drug safety reference in the clinic.

Introduction

Supraventricular tachycardia (SVT) is a dysrhythmia originating at or above the atrioventricular node that is defined by a narrow complex (QRS ≤ 120 ms); however, this can sometimes be relatively wide at a relatively regular heart rate of ≥100 beats per minute. ¹ An electrocardiogram (ECG) shows SVT as either a widening or a narrowing of the QRS wave, accompanied by a relatively regular heart rate during the event. ² SVT is a relatively common clinical arrhythmia, and most patients do not have obvious heart disease. Types of SVT include sinus tachycardia, inappropriate sinus tachycardia, ectopic atrial tachycardia, multifocal atrial tachycardia, atrial fibrillation/flutter; the group of paroxysmal supraventricular tachycardias (sudden onset and end) including atrioventricular nodal reentrant tachycardia (the most common type); and the group of pre-excitation syndromes mediated by abnormal accessory pathways of congenital origin (atrioventricular reciprocating tachycardias). ² SVT manifests as sudden onset and stop. Patients may experience palpitations, chest tightness, shortness of breath, sweating, and other symptoms when the condition first occurs. In some cases, these symptoms may also be accompanied by dizziness or impaired consciousness. ³ SVT is the most prevalent non-sinus tachyarrhythmia; it is more common in women than in men and more common in older people than in younger people. Paroxysmal SVT can precipitate heart failure, angina pectoris, and syncope in susceptible patients, and physiological sinus tachycardia may be the clinical expression of decompensated heart failure and accompany the clinical picture of angina pectoris.^4–7 Notably, drugs are one of the primary causes of SVT.^8–10

The occurrence of drug-related SVT is frequently disregarded during clinical drug therapy with cholinergic inhibitors, calcium antagonists, and other drugs that are likely to cause SVT. The incidence and severity of SVT caused by different drugs vary, and there is no clear link between the dose and duration of most drugs and the occurrence of SVT, frequently making this outcome unpredictable.^11–13 When treating patients, doctors should use the most appropriate drugs in clinical practice. If drug-related SVT is detected early, medications should be stopped promptly. In severe cases, propafenone, amiodarone, and other medications can be used, or cardiac catheter radiofrequency ablation can be used for surgical treatment.^14,15 Such treatments can effectively reduce the frequency of adverse reactions and prevent SVT from worsening owing to medication, thereby improving patient prognosis and reducing their hospital stay. However, there is currently no research on which medications can result in SVT.

The OpenVigil database is a new web-based tool for analyzing drug-related adverse reactions, incorporating data from both the United States Food and Drug Administration (FDA) adverse event reporting system and the German pharmacovigilance database. The database features highly customizable search functions and output filters. To detect proportional imbalances in adverse event signal detection, adverse reactions are analyzed using the proportional reporting ratio (PRR) and reporting odds ratio (ROR) methods, which are well-established measures for detecting adverse event signals. The results can be viewed, sorted, and filtered and can also be saved in statistical software for further analysis.^16,17 Drug instructions and clinical guidelines often do not offer timely information on potential adverse reactions to medication. Clinicians need quick access to information on medications that may cause adverse reactions, allowing them to apply relevant countermeasures and develop early warning systems. These objectives can be achieved by analyzing adverse drug reactions using the OpenVigil database.^18,19 A total of 126 drugs, including sex hormones, anti-diabetics, peptidase inhibitors, antivirals, nicotinic acetylcholine receptor agonists, and tyrosine kinase inhibitors, have previously been identified in a search of the OpenVigil database for reverse signals to find drugs targeting viral respiratory infections or diseases caused by RNA viruses. ²⁰

In the present research, we used the OpenVigil database to analyze the risk of drug-related SVT and to categorize, organize, and summarize those drugs that might cause SVT. This analysis could aid in the early detection of adverse drug events and in countermeasures during the administration of therapeutic medication.

