|Year : 2019 | Volume
| Issue : 1 | Page : 17-23
Treatment delayed is treatment denied: The tortuous pathway to care for acute coronary syndrome
Kathiresan Jeyashree1, Yogakeerthiga Paramasivam1, A Mathavan2, Ramalingam Vadivelu2, Rizwan Suliankatchi Abdulkader3
1 Department of Community Medicine, Velammal Medical College Hospital and Research Institute, Madurai, Tamil Nadu, India
2 Department of Cardiology, Velammal Medical College Hospital and Research Institute, Madurai, Tamil Nadu, India
3 Department of Statistics, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India
|Date of Web Publication||10-May-2019|
Dr. Rizwan Suliankatchi Abdulkader
Department of Statistics, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, Tamil Nadu
Source of Support: None, Conflict of Interest: None
Background: Time duration between symptom onset and treatment in acute coronary syndrome (ACS) is important in determining survival outcomes. This study aimed to determine the extent of delays that occur in the pathway to seeking specific treatment among ACS patients and to explore the associated factors. Materials and Methods: This cross-sectional study was conducted in the emergency department of a tertiary care hospital in Madurai, Tamil Nadu, India, among patients with ACS. A questionnaire was used to collect information on demographic details, clinical features, time duration, and health system-related factors. The time delay at various levels was expressed as median and interquartile range (IQR). Nonparametric tests were applied to test for statistical differences in subgroups. Results: Among 232 ACS patients, the median (IQR) delay from symptom onset to decision-making was 30 min (10, 240), from decision-making to arriving at the first facility was 30 min (15, 45), and from decision-making to receiving specific treatment was 23.3 h (1, 170). Nearly 91% of the patients contacted private health facilities first and only 21.1% received any specific treatment at the first facility they contacted. The two most common reasons for referral from a lower level health facility were lack of infrastructure and lack of a specialist. Conclusions: Significant delays occurred in the pathway to receiving specific treatment for ACS, especially due to delays in decision-making and number of facilities contacted in the initial period not being able to provide specific treatment. Private health facilities are more sought after for emergency care of ACS.
Keywords: Acute coronary syndrome, emergency treatment, tertiary health care, three-delay model
|How to cite this article:|
Jeyashree K, Paramasivam Y, Mathavan A, Vadivelu R, Abdulkader RS. Treatment delayed is treatment denied: The tortuous pathway to care for acute coronary syndrome. J Indian coll cardiol 2019;9:17-23
|How to cite this URL:|
Jeyashree K, Paramasivam Y, Mathavan A, Vadivelu R, Abdulkader RS. Treatment delayed is treatment denied: The tortuous pathway to care for acute coronary syndrome. J Indian coll cardiol [serial online] 2019 [cited 2019 May 19];9:17-23. Available from: http://www.joicc.org/text.asp?2019/9/1/17/257952
| Introduction|| |
Acute coronary syndrome (ACS) is an umbrella term for three conditions characterized by acute myocardial ischemia-unstable angina, non-ST elevation myocardial infarction (NSTEMI), and ST-elevation myocardial infarction (STEMI). It is the leading cause of death and disability in India, with the burden increasing every year. Between 2005 and 2015, there has been a 16% increase in deaths due to ischemic heart diseases (IHDs). According to the Global Burden of Diseases study, IHDs were responsible for 40.3 (38.2, 42.1) million disability-adjusted life years (DALYs) lost and 1.7 (1.6, 1.8) million deaths in 2016 in India. The state of Tamil Nadu ranks second among all the states in India in terms of DALY per 100,000 population lost due to IHD. The persistently high and rising burden of ACS, despite the treatments for ACS becoming refined and technologically sophisticated with passing time, clearly necessitates the need for further systematic breakdown of the pathway to care from symptom onset to delivery of specific care.
Time to treatment is one of the primary factors that decides both the treatment options and the outcome., The prehospital time delay can happen at three levels: the time to make a decision to call for help, the time to reach appropriate health-care facility, and the door-to-needle time. These are otherwise referred to as patient delay, emergency medical transport delay, and doctor delay. While these delays have reduced over the past few decades, they are still greater than the optimal recommendations. Some of the factors identified as being responsible for the prehospital delay are female sex, older age, poor insurance coverage, poor transport facilities, and lengthy referral pathways.,,
While these delays exist worldwide, they contribute differentially to the prehospital delay in developed and developing nations. Developing countries, however, do face additional challenges in surmounting these delays due to low awareness among patients, consent and affordability issues, poor health insurance coverage, and inadequacies in the health-care delivery system.
