Yagi K, Chujo D, Usui I, Liu JH, Nohara A, Shirozu AE, Takikawa A, Honoki H, Fujisaka S, Origasa H, Tada H. B-type natriuretic peptide efficacy compared to fragmented QRS for diastolic dysfunction screening in patients with type 2 diabetes. World J Diabetes 2025; 16(4): 103551 [DOI: 10.4239/wjd.v16.i4.103551]
Corresponding Author of This Article
Kunimasa Yagi, MD, PhD, Professor, Department of Internal Medicine, Kanazawa Medical University Hospital, 1-1 Daigaku, Uchinada, Kahoku 920-0293, Ishikawa, Japan. yagikuni@icloud.com
Research Domain of This Article
Endocrinology & Metabolism
Article-Type of This Article
Retrospective Cohort Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Kunimasa Yagi, Department of Internal Medicine, Kanazawa Medical University Hospital, Kahoku 920-0293, Ishikawa, Japan
Daisuke Chujo, Center for Clinical and Translational Research, Toyama University Hospital, Toyama 930-0152, Toyama, Japan
Isao Usui, Department of Endocrinology and Metabolism, Dokkyo Medical University, Utsunomiya 321-0293, Tochigi, Japan
Jian-Hui Liu, Department of Cardiology, Ningbo Medical Center of Lihuili Hospital, Ningbo 315041, Zhejiang Province, China
Atsushi Nohara, Department of Clinical Genetics, Ishikawa Prefectural Central Hospital, Kanazawa 920-8530, Ishikawa, Japan
Asako Enkaku Shirozu, Akiko Takikawa, Hisae Honoki, Shiho Fujisaka, The First Department of Internal Medicine, Toyama University Hospital, Toyama 930-0152, Toyama, Japan
Hideki Origasa, Data Science and AI Innovation Research Promotion Center, Institute of Statistical Mathematics, Shiga University, Hikone 525-0034, Shiga, Japan
Hayato Tada, Division of Cardiovascular Medicine, Kanazawa University, Graduate School of Medicine, Kanazawa 920-8640, Ishikawa, Japan
Author contributions: Yagi K acquired funding; Yagi K, Usui I, and Nohara A contributed to the design of the study; Chujo D, Liu JH, Shirozu AE, Takikawa A, Honoki H, and Fujisaka S contributed to the data curation; Yagi K, Origasa H, and Tada H participated in the statistical analysis; Yagi K wrote the original draft; Chujo D, Usui I, Liu JH, Nohara A, Origasa H, and Tada H participated in the review and editing; All the authors approved the final draft of the manuscript.
Supported by the JSPS KAKENHI, No. JP21K10300 and No. JP24K02714.
Institutional review board statement: This study was reviewed and approved by the Institutional Review Board of the Ethics Committee of Toyama University Hospital (No. R2020141).
Informed consent statement: All participants provided informed consent and web-based notifications were used to inform them of their right to opt out at any time.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
STROBE statement: The authors have read the STROBE Statement—a checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-a checklist of items.
Data sharing statement: Data supporting the findings of this study are available from the corresponding author upon reasonable request.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Kunimasa Yagi, MD, PhD, Professor, Department of Internal Medicine, Kanazawa Medical University Hospital, 1-1 Daigaku, Uchinada, Kahoku 920-0293, Ishikawa, Japan. yagikuni@icloud.com
Received: November 25, 2024 Revised: January 4, 2025 Accepted: January 21, 2025 Published online: April 15, 2025 Processing time: 98 Days and 3.7 Hours
Abstract
BACKGROUND
Early diagnosis of left ventricular diastolic dysfunction (LVDD) is essential for preventing heart failure. B-type natriuretic peptide (BNP) is a viable marker for predicting LVDD, as elevated BNP levels have been associated with worsening LVDD in patients with diabetes over time. However, the utility of BNP as a diagnostic marker in diabetes is controversial, as BNP levels are often low in overweight individuals.
AIM
To examine the effectiveness of BNP levels and fragmented QRS (fQRS) on electrocardiography for diagnosing LVDD in patients with type 2 diabetes.
METHODS
This retrospective cohort study included 303 patients with type 2 diabetes (67.4 ± 12.3 years old) with preserved ejection fraction (EF) ≥ 50% admitted to Toyama University Hospital for glycemic management and comorbidity evaluation between November 2017 and April 2021. All participants underwent plasma BNP measurement, electrocardiography, and echocardiography. Cardiologists who were blinded to the BNP results assessed the electrocardiograms and echocardiograms. Subgroup analyses were conducted for overweight individuals.
RESULTS
Receiver operating characteristic (ROC) curve analysis determined optimal BNP cut-off values of 34.8 pg/mL and 7.2 pg/mL for diagnosing LVDD in non-overweight [area under the ROC curve (AUC): 0.70] and overweight (AUC: 0.55) groups, respectively (P = 0.040). In the overweight subgroup, fQRS showed greater diagnostic accuracy for LVDD (AUC: 0.67), indicating moderate diagnostic utility compared with the low performance of the BNP cutoff of 35 pg/mL (AUC: 0.52) (P = 0.010). Multivariate analyses confirmed that fQRS was superior to BNP for LVDD diagnosis regardless of the patient’s weight.
CONCLUSION
A BNP level ≥ 35 pg/mL in non-overweight individuals may be a reliable LVDD marker. Additionally, fQRS was more effective than BNP in diagnosing LVDD irrespective of the patient’s weight. fQRS can complement BNP in the early detection of LVDD, especially in overweight patients, potentially improving early detection and mitigating progression to heart failure with preserved EF in patients with type 2 diabetes.
Core Tip: Early diagnosis of left ventricular diastolic dysfunction (LVDD) is critical for preventing heart failure in patients with type 2 diabetes. The effectiveness of B-type natriuretic peptide (BNP) measurement for diagnosing LVDD in patients with type 2 diabetes remains controversial. BNP levels may underestimate the severity of LVDD because of their lower values in individuals with high body mass index or insulin resistance. A fragmented QRS (fQRS) on electrocardiography mainly reflects myocardial fibrosis and has been reported to reflect diastolic dysfunction in individuals with type 2 diabetes. Here, we compared the diagnostic efficacy of BNP and fQRS for LVDD, highlighting the utility of fQRS evaluation.
