Prevalence of the QT interval prolongation and its risk factors in hospitalized geriatric patients: findings of a single center cross-sectional study in Pakistan

Abstract

Background and objectives

Geriatric inpatients are particularly susceptible to Torsades de Pointes (TdP) because they are usually polymorbid and often take QT-prolonging drugs. Since polypharmacy is common in hospitalized geriatrics, it may lead to QT-prolonging Drug-Drug interactions, resulting in adverse cardiac events. Additionally, a significant portion of geriatric patients likely have a combination of other risk factors such as heart failure, hypertension, left ventricular hypertrophy, myocardial infarction, ischemic heart disease, bradycardia, diabetes mellitus, and electrolyte imbalances. However, there is a lack of published data regarding the prevalence of risk factors for QT interval prolongation in this population. This study aimed to determine the prevalence of QT interval prolongation and its risk factors among hospitalized geriatric patients, shedding light on potential contributors to this life-threatening condition in a vulnerable population.

Methods

This cross-sectional study was conducted at Ayub Teaching Hospital, Abbottabad from December 17, 2023, to May 9, 2024. During this study, 384 patients aged 65 years and older were analyzed. Various QT-prolonging medications were assessed using the CredibleMeds^® database, while drug-drug interactions were evaluated using the Lexicomp interactions database. Logistic regression was used to identify the predictors of QT interval prolongation.

Results

Our study found that QT prolongation was more common in females (50.8%) than in males (49.2%). Among these patients, 60.2% presented with six QT-prolonging risk factors. Overall, QT-prolonging drugs were prescribed to 99.5% of patients. A total of 970 QT-prolonging drugs were identified, with the majority (70.4%) carrying a conditional risk of Torsades de Pointes. The most frequently prescribed category of QT-prolonging drugs was diuretics, accounting for 228 instances. QT-prolonging Drug-Drug Interactions were identified in 23.2% of patients. Statistically significant differences were found between the two groups (Prolonged QT interval vs. normal QT interval) in various factors such as all DDIs (p = 0.008), triglycerides (p = 0.03), ischemic heart disease (p = 0.02), myocardial infarction (p = 0.01), antimicrobials (p = 0.004), anti-emetics (p = 0.01), and analgesics (p = 0.05). Univariate analysis showed a statistically significant association of QT interval prolongation with 6–10 DDIs (p = 0.03); 11–15 DDIs (p = 0.001), > 15 DDIs (p = 0.01), ischemic heart disease (p = 0.02), myocardial infarction (p = 0.01), antimicrobials (p = 0.04), and antiemetic’s (p = 0.01). In multivariate analysis, a statistically significant association of QT interval prolongation was found with 11–15 DDIs (p = 0.03).

Conclusion

This study identified a high prevalence of various risk factors for QT interval prolongation. When prescribing medications to this patient population, clinicians should conduct comprehensive medication reviews, regularly monitor the QT interval, and consider alternative therapies. Educating patients on medication risks and adherence to monitoring is crucial for early detection and reporting of adverse effects.

Background

Age-related changes, many morbidities, and polypharmacy all play a role in the complicated issue of QT interval lengthening in elderly populations. It has important clinical ramifications, such as an elevated risk of arrhythmias, falls, and sudden cardiac death. The lengthening of QT interval in geriatrics is a serious issue due to its correlation with the onset of severe cardiovascular events and sudden cardiac death [1] Patients who present with sudden cardiac death typically have abrupt loss of consciousness or unexpected death. Ventricular arrhythmias, which may be linked to a lengthening of the QT interval, are responsible for the majority of these deaths [2]. Torsade de Pointes, an erratic heartbeat that may result in life-threatening ventricular arrhythmias and abrupt cardiac death, is one possible consequence of a prolonged QT interval [3].

The yearly mortality rate due to ventricular tachyarrhythmia’s leading to sudden cardiac death (SCD) is estimated to be around 6 million worldwide. In developed nations, SCD is thought to be the cause of 20% of fatalities [4]. A significant issue in geriatrics is multimorbidity, as age-related modification of organs and systems frequently results in older people having many chronic illnesses or geriatric disorders [5]. The simultaneous presence of two or more chronic diseases in one person is referred to as multimorbidity. Rates among the elderly range from 12.9 to 95.1%, indicating a significant prevalence [6].

Multimorbidity is a major healthcare burden because of its complexity and effect on patient outcomes, particularly higher mortality in the elderly due to poor health outcomes. When treating geriatric multimorbidity, factors such as polypharmacy must be taken into account [5]. Polypharmacy is a serious issue and is quite prevalent in the elderly population. Providing older population with high-quality care requires proactive measures to maximize drug utilization, minimize improper prescribing, and enhance medication safety [7]. QT-prolonging medications are mainly provided to elderly patients who are hospitalized. Several medications may result in TdP. For both cardiovascular and non-cardiovascular purposes, these medications are frequently utilized. A higher chance of encountering possible drug-drug interactions is associated with polypharmacy [8]. Age-related alterations in medication pharmacokinetics, polypharmacy, and greater prevalence of comorbidities in older individuals increase the susceptibility to DDIs that result in QT prolongation. The prevention of this risk in older persons requires careful drug assessment, dosage adjustment, and ECG monitoring [9]. Several risk variables are independently linked to the usage of QT-prolonging medications in elderly patients who are hospitalized. One of the many established indicators of risk for the onset of TdP linked to QT-prolonging medication usage is advanced age [8]. Medical elder inpatients are especially susceptible to TdP because they are often polymorbid and take several medicines, particularly QT-prolonging medications, to manage comorbidities [10].

