Abstract
Background
No population representative data on characteristics, treatment, and outcome were available in acute coronary syndrome (ACS) patients.
Methods
The clinical characteristics, treatment, and in-hospital outcome of 5180 ACS patients registered in multicenter ACS Registry across 33 hospitals in the state since January 2012 to December 2014 are reported. ACS was diagnosed using standard criteria.
Result
70.8% were men; mean age was 60.9 ± 12.1. NSTEMI was more frequent than STEMI (54.5% vs. 45.5%). 83.3% of the ACS population were from rural area. Pre-hospital delay was long, with a median of 780 min. 35.6% of STEMI patients received thrombolytic therapy. Evidence-based treatment was prescribed in more than 80% of ACS patients, and the treatment was similar in men and women across all types of health care centers. In-hospital mortality was 7.6%, and was more frequent in STEMI than in NSTEMI (10.8% vs. 5.0%, p < 0.001).
Interpretation
Pre-hospital delay was long, and use of reperfusion therapy was significantly lower. The in-hospital death rates are higher.
Keywords: Acute coronary syndrome, Registry, Outcomes
1. Introduction
Coronary artery disease (CAD) is a leading cause of morbidity and mortality in developed and developing countries.1, 2 Acute coronary syndrome (ACS) is a commonest mode of presentation among patients of CAD and carrying high rate of morbidity and mortality.3 The incidence and outcomes of ACS depend upon population exposure to risk factors, access to quality health care services, and health seeking behavior of the community.3, 4, 5, 6, 7, 8, 9, 10 These, in turn, are influenced by socioeconomic status, access to health information, geographical characteristics, and cultural practices. Thus, the characteristics, the treatment, and outcomes are likely to vary in different parts of the country and between the countries.3, 4, 5, 6, 7, 8, 9, 10, 11 The CREATE registry data are based on data captured by volunteered participating ACS Registry centers, which are mostly tertiary care centers catering to urban population.12 80% of the population in India live in rural area. The characteristics of rural and urban ACS populations are not reported in any of the ACS registries from India. The Kerala ACS Registry, one of the largest state based registries of India, has reported the characteristics, treatment, and outcomes that are different from CREATE registry.13 The hill state of Himachal Pradesh (HP) has better literacy rate14 and health indicators15 than national average. The health care services in HP are delivered primarily by the health care centers in the government sector. There are only two teaching institutions in the state, and IGMC Shimla is the only tertiary care center with facilities for coronary angioplasty and CABG. The ACS patients are admitted and treated by secondary care hospitals; district hospitals, civil hospitals located at district head quarters, and at some block level, respectively. In some districts, there are private hospitals with indoor facilities, where patients of ACS are admitted and treated. The health care access to rural ACS population is limited by the long traveling time (Fig. 1). There are no population representative data available on patient's characteristics, treatment practices, and outcomes.
Fig. 1.
District wise distribution of different levels of ACS registry centers.
Thus, the multicentric ACS Registry was initiated in March 2012 through networking of two teaching hospitals in the state, all physician-manned hospitals in the government sector, and volunteered hospitals in the private sector to capture the data in predesigned format. This actual preparatory work for establishing HP ACS Registry was started since May 2011 that involved training and meeting with physicians, designing and development of web-based e-recording format.
2. Methods
2.1. Ethical approval
The registry protocol was approved by the institutional review board of IGMC Shimla, and data were collected from the eligible patients after obtaining their informed consent.
2.2. Selection of registry centers
All physician-manned centers in non-teaching hospital treating patients of ACS in the state of HP in government sectors were listed and were included in the registry. All new centers with physicians posted after start of registry were tracked and were also included subsequently. The district hospital of Una did not participate actively and were excluded. One tribal district hospital of Lahaul and Spiti, which was without the physician, was not included, as ACS patients were not treated and admitted. Thus, the registry covered 10 out of 12 districts. The registry also included both teaching hospitals, IGMC Shimla with catheterization facilities, and RPGMC hospital Tanda, which was without catheterization services. The hospitals in private sectors treating patients of ACS were also enlisted, and request was sent to these individual centers. Those volunteered to participate in the registry were linked to this multicenter HP ACS Registry. Out of 12 hospitals in private sector treating patients of ACS, only 7 volunteered to participate. Thus, a total of 33 registry centers, two teaching hospitals, 24 secondary care hospitals in government sector, and 7 private hospitals formed the part of multicentric HP ACS Registry. These registry centers are covering more than 90% of the HP population both rural and urban equally. The non-physician-manned primary care hospitals do not treat and admit these patients and are referred to these secondary level hospitals in govt. sector. The teaching hospital IGMC, having catheterization facility, functioned as the coordinating and central registry center.
2.3. Diagnosis of ACS
Patients are diagnosed as ACS if presenting with acute chest pain suggestive of ACS within preceding one week, associated with any one of the following:
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ECG changes of ST segment deviation or T wave inversion or Q waves in two or more contiguous leads
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Elevated markers of myocardial injury
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History of established CAD in past
2.4. STEMI
The patient is diagnosed when chest pain suggestive of ACS is associated with ST segment elevation with and without ST segment depression in two or more contiguous leads.
2.5. NSTEMI
The patient is diagnosed when chest pain suggestive of ACS is associated with ST segment depression and or T wave inversion, Q waves in two or more contiguous leads associated with elevated levels of markers of myocardial injury.
2.6. Unstable angina
The unstable angina was diagnosed in patients presenting with chest pain suggestive of ACS without ECG and without elevated level of myocardial injury but with past history of documented CAD.