Methods

Data information and flowchart

Table 1 presents information about the databases and sources used in this research. Figure 1 depicts the research flowchart. Patient consent was waived in this study owing to the nature of the data source. The human-related datasets used in this study were provided by a third party (i.e., OpenVigil database) and are publicly available for any research program. This research did not require ethics committee or institutional review board approval. The researchers cannot possess any information regarding participants in the data collection or experiments. The authors have neither the authority nor responsibility to obtain personal information needed for the approval process.

Table 1.

Data source information.

Database or tool	Information
OpenVigil database	https://openvigil.sourceforge.net/
RxNav database	https://lhncbc.nlm.nih.gov/RxNav/
WHO ATC/DDD Index database	https://www.whocc.no/atc_ddd_index/
SIDER database	http://sideeffects.embl.de/
China National Knowledge Infrastructure	https://www.cnki.net/
PubMed database	https://pubmed.ncbi.nlm.nih.gov/
GeenMedical database	https://www.geenmedical.com/
Rational Drug Use System	Hangzhou, Yiyao, China

WHO, World Health Organization; ATC, Anatomical Therapeutic Chemical Classification Coding; DDD, defined daily dose; SIDER, Side Effect Resource.

Figure 1.

Study flowchart.

Access to SVT-related drugs

In the OpenVigil database, we selected “Primary suspect” in the option “Role of drug,” then “PT” in the “Adverse event” option. We entered the term “supraventricular tachycardia” in the data display and calculation analysis module. In the data presentation and analysis module, we chose “Frequency” and “Frequentis_methods.” Finally, we selected “Excel_CSV” file type as the query result output format to obtain a list of medications that could lead to SVT.

Sorting and screening SVT-related drugs

We performed disaggregation, searching the RxNax database for each drug’s trade name and generic name. After merging all data for the same drug, we recalculated the related data using the PRR and chi-squared (χ²). We established the filtering standards as drug events ≥ 30, PRR > 2, and χ² > 4, and the abovementioned medications underwent further screening.

Plotting the Asahi diagram

Each drug’s Anatomical Therapeutic Chemical Classification Coding System (ATC) code was determined by querying the World Health Organization (WHO) ATC/defined daily dose (DDD) Index database. Subsequently, each drug was classified using the ATC code. An Asahi diagram was plotted using Microsoft Office 2016 software (Microsoft Corporation, Redmond, WA, USA) to illustrate the classification of each drug, according to the magnitude of the PRR.

Evaluation of SVT-related drugs

To identify medications that could cause SVT, we searched the Side Effect Resource (SIDER) database using the keyword “supraventricular tachycardia.” We then carried out an extensive assessment of the aforementioned medications using the China National Knowledge Infrastructure, PubMed, and GeenMedical scientific literature databases to validate and evaluate the results of past research.

Results

Drugs with a high reporting frequency of SVT

In this study, we extracted data on adverse drug reactions involving SVT from the OpenVigil database, the FDA adverse reaction reporting system in the United States, and the pharmacovigilance database in Germany. A total of 2435 drugs were identified as causing SVT between the first quarter of 2004 and 2023. A total of 22,375 adverse events related to SVT were reported over the study period. Table 2 lists the 30 drugs with the most frequently reported adverse reactions.

Table 2.

Thirty most frequently reported drugs in the OpenVigil database.

