Acute myocardial infarction treatment delay in South Asia: a systematic review and meta-analysis

ABSTRACT

Background

Acute Myocardial Infarction (AMI) necessitates timely treatment to improve outcomes. Identifying treatment delays across different South Asian countries can aid in formulating policies to reduce these delays. Objectives: To estimate the average treatment delay in AMI patients in South Asia and identify contributing factors.

Methods

Using the CoCoPop framework (Condition, Context, Population), studies were reviewed on AMI treatment delays in South Asia from 2000 to 2022. Databases searched included PubMed Central, Embase and Google Scholar. Eligible studies were cross-sectional and analytical that reported exact delay times, excluding knowledge, attitude, practice studies, narrative reviews, and case reports.

Results

The search yielded 2954 records, with 42 studies meeting the inclusion criteria. The pooled median prehospital delay was 531 minutes (95% CI: 366–769 minutes). The pooled mean door-to-ECG time was 9.18 minutes (95% CI: 2.52–15.84 minutes). The door-to-needle and door to balloon time among STEMI patients were 37.95 (95% CI: 30.11–45.78 minutes) minutes and 62.92 minutes (95% CI: 45.28–80.56 minutes), respectively with significant heterogeneity. Factors associated with delays included old age, female gender, low literacy, ignorance, financial constraints, and rural location.

Conclusion

Significant treatment delays for AMI patients in South Asia are identified, with socio-economic and logistical barriers contributing to these delays.

Plain Language Summary

Acute Myocardial Infarction (AMI), also known as heart attack, needs timely treatment to save lives and improve recovery. Identifying treatment delays in South Asia guides policies to effectively reduce such delays. Studies published between 2000 and 2022 from countries in South Asia were reviewed. It was observed that patients had delays of an average of 531 minutes (nearly 9 hours) to reach the hospital after symptoms began. Once at the hospital, it took around 9 minutes for patients to get an ECG. For patients with a serious heart artery blockage (STEMI) the average time taken from reaching the hospital to starting medicine to break the clot (medicine given through a drip to open blocked arteries) was about 38 minutes. On average, the time taken to reach and start treatment with a heart balloon procedure was about 63 minutes. These times varied widely between studies. Several factors caused delays in treatment, including older age, being female, low education levels, not knowing heart attack symptoms, financial problems, and living in rural areas. This study shows that many people in South Asia experience long delays before getting treatment for heart attacks. Reducing these delays through public awareness, improved transport, and hospital readiness could help save more lives.

1. Introduction

Ischemic Heart diseases (IHDs) are the spectrum of coronary artery manifests which either narrow down the arteries or completely block them, thereby leading to severe chest pain or even heart attack. Being the leading cause of mortality worldwide, it contributes to suffering and disability of not only the patient but cripples the entire family. Health Metrics for India reveal that there has been a 16% increase in deaths due to IHDs from 2005 to 2015 [1,2]. The global rise of cardiovascular disease (CVD) accounting for nearly 30% of deaths worldwide denotes that despite the improved health care services to treat and diagnose CVDs, there is a need to explore opportunities to improve the care of patients with STEMI” [3]. This necessitates a comprehensive review into the pathway from disease inception to treatment, with finer precision.

The time to treatment is an established factor which affects the outcome, either as a pre-hospital delay, transport delay, or delay at the level of doctor. Much attention has been dedicated to this, and has demonstrated promising results in reducing these delays, thereby improving outcomes; however, there is still a long way to go [4]. Studies in Low- and Middle-Income Countries (LMIC) have identified female gender, age, poor insurance coverage, referral pathways and inadequate transport as the responsible factors for these delays. Furthermore, developing countries encounter inadequacies in their health care facilities as well as challenges regarding the availability of properly trained doctors at the peripheral centers or first points of referral [5–7]. Additionally, it has been found that patients are unaware of the premonitory signs and symptoms that ought to cause concern [8,9].

Numerous studies have highlighted this problem. Some identified levels of delay and its reason, point of care, or treatment cornerstones. Barriers to effective care including time and distance with affordability, appropriate referral for MI care management in LMIC, reperfusion therapy guidelines, intensive care access, risk stratification, and adjunct medication use have also been studied.

1.1. Need for the study

A major barrier to effective care is the golden hour. Research indicates that MI patients in LMICs experience higher average delays than patients in other settings in reaching an appropriate place for treatment [6,7]. Despite guidelines recommending that MI patients receive their percutaneous coronary intervention (PCI) within 120 minutes of symptom onset, the average delay in China is between 13 and 15 hours [10]. As a result, the golden hour is missed and the opportunity to improve the outcome in STEMI patients is lost. An effort to segregate and conceptualize the facts according to different countries and contexts will facilitate in formulating a common policy to effectively address and prevent the delays in managing patients with acute myocardial infarction (AMI). The government’s investments in improving tertiary level care centers, roads, transportation systems, ambulances, and health staff training, as well as implementing sophisticated innovations, will be ineffective if the primary goal of reducing pre-treatment delays among AMI patients is not achieved. Therefore, conducting a systematic review of the available evidence will help focus efforts on identifying the factors contributing to pre-treatment delays among AMI patients and identify more effective solutions. Thus, this review was conducted to estimate the average delay in the treatment of patients with AMI in South Asia and to identify the factors associated with the delay.