Citation: Yagi K, Chujo D, Usui I, Liu JH, Nohara A, Shirozu AE, Takikawa A, Honoki H, Fujisaka S, Origasa H, Tada H. B-type natriuretic peptide efficacy compared to fragmented QRS for diastolic dysfunction screening in patients with type 2 diabetes. World J Diabetes 2025; 16(4): 103551
The incidence of heart failure, particularly heart failure with preserved ejection fraction (HFpEF), is notably high among individuals with diabetes[1]. Asymptomatic left ventricular diastolic dysfunction (LVDD) is associated with type 2 diabetes[2] and often progresses to HFpEF[3]. Recent guidelines for glycemic management recommend assessing the risk of heart failure and chronic kidney disease when selecting diabetes medications to leverage the pleiotropic benefits of these therapies[4]. Early detection of LVDD facilitates the selection of diabetes medications with demonstrated efficacy in preventing heart failure.
LVDD screening should be included in routine evaluations when selecting medications for patients with diabetes. However, this is not always the case. Transthoracic echocardiography (TTE)[5], the gold standard technique for diagnosing LVDD, often presents challenges in diabetes clinics. Even in environments where TTE is accessible, accurate assessment of LVDD can be complex in patients with obesity due to suboptimal acoustic windows.
Measurement of natriuretic peptide (NP) levels is a viable alternative to TTE[6,7]. NP levels typically increase to compensate for cardiac dysfunction[8]. Elevated B-type NP (BNP) levels have been reported to be associated with the worsening of LVDD over an 8-year follow-up period in patients with type 2 diabetes[9]. However, the utility of NP measurement for LVDD screening in patients with diabetes remains controversial because NP levels are often significantly low and do not reflect the degree of cardiac load in patients with a higher body mass index (BMI) and insulin resistance[10,11]. Therefore, the utility of BNP should be evaluated by considering the weight of the patient, and an alternative diagnostic tool is needed for LVDD screening in cases where BNP levels are unreliable.
The fragmented QRS complex (fQRS) is an electrocardiographic (ECG) finding indicative of myocardial fibrosis[12]. In patients with diabetes, myocardial perivascular fibrosis develops early and contributes to LVDD[13]. A previous study showed that fQRS is more prevalent in individuals with diabetes than in those with prediabetes or non-diabetes with metabolic risk factors[14]. Additionally, fQRS-positive patients with LVDD have higher BNP levels than fQRS-negative patients, suggesting a higher likelihood of HFpEF development[15]. The higher prevalence of fQRS in patients with diabetes implies that LVDD in patients with diabetes may progress more readily to HFpEF.
Furthermore, fQRS is associated with LVDD and the incidence of LVDD increases with the number of diabetes-related complications in fQRS-positive patients[16]. These findings underscore the importance of considering both fQRS and BNP levels to understand the pathophysiology of LVDD and its progression to HFpEF in patients with diabetes.
Thus, it is worthwhile to investigate the utility of fQRS as a marker for assessing LVDD. In this study, we evaluated the diagnostic accuracy of BNP by comparing it with fQRS for detecting diastolic dysfunction in both overweight and non-overweight patients with diabetes.
MATERIALS AND METHODS
Study design
This retrospective cohort study was performed in accordance with the ethical standards of the Committee responsible for Human Experimentation and the tenets of the 1964 Declaration of Helsinki and its later versions. The study protocol was approved by the Ethics Committee of Toyama University Hospital (No. R2020141). All participants provided informed consent and web-based notifications informed them of their right to opt out at any time.
Study population
We analyzed data from patients admitted to Toyama University Hospital for glycemic management between November 2017 and April 2021. The inclusion criteria were as follows: Type 2 diabetes with available ECG, TTE, and plasma BNP level data; Left ventricular (LV) ejection fraction (EF) ≥ 50%; And no symptoms of heart failure or coronary artery disease (CAD). There were no age restrictions for adults capable of providing informed consent, with the age range set between 20 and 89 years. The exclusion criteria included: Type 1 diabetes, secondary diabetes, genetically identified cardiomyopathy or severe valvular disease, persistent and paroxysmal atrial fibrillation, percutaneous coronary intervention or transcatheter aortic valve implantation within 1 year; Hemodialysis dependence and renal dysfunction with serum creatinine levels > 2.5 mg/dL; Severe hepatic dysfunction (Child-Pugh score ≥ 10), and refractory malignant diseases.
Medical records review and sample collection
All data analyzed in this study were obtained during patient admission for glycemic management, and no emergency admissions were included. Data were collected through comprehensive examinations including medical history reviews, physical assessments, and laboratory tests.
Blood samples were collected by experienced nurses early in the morning, after a period of rest on the day following admission. ECG recordings were performed on the same day. Urinary albumin excretion was determined using 24-hour urine collection starting the day after admission and was adjusted for urinary creatinine.
Variable definitions
Diabetes was diagnosed based on the American Diabetes Association (ADA) diagnostic criteria[6], or if the health questionnaire indicated current diabetes medications. Hypertension was diagnosed if peripheral blood pressure was ≥ 140/90 mmHg, or if the health questionnaire indicated current antihypertensive medications[17]. Based on the World Health Organization BMI cut-off criteria, individuals with BMI ≥ 25 was classified as overweight[18]. CAD was diagnosed in patients with significant coronary artery stenoses ≥ 75% on coronary cineangiography or ≥ 50% on coronary computed tomography angiography examined within 3 years.
Plasma BNP measurement and thresholds
Plasma BNP levels were measured using a chemiluminescent immunoassay (BNP-JP; Abbott Japan Co. Ltd., Tokyo, Japan). A BNP level ≥ 50 pg/mL is regarded as abnormal based on ADA criteria[6] with an upper standard limit of 18.4 pg/mL, and a BNP level ≥ 35 pg/mL is regarded as elevated, supporting a diagnosis of heart failure[19,20].
Criteria for fQRS and diastolic dysfunction
The resting 12-lead ECG (filter range: 0.16-100 Hz, AC filter: 60 Hz, 25 mm/second, 10 mm/mV) (FCP-7431 ECG machine; Fukuda Denshi Co., Ltd., Tokyo, Japan) was analyzed by a single cardiologist and confirmed by another cardiologist, both of whom were blinded to the patient’s clinical information. fQRS was defined according to the Das criteria[12,21]. For patients without bundle branch blocks, fQRS morphologies included various RSR’ patterns, such as an additional R wave (R′), notching of the R or S wave, or the presence of more than one R′ (fragmentation) in at least two contiguous leads corresponding to the LV segment[12]. For patients with bundle branch block, fQRS was identified from various RSR′ patterns, with or without a Q wave, exhibiting more than two R waves (R′), more than two notches in the R wave, or more than two notches in either the downstroke or upstroke of the S wave in at least two contiguous leads corresponding to the LV segment[21].
