Clinical predictive model of new-onset atrial fibrillation in patients with acute myocardial infarction after percutaneous coronary intervention

doi:10.21203/rs.3.rs-4932460/v1

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Clinical predictive model of new-onset atrial fibrillation in patients with acute myocardial infarction after percutaneous coronary intervention

https://doi.org/10.21203/rs.3.rs-4932460/v1

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Background

New-onset atrial fibrillation (NOAF) is associated with increased morbidity and mortality. Despite identifying numerous factors contributing to NOAF, the underlying mechanisms remain uncertain. This study introduces the triglyceride-glucose index (TyG index) as a predictive indicator and establishes a clinical predictive model.

Materials and Methods

We included 551 patients with acute myocardial infarction (AMI) without a history of atrial fibrillation (AF). These patients were divided into two groups based on the occurrence of postoperative NOAF during hospitalization: the NOAF group (n = 94) and the sinus rhythm (SR) group (n = 457). We utilized a regression model to analyze the risk factors of NOAF and to establish a predictive model. The predictive performance, calibration, and clinical effectiveness were evaluated using the receiver operational characteristics (ROC), calibration curve, decision curve analysis, and clinical impact curve.

Results

94 patients developed NOAF during hospitalization. TyG was identified as an independent predictor of NOAF and was significantly higher in the NOAF group. Left atrial (LA) diameter, age, the systemic inflammatory response index (SIRI), and creatinine were also identified as risk factors for NOAF. Combining these with the TyG to build a clinical prediction model resulted in an area under the curve (AUC) of 0.780 (95% CI: 0.888, 0.358). The ROC, calibration curve, decision curve, and clinical impact curve demonstrated that the performance of the new nomogram was satisfactory.

Conclusion

By incorporating the TyG index into the predictive model, NOAF after AMI during hospitalization can be effectively predicted. Early detection of NOAF can significantly improve the prognosis of AMI patients.

New-onset atrial fibrillation

triglyceride-glucose index

acute myocardial infarction

systemic inflammatory response index

predictive model

Acute myocardial infarction (AMI) remains one of the leading causes of death globally and is considered a severe cardiovascular disease with a poor prognosis¹. Percutaneous coronary intervention (PCI) and fibrinolysis, through agents like streptokinase, are the primary treatments aimed at restoring blood flow to ischemic tissues². Atrial fibrillation (AF), a commonly encountered arrhythmia in AMI patients, has an incidence of 6–21%³. The occurrence of new-onset atrial fibrillation (NOAF) post-AMI is linked to increased morbidity and mortality⁴. Clinical trials indicate that NOAF significantly worsens both short- and long-term outcomes in patients with ST-segment elevation myocardial infarction (STEMI) during the PCI era^5,6. Thus, detecting NOAF is crucial for prognosis. Recent studies have investigated AMI patients with NOAF^7,8,9,10,11, identifying several risk factors including age, body mass index (BMI), diabetes, serum albumin and uric acid levels, left atrial (LA) diameter, left ventricular ejection fraction (LVEF), the lymphocyte to C-reactive protein ratio (LCR), left circumflex artery (LCX) stenosis > 50%, and Killip class > II⁷. Despite these findings, the precise mechanisms behind NOAF remain elusive¹², necessitating an efficient predictive tool for its occurrence post-AMI.

Insulin resistance (IR) is a pathological state where cells fail to respond adequately to insulin. In clinical practice, IR is associated with cardiovascular disease (CVD)¹³. The triglyceride-glucose (TyG) index is a recognized reliable and convenient marker of IR¹⁴. Previous research has demonstrated that TyG index's close association with the recurrence of atrial fibrillation post-radiofrequency catheter ablation, suggesting its potential as an effective predictor of post-infarction atrial fibrillation¹⁵. TyG index plays a key role in predicting CVD, cardiovascular mortality, stroke, hypertension^16,17,18. To date, the predictive value of the TyG index for atrial fibrillation post-infarction has not been confirmed.

In recent years, the integration of artificial intelligence with medicine has enhanced the use of predictive modeling and other statistical tools in clinical settings, improving disease prediction accuracy. This study aims to conduct a retrospective analysis using clinical data from patients with AMI at our center. It will explore the impact of the TyG index and other novel indicators on the incidence of AF in patients with primary AMI. Furthermore, this study will develop and evaluate a predictive model to introduce an effective prediction tool that can provide clinical guidance and improve treatment outcomes for patients.

