- Academic Editor
†These authors contributed equally.
Background: Although compliance with the guideline recommendations for
heart failure (HF) is associated with improved survival, the effects of
medication on clinical practice often fail to meet expectations due to physician
and/or patient-related reasons that are unclear. This study analyzed physicians’
compliance with guideline-directed medical therapy (GDMT) based on real-world
clinical data and identified risk factors of low compliance. Methods:
This study included patients with HF, who were treated at the Affiliated Hospital
of North Sichuan Medical College from July 2017 to June 2021. All patients were
divided into high compliance, moderate compliance, and low compliance with GDMT
groups. The proportion of patients receiving treatment in compliance with GDMT
was analyzed, the relationship between compliance with GDMT and clinical outcomes
was evaluated, and the risk factors of low compliance were identified.
Results: Of all patients with HF included in the study, 498 (23.8%) had
low compliance with GDMT, 1413 (67.4%) had moderate compliance with GDMT, and
185 (8.8%) had high compliance with GDMT. The readmission rate of patients in
the moderate compliance with GDMT group was significantly higher than that in the
high and low compliance groups (p = 0.028). There were no significant
differences in the rates of severe cardiovascular disease among the three groups.
The mortality rate of patients in the high compliance with GDMT group was
significantly higher than that of the other groups (p
Chronic heart failure (HF) is a major public health problem worldwide, placing a significant burden on health systems [1]. HF is associated with high morbidity and mortality, with 50–75% of patients dying within 5 years of diagnosis [2]. Globally, the number of cases of end-stage HF is increasing at a rate of more than 800,000 per year, with a 1-year mortality rate of 70% and a sudden death rate of 60% [3]. A National Population-Based Analysis in China found that the age-standardized prevalence and incidence of HF are 1.10% and 275/100,000 person-years, respectively, and both prevalence and incidence increase with age [4]. Although treatment outcomes for chronic HF have improved with the development of new drugs and medical devices, HF is still associated with high rates of mortality and readmissions [5]. There are many potential reasons for this phenomenon, and non-compliance with guidelines is one of the important influencing factors.
Medication is a major component of HF treatment, which can not only relieve symptoms and prevent disease progression but also improve the quality of life and prolong the survival of HF patients. HF guidelines recommend the use of the maximum tolerated target dose of angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin-receptor blockers (ARBs), beta blockers (BBs), mineralocorticoid receptor antagonists (MRAs), ivabradine, and angiotensin-receptor-neprilysin inhibitors (ARNIs) to reduce mortality and/or readmission rates due to HF [5, 6]. Several studies have also shown that the use of these drugs can reduce morbidity and mortality in patients with HF [7, 8]. One study found that better compliance with HF with reduced ejection fraction (HFrEF) guidelines is associated with better 60-day composite endpoints in HF with preserved EF (HFpEF) patients with atrial fibrillation [9]. Although compliance with the guideline recommendations for HF is associated with improved survival, the effects of medication in clinical practice often fail to meet expectations due to physicians and/or patient-related reasons that are unclear [10, 11, 12]. There is a persistent and observable gap in outpatient and inpatient HFrEF patients receiving guidance-directed medication (GDM) [13]. It takes a lot of time and work to implement the guideline recommendations into clinical practice.
Drug noncompliance is a major challenge for many chronic diseases with complex daily medication regiments. The Adherence to guideline-directed medical and device Therapy in outpAtients with heart failure with reduced ejection fraction (ATA) study showed that the majority of eligible HFrEF patients did not receive pharmacological therapy at the target dose or treatment with the device recommended by the guidelines [14]. Another study found that non-compliance with guidance-recommended medication in patients with HF is significantly associated with worsening symptoms, frequent hospitalizations, and premature death [15]. In addition, non-compliance with guidelines can lead to unnecessary treatments, tests, and invasive interventions that put patients at risk and waste significant financial costs [16, 17]. Therefore, improving compliance with guidelines is helpful for improving treatment outcomes, prognosis, and quality of life in patients with HF. There are many factors that affect guideline compliance, including patient-related factors (e.g., age, sex, financial income, disease awareness, education, comorbidities, disease severity) and physician-related factors (e.g., inadequate understanding of guidelines, safety concerns, patient’s personal reasons for dosing adjustment, doctor’s personal prescribing habits) [18, 19, 20].
At present, there is a lack of research analyzing the compliance of Chinese physicians with the treatment guidelines for HF and the risk factors. Based on real-world clinical data, this study analyzed physicians’ compliance with guidelines when treating patients with HF, as well as the impact on the clinical outcome of patients, and identified the risk factors of low compliance with GDM therapy (GDMT) from the patient’s perspective.