Ranking	Number of reported cases	Drug name	PRR	ROR	χ2
1	350	metoprolol	7.16	7.179	658.4325
2	306	aspirin	3.7585	3.76325	278.1345
3	286	rosiglitazone	5.165	5.173	465.32
4	248	rofecoxib	8.098	8.1205	743.6255
5	230	furosemide	3.4525	3.456	174.4015
6	218	salbutamol	2.9975	3.0005	120.283
7	199	metformin	2.144	2.145	115.104
8	181	amlodipine	2.3705	2.372	53.0105
9	176	levothyroxine	2.101	2.102	96.504
10	162	warfarin	3.08	3.083	95.792
11	147	pantoprazole	2.5245	2.526	54.0915
12	145	omeprazole	2.249	2.25	96.187
13	143	ethinylestradiol	2.217	2.218	45.8315
14	138	digoxin	10.8	10.842	1180.645
15	137	hydrochlorothiazide	2.63	2.632	132.657
16	131	lisinopril	2.347	2.349	97.082
17	128	clopidogrel	2.3955	2.3965	54.204
18	122	paroxetine	3.718	3.722	113.5655
19	118	dexamethasone	2.25	2.251	78.566
20	115	amiodarone	9.036	9.065	793.43
21	110	alendronate	4.163	4.1685	123.618
22	106	simvastatin	2.11	2.111	59.392
23	97	verapamil	11.668	11.718	915.709
24	95	quetiapine	2.22	2.221	61.173
25	94	formoterol	2.293	2.2945	31.6305
26	93	folic acid	2.46	2.461	77.503
27	89	bupropion	2.954	2.956	110.9
28	85	ondansetron	3.925	3.929	178.969
29	83	atenolol	3.565	3.568	147.864
30	83	diltiazem	5.466	5.475	292.993

PRR, proportionate reporting ratio; ROR, reporting odds ratio.

Database mining results

After the initial list of drugs was screened using our specified criteria, 93 drugs remained. The ATC codes for these drugs were retrieved from the WHO ATC/DDD Index database and used to categorize the drugs according to ATC code. When multiple ATC codes were available for a specific drug, the code corresponding to the primary indication was selected. The Asahi diagram (Figure 2) was constructed using PRR data, with the first level of ATC codes, or the anatomical main groupings, displayed in the inner circle. The tertiary subgroup categorization for therapeutics and pharmacology is displayed in circles at the second level. The width of each slice represents the relative PRR, with larger slices indicating a higher signal. The Asahi diagram graphically depicts the potential of each drug to cause SVT on the basis of information gleaned from previous studies. Figure 3 shows the cumulative PRR for SVT triggers using the ATC categorization scheme. The five leading drugs in this regard include those for use to treat disorders of the respiratory system, nervous system, and cardiovascular system as well as the alimentary tract and metabolism, and antineoplastic and sex hormones.

Figure 2.

Asahi diagram of drugs that may cause SVT, according to ATC classification. SVT, supraventricular tachycardia; ATC, Anatomical Therapeutic Chemical Classification Coding System; A, alimentary tract and metabolism; B, blood and blood forming organs; C, cardiovascular system; D, dermatologicals; G, genitourinary system and sex hormones; H, systemic hormonal preparations, excluding sex hormones and insulins; J, systemic anti-infectives; L, antineoplastic and sex hormones; M, musculoskeletal system; N, nervous system; R, respiratory system; V, remaining drugs.

Figure 3.

Cumulative PRR for SVT under each ATC classification system. SVT, supraventricular tachycardia; PRR, proportionate reporting ratio; ATC, Anatomical Therapeutic Chemical Classification Coding System.

Evaluation of SVT-related drugs

The SIDER database lists 80 drugs that might cause SVT, including decitabine, aprotinin, daptomycin, alprostadil, aminophylline, and others. The Hangzhou Yiyao Rational Drug Use System also lists numerous drugs that might cause SVT, such as theophylline, sotalol, verapamil, digoxin, amiodarone, and others. Adenosine, theophylline, flecainide, sotalol, verapamil, propofol, digoxin, amiodarone, Avandamet, and rofecoxib were the 10 drugs that had the strongest association with SVT (Table 3), based on PRR values of drugs that might cause SVT obtained from the OpenVigil database.

Table 3.

Ten leading drugs having the strongest associations with SVT.