Echocardiographic image recordings and measurements were obtained using a 3.75-MHz standard probe (EPIQ G7; Philips, Amsterdam, Netherlands). Standard echocardiographic parameters were measured, including the peak early diastolic (E) to peak atrial systolic (A) transmitral flow velocity ratio (E/A) and E-wave to E’ ratio (E/E’) on tissue doppler imaging. LVDD was diagnosed according to the American Society of Echocardiography 2016 guidelines[5] in patients with an EF ≥ 50% and two abnormal parameters: E/E’ ratio, E’ velocity, and tricuspid regurgitation (TR) velocity. An average E/E’ ratio > 14, lateral E/E’ ratio > 13, or septal E/E’ ratio > 15 was considered abnormal. A septal E’ velocity < 7 cm/second or lateral E’ velocity < 10 cm/second was considered abnormal. A TR velocity > 2.8 m/second was considered abnormal. Although the left atrial volume index was not routinely assessed, a diagnosis was still made by following the guidelines based on other parameters.
Sample size determination
A total of 300 patients with type 2 diabetes were required to detect a 15% difference in the area under the curve (AUC) of the receiver operating characteristic (ROC) analysis under a specific model for LVDD diagnosis between overweight and non-overweight groups with 80% power and a two-sided significance level of 0.05. Concerning the group of overweight patients with type 2 diabetes, 90 patients were necessary to detect a 15% difference in AUC between two diagnostic models using fQRS and BNP.
Statistical analysis
Continuous variables are expressed as mean ± SD and categorical variables as numbers and percentages. Continuous variables were compared using the unpaired t test or Mann-Whitney U test. Categorical variables between groups were compared using the χ2 test with Yates’ continuity correction.
The AUC of ROC was calculated to evaluate the discriminatory ability of each parameter, and the confidence interval (CI) and SE of the AUC were computed. The AUC was interpreted as described previously 2021. The DeLong method was used for AUC comparisons[22]. The optimal threshold for the predictor on the x-axis of the ROC curve was determined using Youden’s index.
Multivariate analyses were performed to assess the predictive abilities of BNP and fQRS in the presence of LVDD, adjusted for other potential contributing factors. Of the established risk factors for HFpEF identified in the H2PEF score (obesity, hypertension, atrial fibrillation, age, and E/E’)[23], we selected BMI, age, sex, systolic blood pressure, diagnosis of ischemic heart disease, and heart rate as crucial variables.
Statistical analyses were performed using R version 4.3.0, GUI version 1.79, and R Studio ver. 2023.06.0 + 421 on a Macintosh computer (Apple, Cupertino, CA, United States).
RESULTS
Clinical characteristics of the study population
The clinical characteristics of the study population, which included 303 patients, are presented in Table 1. Supplementary Figure 1 shows the selection process of the study population. FQRS complexes were observed in 112 patients (37%), and 74 patients (24%) showed diastolic dysfunction.
Table 1 Clinical characteristics of the study population, mean ± SD/n (%).
Characteristic
Total (n = 303)
Overweight (n = 140)
Non-overweight (n = 163)
P value
Age, years
67.4 ± 12.3
63.5 ± 13.5
70.7 ± 10.0
< 0.0001
Male sex
191 (63.0)
74 (52.9)
117 (71.8)
0.0010
Duration of diabetes, years
15.9 ± 11.7
13.9 ± 9.8
17.6 ± 12.9
0.0047
Systolic BP, mmHg
134.8 ± 17.7
137.0 ± 16.7
132.8 ± 18.3
0.0371
Diastolic BP, mmHg
81.0 ± 12.0
83.4 ± 10.6
79.0 ± 12.8
0.0013
Heart rate, /minute
73.0 ± 13.0
75.1 ± 12.8
71.2 ± 12.9
0.0094
CAD
72 (23.8)
31 (22.1)
41 (25.2)
0.6323
BMI, kg/m2
25.2 ± 5.0
29.3 ± 4.1
21.7 ± 2.2
< 0.0001
eGFR, mL/minute
71.9 ± 25.3
74.5 ± 26.1
69.7 ± 24.5
0.1035
ALT, U/L
27.9 ± 23.3
29.6 ± 23.3
26.5 ± 23.4
0.2566
LDL-C, mmol/L
2.74 ± 0.90
2.88 ± 0.92
2.62 ± 0.86
0.0108
TG, mmol/L
1.61 ± 0.91
1.82 ± 1.00
1.42 ± 0.79
0.0002
HbA1c, %
9.5 ± 1.6
9.5 ± 1.6
9.5 ± 1.7
0.9957
HbA1c, mmol/mol
80 ± 18
80 ± 18
80 ± 19
UAE, mg/g
192.1 ± 512.4
167.9 ± 432.9
213.1 ± 572.8
0.4365
BNP, pg/mL
38.5 ± 65.6
29.1 ± 48.7
46.5 ± 76.4
0.0169
Medications
Insulin
116 (37.2)
55 (36.9)
61 (35.5)
0.8306
GLP-1a
37 (12.2)
32 (22.9)
5 (3.1)
0.0031
SGLT2i
27 (8.9)
19 (13.6)
8 (4.9)
0.0148
RASi
124 (40.9)
65 (46.4)
59 (36.2)
0.097
Beta blockers
47 (15.5)
22 (15.7)