This was a real-world study involving patients with HF who were treated at the
Affiliated Hospital of North Sichuan Medical College (Nanchong, China) from July
2017 to June 2021. Inclusion criteria included meeting the diagnostic and
treatment standards for HFrEF in the “Chinese HF Diagnosis and Treatment
Guidelines 2018”, follow-up for at least 6 months, receiving inpatient or
outpatient care at the hospital,
The data used in this study were extracted from a database constructed by combining information from multiple data sources including the Hospital Information System, Laboratory Information Management System, Picture archiving and Communication Systems, and Electronic Medical Record of the Affiliated Hospital of North Sichuan Medical College. The variables of interest in this study included patients’ sociodemographic information, drinking history, smoking history, previous medical history, comorbidities, HF-related characteristics, New York Heart Association (NYHA) functional class, laboratory parameters, medication, and other treatments.
Compliance with guideline score was based on physicians’ compliance with the
latest European Society of Cardiology (ESC) HF guideline recommendations at the
time the study registry was established [21]. Scores were related to the
following five classes of medications recommended by the ESC: ACEI, ARB (if ACEI
was not tolerated), ARNI, BB (it is recommended that all patients with HFrEF be
prescribed an ACEI [or ARB or ARNI] and BB, except in cases of contraindications
or intolerance), MRA (I. Patients with HFrEF NYHA class II–IV with EF
The compliance score was the ratio of treatment actually prescribed to what
should theoretically have been prescribed. The treatment score was calculated for
every drug for every patient prescribed, taking into account treatment
eligibility criteria, guideline-based contraindications to drugs, side effects of
the drug, and intolerance to the drug documented. The score was calculated for
each patient by summing the points as follows: 0 points for non-prescription in
the absence of contraindications and 1 point for the use of medicine;
non-administration of the recommended drugs because of specific contraindications
or intolerance was scored as compliance with guidelines. The total score ranged
from 0 (very poor) to 1 (good) and we defined three levels of compliance: good
compliance (score = 1), moderate compliance (score
Continuous variables were checked for normality by using the Kolmogorov–Smirnov
test. The normally distributed continuous variables are presented as the mean
A total of 2096 patients with HF were included in this study, with an average
age of 69.5
Variables | Total (N = 2096) | Physicians’ guideline adherence | p value | |||
Low (N = 498) | Moderate (N = 1413) | High (N = 185) | ||||
Demographic characteristics | ||||||
Age (years) | 69.5 |
68.8 |
69.7 |
70.4 |
0.169 | |
Sex | 0.366 | |||||
Female | 866 (41.3) | 193 (38.8) | 592 (41.9) | 81 (43.8) | ||
Male | 1230 (58.7) | 305 (61.2) | 821 (58.1) | 104 (56.2) | ||
Height (cm) | 160.9 |
161.2 |
160.8 |
160.2 |
0.339 | |
Weight (kg) | 61.6 |
61.7 |
61.5 |
61.7 |
0.921 | |
BMI (kg/m |
23.7 |
23.7 |
23.7 |
23.9 |
0.697 | |
Employment | 0.460 | |||||
No | 176 (8.4) | 37 (7.4) | 126 (8.9) | 13 (7.0) | ||
Yes | 1920 (91.6) | 461 (92.6) | 1287 (91.1) | 172 (93.0) | ||
Education level | 0.234 | |||||
Primary school or below | 1377 (65.7) | 311 (62.4) | 936 (66.2) | 130 (70.3) | ||
Middle school | 612 (29.2) | 158 (31.7) | 403 (28.5) | 51 (27.6) | ||