Drug	DE	PRR	ROR	In the SIDER database	In the Rational Drug Use System	In the published literature	ATC classification
adenosine	50	122.928	129.232	No	No	No	C01EB10
flecainide	60	20.387	20.546	No	No	No	R03DA04
sotalol	45	13.868	13.94	No	Yes	No	C01BC04
verapamil	97	11.668	11.718	No	Yes	No	C07AA07
propofol	54	10.801	10.843	Yes	No	Yes	C08DA01
digoxin	138	10.8	10.842	No	Yes	No	N01AX10
amiodarone	115	9.036	9.065	No	Yes	No	C01AA05
Avandamet	30	8.929	8.957	No	Yes	No	C01BD01
rofecoxib	248	8.098	8.1205	No	No	No	A10BD03
midazolam	33	7.534	7.554	No	Yes	No	M01AH02

SVT, supraventricular tachycardia; PRR, proportionate reporting ratio; ROR, reporting odds ratio; SIDER, Side Effect Resource; ATC, Anatomical Therapeutic Chemical Classification Coding System.

Salbutamol, paroxetine, formoterol, paclitaxel, venlafaxine, and theophylline medications listed in the OpenVigil database, SIDER database, and Hangzhou Yiyao Rational Drug Use System, and also mentioned in the pertinent literature (Table 4).

Table 4.

Drugs listed in all data sources.

Drug	DE	PRR	ROR	In the SIDER database	In the rational drug use system	In the published literature	ATC classification
salbutamol	218	2.9975	3.0005	Yes	Yes	Yes	R03AC02
paroxetine	122	3.718	3.722	Yes	Yes	Yes	N06AB05
formoterol	94	2.293	2.2945	Yes	Yes	Yes	R03AC13
paclitaxel	79	4.007	4.012	Yes	Yes	Yes	L01CD01
venlafaxine	72	2.434	2.435	Yes	Yes	Yes	N06AX16
theophylline	54	22.14	22.329	Yes	Yes	Yes	R03DA04

PRR, proportionate reporting ratio; ROR, reporting odds ratio; SIDER, Side Effect Resource; ATC, Anatomical Therapeutic Chemical Classification Coding System.

Discussion

With rapid advancement in science and technology, a growing number of new medications are being developed and widely used in clinical settings. It is noteworthy that reports of adverse drug reactions associated with SVT are increasing annually. ²¹ However, awareness among clinical staff about drug-derived SVT is far from adequate. Early identification and advance diagnosis of SVT will facilitate the provision of necessary treatment to enhance patient outcomes, minimize hospitalization, and lessen the burden of patient care. ²² However, no studies have investigated relevant medications that can result in SVT.

To gain a better understanding of drugs related to adverse reactions, researchers and academics have recently focused on monitoring adverse drug events through adverse reaction reporting systems. For this, several types of detection methods with different characteristics are in use, including the ORR method, PRR method, and comprehensive standard method. ²³ The ORR method and PRR method are the two analytical techniques used in the OpenVigil database. Both exhibit strong agreement when analyzing data on adverse drug reactions and can be used to effectively investigate medications that may cause significant adverse responses. ²⁴ However, data mining is only used as a means of discovering suspicious signals. Further analysis and evaluation through rigorous in vivo and in vitro experiments, as well as thorough medical evaluations, are necessary.^25,26

A total of 2435 drugs were found to have caused SVT, according to data extracted from the OpenVigil database. A total of 22,375 adverse events of SVT were reported. Thirty drugs had the greatest number of reported cases (Table 2) according to the Medicines and Healthcare products Regulatory Agency signal detection criteria in the proportional imbalance method:^27,28 PRR ≥ 2, χ² > 4 and a ≥ 3, where “a” is the number of reported cases. The findings demonstrated that all 30 medications met these criteria.