25 (15.3)
0.004
Diuretics
69 (22.8)
43 (30.7)
26 (16.0)
0.168
TTE findings
LAD, mm
36.8 ± 6.0
37.6 ± 5.4
36.1 ± 6.4
0.0251
LVDD, mm
45.1 ± 5.0
45.8 ± 5.0
44.6 ± 4.9
0.0299
IVS, mm
9.4 ± 1.3
9.7 ± 1.2
9.2 ± 1.3
0.0013
EF, %
67.4 ± 7.2
67.0 ± 7.4
67.7 ± 7.1
0.4056
Septal E/E’
12.0 ± 4.1
12.1 ± 3.9
12.0 ± 4.3
0.9078
Lateral E/E’
9.1 ± 3.4
9.1 ± 3.4
9.0 ± 3.4
0.7779
Average E/E’
10.6 ± 3.5
10.6 ± 3.4
10.5 ± 3.6
0.8230
Septal E’, m/second
5.6 ± 1.7
5.6 ± 1.9
5.5 ± 1.6
0.6822
Lateral E’, m/second
7.5 ± 2.3
7.5 ± 2.6
7.4 ± 2.1
0.7016
TRV, m/second
2.3 ± 0.3
2.3 ± 0.2
2.3 ± 0.4
0.8226
E/A
0.81 ± 0.26
0.82 ± 0.27
0.80 ± 0.26
0.5415
Diastolic dysfunction
74 (24.4)
32 (22.9)
42 (25.8)
0.5933
ECG findings
Blocks
AVB
4 (1.3)
2 (1.4)
2 (1.2)
1.0000
RBBB
25 (8.3)
8 (5.7)
17 (10.4)
0.2013
LBBB
2 (0.7)
1 (0.7)
1 (0.6)
1.0000
fQRS
112 (37.0)
46 (32.9)
66 (40.5)
0.2102
fQRS region
Inferior leads
95 (31.4)
42 (30.0)
53 (32.5)
0.7291
Anterior leads
44 (14.5)
14 (10.0)
30 (18.4)
0.0566
Lateral leads
11 (3.6)
4 (2.9)
7 (4.3)
0.7197
Multiple regions
34 (11.2)
10 (7.1)
24 (14.7)
0.0572
fQRS morphologies
Fragmented QRS
15 (5.0)
3 (2.1)
12 (7.4)
0.1966
RSR’
11 (3.6)
3 (2.1)
8 (4.9)
0.3296
Notched S
82 (27.1)
38 (27.1)
44 (27.0)
1.0000
RSR’
13 (4.0)
3 (2.1)
10 (5.5)
0.2270
Notched R
87 (28.7)
36 (25.7)
51 (31.3)
0.3463
RSR’ with ST elevation
3 (1.0)
1 (0.7)
2 (1.2)
1.0000
Plasma BNP levels for LVDD diagnosis in non-overweight patients
According to ROC curve analysis, the optimal threshold for plasma BNP levels in non-overweight patients positive for LVDD was 34.8 pg/mL, showing a strong positive correlation with an AUC of 0.70 (95%CI: 0.61-0.79) (Figure 1A). The threshold in individuals with BMI ≥ 25 was significantly lower at 7.2 pg/mL with an AUC of 0.55 (95%CI: 0.43-0.66).
Figure 1 Receiver operating characteristic curves.
A: B-type natriuretic peptide (BNP) level and the presence of left ventricular diastolic dysfunction (LVDD). The non-overweight group (closed circles, n = 163) has an area under the receiver operating characteristic (ROC) curve (AUC) threshold of 0.70 [95% confidence interval (CI): 0.61-0.79] threshold of 34.8 pg/mL. Overweight group (crosses, n = 140): AUC: 0.55 (95%CI: 0.43-0.66) threshold 7.2 pg/mL; B: Non-overweight group ROC curves of fragmented QRS (fQRS) and BNP with various cut-offs and the presence of LVDD. BNP (black closed circles, AUC: 0.70), BNP at a cut-off of 18.4 pg/mL (blue long dashed line, AUC: 0.66), BNP at a cut-off of 35 pg/mL (blue long dashed line, AUC: 0.69), BNP at a cut-off of 50 pg/mL (purple dotted dashed line, AUC: 0.62), and fQRS (red solid line, AUC: 0.63); C: Overweight group ROC curves of BNP (black crosses, AUC: 0.55), BNP at a cut-off of 18.4 pg/mL (green dotted line, AUC: 0.52), BNP at a cut-off of 35 pg/mL (blue long dashed line, AUC: 0.52), BNP at a cut-off of 50 pg/mL (purple dotted dashed line, AUC: 0.55), and fQRS (red solid line, AUC: 0.67). AUC: Area under the receiver operating characteristic curve; BNP: B-type natriuretic peptide; fQRS: Fragmented QRS; CI: Confidence interval.
The BNP threshold of 34.8 pg/mL was consistent with the cutoff value of 35.0 pg/mL in the universal guidelines[19]. Therefore, the BNP cut-off was set at 35.0 pg/mL for 163 non-overweight patients, resulting in an AUC of 0.69 (95%CI: 0.61-0.77). Adjusting the cut-off to the upper standard limit of BNP of 18.4 pg/mL and 50.0 pg/mL yielded AUCs of 0.66 (95%CI: 0.59-0.73) and 0.62 (95%CI: 0.53-0.70), respectively (Figure 1B).
Plasma BNP levels and fQRS for LVDD diagnosis in overweight patients
Further examination of 140 patients with BMI ≥ 25 revealed AUCs of 0.52 (95%CI: 0.44-0.60) for a BNP cut-off of 35.0 pg/mL, 0.52 (95%CI: 0.43-0.62) for 18.4 pg/mL, and 0.55 (95%CI: 0.48-0.63) for 50.0 pg/mL (Figure 1C). fQRS for LVDD diagnosis in this BMI subgroup yielded a moderate positive correlation with a significantly greater AUC of 0.67 (95%CI: 0.58-0.77) than that of BNP levels.
Utility of fQRS and BNP in three LVDD diagnostic models
We subsequently investigated the utility of fQRS and BNP by conducting comparative analyses of three diagnostic models in non-overweight (Table 2) and overweight patients (Table 3). Model 1 included BNP, model 2 incorporated fQRS, and model 3 combined BNP and fQRS.
Table 2 Multivariate model analysis on the diastolic dysfunction diagnosis in non-overweight participants.