2. Methods

2.1. Registration of the review

The protocol for the review was registered at PROSPERO 2021 (CRD42021243382). Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) recommendations were followed while conducting and reporting this review.

2.2. Search strategy

The CoCoPop (Condition, Context, Population) framework to include all key components prior to starting the review was used. A comprehensive literature review was conducted to identify the studies that fulfil the inclusion criteria. The search strategy was refined and developed in collaboration with team members by searching PubMed and other literature databases related to the study’s objectives. The searches included all synonyms and keywords pertaining to the concepts of acute coronary syndrome (such as ST segment elevation, chest pain, angina), delay (including pre-hospital delay, treatment seeking delay, time-to-treatment, delay in care), and South Asia, to prevent the omission of potentially relevant articles. The search was restricted to studies that were published in the English language between 2000 to 2022 and databases were comprehensively searched such as MEDLINE, EMBASE, PUBMED CENTRAL, and Google Scholar by retrieving studies using a reference management system. The MeSH terms were searched in the title and abstract using Boolean operators, and the articles were screened using the Covidence software.

2.3. Eligibility

All articles with cross-sectional and analytical studies with similar objectives or results and studies that reported the outcome of exact time of delay were included, whereas the studies on knowledge, attitude and practice of delay and narrative reviews and case reports were excluded.

2.4. Screening

Two researchers performed the initial screening of abstracts. The checklist employed for the selection of observational analytical studies was as per the guidelines of Cochrane. The software was used for the repository of articles during abstract screening. The screened abstracts were later checked with the checklist to determine whether they be included or not. Two researchers performed the screening independently. A third researcher also used the checklist for selection of the publications and the review. The main intention of the third reviewer was to identify and segregate the articles as cross-sectional or analytical. If there was any problem or conflict, then fourth reviewer was used to resolve it. The weightage for studies and grading of the study was done using software. The fifth and sixth authors investigated methods and results and checked through the checklist. They also performed the selection of articles for final extraction.

2.5. Data extraction

Following the selection of articles to be included in the present review, three researchers extracted the data. One researcher prepared a form which was used for screening and plotting the relevant information to be extracted. From the selected studies, the following information was extracted from each of the articles depending on the objectives: title, author, year, journal, research question and specific aims, conceptual framework, hypothesis, research methods or study type, treatment delay, co-morbidities, and associated factors. A single final form was created which was continuously updated after taking into consideration all the fallacies and disagreements. A table of the characteristic features of each article was prepared to get idea on the article characteristics.

2.6. Quality assessment

Two mid-level experienced researchers independently performed a qualitative critical appraisal of the studies (quality assessment). The common guide included appropriateness of study design to research objective, choice of outcomes, statistical methods employed, quality of reporting, and generalizability. STROBE guidelines were used.

3. Results

3.1. Search results

This review was conducted to estimate the pre-treatment delay in patients with acute myocardial infarction in South Asia from 2000 to 2022. Figure 1 depicts the process of the search strategy and selection of studies included in the review. Using the search strategy, 2954 records were retrieved across the databases. After removing the duplicate records, primary screening of the title and abstract was done for 2912 records. 1112 articles were found to be relevant and the full text publications were retrieved for these articles and they underwent secondary screening against the inclusion criteria. Finally, data from 42 studies were included that satisfied the inclusion criteria.

Figure 1.

PRISMA flow chart of the screening process.

3.2. Baseline characteristics

Table 1 depicts the characteristics of the included studies (n = 42). These studies were published from 2000 to 2022 (2000 to 2019 (pre covid pandemic) − 28 articles; 2020 to 2022 (covid pandemic) − 14 articles). In South Asia, the majority of the included studies were conducted in India (22), followed by Pakistan (10), Bangladesh (4), Nepal (4) and Sri Lanka (2). Based on the study designs, the studies were categorized as cross sectional (32), longitudinal analytical (1), cohort study (4), and retrospective record review (5). Half of the included studies reported both the pre-treatment delay in patients with AMI and the factors associated with the delay. The remaining studies (n = 21) documented only the delay without mentioning the associated factors. A total of 44,568 participants were studied, and the sample size varied from 67 to 20,937. The mean age of the study participants ranged from 35.5 to 67.1 years.

Table 1.