2.7. Case ascertainment
The case ascertainment in each registry center is done by the local treating physician, and data from case record are entered by the trained data entry operator of the respective center in consultation with treating physician in the pre-structured recording format. Thus, the diagnosis of ACS and type of ACS, STEMI, or NSTEMI (including unstable angina) was based on clinical documentation by treating physician, and no core lab was used. The data are uploaded in the e-recording format in the HP ACS web site after discharge or death.
2.8. Data collection
Data related to demographics, education, status of urban/rural background, mode of transportation, pre-hospital delay, CV risk factors, hemodynamic status, ACS types, treatments, and in-hospital outcomes were recorded.
2.9. Quality control
Internal quality control is addressed by training of all participating physician and data entry operators employed by the state health services for reporting other national program-related health data. The quality of data and any missing data were monitored on a daily basis at coordinating center by the data manager, and any discrepancy in data entered are immediately communicated to the respective registry center and is corrected and settled. The external quality control was monitored by periodic site visit by the field research assistant and the data entered are crosschecked from the source document.
The external quality control is monitored by periodic visit by the project field research assistant at each site to validate the data entered from the source document and case file.
2.10. Statistical analysis
Data are reported as absolute frequency and relative frequency for categorical variables and mean ± SD for continuous variables. The significance of differences in the distribution of categorical variables among STEMI and NSTEMI population, urban and rural population, men and women were analyzed using χ2-test for normal distribution and Mann–Whitney test for skewed distribution of the characteristics. The difference in distribution of continuous variables was compared using unpaired t-test for normally distributed variable and Mann–Whitney test for skewed distribution. Multi-variable logistic modeling of clinical characteristics was done to estimate the independent predictors of in-hospital mortality. Since pre-hospital delay variable had skewed distribution, thus was log transformed before modeling. The log-transformed pre-hospital delay had normal distribution. Two-tailed significance at <0.05 was taken as statistically significant. Statistical analysis was done using statistical software STA version 13.
3. Results
3.1. Sociodemographic characteristics
Detailed description of sociodemographic characteristics is given in Table 1, Table 3, Table 5. In brief, the mean age of the ACS population was 60.9 ± 12.1. STEMI ACS population was younger than NSTEMI (59.9 ± 12.1 vs. 61.8 ± 12.0, p < 0.01). The rural and women ACS populations were older than urban and men ACS populations. NSTEMI was more common than STEMI (54.5% vs. 45.5%, p < 0.01). ACS was more frequent in men than in women (70.8% vs. 29.2%). NSTEMI was relatively more common in women, while STEMI was more common in men. The level of education was not different among STEMI and NSTEMI groups. Proportion of ACS population (both STEMI and NSTEMI) from rural background was significantly higher than from urban background (83.3% vs.16.7%). The ACS population with STEMI were more frequently admitted in tertiary care centers, while NSTEMI were admitted in secondary care hospitals.
Table 1.
Sociodemographic characteristics, pre-hospital delay, mode of transportation and types of hospital wise distribution of ACS population.
| Characteristics | Overall group n = 5807 |
STEMI n = 2641 (45.5%) |
NSTEMI/Unstable angina n = 3166 (54.5%) |
p value |
|---|---|---|---|---|
| Demographic characteristics | ||||
| Age strata | 60.94 ± 12.10 | 59.89 ± 12.10 | 61.82 ± 12.02 | 0.001 |
| <40 years | 260 (4.5%) | 140 (5.3%) | 120 (3.8%) | 0.001 |
| 41–50 years | 1018 (17.5%) | 498 (18.9%) | 520 (16.4%) | |
| 51–60 years | 1679 (28.9%) | 793 (30%) | 886 (28.0%) | |
| 61–70 years | 1655 (28.5%) | 740 (28%) | 915 (28.9%) | |
| >71 years | 1195 (20.6%) | 470 (17.8%) | 725 (22.9%) | |
| Gender (men) | 4112 (70.8%) | 2043 (77.4%) | 2069 (65.4%) | 0.001 |
| Education (median inter quartile range) | 5977 | 2636 | 3163 | 0.2 |
| 8.0 (0.0–10.0) | 8.0 (0.0–10.0) | 8.0 (0.0–20.0) | ||
| Urban | 971 (16.7%) | 464 (17.6%) | 507 (16.0%) | 0.1 |
| Rural | 4836 (83.3%) | 2177 (82.4%) | 2659 (84.0%) | |
| Pre-hospital delay (median, inter quartile range) minutes | 780 (242–2358) | 780.0 (258–2298) | 780.0 (240–2410) | 0.45 |
| Proportion reaching hospital within 6 h | 1915 (33.0%) | 844 (32.0%) | 1071 (33.8%) | 0.13 |
| Proportion reaching within 12 h | 858 (14.8%) | 412 (15.6%) | 446 (14.1%) | 0.81 |
| Reasons for delayed reporting | ||||
| Ignorant of symptoms of ACS | 1617 (33%) | 722 (31.4%) | 895 (34.3%) | 0.03 |
| Consulting local practitioners/PHC | 1426 (43.1%) | 752 (47.6%) | 674 (39.0%) | |
| Mode of transportation | 0.001 | |||
| 108 ambulance | 862 (15.4%) | 371 (14.4%) | 491 (16.1%) | |
| Private vehicle | 2371 (42.3%) | 1094 (42.6%) | 1277 (42.0%) | |
| Taxi | 1395 (24.9%) | 635 (24.7%) | 760 (25.0%) | |
| Public transport | 450 (8.0%) | 190 (7.4%) | 260 (8.5%) | |
| Ambulance | 382 (6.8%) | 216 (8.4%) | 166 (5.5%) | |
| Any other | 149 (2.7%) | 62 (2.4%) | 87 (2.9%) | |
| Teaching hospitals with catheterization facilities | 1701 (29.3%) | 882 (33.4%) | 819 (25.9%) | 0.001 (trends) |
| RPGMC | 1158 (19.9%) | 487 (18.4%) | 671 (21.2%) | |
| Non-teaching hospital (govt. sector) | 2424 (41.7%) | 1025 (38.8%) | 1399 (44.2%) | |
| Non-teaching hospital (private hospital) | 524 (9.0%) | 247 (9.4%) | 277 (8.7%) | |
Table 3.
