Establishment of a risk prediction model for prolonged mechanical ventilation after lung transplantation: a retrospective cohort study.

ABSTRACT

BACKGROUND: Prolonged mechanical ventilation (PMV), mostly defined as mechanical ventilation > 72 h after lung transplantation with or without tracheostomy, is associated with increased mortality. Nevertheless, the predictive factors of PMV after lung transplant remain unclear. The present study aimed to develop a novel scoring system to identify PMV after lung transplantation. METHODS: A total of 141 patients who underwent lung transplantation were investigated in this study. The patients were divided into PMV and non-prolonged ventilation (NPMV) groups. Univariate and multivariate logistic regression analyses were performed to assess factors associated with PMV. A risk nomogram was then established based on the multivariate analysis, and model performance was further examined regarding its calibration, discrimination, and clinical usefulness. RESULTS: Eight factors were finally identified to be significantly associated with PMV by the multivariate analysis and therefore were included as risk factors in the nomogram as follows the body mass index (BMI, P = 0.036); primary diagnosis as idiopathic pulmonary fibrosis (IPF, P = 0.038); pulmonary hypertension (PAH, P = 0.034); primary graft dysfunction grading (PGD, P = 0.011) at T0; cold ischemia time (CIT P = 0.012); and three ventilation parameters (peak inspiratory pressure [PIP, P < 0.001], dynamic compliance [Cdyn, P = 0.001], and P/F ratio [P = 0.015]) at T0. The nomogram exhibited superior discrimination ability with an area under the curve of 0.895. Furthermore, both calibration curve and decision-curve analysis indicated satisfactory performance. CONCLUSION: A novel nomogram to predict individual risk of receiving PMV for patients after lung transplantation was established, which may guide preventative measures for tackling this adverse event.