Passive Prescription of Secondary Prevention Medical Therapy during Index Hospitalization for Acute Myocardial Infarction Is Prevalent and Associated with Adverse Clinical Outcomes.

Secondary prevention therapy reduces death and reinfarction after acute myocardial infarction (AMI), but it is underutilized in clinical practice. Mechanisms for this therapeutic gap are not well established. In this study, we have explored and evaluated the impact of passive continuation compared to active initiation of secondary prevention therapy for AMI during the index hospitalization. For this purpose, we have analyzed 1083 consecutive patients with AMI to a tertiary referral hospital in Hong Kong and assessed discharge prescription rates of secondary prevention therapies (aspirin, beta-blockers, statins, and ACEI/ARBs). Multivariate analysis was used to identify independent predictors of discharge medication, and Kaplan-Meier survival curve was used to evaluate 12-month survival. Overall, prescription rates of aspirin, beta-blocker, statin, and ACEI/ARBs on discharge were 94.8%, 64.5%, 83.5%, and 61.4%, respectively. Multivariate analysis showed that prior use of each therapy was an independent predictor of prescription of the same therapy on discharge: aspirin (odds ratio (OR) = 4.8, 95% CI = 1.9-12.3, P < 0.01), beta-blocker (OR = 2.5, 95% CI = 1.8-3.4, P < 0.01); statin (OR = 8.3, 95% CI = 0.4-15.7, P < 0.01), and ACEI/ARBs (OR = 2.9, 95% CI = 2.0-4.3, P < 0.01). Passive continuation of prior medication was associated with higher 1-year mortality rates than active initiation in treatment-naïve patients (aspirin (13.7% vs. 5.7%), beta-blockers (12.9% vs. 5.6%), and statins (11.0% vs. 4.6%); all P < 0.01). Overall, the use of secondary prevention medication for AMI was suboptimal. Our findings suggested that the practice of passive continuation of prior medication was prevalent and associated with adverse clinical outcomes compared to active initiation of secondary preventive therapies for acute myocardial infarction during the index hospitalization.

1. Introduction

Acute myocardial infarction (AMI) has become one of the most common causes for hospital admission and is associated with a significant risk of mortality and morbidity in Asia [1], especially in China [2]. Although guideline recommendations were well established, many AMI patients still do not receive optimal care resulting in suboptimal clinical outcomes [3-5]. Multiple studies have been performed to explore the factors associated with nonadherence in order to improve the quality of care. Many factors have been reported to be associated with this gap including patient's risk status [6], age [7], relevant medical history [8], ethnicity [9], the type of institution, and clinician [10]. However, the related reasons for this treatment gap among Asia especially the Chinese population still remain unknown. Moreover, most of the investigations were mainly focused on the inpatient quality of care during the acute period of disease and discharge time point, while the potential factors related to the 6-month prescription were still unclear.

In this paper, we mainly sought to (1) analyze the potential reasons for the therapeutic gap by discharge and 6-month physician adherence and (2) estimate the clinical impact of these gaps for patients with AMI, in particular of the comparison between passive prescription and active prescription. For this purpose, we have analyzed 1083 consecutive patients with AMI to a tertiary referral hospital in Hong Kong and assessed discharge prescription rates of secondary prevention therapies (aspirin, beta-blockers, statins, and ACEI/ARBs). Multivariate analysis was used to identify independent predictors of discharge medication, and Kaplan–Meier survival curve was used to evaluate 12-month survival.

The remaining paper is organized according to the following structure. In Section 2, the materials and methodology which are used in the proposed setup are described in detail. In Section 3, experimental results and observations were presented along with the effective performance of the proposed scheme in resolving the issue. Predictors to adherence to guideline therapy are described in Section 4. A generalized discussion on both existing state-of-the-art and proposed techniques is provided in Section 5. Finally, concluding remarks are given in Section 6.