Characteristics summary of the included studies (n = 42).

					Age of the participants
Author	Year of Publication	Country	Condition of the participants	Sample size	Mean	SD	Outcome measures	Strobe score
Khan et al. [11]	2007	Pakistan	ACS	720	54	12	Prehospital delay – Mean & SD and (%)	19
Xavier et al. [7]	2008	India	ACS	20937	57.5	12.1	Prehospital delay – Median & IQR	19
Acharya et al. [12]	2009	Nepal	STEMI	100	62	10.4	Prehospital delay – Mean	27
Shreshta et al. [13]	2011	Nepal	ACS	153	62	12	Prehospital delay – (%)	40
Goel et al. [14]	2012	India	STEMI	609	56.3	19–98	Prehospital delay – (%)	17
Subban et al. [15]	2014	India	STEMI	672	52	13.4	Prehospital delay – Median & IQR and (%)	16
Bandara et al. [16]	2015	Sri Lanka	STEMI	81	61.7	10.7	Prehospital delay – Mean	18
Khursheed et al. [17]	2015	Pakistan	ACS	230	–	–	Prehospital delay – Mean & SD Door to needle or ballon time – Mean & SD	–
Medagama et al. [18]	2015	Sri Lanka	ACS	256	63.2	11.1	Prehospital delay – Median	18
Allana et al. [19]	2015	Pakistan	ACS	249	56.46	11.67	Prehospital delay – Median & IQR	18
Iqbal and Barkataki [20]	2016	India	ACS	704	56.5	–	Prehospital delay – Mean	15
Tanveer et al. [21]	2016	India	STEMI	190	54.37	11.73	Prehospital delay – (%)	68
Khan et al. [22]	2016	India	STEMI	198	–	–	Prehospital delay – Mean & SD and %; Door to ECG time – Mean & SD; Door to needle or ballon time – Mean & SD	10
Nanjappa et al. [23]	2016	India	STEMI	133	64.4	11	Prehospital delay – (%)	15
Negi et al. [24]	2016	India	ACS	5180	60.9	12.1	Prehospital delay – Median & IQR and (%)	48
Khan et al. [25]	2016	Pakistan	ACS	325	–	–	Prehospital delay – (%)	15
Mawani et al. [26]	2016	Pakistan	ACS	310	59.2	15.1	Prehospital delay – Median & IQR	15
Agrawal et al. [27]	2016	India	ACS	100	58.9	–	Prehospital delay – (%)	18
Dhungel et al. [28]	2017	Nepal	STEMI	79	56	11.2	Prehospital delay – (%)	43.5
George et al. [29]	2017	India	STEMI	96	55	11	Prehospital delay – (%)	8
Khan et al. [30]	2017	India	STEMI	1386	–	–	Prehospital delay – Mean & SD; Door to needle or ballon time – Mean & SD	18
Ahmed et al. [31]	2018	Pakistan	ACS – Diabetic	130	67	9.2	Prehospital delay – (%)	17
Venkatesan et al. [32]	2018	India	ACS	93	–	–	Prehospital delay – (%)	39
Doddipalli et al. [33]	2018	India	STEMI	346	55.3	12.1	Prehospital delay – Mean & SD; Door to ECG time – Mean & SD	19
Mohan et al. [34]	2018	India	STEMI	619	–	–	Prehospital delay – (%)	17
Sharma et al. [35]	2019	India	STEMI	147	58.7	11.1	Prehospital delay – Median & IQR	14
Choudhary et al. [36]	2019	India	ACS	1328	61.8	10	Prehospital delay – Mean & SD andMedian & IQR	6
Kim et al. [37]	2019	Bangladesh	STEMI	164	57.07	12.4	Prehospital delay – Mean & SD	15
Koirala et al. [38]	2019	Nepal	STEMI	232	57.39	12.97	Prehospital delay – (%)	18
Sidhu et al. [39]	2020	India	ACS	621	56.06	11.29	Prehospital delay – Median & IQR	56
Jalbani et al. [40]	2020	Pakistan	ACS	360	55.29	6.9	Prehospital delay – Mean & SD and (%)	14
Rafi et al. [41]	2020	Bangladesh	ACS	337	54.37	12.58	Prehospital delay – (%)	20
Krishnan et al. [42]	2021	India	STEMI	500	58.6	12.05	Door to ECG time – Mean & SD; Door to needle or ballon time – Mean & SD	42
Sharma et al. [43]	2021	India	ACS	1203	58.14	12.5	Prehospital delay – Median & IQR	48
Mujtaba et al. [44]	2021	Pakistan	STEMI	240	53.26	10.94	Prehospital delay – Median & IQR and (%)	18
Panda et al. [45]	2021	India	ACS	130	61.3	13.4	Prehospital delay – Median & IQR and (%)	18
Alam et al. [46]	2021	Bangladesh	ACS	98	53	12	Prehospital delay – Mean	–
Chowdhury et al. [47]	2021	Bangladesh	ACS	333	53.8	11.2	Prehospital delay – (%)	20
Revaiah et al. [48]	2021	India	ACS	182	35.5	4.7	Prehospital delay – Median & IQR and (%)	19
Sheikh et al. [49]	2022	Pakistan	ACS	4480	59.1	12	Prehospital delay – Median & IQR	15
Aijaz et al. [50]	2022	Pakistan	ACS	67	62.7	11.1	Prehospital delay – (%)	14
Bhalerao et al. [51]	2022	India	ACS	100	–	–	Door to needle or ballon time – Mean & SD	18