The studies addressing this particular problem were carried out in the past. However, many studies lacked specific information on the methods that were used to acquire the data. The studies employed a single criterion for risk assessment and paid less attention to other possible risk variables mainly concentrated on identifying drugs with known or potential QT-prolonging effects, but did not provide a thorough assessment of individual patient risk factors, such as electrolyte imbalances, cardiac conditions already present, or other pertinent factors that could influence the possibility of lengthening of QT interval and its consequences, including fatal arrhythmia and abrupt cardiac demise. QT interval prolongation in older populations is a complex problem with important clinical consequences. Understanding the risk factors and management techniques linked to QT interval prolongation is crucial for maximizing the care and safety of older population as the aged population continues to rise. However, our understanding of this topic needs to be advanced by more studies to direct evidence-based clinical treatment. Our study aimed to evaluate the prevalence of QT interval prolongation and its associated risk factors in hospitalized geriatric patients.

Methods

Study design and setting

This analytical cross-sectional study was carried out at a tertiary care hospital in Abbottabad from December, 2023, to May, 2024 to determine the prevalence of QT interval prolongation and its associated risk factors in the hospitalized geriatric patients. It is the only tertiary care hospital complex located in Hazara Division, Khyber Pakhtunkhwa, which covers the majority of the patients of all Hazara regions.

Ethical considerations

The study was carried out after getting ethical approval from the Institutional Ethical Review Committee of Medical Teaching Institute (MTI), Ayub Teaching Hospital (ATH), Abbottabad vide reference number RC-EA-2023/172. Written informed consent was obtained from all participating patients and/or their attendants. Participants were informed about the study and were assured about the confidentiality of their data.

Sample source and time-frame

Geriatric patients admitted in Cardiology, CCU, and Internal Medicine (A, B, C, & D) at Medical Teaching Institute (MTI), Ayub Teaching Hospital (ATH), Abbottabad were constituted as a sample source. Data collection was conducted from 17th December 2023 to 9th May 2024. Data from 384 patients was obtained. Non-probability consecutive sampling technique was used.

Data collection

Following data such as age, gender, ECG, diagnosis, comorbid illnesses, a complete list of medications, serum electrolyte levels, and other lab data of study participants, were obtained from medical records of the patients and documented in the data collection form.

Inclusion criteria

• Both genders

• Age > 65 years

• ECG report

Exclusion criteria

• Age less than 65 years

• Patients without ECG report

• Patients hospitalized for less than 24 h

• Patients with incomplete medical records

Data analysis

CredibleMeds^®

Various categories of QT interval prolonging medications were identified by using an online database called CredibleMeds^®. The CredibleMeds^® encourages the safe utilization of medications. Its beneficial influence on clinical prescription makes it extremely valuable. This database and many tools are maintained by AZCERT, which is primarily recognized for the list of QT Drugs having a susceptibility to prolonging the QT segment as well as TdP, a potentially fatal cardiac arrhythmia, TdP [11].

ATC codes

ATC codes at level five were used for the identified QT-prolonging Drugs. The active components in drugs are classified using the Anatomical Therapeutic Chemical (ATC) Classification System, which also takes into account the chemical, pharmacological, and therapeutic qualities of the medications as well as the organ or system on which they function. It serves as a tool for research to enhance the quality of medicine usage as well as for drug use monitoring. This is under the jurisdiction of the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC) [12].

ECG evaluation

A standard 12-lead ECG was performed for each patient following hospital admission, though the exact timing post-admission varied and was not specifically recorded. QT intervals were measured by the lead researcher and later on independently validated by a cardiologist and blinding to individual patient treatment and clinical details was maintained throughout the study period. Inconsistencies were resolved through consensus discussion. This two-tier process ensured both accuracy and objectivity of the collected data. QT interval using standard QTc cutoffs (> 450 milli seconds for males and > 470 milli seconds for females) consistent with established clinical guidelines [13].

Fridericia formula

The measured QT intervals were adjusted for heart rate using Fredericia’s formula [14]. Fridericia formula is expressed as:

• QTc = QT/∛RR

Lexicomp^® database

To assess the frequency of potential drug-drug interactions (DDIs) in geriatric patients who are hospitalized, the Lexicomp^® database was utilized for the evaluation of potential drug-drug interactions (DDIs) [15]. The detected potential DDIs were categorized based on reliability rating, severity, and interaction risk ratings as follows:

Reliability rating

Shows how much and what kind of documentation there is for a certain interaction. Reliability rating is further classified as:

• Poor

• Fair

• Good

• Excellent

Severity rating

Showed the extent of the consequence of an interaction and informs regarding what has to be done in response to an encounter and how urgent it is. The severity rating is further classified as:

• Major: This interaction might be fatal or result in irreversible harm. The consequences might include hospitalization, therapy failure, death, or lifelong harm.

• Moderate: The combination might cause the patient’s condition to worsen. It might be necessary to take extra precautions. Medical intervention is required to address the consequences.

• Minor: An encounter that is disagreeable yet not harmful to health overall. In most circumstances, the effects would be deemed bearable; no medical intervention would be necessary.

There are further risk assessment categories of risk rating: A, B, C, D, or X. The need to respond to the facts with greater urgency increases as one moves from A to X. Each risk rating has the following definition:

• A: No known interaction

  Pharmacokinetic and pharmacodynamic interactions are not supported by any evidence.