2.1 Study Population

We enrolled 707 consecutive AMI patients > 18 years old who were admitted to the Department of Cardiology at the First Hospital of Jilin University from February 2018 to February 2024. The included patients met the diagnostic criteria for AMI established by the European Society of Cardiology/American College of Cardiology¹⁹. Inclusion criteria were: [1] age > 18 years and [2]diagnosed with AMI following PCI. Patients were excluded if they: [1] lacked crucial laboratory results; [2] had severe liver insufficiency or end-stage renal disease; [3] were undergoing thrombolytic therapy or emergent coronary artery bypass grafting (CABG); [4] had a history of AF or atrial flutter; [5] were diagnosed with hyperthyroidism or heart valve disease; or [7] had a history of PCI or AMI. Ultimately, 551 patients were included in the analysis (Fig. 1). Of these, 386 and 165 were allocated to the training and validation cohorts, respectively. The ethics committee of the The First Hospital of Jilin University approved the study, which adhered to the Declaration of Helsinki. Due to the retrospective nature of the study, the requirement for informed consent was waived.

2.2 Definitions

NOAF was defined as a type of AF occurring during postoperative hospitalization in patients with no prior history of AF. It was identified through episodes of AF lasting at least 30 seconds, detected by continuous telemetry, 12-lead electrocardiogram (ECG), or Holter monitoring during hospitalization. The Killip class was defined as follows: Class I, no signs of heart failure; Class II, rales present in the lungs but covering less than half of the lung field; Class III, rales covering more than half of the lung field; and Class IV, cardiogenic shock with varying degrees of hemodynamic change²⁰. The TyG index, a composite value of triglyceride (TG) and fasting blood glucose (FBG) levels, was calculated as ln [TG (mg/dl) × FBG (mg/dl)/2] ²¹. The Systemic Inflammatory Response Index (SIRI) is defined as the product of neutrophils and monocytes divided by lymphocytes²². Coronary artery stenosis was defined as ≥ 50% stenosis in any coronary artery (including the left main artery, left anterior descending branch, left circumflex branch, and right coronary artery) as demonstrated by coronary angiography.

2.3 Data Collection

We collected clinical information from patient’s medical records, including demographic data (age, sex, heart rate, systolic blood pressure, diastolic blood pressure, smoking and drinking status, and medication use); electrocardiogram (ECG) results; laboratory parameters; angiographic findings; echocardiography results (left atrial [LA] diameter, left ventricular [LV] diameter, and left ventricular ejection fraction [LVEF]); and presence of comorbidities (hypertension, diabetes, history of stroke). Laboratory test results included leukocytes, neutrophils, monocytes, lymphocytes, hemoglobin, serum albumin, fasting blood glucose, total cholesterol, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, creatinine, uric acid, B-type natriuretic peptide [BNP], and cardiac troponin I [cTnI].

2.4 Statistical Analysis

We used software xtitle to select an appropriate TyG index cutoff value. Univariate and multivariate logistic regression models were employed to explore the relationship between the variables and NOAF. Using atrial fibrillation as the outcome, the predictive model was developed and evaluated by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC) to assess the model's predictive value. The model's accuracy was further evaluated using a decision curve analysis (DCA). Continuous variables were analyzed using the T-test, and categorical variables were assessed with the χ2 test. Statistical analyses were performed using R statistical software (version 4.0.2), and a two-sided P-value < 0.05 was considered statistically significant.

We enrolled 551 AMI patients without a previous history of AF, including 457 with SR and 94 who developed AF during hospitalization after PCI. The incidence of NOAF was 17.1%. The baseline characteristics are detailed in Table 1. In the SR group, there were 31 patients with elevated serum creatinine, 178 with higher BNP levels, and 41 with increased D-dimer levels; corresponding figures in the NOAF group were 19, 51, and 15, respectively. Among NOAF patients, 64 (88.1%) had left circumflex artery stenosis > 50%, compared to 241 in the SR group. NOAF patients were generally older, had higher heart rates, larger LA diameters, and elevated levels of fasting blood glucose, white blood cell and neutrophil counts, uric acid, aspartate aminotransferase(AST), alanine aminotransferase (ALT), HbA1C, TyG index, and SIRI. Conversely, they had significantly lower admission systolic and diastolic blood pressure, albumin levels, and low-density lipoprotein cholesterol.