After the drugs were screened, 93 drugs were ultimately obtained. We checked the ATC codes of these drugs against the WHO ATC/DDD Index database, and categorized the drugs in accordance with the ATC codes. On this basis, we determined that the five drugs most likely to cause SVT (in order) were medications used to treat respiratory system disorders, the alimentary tract and metabolism, nervous system disorders, disorders involving antineoplastic and sex hormones, and cardiovascular system disorders. The 10 medications with the greatest risk of causing SVT were further investigated and rated (Table 3) based on their PRR value. We found that 6 of the 10 leading high-risk drugs (adenosine, flecainide, sotalol, verapamil, digoxin, and amiodarone) were medications for the cardiovascular system. The remaining medications included one each for the respiratory system, musculoskeletal system, nervous system, and alimentary tract and metabolism, as shown in Table 3 and Figure 3. Anti-arrhythmic medications may be related to a high frequency of adverse reactions, such as ventricular tachycardia, atrioventricular block, bradycardia, ventricular fibrillation, and prolongation of the QT interval, as well as a variety of extra-cardiac side effects such as pigmentation, nausea, vomiting, modified thyroid function, pulmonary interstitial fibrosis, and corneal pigmentation.^29,30 Drugs used to treat arrhythmias may cause new arrhythmias or may be linked to concurrent drug use, which raises the risk of SVT. ³¹ Furthermore, the reporting of adverse reactions adheres to the principle of reporting when suspected. Therefore, additional verification is required before definitive conclusions can be drawn.^32–34

Using the OpenVigil database, we identified 10 medicines with a high risk of leading to SVT. These 10 drugs were also identified in our searches of the Hangzhou Yiyao Rational Drug Use System, SIDER database, and scientific literature databases. Only one instance involving each drug has been documented in the literature. In a case study, a 36-year-old female patient received 1.5 mg/kg of propofol intravenously for sedation. On the seventh day of hospitalization, an ECG revealed SVT. Following a comprehensive evaluation of the patient’s condition, the propofol dosage was gradually reduced until it was discontinued; the patient’s ECG returned to normal within 72 hours. ³⁵ The exact mechanism of action of propofol-induced SVT requires further research. However, relevant studies have demonstrated that early detection and prevention are effective methods for preventing this condition. ³⁶

Salbutamol, paroxetine, formoterol, paclitaxel, venlafaxine, and theophylline (three respiratory drugs, two neurological drugs, and one antitumor and immunomodulatory drug, respectively) were identified in the OpenVigil database, SIDER database, and Hangzhou Yiyao Rational Drug Use System, and also reported in the relevant literature. Salbutamol was identified as a risk factor for SVT through conditional logistic regression analysis in a retrospective study that included patients using the medication from 2006 to 2015. The most frequent side effects of salbutamol were reflex cardiac stimulation brought on by peripheral vasodilatation, direct stimulation of the atrial β₂ receptor, and myocardial β₁ receptor-induced tachycardia. It should also be noted that high doses of salbutamol are a risk factor for the induction of SVT. ³⁷ A phase III randomized, double-blind, crossover, active-controlled study was conducted to assess the allosteric effects of formoterol in patients aged 15 to 65 years with persistent asthma. The research aimed to identify and compare adverse drug reactions. It was found that 13% of patients experienced SVT, which occurred through a mechanism similar to that of salbutamol. ³⁸

According to research, theophylline has a narrow therapeutic window and is prone to causing severe side effects, including SVT. An older patient with SVT was taken to the hospital and found to be taking theophylline (200 mg, twice a day) for chronic obstructive pulmonary disease. An ECG revealed that the patient’s SVT ventricular rate was 211 beats per minute. Clinicians used mechanical circulatory assistance to stabilize the patient’s hemodynamics. The patient’s systemic condition improved as a result of the hemodynamic stabilization enabled by mechanical circulatory support and continuous hemodialysis filtration to remove theophylline, which was associated with the drug’s ability to improve atrial arrhythmia and intracardiac conduction. ³⁹ Toxic or even usual therapeutic doses of theophylline or other derivatives of xanthines have the potential to trigger tachyarrhythmias in susceptible patients with preexisting arrhythmogenic substrate, as in this particular case.