The Indian health-care system differs in many ways from that of the other countries. With wide inter-regional differences and where each state could be a country on its own, the factors contributing to delay in one state and the solutions offered might not be applicable to another. The inadequate availability and disproportionate distribution of skilled health workforce and infrastructure, dismally low level of health insurance coverage, belief in alternative medicine, and other social and economic determinants influence the health care-related choices of an individual. Only a few studies such as the CREATE study, ACS registries in Himachal Pradesh and Kerala, and individual hospital experiences have studied the prehospital delay in ACS., An in-depth analysis of the patterns of delay in our study settings is a prerequisite to be able to design appropriate community- and hospital-level interventions to improve the health care-seeking behavior and treatment outcomes of ACS.
This study was designed to identify the extent of delay that occurred along the pathway to seeking specific care for ACS beginning from symptom onset and the factors associated among patients diagnosed with ACS who visited the emergency department of a tertiary care hospital in Madurai.
| Materials and Methods|| |
This analytical cross-sectional study was conducted in the emergency department of a 600-bedded private tertiary health-care teaching hospital in Madurai. Madurai is a city in South India with a population of about 1 million. All patients reporting to the emergency department between April 2017 and November 2017 who were diagnosed with ACS (unstable angina and myocardial infarction) were consecutively enrolled into the study till the requisite sample size was achieved. The sample size was calculated based on prior studies in India, which reported the mean (standard deviation) prehospital delay as 13.5 (±22.7) h. Allowing an alpha error of 5%, the desired sample size was calculated as 230. After getting consent from the patient or primary caregiver who was with the patient for majority of the time during the course of events, the interviewer administered a structured questionnaire. The questionnaire consisted of sections for sociodemographic details, clinical details, and time-related data for the current illness episode. For recording patient outcomes after discharge from the hospital, follow-up interviews were conducted over phone.
Delays in the treatment pathway were calculated at three levels based on the three-delay model – symptom onset to decision-making, decision-making to arriving at the first health facility, and decision-making to receiving specific treatment. The three-delay model was first used in the context of maternal mortality but has been used for explaining treatment delays for other health conditions., In this study, we have also used this model to explain delays in the treatment of ACS. Symptom onset was calculated based on the first symptom that started off the current train of events leading to the presentation at the emergency department of the study hospital. A health facility was defined as a clinic/polyclinic/hospital/dispensary where the patient consulted a qualified medical practitioner (allopathic or alternative medicine). Specific treatment was defined as any measures taken to re-establish perfusion, namely, thrombolysis, percutaneous coronary intervention (PCI), or coronary artery bypass graft surgery. The delays were expressed as median and interquartile range. Subgroup analysis was conducted to determine factors associated with the delays, and statistical tests (Mann–Whitney test or Kruskal–Wallis test) were applied to determine statistically significant differences. P < 0.05 was considered statistically significant. All analyses were conducted in SPSS software version 20 (IBM Corp., version 20 Armonk, NY: USA).
The institutional ethics committee of the study institution approved the study. All participants provided an informed consent.
| Results|| |
Demographic and clinical features of the patients
Among 232 participants with ACS who were included in the study, 85.3% were male. About two-thirds of them had received any schooling, and nearly half of the patients were employed as semi-skilled or unskilled workers [Table 1]. Forty-seven percent were diabetic, 32% were hypertensive, and about 9% had preexisting cardiac illness. Chest pain was the major presenting symptom in 89% of the patients, followed by dyspnea (33%), pain at other sites (13.4%), and syncope (9.5%). The final diagnosis was STEMI in 167 (72.6%), NSTEMI in 34 (14.8%), and unstable angina in 14 patients (6.1%), and the remainder had a generic diagnosis written as ACS or coronary artery disease. A follow-up of the patients showed that 90.1% had received a specific treatment for ACS, and the details for the remaining could not be retrieved even after two attempts to contact them post discharge. Among those who received specific treatment, only one patient died.
|Table 1: Profile of patients presenting with or diagnosed with acute coronary syndrome|
Click here to view
Delays in the pathway to seeking treatment of acute coronary syndrome and factors associated
The flow of patients from symptom onset, through the study facility and specific treatment, is presented in [Figure 1]. The median time taken from symptom onset to decision-making (Delay 1) was 30 min (interquartile range [IQR]: 10–240) and that from decision-making to arrival at the first health facility (Delay 2) was also 30 min (15–45). Among those who received specific treatment, the median time from decision-making to receiving specific treatment (Delay 3) was 23.3 h (1–170) [Table 2].
