Predictors of Pre-hospital Delay in Myocardial Infarction: A Cross-sectional Study at a Tertiary Care Center of Ahmedabad, India

Abstract

Background:

Myocardial infarction (MI) is a leading cause of death globally, with early treatment being critical in reducing mortality. Understanding determinants contributing to pre-hospital delay is essential to improve the outcome.

Objective:

The present study evaluated the time distribution between symptom onsets to hospital arrival and associated factors among patients with MI.

Materials and Methods:

A cross-sectional study included 105 MI patients admitted to a tertiary care teaching hospital in Ahmedabad city during March to August 2024. Patients fulfilling the inclusion criteria were interviewed using a pre-tested, semi-structured questionnaire. Pre-hospital delay was considered as the time elapsed from symptom onset to arrival at a tertiary care hospital among MI patients. Delay was categorized into two groups: ≤2 hours and >2 hours. Appropriate statistical tests were used to assess the association between factors influencing the time from symptom onset to hospital arrival.

Results:

The median time from symptom onset to tertiary hospital arrival was 90 minutes (Interquartile range: 45–210 minutes). Out of a total of 105 participants, 37 (35.2%) experienced delays of >2 hours. Younger age (odds ratio [OR]: 2.16; P = 0.171), female gender (OR: 3.2; P = 0.03), and nuclear family structure (OR: 1.56; P = 0.39) were predictors of pre-hospital delay. Patients having previous similar episodes of symptoms or who misinterpreted them as non-heart-related also reported longer delays.

Conclusion:

Approximately one-third of patients reported a delay of >2 hours. Younger age, female gender, nuclear family structure, previous episodes of symptoms, and misinterpretation of symptoms were significant determinants of pre-hospital delay in MI patients.

INTRODUCTION

Myocardial infarction (MI), commonly known as a heart attack, occurs when part of the heart muscle is damaged or dies due to a lack of blood supply. It is a major presentation of acute coronary syndrome and remains one of the leading causes of death globally. Coronary artery disease (CAD), which often leads to MI, is responsible for about 17.8 million deaths every year.[1]

Timely treatment is critical in managing MI. Many times, deaths occur within the first 24 hours of symptom onset, with nearly half taking place in the first hour, the so-called “Golden Hour.”[2] Studies have shown that starting treatment within this first hour can reduce the risk of death by up to 50%. In contrast, for every 30-minute delay, the risk of dying within a year increases by approximately 7.5%.[3] Mortality benefits can still be observed if treatment is initiated within six hours, but the earlier, the better.[2,3]

Unfortunately, only a small proportion of patients arrive at the hospital within this crucial window. Previous research suggests that the average time from the start of symptoms to reaching medical care ranges from 1.5 to 6 hours.[3,4] This delay significantly reduces the effectiveness of life-saving treatments like thrombolysis or primary percutaneous coronary intervention (PCI).

Several factors can contribute to delays in seeking care. These include misinterpreting the symptoms, choosing to self-medicate, first consulting non-qualified practitioners, and facing issues with transport or access.[5,6,7]

While studies in other regions have explored these issues, there is limited evidence from Gujarat, especially from urban tertiary care hospitals. Understanding why patients delay seeking treatment can help design better awareness campaigns, improve emergency response systems, and ultimately save lives. This study aims to assess the extent of pre-hospital delay and identify its associated factors among MI patients presenting to a tertiary care center in Ahmedabad, Gujarat.

MATERIALS AND METHODS

Study design and setting

This cross-sectional study aimed to assess the time interval from symptom onset to arrival at a tertiary care hospital among patients diagnosed with MI and to identify determinants of pre-hospital delay. The study was conducted at a tertiary care center in Ahmedabad, purposively selected for its operational feasibility and extensive cardiac care services.

Study participants

Eligible participants included adult patients (aged >18 years) diagnosed with ST-elevation or non-ST-elevation MI who were admitted to the facility and could reliably recall the time from symptom onset to hospital arrival.

Exclusion criteria

Patients were excluded if they:

• Were disoriented to person, place, time, or situation;

• Could not report symptom onset time;

• Had psychiatric conditions impairing questionnaire response.