Demographics, clinical characteristics, treatment, and outcomes of urban and rural ACS population.
| Characteristics | Overall | Urban | Rural | p value |
|---|---|---|---|---|
| Age (mean ± SD) | 60.9 ± 12.1 | 58.6 ± 12.2 | 61.4 ± 12.0 | 0.001 |
| Age groups | 0.0001 | |||
| <40 years | 260 (4.5%) | 69 (7.1%) | 191 (3.9%) | |
| 41–50 years | 1018 (17.5%) | 202 (20.8%) | 1816 (16.9%) | |
| 51–60 years | 1679 (28.9%) | 299 (30.8%) | 1380 (28.5%) | |
| 61–70 years | 1655 (28.5%) | 234 (24.6%) | 1421 (29.4%) | |
| >71 years | 1195 (20.6%) | 167 (17.2%) | 1028 (21.3%) | |
| Gender | 0.001 | |||
| Men | 4112 (70.8%) | 753 (77.5%) | 3359 (69.5%) | |
| Women | 1695 (29.5%) | 218 (22.5%) | 1477 (30.5%) | |
| Awareness about symptoms of ACS | 2286 (40.6%) | 413 (44.1%) | 1873 (40.0%) | 0.01 |
| Mode of transportation | 0.0001 | |||
| 108 services | 862 (15.4%) | 110 (11.7%) | 752 (16.1%) | |
| Ambulance | 382 (6.8%) | 57 (6.0%) | 325 (7.0%) | |
| Personal vehicle | 2371 (42.3%) | 532 (56.4%) | 1839 (39.4%) | |
| Public Transportation | 450 (8.0%) | 39 (4.1%) | 411 (8.8%) | |
| Taxi | 1395 (24.9%) | 167 (17.7%) | 1228 (26.3%) | |
| Any other | 149 (2.7%) | 38 (4.0%) | 111 (2.4%) | |
| Median pre-hospital delay (I.Q. range), minutes | 780 (242–2358) | 450 (139–1486) | 849 (280–2532) | 0.0001 |
| STEMI | 2641 (45.5%) | 464 (47.8%) | 2177 (45.0%) | 0.11 (trend) |
| NSTEMI | 3166 (54.5%) | 507 (52.2%) | 2659 (55.0%) | |
| CV risk factors | ||||
| Diagnosed hypertension | 2510 (43.4%) | 493 (51.0%) | 2017 (41.9%) | 0.00001 |
| Diagnosed diabetes | 1022 (17.7%) | 233 (24.1%) | 784 (16.4%) | 0.0001 |
| Tobacco smokers | 3658 (63.4%) | 562 (58.2%) | 3096 (64.5%) | 0.0001 |
| Sedentary | 1716 (29.7%) | 466 (48.2%) | 1250 (25.9%) | 0.0001 |
| BMI | 4953 (24.0 ± 4.2) | 777 (24.3 ± 4.2) | 4176 (23.9 ± 4.2) | 0.04 |
| Treatment | ||||
| ASA | 5792 (99.8%) | 967 (99.8%) | 4825 (99.8%) | 0.8 |
| Clopidogrel | 5792 (98.8%) | 967 (99.8%) | 4825 (99.8%) | 0.8 |
| ACE inhibitors/ARB | 4815 (83.7%) | 806 (83.8%) | 4009 (83.6%) | 0.9 |
| B Blockers | 4706 (81.1%) | 797 (82.08%) | 3909 (80.8%) | 0.3 |
| Statins | 5057 (87.1%) | 852 (87.7%) | 4205 (86.9%) | 0.5 |
| Thrombolytic therapy | 939 (16.2%) | 230 (23.7%) | 709 (14.6%) | 0.001 |
| Hypotension | 628 (12.6%) | 89 (9.9%) | 539 (13.2%) | 0.01 |
| Cardiogenic shock | 455 (10.2%) | 69 (8.3%) | 386 (10.6%) | 0.04 |
| A Fib | 5 (0.1%) | 1 (0.1%) | 4 (0.1%) | 0.86 |
| CHB | 246 (4.7%) | 49 (5.4%) | 197 (4.5%) | 0.2 |
| VT/VFib. | 50 (0.9%) | 13 (1.4%) | 37 (0.8%) | 0.09 |
| Coronary angiography done | 83 (1.4%) | 20 (2.1%) | 63 (1.3%) | 0.07 |
| Heparin | 4460 (76.8%) | 726 (74.8%) | 3734 (77.3%) | 0.09 |
| VT/VFib. | 50 (0.9%) | 13 (1.4%) | 37 (0.8%) | 0.09 |
| Coronary angiography done | 83 (1.4%) | 20 (2.1%) | 63 (1.3%) | 0.07 |
| Outcomes | ||||
| Death | 442 (7.6%) | 61 (6.3%) | 381 (7.9%) | 0.08 |
| Bleeding | 55 (1.0%) | 7 (0.8%) | 48 (1.2%) | 0.6 |
Table 5.