Factors
OR
95%CI
P value
Model 1; AIC: 181.33
Age, 10-year increments
1.59
1.04-2.57
0.044
Sex, male/female
0.52
0.23-1.19
0.118
CAD
0.84
0.33-2.06
0.715
sBP, every 10 mmHg
1.18
0.96-1.47
0.122
HR, every 10/minute
0.98
0.71-1.34
0.881
BNP, per SD
1.69
1.10-2.81
0.028
Model 2; AIC: 176.14
Age, 10-year increments
1.75
1.14-2.84
0.028
Sex, male/female
0.38
0.16-090
0.055
CAD
0.88
0.33-2.21
0.794
sBP, every 10 mmHg
1.22
0.99-1.53
0.073
HR, every 10/minute
0.89
0.65-1.22
0.478
fQRS
3.76
1.71-8.72
0.001
Model 3; AIC: 171.06
Age, 10-year increments
1.59
1.02-2.60
0.047
Sex, male/female
0.40
0.16-0.96
0.040
CAD
0.66
0.24-1.72
0.410
sBP, every 10 mmHg
1.23
0.99-1.54
0.067
HR, every 10/minute
0.96
0.69-1.31
0.786
BNP, per SD
1.89
1.16-3.39
0.023
fQRS
4.17
1.86-9.88
< 0.001
Table 3 Multivariate model analysis on the diastolic dysfunction diagnosis in overweight participants.
Factors
OR
95%CI
P value
Model 1; AIC: 128.79
Age, 10-year increments
2.22
1.43-3.71
< 0.001
Sex, male/female
1.06
0.41-2.79
0.908
CAD
0.30
0.07-1.01
0.068
sBP, every 10 mmHg
1.66
0.72-2.30
< 0.001
HR, every 10/minute
1.91
1.29-2.99
0.002
BNP, per SD
1.19
0.77-1.82
0.387
Model 2; AIC: 114.95
Age, 10-year increments
2.60
1.57-4.77
< 0.001
Sex, male/female
0.87
0.30-2.48
0.790
CAD
0.18
0.03-0.71
0.024
sBP, every 10 mmHg
1.75
1.28-2.51
< 0.001
HR, every 10/minute
1.82
1.18-2.96
0.010
fQRS
7.15
2.55-22.4
< 0.001
Model 3; AIC: 116.52
Age, 10-year increments
2.57
1.55-4.72
< 0.001
Sex, male/female
0.87
3.03-2.48
0.789
CAD
0.17
0.03-0.69
0.022
sBP, every 10 mmHg
1.73
1.27-2.48
0.001
HR, every 10/minute
1.87
1.21-3.05
0.008
BNP, per SD
1.16
0.71-1.78
0.497
fQRS
7.07
2.51-22.2
< 0.001
In non-overweight patients (Table 2), BNP contributed modestly in model 1 [odds ratio (OR) = 1.69, 95%CI: 1.10-2.81, P = 0.028] compared with that of the more substantial effect of fQRS in model 2 (OR = 3.76, 95%CI: 1.71-8.72, P = 0.001). In model 3, fQRS (OR = 4.27, 95%CI: 1.86-9.88, P < 0.001) demonstrated a more vital contribution to LVDD diagnosis than BNP (OR = 1.89, 95%CI: 1.16-3.39, P = 0.023).
In overweight patients (Table 3), BNP in model 1 did not significantly contribute to LVDD diagnosis (OR = 1.19, 95%CI: 0.77-1.82, P = 0.387), whereas fQRS in model 2 showed a strong effect (OR = 7.15, 95%CI: 2.55-22.4, P < 0.001). In model 3, fQRS (OR = 7.07, 95%CI: 2.51-22.2, P < 0.001) again demonstrated a more substantial contribution to LVDD diagnosis than the non-significant BNP (OR = 1.16, 95%CI: 0.71-1.78, P = 0.497). Thus, fQRS significantly contributed to LVDD diagnosis in both overweight and non-overweight participants.
Integrating fQRS and BNP into LVDD diagnostic model
Integrating fQRS and BNP into the diagnostic model significantly improved the accuracy of LVDD detection, as indicated by a lower Akaike’s information criterion (AIC) value in non-overweight individuals (model 3, Table 2). This finding underscores the potential of a multi-marker approach to enhance diagnostic precision. Conversely, for overweight patients, the AIC value was lower in model 2 than in model 3, suggesting that BNP levels did not further improve the diagnostic accuracy of LVDD when added to fQRS. The combined use of fQRS and BNP demonstrated the highest diagnostic performance with an AUC of 0.70 for the total cohort, 0.65 for overweight individuals, and 0.73 for non-overweight individuals, as shown in Supplementary Figure 2.
DISCUSSION
This study aimed to precisely and reliably compare the effectiveness of fQRS and BNP levels in the diagnosis of LVDD. The findings are summarized as follows. First, BNP was demonstrated to be a significant diagnostic marker for LVDD in patients with type 2 diabetes, although this utility was limited to non-overweight patients. The observed threshold value was close to 35 pg/mL, which is consistent with the levels considered to be elevated by universal heart failure guidelines[19]. Second, fQRS could be an alternative diagnostic marker for LVDD, independent of overweight status. In non-overweight participants, the combined use of BNP levels and fQRS further improved the diagnostic reliability of LVDD.
The study included patients with type 2 diabetes who had available records of TTE, ECG, and BNP levels and excluded patients with renal dysfunction and atrial fibrillation to eliminate their influence on BNP levels. The detection of LVDD in 24% of patients was consistent with findings of Bouthoorn et al[24]. This study was conducted before the publication of large-scale trials on the treatment of HFpEF with sodium-glucose co-transporter 2 inhibitors[25,26] and glucagon-like peptide-1 receptor agonists (GLP-1a)[27,28]; hence, the influence of these medications on the patients was minimal. Since this study was conducted in a Japanese cohort, our findings reflect the specific metabolic characteristics of Asian populations, which are sensitive to adiposity and develop metabolic impairment at approximately 5 kg/m2 lower than those in Western populations[29]. Thus, the effect of adiposity on BNP levels was more pronounced in this study, highlighting the utility of fQRS in diagnosing LVDD.
The risk prediction cut-offs for NP are lower in overweight and obese patients than in non-overweight patients[30], which is consistent with the observation that NP levels are reduced by high BMI[10,31]. Decreased plasma NP levels in individuals with high BMI are attributable to the expression of neprilysin and NP clearance receptors in adipocytes[32]. Reduction in adipose tissue volume following bariatric surgery has been shown to increase NP levels[33,34]. In the AHEAD intervention, NT-proBNP increased significantly in the intervention arm that achieved weight loss compared to the control arm at 1 year[35]. Moreover, myocardial stretch stress and BNP elevation are less likely to occur in patients with LVDD than in those with reduced EF status[36]. The benchmark for elevated NP levels has gradually decreased, reflecting the relative increase in HFpEF over heart failure with reduced EF[19], and A BNP level of 35 pg/mL has recently been defined as abnormal for diagnosing heart failure across ethnicities[19]. The present study indicated an optimal BNP threshold of 34.75 pg/mL for LVDD diagnosis in non-overweight patients, and an AUC > 0.7 reinforced the reliability of this benchmark[37].