Table 1

The baseline characteristics of the patients :
Variables	SR(N = 457)	NOAF(N = 94)	P value
Demographic data
Age(years)	58.83 ± 10.61	65.71 ± 10.91	< 0.001
Males (n%)	323 (70.68)	61 (64.89)	0.266
SBP (mmHg)	131.00 (120.00-146.00)	123.00 (107.00-135.00)	< 0.001
DBP (mmHg)	80.00 (70.00–87.00)	68.50 (63.00–82.00)	< 0.001
HR (bpm)	78.00 (70.00–84.00)	80.00 (74.00–92.00)	0.002
Smoking(n%)	194 (42.45)	42 (44.68)	0.691
Alcohol intake (n%)	88 (19.26)	12 (12.77)	0.137
Drug use in hospital
ACEI/ARB (n%)	240 (52.52)	47 (50.00)	0.656
Beta blocker (n%)	213 (46.61)	45 (47.87)	0.823
Laboratory data
FBG(mmol/L)	5.98 (5.21–7.81)	7.66 (6.17–10.59)	< 0.001
Albumin (g/L)	37.70 (35.20–40.00)	36.95 (34.82–39.45)	0.047
WBC(\(\:{10}^{9}\)/L)	9.01 (7.25–11.26)	10.79 (8.62–13.77)	< 0.001
Neutrophil(\(\:{10}^{9}\)/L)	6.50 (4.71–8.64)	8.91 (6.38–11.01)	< 0.001
Lymphocyte(\(\:{10}^{9}\)/L)	1.64 (1.18–2.22)	1.48 (1.04–2.36)	0.3
Monocyte(\(\:{10}^{9}\)/L)	0.53 (0.39–0.68)	0.58 (0.41–0.80)	0.064
Hemoglobin(g/L)	146.00 (135.00-158.00)	142.50 (129.75-156.75)	0.312
Platelet(\(\:{10}^{9}\)/L)	231.00 (194.00-270.00)	226.50 (194.25–259.00)	0.561
Uric acid (µmol/L)	334.00 (275.00-402.00)	366.50 (303.00-452.00)	0.008
AST(U/L)	61.30 (28.20-139.60)	138.40 (51.25-310.35)	< 0.001
ALT(U/L)	28.30 (18.70–45.10)	40.60 (25.35–71.05)	< 0.001
cTnI (ng/mL)	2.26 (0.31–9.85)	4.75 (0.30–19.30)	0.153
Triglyceride (mmol/L)	1.67 (1.13–2.46)	1.77 (1.23–2.47)	0.344
TC (mmol/L)	5.01 (4.35–5.66)	4.72 (4.04–5.64)	0.075
LDL-C(mmol/L)	3.18 (2.69–3.67)	2.83 (2.37–3.57)	0.015
HDL-C(mmol/L)	1.08 (0.93–1.26)	1.07 (0.96–1.21)	0.997
HbA1C (%)	6.00 (5.60–7.30)	6.40 (5.82–7.70)	0.021
Creatinine (n%)	31 (6.78)	19 (20.21)	< 0.001
BNP (n%)	178 (38.95)	51 (54.26)	0.006
D-D (n%)	41 (8.97)	15 (15.96)	0.041
Echocardiography data
Lvef (%)	58.00 (55.00–61.00)	55.50 (47.25-57.00)	< 0.001
Left atrium diameter (mm)	34.00 (32.00–37.00)	37.00 (33.00–40.00)	< 0.001
Left ventricular diameter (mm)	49.00 (46.00–51.00)	49.50 (47.00-52.75)	0.041
TyG index	7.43 (6.99–7.95)	7.64 (7.32–8.23)	0.003
SIRI	2.05 (1.22–3.19)	3.00 (1.81–5.55)	< 0.001
AMI Type, (n%)
STEMI(n%)	218 (47.70)	44 (46.81)	0.874
comorbidities
Hypertension(n%)	221 (48.36)	52 (55.32)	0.219
Diabetes mellitus (n%)	132 (28.88)	33 (35.11)	0.23
stroke(n%)	20 (4.38)	8 (8.51)	0.096
Killip ≥ III(n%)	95 (20.79%)	37 (39.36%)	< 0.001
Angiographic data: Coronary artery stenosis > 50%, (n%)
LM(n%)	7 (1.53)	2 (2.13)	0.678
LAD(n%)	318 (69.58)	71 (75.53)	0.249
LCX(n%)	241 (52.74)	64 (68.09)	0.006
RCA(n%)	263 (57.55)	60 (63.83)	0.26
SR, sinus rhythm; NOAF, new-onset atrial fibrillation; SBP, systolic blood pressure;
DBP, diastolic blood pressure; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; FBG, fast blood glucose; WBC, white blood cell; AST, aspartate aminotransferase; ALT, alanine aminotransferase; CTnI, troponin I; TC, total cholesterol; HDL-c, high-density lipoprotein cholesterol; LDL-c, low-density lipoprotein cholesterol; BNP, B-type natriuretic peptide; D-D, d-dimer; TyG index, triglyceride-glucose index; Lvef, left ventricular ejection fraction; SIRI, systemic immune response index; STEMI, ST-segment elevation myocardial infarction; LM, left main artery; LAD, left anterior descending coronary artery; LCX, left circumflex coronary artery; RCA, right coronary artery.