ACS = Acute Coronary Syndrome; ECG = Electrocardiogram; IQR = Inter Quartile Range; STEMI = ST segment Elevated Myocardial Infarction; SD = Standard Deviation.

3.3. Comorbidities

Comorbidities such as Diabetes mellitus, Hypertension and coronary artery disease (CAD) and risk factors such as dyslipidaemia, smoking and family history of CAD were reported. About 27 articles mentioned at least any one of the above-mentioned conditions (Table 2).

Table 2.

Distribution of co-morbidities.

Condition	No of included studies that reported the condition	Minimum (%)	Maximum (%)	Median (IQR)
Diabetes mellitus	27	14	60	34.9 (29.4–42)
Hypertension	26	2.6	68.9	39.9 (35–53.8)
CAD	12	3.6	24.6	10.4 (6.1–17.5)
Dyslipidaemia	11	7	83	27.8 (15.3–47)
Smoking	21	14.9	75.9	40.2(25.1–54.8)
Family history of CAD	13	3.3	55	22 (7.5–34.1)

CAD – Coronary Heart Disease; IQR = Inter Quartile Range.

3.4. Methodological quality of included studies

The quality of the included studies was assessed using Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist. It is a 22-item checklist, and each item has 1 to 7 subitems. Each sub item has a possible score of either 0 or 1. The weightage to the checklist was assigned as follows: Title and abstract − 10%, introduction − 15%, methods − 60%, results −10%, discussion − 5%. Accordingly, the included articles were given a minimum and maximum score of 6 and 68 respectively. The weightage also assigned ranges from 27 to 91. This analysis aimed to assess the robustness of the findings when weighting by reporting quality.

3.5. The treatment delay in patients with AMI

Our review revealed that the included articles reported one or more of the delays in treatment pathway of patients with AMI as follows:

1. Prehospital delay (time from onset of symptoms to reaching the health facility),

2. Door to ECG time (time from reaching the health facility to investigation),

3. Door to Needle or Ballon time (time from reaching the health facility to initiation of treatment). These delays are reported in varying outcome measures such as median with interquartile range (IQR), Mean and standard deviation, and percentage delay and the meta-analysis was done accordingly. Due to the typically skewed distribution of prehospital delay times, the data extracted or calculated the median and IQR for each study wherever possible. The median was chosen over the mean as it better represents the central tendency in datasets where a small number of patients experience extreme delays (e.g., > 24 hours). This approach improves the accuracy and comparability of delay estimates across studies. The analysis was done using the STATA version 14.0 and R (4.5.0) software.

3.5.1. The pooled estimate of prehospital delay

The pooled estimate was calculated after adjusting for sample size weights and depicted graphically. The pooled median prehospital delay of patients with AMI in South Asia was 531 minutes (95% CI: 366–769 minutes). There was a significant heterogeneity among the studies included for the analysis. (I² = 66%; p < 0.001). Subgroup analysis based on region found that the median prehospital delay was higher in Pakistan with 556 minutes (95% CI: 301–1026 minutes) (I² = 87%; p < 0.001), followed by India with 388 minutes (95% CI: 261–579 minutes) (I² = 38%; p = 0.033) (Figure 2).

Figure 2.

Forest plots of (a) median prehospital delay, (b) median prehospital delay in India, (c) median prehospital delay in Pakistan.

Figure 2.

(Continued).

Some studies reported the prehospital delay in percentages. Accordingly, the pooled percentage of prehospital delay of less than 6 hours was 39% (95% CI: 28.14% to 49.95%). The highest percentage of delay of less than 6 hours was reported in Pakistan 57% (95% CI: 28.25% − 86.24%) and the lowest percentage was reported in Nepal 17.52% (95% CI: 4.78% − 30.26%). There was significant heterogeneity among the studies included (I² = 99.14%; p = 0.00) (Figure 3).

Figure 3.

Forest plot of pooled % prehospital delay of less than 6 hours.