2. Proposed Methods

2.1. Study Population and Definition

We have analyzed consecutive patients presenting AMI to a tertiary referral hospital in Hong Kong from February 2006 to March 2012. Besides unstable angina (UA), our registry consecutively recruited 1083 patients with AMI (ST-elevation and non-ST-elevation myocardial infarction (STEMI/NSTEMI)), involving all causes of AMI in our health institution. And UA patients were excluded from the analysis because of the AMI performance measures we used. The study protocol was approved by the ethics committee of the joint Chinese University of Hong Kong—New Territories East Cluster and was performed in accordance with the Declaration of Helsinki and Good Clinical Practice (ICH-GCP). All subjects provided written informed consent to participate.

2.2. Adherence to Clinical Guidelines

We have selected five of the secondary prevention therapies recommended by the American College of Cardiology (ACC) and American Heart Association (AHA), and classified as performance measures by ACC/AHA (aspirin, clopidogrel, beta-blockers, angiotensin-converting enzyme inhibitor, or angiotensin II receptor blockers (ACEI/ARBs) for patients with left ventricular systolic dysfunction (LVSD), and statins (if serum LDL-cholesterol >100 mg/dL)). In order to measure the physician adherence rate more accurately, we analyzed their use in patients without known contraindications that were clearly defined in ACC/AHA 2008 performance measures for AMI [11]. Thus, if the patients had the following situation and were not prescribed with the guideline recommendations, we did not regard it as the nonadherence. And in order to measure more carefully, we excluded these patients in this paper's analysis.

The prescription of evidence-based therapies in two time points, discharge and six months, was assessed. The use of medications on discharge was examined in patients who survived to hospital discharge, and the rate of 6-month prescription was assessed among patients who had survived up to 6-month post discharge.

2.3. Follow-Up (Outcomes)

In this part, the clinical impact was calculated in two time points: six-month post discharge and one-year post discharge. Clinical outcome was defined as an event (including death, recurrent myocardial infarction (MI), cardiac hospitalization, and bleeding). Mortality involved the death caused by all reasons. Outside-hospital clinical events of 6 months and one year were got from the record of the patient's primary care system or through phone call follow-up.

2.4. Statistical Analysis

Briefly, continuous variables, distributed in a normal pattern, were presented as mean values (SD), and otherwise displayed as median (IQR). Normality was tested by the Shapiro–Wilks test; discrete variables were expressed as percent values. Student's t-test was used in comparison of continuous variables. Fisher's exact test or Pearson's chi-square test was performed among the contrast of categorical variables. Logistic regression was undergone to discover the potential mechanism of adherence and influence of adherence on clinical outcomes. Univariate regression including independent variables (previous selected clinical characteristics, laboratory results, and medication variables with P < 0.20) and multivariate regression models (P < 0.05) were established and remained those independent variables who were demonstrated to be statistically significant in univariate regression. Backward selection mode was used to make the adjustment. Survival was evaluated by the Kaplan–Meier method, and differences in survival between groups were calculated by the log-rank test. All calculated P values were two-sided, and P value <0.05 was considered statistically significant. Statistical analysis was performed using SPSS version 22.0 (SPSS Inc., Chicago, IL, USA).

3. Experimental Results and Observations

3.1. Patient Population and Performance Measure (Quality of Care)

During the whole study period, a total of 1083 AMI patients with 459 of STEMI (42.4%) or 624 of NSTEMI (57.6%) were enrolled. 54 patients died before discharge; therefore, the analysis of prescription of discharge guideline-recommended medications was estimated with 1029 patients. 108 AMI patients died within six months after admission, so 975 patients were taken into account for 6-month physician adherence assessment. Finally, totally 149 patients cumulatively lost life during the one-year period post admission as AMI in our registry.

Overall, physician adherence rates for the key performance measures (discharge medication) were as follows: 94.8% for aspirin, 54.2% for clopidogrel, 64.5% for beta-blockers, 61.4% for ACEI/ARBs use in patients with LVSD, 83.5% for statins.

For secondary prevention adherence at 6-month: calculated by the type of AMI, the STEMI population had a significantly higher usage than NSTEMI of aspirin (94.8% vs. 91.4%, P=0.042), ACEI/ARBs (67.6% vs. 58.8%, P=0.006), statins (90.3% vs. 81.5%, P < 0.001), and all 5 medications (21.1% vs.14.4%, P=0.012).