LVEF was lower in the NOAF group compared to the SR group. No significant differences were observed between the groups in terms of gender, history of hypertension, diabetes, stroke, smoking, alcohol consumption, drug use, levels of total cholesterol, triglycerides, platelet count, lymphocytes, monocytes, hemoglobin, high-density lipoprotein cholesterol, cTnI, AMI type, or the proportion of patients with stenosis > 50% in the left main, left anterior descending, or right coronary arteries.

The study comprised 386 patients in the training cohort and 165 in the validation cohort (Table 2).

Table 2

Validation cohort and Training cohort
Characteristic	Validation cohort	Training cohort	P value
N	165	386
Age(years)	60.61 ± 10.95	59.74 ± 10.98	0.395
Males (n%)	119 (72.12%)	265 (68.65%)	0.417
SBP (mmHg)	130.00 (117.00-142.00)	130.00 (118.00-145.00)	0.821
DBP (mmHg)	78.00 (70.00–87.00)	79.00 (68.25-86.00)	0.728
HR (bpm)	78.00 (72.00–85.00)	78.00 (70.00–84.00)	0.035
Smoking(n%)	71 (43.03%)	165 (42.75%)	0.951
Alcohol intake (n%)	31 (18.79%)	69 (17.88%)	0.799
Drug use in hospital
ACEI/ARB (n%)	77 (46.67%)	210 (54.40%)	0.096
Beta blocker (n%)	77 (46.67%)	181 (46.89%)	0.961
Laboratory data
FBG(mmol/L)	6.33 (5.28–8.86)	6.25 (5.25–8.13)	0.458
Albumin (g/L)	36.90 (34.50–39.50)	37.95 (35.30–40.00)	0.023
WBC(\(\:{10}^{9}\)/L)	8.85 (7.42–11.38)	9.50 (7.43–11.82)	0.316
Neutrophil(\(\:{10}^{9}\)/L)	6.52 (4.61–9.27)	7.02 (4.99-9.00)	0.465
Lymphocyte(\(\:{10}^{9}\)/L)	1.48 (1.07–2.12)	1.71 (1.18–2.28)	0.026
Monocyte(\(\:{10}^{9}\)/L)	0.52 (0.37–0.70)	0.54 (0.40–0.71)	0.57
Platelet(\(\:{10}^{9}\)/L)	227.00 (189.00-260.00)	232.00 (195.00-271.00)	0.286
mono	0.52 (0.37–0.70)	0.54 (0.40–0.71)	0.57
Hemoglobin(g/L)	146.00 (135.00-159.00)	146.00 (135.00-157.75)	0.893
Uric acid (µmol/L)	327.00 (271.00-402.00)	341.50 (282.25-407.75)	0.294
AST(U/L)	61.60 (27.50-155.80)	71.05 (30.25-172.35)	0.317
ALT(U/L)	27.10 (16.30–49.10)	31.05 (20.70–47.40)	0.067
cTnI (ng/mL)	2.03 (0.41–11.40)	2.59 (0.28–11.17)	0.978
Triglyceride (mmol/L)	1.66 (1.11–2.28)	1.71 (1.18–2.56)	0.357
TC (mmol/L)	4.64 (4.04–5.41)	5.08 (4.42–5.78)	< 0.001
LDL-C(mmol/L)	2.90 (2.52–3.47)	3.22 (2.70–3.71)	0.001
HDL-C(mmol/L)	1.06 (0.92–1.23)	1.08 (0.95–1.26)	0.402
HbA1C (%)	6.20 (5.70–7.50)	6.10 (5.62–7.30)	0.273
Creatinine (n%)	26 (15.76%)	36 (9.33%)	0.753
BNP (n%)	67 (40.61%)	162 (41.97%)	0.766
D-D (n%)	16 (9.70%)	40 (10.36%)	0.813
Echocardiography data
Lvef (%)	58.00 (56.00–61.00)	57.00 (54.00–60.00)	0.106
Left atrium diameter (mm)	35.00 (32.00–37.00)	34.00 (31.00–38.00)	0.707
Left ventricular diameter (mm)	48.00 (46.00–51.00)	49.00 (46.00–52.00)	0.432
TyG	7.40 (7.02–7.96)	7.48 (7.02–7.97)	0.618
SIRI	2.22 (1.27–4.05)	2.15 (1.31–3.42)	0.532
STEMI(n%)	87 (52.73%)	175 (45.34%)	0.112
atrial_fibrillation	29 (17.58%)	65 (16.84%)	0.833
comorbidities
Hypertension(n%)	78 (47.27%)	195 (50.52%)	0.485
Diabetes mellitus (n%)	59 (35.76%)	106 (27.46%)	0.051
stroke(n%)	6 (3.64%)	22 (5.70%)	0.313
Killip ≥ III(n%)	32 (19.39%)	100 (25.91%)	0.101
Angiographic data: Coronary artery stenosis > 50%, (n%)
LM(n%)	3 (1.82%)	6 (1.55%)	0.823
LAD(n%)	113 (68.48%)	276 (71.50%)	0.476
LCX(n%)	95 (57.58%)	210 (54.40%)	0.493
RCA(n%)	90 (54.55%)	233 (60.36%)	0.204