The pooled percentage of prehospital delay of more than 6 hours was 41.74% (95% CI: 33.67% − 49.81%). Nepal had the highest reported percentage of prehospital delay of more than 6 hours 54.97% (95% CI:13.81% − 96.13%) and Pakistan had the lowest percentage of 34.52% (95% CI: 18.65% − 50.40%). There was significant heterogeneity among the included studies (I² = 96.87%; p = 0.00) (Figure 4).

Figure 4.

Forest plot of pooled % prehospital delay of more than 6 hours.

Although the pooled median prehospital delay reported from Pakistan was high (556 minutes), some studies from the region also reported a higher percentage of patients presenting within 6 hours (57%). Prehospital delay data tend to be right-skewed, where a small group of patients experiences extremely long delays. This can significantly increase the median value, even if a large proportion of patients present early (e.g., within 6 hours). This apparent discrepancy arises because the estimates were derived from different subsets of studies, each with varying methodologies, populations, and definitions. Additionally, the wide confidence intervals and high heterogeneity indicate substantial variability across studies.

3.5.2. The pooled estimate of door to ECG time

The pooled mean of door to ECG time of patients with AMI was 9.18 minutes (95% CI:2.52–15.84 minutes). There was a significant heterogeneity among the studies included for the analysis (I² = 99.91%; p = 0.01) (Figure 5(a)).

Figure 5.

Forest plots of (a) pooled mean door to ECG time, (b) pooled mean door to needle time, (c) pooled mean door to balloon time, (d) pooled % of door to balloon time.

3.5.3. The pooled estimate of door to needle and door to balloon time

Only studies including data of STEMI patients were used for this analysis. The pooled mean of door to needle time of patients with AMI in STEMI was 37.95 minutes (95% CI: 30.11–45.78 minutes). There was significant heterogeneity among the studies included for the analysis (I2 = 97.33%; p = 0.00) (Figure 5(b)).

The pooled mean of door to ballon time of patients with AMI in STEMI was 62.92 minutes (95% CI: 45.28–80.56 minutes). There was a significant heterogeneity among the studies included for the analysis (I² = 98.92%; p = 0.00) (Figure 5(c)).

Some studies documented the percentage of patients with a door to ballon time within 90 minutes. The pooled estimate of 63.49% (95% CI- 29.19% −97.79%) had started PCI within 90 minutes of reaching the health facility. There was significant heterogeneity among the studies (I² = 99.34%; p = 0.00) (Figure 5(d)).

3.6. Treatment delay using STROBE scoring as weightage

3.6.1. The pooled estimate of prehospital delay using STROBE scoring as weightage

The pooled estimate was calculated after adjusting for strobe weights and depicted graphically (Supplementary files). The pooled median prehospital delay of patients with AMI in South Asia was 231.1 minutes (95% CI: 31.4–1701 minutes). There was significant heterogeneity among the studies included for the analysis. (I² = 98.7%; p < 0.001) (Supplementary figure S1).

3.6.2. The pooled estimate of door to ECG time using STROBE scoring as weightage

The pooled mean of door to ECG time of patients with AMI was 6.79 minutes (95% CI: 2.00–11.57 minutes). There was no significant heterogeneity among the studies included for the analysis. (I² = 0%; p = 0.01) (Supplementary figure S2.a).

3.6.3. The pooled estimate of door to needle and Ballon time using STROBE scoring as weightage

The pooled mean of door to needle and door to ballon time of patients with AMI in STEMI were 38.03 minutes (95% CI: 30.19–45.87 minutes) and 62.99 minutes (95% CI: 45.35–80.63 minutes) (Supplementary figure S2b,c).

3.7. Factors associated with delay of AMI treatment

The non – modifiable factors associated with pretreatment delay of patients with AMI were old age and female gender. Illiteracy or low literacy level had a significant role in late presentation to the health facility. Ignorance, misinterpretation, and lack of perceived seriousness about the symptoms of MI are associated with prehospital delay. Decision making, inadequate family and social support were the factors related to treatment seeking delay among adults diagnosed with AMI. Low- and middle-income households and financial constraints were major socioeconomic factors influencing timely treatment. Patients with diabetes had a decreased prehospital delay, whereas no significant association was found between other comorbidities and prehospital delay. Based on the type of ACS, mean time of presentation was higher in NSTEMI/UA than STEMI.

Rural and hilly terrains are the geographical restrictions for seeking early treatment. A study conducted in Bangladesh reported significantly longer pre-hospital delays in patients located beyond 30 kms from the hospital at symptom onset. Lack of rapid transport modalities like ambulance, long traveling time, delay in getting transport were the major reasons for late presentation at the health facilities. Indian studies reported that personal vehicles like cars were the commonest mode of transportation and the average time delay of patients reaching the hospital by ambulance was less than for those with private vehicles. “108” national ambulance service was more frequently used by the rural than the urban AMI population.