Junior college | 46 (2.2) | 15 (3.0) | 30 (2.1) | 1 (0.5) | ||
Bachelor or above | 61 (2.9) | 14 (2.8) | 44 (3.1) | 3 (1.6) | ||
Medical insurance | 0.212 | |||||
No | 183 (8.7) | 48 (9.6) | 125 (8.8) | 10 (5.4) | ||
Yes | 1913 (91.3) | 450 (90.4) | 1288 (91.2) | 175 (94.6) | ||
Smoking | 844 (40.3) | 210 (42.2) | 557 (39.4) | 77 (41.6) | 0.519 | |
Drinking | 651 (31.1) | 157 (31.5) | 440 (31.1) | 54 (29.2) | 0.836 | |
Diseases history | ||||||
MI | 91 (4.3) | 13 (2.6) | 68 (4.8) | 10 (5.4) | 0.088 | |
Angina | 78 (3.7) | 17 (3.4) | 50 (3.5) | 11 (5.9) | 0.244 | |
Arrhythmia | 117 (5.6) | 21 (4.2) | 82 (5.8) | 14 (7.6) | 0.194 | |
VHD | 31 (1.5) | 5 (1.0) | 24 (1.7) | 2 (1.1) | 0.487 | |
HCM | 2 (0.1) | 0 (0.0) | 2 (0.1) | 0 (0.0) | 0.616 | |
DCM | 16 (0.8) | 3 (0.6) | 11 (0.8) | 2 (1.1) | 0.810 | |
Pulmonary hypertension | 2 (0.1) | 0 (0.0) | 2 (0.1) | 0 (0.0) | 0.616 | |
COPD | 78 (3.7) | 18 (3.6) | 52 (3.7) | 8 (4.3) | 0.900 | |
Diabetes | 558 (26.6) | 119 (23.9) | 378 (26.8) | 61 (33.0) | 0.057 | |
Hypertension | 1201 (57.3) | 267 (53.6) | 816 (57.7) | 118 (63.8) | 0.048 | |
Renal insufficiency | 30 (1.4) | 3 (0.6) | 17 (1.2) | 10 (5.4) | ||
Hyperlipidemia | 32 (1.5) | 6 (1.2) | 20 (1.4) | 6 (3.2) | 0.130 | |
Myocarditis | 4 (0.2) | 1 (0.2) | 2 (0.1) | 1 (0.5) | 0.504 | |
Sleep disorders | 13 (0.6) | 6 (1.2) | 7 (0.5) | 0 (0.0) | 0.118 | |
Hyperuricemia | 18 (0.9) | 2 (0.4) | 14 (1.0) | 2 (1.1) | 0.445 | |
Thyroid function | 0.528 | |||||
Normal | 2063 (98.4) | 488 (98.0) | 1394 (98.7) | 181 (97.8) | ||
Hyperthyroidism | 28 (1.3) | 9 (1.8) | 15 (1.1) | 4 (2.2) | ||
Hypothyroidism | 5 (0.2) | 1 (0.2) | 4 (0.3) | 0 (0.0) | ||
Family history | ||||||
Hypertension | 80 (3.8) | 22 (4.4) | 55 (3.9) | 3 (1.6) | 0.230 | |
Diabetes | 22 (1.0) | 6 (1.2) | 13 (0.9) | 3 (1.6) | 0.629 | |
CHD | 38 (1.8) | 13 (2.6) | 23 (1.6) | 2 (1.1) | 0.271 | |
Stroke | 5 (0.2) | 2 (0.4) | 3 (0.2) | 0 (0.0) | 0.595 | |
Myocardiopathy | 3 (0.1) | 1 (0.2) | 2 (0.1) | 0 (0.0) | 0.826 | |
MI | 5 (0.2) | 1 (0.2) | 4 (0.3) | 0 (0.0) | 0.745 | |
Heart failure | 58 (2.8) | 19 (3.8) | 37 (2.6) | 2 (1.1) | 0.128 | |
Operation History | ||||||
PCI | 329 (15.7) | 75 (15.1) | 232 (16.4) | 22 (11.9) | 0.255 | |
CABG | 18 (0.9) | 3 (0.6) | 15 (1.1) | 0 (0.0) | 0.263 | |
ICD | 7 (0.3) | 2 (0.4) | 5 (0.4) | 0 (0.0) | 0.703 | |
CRT | 3 (0.1) | 1 (0.2) | 1 (0.1) | 1 (0.5) | 0.262 | |
NYHA classification | ||||||
I | 438 (20.9) | 143 (28.7) | 279 (19.7) | 16 (8.6) | ||
II | 932 (44.5) | 253 (50.8) | 612 (43.3) | 67 (36.2) | ||
III | 588 (28.1) | 86 (17.3) | 426 (30.1) | 76 (41.1) | ||
IV | 138 (6.6) | 16 (3.2) | 96 (6.8) | 26 (14.1) |
Note: BMI, body mass index; MI, myocardial infarction; VHD, valvular heart disease; HCM, hypertrophic cardiomyopathy; DCM, dilated cardiomyopathy; COPD, chronic obstructive pulmonary disease; CHD, coronary heart disease; PCI, percutaneous coronary intervention; CABG, coronary artery bypass grafting; ICD, implantable cardioverter-defibrillator; CRT, cardiac resynchronization therapy; NYHA, New York Heart Association.
Our study found that 767 (36.59%) HF patients received ACEI/ARB/ARNI treatment, 1684 (80.34%) HF patients received beta blocker treatment, 1492 (71.18%) HF patients received ivabradine treatment, and 1614 (77.00%) HF patients received MRA treatment in compliance with GDMT (Fig. 1). Of all patients with HF included in the study, 498 (23.8%) had low compliance with GDMT, 1413 (67.4%) had moderate compliance with GDMT, and 185 (8.8%) had high compliance with GDMT (Fig. 2).