Clinical characteristics, treatment, and outcomes in ACS population registered across types of hospitals.
| Characteristics | Overall | Teaching hospitals with catheterization facilities (IGMC) | Teaching hospital without catheterization facilities | Secondary level institutions in Govt. sector | Secondary level hospitals in private sector | p value |
|---|---|---|---|---|---|---|
| Age | 60.9 ± 12.1 | 59.5 ± 12.2 | 59.5 ± 12.2 | 613 ± 11.9 | 62.0 ± 12.2 | 0.001 |
| Sex (men) | 4112 (70.8%) | 1290 (75.8%) | 782 (67.5%) | 1666 (68.7%) | 374 (71.4%) | 0.001 |
| Education level, median (I.Q. range) | 8 (0–10) | 8 (0–10) | 8 (0–10) | 6 (0–10) | 8 (0–10) | 0.01 |
| Urban | 971 (16.7%) | 446 (26.2%) | 52 (4.5%) | 413 (17.0%) | 60 (11.5%) | 0.001 |
| Rural | 4836 (83.3%) | 1255 (73.8%) | 1106 (95.5%) | 2011 (83.0%) | 464 (88.5%) | |
| Pre-hospital delay, median (I.Q. range), minutes | 780 (242–2358) | 970 (330–2784) | 630 (265–1920) | 544 (195–1710) | 795 (183–2655) | 0.0001 |
| Proportion of ACS reporting within 6 h | 1915 (33.0%) | 366 (21.5%) | 405 (35.0%) | 952 (39.5%) | 192 (36.6%) | 0.001 |
| CV risk factors | ||||||
| Hypertension | 2510 (43.4%) | 635 (37.4%) | 502 (43.4%) | 1104 (45.7%) | 269 (52.3%) | 0.001 |
| Diabetes | 1022 (17.7%) | 304 (17.9%) | 248 (21.4%) | 386 (16.0%) | 84 (16.4%) | 0.01 |
| Tobacco Smoker | 3658 (63.4%) | 1189 (70.0%) | 733 (63.4%) | 1451 (60.5%) | 285 (52.2%) | 0.001 |
| Sedentary | 1716 (29.7%) | 675 (39.7%) | 181 (15.6%) | 698 (29.0%) | 162 (31.5%) | 0.0001 |
| BMI | 4953 (24.0 ± 4.2) | 1200 (22.2 ± 3.4) | 1119 (23.6 ± 4.1) | 2166 (25.2 ± 4.3) | 468 (23.9 ± 4.2) | 0.001 |
| STEMI | 2641 (45.5%) | 882 (51.9%) | 487 (42.1%) | 1025 (42.3%) | 247 (48.1%) | 0.001 |
| NSTEMI | 3166 (54.5%) | 819 (48.1%) | 671 (57.9%) | 1399 (57.7%) | 277 (52.9%) | 0.01 |
| Cardiogenic shock | 455 (10.2%) | 164 (11.2%) | 111 (14.3%) | 143 (7.7%) | 37 (10.0%) | 0.001 |
| Hypertensive | 628 (12.6%) | 220 (13.4% | 164 (17.8%) | 204 (10.1%) | 40 (9.6%) | 0.001 |
| CHB | 246 (4.7%) | 91 (6.0%) | 43 (4.2%) | 89 (3.9%) | 23 (4.8%) | 0.02 |
| VT/VF | 50 (0.9%) | 12 (0.8%) | 12 (1.2%) | 17 (0.8%) | 9 (1.9%) | 0.08 |
| Treatment | ||||||
| ASA | 5780 (100%) | 1695 (100%) | 1158 (100%) | 2405 (100%) | 522 (100%) | |
| Clopidogrel | 5789 (100%) | 1696 (100%) | 1158 (100%) | 2412 (100%) | 523 (100%) | |
| B Blockers | 4706 (81.1% | 1297 (76.3%) | 923 (79.7%) | 2059 (84.9%) | 427 (81.6%) | 0.001 |
| ACE inhibitors/ARB | 4716 (82.5%) | 1394 (83.5%) | 877 (77.0%) | 2013 (86.3%) | 382 (74.3%) | 0.0001 |
| Statins | 5650 (97.3%) | 1664 (97.9%) | 1150 (99.3%) | 2364 (97.5%) | 472 (90.2%) | 0.001 |
| Proportion of STEMI received thrombolytic therapy | 939 (35.6%) | 286 (32.5%) | 207 (42.5%) | 322 (31.4%) | 124 (50.2%) | 0.001 |
| PCI | 37 (0.6%) | 37 (2.2%) | Nil | Nil | Nil | |
| Outcomes | ||||||
| Death | 442 (7.6%) | 184 (10.8%) | 123 (10.6%) | 104 (4.3%) | 31 (5.9%) | 0.0001 |
| Bleeding | 55 (1.0%) | 10 (0.7%) | 15 (1.5%) | 25 (1.1%) | 5 (1.0%) | 0.02 |
| Post-MI angina | 125 (2.4%) | 42 (2.8%) | 5 (0.5%) | 65 (2.9%) | 13 (2.7%) | 0.03 |
| Hospital stay, Median (I.Q. range) days | 4 (3–6) | 5 (3–7) | 4 (2–5) | 5 (3–6) | 5 (2–6) | 0.001 |
3.2. Pre-hospital delay and mode of transportation
Median pre-hospital delay was 780 min and was similar in both STEMI and NSTEMI. Only 33% reached within 6 h. The characteristics of the ACS population reporting late in the hospitals were women, patients from rural background, and those reporting tertiary care centers and private hospitals. The reasons for late reporting were ignorant about the symptoms of ACS, intervened by primary care physicians, and long traveling time. A personal vehicle was the commonest mode of transportation, “108” national free ambulance service was used only by 15.4%. Public transportation and “108” national ambulance service were more frequently used by rural than urban ACS population (Table 1, Table 3, Table 4, Table 5).