Table 1 shows the baseline clinical characteristics of two different discharge prescription patterns (active vs. passive). Additionally, this pattern of active prescription is those lower risk patients (who were younger, had less medical histories, and had better left ventricular ejection fraction (LVEF) value) who were treated more aggressively with secondary prevention on discharge. However, for clopidogrel, the discharge medication pattern was highly associated with the patients' vascular histories and in-hospital percutaneous coronary intervention (PCI) treatment.

Table 1

Baseline characteristics and intervention therapy of active and passive prescription patterns.

Prescription Pattern	Aspirin		P value	Clopidogrel		P value	Beta-blocker		P value	ACEI/ARBs		P value	Statins		P value
	Active	Passive		Active	Passive		Active	Passive		Active	Passive		Active	Passive
Number	652	307		542	16		392	249		393	215		545	219
Demographics
Age, mean (SD)	66.2 ± 12.7	73.4 ± 1.3	<0.001	64.7 ± 11.8	66.0 ± 14.4	0.657	66.5 ± 13.3	70.9 ± 11.3	<0.001	67.8 ± 13.3	70.2 ± 11.2	0.018	66.2 ± 12.8	70.1 ± 11.2	<0.001
Male, no. (%)	71.9	61.6	0.001	75.5	87.5	0.268	70.9	60.6	0.007	73.5	63.3	0.008	72.8	63.9	0.015
Smoker, no. (%)	51.7	42.0	0.005	54.1	56.2	0.862	50.3	39.8	0.009	53.7	43.7	0.019	53.2	45.2	0.045
Medical histories, no. (%)
MI	2.5	29.0	<0.001	7.9	43.8	<0.001	3.1	21.7	<0.001	6.6	21.4	<0.001	3.1	27.4	<0.001
PVD	0.9	3.3	0.008	0.6	12.5	<0.001	1.3	1.6	0.729	1.8	2.3	0.645	0.6	3.2	0.004
Stroke	2.0	22.5	<0.001	5.0	25.0-	0.001	7.4	11.2	0.095	5.6	14.0	<0.001	4.2	16.0	<0.001
Diabetes mellitus	26.8	49.8	<0.001	28.2	43.8	0.176	29.3	48.6	<0.001	26.2	58.6	<0.001	26.2	48.4	<0.001
Hypertension	52.0	73.3	<0.001	54.6	43.8	0.390	54.8	81.5	<0.001	54.2	81.4	<0.001	53.6	71.2	<0.001
Hyperlipidemia	21.9	34.5	<0.001	25.3	18.8	0.553	23.0	34.5	0.001	21.6	34.9	<0.001	16.5	57.5	<0.001
CRF	4.3	20.2	<0.001	5.4	6.2	0.875	5.6	19.7	<0.001	3.6	15.3	<0.001	4.2	18.7	<0.001
CHF	2.8	19.5	<0.001	3.9	25.0	<0.001	4.6	14.1	<0.001	5.1	15.8	<0.001	5.0	13.7	<0.001
Physical characteristics at admission
Heart rate (bpm), mean (SD)	79.7 ± 20.9	85.6 ± 21.6	<0.001	79.1 ± 19.0	96.2 ± 23.9	0.002	84.2 ± 20.1	81.9 ± 21.0	0.186	82.4 ± 22.4	83.8 ± 20.9	0.486	80.6 ± 20.3	83.1 ± 21.4	0.139
SBP (mmHg), mean (SD)	141.6 ± 30.1	148.5 ± 33.0	0.003	29.2 ± 1.3	30.7 ± 8.5	0.580	147.3 ± 28.1	150.6 ± 33.1	0.196	145.1 ± 30.2	151.4 ± 33.9	0.028	144.3 ± 29.8	145.7 ± 35.2	0.583
LVEF(%), mean (SD)	51.6 ± 12.1	49.2 ± 13.9	0.039	11.7 ± 0.6	17.8 ± 6.7	0.224	11.7 ± 0.70	13.2 ± 1.0	0.418	50.1 ± 12.4	51.2 ± 13.9	0.416	51.0 ± 12.2	50.5 ± 13.8	0.639
Killip class III–IV, no. (%)	8.1	11.7	0.073	5.7	6.2	0.928				7.1	9.8	0.252	6.2	11.4	0.015
STEMI, no. (%)	50.6	22.8	<0.001	51.7	25.0	0.036	42.9	29.7	0.001	50.6	30.2	<0.001	51.0	28.3	<0.001
Laboratory results, mean (SD)
Serum LDL (mmol/L)	3.1 ± 1.0	2.4 ± 1.0	<0.001	3.0 ± 1.0	1.8 ± 0.8	<0.001	3.1 ± 1.0	2.6 ± 1.1	<0.001	2.9 ± 1.0	2.6 ± 1.0	<0.001	3.2 ± 1.0	2.4 ± 1.1	<0.001
eGFR, mL/min/1.73 m2	65.2 ± 25.3	49.6 ± 13.9	<0.001	64.9 ± 21.8	72.5 ± 35.3	0.408	65.1 ± 22.9	50.6 ± 26.5	<0.001	51.3 ± 25.9	50.1 ± 12.4	<0.001	65.4 ± 25.0	52.6 ± 26.1	<0.001
In-hospital treatment
PCI, no. (%)	63.3	38.8	<0.001	89.1	56.2	<0.001	61.0	45.4	<0.001	60.8	47.0	0.001	65.5	48.9	<0.001

