Noninvasive screening for surgery in adults with simple shunting congenital heart disease

Abstract

Objectives

The aim of this study was to use non-invasive parameters to assess eligibility to undergo surgery in patients with adult congenital heart disease (CHD).

Methods

This is a single-center retrospective study that collected patients’ demographic data, right heart catheterization, complete blood count, N-terminal pro-brain natriuretic peptide (NT-proBNP), arterial blood gas, pulmonary function test (PFT), cardiopulmonary exercise testing (CPET), and echocardiographic parameters. Surgical eligibility of patients was evaluated based on hemodynamic parameters, specifically pulmonary vascular resistance (PVR) < 4.6 WU and pulmonary vascular resistance index (PVRI) < 8 WU ▪ m². Univariate analysis was employed to compare baseline parameters between the two patient groups. Independent predictive factors were determined using binary logistic regression analysis.

Results

A total of 81 patients with adult CHD were included in the study, with 34 qualifying for surgery and 47 not meeting the criteria for surgery. The group eligible for surgery exhibited superior hemodynamics, higher oxygen partial pressure, and better diffusion capacity compared to the non-eligible group. Echocardiographic findings revealed lower systolic pulmonary artery pressure (SPAP) in the eligible group (79.23 ± 23.56 vs. 98.91 ± 20.23 mmHg, p = 0.00), higher tricuspid annular plane systolic excursion (TAPSE) (2.10 (1.88, 2.50) vs. 1.81 (1.70, 2.01) cm, p = 0.001), and increased TAPSE/SPAP ratio (0.32 ± 0.14 vs. 0.20 ± 0.59 mm/mmHg, p = 0.00). TAPSE/SPAP emerged as an independent predictor, with an area under the receiver operating characteristic (ROC) curve of 0.81 (95% CI: 0.70–0.92, p < 0.01).

Conclusions

The echocardiographic parameter TAPSE/SPAP may be a valuable index to evaluate whether patients with coronary artery disease are suitable for surgery.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-025-05331-1.

Congenital heart disease (CHD) stands as a prevalent etiology of neonatal malformations, with a reported incidence of 0.8% in European newborns between 2000 and 2005 [1]. In China, despite regional disparities, the detection rates for CHD typically range from 2.9% to 16% [2]. Advances in medical technology have extended the lifespan of most CHD patients. Nonetheless, approximately 20% of these individuals necessitate surgical intervention in the adult phase [3] .

The judicious timing of surgery for adult CHD patients is paramount. As stipulated in the 2015 ESC/ERS Pulmonary Hypertension Guidelines [4], parameters derived from right heart catheterization (RHC), specifically pulmonary vascular resistance (PVR) < 4.6 WU and pulmonary vascular resistance index (PVRI) < 8 WU ▪ m², serve as indicators guiding surgical eligibility. However, the invasive nature of this procedure and its limited availability in numerous institutions necessitate exploration of non-invasive and easily accessible parameters. The primary objective of this study is to employ noninvasive examination parameters for predicting surgical candidacy.

Methods

This study enrolled 82 CHD patients who sought medical attention at the Shanghai Pulmonary Hospital from 2013 to 2023. All patients underwent RHC for the definitive diagnosis of CHD with shunting. Within one month of RHC, participants underwent a comprehensive assessment, including complete blood count, arterial blood gas analysis, pulmonary function testing, cardiopulmonary exercise testing, and echocardiography. Exclusion criteria encompassed: (1) repaired CHD; (2) tetralogy of fallot;3) anomalous great artery connection, such as transposition of the great arteries;4) single ventricle, single atrium; 5) availability of complete data; 6) age less than 18 years.

The study was approved by the Shanghai pulmonary hospital’s ethics committee.

RHC

RHC was performed after other examinations were completed during hospitalization. Measurements including right atrial pressure (RAP), mean pulmonary artery pressure (mPAP), pulmonary arterial wedge pressure (PAWP), quantity of pulmonary blood flow (Qp), quantity of systemic blood flow (Qs) and PVR were recorded.

