Method for severe intestinal aGVHD model based on aGVHD biomarker
A kind of intestinal and severe technology, applied in the fields of genomics, instrumentation, proteomics, etc., can solve the problem that the conclusion cannot be directly applied to the patient population
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Embodiment 1
[0025] see figure 1 , a method for severe intestinal aGVHD model based on aGVHD biomarker, including four steps of aGVHD biomarker index monitoring, data screening and grouping, single severe intestinal aGVHD model training, multi-model fusion, and prospective verification of severe intestinal aGVHD model performance :
[0026] S1. Monitoring of aGVHD biomarker indicators and data screening and grouping, dynamic monitoring of aGVHD biomarker indicators at important time points in multi-center patient groups (patients with severe intestinal aGVHD accounted for 13%), aGVHD biomarker indicators include sST2, REG3α, IL-6 , IL-8, and TNFR1, and were grouped according to whether intestinal aGVHD occurred within 100 days after HSCT. Among them, the aGVHD biomarker index monitored at the time point of the screening event occurred in the group with intestinal aGVHD, and the group without intestinal aGVHD was divided into groups. Screen the monitored aGVHD biomarker indicators in the s...
Embodiment 2
[0031] Using the method of a severe intestinal aGVHD model based on aGVHD biomarker in Example 1, dynamically monitor the aGVHD biomarker index at important time points of the multi-center patient population (severe intestinal aGVHD accounts for about 13%), and according to the method within 100 days after transplantation Whether intestinal aGVHD occurs or not is grouped for data screening, using one data per patient, the patients in the intestinal aGVHD occurrence group take the biomarker data at the occurrence of intestinal aGVHD, and the patients in the intestinal aGVHD non-occurrence group follow the same principle as the occurrence group biomarker data measurement time distribution Filter the data to form a model dataset.
[0032] The above model data set is randomly divided into a training set and a test set in proportion, and the patient's aGVHD biomarker index is correlated with whether severe intestinal aGVHD occurs clinically, and stacking and logistic regression tech...
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