|Figure 1: Pathway to specific treatment for acute coronary syndrome in patients presenting with or diagnosed with acute coronary syndrome|
Click here to view
|Table 2: Factors affecting time to receiving specific treatment for patients presenting with or diagnosed with acute coronary syndrome|
Click here to view
Wide variations were noted in these time delays in different subgroups. In general, the delays were greater for women, older persons (≥45 years), unemployed persons, those with a similar past episode, those with no positive family history, and those with a diagnosis of unstable angina compared to those who had STEMI/NSTEMI. However, statistically significant differences were noted only for a few factors. Delay 2 was higher in unemployed persons (25 vs. 15 min) as compared to persons employed as skilled worker or professional or businessman, among people with diabetes (30 vs. 15 min), as compared to those without diabetes and among those who had preexisting cardiac illness (30 vs. 20 min) as compared to those who had none. Delay 3 was significantly higher among those with a diagnosis of unstable angina (159.6 vs. 6 h) as compared to STEMI patients [Table 2]. Seventy percent of the patients were covered by the government insurance scheme and 10% by other private insurance schemes. Only 5 (2.2%) had used a government ambulance for transport to hospital for treatment.
Health system factors in seeking care for acute coronary syndrome
Only 2.6% came directly to the study facility. Specific treatment was received at the first facility visited by only 21.1% of the patients. Almost 50% of the patients received specific treatment only in the second facility that they had visited, 17.7% in the third facility, and 3.4% received specific treatment only in the fourth facility visited. For 9.5% of the patients, the status of receipt of specific treatment could not be obtained despite two attempts to contact them over phone after discharge from the study facility [Figure 1]. The proportion of those who received specific treatment increased with the number of facilities visited – 66.7%, 90.4%, 90.3%, and 100% for those who visited one, two, three, and four facilities, respectively. Interestingly, among those who visited two facilities, 30.4% had already received specific treatment in the previous facility. Similarly, 45.8% of those who visited three facilities and 57.9% of those who visited four facilities had already received specific treatment in the previous facilities.
Nearly 91% of the first health facility approached by the ACS patients was a private facility. The most common factor that affected decision-making was fear for life (97.7%), followed by financial concerns (28.8%) and concerns about a caretaker (14.9%). The most common reasons for referral were lack of infrastructure (90%), lack of a specialist (19.3%), and unaffordability (9.3%) [Table 3].
|Table 3: Health system-related factors in patients presenting with or diagnosed with acute coronary syndrome|
Click here to view
| Discussion|| |
Our study on 232 patients who presented with or were diagnosed with ACS in a tertiary health-care facility in Madurai, India, showed that significant delays occurred in the pathway to receiving specific treatment for ACS. These delays occurred in decision-making and contacting more than one facility before receiving specific treatment. The decision delay found in our study was much less than that reported elsewhere in India., Less than a quarter of patients received any specific treatment for their condition at the first facility they had contacted. On an average, one whole day passed before an ACS patient received an ACS-specific treatment. This delay is well past the golden period of 120 min before reperfusion.
A recent study conducted among ACS patients presenting at a tertiary care hospital in Mumbai found that prehospital delays were significant, with 83% reaching the hospital 2 h after symptom onset. Xavier et al. reported that the median prehospital delay was 6 h. The pattern of ACS diagnoses was similar to what has been reported by other studies in the Indian scenario, with a preponderance of STEMI (72.6%). In our study, the median time to reach the first health facility after symptom onset was found to be about 60 min, but the median delay from decision made to seek care to receiving a specific treatment was 23 h. This could be due to many reasons.
First, while patients were able to decide fairly quickly that they have to seek medical attention for their symptoms and also reached the first facility within the next hour, they did not reach an appropriate facility capable of offering specific treatment for ACS. In our study, around 80% of patients had to visit at least two facilities before receiving specific treatment. This is probably because of the lack of awareness about the ACS treatment options and the facilities capable of offering them, leading to time lost in hopping between facilities. In a qualitative study from Kerala, the authors found that making multiple stops before arriving at a facility capable of any specific treatment for ACS was not uncommon. Time lost in traveling through and between facilities could alter the treatment options and outcome. Knowing and reaching out to an appropriate facility are crucial to reducing total ischemic time and saving the salvageable myocardium. This reason for delay can be addressed by interventions planned to target the patients, providers, and/or the health-care facilities. Only when the patient is aware of the possibility that his/her symptoms could be due to ACS, can s/he proceed to choosing an appropriate facility for specific treatment. A multicomponent intervention targeting patients promoting self-diagnosis of ACS and self-referral and self-treatment with aspirin was found to reduce the time to thrombolysis.