The evaluation of fQRS, which requires only standard ECG records, represents a feasible method for LVDD screening in patients with type 2 diabetes. Although no previous studies have compared the significance of BNP and fQRS in LVDD detection in patients with diabetes, fQRS is correlated with LVDD in other endocrinological diseases. Among patients with subclinical hypothyroidism, fQRS + patients showed longer isovolumic relaxation time[38]. Similarly, in patients with acromegaly, fQRS correlated with elevated E/E’ and decreased E’[39]. The determination of fQRS involves the detection of fragments in two contiguous leads corresponding to the LV segments[40,41]. The fQRS is consistent and reliable when these notches can be distinguished from the artifacts. The widespread availability of ECG makes fQRS a promising tool for LVDD screening by diabetologists. The significant increase in OR for diagnosing LVDD using fQRS is consistent with observations that fQRS serves as a qualitative indicator of the presence and extent of myocardial fibrosis. Significant alterations in fQRS are less likely to occur over short periods, although changes in Q waves over time indicate the dynamic nature of myocardial fibrosis. In addition, BNP levels provide quantitative insights into cardiac load states. The OR for diagnosing LVDD was enhanced by adding BNP level and fQRS to the traditional HFpEF risk factors. The diverse nature of BNP and fQRS emphasizes the need for an integrated approach that leverages the quantitative strength of BNP for short-term evaluation and qualitative insights from fQRS for long-term monitoring.
HFpEF is a heterogeneous condition broadly classified into two types: One associated with younger age, male sex, and obesity and another associated with older age, female sex, and lower BMI[42]. Recent studies such as STEP-HFpEF[27] and SELECT[28] have demonstrated the effectiveness of the GLP-1a semaglutide in improving exercise tolerance and reducing total mortality in younger, male, and obese patients with HFpEF. There appears to be a medical rationale for routine fQRS evaluation while reaffirming the GLP-1a indication in overweight patients and those with type 2 diabetes. Furthermore, machine learning (ML) applications have become widely used for identifying and interpreting fQRS[43]. Prospective studies are needed to validate ML-mediated fQRS-guided GLP-1a interventions for the prevention and progression of HFpEF.
This study has several limitations. Given its retrospective design in a single facility, we could not entirely exclude the potential for inherent bias, unlike prospective multicenter analyses. Our cohort was not very large; however, it was sufficiently robust to detect highly applicable differences in clinical practice. We excluded patients with significant renal impairment by omitting those on dialysis or having a creatinine levels ≥ 2.5 mg/dL. This limited our ability to explore the impact of renal function on BNP levels, as discussed by Tsutamoto et al[44]. However, it allowed for a more concentrated analysis of the effects of being overweight. This study analyzed BNP, rather than NT-proBNP, which is commonly used in Western countries. However, BNP is considered less susceptible to the effects of renal impairment[45] and both BNP and NT-proBNP are equally predictive of all-cause death and HF rehospitalization[46]. This potentially increased the reliability of these findings. Furthermore, the retrospective cohort design with a cross-sectional approach restricts the assessment of the long-term predictive value of BNP and fQRS for LVDD or HFpEF progression, highlighting an area for future research.
CONCLUSION
This study emphasizes the clinical relevance of BNP and fQRS as indicators of HFpEF risk in patients with type 2 diabetes. In non-overweight individuals, a BNP level ≥ 35 pg/mL may be a reliable LVDD marker. Additionally, fQRS demonstrated greater effectiveness than BNP in diagnosing LVDD, regardless of patient weight. Using BNP and fQRS as markers to guide therapeutic interventions targeting LVDD may help mitigate the progression to HFpEF in patients with type 2 diabetes.
ACKNOWLEDGEMENTS
The authors express their gratitude to Dr. Konno T, former research director of the Department of Cardiology, Kanazawa University, for inspiring the fQRS analysis.
Footnotes
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Endocrinology and metabolism
Country of origin: Japan
Peer-review report’s classification
Scientific Quality: Grade B, Grade C, Grade C, Grade C
Novelty: Grade B, Grade B, Grade B
Creativity or Innovation: Grade B, Grade C, Grade C
Scientific Significance: Grade B, Grade B, Grade C
P-Reviewer: Gutiérrez-Cuevas J; Horowitz M; Luo BB; Shah SIA S-Editor: Fan M L-Editor: A P-Editor: Zhao S
Boonman-de Winter LJ, Rutten FH, Cramer MJ, Landman MJ, Liem AH, Rutten GE, Hoes AW. High prevalence of previously unknown heart failure and left ventricular dysfunction in patients with type 2 diabetes.Diabetologia. 2012;55:2154-2162.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 187][Cited by in RCA: 213][Article Influence: 16.4][Reference Citation Analysis (0)]
Antakly-Hanon Y, Ben Hamou A, Garçon P, Moeuf Y, Banu I, Fumery M, Voican A, Abassade P, Oriez C, Chatellier G, Dupuy O, Cador R, Komajda M. Asymptomatic left ventricular dysfunction in patients with type 2 diabetes free of cardiovascular disease and its relationship with clinical characteristics: The DIACAR cohort study.Diabetes Obes Metab. 2021;23:434-443.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 2][Cited by in RCA: 7][Article Influence: 1.8][Reference Citation Analysis (0)]
Hoek AG, Dal Canto E, Wenker E, Bindraban N, Handoko ML, Elders PJM, Beulens JWJ. Epidemiology of heart failure in diabetes: a disease in disguise.Diabetologia. 2024;67:574-601.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 3][Reference Citation Analysis (0)]