PFT

PFT were carried out using Master Screen Diffusion (Erich Jaeger Inc, Germany) according to standard protocols [5]. Measurements including forced vital capacity (FVC), forced expiratory volume in1 sec (FEV₁) and diffusing capacity for carbon monoxide (DLCO) were recorded. The results were expressed as percent of predicted (% pred).

CPET

CPET was performed on an electronically braked cycle ergometer with breath-by-breath system (Mastercreen-CPX; Jaeger; Hoechberg; Germany) according to standard procedures [6]. Measurements including peak oxygen uptake (peak VO₂), and peak load were recorded. Ventilatory efficiency (VE/VCO₂ slope) was determined as the linear regression slope of VE and VCO₂. Lowest VE/VCO₂ was determined by averaging the lowest consecutive 90 s data points. The oxygen uptake efficiency plateau (OUEP) was the highest 90-s consecutive stretch of VO₂ (mL/min)/VE (L/min) [7]. Oxygen uptake efficiency slope (OUES) was computed by linear square regression of the oxygen uptake on the logarithm of the minute ventilation according to the following equation: VO₂ = a*lgVE + b, which the constant “a” is called the OUES [7].

Echocardiography.

A specialist performed the Echocardiography as per recognized standards [8]. Measurements including systolic pulmonary artery pressure (SPAP), ascending aorta (Ao), pulmonary artery (PA), tricuspid annular plane systolic excursion (TAPSE) and the ejection fraction of the left ventricle (EF).

Statistical analyses

Statistical analyses were conducted utilizing SPSS 21.0 and Medcalc software. Continuous variables were expressed as mean ± standard deviation or median with interquartile range, while categorical variables were presented as counts and proportions. Continuous data were analyzed using T-test or Mann–Whitney U-test, and categorical data were assessed using the chi-squared test. Independent predictors were determined through multivariate analysis, and only parameters demonstrating statistical significance were retained. Receiver Operating Characteristic (ROC) curves were employed for validation. Statistical significance was defined as a two-sided P-value < 0.05.

Results

Patient characteristics

We included a total of 81 patients with CHD, comprising 33 with atrial septal defect (ASD), 20 with ventricular septal defect (VSD), 16 with patent ductus arteriosus (PDA), and 12 with other conditions. In the CHD group eligible for surgery, the “other” category (5 cases) included: 2 cases of ASD combined with PDA, 1 case of coronary artery-pulmonary artery fistula, 1 case of partial atrioventricular septal defect (PAVSD), and 1 case of VSD combined with PDA. In the CHD group who were not eligible for surgery, the “other” category (7 cases) included: 2 cases of ASD combined with PDA, 4 cases of VSD combined with PDA, 1 case of ASD combined with patent foramen ovale (PFO). The patients were stratified into two groups based on PVR and PVRI, based on this 34 qualified for surgery and 47 did not. There were no statistically significant differences in demographic parameters between the two groups. Patients in the non-operative group had a higher proportion of New York Heart Association (NYHA) class III–IV (75%), worse hemodynamic parameters (with statistically significant differences in mPAP, Qp, Qs, and Qp/Qs), and more severe hypoxia (PaO₂: 75.52 ± 16.04 vs. 62.76 ± 17.88, p < 0.05). Significant intergroup differences were also observed in echocardiographic parameters, including SPAP, EF%, TAPSE, and TAPSE/SPAP. Detailed hemodynamic and clinical parameters are provided in Tables 1 and 2.

Table 1.