4. Predictors of Adherence to Guideline Therapy

4.1. Discharge Prescription

As shown in Table 2, increasing age was related to the lower prescription rates of aspirin (OR = 0.94; 95% CI = 0.91–0.97; P=0.001, per year), beta-blockers (OR = 0.97; 95% CI = 0.96–0.99; P < 0.001, per year), and statins (OR = 0.95; 95% CI = 0.94–0.97; P < 0.001, per year) on discharge. Traditional risk features (corresponding medical history and comorbidities) were correlated with lower usage of beta-blockers and ACEI/ARBs but did not significantly influence the prescription rate of aspirin and statins on discharge.

Table 2

Predictors of adherence to discharged and six-month medical therapy.

	Adjusted OR	95% CI	P value
Aspirin on discharge
Age (per year)	0.94	0.91–0.97	0.001
Index revascularization	2.77	1.13–6.82	0.026
Aspirin before admission	4.84	1.90–12.30	0.001
Clopidogrel on discharge
eGFR (mL/min/1.73 m2)	1.01	1.00–1.02	0.037
Hemoglobin (g/dL)	1.17	1.05–1.29	0.004
Killip class (III-IV)	0.45	0.23–0.86	0.016
Index revascularization	34.28	22.94–51.22	<0.001
Beta-blockers on discharge
Age (per year)	0.97	0.96–0.99	<0.001
NSTEMI	1.54	1.18–2.02	0.002
History of hypertension	1.75	1.31–2.32	<0.001
Heart failure on admission	0.58	0.35–0.95	0.032
Beta-blockers before admission	2.50	1.83–3.42	<0.001
ACEI/ARBs on discharge
Systolic blood pressure (per mmHg)	1.01	1.00–1.02	<0.001
Diabetes mellitus	1.43	1.03–1.98	0.033
ACEI/ARBs before admission	2.93	1.97–4.34	<0.001
Statins on discharge
Age (per year)	0.95	0.94–0.97	<0.001
STEMI	1.94	1.32–2.87	0.001
Albumin (per g/l)	1.08	1.04–1.12	<0.001
Peak creatinine (per μmol/l)	0.99	0.98–1.01	0.018
Statins before admission	8.27	0.35–15.71	<0.001
Combined medication use on discharge
Diastolic blood pressure (per mmHg)	1.01	1.00–1.03	0.005
Index revascularization	9.6	5.60–16.5	<0.001
ACEI/ARBs before admission	1.92	1.26–2.92	0.003
Aspirin at 6-month
Index revascularization	1.97	1.08–3.58	0.027
Aspirin on discharge	15.13	7.13–32.13	<0.001
Clopidogrel at 6-month
Albumin during hospitalization (g/L)	1.05	1.01–1.09	0.018
History of revascularization	2.04	1.15–3.63	0.015
Index revascularization	2.51	1.45–4.35	0.001
Clopidogrel on discharge	6.02	3.42–10.60	<0.001
Beta-blockers at 6-month
Current smoker	0.54	0.34–0.85	0.008
Heart rate during hospitalization (bpm)	1.01	1.00–1.02	0.029
Albumin during hospitalization (g/L)	1.04	1.01–1.08	0.047
Beta-blockers on discharge	19.70	13.00–29.85	<0.001
ACEI/ARBs at 6-month
Hypertension	1.51	1.01–2.26	0.044
CHF	2.27	1.32–3.91	0.003
AF	0.274	0.09–0.87	0.029
Presented with STEMI	1.92	1.28–2.89	0.002
ACEI/ARBs on discharge	15.43	10.36–22.98	<0.001
Statins at 6-month
Statins on discharge	26.30	15.90–43.51	<0.001
Combined medication use at 6-month
History of revascularization	1.97	1.09–3.53	0.024
Index revascularization	2.80	1.59–4.93	<0.001
Statins on discharge	8.24	5.35–12.70	<0.001