Second, the logistic and workforce issues due to which 24 × 7 specialist (internal medicine) services are unavailable in most public health-care facilities below the level of a district hospital. The lack of a specialist doctor and infrastructure or round-the-clock services in the lower level health facilities was one of the major reasons for referral in our study. Keeping in mind the limitations in low- and middle-income countries like India, thrombolysis has been suggested as a reasonable first-line intervention where reaching a facility for PCI could be delayed. But even that was available to only about 80% of the patients until they reached the second health-care facility. Urban-centric, asymmetrical development of specialist health-care services is a reason why, despite rapid strides being made in the field of interventional cardiology, specific treatment within the golden hour is still a distant dream for many ACS patients in India. For majority of the patients in our study, a private health facility was the first option establishing the undeniable role of private facilities in the pathway to care for ACS patients. But even among private hospitals, which are a heterogeneous group of private clinics, polyclinics, or corporate hospitals, not all offer specific treatment for ACS. They also pose affordability issues to the patient, which is not the case with public facilities. Government-funded insurance which had protected a significant number of patients offers some cushion to protect patients from impoverishment due to ACS treatment, which prevents nonseeking of treatment due to financial reasons.
Third, the delay could have occurred in the emergency transport between facilities. Though a public ambulance helpline is in place in the study area, most of the patients have used a private ambulance or a private vehicle to travel to the hospital. The geographical distance and terrain are not expected to be major players in our scenario, populated by many private hospitals and one of the state's largest private teaching hospitals.
The assessment of factors associated with the delay showed that the delay was more common in vulnerable groups such as the unemployed and those with preexisting illnesses. However, the sample size was not sufficient to establish statistical significance for many other factors.
Strengths and limitations
Unlike previous hospital-based studies in India, which did not explore the pathway to seeking care and the health system-related factors associated with ACS, we have attempted to identify the referral pattern of patients presenting to our hospital with ACS and to understand the health system-related factors that may affect the provision of care. A few limitations must be considered as well. First, the patients were enrolled in a consecutive fashion and not randomly, which is likely to raise concerns of generalizability. However, the demographic profile of patients does not seem to suggest any major deviation from the general population. Second, the fact that the study was conducted in a private hospital limits our ability to generalize the findings to a group of patients who might have sought care from a government tertiary care hospital, which is mostly accessed by poor patients. Third, we were unable to associate the treatment outcomes with delay because of insufficient number of unfavorable outcomes. Finally, this being a hospital-based, cross-sectional study would not have captured those who did not seek treatment or those who had severe disease and collapsed before they could seek help.
| Conclusions|| |
This study has found significant delays in the pathway to receiving specific treatment for ACS. These delays could significantly affect the health outcomes in such patients.
- Raising awareness in the community about the golden hour in ACS and the need to reach out to an appropriate facility to have better patient outcomes has to be emphasized
- Strengthening health-care facilities with diagnostic facilities such as electrocardiogram (ECG) and cardiac enzymes coupled and provisions to thrombolyse and stabilize the patient en route to a higher facility for definitive treatment is the need of the hour
- Training of workforce at 24 × 7 primary health-care centers to diagnose and administer first-level care to ACS patients along with establishing proper referral linkages and emergency transport will also reduce the total ischemic time
- In resource-limited settings like India, remote support by specialists can quicken the diagnosis of ACS using various telemedicine applications to interpret ECG findings and cardiac enzyme reports and help make the decision to shift the patient for early treatment.
The authors acknowledge the hospital staff in facilitating data collection and providing the necessary information as and when required.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Fuster V, Kovacic JC. Acute coronary syndromes: Pathology, diagnosis, genetics, prevention, and treatment. Circ Res 2014;114:1847-51.
Institute for Health Metrics and Evaluation. India Country Profile. Seattle, WA: IHME, University of Washington; 2016. Available from: http://www.healthdata.org/india
. [Last accessed on 2017 Jan 07].
Institute for Health Metrics and Evaluation. GBD Compare Data Visualization. Seattle, WA: IHME, University of Washington; 2016. Available from: http://www.vizhub.healthdata.org/gbd-compare
. [Last accessed on 2017 Dec 10].