Characteristics of congenital heart disease in enrolled patients

Variables	PVR < 4.6 WU and PVRI < 8 WU ▪ m2	PVR ≥ 4.6 WU or PVRI ≥ 8 WU ▪ m2	p
Types of CHD			0.004
ASD, n (%)	21(61.8%)	12(25.5%)
VSD, n (%)	6(17.6%)	14(29.8%)
PDA, n (%)	2(5.9%)	14(29.8%)
PDA, n (%)	2(5.9%)	14(29.8%)
ASD + PDA, n (%)	2(5.9%)	2(4.3%)
VSD + PDA, n (%)	1(2.9%)	4(8.5%)
ASD + PFO, n (%)	0(0.0%)	1(2.1%)
CAPAF, n (%)	1(2.9%)	0(0.0%)
PAVSD, n (%)	1(2.9%)	0(0.0%)

Values are expressed as the mean ± SD, n (%), or median (interquartile range) and percentage of measured to predicted values (%pred)

BMI Body mass index, BSA Body surface area, WHO-FC World Health Organization functional classification, ASD Atrial septal defect, VSD Ventricular septal defect, PDA Patent ductus arteriosus, PFO Patent foramen ovale, PAVSD partial atrioventricular septal defect, CAPAF Coronary artery-pulmonary artery fistula

P < 0.05 is represented by bold values

Table 2.

Comparison of clinical and laboratory parameters between groups stratified by PVR and PVRI

Variables	PVR < 4.6 WU and PVRI < 8 WU ▪ m2	PVR ≥ 4.6 WU or PVRI ≥ 8 WU ▪ m2	p
men/females, n(%)	9(26.5%)/25༈73.5%༉	18(38.3%)/29༈61.7%༉	0.388
Age, years	43.09 ± 14.48	36.74 ± 11.95	0.041
height, cm	156.20 ± 6.57	160.41 ± 8.58	0.357
weight, kg	47.00(43.25, 51.75)	54.05(45.75, 60.25)	0.908
BMI, kg/m2	19.14(18.26, 22.23)	21.09(18.65, 22.44)	0.515
BSA, m2	1.39(1.33, 1.48)	1.54(1.39, 1.62)	0.667
WHO-FC			<0.001
I-II, n(%)	23(67.6%)	11(25%)
III-IV, n(%)	11(32.4%)	36(75%)
Pulmonary hemodynamics
mPAP, mmHg	47.25 ± 13.68	74.57 ± 19.92	<0.001
PAWP, mmHg	8.50(4.25, 12.75)	5.50(4.25, 9.00)	0.246
PVR, WU	2.91 ± 2.85	22.77 ± 8.88	<0.001
PVRI	3.41 ± 2.06	11.04 ± 8.85	<0.001
Qp, L/min	8.49 ± 4.24	3.44 ± 0.92	<0.001
Qs, L/min	5.01(3.58, 7.44)	3.82(3.48, 4.68)	0.008
Qp/Qs	1.40(1.02, 2.55)	0.86(0.76, 1.00)	<0.001
Blood gas analysis
PH	7.44(7.42, 7.45)	7.43(7.40, 7.44)	0.126
PCO2, mmHg	35.08 ± 5.34	35.99 ± 7.20	0.772
PO2, mmHg	75.52 ± 16.04	62.76 ± 17.88	0.011
SPO2, %	95.10(92.58, 97.25)	91.80(87.45, 96.23)	0.001
Laboratory parameters
MPV, FL	10.94 ± 1.27	11.67 ± 1.17	0.22
PDW, FL	13.50(11.03, 15.00)	14.60(13.50, 16.25)	0.023
PLT, 10*9/L	180.75 ± 57.29	152.72 ± 62.64	0.117
NT-proBNP, pg/mL	444.00(132.25, 1229.50)	139.50(62.25, 139.50)	0.144
Pulmonary function test
FVC, % predicted	74.05 ± 19.79	72.29 ± 17.44	0.962
FEV1, % predicted	67.11 ± 19.21	64.32 ± 17.56	0.785
DLCO, % predicted	104.70(86.65, 118.75)	90.70(83.90, 105.10)	0.001
Cardiopulmonary exercise testing
OUEP, mL/L	26.27(23.88, 30.04)	25.83(22.08, 29.43)	0.342
LOWEST VE/VCO2	43.72 ± 9.46	45.77 ± 10.38	0.395
VE/VCO2 slope	42.74(33.38, 53.27)	45.29(40.72, 70.01)	0.528
OUES	0.85(0.68, 1.18)	1.15(0.79, 1.63)	0.881
Peak VO2/Kg, mL/Kg/min	14.38 ± 4.49	13.67 ± 4.21	0.426
Peak VO2, % predicted	44.00(35.00, 53.00)	38.5(32.00, 45.50)	0.049
Peak Load, % predicted	46.50(33.75, 72.25)	44.50(40.50, 68.00)	0.436
Echocardiography
Ao, cm	2.45 (2.20, 2.73)	2.70(2.30, 2.90)	0.587
LA, cm	3.18(2.88, 3.47)	3.20(2.89, 3.43)	0.632
LVEDD, cm	4.10 (3.60, 4.75)	4.00 (3.60, 4.50)	0.817
LVESD, cm	2.40 (1.90, 2.90)	2.40 (2.20, 2.70)	0.496
IVS, cm	0.70 (0.70, 0.85))	0.70 (0.60, 0.90)	0.683
SPAP, mmHg	79.23 ± 23.56	98.91 ± 20.23	<0.001
EF, %	74.50(67.75, 80.25)	70.00(67.00, 72.00)	0.045
TAPSE, cm	2.10(1.88, 2.50)	1.81(1.70, 2.01)	0.001
TAPSE/SPAP, mm/mmHg	0.32 ± 0.14	0.20 ± 0.59	<0.001