Table 4.
Clinical characteristics treatment and outcomes in men and women ACS population.
| Characteristics | Overall | Men | Women | p value |
|---|---|---|---|---|
| Age | 5807 (60.9 ± 12.1) | 4112 (59.7 ± 12.0) | 1695 (63.7 ± 11.8) | 0.0001 |
| STEMI | 2641 (45.5%) | 2043 (49.7%) | 598 (35.3%) | 0.001 |
| NSTEMI | 3166 (54.5%) | 2069 (50.3%) | 1097 (64.7%) | 0.001 |
| Urban | 971 (16.7%) | 753 (18.3%) | 218 (12.9%) | 0.001 |
| Rural | 4836 (83.3%) | 3359 (81.7%) | 1477 (87.1%) | 0.001 |
| Pre-hospital delay, median (I.Q. range), minutes | 780 (242–2358) | 734 (240–2253) | 856 (285–2587) | 0.001 |
| Hypertension | 2510 (43.4%) | 1642 (40.1%) | 868 (51.4%) | 0.001 |
| Diabetes | 1022 (17.7%) | 644 (15.7%) | 378 (22.4%) | 0.001 |
| BMI | 4953 (24.0 ± 4.2) | 3481 (23.9 ± 4.1) | 1472 (24.2 ± 4.4) | 0.07 |
| Fasting blood glucose (>126), mg/dl | 1452 (53%) | 980 (53.0%) | 472 (55.2%) | 0.2 |
| Tobacco consumption | 3658 (63.4%) | 3296 (80.6%) | 362 (21.5%) | 0.001 |
| Sedentary | 1716 (29.7%) | 1217 (29.7%) | 499 (29.6%) | 0.93 |
| Hemodynamic status | ||||
| Hypotension | 628 (12.6%) | 444 (12.5%) | 184 (12.8%) | 0.7 |
| Cardiogenic shock | 455 (10.2%) | 328 (10.3%) | 127 (9.9%) | 0.6 |
| Treatment | ||||
| ASA | 5780 (100%) | 4094 (100%) | 1686 (100%) | |
| Clopidogrel | ||||
| B Blockers | 4706 (81.1%) | 3307 (80.4%) | 1399 (82.6%) | 0.05 |
| ACE inhibitors/ARB | 4716 (82.5%) | 3328 (82.3%) | 1388 (83.1%) | 0.5 |
| Statins | 5650 (97.3%) | 3992 (97.1%) | 1658 (97.9%) | 0.07 |
| Thrombolytic therapy | 939 (35.6%) | 783 (38.3%) | 156 (26.1%) | 0.01 |
| PCI | 37 (0.6%) | 29 (0.5%) | 8 (0.05%) | 0.4 |
| Outcomes | ||||
| Death | 442 (7.6%) | 289 (7.0%) | 153 (9.0%) | 0.01 |
| Bleeding | 55 (1.0%) | 42 (1.1%) | 13 (0.8%) | 0.5 |
| Post-MI angina | 125 (2.4%) | 106 (2.8%) | 19 (1.2%) | 0.001 |
3.3. CV risk factors
The detailed description of risk factors treatment and outcome is described in Table 2, Table 3, Table 4, Table 5. In brief, self-reported hypertension and diabetes were recorded in 43.4% and 17.7%, respectively. Diabetes, hypertension, and obesity were more frequently associated with NSTEMI, women and urban ACS population, while tobacco consumption was significantly high in STEMI than in NSTEMI. Tobacco consumption was reported in 63.4% and was mostly in men than in women (80.6% vs. 21.5%). Overweight and obesity were reported in 27.6% and 7.8%, respectively and were significantly high in women, in NSTEMI, and in urban ACS population. The distribution of CV risk factors among ACS population reporting in different level of hospitals was significantly different.
Table 2.
CV risk factors, characteristics, treatment received, and outcome in STEMI and NSTEMI ACS population.