All the data used in analyzing were adjusted with excluding the patients who have contraindications. +Analysis was performed using multivariable logistic regression, and variables in the model include baseline characteristics, medical histories, medication before admission, presentation of AMI, laboratory results during hospitalization, and procedures during index admission. ACEI/ARBs, angiotensin-converting enzyme inhibitor (ACEI), or angiotensin II receptor blockers (ARB); CI, confidence interval; OR, odds ratio; STEMI, ST-elevated myocardial infarction; NSTEMI, non-ST-elevated myocardial infarction; eGFR, estimated glomerular filtration rate; CHF: congestive heart failure; AF: atrial fibrillation; Combined medication: combined using five medications including aspirin, clopidogrel, beta-blockers, ACEI/ARBs, and statins

Invasive procedures during hospitalization including PCI or coronary artery bypass grafting (CABG) were strongly associated with discharged prescription of aspirin. In-hospital revascularization was strongly associated with clopidogrel discharge prescription, as well as the higher level of estimated glomerular filtration rate (eGFR) and hemoglobin during hospitalization and lower standard Killip class (I or II). Presenting with NSTEMI and having a medical history of hypertension (HT) were associated with a higher likelihood to be discharged on beta-blockers, whereas having congestive heart failure (CHF) on admission was a block to its use. Presenting with higher systolic blood pressure and comorbid with diabetes mellitus (DM) were predictors of ACEI/ARBs prescription in eligible patients. In those diagnosed as STEMI, higher albumin level during hospitalization predicted the use of statins on discharge, while lower peak creatinine level during index hospitalization was less likely to be prescribed with statin. Patients who underwent revascularization during index hospitalization were more likely to be prescribed with combined therapy on discharge (Table 2).

Patients who were not on a given medication before admission were less likely to be prescribed it on discharge, and this was consistent through the four medications, except clopidogrel. Besides, clopidogrel was highly influenced by invasive interventions like PCI or CABG, taking a specific medication before admission was still the strongest independent predictor to take it on the discharge of other four drugs: aspirin before admission (OR = 4.84; 95% CI = 1.90–12.30; P=0.001) for aspirin on discharge; beta-blockers before admission (OR = 2.50; 95% CI = 1.83–3.42; P < 0.001) for beta-blockers on discharge; ACE/ARBs before admission (OR = 2.93; 95% CI = 1.97–4.34; P < 0.001) for ACE/ARBs on discharge; statins before admission (OR = 8.27; 95% CI = 0.35–15.71; P < 0.001) for statins on discharge. And for combination guideline prescription, ACEI/ARBs used before admission played an ignorable role in the medical part (OR = 1.92; 95% CI = 1.26–2.92; P=0.003) (Table 2).

4.2. Six-Month Prescription

Table 2 exhibits the attributes of the disparate 6-month utilization of individual recommendation and composited treatment pattern from adherence part in a multivariable model.