Values are expressed as the mean ± SD, n (%), or median (interquartile range) and percentage of measured to predicted values (%pred)

BMI Body mass index, BSA Body surface area, WHO-FC World Health Organization functional classification, mPAP Mean pulmonary artery pressure, PAWP Pulmonary arterial wedge pressure, Qp Quantity of pulmonary blood flow, Qs Quantity of systemic blood flow, PVR Pulmonary vascular resistance, PVRI Pulmonary vascular resistance index, PaCO₂ arterial carbon dioxide tension, PaO2 arterial oxygen tension, SaO2 oxygen saturation, MPV Mean platelet volume, PDW Patent distribution width, PLT Platelet, NT-proBNP N-terminal pro-brain natriuretic peptide, FVC Forced vital capacity, FEV1 Forced expiratory volume in 1 s, DLCO Carbon monoxide diffusing capacity, VO₂ Oxygen uptake, VCO₂ rate of carbon dioxide production, VE Minute ventilation, OUEP Oxygen uptake efficiency plateau, OUES Oxygen uptake efficiency slope, SPAP Systolic pulmonary artery pressure, Ao Ascending aorta, LA, Left atrial diameter; LVEDD Left ventricular end-diastolic dimension, LVESD Left ventricular end-systolic dimension, IVS Interventricular septum, EF The ejection fraction of the left ventricle; TAPSE, tricuspid annular plane systolic excursion

P < 0.05 is represented by bold values

Prediction of eligibility for surgery

The univariate analysis presented in Table 1 delineates that the cohort deemed eligible for surgery manifests superior hemodynamics, better functional classification, elevated oxygen partial pressure, and enhanced diffusion function (p < 0.05). Statistically significant distinctions (p < 0.05) are also evident in SPAP, EF, TAPSE, and TAPSE/SPAP between the two groups. Conversely, no statistically significant differences are observed in NT-proBNP, exercise endurance, and gas exchange function. Employing these parameters for logistic regression analysis, TAPSE/SPAP emerged as an independent predictive factor(Table 3), with an area under the ROC curve of 0.81 (95% CI: 0.70–0.92, p < 0.01) (Fig. 1). This indicates clinically valuable specificity (81.11%) and sensitivity (71.40%) (Table 4).

Table 3.