Proportion of patients with HF receiving different drugs in compliance with GDMT. HF, heart failure; GDMT, guideline-directed medical therapy; ACEI, angiotensin-converting enzyme inhibitor; ARBs, angiotensin-receptor blockers; ARNI, angiotensin-receptor-neprilysin inhibitor; MRAs, mineralocorticoid receptor antagonists.
Physicians’ guideline compliance.
The effect of compliance with treatment guidelines on clinical outcomes was
analyzed. The readmission rate of patients in the moderate compliance with GDMT
group was significantly higher than that in the high and low compliance with GDMT
groups (p = 0.028; Fig. 3A). There were no significant differences in
severe cardiovascular disease (CVD) rate among the low, moderate, and high
compliance with GDMT groups (p = 0.569; Fig. 3B). Interestingly,
patients in the high compliance with GDMT group had significantly higher
mortality rates than those in the low and moderate compliance with GDMT groups
(p
Correlation of clinical outcomes with physicians’ guideline compliance. (A) The correlation of readmission with physicians’ guideline adherence; (B) The correlation of severe CVD rate with physicians’ guideline adherence; (C) The correlation of mortality with physicians’ guideline adherence. CVD, cardiovascular disease.
There were significant differences in heart rate (p
Physical indexes | Total (N = 2096) | Physicians’ guideline adherence | p value | |||
Low (N = 498) | Moderate (N = 1413) | High (N = 185) | ||||
Physical examination | ||||||
Body temperature | 36.5 (36.3–36.7) | 36.5 (36.3–36.7) | 36.5 (36.4–36.7) | 36.5 (36.3–36.7) | 0.186 | |
Heart rate | 80.0 (73.0–95.0) | 79.0 (68.0–80.0) | 80.0 (75.0–99.0) | 80.0 (74.5–102.0) | ||
DBP | 88.0 (82.0–95.0) | 88.0 (82.0–93.0) | 88.0 (82.0–96.0) | 89.0 (82.0–98.0) | 0.030 | |
SBP | 145.0 (134.0–156.0) | 145.0 (135.0–154.0) | 145.0 (134.0–156.0) | 150.0 (136.0–162.0) | 0.007 | |
Cardiac function indexes | ||||||
CK-MB | 1.960 (1.390–2.902) | 1.875 (1.220–2.537) | 1.960 (1.430–2.910) | 2.340 (1.710–3.580) | ||
hsTnT | 0.021 (0.012–0.047) | 0.016 (0.009–0.034) | 0.021 (0.012–0.049) | 0.033 (0.020–0.070) | ||
NT-proBNP | 773.3 (185.0–2259.2) | 333.5 (90.4–1126.8) | 786.8 (227.1–2329.0) | 1688.0 (773.3–5569.0) | ||
Renal function indexes | ||||||
Uric acid | 371.7 (308.7–448.0) | 359.8 (296.7–418.6) | 371.7 (311.4–450.1) | 418.9 (338.9–533.5) | ||
eGFR | 78.1 (62.5–94.0) | 82.2 (67.9–97.6) | 78.1 (62.7–92.8) | 65.6 (40.8–84.6) | ||
Urea nitrogen | 6.100 (4.908–7.790) | 5.760 (4.710–7.135) | 6.100 (4.900–7.760) | 7.320 (5.800–11.220) | ||
Serum creatinine | 76.9 (64.3–93.8) | 74.1 (61.4–85.6) | 76.9 (64.7–93.0) | 90.7 (72.3–134.7) | ||
Liver function indexes | ||||||
ALT | 19.3 (13.7–28.9) | 19.3 (13.0–26.0) | 19.3 (14.0–29.3) | 19.3 (13.3–31.0) | 0.046 | |
AST | 24.4 (19.9–32.0) | 24.1 (18.8–28.4) | 24.4 (20.1–33.3) | 24.4 (20.6–33.2) | ||
ALP | 81.7 (69.0–95.5) | 81.7 (67.0–92.8) | 81.7 (70.0–96.1) | 81.7 (69.9–97.7) | 0.028 | |