Several risk factors, including age, systolic and diastolic blood pressures, heart rate, fasting blood glucose, white blood cell and neutrophil counts, albumin, uric acid, AST, ALT, monocytes, D-dimer, serum creatinine, LA diameter, LV diameter, TyG index, and SIRI, were identified as significant predictors of NOAF (all P values < 0.05) (Table 3). Multivariate analyses indicated that age, serum creatinine, TyG index, LA diameter, and SIRI were independent predictors of NOAF (Table 4). These variables were used to construct a novel nomogram (Fig. 2). The ROC curve demonstrated good predictive performance, with an AUC of 0.780 (95% CI: 0.538–0.888) in the training cohort and 0.804 (95% CI: 0.690–0.838) in the validation cohort (Fig. 3). The calibration curve showed a high concordance between predicted and observed risks of NOAF, with an average absolute error of 0.019 in the training cohort and 0.029 in the validation cohort (Fig. 4 and Fig. 5). Decision curve analysis (DCA) revealed that the nomogram provided significant net clinical benefits (Fig. 6 and Fig. 7), and the clinical impact curve (CIC) confirmed the model's high clinical efficacy.

Table 3

Potential Clinical Predictors for NOAF After AMI univariate logistic regression analysis
Characteristics	OR	95% CI	P-value
Age (years)	1.059	1.03–1.088	0
Sex (n%)	0.948	0.536–1.677	0.855
SBP (mmHg)	0.978	0.964–0.991	0.001
DBP (mmHg)	0.964	0.943–0.985	0.001
HR (bpm)	1.047	1.025–1.07	0
Smoking(n%)	1.181	0.692–2.017	0.543
Alcohol intake (n%)	0.598	0.272–1.318	0.203
ACEI/ARB (n%)	1.027	0.602–1.754	0.921
Beta blocker (n%)	1.117	0.654–1.907	0.687
FBG(mmol/L)	1.146	1.059–1.239	0.001
Albumin (g/L)	0.907	0.846–0.974	0.006
WBC(\(\:{10}^{9}\)/L)	1.211	1.121–1.307	0
Neutrophil(\(\:{10}^{9}\)/L)	1.224	1.129–1.326	0
Lymphocyte(\(\:{10}^{9}\)/L)	0.985	0.715–1.355	0.924
Monocyte(\(\:{10}^{9}\)/L)	2.823	1.234–6.455	0.014
Hemoglobin(g/L)	0.994	0.979–1.01	0.459
Platelet(\(\:{10}^{9}\)/L)	1	0.997–1.004	0.849
Uric acid (µmol/L)	1.004	1.002–1.006	0.001
AST(U/L)	1.004	1.002–1.006	0
ALT(U/L)	1.024	1.014–1.034	0
cTnI (ng/mL)	1.018	0.998–1.038	0.076
Triglyceride (mmol/L)	1.538	1.102–2.146	0.011
TC (mmol/L)	0.869	0.675–1.119	0.276
LDL-C(mmol/L)	0.754	0.537–1.058	0.103
HDL-C(mmol/L)	0.724	0.26–2.019	0.538
HbA1C (%)	1.132	0.956–1.34	0.148
Creatinine (n%)	3.731	1.792–7.765	0
BNP (n%)	1.654	0.968–2.824	0.066
D-D (n%)	2.722	1.318–5.622	0.007
Lvef (%)	0.928	0.897–0.959	0
Left atrium diameter (mm)	1.137	1.077–1.202	0
Left ventricular diameter (mm)	1.054	0.994–1.118	0.075
TyG index	1.538	1.102–2.146	0.011
SIRI	1.128	1.051–1.211	0.001
STEMI(n%)	0.77	0.447–1.324	0.344
Hypertension(n%)	1.174	0.687–2.005	0.556
Diabetes mellitus (n%)	1.014	0.559–1.84	0.963
stroke(n%)	1.939	0.729–5.155	0.185
Killip ≥ III(n%)	2.617	1.5-4.566	0.001
LM stenosis > 50%(n%)	0	0-Inf	0.988
LAD stenosis > 50%(n%)	0.958	0.533–1.721	0.886
LCX stenosis > 50%(n%)	1.956	1.113–3.44	0.02
RCA stenosis > 50%(n%)	1.463	0.832–2.573	0.187

