Linse Björn, Ohlsson Mattias, Stehlik Joseph, Lund Lars H, Andersson Bodil, Nilsson Johan
Computational Biology and Biological Physics, Lund University, Lund, Sweden.
Center for Applied Intelligent Systems Research, Halmstad University, Sweden.
Heliyon. 2023 Mar 5;9(3):e14282. doi: 10.1016/j.heliyon.2023.e14282. eCollection 2023 Mar.
Primary graft failure (PGF) remains the most common cause of short-term mortality after heart transplantation. The main objective was to develop and validate a risk model for prediction of short-term mortality due to PGF after heart transplantation using the ISHLT Heart Transplant Registry.
We developed a non-linear artificial neural networks (ANN) model to evaluate the association between recipient-donor variables and post-transplant PGF. Patients in the ISHLT registry were randomly divided into derivation and an independent internal validation cohort. The primary endpoint was PGF defined as death within 30 days due to Graft failure or Cardiovascular causes or retransplant within 30 days for causes other than rejection.
Among 64,964 adult recipients transplanted between 1994 and 2013, mean age was 51 years and 22% were female. The incidence of PGF up to 30 days was 3.7%. The ANN model selected 33 of 77 risk variables as relevant for PGF prediction. The C-index in the test cohort was 0.70 (95% CI: 0.68-0.71). The risk variables which most influenced the PGF were underlying HF diagnosis, ischemia time and sex, while renal function had a lower influence.
An ANN model to predict primary graft dysfunction was derived and independently validated. The good discrimination of the ANN model likely results from its flexibility to model potentially non-linear relationships and interactions. Whether this model with improved discrimination can assist in clinical decisions at the time of transplant should be tested.
原发性移植心脏功能衰竭（PGF）仍然是心脏移植术后短期死亡的最常见原因。主要目的是利用国际心脏和肺移植学会（ISHLT）心脏移植登记处的数据，开发并验证一种用于预测心脏移植术后因PGF导致短期死亡的风险模型。
我们开发了一种非线性人工神经网络（ANN）模型，以评估受者-供者变量与移植后PGF之间的关联。ISHLT登记处的患者被随机分为推导队列和独立的内部验证队列。主要终点是PGF，定义为因移植心脏功能衰竭或心血管原因在30天内死亡，或因非排斥原因在30天内再次移植。
在1994年至2013年间接受移植的64964名成年受者中，平均年龄为51岁，22%为女性。30天内PGF的发生率为3.7%。ANN模型从77个风险变量中选择了33个与PGF预测相关的变量。测试队列中的C指数为0.70（95%CI：0.68-0.71）。对PGF影响最大的风险变量是潜在的心力衰竭诊断、缺血时间和性别，而肾功能的影响较小。
推导并独立验证了一种用于预测原发性移植心脏功能障碍的ANN模型。ANN模型良好的区分能力可能源于其对潜在非线性关系和相互作用进行建模的灵活性。这种具有改进区分能力的模型是否能在移植时协助临床决策有待进一步测试。