• B: No action needed

  While there is little to no clinical data to support the possibility of medication interactions, evidence suggests that they may occur.

• C: Monitor therapy

  There is evidence that the two medications might interact in a clinically important way. When these two drugs are used together, the advantages typically exceed the disadvantages. The implementation of a suitable monitoring strategy is necessary to prevent any unfavorable consequences.

• D: Consider therapy modification

  There is evidence to support the possibility of a clinically meaningful interaction between the two drugs. To reduce the toxicity brought on by taking the drugs at the same time, specific steps must be taken.

• X: Avoid combination

  There is therapeutic importance to the way the two medications interact. Combining these medications at the same time usually has more dangers than benefits and is generally not advised [15].

Statistical analysis

All statistical analyses were conducted using SPSS (IBM SPSS Statistics 23) software. Continuous variables were expressed as means and standard deviations, while categorical variables were reported as frequencies and percentages. Group comparisons were performed using t-tests and chi-square tests. Logistic regression was used to identify the predictors of QT interval prolongation.

Results

Patients’ characteristics

A total of 384 patients were included in the study. Among these, 195 (50.8%) were female and 189 (49.2%) were male. The majority of the patients (46.6%) were within the age range of 65 to 70 years. About the usage of QT-prolonging medications, 55.7% of the patients were prescribed 1–2 drugs, 34.9% were using 3–4 drugs concomitantly, and 8.9% were taking more than 4 drugs. In terms of QT drug-drug interactions (QT DDIs), 14.8% of the patients encountered one QT DDI, while more than one QT DDI was present in 8.6% of the patients. Additionally, 46.4% of the patients had six risk factors, whereas greater than six risk factors were present in 53.6% of the patients. The most frequently observed comorbidity was hypertension, present in 78.6% of the patients, followed by ischemic heart disease (46.1%), diabetes mellitus (35.1%), myocardial infarction (29.1%), heart failure (16.7%), chronic kidney disease (15.4%), lower respiratory tract infection (12%), stroke (12%), cardiomyopathy (11.7%), chronic obstructive pulmonary disease (8.3%), pneumonia (3.9%), atrial fibrillation (6%), and sepsis (3.1%), as shown in Table 1.

Table 1.

Patients’ characteristics

Variables	n (%)*
Gender
Female	195 (50.8)
Male	189 (49.2)
Age categories
65–70	179 (46.6)
71–75	77 (20.1)
76–80	72 (18.8)
> 80	56 (14.6)
QT-prolonging medications prescribed
1–2	214 (55.7)
3–4	134 (34.9)
> 4	34 (8.9)
QT-prolonging drug-drug interactions
1	57 (14.8)
> 1	33 (8.6)
QT risk factors
6	178 (46.4)
> 6	206 (53.6)
Diagnoses
Hypertension	302 (78.6)
Ischemic heart disease	177 (46.1)
Diabetes mellitus	135 (35.1)
Myocardial infarction	112 (29.1)
Heart failure	64 (16.7)
Chronic kidney disease	59 (15.4)
Lower respiratory tract infection	46 (12)
Stroke	46 (12)
Cardiomyopathy	45 (11.7)
Chronic obstructive pulmonary disease	32 (8.3)
Pneumonia	25 (6.5)
Atrial fibrillation	23 (6)
Sepsis	12 (3.1)

*The percentages were calculated in total number of patients, i.e., n = 384

QT-prolonging risk factors

Figure 1 depicts the prevalence of various risk factors linked with QT lengthening. Advanced age was the most prevalent risk variable for QT prolongation (100%). More pertinently, 99.5% (n = 382 out of 384) of the study sample were taking ≥ 1 QT-extending medications. Other frequent risk variables for QT prolongation in our study population were hypertension 78.6%, female gender 50.8%, ischemic heart disease 46.1%, diabetes mellitus 35.2%, myocardial infarction 29.2%, presence of ≥ 1 QT-prolonging DDIs 23.2%, heart failure 16.7%, chronic kidney disease15.4%.

Fig. 1.

Prevalence of the risk factors for QT interval prolongation

The frequencies of QT-prolonging medications

A total of 970 QT-prolonging medications were identified in this study. The most commonly given category of QT lengthening medications was diuretics, accounting for 228 instances. Among diuretics, furosemide was the most commonly used, constituting 20.5% of the prescriptions. The second most commonly prescribed group was antimicrobials, with 202 instances. Within this category, metronidazole was the most frequently used (5.2%), followed by piperacillin-tazobactam (4.4%) and azithromycin (4.2%). Antiemetics were also commonly prescribed, with 178 instances noted. Metoclopramide emerged as the most frequently used antiemetic, representing 13.7% of the prescriptions. Proton pump inhibitors constituted another significant class of drugs, with 163 instances recorded. Omeprazole was the predominant drug in this category, comprising 16.5% of the prescriptions. Among analgesics, which accounted for 84 instances, tramadol was the most widely prescribed, representing 8.7% of the analgesic prescriptions, as illustrated by Table 2.

Table 2.