Table 4

Independent Clinical Predictors for NOAF After AMI Multivariate Analysis
characteristics	OR	CI	P
Age (years)	1.065	1.032–1.099	0
Creatinine (n%)	2.584	1.095–6.097	0.03
Left atrium diameter (mm)	1.118	1.054–1.186	0
TyG index	1.981	1.344–2.92	0.001
SIRI	1.102	1.021–1.19	0.013

Our study established and validated a predictive model for NOAF in 551 AMI patients post-PCI, identifying age, serum creatinine, the TyG index, LA diameter, and the SIRI as independent risk factors. We developed a regression model using these predictors, which demonstrated good calibration, discrimination, and clinical utility after validation through multiple methods.

IR is known to be associated with several diseases, including hypertension and CAD^18,23. The TyG index serves as a reliable surrogate marker of IR¹⁴. Research has shown that the TyG index is positively associated with metabolic risk factors and cardiovascular outcomes. For instance, Zheng et al. demonstrated that the TyG index was independently associated with an increased risk of hypertension, even after adjusting for potential confounding variables¹⁸. Additionally, Li et al. analyzed 1516 patients with symptomatic CAD at Tianjin Union Medical Center from January 2016 to December 2022, finding that higher TyG index values were predictive of a high rate of coronary lesions and plaques²⁴. The TyG index has also been recognized as an effective indicator of postoperative NOAF following septal myectomy²⁵. Furthermore, Wang et al. conducted a retrospective survey of 2242 AF patients over a one-year follow-up period, from June 2018 to January 2022, at two hospitals in China, concluding that the TyG index could be independently associated with AF recurrence following ablation²⁶. Several mechanisms may explain the correlation between the TyG index and AF. Primarily, insulin resistance (IR) can lead to excessive lipid accumulation in cardiomyocytes, resulting in a phenomenon known as "cardiotoxicity." This accumulation causes cellular dysfunction, cardiomyocyte apoptosis, and impaired myocardial metabolism, potentially altering the function and structure of cardiomyocytes and increasing the risk of AF²⁷. IR also promotes CaMKIIδ oxidation, leading to abnormal intracellular calcium homeostasis and atrial structural remodeling²⁸. Animal studies have demonstrated that IR impairs the transport and expression of the major myocardial isoform of the glucose transporter protein (GLUT) 4 and the novel subtype GLUT8, heightening susceptibility to AF. IR is associated with various aspects of left atrial (LA) remodeling, including increased oxidative stress, elevated expression of hyperphosphorylated calcium-related proteins, and cardiac fibrosis²⁹. Increased oxidative stress and inflammation related to impaired IR and insulin secretion contribute to atrial electrical remodeling, LA fibrosis, and the formation of LA low-pressure areas³⁰.