GGT | 30.5 (20.2–50.0) | 30.0 (16.9–38.1) | 30.5 (20.9–52.5) | 31.0 (23.9–58.6) | ||
Total protein | 66.2 (63.1–69.5) | 66.2 (63.4–69.1) | 66.2 (63.2–69.7) | 66.2 (61.5–68.3) | 0.024 | |
Albumin | 38.0 (35.6–40.3) | 38.0 (36.4–40.6) | 38.0 (35.5–40.2) | 37.7 (34.1–39.6) | ||
TBIL | 13.6 (10.5–17.5) | 13.6 (10.5–16.5) | 13.6 (10.7–18.0) | 13.6 (9.9–17.0) | 0.139 | |
DBIL | 3.400 (2.415–4.953) | 3.400 (2.400–4.400) | 3.400 (2.438–5.000) | 3.400 (2.500–5.145) | 0.207 | |
IBIL | 10.0 (7.7–12.8) | 10.0 (7.7–12.4) | 10.0 (7.8–13.0) | 10.0 (7.0–12.3) | 0.077 | |
Coagulation function | ||||||
Prothrombin time | 13.5 (12.8–14.4) | 13.4 (12.7–14.1) | 13.5 (12.9–14.5) | 13.5 (13.0–14.8) | 0.001 | |
APTT | 35.5 (32.4–39.0) | 35.5 (32.2–38.8) | 35.5 (32.4–38.9) | 35.5 (32.8–39.6) | 0.647 | |
TT | 17.2 (16.4–18.2) | 17.2 (16.4–18.2) | 17.2 (16.4–18.2) | 17.2 (16.7–18.4) | 0.328 | |
Fibrinogen | 3.354 (2.882–3.970) | 3.331 (2.812–3.828) | 3.354 (2.900–4.010) | 3.354 (2.982–4.200) | 0.011 | |
Antithrombin III | 89.4 (81.3–98.6) | 89.6 (82.0–99.0) | 89.4 (81.3–98.3) | 89.4 (79.9–98.9) | 0.169 | |
Blood routine examination | ||||||
WBC | 6.300 (5.270–7.580) | 6.300 (5.325–7.690) | 6.300 (5.270–7.510) | 6.300 (5.220–7.920) | 0.392 | |
RBC | 4.170 (3.800–4.530) | 4.170 (3.850–4.570) | 4.170 (3.810–4.520) | 4.130 (3.550–4.550) | 0.032 | |
HGB | 132.0 (32.5–317.0) | 132.0 (32.6–317.0) | 132.0 (32.7–317.0) | 128.0 (31.7–310.0) | 0.136 | |
HCT | 0.389 (0.350–0.422) | 0.390 (0.354–0.427) | 0.389 (0.350–0.421) | 0.384 (0.331–0.420) | 0.064 | |
Platelet | 172.0 (135.0–210.0) | 172.0 (138.0–215.0) | 172.0 (133.0–209.0) | 170.0 (131.0–207.0) | 0.588 | |
Thyroid function indexes | ||||||
TSH | 1.914 (1.358–2.788) | 1.914 (1.529–2.780) | 1.914 (1.293–2.825) | 1.914 (1.225–2.727) | 0.276 | |
FT3 | 2.685 (2.420–2.920) | 2.685 (2.540–2.960) | 2.685 (2.410–2.910) | 2.685 (2.180–2.840) | ||
FT4 | 1.270 (1.170–1.420) | 1.270 (1.140–1.350) | 1.270 (1.170–1.440) | 1.270 (1.150–1.400) | ||
Echocardiography | ||||||
LVDd | 50.0 (45.00–55.00) | 45.00(49.00–52.00) | 45.0 (50.00–55.00) | 50.0 (53.00–60.00) | ||
Electrocardiograph | ||||||
Arrhythmia | 117 (5.6) | 21 (4.2) | 82 (5.8) | 14 (7.6) | 0.194 |
Note: SBP, systolic blood pressure; DBP, diastolic blood pressure; CK-MB, creatine kinase-MB; hsTnT, high-sensitivity troponin T; NT-proBNP, N-terminal pro-B-type natriuretic peptide; eGFR, estimate glomerular filtration rate; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALP, alkaline phosphatase; GGT, gamma-glutamyl transpeptidase; TBIL, total bilirubin; DBIL, direct bilirubin; IBIL, indirect bilirubin; APTT, activated partial thromboplastin time; TT, thrombin time; WBC, white blood cell; RBC, red blood cell; HGB, hemoglobin; HCT, hematocrit; TSH, thyroid stimulating hormone; FT3, free triiodothyronine; FT4, free thyroxine; LVDd, left ventricular end-diastolic diameter.