Multivariable analysis of factors for surgical eligibility

	HR	p-valoe
TAPSE/SPAP, mm/mmHg	0.0001(95% CI: 0.000–0.077.000.077)	0.003

Fig. 1.

ROC curve for predicting surgical eligibility in patients with congenital heart disease using TAPSE/APAP

Table 4.

Area under the curve of TAPSE/SPAP indices

parameter	Area under the curve	95% CI	Cut-off value	Sensitivity (%)	Specificity (%)
TAPSE/SPAP, mm/mmHg	0.81	0.70–0.92	0.21	71.40	81.11

An additional flowchart has been created to optimize the clinical management pathway for CHD, thereby facilitating patient care (supplementary file).

Discussion

The primary objective of this study was to develop a predictive framework for determining the surgical eligibility of patients with CHD using readily accessible, non-invasive clinical parameters. To establish a definitive classification of operability, we utilized right heart catheterization-derived measurements—specifically, PVR and PVRI—as the reference standard for categorizing patients. Through this approach, the TAPSE/SPAP index was identified as an independent and non-invasive predictor of surgical candidacy in the CHD population. The robustness of this parameter was underscored by an area under the ROC curve of 0.81, indicating a high level of predictive accuracy.

The echocardiographic parameter TAPSE is widely recognized as a pivotal clinical metric for evaluating right ventricular systolic function and has been consistently associated with prognostic value in predicting patient outcomes [9, 10]. Similarly, SPAP serves as a key indicator of right ventricular afterload and is extensively utilized in the screening and assessment of patients with pulmonary hypertension [4]. The ratio TAPSE/SPAP has emerged as a practical, non-invasive surrogate for assessing right ventricular-pulmonary arterial (RV-PA) coupling. A decline in this ratio reflects an uncoupling between ventricular contractility and afterload, signaling a deterioration in RV-PA coordination [11–14]. The clinical relevance of RV-PA coupling is substantial, particularly in the early identification and risk stratification of patients with pulmonary arterial hypertension (PAH), with implications for prognosis and treatment strategy [15–18]. Previous studies in heart failure with preserved ejection fraction (HFpEF) have proposed TAPSE/SPAP as a discriminative risk stratification tool, showing a negative correlation with WHO Functional Classification and a capacity to predict adverse clinical events [12, 19]. A growing body of literature continues to adopt TAPSE/SPAP as a reliable non-invasive indicator of the delicate balance between RV function and pulmonary vascular load, effectively serving as a substitute for the gold-standard Ees/Ea ratio [19–24].

In a related investigation, Yang Ziyang et al. [25] developed a prognostic model for surgical eligibility in CHD patients based on echocardiographic parameters. Their model, which incorporated estimated SPAP, right atrial diameter, left atrial diameter, and peak pulmonary valve velocity, demonstrated considerable predictive efficacy. However, in our view, the omission of RV-PA coupling parameters such as TAPSE/SPAP represents a notable limitation. The integration of such parameters in future multi-center studies could substantially enhance the discriminative power and clinical applicability of predictive models.

Currently, the decision regarding surgical intervention in CHD patients heavily relies on findings from right heart catheterization. However, this invasive procedure is not routinely available in many secondary hospitals and carries inherent risks related to its invasiveness and exposure to radiation, which also limits its feasibility for repeated assessments. In contrast, echocardiography is widely accessible across all levels of healthcare institutions. Its non-invasive nature, safety profile, and reproducibility make it an ideal tool for initial patient evaluation. The use of echocardiographic parameters such as TAPSE/SPAP can assist clinicians in primary and secondary care settings in conducting efficient preliminary assessments, identifying patients who may benefit from further invasive evaluation at specialized centers, and ultimately facilitating more timely surgical referrals and interventions.