First visit to local dispensary for consultation and stay at first medical contact, and time taken from first medical contact to reach health facility for thrombolysis or PCI were the significant factors associated with pre-treatment delays. Misdiagnosis and lack of equipment were factors associated with in-hospital delays. Lack of health facilities equipped with PCI/Coronary Artery Bypass Graft (CABG) was also one of the reasons for pretreatment delay of patients with AMI.

4. Discussion

The analysis of 42 studies on AMI treatment delays revealed a focus on South Asia, predominantly India. This systematic review and meta-analysis highlights the causes for treatment delays in AMI patients and factors causing such delays. These studies were conducted between 2000 and 2022 and highlighted a surge in studies pre-COVID. Most of these studies were cross-sectional, investigating both treatment delays and associated factors for delays in half of the cases, while the remaining focused on only documenting delays. It was not intended to assess relationship between delays and mortality. A total of 44,568 participants were studied, representing a diverse variety of demographics. This research emphasizes the importance of comprehending the factors that cause delays in treatment, keeping the golden hour concept which is known to have favorable outcomes in AMI management, particularly by conducting more long-term studies to effectively address the gaps in existing research.

The prevalence of diabetes varies between 14% to 60%, consistent with the global trend of increasing diabetes burden due to aging and lifestyle changes [52]. The prevalence of Hypertension ranged from 2.6% to 68.9%, reflecting disparities in global healthcare access and control [53]. CAD itself had a lower prevalence, suggesting the impact of traditional risk factors on its incidence [54]. Dyslipidaemia and smoking, with their broad prevalence ranges, underscore the influence of dietary patterns, genetic predisposition, and tobacco use on cardiovascular risk [55,56]. The variation in family history of CAD prevalence emphasizes the role of genetic factors’ in cardiovascular risk [57]. These findings highlight the need for targeted public health interventions and the significance of managing modifiable risk factors to prevent CAD.

The utilization of the STROBE checklist to assess the quality of observational epidemiology studies reflects a commitment to enhancing research integrity through detailed reporting, and assigning scores based on a structured weighting system emphasized in the Methods section [58]. This approach emphasizes the significance of transparent and rigorous methodological documentation in ensuring the validity and dependability of research findings by addressing the variation in reporting quality [59]. The scoring variability among included studies emphasizes the need for adherence to high-quality reporting standards and guidelines to increase research quality and utility [60].

In our review, AMI patients in South Asia had a pooled median prehospital delay of 531 minutes, with significant heterogeneity among the studies and geographical variances (Pakistan at 556 minutes, India at 388 minutes). This is consistent with global observations of significant prehospital delays; however, durations vary by region and population [61]. Compared to international norms, the pre-hospital delays in South Asia are considerably longer than those recommended for effective AMI treatment. According to systematic reviews, educational interventions can greatly reduce these delays by increasing public awareness of AMI symptoms and appropriate responses [62].

A systematic review on acute coronary syndrome treatment delays in LMIC done by Beza et al. including 29 studies in 14 LMIC with 29,731 participants highlighted that prehospital delay times varied widely, with a mean time from symptom onset to first medical contact of 12.7 hours. Factors such as gender, age, educational status, income, and knowledge of ACS symptoms significantly influenced these delay times. Notably, male gender and younger age were associated with shorter delays. Comorbid conditions like diabetes and hypertension also played a role in influencing the time to treatment [63]. Our analysis found prehospital delay times in Pakistan and India that are consistent with LMIC findings that attribute delays to systemic, socio-economic, and individual factors. A comprehensive evaluation of emergency care in LMICs found that poor infrastructure, lack of transportation, financial constraints, and low public awareness delayed medical care [64].

Our review observed that Pakistan has more prehospital delays of fewer than 6 hours (57%) than Nepal (17.52%). This variability demonstrates how regional differences influence healthcare-seeking. Healthcare infrastructure, public awareness of AMI symptoms, and accessibility to medical services can cause such discrepancies. The impact of diverse factors contributing to prehospital delay across various settings were further emphasized by the substantial heterogeneity (I2 = 99.14%) among the studies.

Socio-demographic characteristics and inadequate cardiovascular disease knowledge delay myocardial infarction treatment from symptom onset to medical intervention, according to Russian research. Geographical constraints and urban-rural healthcare access further complicate timely treatment [65]. The hospital arrival rate within the golden time after AMI symptom onset was reported at 44% in a South Korean study. This indicates that over half of AMI patients arrived at emergency medical centers after the optimal time for treatment [66]. This aligns with our study findings indicates a global challenge in providing timely AMI medical care. The median delay times in the decade-long trends study of elderly patients in Massachusetts did not exhibit any substantial changes over time. This suggests that there are persistent challenges in reducing prehospital delays across various patient demographics [67].