Predictors of losing prescription at 6-month were different for each drug. For aspirin, less in-hospital revascularization including PCI or CABG and not prescribed with aspirin on discharge was discovered as the independent predictors. For clopidogrel, the strong predictors associated with lower continuous use were no history of revascularization, decreasing level of albumin, no index procedure of PCI or CABG, and without clopidogrel prescription on discharge. As for beta-blockers, current smoker, less heart rate beats, lower in-hospital laboratory level of albumin, and no prescription of beta-blockers were contributed to inconstant use. In term of ACEI/ARBs, independent predictors of withhold prescription were the presence of AF, the absence of CHF and hypertension, presentation with NSTEMI, and not prescribed ACEI/ARBs on discharge. Inconstant use of statins was the result of no prescription on discharge. For low prescription rate of combined medication, without revascularization, no matter before admission or during hospitalization nor statin therapy on discharge was considered to be independent factors (Table 2).

4.3. Impact of Passive Prescription

According to Table 3, the patients who received the prescription on discharge and did not get the same medication before admission (active prescription pattern) had the lower risk in 6-month and 1-year mortality compared with the patients who adhered to discharge guideline recommendation and already treated with the same therapy before admitted to hospital (passive prescription pattern). Specifically, for 6-month mortality post discharge, active prescription compared with passive prescription of aspirin (2.9% vs. 7.5%, P < 0.001), beta-blockers (2.3% vs. 6.8%, P=0.005), statins (2.4% vs. 5.9%, P=0.014); for one-year mortality post discharge, active prescription compared with passive prescription of aspirin (5.7% vs. 13.7%, P < 0.001), beta-blockers (5.6% vs. 12.9%, P=0.001), statins (4.6% vs. 11.0%, P=0.001). Even after adjustment for age, gender, smoking status, cardiovascular disease histories, diabetes, hypertension, hyperlipidemia, and discharge diagnosis, active prescription still demonstrated survival benefit compared to passive prescription: for 6-month mortality, aspirin (OR = 0.436, 95% CI = 0.225–0.845, P=0.014), beta-blockers (OR = 0.374, 95% CI = 0.163–0.861, P=0.021); for 1-year mortality, aspirin (OR = 0.538, 95% CI = 0.326–0.887, P=0.015), beta-blockers (OR = 0.481, 95% CI = 0.260–0.892, P=0.020), statins (OR = 0.514, 95% CI = 0.277–0.956, P=0.036).

Table 3

Impact of passive prescription on 6-month &1-year mortality post discharge.

Variables	Prescription Pattern	6-month mortality	P value	Adjusted# OR of 6-month mortality		P value	1-year mortality	P value	Adjusted# OR of 1-year mortality		P value
		No. (%)		OR	95% CI		No. (%)		OR	95% CI
Aspirin	Active	2.9	<0.001	0.436	0.225-0.845	0.014	5.7	<0.001	0.538	0.326-0.887	0.015
	Passive	7.5		1.000	Reference		13.7		1.000	Reference
Clopidogrel	Active	0.0	0.730	—	—	—	0.0	0.547	—	—	—
	Passive	0.7		—	—	—	2.2		—	—	—
Beta-Blockers	Active	2.3	0.005	0.374	0.163-0.861	0.021	5.6	0.001	0.481	0.260-0.892	0.020
	Passive	6.8		1.000	Reference		12.9		1.000	Reference
ACEI/ARBs	Active	4.8	0.919	—	—	—	6.9	0.283	—	—	—
	Passive	4.7		—	—	—	9.3		—	—	—
Statins	Active	2.4	0.014	0.538	0.326-0.887	0.125	4.6	0.001	0.514	0.277-0.956	0.036
	Passive	5.9		1.000	Reference		11.0		1.000	Reference

ACEI/ARBs, angiotensin-converting enzyme inhibitor (ACEI) or angiotensin II receptor blockers (ARB), odds ratio (OR). ∗All the data used in analyzing were after adjusted for age, gender, cardiovascular medical histories, diabetes, hypertension, hyperlipidemia, smoking status, discharge diagnosis.