Sample size and sampling technique

Based on an expected prevalence of pre-hospital delay >2 hours at 42% (from a prior study by Khan et al.),[8] with an absolute precision of 10% and a 10% non-response rate, the sample size was calculated using the formula 4 pq/L². The final required sample size was 105. Patients were selected using a fixed interval approach. Every 2^nd eligible patient was included from the continuous stream of attendees during the study period till the sample size was achieved. Interviews were conducted at the time of discharge to ensure optimal recall.

Ethical considerations

Ethical approval was obtained from the Institutional Ethics Committee. Written informed consent was obtained from all participants.

Data collection tools and variables

Data were collected using a pre-designed, pre-validated questionnaire comprising socio-demographic profile, clinical and personal history (diabetes, hypertension, MI symptoms), time metrics from symptom onset to hospital arrival, awareness and interpretation of symptoms, and socioeconomic classification using the Modified B G Prasad Scale, updated for the year 2023.[9]

Operational definitions

• Pre-hospital delay: Time from symptom onset to arrival at a reperfusion-capable hospital; categorized as ≤2 hours (Group I) or >2 hours (Group II).[8]

• Acute Myocardial Infarction (MI): Defined by elevated troponin or Creatine Kinase-MB (CK-MB) levels with any of the following: ischemic symptoms, pathologic Q waves, or ischemic ECG changes (ST elevation/depression).[10]

Statistical analysis

Data were analyzed using Jamovi (v2.3.28). Quantitative data were expressed as mean ± standard deviation (SD) or median (interquartile range [IQR]), depending on normality. Categorical variables were presented as frequencies and percentages. Non-parametric tests (Mann–Whitney U, Kruskal–Wallis H) were used due to non-normal data distribution. Variables significant in univariate analysis were included in multivariate logistic regression to identify predictors of pre-hospital delay. A P value <0.05 was considered statistically significant.

RESULTS

Most of the study participants were aged between 50 and 60 years (32, 30.5%). The mean age of the participants was 50 years with a SD of 11.6 years. The gender distribution revealed male preponderance (71, 71.4%). Most participants were married (99, 94.3%). Regarding education, 46.7% had completed higher secondary or above, while 5.7% had no formal education. A significant proportion of participants were non-working (62, 59.0%), and 50 (47.6%) belonged to nuclear families [Table 1].

Table 1.

Socio-demographic characteristics of patients with myocardial infarction (n=105)

		Frequency		Percentage
Age group (years)
<40		15		14.3
40–50		21		20.0
50–60		32		30.5
60–70		29		27.6
>70		8		7.6
Gender
Female		30		28.6
Male		75		71.4
Marital status
Married		99		94.3
Widow/er		6		5.7
Education
No formal education		6		5.7
Primary		23		21.9
Secondary		27		25.7
Higher secondary or above		49		46.7
Occupation
Non-Working		62		59.0
Working		43		41.0
Type of family
Nuclear		50		47.6
Joint		49		46.7
Three generation		6		5.7
Socioeconomic class
Class 1		58		55.2
Class 2		42		40.0
Class 3		03		2.9
Class 4		02		1.9

In this study, the median time from symptom onset to hospital arrival (pre-hospital delay) was 90 minutes (IQR: 45–210 minutes). Out of the total participants, 37 (35.2%) reported pre-hospital delay of more than 2 hours.

Symptom onset to hospital arrival time was found to significantly vary by age, with the median delay decreasing with increasing age (P = 0.005). Those aged <40 years had the longest median delay (200 minutes), while participants aged >70 years experienced the shortest delay (42.5 minutes). Gender differences in pre-hospital delay were not statistically significant (P = 0.127), but females had a longer delay (180 minutes) compared to males (75 minutes). Socioeconomic class was significantly associated with pre-hospital delay (P = 0.010), with Class 4 participants having the longest delay (3000 minutes). Family type also showed borderline significance (P = 0.080), with three-generation families reporting the longest delay (240 minutes). Working status was significant (P = 0.015), with part-time workers experiencing the longest delay (1440 minutes). There was no significant association between comorbidities and pre-hospital delay (P = 0.540) [Table 2].