The average door-to-ECG, door-to-needle, and door – to- balloon times for AMI patients in our study were 9.18, 37.95 and 62.92 minutes, respectively. About 63.49% of patients started PCI within 90 minutes of hospital arrival. The door-to-balloon time (DTBT) ≤90 min was associated with improved short- and long-term outcomes in high- and low-risk STEMI patients according to a study by Yudi et al. [68,69]. Park et al. found that shortening DTBT time significantly improves survival, even from 90 to 60 minutes. This emphasizes fulfilling the 90-minute goal and reducing DTBT timelines for better patient outcomes [70]. Factors such as patient arrival method and hospital capabilities significantly impact door-to-balloon times, as indicated by research. System-wide reforms and public education on the importance of utilizing EMS for suspected STEMI could reduce prehospital delays and enhance door-to-balloon time goals.

Our study found that age, gender, low literacy levels, socio-economic challenges, and certain health conditions like diabetes significantly affect treatment delay in AMI patients, with variations amongst ACS types [71]. Ignorance, misinterpretation, and lack of perceived seriousness of MI symptoms are common reasons of prehospital delays. Decision-making challenges and inadequate family or social support also play crucial roles in treatment-seeking delays. These results are consistent with studies that show behavioral and psychological factors have a major impact on delay times [72]. Studies from different regions demonstrate that socioeconomic status delays AMI treatment [73]. Patients with diabetes have been found to have decreased prehospital delays, a finding consistent with other studies indicating that certain comorbidities can influence patient responsiveness to AMI symptoms. The difference in mean presentation time between NSTEMI/UA and STEMI also highlights the varied perceptions and responses to different types of ACS [74]. While our primary objective was to assess treatment delays in AMI, both STEMI and broader ACS populations – including NSTEMI and unstable angina were included – because this reflects the real-world spectrum of acute coronary presentations encountered in South Asia. Inclusion of ACS studies allowed us to capture delay patterns beyond the STEMI-specific metrics, providing insights into delays associated with diagnostic uncertainty, lower triage priority, and atypical symptom profiles common in NSTEMI/UA. These cases often present significant challenges in LMIC settings due to resource constraints and limited access to early risk stratification tools. This inclusive approach enabled a comprehensive analysis of treatment delays and has implications for emergency care system planning across diverse clinical presentations.

Patient awareness of AMI symptoms is critical for reducing prehospital delays. Studies suggest that improving public knowledge and awareness about AMI symptoms, especially atypical symptoms, can significantly reduce delay times and improve outcomes [75].

4.1. Implications of weighting methods

For the pooled median prehospital delay of patients with AMI in South Asia, two different statistical methods were used for weighting the data: sample size weights and STROBE weights. The results show a difference in the pooled median prehospital delay: 531 minutes with sample size weights and 231.1 minutes with STROBE weights.

The difference in the pooled median prehospital delay between the two weighting methods is statistically negligible. This indicates that both sample size and the quality of reporting (as assessed by STROBE) yield almost identical estimates of prehospital delay in AMI patients in South Asia. This minimal difference suggests that the factors contributing to prehospital delay are consistent across studies of varying sizes and reporting quality. The extremely high heterogeneity observed with both weighting methods points to the diversity in study conditions, methodologies, and populations. Such heterogeneity implies that while the average delay is informative, individual study contexts, such as geographical location, healthcare infrastructure, patient awareness, and socioeconomic factors, greatly influence prehospital delay times.

Sample size weighting gives more influence on studies with larger sample sizes on the assumption that they provide more reliable estimates due to their statistical power. However, it may overlook the quality of the study design and reporting. STROBE weighting considers the quality of study reporting, based on the STROBE checklist, which could include aspects like the clarity of defining outcomes, completeness of data, and the extent of bias discussion. This method assumes that better-reported studies provide more reliable evidence, regardless of their sample size. This highlights the importance of both large-scale studies and high-quality reporting in understanding and addressing prehospital delays in AMI care.

4.2. Recommendations

The data highlight the critical need for targeted public health interventions to reduce prehospital delays in AMI treatment. Efforts should focus on enhancing public awareness about AMI symptoms and the importance of immediate medical attention.

There’s also a need for region-specific strategies to address the identified barriers to timely hospital arrival, such as improving transportation and access to emergency medical services, especially in rural or underserved areas like those suggested by the disparities observed between Pakistan and Nepal. Further research is needed to explore the underlying reasons for the variability in prehospital delay times, especially focusing on sociocultural, economic, and systemic healthcare factors that may influence patient behavior and access to care in different regions.Policymakers should consider these findings to develop and implement more effective strategies for reducing prehospital delays. This might include community education programs, improved emergency response systems, and policies that facilitate rapid access to care for AMI patients.

