Pediatric Intensive Care Unit, Bambino Gesù, Children's Hospital, IRCCS, Rome, Italy.
Latner Thoracic Research Laboratories, Toronto Lung Transplant Program, University Health Network, Toronto, ON, Canada.
Am J Transplant. 2021 Nov;21(11):3704-3713. doi: 10.1111/ajt.16616. Epub 2021 Jun 11.
Ex vivo lung perfusion (EVLP) has being increasingly used for the pretransplant assessment of extended-criteria donor lungs. Mathematical models to predict lung acceptance during EVLP have not been reported so far. Thus, we hypothesized that predictors of lung acceptance could be identified and used to develop a mathematical model describing the clinical decision-making process used in our institution. Donor lungs characteristics and EVLP physiologic parameters included in our EVLP registry were examined (derivation cohort). Multivariable logistic regression analysis was performed to identify predictors independently associated with lung acceptance. A mathematical model (EX vivo lung PerfusIon pREdiction [EXPIRE] model) for each hour of EVLP was developed and validated using a new cohort (validation cohort). Two hundred eighty donor lungs were assessed with EVLP. Of these, 186 (66%) were accepted for transplantation. ΔPO and static compliance/total lung capacity were identified as independent predictors of lung acceptance and their respective cut-off values were determined. The EXPIRE model showed a low discriminative power at the first hour of EVLP assessment (AUC: 0.69 [95% CI: 0.62-0.77]), which progressively improved up to the fourth hour (AUC: 0.87 [95% CI: 0.83-0.92]). In a validation cohort, the EXPIRE model demonstrated good discriminative power, peaking at the fourth hour (AUC: 0.85 [95% CI: 0.76-0.94]). The EXPIRE model may help to standardize lung assessment in centers using the Toronto EVLP technique and improve overall transplant rates.
离体肺灌注 (EVLP) 越来越多地用于移植前评估扩展标准供体肺。到目前为止，还没有报道用于预测 EVLP 期间肺接受的数学模型。因此，我们假设可以确定预测肺接受的指标，并将其用于开发描述我们机构中使用的临床决策过程的数学模型。
检查了纳入我们 EVLP 注册中心的供体肺特征和 EVLP 生理参数（推导队列）。进行多变量逻辑回归分析，以确定与肺接受独立相关的预测因素。使用新队列（验证队列）为 EVLP 的每小时开发和验证一个数学模型（离体肺灌注预测 [EXPIRE] 模型）。
对 280 个供体肺进行了 EVLP 评估。其中，186 个（66%）接受了移植。ΔPO 和静态顺应性/总肺容量被确定为肺接受的独立预测因素，并确定了它们各自的截止值。EXPIRE 模型在 EVLP 评估的第一个小时显示出较低的区分能力（AUC：0.69 [95% CI：0.62-0.77]），直到第四个小时逐渐提高（AUC：0.87 [95% CI：0.83-0.92]）。在验证队列中，EXPIRE 模型在第四个小时显示出良好的区分能力（AUC：0.85 [95% CI：0.76-0.94]）。EXPIRE 模型可能有助于使用多伦多 EVLP 技术的中心标准化肺评估，并提高整体移植率。