The frequencies of QT-prolonging medications concerning their therapeutic class, generic, ATC codes, AZCERT risk class

Therapeutic Class	Generic	ATC Code	Risk Category	n (%)*
	Furosemide	C03CA01	Conditional Risk of TdP	199 (20.5)
Diuretic (n = 228)	Metolazone	C03BA08	Conditional Risk of TdP	21 (2.2)
	Hydrochlorothiazide	C03AA03	Conditional Risk of TdP	6 (0.6)
	Indapamide	C03BA11	Conditional Risk of TdP	2 (0.2)
	Metronidazole	J01XD01	Conditional Risk of TdP	50 (5.2)
	Piperacillin + Tazobacatam	J01CR05	Conditional Risk of TdP	43 (4.4)
	Azithromycin	J01FA10	Known Risk of TdP	41 (4.2)
	Moxifloxacin	J01MA14	Known Risk of TdP	27 (2.8)
	Clarithromycin	J01FA09	Known Risk of TdP	17 (1.8)
Antimicrobial (n = 202)	Levofloxacin	J01MA12	Known Risk of TdP	8 (0.8)
	Fluconazole	J02AC01	Known Risk of TdP	7 (0.7)
	Ciprofloxacin	J01MA02	Known Risk of TdP	5 (0.5)
	Moxifloxacin	J01MA14	Known Risk of TdP	1 (0.1)
	Clarithromycin	J01FA09	Known Risk of TdP	1 (0.1)
	Voriconazole	J02AC03	Conditional Risk of TdP	1 (0.1)
	Artemether + Lumefantrine	P01BF01	Possible Risk of TdP	1 (0.1)
	Metoclopramide	A03FA01	Conditional Risk of TdP	133 (13.7)
Antiemetic (n = 178)	Ondansetron	A04AA01	Known Risk of TdP	33 (3.4)
	Domperidone	A03FA03	Known Risk of TdP	12 (1.2)
	Omeprazole	A02BC01	Conditional Risk of TdP	160 (16.5)
Proton pump inhibitor (n = 163)	Pantoprazole	A02BC02	Conditional Risk of TdP	2 (0.2)
	Esomeprazole	A02BC05	Conditional Risk of TdP	1 (0.1)
Analgesic (n = 84)	Tramadol	N02AX02	Possible Risk of TdP	84 (8.7)
Antianginal (n = 35)	Ranolazine	C01EB18	Conditional Risk of TdP	19 (2)
	Ivabradine	C01EB17	Known Risk of TdP	16 (1.6)
Antiarrhythmic (n = 29)	Amiodarone	C01BD01	Known Risk of TdP	29 (3)
	Haloperidol	N05AD01	Known Risk of TdP	15 (1.5)
	Quetiapine	N05AH04	Conditional Risk of TdP	5 (0.5)
Antipsychotic (n = 24)	Thioridazine	N05AC02	Known Risk of TdP	2 (0.2)
	Chlorpromazine	N05AA01	Known Risk of TdP	1 (0.1)
	Risperidone	N05AX08	Conditional Risk of TdP	1 (0.1)
	Escitalopram	N06AB10	Known Risk of TdP	2 (0.2)
	Fluoxetine	N06AB03	Conditional Risk of TdP	2 (0.2)
Antidepressant (n = 8)	Sertraline	N06AB06	Conditional Risk of TdP	1 (0.1)
	Dextromethorphan	R05DA09	Possible Risk of Tdp	1 (0.1)
	Amitriptyline	N06AA09	Conditional Risk of TdP	1 (0.1)
	Mirtazapine	N06AX11	Possible Risk of TdP	1 (0.1)
Antihistamines (n = 7)	Famotidine	A02BA03	Conditional Risk of TdP	4 (0.4)
	Diphenhydramine	D04AA32	Conditional Risk of TdP	3 (0.3)
Antihypertensive (n = 5)	Diltiazem	C08DB01	Conditional Risk of TdP	5 (0.5)
Opioid agonist (n = 4)	Loperamide	A07DA03	Condtional Risk of TdP	4 (0.4)
Anti-seizure (n = 3)	Levetiracetam	N03AX14	Possible Risk of TdP	3 (0.3)

*The percentages were calculated in the total number of QT-prolonging medications i.e., n = 970.

Risk classification of QT-prolonging medications

Figure 2 demonstrates the risk classification of QT-extending medications. 20.6% of medications were included in list-1 (known risk of TdP), 9.0% in list-2 (possible risk of TdP), and 70.4% in list-3 (conditional risk of TdP) of the AZCERT risk categorization.

Fig. 2.

The AZCERT risk classification of QT-prolonging drugs

The frequencies of potential QT DDIs

A total of 160 potential QT DDIs were identified in our study population. Table 3 shows the pairs that interact the most frequently were metoclopramide – ondansetron 6.3% (n = 10), azithromycin – metoclopramide 6.3% (n = 10) followed by metronidazole – ondansetron 4.4% (n = 7), amiodarone – metoclopramide 3.8% (n = 6), azithromycin – metronidazole 3.8% (n = 6), haloperidol – salbutamol 2.5% (n = 4), domperidone – metoclopramide 2.5% (n = 4).

Table 3.