In the liver, cytochrome P450 oxidase (CYP2D6) breaks down paroxetine, a potent and specific inhibitor of 5-hydroxytryptamine reuptake, and excretes it in the urine. One study demonstrated that treatment with paroxetine (10 mg, once a day) in older female patients with renal insufficiency resulted in adverse effects such as SVT, which subsided within a week of ceasing the medication. The exact mechanism was related to the medication’s impact on serotonin, and the risk of adverse effects is higher in patients with renal insufficiency. ⁴⁰

The microtubule toxicant paclitaxel, originally discovered and extracted from the coast redwood tree, promotes microtubule assembly and inhibits depolymerization by binding to microtubule protein subunits. According to one report, a patient with breast cancer developed cardiotoxicity, including the onset of SVT, after receiving systemic chemotherapy with paclitaxel. Her condition was significantly relieved with prompt symptomatic treatment. ⁴¹ Venlafaxine is a serotonergic and noradrenergic antidepressant, sharing the same 5-hydroxytryptaminergic side effects as selective 5-hydroxytryptamine (serotonin) reuptake inhibitor (SSRI) antidepressants. SSRIs can also cause noradrenergic side effects, particularly cardiovascular disorders; however, it has not been demonstrated that venlafaxine is more effective than SSRIs. An analysis of hundreds of reports of suicide attempts via venlafaxine overdose, as well as a cohort study of 50 older patients, showed that venlafaxine overdose carries a risk of prolonging the QT interval. The side effect of QT interval prolongation is well known in SSRIs and can potentially trigger torsades de pointes, a potentially fatal tachyarrhythmia. Torsades de pointes is a particular form of polymorphic ventricular tachycardia, but not SVT. Nevertheless, lengthening of the QT interval can result in potentially lethal SVT. ⁴²

This study has several limitations. The OpenVigil database is a spontaneous presentation system database that is derived from actual clinical medication use. However, the database may be biased by a number of confounding factors; identification of drugs that may cause SVT in the OpenVigil database does not indicate a drug–adverse event causal association.^43,44 Additionally, in our study, drugs found in the OpenVigil database that could potentially cause SVT were further assessed using the Hangzhou Yiyao Rational Use System, SIDER database, and several research literature databases. However, the aforementioned sources have a certain lag time, such as lengthy update time of drug specifications. Moreover, only 80 results were queried using the SIDER database, indicating that the results of our research include some bias.^45–49 Other limitations mentioned earlier in this section include the study results being limited owing to a lack of specific in vivo and in vitro research, which prevents us from establishing a definitive causal relationship between the drugs identified in the database and adverse events. Given that this study was limited to computer simulations, our findings require validation through both in vivo and clinical trials. In vivo experimental studies on the medications most likely to cause SVT using animal models can clarify the drugs’ mechanisms of action, with clinical samples analyzed to corroborate the findings. Finally, there may be inaccuracies in the data owing to the reporting groups, which include physicians, nurses, pharmacists, and the general public; further validation of the present results is necessary.

Despite the above limitations, using the mature OpenVigil database for adverse drug event signal detection is inexpensive and can be used to quickly mine important information from massive data as well as evaluate signals in combination with rigorous medical assessment. This approach can provide informational support for ensuing related research in numerous disciplines, such as clinical medicine, pharmacy, and epidemiology, and can be used in preventing adverse events as well as early warning when administering these SVT-related medications in the clinic.

Conclusion

In this research, we identified drugs that may cause SVT using the OpenVigil database and presented relevant pharmacological data in a comprehensive manner. It is essential to be aware of symptoms such as palpitations, chest tightness, shortness of breath, and sweating when administering medications to patients, and to regularly improve laboratory tests such as ECGs. This will help to identify the risk of SVT at an early stage and enable the implementation of appropriate interventions to prevent the progression of SVT and minimize adverse clinical outcomes.

Availability of data and materials

The data used to support the findings of this research are available from the corresponding author upon request. All raw data in this research can be found in the following online database: OpenVigildatabase (https://openvigil.sourceforge.net/).