Davies MJ, Aroda VR, Collins BS, Gabbay RA, Green J, Maruthur NM, Rosas SE, Del Prato S, Mathieu C, Mingrone G, Rossing P, Tankova T, Tsapas A, Buse JB. Management of Hyperglycemia in Type 2 Diabetes, 2022. A Consensus Report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD).Diabetes Care. 2022;45:2753-2786.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 533][Cited by in RCA: 673][Article Influence: 224.3][Reference Citation Analysis (1)]
Nagueh SF, Smiseth OA, Appleton CP, Byrd BF 3rd, Dokainish H, Edvardsen T, Flachskampf FA, Gillebert TC, Klein AL, Lancellotti P, Marino P, Oh JK, Popescu BA, Waggoner AD. Recommendations for the Evaluation of Left Ventricular Diastolic Function by Echocardiography: An Update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging.J Am Soc Echocardiogr. 2016;29:277-314.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 2879][Cited by in RCA: 3623][Article Influence: 402.6][Reference Citation Analysis (0)]
Iwanaga Y, Nishi I, Furuichi S, Noguchi T, Sase K, Kihara Y, Goto Y, Nonogi H. B-type natriuretic peptide strongly reflects diastolic wall stress in patients with chronic heart failure: comparison between systolic and diastolic heart failure.J Am Coll Cardiol. 2006;47:742-748.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 373][Cited by in RCA: 383][Article Influence: 20.2][Reference Citation Analysis (0)]
Kroon MH, van den Hurk K, Alssema M, Kamp O, Stehouwer CD, Henry RM, Diamant M, Boomsma F, Nijpels G, Paulus WJ, Dekker JM. Prospective associations of B-type natriuretic peptide with markers of left ventricular function in individuals with and without type 2 diabetes: an 8-year follow-up of the Hoorn Study.Diabetes Care. 2012;35:2510-2514.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 26][Cited by in RCA: 27][Article Influence: 2.1][Reference Citation Analysis (0)]
Buckley LF, Canada JM, Del Buono MG, Carbone S, Trankle CR, Billingsley H, Kadariya D, Arena R, Van Tassell BW, Abbate A. Low NT-proBNP levels in overweight and obese patients do not rule out a diagnosis of heart failure with preserved ejection fraction.ESC Heart Fail. 2018;5:372-378.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 45][Cited by in RCA: 48][Article Influence: 6.9][Reference Citation Analysis (0)]
Yagi K, Nagata Y, Yamagami T, Chujo D, Kamigishi M, Yokoyama-Nakagawa M, Shikata M, Enkaku A, Takikawa-Nishida A, Honoki H, Fujisaka S, Origasa H, Tobe K. High prevalence of fragmented QRS on electrocardiography in Japanese patients with diabetes irrespective of metabolic syndrome.J Diabetes Investig. 2021;12:1680-1688.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 3][Cited by in RCA: 2][Article Influence: 0.5][Reference Citation Analysis (0)]
Onoue Y, Izumiya Y, Hanatani S, Kimura Y, Araki S, Sakamoto K, Yamamoto E, Tsujita K, Tanaka T, Yamamuro M, Kojima S, Kaikita K, Hokimoto S, Ogawa H. Fragmented QRS complex is a diagnostic tool in patients with left ventricular diastolic dysfunction.Heart Vessels. 2016;31:563-567.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 14][Cited by in RCA: 15][Article Influence: 1.5][Reference Citation Analysis (0)]
Yagi K, Imamura T, Tada H, Liu J, Miyamoto Y, Ohbatake A, Ito N, Shikata M, Enkaku A, Takikawa A, Honoki H, Fujisaka S, Chujo D, Origasa H, Kinugawa K, Tobe K. Fragmented QRS on electrocardiography as a predictor for diastolic cardiac dysfunction in type 2 diabetes.J Diabetes Investig. 2022;13:1052-1061.
[PubMed] [DOI][Cited in This Article: ][Reference Citation Analysis (0)]
Shimamoto K, Ando K, Fujita T, Hasebe N, Higaki J, Horiuchi M, Imai Y, Imaizumi T, Ishimitsu T, Ito M, Ito S, Itoh H, Iwao H, Kai H, Kario K, Kashihara N, Kawano Y, Kim-Mitsuyama S, Kimura G, Kohara K, Komuro I, Kumagai H, Matsuura H, Miura K, Morishita R, Naruse M, Node K, Ohya Y, Rakugi H, Saito I, Saitoh S, Shimada K, Shimosawa T, Suzuki H, Tamura K, Tanahashi N, Tsuchihashi T, Uchiyama M, Ueda S, Umemura S; Japanese Society of Hypertension Committee for Guidelines for the Management of Hypertension. The Japanese Society of Hypertension Guidelines for the Management of Hypertension (JSH 2014).Hypertens Res. 2014;37:253-390.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 643][Cited by in RCA: 812][Article Influence: 73.8][Reference Citation Analysis (0)]
Bozkurt B, Coats AJ, Tsutsui H, Abdelhamid M, Adamopoulos S, Albert N, Anker SD, Atherton J, Böhm M, Butler J, Drazner MH, Felker GM, Filippatos G, Fonarow GC, Fiuzat M, Gomez-Mesa JE, Heidenreich P, Imamura T, Januzzi J, Jankowska EA, Khazanie P, Kinugawa K, Lam CSP, Matsue Y, Metra M, Ohtani T, Francesco Piepoli M, Ponikowski P, Rosano GMC, Sakata Y, SeferoviĆ P, Starling RC, Teerlink JR, Vardeny O, Yamamoto K, Yancy C, Zhang J, Zieroth S. Universal Definition and Classification of Heart Failure: A Report of the Heart Failure Society of America, Heart Failure Association of the European Society of Cardiology, Japanese Heart Failure Society and Writing Committee of the Universal Definition of Heart Failure.J Card Fail. 2021;.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 330][Cited by in RCA: 394][Article Influence: 98.5][Reference Citation Analysis (0)]
Authors/Task Force Members:; McDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, Böhm M, Burri H, Butler J, Čelutkienė J, Chioncel O, Cleland JGF, Coats AJS, Crespo-Leiro MG, Farmakis D, Gilard M, Heymans S, Hoes AW, Jaarsma T, Jankowska EA, Lainscak M, Lam CSP, Lyon AR, McMurray JJV, Mebazaa A, Mindham R, Muneretto C, Francesco Piepoli M, Price S, Rosano GMC, Ruschitzka F, Kathrine Skibelund A; ESC Scientific Document Group. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: Developed by the Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). With the special contribution of the Heart Failure Association (HFA) of the ESC.Eur J Heart Fail. 2022;24:4-131.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 205][Cited by in RCA: 1102][Article Influence: 367.3][Reference Citation Analysis (0)]
DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.Biometrics. 1988;44:837-845.