The most frequent pairs (n ≥ 2) of potential QT DDIs

DDIS	Risk Rating	Severity	Reliability Rating	n (%)*
Metoclopramide-Ondansetron	B	Minor	Fair	10 (6.3)
Azithromycin-Metoclopramide	B	Minor	Fair	10 (6.3)
Metronidazole-Ondansetron	B	Minor	Fair	7 (4.4)
Amiodarone-Metoclopramide	C	Moderate	Fair	6 (3.8)
Azithromycin-Metronidazole	B	Minor	Fair	6 (3.8)
Haloperidol-Salbutamol	C	Moderate	Fair	4 (2.5)
Domperidone-Metoclopramide	B	Minor	Fair	4 (2.5)
Moxifloxacin-Salbutamol	B	Minor	Fair	3 (1.9)
Metoclopramide-Moxifloxacin	B	Minor	Fair	3 (1.9)
Haloperidol-Metronidazole	C	Moderate	Fair	3 (1.9)
Azithromycin-Budesonide and Formoterol	B	Minor	Fair	3 (1.9)
Azithromycin-Salbutamol	B	Minor	Fair	3 (1.9)
Azithromycin-Moxifloxacin	C	Moderate	Fair	3 (1.9)
Azithromycin-Metronidazole	B	Minor	Fair	3 (1.9)
Amiodarone-Budesonide and Formoterol	C	Moderate	Fair	3 (1.9)
Clarithromycin-Salbutamol	B	Minor	Fair	3 (1.9)
Moxifloxacin-Salbutamol	B	Minor	Fair	3 (1.9)
Amiodarone-Moxifloxacin	X	Major	Fair	2 (1.3)
Amiodarone-Metronidazole	C	Moderate	Fair	2 (1.3)
Metronidazole-Moxifloxacin	B	Minor	Fair	2 (1.3)
Metoclopramide-Moxifloxacin	B	Minor	Fair	2 (1.3)
Haloperidol-Quetiapine	C	Moderate	Fair	2 (1.3)
Domperidone-Metoclopramide	B	Minor	Fair	2 (1.3)
Azithromycin-Salbutamol	B	Minor	Fair	2 (1.3)
Azithromycin-Domperidone	D	Moderate	Fair	2 (1.3)
Escitalopram-Moxifloxacin	C	Moderate	Fair	2 (1.3)
Levofloxacin-Salbutamol	B	Minor	Fair	2 (1.3)
Levofloxacin-Terbutaline	C	Moderate	Fair	2 (1.3)
Levofloxacin-Metronidazole	B	Minor	Fair	2 (1.3)

*The percentages were calculated in the total number of potential QT-DDIs, i.e., n = 160

The classification of potential QT DDIs based on severity, reliability, and risk ratings

Figure 3 shows the distribution of different categories within three distinct variables: Reliability Rating, Severity, and risk rating. In terms of Reliability Rating, the categories are “Good,” and “Fair, with the “Fair” category dominating at 99.4% (n = 159) and “Good” at a minimal 0.6% (n = 1). This indicates a significant skew towards the “Fair” rating. Regarding Severity, the categories “Minor”, “Moderate” and “Major” are depicted, with “Minor” significantly higher at 63.1% (n = 101) compared to “Moderate” at 33.1% (n = 53) and “Major” at 3.8% (n = 6). This suggests that minor severity cases were far more prevalent. Lastly, the severity rating shows “B” as the most frequent at 63.1% (n = 101), followed by “C” at 30.6% (n = 49), and both “X” and “D” at 3.1% (n = 5).

Fig. 3.

The severity, reliability, and risk ratings of potential QT-DDI

The comparison of patients with prolonged QT interval and normal QT interval concerning various clinical and lab parameters is illustrated in Table 4. Significant differences were observed between the two groups (normal QT vs. prolonged QT) concerning all potential DDIs (p = 0.008), and triglycerides (p < 0.03).

Table 4.

Comparison of two patients’ groups (prolonged QT vs. normal QT intervals) concerning various clinical and lab parameters

Variables	No QT prolongation	QT prolongation	p value*
	Mean ± SD	Mean ± SD
Age	73.7 ± 8.6	73.6 ± 0.5	0.9
All prescribed medications	11.9 ± 4.3	12.2 ± 3.9	0.5
All potential DDIs	11.5 ± 10.2	14.9 ± 12.7	0.008
Potential QT-DDIs	0.5 ± 1.2	0.4 ± 0.9	0.5
QT-prolonging drugs	2.6 ± 1.6	2.5 ± 1.4	0.5
Overall QT-prolonging risk factors	6.1 ± 1.4	6.2 ± 1.3	0.4
Random blood sugar	216.7 ± 114	213.6 ± 117.5	0.8
Blood urea nitrogen	70.1 ± 63.6	61.4 ± 46.6	0.2
Serum creatinine	2.1 ± 6.6	1.5 ± 1.3	0.3
Serum bilirubin	1.3 ± 3.7	0.8 ± 0.8	0.07
Alanine transaminase	54.8 ± 108	63.1 ± 200.5	0.6
Alkaline phosphatase	122.6 ± 66.5	140.6 ± 92.2	0.7
Serum potassium	4.1 ± 0.8	4.3 ± 0.8	0.06
Serum calcium	8 ± 0.8	8 ± 1	0.8
Triglycerides	2.3 ± 0.1	104 ± 33.6	0.03
Serum albumin	3.1 ± 0.7	3.1 ± 0.5	0.9
Uric acid	6.9 ± 2.4	7.4 ± 2.4	0.6
HbA1C	7 ± 2.4	7.4 ± 2.4	0.8
Triiodothyronine (T3)	1.1 ± 0.2	1.5 ± 2.2	0.7
Tetraiodothyronine (T4)	25.2 ± 35.2	46.6 ± 93.5	0.7
C-reactive protein (CRP)	79.4 ± 67.9	111.2 ± 159.6	0.2

**p values were calculated using t-tests

Table 5 illustrates the comparison of patients with prolonged QT intervals and normal QT intervals concerning diagnoses and QT-prolonging medications. Significant differences were observed between the two groups (normal QT vs. prolonged QT) with respect to ischemic heart disease (p = 0.02), myocardial infarction (p = 0.01), anti-microbial (p = 0.004), anti-emetics (p = 0.01) and analgesics (p = 0.05).