Ordinal logistic regression results showed that a history of hypertension (odds
ratio [OR] = 1.332, 95% confidence interval [CI]: 1.090–1.628; p =
0.005); NYHA classification (III vs. I) (OR = 1.569, 95% CI: 1.168–2.111;
p = 0.003); NYHA classification (IV vs. I) (OR = 1.874, 95% CI:
1.180–2.974; p = 0.008); and abnormal heart rate (OR = 1.627, 95% CI:
1.312–2.021; p
Variables | SE | Wald |
p value | OR (95% CI) | ||
History of MI | 0.26 | 0.2342 | 1.1102 | 0.267 | 1.297 (0.821–2.055) | |
History of hypertension | 0.286 | 0.1023 | 2.798 | 0.005 | 1.332 (1.090–1.628) | |
History of renal insufficiency | 1.044 | 0.4151 | 2.5159 | 0.012 | 2.841 (1.238–6.329) | |
NYHA classification | ||||||
I | Ref (1.000) | |||||
II | 0.087 | 0.1233 | 0.705 | 0.481 | 1.091 (0.856–1.388) | |
III | 0.451 | 0.151 | 2.985 | 0.003 | 1.569 (1.168–2.111) | |
IV | 0.628 | 0.2358 | 2.663 | 0.008 | 1.874 (1.180–2.974) | |
Heart rate (abnormal) | 0.487 | 0.1101 | 4.419 | 1.627 (1.312–2.021) | ||
DBP (abnormal) | 0.136 | 0.1036 | 1.310 | 0.190 | 1.145 (0.935–1.404) | |
SBP (abnormal) | –0.028 | 0.1092 | –0.258 | 0.797 | 0.972 (0.785–1.204) | |
CK-MB (abnormal) | 0.024 | 0.1562 | 0.156 | 0.876 | 1.025 (0.755–1.393) | |
hsTnT (abnormal) | 0.335 | 0.1205 | 2.783 | 0.005 | 1.398 (1.104–1.771) | |
NT-proBNP (abnormal) | 0.387 | 0.1262 | 3.064 | 0.002 | 1.472 (1.150–1.886) | |
Uric acid (abnormal) | 0.335 | 0.1096 | 3.055 | 0.002 | 1.398 (1.128–1.734) | |
eGFR (abnormal) | –0.11 | 0.1153 | –0.950 | 0.342 | 0.896 (0.715–1.123) | |
Urea nitrogen (abnormal) | 0.236 | 0.1227 | 1.925 | 0.054 | 1.267 (0.997–1.613) | |
Serum creatinine (abnormal) | 0.136 | 0.1044 | 1.299 | 0.194 | 1.145 (0.934–1.406) | |
ALT (abnormal) | –0.024 | 0.1441 | –0.165 | 0.869 | 0.977 (0.737–1.296) | |
AST (abnormal) | –0.028 | 0.1356 | –0.204 | 0.838 | 0.973 (0.746–1.269) | |
ALP (abnormal) | 0.024 | 0.1722 | 0.138 | 0.890 | 1.024 (0.731–1.437) | |
GGT (abnormal) | 0.134 | 0.1167 | 1.146 | 0.252 | 1.143 (0.910–1.438) | |
Total protein (abnormal) | –0.104 | 0.1037 | –1.004 | 0.315 | 0.901 (0.735–1.104) | |
Albumin (abnormal) | 0.017 | 0.1131 | 0.147 | 0.883 | 1.017 (0.814–1.269) | |
Prothrombin time (abnormal) | –0.074 | 0.1102 | –0.673 | 0.501 | 0.928 (0.748–1.153) | |
Fibrinogen (abnormal) | 0.154 | 0.1113 | 1.382 | 0.167 | 1.166 (0.938–1.452) | |
RBC (abnormal) | –0.04 | 0.1069 | –0.372 | 0.710 | 0.961 (0.779–1.185) | |
FT3 (abnormal) | 0.09 | 0.1311 | 0.690 | 0.490 | 1.095 (0.847–1.416) | |
FT4 (abnormal) | 0.277 | 0.1959 | 1.413 | 0.158 | 1.319 (0.899–1.938) | |
LVDd (abnormal) | 0.306 | 0.1102 | 2.773 | 0.006 | 1.358 (1.094–1.686) | |
Threshold 1 | 0.072 | 0.1685 | 0.427 | 0.669 | – | |
Threshold 2 | 3.9623 | 0.1994 | 19.870 | ¡0.001 | – |
Note: MI, myocardial infarction; NYHA, New York Heart Association; DBP, diastolic blood pressure; SBP, systolic blood pressure; CK-MB, creatine kinase-MB; hsTnT, high-sensitivity troponin T; NT-proBNP, N-terminal pro-B-type natriuretic peptide; eGFR, estimate glomerular filtration rate; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALP, alkaline phosphatase; GGT, gamma-glutamyl transpeptidase; RBC, red blood cell; FT3, free triiodothyronine; FT4, free thyroxine; LVDd, left ventricular end-diastolic diameter.