| Characteristics | Overall ACS group | STEMI Group | NSTEMI/Unstable angina Group | p value |
|---|---|---|---|---|
| CV risk factors | ||||
| Self-reported hypertension | 2510 (43.4%) | 986 (37.1%) | 1524 (48.4%) | 0.001 |
| Self-reported diabetes | 1022 (17.7%) | 426 (16.1%) | 596 (19.0%) | 0.01 |
| Tobacco smokers (yes) % | 3658 (62.9%) | 1834 (69.7%) | 1823 (58.1%) | 0.0001 |
| Sedentary (yes) % | 1716 (29.7%) | 746 (28.3%) | 970 (30.9%) | 0.03 |
| BMI status | 4953 (24.0 ± 4.2) | 2230 (23.6 ± 4.1) | 2720 (24.4 ± 4.2) | 0.001 |
| BMI < 19.9 | 960 (18.4%) | 487 (20.7%) | 473 (16.5%) | 0.0001 |
| BMI 20–24.9 | 2411 (46.2%) | 1130 (48.0%) | 1281 (44.6%) | |
| BMI 25–29.9 | 1443 (27.6%) | 596 (25.3%) | 847 (29.5%) | |
| >30 | 408 (7.8%) | 140 (5.9%) | 268 (9.3%) | |
| Past H/O MI | 420 (7.2%) | 111 (4.2%) | 309 (9.8%) | 0.01 |
| Past H/O CABG | 52 (0.9%) | 7 (0.3%) | 45 (1.4%) | 0.01 |
| Past H/O PCI | 86 (1.5%) | 16 (0.6%) | 70 (2.2%) | 0.1 |
| Clinical characteristics | ||||
| Tachycardia | 985 (21.2%) | 482 (21.8%) | 503 (20.7%) | 0.02 |
| Hypotension | 577 (11.7%) | 383 (16.1%) | 245 (9.4%) | 0.001 |
| Killip Class 111/1V | 639 (11.0%) | 353 (13.4%) | 286 (9.0%) | 0.001 |
| A Fib | 5 (0.1%) | 3 (0.1%) | 2 (0.1%) | 0.5 |
| VT/VF | 50 (0.9%) | 30 (1.4%) | 20 (0.6%) | 0.04 |
| CHB | 246 (4.7%) | 145 (6.0%) | 101 (3.5%) | 0.0001 |
| Stress hyperglycemia (FBS > 126) | 1253 (46.3%) | 524 (44.1%) | 729 (48.1%) | 0.04 |
| Trop T positive | 2309 (40.3%) | 1133 (43.0%) | 1176 (37.8%) | |
| Treatment | ||||
| ASA | 5796 (100%) | 2634 (100%) | 3162 (100%) | |
| Clopidogrel | 5796 (100%) | 2634 (100%) | 3162 (100%) | |
| Statin | 5650 (97.3%) | 2579 (97.7%) | 3071 (97.1%) | 0.5 |
| B Blocker | 4706 (81.1%) | 2017 (76.4%) | 2689 (85.0%) | 0.001 |
| ACE inhibitor/ARB | 4716 (82.5%) | 2035 (78.5%) | 2681 (86.0%) | 0.01 |
| Thrombolytic therapy | 948 (16.3%) | 939 (35.6%) | 9 (0.3%) | |
| PCI | 37 (0.6%) | 25 (0.9%) | 12 (0.4%) | 0.01 |
| Outcome | ||||
| Death | 442 (7.6%) (6.9–8.3%) | 284 (10.8%) | 158 (5.0%) | 0.0001 |
| Bleeding | 55 (1.0%) | 33 (1.4%) | 22 (0.8% | 0.07 |
| Post-MI angina | 125 (2.4%) | 68 (2.8%) | 57 (2.0%) | 0.054 |
| Cardiogenic shock | 455 (10.2%) | 280 (13.0%) | 175 (7.6%) | 0.001 |
3.4. Hemodynamic status
The hemodynamic instability state characterized by presence of hypotension, tachycardia, Killip Class 111/1V and cardiogenic shock was significantly more common in STEMI than in NSTEMI and in women ACS population (Table 2, Table 3, Table 4, Table 5). Hypotension and Killip Class 111/1V and cardiogenic shock were reported in 11.7%, 11%, and 10.2%, respectively, and were more often observed among rural ACS population and those reporting teaching hospitals.
3.5. Electrocardiographic abnormalities
Fifty patients (0.9%) had VT/V.Fib., and was significantly more frequent among STEMI than in NSTEMI patients (1.4% vs. 0.6%, p < 0.01). 4.7% had CHB and was significantly higher among STEMI than in NSTEMI patients (6.0% vs. 3.5%, p < 0.001) and 5 (0.1%) were in atrial fibrillation (Table 2).
3.6. Treatment at and during hospitalization
Antiplatelets therapy, both ASA and Clopidogrel, was prescribed in all patients both in STEMI and NSTEMI. 82.5% received ACE inhibitors/ARBs and their use was similar in men and women and urban and rural ACS population. Use of ACE inhibitors/ARB was significantly lower in STEMI than in NSTEMI both in men and women and ACS population admitted in private hospitals than in govt. hospitals at secondary and tertiary care hospitals. Statins were prescribed in 97.3% of ACS patients and was not different in men and women and were prescribed equally among STEMI and NSTEMI patients as well as in rural and urban ACS population. However, Statins were prescribed less frequently in private hospitals than in government hospitals. 81.1% received B Blockers. STEMI ACS population received B Blockers less often than NSTEMI patients 76.4% vs. 85.0%, respectively. (p < 0.001). Patients admitted in tertiary care center received B Blockers less frequently than those admitted in secondary level hospitals both in govt. and private sectors. Overall thrombolytic therapy was used only in 35.6% of STEMI population. Use of thrombolytic therapy was least in STEMI patients admitted in tertiary care hospitals (32.5%), and was highest in STEMI patients admitted in private hospitals (50.2%). Proportion of STEMI population receiving thrombolytic therapy was significantly higher in urban than in rural STEMI population (49.6% vs. 32.6%, p < 0.01). Only 37 (0.6%) of ACS population underwent PCI. 0.9% of STEMI population underwent PCI compared to 0.4% NSTEMI patients (p < 0.01). However, tertiary care center with cath facilities PCI rate was 2.2%. None of the ACS patient underwent CABG (Table 2, Table 3, Table 4, Table 5).