Indian Council of Medical Research, Public Health Foundation of India, and Institute for Health Metrics and Evaluation. GBD India Compare Data Visualization. New Delhi: ICMR, PHFI, and IHME; 2017. Available from: http://www.vizhub.healthdata.org/gbd-compare/india
. [Last accessed on 2017 Dec 10].
Cullen L, Greenslade JH, Menzies L, Leong A, Than M, Pemberton C, et al.
Time to presentation and 12-month health outcomes in patients presenting to the emergency department with symptoms of possible acute coronary syndrome. Emerg Med J 2016;33:390-5.
George E, Savitha D, Pais P. Pre hospital issues in acute myocardial infarction. J Assoc Physicians India 2001;49:320-3.
Mol KA, Rahel BM, Meeder JG, van Casteren BC, Doevendans PA, Cramer MJ. Delays in the treatment of patients with acute coronary syndrome: Focus on pre-hospital delays and non-ST-elevated myocardial infarction. Int J Cardiol 2016;221:1061-6.
O'Gara PT, Kushner FG, Ascheim DD, Casey DE Jr., Chung MK, de Lemos JA, et al.
2013 ACCF/AHA guideline for the management of ST-elevation myocardial infarction: A report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Circulation 2013;127:e362-425.
Mehta S, Granger C, Grines CL, Jacobs A, Henry TD, Rokos I, et al.
Confronting system barriers for ST-elevation MI in low and middle-income countries with a focus on India. Indian Heart J 2018;70:185-90.
McKee G, Mooney M, O'Donnell S, O'Brien F, Biddle MJ, Moser DK. Multivariate analysis of predictors of pre-hospital delay in acute coronary syndrome. Int J Cardiol 2013;168:2706-13.
Xavier D, Pais P, Devereaux PJ, Xie C, Prabhakaran D, Reddy KS, et al.
Treatment and outcomes of acute coronary syndromes in India (CREATE): A prospective analysis of registry data. Lancet 2008;371:1435-42.
Wechkunanukul K, Grantham H, Clark RA. Global review of delay time in seeking medical care for chest pain: An integrative literature review. Aust Crit Care 2017;30:13-20.
Mehta S, Granger CB, Henry TD, Grines CL, Lansky A, Rokos I, et al.
Reducing system delays in treatment of ST-elevation myocardial infarction and confronting the challenges of late presentation in low and middle-income countries. Indian Heart J 2017;69 Suppl 1:S1-5.
Negi PC, Merwaha R, Panday D, Chauhan V, Guleri R. Multicenter HP ACS registry. Indian Heart J 2016;68:118-27.
Patel A, Mohanan PP, Prabhakaran D, Huffman MD. Pre-hospital acute coronary syndrome care in Kerala, India: A qualitative analysis. Indian Heart J 2017;69:93-100.
Waiswa P, Kallander K, Peterson S, Tomson G, Pariyo GW. Using the three delays model to understand why newborn babies die in Eastern Uganda. Trop Med Int Health 2010;15:964-72.
Barnes-Josiah D, Myntti C, Augustin A. The “three delays” as a framework for examining maternal mortality in Haiti. Soc Sci Med 1998;46:981-93.
George L, Ramamoorthy L, Satheesh S, Saya RP, Subrahmanyam DK. Prehospital delay and time to reperfusion therapy in ST-elevation myocardial infarction. J Emerg Trauma Shock 2017;10:64-9.
] [Full text]
Khan A, Phadke M, Lokhandwala YY, Nathani PJ. A study of prehospital delay patterns in acute myocardial infarction in an urban tertiary care institute in Mumbai. J Assoc Physicians India 2017;65:24-7.
Lee ES, Vedanthan R, Jeemon P, Kamano JH, Kudesia P, Rajan V, et al.
Quality improvement for cardiovascular disease care in low- and middle-income countries: A systematic review. PLoS One 2016;11:e0157036.
Prabhakaran D, Jeemon P, Mohanan PP, Govindan U, Geevar Z, Chaturvedi V, et al.
Management of acute coronary syndromes in secondary care settings in Kerala: Impact of a quality improvement programme. Natl Med J India 2008;21:107-11.
Dalal J, Sahoo PK, Singh RK, Dhall A, Kapoor R, Krishnamurthy A, et al.
Role of thrombolysis in reperfusion therapy for management of AMI: Indian scenario. Indian Heart J 2013;65:566-85.
Ramakrishnan S, Mishra S, Chakraborty R, Chandra KS, Mardikar HM. The report on the Indian coronary intervention data for the year 2011 – National Interventional Council. Indian Heart J 2013;65:518-21.
[Table 1], [Table 2], [Table 3]