This study conducted stratified analysis on patients with HF with different NYHA scores and compared the impact of guideline compliance on clinical outcomes in these patients. In patients with I/II NYHA, there were significant differences in readmission rates (p = 0.033) and mortality rates (p = 0.001) among the high, moderate, and low compliance with GDMT groups. There were no significant differences in severe CVD rates (p = 0.913) among the three groups. In patients with III/IV NYHA, there were no significant differences in readmission rates (p = 0.317), mortality rates (p = 0.766), and severe CVD rates (p = 0.725) among the three groups (Table 4). This study also compared the influence of basic characteristics and laboratory index of patients with I/II NYHA on physicians’ compliance with guidelines. The results are shown in Supplementary Tables 1,2.
Variables | Physicians’ guideline adherence | p value | ||||
Low | Moderate | High | ||||
Patients with I/II NYHA (N = 1370) | ||||||
Readmission | 0.033 | |||||
No | 302 (76.26) | 631 (70.82) | 67 (80.72) | |||
Yes | 94 (23.74) | 260 (29.18) | 16 (19.28) | |||
Death | 0.001 | |||||
No | 377 (95.20) | 823 (92.37) | 69 (83.13) | |||
Yes | 19 (4.80) | 68 (7.63) | 14 (16.87) | |||
Severe CVD | 0.913 | |||||
No | 357 (90.15) | 803 (90.12) | 76 (91.57) | |||
Yes | 39 (9.85) | 88 (9.88) | 7 (8.43) | |||
Patients with III/IV NYHA (N = 726) | ||||||
Readmission | 0.317 | |||||
No | 68 (66.67) | 358 (68.58) | 77 (75.49) | |||
Yes | 34 (33.33) | 164 (31.42) | 25 (24.51) | |||
Death | 0.766 | |||||
No | 84 (82.35) | 435 (83.33) | 82 (80.39) | |||
Yes | 18 (17.65) | 87 (16.67) | 20 (19.61) | |||
Severe CVD | 0.725 | |||||
No | 93 (91.18) | 483 (92.53) | 96 (94.12) | |||
Yes | 9 (8.82) | 39 (7.47) | 6 (5.88) |
?Note: NYHA, New York Heart Association; CVD, cardiovascular disease.
Our study provides important real-world data on physician’s compliance with GDMT in patients with HFrEF in China and its impact on clinic outcomes. Our analysis showed that: (i) older age and comorbidities including hypertension, DM, and renal dysfunction were more common in Chinese HF patients with decreased EF; (ii) physicians’ compliance with GDMT and overall compliance score were good in 8.8%, moderate in 67.4%, and low in 23.8% of patients; and (iii) physicians’ high compliance was associated with better outcomes (reduction in CVD and HF rehospitalization) at the 24-month follow-up according to univariate analysis.
Our research describes in detail the clinical profile of our patients, who had a mean age of 69.5 years, tended to be older than those reported in previous studies, had an SBP of 145 mmHg, and had higher rates of common comorbidities including hypertension (57.3%) and DM (26.6%). Compared with the ASIAN-HF registry [22], device use was low in our study, with only 0.3% using an implantable cardioverter defibrillator and 0.1% undergoing CRT; this discrepancy may be partly related to socioeconomic considerations.
There is clear evidence from drug research studies showing that drugs recommended by the guidelines improve the clinical outcome of HF patients [23, 24]; however, there is still a large gap between GDMT and clinical practice [22, 25, 26]. We found that physicians’ compliance score was good in 8.8%, moderate in 67.4%, and low in 23.8% of patients, which was significantly lower than the QUALIFY global survey in which adherence was good in 67%, moderate in 25% and poor in 8% of patients [27]. Thus, in our study, there were still a large number of patients who received unoptimized treatment with just one or two of these medications, opposed to combination therapy, as recommended by guidelines, and only 8% of the patients received optimized treatment. Among eligible candidates with HFrEF, physicians tended to be in relative moderate compliance with BBs (80.3%) and MRAs (77%), similar to that of European countries; and in poor compliance with ACEI /ARB/ARNI (added up to 36.6%), which was much lower than that of European countries [12, 26, 28, 29] and the China PEACE Retrospective acute myocardial infarction (AMI) Study [30]. The high rate of use of MRAs in our study could be attributed to a nationwide quality assessment evaluation program [31] and the low cost of MRA. Poor compliance with ACEI was presumably due, in part, to the higher prevalence of persistent cough resulting from ACEI [32]. Contraindication to severe chronic kidney disease or renal failure also plays an important role. However, physicians’ awareness, including concern of adverse effects after combination therapy or during dose escalation for older patients, and economic factors also affecting physicians’ compliance level.