Table 5.

Comparison of two patients’ groups (prolonged QT vs. normal QT intervals) concerning diagnoses and QT-prolonging medications

Variables	No QT prolongation	QT prolongation	p value*
	n (%)**	n (%)**
Hypertension	112 (37.1)	190 (62.9)	0.9
Diabetes mellitus	44 (32.6)	91 (67.4)	0.2
Ischemic heart disease	55 (31.1)	122 (68.9)	0.02
Myocardial infarction	31 (27.7)	81 (72.3)	0.01
Heart failure	21 (32.8)	43 (67.2)	0.4
Chronic kidney disease	25 (42.4)	34 (57.6)	0.3
Lower respiratory tract infection	21 (45.7)	25 (54.3)	0.2
Stroke	21 (45.7)	25 (54.3)	0.2
Cardiomyopathy	11 (24.4)	34 (75.6)	0.06
Chronic obstructive pulmonary disease	13 (40.6)	19 (59.4)	0.6
Pneumonia	14 (56)	11 (44)	0.04
Atrial fibrillation	13 (56.5)	10 (43.5)	0.04
Sepsis	5 (41.7)	7 (58.3)	0.7
Diuretics	71 (35.3)	130 (64.7)	0.5
Antimicrobials	68 (43)	90 (57)	0.04
Antiemetics	48 (29.8)	113 (70.2)	0.01
Proton pump inhibitors	63 (39.9)	95 (60.1)	0.3
Analgesics	22 (27.8)	57 (72.2)	0.05

*p values were calculated using chi-square tests

**Row-wise percentages were calculated

In logistic regression (Table 6), univariate analysis demonstrates a significant association of QT prolongation with various categories of potential DDIs 6–10 (OR = 2; 95% CI = 1.1–3.6; p = 0.03), 11–15 (OR = 2.9; 95% CI = 1.5–5.4; p = 0.001), > 15 (OR = 2.5; 95% CI = 1.4–4.2; p = 0.001), ischemic heart disease (OR = 1.6; 95% CI = 1.1–2.5; p = 0.02), myocardial infarction (OR = 1.8; 95% CI = 1.1–2.9; p = 0.01), antimicrobials (OR = 0.6; 95% CI = 0.4-1; p = 0.04) and antiemetics (OR = 1.7; 95% CI = 1.1–2.6; p = 0.01). The overall fit of the logistic regression model was assessed using the Hosmer-Lemeshow goodness-of-fit test, which yielded a chi-square value of 9.9 with 8 degrees of freedom (p = 0.273), indicating no significant discrepancy between observed and predicted outcomes and thus a good model fit. The − 2 Log Likelihood value for the model was 453.025, reflecting appropriate model convergence. Additionally, the model explained approximately 13–18% of the variance in QT interval prolongation, as indicated by the Cox & Snell R² (0.13) and Nagelkerke R² (0.18) values. Together, these findings suggest that the logistic regression model demonstrated an adequate fit and reasonable explanatory power in identifying predictors of QT interval prolongation in this patient population. The multivariate analysis illustrates that potential DDIs 11–15 (OR = 2.4; 95% CI = 1.1–5.4; p = 0.03) and antiemetics (OR = 2.1; 95% CI = 1.1–3.8; p = 0.02) were independently associated with QT prolongation at a statistically significant level.

Table 6.

Logistic regression analysis

Variables	Univariate analysis		Multivariate analysis
	OR (95% CI)	p-value	OR (95% CI)	p-value
Gender
Female	Reference	Reference	Reference	Reference
Male	1.5 (1-2.3)	0.06	1.5 (1-2.5)	0.07
Age-group
65–70	Reference	Reference	-	-
71–75	0.9 (0.5–1.5)	0.6	-	-
76–80	1 (0.6–1.8)	0.9	-	-
> 80	1.1 (0.6–2.1)	0.7	-	-
All prescribed medications
≤ 8	Reference	Reference	-	-
9–10	1 (0.6-2)	0.9	-	-
11–12	1.6 (0.8–3.2)	0.1	-	-
> 12	1.3 (0.7–2.4)	0.3	-	-
Potential drug-drug interactions
≤ 5	Reference	Reference	Reference	Reference
6–10	2 (1.1–3.6)	0.03	1.8 (0.9–3.6)	0.09
11–15	2.9 (1.5–5.4)	0.001	2.4 (1.1–5.4)	0.03
> 15	2.5 (1.4–4.2)	0.001	1.9 (0.9–3.9)	0.1
QT-prolonging medications prescribed
1–2	Reference	Reference
3–4	0.5 (0.3–0.8)	0.004	0.4 (0.2–0.7)	0.002
> 4	1.6 (0.7–3.6)	0.3	0.7 (0.2–2.1)	0.5
Potential QT-prolonging drug-drug interactions
1	Reference	Reference	-	-
> 1	0.9 (0.4–1.9)	0.8	-	-
QT risk factors
6	Reference	Reference	-	-
> 6	1.2 (0.8–1.8)	0.4	-	-
Diagnoses
Hypertension	1 (0.6–1.6)	0.9	1.1 (0.6–1.9)	0.9
Diabetes mellitus	1.3 (0.9–2.1)	0.2	1.2 (0.7-2)	0.5
Ischemic heart disease	1.6 (1.1–2.5)	0.02	1.3 (0.8–2.2)	0.3
Myocardial infarction	1.8 (1.1–2.9)	0.01	1.4 (0.7–2.7)	0.3
Heart failure	1.2 (0.7–2.2)	0.4	1.8 (0.9–3.7)	0.1
Chronic kidney disease	0.8 (0.4–1.3)	0.3	0.9 (0.5–1.8)	0.8
Lower respiratory tract infection	0.7 (0.4–1.2)	0.2	0.6 (0.3–1.2)	0.1
Stroke	0.7 (0.4–1.2)	0.2	1.5 (0.6–3.3)	0.4
Cardiomyopathy	1.9 (1–4)	0.07	2.2 (0.9–5.4)	0.09
Chronic obstructive pulmonary disease	0.8 (0.4–1.8)	0.6	1 (0.4–2.3)	0.98
Pneumonia	0.4 (0.2-1)	0.04	0.7 (0.2–1.8)	0.4
Atrial fibrillation	0.4 (0.2-1)	0.05	0.5 (0.2–1.3)	0.2
Sepsis	0.8 (0.3–2.6)	0.7	1.6 (0.4–5.9)	0.51
The most frequent therapeutic classes of QT medications
Diuretics	1.2 (0.8–1.8)	0.5	1.1 (0.6–1.8)	0.8
Antimicrobials	0.6 (0.4-1)	0.04	1.2 (0.7–2.1)	0.5
Antiemetics	1.7 (1.1–2.6)	0.01	2.1 (1.1–3.8)	0.02
Proton pump inhibitors	0.8 (0.5–1.2)	0.3	1 (0.6–1.6)	0.9
Analgesics	1.7 (1-2.9)	0.06	1.5 (0.8-3)	0.2