In Kaplan–Meier survival analysis, active prescription of aspirin had the better one-year survival (log-rank χ2 = 17.8, P < 0.001), active prescription of beta-blockers also brings the significant benefit in longer one-year live time (log-rank χ2 = 10.3, P=0.001), as well as the active use of statins (log-rank χ2 = 10.7, P=0.001) (Figure 1). After adjustment with age, gender, smoking status, cardiovascular disease histories, diabetes, hypertension, hyperlipidemia, and discharge diagnosis, the hazard ratios of active prescription of aspirin were [HR = 1.750, 95% CI = 1.091–2.805, P=0.020], of beta-blockers were [HR = 1.955, 95% CI = 1.099–3.477, P=0.022], of statins were [HR = 1.891, 95% CI = 1.056–3.388, P=0.032].

Figure 1

Kaplan–Meier survival curve: (a) one-year mortality in 2 prescription patterns of aspirin; (b) one-year mortality in 2 prescription patterns of beta-blockers; (c) one-year mortality in 2 prescription patterns of statins.

5. Discussion

With the increasing realization of the size of the treatment gap, increasing attention has been paid to the possible reasons for withholding such important medications [10,12,13]. Our study results are consistent with other publications, which have identified that the younger group and the traditional high-risk group with relevant medical histories and comorbidities were the significant predictors of physician adherence to the various performance measures after AMI.

However, we have found a novel reason that in our regression model, prior medication is a strong independent predictor of prescription of the same discharge drugs. We could clearly see that a pattern that the patient who was not on that medication on admission was less likely to be prescribed it on discharge, and this was persistent through all four medications including aspirin, beta-blockers, ACEI/ARBs, and statins. And the similar pattern could be found in 6-month prescription as well. Previous prescription (discharge medications) highly affected 6-month prescription. This pattern of active prescription is those lower-risk patients who were treated more aggressively with PCI and secondary prevention on discharge and therefore had better outcomes.

Another potential explanation for this phenomenon could be human behavior. Perhaps, the physician assumed that the doctor before him already had prescribed the right medication, and no further dose titration was necessary or maybe it was due to a lack of awareness of the guidelines and the importance of adequate dosages of preventive medication post discharge to reduce later mortality. This may explain the continuation of subtherapeutic doses from admission to discharge and follow-up with passive prescription. Further studies to estimate doctors' awareness of guidelines seem essential and urgent. Another strategy could be a formal discharge checking system with the recommended discharge medications for all AMI patients before discharge to see if that will increase the prescription rate and consequently improve the clinical outcome. This reminder system could ensure that patients leave the hospital on the most optimal medication for them. More importantly, given the incomplete physician adherence and the potential source we found from this study, relying on an informal educational system does not seem to be adequate.

In addition, our results also showed patients who had the lower mortality at 6-month and 1-year were the ones who had the greater likelihood to receive the active prescription. In particular, the active prescription of medications of aspirin, beta-blockers, and statins could bring the statically significant survival benefit to MI patients after they recovered. Therefore, our findings indicated that substantial support and encouragement of prescription of secondary prevention recommendations in an active insight were needed to be highlighted to maximize the survival benefit and minimize the mortality risk.

Our results raised a hypothesis that discharge on secondary preventive medication alone may not be enough. The discharge dosage or subsequent dose titration may impact on clinical outcomes and should be assessed in the quality of care of acute myocardial patients.

6. Conclusion

Despite advances in cardiovascular care and the establishment of management guidelines, there remained gaps in the application of the best treatments and strategies for MI patients in Hong Kong. In total, the prescription rate of guideline recommendations about secondary preventive medication, including discharge time point and in 6-month use, post AMI was not high enough, typically among patients who were treatment naïve previous. As a result, the outcomes of STEMI and NSTEMI patients were suboptimal. These findings that previous medication influenced discharged medications and discharged prescription highly affected 6-month prescription indicated a potential reason of nonadherence that physicians or patients prefer the continuation of prior medication rather than initiating new secondary preventive therapies. Better understanding and narrowing these gaps between guidelines and practice will contribute to improving the quality of care and clinical outcomes of MI patients.