This study has several limitations. First, its single-center, retrospective design introduces potential selection bias and limits the generalizability of the findings. The modest sample size may have reduced statistical power to detect subtle differences and prevented robust subgroup analyses. The extended data collection period also introduces the potential for temporal biases due to evolving echocardiographic technology and practices. Second, the analysis was challenged by missing echocardiographic parameters, which may not be random and could introduce bias, despite statistical handling of missing data. Third, the model’s applicability is constrained as it was derived from and is only relevant to patients with simple defects (ASD, VSD, PDA), and it requires external validation in independent cohorts to confirm its generalizability. Finally, the model’s simplicity, while a goal for ease of use, may oversimplify complex clinical decisions and should only serve as an adjunct to, not a replacement for, clinical judgment.

Future multi-center, prospective studies with larger, diverse cohorts and standardized data collection are needed. Research should also focus on complex congenital heart disease, and external validation is essential for this model’s clinical adoption.

Conclusion

In the preoperative assessment of adults with congenital heart disease, alongside comprehensive history-taking, physical examination, and laboratory investigations, the TAPSE/SPAP ratio measured by non-invasive echocardiography may serve as one of the auxiliary tools for screening surgical candidates. This parameter demonstrates relatively high specificity (approximately 81.11%) in predicting operability, a characteristic that can offer some value for initial screening in primary care settings. By providing an objective functional reference, the TAPSE/SPAP ratio can assist clinicians in preliminary risk stratification, thereby contributing to more informed triage decisions and potentially facilitating appropriate referrals to tertiary centers. The use of this approach may support improvements in diagnostic efficiency across different levels of healthcare.

Abbreviations

CHD

Congenital heart disease

WHO-FC

World Health Organization functional classification

NT-proBNP

N-terminal pro-brain natriuretic peptide

ASD

Atrial septal defect

VSD

Ventricular septal defect

PDA

Patent ductus arteriosus

RHC

Right heart catheterization

CPET

Cardiopulmonary exercise testing

PFT

Pulmonary function testing

PVR

Pulmonary vascular resistance

PVRI

Pulmonary vascular resistance index

RAP

Right atrial pressure

mPAP

Mean pulmonary artery pressure

SPAP

Systolic pulmonary artery pressure

PAWP

Pulmonary arterial wedge pressure

Qp

Quantity of pulmonary blood flow

Qs

Quantity of systemic blood flow

FVC

Forced vital capacity

FEV₁

Forced expiratory volume in 1s

DLCO

Carbon monoxide diffusing capacity

VO₂

Oxygen uptake

VCO₂

Rate of carbon dioxide production

VE

Minute ventilation

OUEP

Oxygen uptake efficiency plateau

OUES

Oxygen uptake efficiency slope

Ao

Ascending aorta

PA

Pulmonary artery

TAPSE

Tricuspid annular plane systolic excursion

EF

The ejection fraction of the left ventricle

LA

Left atrial diameter

PV

Highest blood flow velocity through the pulmonary valve

RA

Right atrial diameter

RV-PA

Right ventricular-pulmonary artery

HfpEF

Heart failure patients with preserved ejection fraction

ROC

Receiver operating characteristic curves

AUC

Area under the curve

OR

Odds ratio

CI

Confidence interval

Authors’ contributions

Conceived and designed the experiments: SXX GJ.Performed the experiments: SXX XJH YWL BP.Analyzed the data: SXX CY ZHQ GHY GJ.Contributed reagents/materials/analysis tools: SXX XJH CY.Wrote the paper: SXX CY XJH.

Funding

This study was supported by the Scientific research project of Shanghai Municipal Health Commission [202040321] and the Clinical research project of Shanghai Pulmonary Hospital [FKLY20020]. This work was also supported by the Department Support Fund of Shanghai Pulmonary Hospital and the Science and Technology Commission of Shanghai Municipality Project (22xtcx00104, XTCX-KJ-2022-32).

Data availability

All data included in this study are available upon request by contact with the corresponding author.

Declarations

Ethics approval and consent to participate

Informed consent to participate was obtained from all of the participants in the study. Ethical approval by the medical ethics committee of Shanghai pulmonary hospital was obtained.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.