OR Odds Ratios, CI Confidence interval

*p values were calculated using binary logistic regression

Discussion

Geriatric inpatients are considered to be more susceptible to the development of prolonged QTc interval and subsequent arrhythmias. Therefore, the current study evaluated the prevalence of risk variables for QT interval prolongation among such a population. We identified that the majority of our study population was exposed to a variety of risk variables that prolong QT interval. Among them, utilization of QT-prolonging medications was the most prevalent as 99.5% of patients experienced exposure to either ≥ 1 QT-prolonging medication. Most of these patients were taking these medications concomitantly. According to a recent study, polypharmacy is linked to a high frequency of medications associated with TdP [16]. As a result, potential QT Drug-Drug Interactions might happen.

We also highlight the fact that a considerable proportion of the detected potential drug interactions in our study were classified as mild or having a “conditional” relationship. While these interactions elevated the overall prevalence, their individual clinical significance appears limited, particularly regarding direct effects on QT interval prolongation or adverse cardiac events. However, the high prevalence of such interactions underlines the importance of careful medication review in the geriatric population, as mild or conditional interactions can be additive in the presence of other factors such as polypharmacy, comorbidities, and physiological changes associated with elderly. Therefore, although these interactions may not pose significant immediate risk individually, their cumulative effects warrant ongoing clinical monitoring and further prospective evaluation to describe the impact on patient safety.

There are not many studies that concentrate on figuring out how prevalent potential QT DDIs are in clinical settings. We found that 23.2% of the study population had potential QT DDIs. Among these, the most common drug interacting pair comprised of Metoclopramide - Ondansetron and Azithromycin - Metoclopramide. While research indicates that using the highest risk QTc lengthening medication concurrently with any other QTc prolonging medication must be circumvented due to the significant increase in the danger for fatal toxicities, such as the onset of TdP or other severe ventricular tachyarrhythmias of considerable severity [17]. Apart from this, many previous studies considered only certain drug classes in their analysis [18], whereas, we used a broader strategy that took into account all medication classes that were prescribed to hospitalized geriatric patients. Our study found that diuretics, antimicrobials, antiemetics, proton pump inhibitors, and analgesics were the most commonly involved drug classes in QT prolongation. In our investigation, we found 228 cases of diuretics, indicating a significant prevalence. However, diuretics were commonly used by hospitalized patients, and our results align with the results of the earlier research done on older individuals [16]. Among diuretics, furosemide was the most commonly used, constituting 20.5% of the prescriptions. The second most commonly prescribed group was antimicrobials, with 202 instances. Metoclopramide emerged as the most frequently used antiemetic, representing 13.7% of the prescriptions. Another study also reported a similar proportion where its use was documented in 11.1% of cases. The possible negative effects of QT-prolonging medications were disregarded, despite their widespread use.

The majority of patients had been prescribed one or two QT-extending medications, which highlights the necessity for specific resources like AZCERT QT drug lists to help physicians while prescribing QT-prolonging medications to patients who are already compromised [19]. Since QT-lengthening drugs have a bigger impact on mortality than QT-prolonging diagnosis, they draw attention to a significant modifiable mortality risk factor [20]. Previous research revealed a clear link between QT-prolonging medications and a higher risk of sudden death and arrhythmias [21].

It is evident in our study that the proportion of many heart diseases was higher in patients with prolonged QT intervals such as hypertension (78.6%) which is slightly higher than the previous study (73%) indicated that QT prolongation commonly occurred in hypertensive individuals [22]. Ischemic heart disease (46.1%) which is considerably higher than the study, published in the Journal of the American College of Cardiology, indicated that QT prolongation was a relatively common finding in patients with ischemic heart disease, occurring in around 17% of this population [23]. Another prevalent heart disease in our study was myocardial infarction (29.2%) which is consistent with the study demonstrating that QT prolongation is a common occurrence in patients presenting with acute myocardial infarction, with prevalence rates around 30% [24].