[PubMed] [DOI][Cited in This Article: ]
Bouthoorn S, Valstar GB, Gohar A, den Ruijter HM, Reitsma HB, Hoes AW, Rutten FH. The prevalence of left ventricular diastolic dysfunction and heart failure with preserved ejection fraction in men and women with type 2 diabetes: A systematic review and meta-analysis.Diab Vasc Dis Res. 2018;15:477-493.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 65][Cited by in RCA: 88][Article Influence: 12.6][Reference Citation Analysis (0)]
Kosiborod MN, Abildstrøm SZ, Borlaug BA, Butler J, Rasmussen S, Davies M, Hovingh GK, Kitzman DW, Lindegaard ML, Møller DV, Shah SJ, Treppendahl MB, Verma S, Abhayaratna W, Ahmed FZ, Chopra V, Ezekowitz J, Fu M, Ito H, Lelonek M, Melenovsky V, Merkely B, Núñez J, Perna E, Schou M, Senni M, Sharma K, Van der Meer P, von Lewinski D, Wolf D, Petrie MC; STEP-HFpEF Trial Committees and Investigators. Semaglutide in Patients with Heart Failure with Preserved Ejection Fraction and Obesity.N Engl J Med. 2023;389:1069-1084.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 103][Cited by in RCA: 467][Article Influence: 233.5][Reference Citation Analysis (0)]
Vergaro G, Gentile F, Meems LMG, Aimo A, Januzzi JL Jr, Richards AM, Lam CSP, Latini R, Staszewsky L, Anand IS, Cohn JN, Ueland T, Gullestad L, Aukrust P, Brunner-La Rocca HP, Bayes-Genis A, Lupón J, Yoshihisa A, Takeishi Y, Egstrup M, Gustafsson I, Gaggin HK, Eggers KM, Huber K, Gamble GD, Ling LH, Leong KTG, Yeo PSD, Ong HY, Jaufeerally F, Ng TP, Troughton R, Doughty RN, Devlin G, Lund M, Giannoni A, Passino C, de Boer RA, Emdin M. NT-proBNP for Risk Prediction in Heart Failure: Identification of Optimal Cutoffs Across Body Mass Index Categories.JACC Heart Fail. 2021;9:653-663.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 7][Cited by in RCA: 20][Article Influence: 5.0][Reference Citation Analysis (0)]
Poh KK, Shabbir A, Ngiam JN, Lee PSS, So J, Frampton CM, Pemberton CJ, Richards AM. Plasma Clearance of B-Type Natriuretic Peptide (BNP) before and after Bariatric Surgery for Morbid Obesity.Clin Chem. 2021;67:662-671.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 5][Cited by in RCA: 8][Article Influence: 1.6][Reference Citation Analysis (0)]
Bertoni AG, Wagenknecht LE, Kitzman DW, Marcovina SM, Rushing JT, Espeland MA; Brain Natriuretic Peptide Subgroup of the Look AHEAD Research Group. Impact of the look AHEAD intervention on NT-pro brain natriuretic peptide in overweight and obese adults with diabetes.Obesity (Silver Spring). 2012;20:1511-1518.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 24][Cited by in RCA: 26][Article Influence: 2.0][Reference Citation Analysis (0)]
van Veldhuisen DJ, Linssen GC, Jaarsma T, van Gilst WH, Hoes AW, Tijssen JG, Paulus WJ, Voors AA, Hillege HL. B-type natriuretic peptide and prognosis in heart failure patients with preserved and reduced ejection fraction.J Am Coll Cardiol. 2013;61:1498-1506.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 299][Cited by in RCA: 314][Article Influence: 26.2][Reference Citation Analysis (0)]
Mukaka MM. Statistics corner: A guide to appropriate use of correlation coefficient in medical research.Malawi Med J. 2012;24:69-71.
[PubMed] [DOI][Cited in This Article: ]
Yılmaz E, Aydın E, Çamcı S, Aydın E. Frequency of fragmented QRS on ECG and relationship with left ventricular dysfunction in patients with subclinical hypothyroidism.Eur Rev Med Pharmacol Sci. 2022;26:3677-3685.
[PubMed] [DOI][Cited in This Article: ][Reference Citation Analysis (0)]
Dereli S, Özer H, Özer N, Bayramoğlu A, Kaya A. Association between fragmented QRS and left ventricular dysfunction in acromegaly patients.Acta Cardiol. 2020;75:435-441.
[PubMed] [DOI][Cited in This Article: ][Reference Citation Analysis (0)]
Konno T, Hayashi K, Fujino N, Oka R, Nomura A, Nagata Y, Hodatsu A, Sakata K, Furusho H, Takamura M, Nakamura H, Kawashiri MA, Yamagishi M. Electrocardiographic QRS Fragmentation as a Marker for Myocardial Fibrosis in Hypertrophic Cardiomyopathy.J Cardiovasc Electrophysiol. 2015;26:1081-1087.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 37][Cited by in RCA: 42][Article Influence: 4.2][Reference Citation Analysis (0)]
Tromp J, Shen L, Jhund PS, Anand IS, Carson PE, Desai AS, Granger CB, Komajda M, McKelvie RS, Pfeffer MA, Solomon SD, Køber L, Swedberg K, Zile MR, Pitt B, Lam CSP, McMurray JJV. Age-Related Characteristics and Outcomes of Patients With Heart Failure With Preserved Ejection Fraction.J Am Coll Cardiol. 2019;74:601-612.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 48][Cited by in RCA: 107][Article Influence: 21.4][Reference Citation Analysis (0)]
Villa A, Vandenberk B, Kenttä T, Ingelaere S, Huikuri HV, Zabel M, Friede T, Sticherling C, Tuinenburg A, Malik M, Van Huffel S, Willems R, Varon C. A machine learning algorithm for electrocardiographic fQRS quantification validated on multi-center data.Sci Rep. 2022;12:6783.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 1][Cited by in RCA: 4][Article Influence: 1.3][Reference Citation Analysis (0)]
Tsutamoto T, Wada A, Sakai H, Ishikawa C, Tanaka T, Hayashi M, Fujii M, Yamamoto T, Dohke T, Ohnishi M, Takashima H, Kinoshita M, Horie M. Relationship between renal function and plasma brain natriuretic peptide in patients with heart failure.J Am Coll Cardiol. 2006;47:582-586.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 144][Cited by in RCA: 142][Article Influence: 7.5][Reference Citation Analysis (0)]
