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临床试验/NCT05284500
NCT05284500已完成不适用

Predictive Pre and Peroperative Factors for Multiple Organ Dysfunction Score 2 (MODS-2) in Pediatric Cardiac Surgery

Brugmann University Hospital1 个研究点 分布在 1 个国家目标入组 152 人开始时间: 2022年3月18日最近更新:
适应症

试验速览

阶段
不适用
状态
已完成
入组人数
152
试验地点
1
主要终点
MODS2

研究概览

简要总结

Pediatric cardiac surgery has a relatively high morbi-mortality. Despite great advances in surgical techniques, today the mortality rate is about 3% and morbidity is about 30-40%. Outcome has been related to demographic factors, like age; peroperative factors, like duration of cardiopulmonary bypass as well as postoperative factors like positive fluid balance. Willems et al defined a new score (MODS2), an outcome score combining either patient's death or a high postoperative morbidity. This morbidity is defined as minimum of 2 organ failures: either respiratory insufficiency, prolonged use of inotropic agents or renal insufficiency. The aim of this study is to identify pre and peroperative factors which are predictors of MODS2. Patients operated between 2008 and 2018 for pediatric cardiac surgery with cardiopulmonary bypass will be included. Variables extracted from our database will be: sex, ASA score, cyanotic cardiac pathology, redo surgery, RACH1 score, use of antifibrinolytic agents, aortic cross-clamping, deep hypothermic circulatory arrest, selective cerebral perfusion, red cell transfusion in the operating room, administration of fresh frozen plasma in the operating room, age, preoperative weight, weight difference between preop weight and weight at postop day 2, emergency surgery, duration of aortic cross clamping, duration of selective cerebral perfusion, duration of cardiopulmonary bypass, duration off deep hypothermic circulatory arrest, duration of surgery, minimal core temperature, cardiopulmonary priming volume, calculated hemodilution, use of red blood cells in the cardiopulmonary bypass priming, preoperative hemoglobin, preoperative hematocrit, preoperative platelet count, preop international normalized ratio, preop fibrinogen, preop creatinin, toal fluid balance, blood loss during surgery. A statistical analysis (see detailed description) will be used to establish a prediction model for MODS2. The variables describing best the MODS2 outcome will be retained.

详细描述

Detailed statistical analysis:

5 multiple imputations via the mice R package will be performed, which is the most appropriate method for a risk model. We then take the mean of the imputed datasets in order to start the data mining models on one single dataset. All of the variables will be entered in the model. Before to run the data mining models, we will perform three transformations on the continuous variables: 1) standardization; 2) best normalisation via the bestNormalize R package and 3) taking the variable from its power 2 to its power 10. The dataset will be split into a training set (75% of the cases) and test set (25% of the cases). For all tested data mining models, we will use a 10-fold cross-validation method on the training set before applying the retained model on the test set. The following data mining models will be tested: 1) a regression tree, 2) a logistic regression (GLM), 3) a Neural Network (NN); 4) a Support Vector Machine (SVM); 5) a Random Forest (RF); 6) a Multivariate Adaptive Regression Spline model (MARS) and 7) a Non-Linear Support Vector Machine (SVM NL). The models will be drawn with the caret R package. The confusion matrix, reporting the sensibilities, specificities, accuracies will be drawn on the test set based on the models developed on the training set, and the calibration plot will be drawn for three model competitors. The R software (R Core Team, 2019), version 3.6.1. will be used to produce the results.

研究设计

研究类型
Observational
观察模型
Cohort
时间视角
Retrospective

入排标准

年龄范围
— 至 16 Years(Child)
性别
All
接受健康志愿者

入选标准

  • patients undergoing pediatric cardiac surgery with cardiopulmonary bypass between 2008 and 2018 at our institution
  • accepting blood transfusions
  • ASA score 1-4

排除标准

  • Jehova's witness
  • ASA 5 status

结局指标

主要结局

MODS2

时间窗: 28 days

Occurence of MODS2 (as percentage) will be extracted. A predictive model will be established from the observed preoperative and peroperative variables. A statistical analysis (see detailed description) will be used to establish a prediction model for MODS2. The variables describing best the MODS2 outcome will be retained.

次要结局

未报告次要终点

研究者

申办方类型
Other
责任方
Principal Investigator
主要研究者

Denis SCHMARTZ

Head, Dept of Anesthesiology, Brugmlann University hospital & HUDERF

Brugmann University Hospital

研究点 (1)

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