The risk of right heart failure after heart transplantation based on preoperative pulmonary artery pressure assessment

doi:10.11958/20250560

Abstract

Abstract:

Objective To evaluate the risk of right heart failure after heart transplantation by establishing nomogram based on preoperative pulmonary artery pressure. Methods A total of 184 patients undergoing heart transplantation were retrospectively collected and divided into the training group (126 cases) and the verification group (58 cases). Patients in the training set were divided into the right heart failure group (60 cases) and the non-right heart failure group (66 cases) according to whether right heart failure occurred after operation. The differences of clinical data between the two groups were compared, and the influencing factors of right heart failure occurred after operation in the training set were screened by Lasso-Logistic regression. According to the screened influencing factors, nomograms were drawn, and the predictive efficiency of the model was evaluated by using the receiver's operating characteristic (ROC) curve, calibration curve, receiver's operating characteristic (ROC) curve and clinical decision curve. Further vertification of the clinical application effect of centralized evaluation model was conducted. Results The Lasso-Logistic regression analysis identified the following independent risk factors for right heart failure after heart transplantation: elevated total bilirubin (OR=2.649, 95%CI: 1.339-5.239), increased mean pulmonary artery pressure (OR=3.082, 95%CI: 1.608-5.910), elevated pulmonary artery resistance (OR=3.171, 95%CI: 1.710-5.879), and widened right ventricular outflow tract diameter (OR=2.681, 95%CI: 1.361-5.281), all of which demonstrated statistical significance (P<0.05). The nomogram model was constructed accordingly. The AUC of the nomogram model was 0.846 （95%CI: 0.813-0.947). The calibration curve demonstrated good fit via the goodness-of-fit test (Hosmer-Lemeshow χ²=0.862, P=0.361). Clinical decision curve analysis revealed that the net benefit rate remained >0 when the high-risk threshold probability ranged from 1% to 95%, indicating favorable clinical utility of this nomogram model. Based on the model predictions, among 58 heart transplant patients in the validation cohort, 34 were classified as high-risk for right heart failure and 24 as low-risk. Actual diagnosis results showed 29 cases with right heart failure and 29 without. The Kappa coefficient reached 0.483 (95%CI: 0.261-0.705), demonstrating high consistency between model predictions and actual clinical outcomes. Conclusion Preoperative pulmonary systolic pressure increase is an independent risk factor for right heart failure after heart transplantation. A nomogram prediction model for right heart failure after heart transplantation is established by combining other clinical risk factors, and it has good prediction efficiency.

Key words: heart transplantation, heart failure, pulmonary artery pressure, prediction model, nomogram

CLC Number:

R541.9

Figures/Tables 8

Tab.1 Comparison of basic data between training set and verification set

组别	n	性别（男/女）	年龄/岁	BMI/（kg/m²）	NYHA心功能分级（Ⅲ级/Ⅳ级）	吸烟史
训练集	126	90/36	49.52±9.02	22.37±1.41	88/38	82（65.1）
验证集	58	44/14	48.64±8.75	22.15±1.36	40/18	32（55.2）
χ²或t		0.394	0.621	0.994	0.344	1.654

Tab.2 Comparison of clinical characteristics between the right heart failure group and the non-failure group

Fig.1 Selection process for Lasso regression

Tab.3 Multivariate Logistic regression analysis of right heart failure after heart transplantation

变量	β	SE	Wald χ²	P	OR	OR 95%CI
TBIL	0.974	0.348	7.834	0.005	2.649	1.339~5.239
mPAP	1.126	0.332	11.503	<0.001	3.082	1.608~5.910
PVR	1.154	0.315	13.421	<0.001	3.171	1.710~5.879
RVOTD	0.986	0.346	8.121	0.004	2.681	1.361~5.281
常数项	-3.210	0.850	14.256	<0.001	0.040

Fig.2 Nomogram for risk prediction of right heart failure after heart transplantation

Fig.3 Calibration curve of the nomogram model

Fig.4 ROC curve of the nomogram model for predicting right heart failure

Fig.5 The decision curve analysis of nomogram model

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