Prediction of Pulmonary Graft Dysfunction After Double-lung Transplantation (PGD3-AI Study)
Prediction of Grade 3 Pulmonary Graft Dysfunction After Double-lung Transplantation From Donor, Recipient and Intraoperative Variables
The thundering evolution of lung transplantation management during the past ten years and primary graft dysfunction (PGD) new definition have led to new predictive factors of PGD. Therefore, we retrospectively analyzed a monocentric database using a machine-learning method, to determine the predictive factors of grade 3 PGD (PGD3), defined as a PaO2/FiO2 ratio < 200 or being under extracorporeal membrane oxygenation (ECMO) at postoperative day 3.
We included all double lung transplantation from 2012 to 2019 and excluded multi-organ transplant, cardiopulmonary bypass, or repeated transplantation during the study period for the same patient. Recipient, donor and intraoperative data were added in a gradient boosting algorithm step-by-step according to standard transplantation stages. Dataset will be split randomly as 80% training set and 20% testing set. Relationship between predictive factors and PGD3 will be represented as ShHapley Additive exPlanation (SHAP) values.
調査の概要
詳細な説明
The standardized anesthetic management has been previously described 18 and is detailed on the web site http://anesthesie-foch.org/protocoles-anesthesie/ ("The Foch lung transplant anesthesia protocol").
Continuous variables are presented as median + interquartile range (IQR) or mean and 95%CI, and were compared using independent T-test or Mann-Whitney test. Categorical variables are presented as n (%) and were compared using Chi-squared test or Fisher's exact test. We applied machine learning algorithm to predict 3-day ahead primary graft dysfunction after lung transplant surgery among patients. Machine learning is a branch of artificial intelligence where computer systems can learn from available data and identify patterns with minimal human intervention. Machine learning algorithm tests on data and performance metrics were used to obtain the higher performing algorithm. In this study, we performed a XGBoost (Gradient Boosting) algorithm which was a combination of decisions trees. Each decision tree typically learned from its precursor and passed on the improved function to the following. The weighted combination of these trees provided the prediction.
No particular data transformation has been performed on numerical variables. Categorical variables have been encoded as integer, without any further pre-processing steps. In particular, no specific processing has been performed to deal with missing data. The default behavior of XGBoost has been used. It consists in treating missing data as a specific modality. During the training step of XGBoost models, missing values are treated as other values, and left or right decisions at any branch of a tree are learned by optimizing the outcome.
In order to reflect the sequential nature of this predictive medicine problem, nine steps have been defined to take into account incrementally observed variables acquired at various stages of the surgery.
Step 1: recipient variables Step 2: donor variables Step 3: arrival in the OR Step 4: after anesthetic induction Step 5: during first pneumonectomy Step 6: after first graft implantation Step 7: second pneumonectomy Step 8: second graft implantation Step 9: end surgery status At each of the nine steps, a cross-validation procedure is employed to assess the predictive performance of a machine learning model (XGBoost). One repetition of the cross-validation procedure is designed as follows: the dataset of subjects is randomly split into eight disjoint parts. Successively, the performance of the XGBoost model on each of the eight subset,while training the machine learning model using the remaining seven subsets. For such a repetition, the predictive probability of 3-day ahead primary graft dysfunction for each subject is retained to finally compute the area under ROC (receiving operator curve). To evaluate the variability of the predictive performance of the machine learning model, this cross-validation procedure is repeated fifty times, with randomly chosen subjects partitions. For each of the fifty times eight times nine (repetitions, partitions, surgical steps), hence 3600 models training, a conservative approach has been adopted for XGBoost training, consisting in a unique set of training parameters. These parameters have been chosen to prevent overfitting due to a relatively small number of subjects compared to the number of variables, especially categorical variables, which yield a high degree of freedom. Specifically XGBoost has been trained for 400 rounds (no early stopping), a maximum depth of 5 for each tree, a minimum child weight of 3, and a learning parameter eta equals to 0.0002. Besides those conservative parameters chosen to prevent overfitting, only 40 percents of available columns are selected for tree construction at each round, and 95 % of subjects. These parameters have been kept fixed and chosen to ensure stability of results. Small perturbations around these values could result in local performance improvements, but would not be practically chosen given the size of the dataset.
In order to gain some insights into the most useful variables in terms of predictive power, we then conducted a post-hoc analysis based on the following methodology: at each surgical step, 400 models have been trained for the repeated cross-validation procedure. For each model, we retain the rank of each variable as given by the variable importance procedure of XGBoost. The average rank of each variable for each step is then computed by averaging the ranks obtained by variables for each of the 400 models. At step 9, variables are ordered based on their average rank (increasing average ranks). They are then incrementally used as input of a new cross-validation procedure (repeated 20 times).
研究の種類
入学 (実際)
参加基準
適格基準
就学可能な年齢
健康ボランティアの受け入れ
受講資格のある性別
サンプリング方法
調査対象母集団
説明
Inclusion Criteria:
- double-lung transplantation
Exclusion Criteria:
- multi-organ transplant
- use of a cardiopulmonary bypass
- repeated transplantation during the study period for the same patient.
研究計画
研究はどのように設計されていますか?
デザインの詳細
コホートと介入
グループ/コホート |
介入・治療 |
---|---|
No grade 3 Pulmonary graft dysfunction at postoperative day 3
patients having not a grade 3 Pulmonary graft dysfunction at postoperative day 3
|
|
Grade 3 Pulmonary graft dysfunction at postoperative day 3
patients having a grade 3 Pulmonary graft dysfunction at postoperative day 3
|
この研究は何を測定していますか?
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
---|---|---|
risk factors for grade 3 pulmonary graft dysfunction at postoperative day 3
時間枠:3 days
|
PaO2/FiO2 ratio < 200 or being under extracorporeal membrane oxygenation (ECMO) at postoperative day 3 due to hypoxemia
|
3 days
|
協力者と研究者
スポンサー
捜査官
- スタディディレクター:Elisabeth Hulier Ammar, PhD、Hopital Foch
研究記録日
主要日程の研究
研究開始 (実際)
一次修了 (実際)
研究の完了 (実際)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (実際)
学習記録の更新
投稿された最後の更新 (実際)
QC基準を満たした最後の更新が送信されました
最終確認日
詳しくは
本研究に関する用語
その他の研究ID番号
- 1111111111111
個々の参加者データ (IPD) の計画
個々の参加者データ (IPD) を共有する予定はありますか?
医薬品およびデバイス情報、研究文書
米国FDA規制医薬品の研究
米国FDA規制機器製品の研究
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