In our study, potential drug-drug interactions (DDIs) were found to be significantly associated with QT interval prolongation among hospitalized geriatric patients. However, due to cross-sectional design of our study, these findings represent associations observed at a single point in time and do not establish causality. Therefore, while potential DDIs appear to be an important risk factor QT prolongation, further prospective studies are needed to determine causal relationships. Similar findings were recorded in other literature as well indicating that pharmacotherapy, which includes drug-drug interactions (DDIs), was a primary cause of QT prolongation. A recent study showed that QTc prolonging is linked to increased mortality and longer hospital stays, even though Torsades de Pointes is the most hazardous and well-known adverse consequence associated with it [25]. It is found in our study that QT prolongation has a significant connection with myocardial infarction, which is in agreement with already existing data [26]. Our study demonstrated a significant alliance of QT prolongation with ischemic heart disease. A similar trend is seen in previous studies as well [27]. Findings in our study suggest a statistically significant relationship between QT prolongation with triglycerides which is by the data published in previous studies [28]. A significant acquaintance of antiemetics with QT prolongation was found in our study, which is consistent with the findings of a previous study [29]. This study demonstrated a significant affiliation of QT prolongation with antimicrobials, a similar result was also evident from other studies [30]. We also identified a significant association of QT prolongation with analgesics, shedding light on the possibility of QT prolongation brought on by medications often used to alleviate pain which is consistency with the findings of a previous study [31].

Patients taking QT combinations or potential QT DDIs should be provided appropriate attention because when there are other additional risk factors present, these drugs and their combinations may have catastrophic consequences. Another significant problem that contributed to the high frequency of QT-prolonging drugs in hospitalized elderly individuals was polypharmacy. Due to the administration of various medications and their comorbidities, elderly individuals were more susceptible to QT-prolonging drugs and potential QT Drug-Drug Interactions. Patients who are more likely to experience deadly arrhythmias should have their QT interval monitored. The antiarrhythmic medication amiodarone, which is known to extend the QT interval as well as TdP, was widely used. A similar trend was also seen in other literature as well [32]. To prevent any potentially negative outcomes, it is therefore advised to thoroughly examine patients who are at risk for QT prolongation and to closely monitor them. This is especially important for patients who are receiving QT-prolonging medications or their combinations in the presence of other risk factors. Effective patient care requires routine practice using risk assessment tools, such as those developed by Vandal et al. [33].

Strengths and limitations

Our study offers several notable strengths. The study was carried out in a large tertiary care hospital in Abbottabad, Pakistan. This study is first of its kind to comprehensively address the prevalence of QT interval prolongation and its associated risk factors in the geriatric population in Pakistan. Given the clinical importance of QT prolongation and its association with serious cardiac events, our findings provide valuable epidemiological data in a population that is often underrepresented in cardiovascular research. The study included rigorous ECG measurement protocols with blinded validation, and applied multivariate logistic regression to identify independent predictors, thereby enhancing the robustness of the analytical approach. Moreover, by focusing on hospitalized older adults, a high-risk group due to multiple comorbidities and polypharmacy, our research underscores the real-world relevance of monitoring QT prolongation and associated drug interactions. These findings align with existing literature highlighting age-related QTc changes and drug-related risks. Despite these strengths, there are certain limitations of the study that need attention. An exact interval between admission and ECG was not uniformly recorded. This represents a study limitation, as the effects of differing hospitalization durations prior to ECG could not be systematically evaluated. However, in future studies it would be important to document ECG timing relative to treatment initiation in order to address this parameter. Another inherent limitation of this study is the cross-sectional design which limits it’s ability to establish temporal relationships or infer causality, emphasizing that our findings represent associations rather than causality. Moreover, we have identified potential drug-drug interactions which represent only identified potential risks rather than the confirmed clinical interactions.

Conclusion

A significant proportion of the study population was found to be exposed to QT-prolonging medications, and the issue of polypharmacy was observed to exacerbate this risk. The association of QT prolongation with various comorbidities, including ischemic heart disease and myocardial infarction, further emphasizes the complex interplay of multiple risk factors within this vulnerable population. Consequently, it is imperative to implement targeted strategies for the management and monitoring of QT intervals in elderly patients to reduce the incidence of life-threatening arrhythmias and enhance overall patient safety. This study underscores the urgent need for heightened awareness and proactive management of QT prolongation risks to enhance the standard of care and safety for geriatric patients.

Authors’ contributions

H.T., M.A., M.S., M.I.,A.I., Q.K.,M.H and A.B wrote the main manuscript text and N.A and W.S. prepared figures and Tables. All authors reviewed the manuscript.”

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This work was funded by the Ongoing Research Funding Program (ORF-2025-856), King Saud University, Riyadh 11451, Saudi Arabia.

Data availability

The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

The study was carried out after getting ethical approval from the Institutional Ethical Review Committee of Medical Teaching Institute (MTI), Ayub Teaching Hospital (ATH), Abbottabad vide reference number RC-EA-2023/172. Written informed consent was obtained from all participating patients and/or their attendants. Participants were informed about the study and were assured about the confidentiality of their data. All study procedures were conducted according to the Declaration of Helsinki guidelines for human research. They were also informed of their right to withdraw from the study at any point in time.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.