4.3. Strengths and limitations

The study on treatment delays for AMI patients in South Asia through systematic review and meta-analysis carries several strengths, including a rigorous approach to data collection and synthesis, which enhances the reliability and validity of the findings. It benefits from geographical diversity, shedding light on challenges across various settings, and examines a comprehensive range of factors affecting treatment delays, thus offering a holistic understanding of the issue. The application of established methodological frameworks like PRISMA and STROBE adds to its methodological rigor, and the identification of specific barriers to AMI care is crucial for targeted interventions.

However, the study faces limitations such as significant heterogeneity among included studies, potential publication bias, and limited data on the efficacy of interventions to reduce delays. Variability in healthcare systems across South Asia may impact the applicability of recommendations, and the study’s temporal and geographic scope could limit its relevance to recent developments or regions not adequately represented. Despite these challenges, the study provides essential insights into improving AMI care in South Asia settings.

5. Conclusion

The pooled median prehospital delay of patients with AMI in South Asia was 531 minutes (95% CI: 366-769 minutes). The pooled mean of door to ECG time, door - to- needle, and door – to- balloon times for AMI patients in our study were 9.18 (95% CI:2.52–15.84 minutes), 37.95 (95% CI: 30.11–45.78 minutes). and 62.92 minutes (95% CI: 45.28–80.56 minutes), respectively. The effects of gender, older age group and socioeconomic status were evident in the treatment delays of patients with AMI.

Funding Statement

This paper was not funded.

Article highlights

• Acute Myocardial Infarction (AMI) patients in South Asia experience significant treatment delays, especially in the prehospital phase.

• This systematic review and meta-analysis included 42 studies from five South Asian countries (India, Pakistan, Bangladesh, Nepal, Sri Lanka) published between 2000 and 2022.

• The pooled median prehospital delay was 531 minutes, far exceeding the recommended golden hour for AMI management.

• Pooled door-to-ECG time was 9.18 minutes and door-to-needle and door-to-balloon time was 37.95 minutes and 62.92 minutes.

• Subgroup analysis showed Pakistan had the highest prehospital delay, followed by India.

• Key factors contributing to delays included older age, sex, low literacy, rural residence, financial hardship, and misinterpretation of symptoms.

• Patients using ambulances had shorter delays than those using private transport, but ambulance access remained limited in rural areas.

• STROBE checklist-based quality scoring was used to assess study reporting rigor and to reweight meta-analysis, yielding similar delay estimates.

• Findings emphasize the need for targeted public health interventions, improved EMS infrastructure, and enhanced awareness about AMI symptoms to reduce delays.

Author contributions statement

Dr. Deepthi Ramamurthy contributed to the methodology, review of literature, data extraction, risk of bias assessment, analysis, writing the original draft, reviewing and editing the manuscript, journal selection, reviewing and agreeing on revisions, and ensuring accountability for the work published.

Dr. Meely Panda contributed to the methodology, review of literature, data extraction, risk of bias assessment, writing the original draft, reviewing and editing the manuscript, journal selection, reviewing and agreeing on revisions, and ensuring accountability for the work published.

Dr. Manjula Rangappa contributed to the methodology, review of literature, data extraction, risk of bias assessment, writing the original draft, reviewing and editing the manuscript, journal selection, reviewing and agreeing on revisions, and ensuring accountability for the work published.

Dr. Suthanthira Kannan contributed to the conceptualization, methodology, review of literature, data extraction, risk of bias assessment, analysis, writing the original draft, reviewing and editing the manuscript, journal selection, reviewing and agreeing on revisions, and ensuring accountability for the work published.

Dr. Rashmi Kundapur contributed to the conceptualization, methodology, review of literature, data extraction, risk of bias assessment, writing the original draft, reviewing and editing the manuscript, journal selection, reviewing and agreeing on revisions, and ensuring accountability for the work published.

Dr. Swetha Rajeshwari contributed to the methodology, review of literature, data extraction, risk of bias assessment, writing the original draft, reviewing and editing the manuscript, journal selection, reviewing and agreeing on revisions, and ensuring accountability for the work published.

Dr. Padmavathi Subbiah contributed to the methodology, review of literature, data extraction, risk of bias assessment, analysis, writing the original draft, reviewing and editing the manuscript, journal selection, reviewing and agreeing on revisions, and ensuring accountability for the work published.

Dr. Pradeep Aggarwal contributed to the conceptualization, methodology, risk of bias assessment, writing the original draft, reviewing and editing the manuscript, journal selection, reviewing and agreeing on revisions, and ensuring accountability for the work published.

Dr. Sumit Aggarwal contributed to the conceptualization, methodology, project administration, reviewing and editing the manuscript, journal selection, reviewing and agreeing on revisions, and ensuring accountability for the work published.

Disclosure Statement

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Data sharing statement

All the data and materials mentioned in the manuscript are available.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/14796678.2025.2541525