Table 2.

Factors associated with time from symptom onset and hospital arrival among myocardial infarction patients (n=105)

Variables				Time from symptom onset to arrival at tertiary hospital (minutes)				P
				Median		IQR
Age (years)		<40		200		90–405		15.01* (0.005)
		40–50		120		65–240
		50–60		120		43.8–240
		60–70		75		45–120
		>70		42.5		36.3–60
Gender		Male		75		45–188		910.5** (0.127)
		Female		180		63.8–330
Socio-economic class		Class 1		70		45–165		11.33* (0.010)
		Class 2		110		60–240
		Class 3		200		160–200
		Class 4		3000		3000–3000
Type of family		Joint		75		50–180		5.06* (0.080)
		Nuclear		82.5		45–203
		3 Generation		240		240–240
Education		Illiterate		60		40–71.3		2.94* (0.401)
		Primary		100		52.5–225
		Secondary		120		40–360
		Graduate and Above		90		50–180
Occupation		Retired or Unemployed		75		40–180		8.43* (0.015)
		Working full time		120		60–233
		Working part-time		1440		1440–3000
Comorbidities (Hypertension or Diabetes)		Yes		75		45–200		1060** (0.540)
		No		120		45–240

IQR=Interquartile range. *Kruskal–Wallis H test, **Mann–Whitney U test. Note: Only two participants belonged to Class 4, and the observed median delay (3000 minutes) should be interpreted cautiously due to extreme data sparsity

A total of 47 (44.8%) patients correctly identified their symptoms as a heart attack. However, a substantial proportion misinterpreted their symptoms, with 28.6% attributing them to muscle pain, 20.0% to indigestion, and 6.7% to fatigue or flu-like symptoms. This misinterpretation highlights the potential risk of delay in seeking appropriate care due to a lack of symptom awareness.

Several factors significantly predicted pre-hospital delay in the current study [Table 3]. Patients younger than 50 years had a higher likelihood of delay (Odds ratio: 2.16; P = 0.171). Gender was a strong predictor, with females experiencing significantly longer delays (odds ratio: 3.20; P = 0.03). Family type also impacted delay, with nuclear families having longer delays compared to joint families (odds ratio: 1.56; P = 0.39). Interestingly, patients who experienced a recurrent episode of symptoms were more likely to delay (odds ratio: 3.70; P = 0.02). Lastly, patients who interpreted their symptoms as non-heart-related were more likely to experience significant delays (Odds ratio: 6.19; P = 0.004). These findings suggest that female gender, multiple episodes of symptoms, and misinterpretation of symptoms are significant predictors of pre-hospital delay in patients with MI.

Table 3.

Binary logistic regression analysis for the predictors of pre-hospital delay (n=105)

Predictor		SE		Z		P		OR		95% CI for OR
Age (<50 vs. ≥50 years)		0.56		1.36		0.171		2.16		0.71–6.5
Gender (female vs. male)*		0.55		2.08		0.037		3.2		1.07–9.5
Family type (nuclear vs. joint)		0.53		0.84		0.398		1.565		0.55–4.42
Education (<10th vs. ≥10th standard)		0.55		0.48		0.627		0.764		0.25–2.26
Place of symptoms (outside home vs. home)		0.78		0.44		0.658		0.7069		0.15–3.28
Transport (own vehicle vs. 108 ambulance)		0.93		1.35		0.175		3.5319		0.5–21.9
Transport (hired vehicle vs. 108 ambulance)		0.98		1.51		0.13		4.406		0.64–30.08
Episode of symptoms (>1 vs. first episode)*		0.56		2.30		0.021		3.7029		1.21–11.2
Interpretation (other than heart attack vs. heart attack)*		0.62		2.90		0.004		6.1915		1.8–21.2

*Statistically significant in the foot note. OR=Odds ratio, CI=Confidence interval, SE=Standard error

DISCUSSION

This study provides valuable insights into the factors contributing to pre-hospital delays among patients with MI, highlighting critical areas for targeted interventions to improve timely care.