3.7. Outcomes
In-hospital mortality was 7.6% and was higher in STEMI patients than in NSTEMI population (10.8% vs. 5.0%, respectively, p < 0.01). The in-hospital mortality was higher among women and those from rural background. The mortality rate was significantly higher in ACS population admitted in teaching hospital with catheterization services than in teaching hospital without cath services, non-teaching hospitals in government, and private sectors; 10.8%, 10.6%, 4.3%, and 5.9%, respectively. 4.7% had CHB and was equally prevalent in patients admitted across the hospitals types. VT VF was reported in 0.9% of the population and was similar across the types of registry centers. 14.8% and 13.4% of the ACS populations admitted in secondary level hospitals in govt. and private sectors, respectively, were referred to tertiary care center due to hemodynamic instability, and/or post-MI angina. 1.0% had major and minor bleeding and was not significantly different among STEMI and NSTEMI patients (1.4% vs. 0.8%, p 0.07). 2.4% had post-MI angina, and there was a trend of higher prevalence among STEMI than in NSTEMI patients (Table 2, Table 3, Table 4, Table 5).
3.8. Predictors of in-hospital deaths
Multi-variable stepwise regression modeling was done to determine the independent predictors of in-hospital death. Age, diabetes, STEMI, and Killip class 111/1V were strong independent predictors of increased in-hospital deaths. Pre-hospital delay adjusted for other predictors was not found to have independent predictive value for increased in-hospital mortality. Literacy status and increasing BMI had lower in-hospital mortality. Gender and geographical characteristics had no significant association with in-hospital mortality (Table 6).
Table 6.
Multivariable logistic regression modeling of independent predictors of in-hospital deaths.
| Outcome status | Odds ratio | p value | [95% Conf. Interval] |
|---|---|---|---|
| Age | 1.02646 | 0.000 | 1.014685–1.038371 |
| Gender | 0.8337839 | 0.223 | 0.6224593–1.116853 |
| Urban | 0.9490128 | 0.795 | 0.6393988–1.40855 |
| Literates | 0.6769331 | 0.010 | 0.5028687–0.9112487 |
| Diabetics | 1.863109 | 0.000 | 1.3989–2.481358 |
| STEMI ACS | 1.941678 | 0.000 | 1.495607–2.52079 |
| BMI | 0.9616202 | 0.016 | 0.9315808–0.9926283 |
| Killip class 111/1V | 8.007989 | 0.000 | 6.144254–10.43705 |
| Pre-hospital delay | 1.049987 | 0.310 | 0.9555333–1.153777 |
4. Discussion
We analyzed data from large HP ACS Registry in the state of HP covering more than 90% of the state population.
4.1. Summary of HP ACS Registry findings
ACS population in hill state of HP is characterized by: mean age of 60 years, NSTEMI is more common than STEMI, mean age is higher than STMI and is more frequent in women. About 83% of ACS population was from rural area. NSTEMI patients were more often associated with hypertension, diabetes and obesity, while STEMI patients were associated with tobacco consumption. Median pre-hospital delay was 780 min and significantly longer for rural ACS population than urban population. Only one-third of the patients reported within 6 h of symptom onset. Lack of awareness of symptoms of ACS, consulting local practitioners, and long traveling time were the main reasons for inordinately longer pre-hospital delay. One-third of the ACS population were admitted in teaching hospital with catheterization facilities, while only about 10% were treated in private secondary care hospitals. About 10% of the ACS population were hemodynamically unstable and were more frequent in STEMI than in NSTEMI. Only 35.6% received thrombolytic therapy and 0.6% underwent PCI. All patients received Antiplatelets; Statins were prescribed in more than 97%, whereas B Blockers and ACE inhibitors/ARB were prescribed in more than 80%. In-hospital mortality was 7.6% and was significantly high in STEMI than NSTEMI (10.8% vs. 5.0%, p < 0.001).
4.2. Comparison with prior ACS Registry
Proportion of ACS population with NSTEMI was more common than STEMI unlike reported in CREATE12 and OASIS10 Registry from India, however, is similar to reports of Kerala ACS Registry.13 The mean age of the ACS population is higher compared to CREATE Registry12 but is similar to Kerala Registry.13 However, the mean age of the STEMI was lower than NSTEMI unlike Kerala ACS Registry,13 where there was no difference in mean age of STEMI and NSTEMI patients. It is intriguing that the characteristics of the ACS population change with transition in sociodemographic characteristics of the general population. With improvement in socioeconomic status of the population, the age of incidence of ACS increases, NSTEMI becomes more frequent, and the difference in the mean age of men and women ACS population narrows. In this context, the characteristics of ACS population in hill state of HP appear similar to Kerala state,13 while other regions of the country reflected by CREATE Registry12 appear to be in trailing stage of epidemiological transition of ACS.