Although previous studies have encouraged uptitration, underdosing still
remained significant in our study. A substantial proportion of patients with
HFrEF received doses considerably below the guideline-recommended doses,
especially for BBs. In view of this situation, the Chinese guidelines emphasize
that BBs should gradually reach the target dose or maximum tolerance to
facilitate clinical implementation, which lowers resting heart rate to 60
beats
Data on the impact of physicians’ compliance with GDMT on clinical outcomes in daily practice are limited, particularly in China. We evaluated the physicians’ compliance with GDMT as well as the relationship between guideline compliance and clinical outcomes in hospitalized and outpatients with HFrEF in China for the first time. We found that high compliance with treatment guidelines was independently correlated with the low rate of HF or CVD rehospitalization, consistent with other studies [22, 37]. By contrast, we observed no significant benefit on mortality, which was inconsistent with another study [38]. Interestingly, we also found that physicians’ compliance was related to mortality in patients with NYHA I/II but not in patients with NYHA III/IV. To explore additional reasons, we conducted a subgroup analysis, which showed that in the group of patients with NYHA I/II, higher mortality was strongly correlated with comorbidities such as hypertension and renal insufficiency. The possibility that compliance had less impact on mortality than on hospitalization may be explained by the fact that mortality for HF is likely to be affected by several medical and non-medical factors including characteristics of the population baseline in our study, such as higher co-morbidities, older age, frailty, and poor financial situation. It is all the more interesting that aggressive treatment is often for patients in the more severe NYHA functional class in order to provide symptom relief. Thus, in our study, physicians’ compliance was better in patients with NYHA I/II with higher mortality risk but not in patients with NYHA III/IV. These findings are in accordance with the previously described “risk-treatment paradox”, where HF patients with the greatest need are less likely to receive appropriate therapy [39]. As quality improvement programs including improving the health care system and medical insurance systems, or a series of physician professional level training programs have been developed over the past years to improve the daily care of patients with HF in China, an increasing number of patients with HFrEF have been treated GDMT. With the increased demand to improve the quality of medical care in China, more efforts are needed to perform improvement measures and optimize the quality of data-based digital management systems, which have been shown to be efficient.
Some important limitations of our study must be acknowledged. First, as this was a hospital-based, retrospective, observational study, it was limited by the nature of its design. We acknowledge that observational data cannot definitively establish causality or drug efficacy. Randomized controlled trials are required for definitive answers. Second, we performed multivariate regression analysis, but other unmeasured and hidden confounding variables such as patients’ compliance, whether patients take medication after discharge, socioeconomic factors, and health care system factors may have obvious impacts on clinical outcome [40]. Third, as with all studies that rely on automated sources of data, it is possible that parameters such as billing codes could be biased and proxies could fail to capture certain factors that are difficult to ascertain from the available clinical data. Finally, we did not analyze the relationship between dose and clinical results in our study, and the compliance score was measured only by the number of classes. We did not take dosage into account, as we only recorded the dosage of a given recommended class, and thus cannot provide a detailed explanation for underdosing or at which stage drug titration occurs.
In our real-word survey of inpatient and outpatient patients with HFrEF, we found that physicians’ compliance with HF class was not satisfactory, with just good in 8.8% of patient, and poor compliance with ACEI/ARB/ARNI. Furthermore, the underdosing of recommended medications was frequently observed, especially for BBs after discharge. This finding calls for action to improve combing drug pattern and uptitration of recommended therapies.
Data is available from the corresponding author upon reasonable request.
GW, LL and HH mainly participated in literature search, study design, writing and critical revision. XW, TY, HX, TZ, JL, HL, YL, LJ and WH mainly participated in data collection, data analysis and data interpretation, and they have all been involved in revising the manuscript critically for important intellectual content. All authors read and approved the final manuscript.
The study was approved by the Ethics Committee of the Affiliated Hospital of North Sichuan Medical College (No. 2022ER314-1), and since the study only involved retrospective analysis of previous clinical data, the requirement for informed consent was waived.
We thank Shanghai Synyi Medical Technology Co., Ltd. for providing the data analysis and statistical platform.
The study was funded by Major Projects of Sichuan Provincial Health Commission (21ZD004), Science and Technology Department of Sichuan Province Project (2021YJ0208, 2021YJ0210), and Research Project Foundation of Affiliated Hospital of North Sichuan Medical College (2022LC009, 2021ZK002, 2021LC011).
The authors declare no conflict of interest, and all authors have no conflict of interest with Shanghai Synyi Medical Technology Co., Ltd.
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