The median pre-hospital delay observed in this study was 90 minutes (IQR: 45–210 minutes). This finding aligns with previous studies conducted by Panda et al. (2021),[11] who reported delays ranging from 20 to 810 minutes, and Rafi et al. (2020),[12] where a median delay of 60 minutes, were observed between symptom onset and hospital arrival. These findings emphasize that delays often exceed the recommended “golden hour” for MI management. The current study revealed that younger patients (<40 years) experienced significantly longer delays compared to those aged > 70 years; this finding contrasts with the study conducted by Krishnan et al. (2023),[5] which reported that older adults often delayed care due to comorbidities and dependency on caregivers. A possible explanation in the present setting is that younger adults may underestimate the seriousness of their symptoms, attributing them to less severe causes, and may delay seeking care due to work or social commitments.

The median delay for females (180 minutes) was more than double as compared to males (75 minutes), similar observation was revealed in the studies conducted by Chowdhury et al. (2021)[4] and McGinn et al. (2005).[13] This aligns with global evidence indicating that women are more likely to misattribute cardiac symptoms to non-cardiac causes, delay help-seeking, or face systemic barriers in accessing timely care. Socioeconomic status emerged as a significant determinant of delay in this study. Participants from Class 3 and Class 4 reported the longer delays as compared to their counter parts, potentially due to limited health literacy, poor access to healthcare facilities, or financial constraints, the finding is consistent with studies by Khan et al. (2017)[8] and Mujtaba et al. (2021),[14] which highlighted the association between lower socioeconomic status and prolonged pre-hospital delays.

Symptom misinterpretation played a pivotal role in delay, with only 44.8% of patients correctly identifying their symptoms as cardiac-related. Alarmingly, 28.6% attributed their symptoms to muscle pain, and 20.0% to indigestion. In the current study, the findings were similar to studies conducted by Mujtaba et al. (2021),[14] Mohan et al. (2018)[15] and Hertz et al. (2022),[16] all of which emphasized the impact of symptom misinterpretation on delayed care-seeking. These findings underscore the urgent need for public health campaigns to improve awareness of both typical and atypical presentations of MI. Patients from nuclear families experienced longer delays compared to those from joint families. This may be attributed to the lack of immediate support or collaborative decision-making in nuclear households, emphasizing the role of social dynamics in influencing healthcare-seeking behavior.

The findings of this study underscore the need for multifaceted strategies to reduce pre-hospital delays. Awareness campaigns, especially those focused on younger individuals and women, can play a crucial role in improving recognition of MI symptoms and promoting early health-seeking behavior to prevent misinterpretation and ensure timely evaluation. Also, there is a need to ensure utilization of available ambulance services (e.g., 108) for speedy transportation of patients to healthcare facilities.

Limitations of the study

Single-center study may reflect specific demographic and healthcare access profiles, which could vary across other settings. While self-reported symptom recognition and delay times provide valuable insights, they may be influenced by recall limitations. Additionally, the study focuses on survivors of MI episodes, providing an important perspective on pre-hospital delays but does not account for cases with fatal outcomes, which could introduce some selection bias. The wide variation in median values across certain subgroups reflects limited sample sizes within these categories, potentially leading to reduced precision, thereby warranting cautious interpretation of subgroup analyses. Differences in symptom severity and urgency between STEMI and NSTEMI could have influenced care-seeking behavior and pre-hospital delay, which could not be explored in the present study.

CONCLUSION

The median pre-hospital delay of 90 minutes (IQR: 45–210 minutes) was observed among patients having MI. The study highlights predictors of pre-hospital delay in MI, including younger age, female gender, nuclear family structure, working status, recurrent symptom episodes, and symptom misinterpretation. Targeted interventions addressing these factors are crucial to minimize delays and improving outcomes for MI patients. Strategies should focus on enhancing symptom awareness, addressing gender and socioeconomic disparities, and raising supportive environments for timely care-seeking behavior.

Conflicts of interest

There are no conflicts of interest.

Funding Statement

Nil.