The use of reperfusion therapy was significantly low in HP ACS Registry than reported in previous registries from India and other developed countries.3, 4, 5, 6, 7, 8, 9, 10, 12, 13 This is because of the fact that pre-hospital delay was significantly high. The median delay of 780 min was greater compared to 300 min in CREATE Registry12 and was much lower reported from developed and other developing countries.3, 4, 5, 6, 7, 8, 9, 10, 12, 13 Only about 30% reported within 6 h of symptom onset. The thrombolysis rate also varied significantly across the types of hospitals. Thrombolytic rate was highest in private hospitals (50%), while it was least in secondary care hospitals in government sector (31.4%). Although pre-hospital delay was least in secondary care hospitals in government sector (median delay of 544 min), the thrombolytic therapy rate was least in the secondary care hospitals. The low rate of thrombolysis in secondary care hospitals in govt. sectors may be due to limited number of physicians posted; thus, they are not available on all days for providing care to ACS patients. The general duty medical officers are not competent to take decision for thrombolytic therapy. Thus, this brings out an important gap in the health care system in government sector that needs to be addressed. The lower rates of thrombolysis in teaching hospital have been primarily due to long pre-hospital delay and thus reporting out of window period. The negligible rate of PCI in HP ACS Registry is due to the fact that only one center in whole of state has the catheterization facilities, and this too only provides PCI services during working hours. The other reason of lower PCI rate of 2.2% in center with PCI facilities is the affordability. Most of the patients pay the cost out of pocket. The use of other evidence-based medicine for secondary prevention: Antiplatelets, Statins, ACE inhibitors, and B Blockers are much higher than reported from previous registries from India and other registries from developed countries.3, 4, 5, 6, 7, 8, 9, 10, 12, 13 This reflects the fact that physicians in the state are well informed about the use of evidence-based medicines. There is a need for further improving the use of ACE inhibitors and B Blockers in high-risk patients, as the use is less frequent in STEMI than in NSTEMI patients. The in-hospital mortality was 7.6% that is higher than in-hospital mortality reported in CREATE12 registry of 5.6% and Kerala ACS Registry.13 The high mortality rate in HP ACS Registry is due to long pre-hospital delay, and lower reperfusion rate; thus, has an implication for making informed interventions to reduce pre-hospital delay and to improve reperfusion therapy. Adjusted analysis of predictors of in-hospital mortality revealed that age, diabetic status, patients with unstable hemodynamic; Killip class 111/1V, and STEMI ACS population as expected carried worse prognosis. However interestingly, increasing BMI and literacy status were associated with lower mortality. The possible reasons for lower mortality among literate population could be higher thrombolysis rate in literate population than in illiterate ACS group (75.8% vs. 24.2%, p < 0.001). Gender, geographical characteristics, and pre-hospital delay were not significantly associated with in-hospital mortality when adjusted for other variables.
Tobacco consumption is the most frequent risk factor prevalent both in rural and urban population, while hypertension, diabetes, obesity and sedentary lifestyle are the major drivers of ACS in urban population. Thus, appropriate health system responses directed toward effective implementation of evidence-based public health intervention, primary and secondary preventive measures are required to reduce the incidence of ACS and its related morbidity and mortality.
4.3. Limitations of the study
The ACS cases recorded in some of the centers may not have been consecutive, especially during the time when the physician at the site is on leave and out of station for some assignments, and patients who died in the casualty before shifting to CCU, and wards in some of the centers may not have been registered, thus the data may not be true representative. The data related to some of the clinical characteristics were missing in some patients, as it could not be corrected due to late uploading of data due to internet connectivity issues. Post-discharge follow-up data are not captured to evaluate the adherence to secondary preventive interventions, and major adverse cardiovascular events.
4.4. Strength
The registry centers included all the hospitals in teaching and non-teaching secondary care hospitals in government sector except two out of 12 districts. Out of 10 private hospitals treating patients of ACS, 5 volunteered to participate. Overall, more than 80% of the ACS population is treated in government sector in HP. Thus, HP ACS Registry data cover more than 90% of the ACS population and is thus representative data of the state reflected by rural urban ACS population distribution mirroring the normal rural urban population distribution.
4.5. Implications for quality improvement
The pre-hospital delay and low rate of thrombolysis are two important barriers and gaps in the health care system. The quality improvement strategies are being recommended to be implemented by the state health department through community education program to improve the awareness about ACS symptoms and influence the health seeking behaviors and educating community about the health care centers to be visited in event of ACS. Some of the quality initiatives could be identifying strategically located high volume primary health care centers and strengthening with tele ECG equipments to provide decision support system by linking with physicians and cardiologists to guide institution of time sensitive treatment before referral to secondary or tertiary care centers. Tele ECG machines are installed in 60 PHC/CHC's and civil and district hospitals. The capacity building exercises across all level of health care centers in the state are planned in near future, and its impact would be recorded and would be reported in subsequent report.
4.6. Conclusions
The ACS population in the hill state of HP is characterized by higher mean age, more frequent NSTEMI, equally prevalent in urban and rural population, STEMI more common in men and NSTEMI being more frequent in women. STEMI patients are younger, more often smoker and more frequent in men, while NSTEMI are older, more often associated with hypertension, diabetes, obesity and more frequent in women. Pre-hospital delay is inordinately higher than reported from other parts of the country. The reperfusion therapy received was significantly low and in-hospital mortality was higher than reported in other registries in India. Use of Antiplatelets, Statins, B Blockers, and ACE inhibitors/ARB's were highest than reported in other registries. These observations have important implications in improving the scope of treatment and outcomes through intervention at health system level.
Funding
The HP ACS Registry is being funded by State NRHM.
Conflicts of interest
The authors have none to declare.
Acknowledgements
We acknowledge the support received from NRHM of HP State, and department of health and family welfare government of HP in providing support for arranging training of stake holders, implementation since its inception in 2011.
Footnotes
Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.ihj.2015.07.027.
Appendix A. Supplementary data
The following are the supplementary data to this article:
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