Regarding the other four variables, previous studies have identified advancing age as an independent risk factor for the development of AF. With aging, the myocardium undergoes anatomical and electrophysiological changes, including the loss of lateral electrical connections between myofibers and reduced electrical conduction in the sinoatrial node, atrioventricular node, and atria³¹. Left atrial diameter has been recognized as an independent predictor for NOAF, with increased left atrial diameter indicating progressive dilatation and remodeling of the left atrial myocardium, which serves as a substrate for AF initiation and maintenance³².

As we know, inflammation plays a role in the development and prognosis of AMI, with certain inflammatory markers, such as C-reactive protein and the SII, correlated with both short- and long-term prognosis of myocardial infarction^33,34. Previous studies have shown that the SIRI can predict prognosis in cardiovascular diseases^35,36. Furthermore, a recent retrospective study of 526 ischemic stroke patients confirmed that SIRI is an independent predictor of AF³⁶. SIRI encompasses neutrophils, lymphocytes, and monocytes. Monocyte aggregation leads to the production of inflammatory factors, such as interleukin (IL)-6 and IL-1β, which contribute to atrial structural and electrical remodeling, ultimately leadingii to atrial fibrillation³⁷. Additionally, neutrophils can produce proinflammatory cytokines, proteases, peroxidases, and reactive oxygen species, which may result in atrial fibrosis³⁸. Finally, T cells and B cells, which are lymphocytes, play roles in AF—T cells primarily regulate the innate immune response, and B cells may affect the condition by secreting autoantibodies. Concerning creatinine, renal dysfunction is associated with both electrical and structural remodeling of the LA, potentially leading to new-onset POAF³⁹.

We constructed a new nomogram that includes the TyG index, SIRI, creatinine, age, and LA diameter. Our model demonstrates high clinical predictive performance and is a practical fit. The nomogram can identify patients who are at high risk of NOAF, facilitating early inter

This study has several limitations. Firstly, it is a single-center study with a small sample size, which may not represent the entire patient population. Therefore, our model should be validated in a larger cohort. Secondly, as a retrospective observational study without randomization or intervention, it is not possible to eliminate all unmeasurable confounding factors, introducing potential bias. However, the study still offers significant advantages, such as investigating new-onset atrial fibrillation following PCI in patients with acute myocardial infarction and introducing the relatively novel TyG indicators. This approach enables early detection of atrial fibrillation and the implementation of measures to reduce the occurrence of adverse events.

In our study, the TyG index, SIRI, creatinine, age, and LA diameter were identified as independent risk factors for atrial fibrillation in patients with AMI post-PCI during hospitalization. The logistic prediction model effectively identifies high-risk patients for atrial fibrillation and demonstrates strong predictive accuracy and recognition capability. Ultimately, this intuitive and practical model will aid clinicians in managing patients with acute myocardial infarction.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by the Medical Ethics Committee of the The First Hospital of Jilin University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Competing interests

The authors declare that they have no competing interests.

Funding

No.

Author contributions

W.X.D wrote the main manuscript text and Z.W. prepared figure1-7. W.Q.W prepared table1-4. Y.X.Y is charge of data curation. W.J.Y. and Y.S. are charge of methodology. T.Q.reviewd the manuscript.

Acknowledgments

We thank the First Hospital of Jilin University for providing the patient's clinical data.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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No competing interests reported.

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Clinical predictive model of new-onset atrial fibrillation in patients with acute myocardial infarction after percutaneous coronary intervention

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Abstract

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Materials and Methods

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1. Introduction

2. Methods

2.1 Study Population

2.2 Definitions

2.3 Data Collection

2.4 Statistical Analysis

3. Results

4. Discussion

5. Limitations

6. Conclusion

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