Biomarker predictive research method for real world data

Abstract

The invention discloses a biomarker predictive research method for real world data, and relates to the technical field of hematopoietic stem cell transplantation. The method comprises the following steps: S1, biomarker index monitoring and data screening and grouping: dynamically monitoring biomarker indexes at important time points of patient groups in the real world; s2, carrying out risk stratification based on the peak concentration of sST2 or REG3alpha, selecting the peak concentration of sST2 and REG3alpha as an index of the worst condition of the patient within 100 days after the patient is transplanted, and carrying out statistical association with the long-term prognosis (survival and non-recurrent death) of the patient; and S3, dynamically monitoring the risk layering change and trend of the patient. The biomarker predictive research method for the real world data is more suitable for the real world data, gets rid of the dependence of a research conclusion on a specific detection time point, has higher clinical reference, can dynamically monitor the risk state and trend of a patient, and is convenient for a doctor to pay attention to and follow up the risk change of the patient in time.

Description

technical field [0001] The invention relates to the technical field of hematopoietic stem cell transplantation, in particular to a biomarker predictive research method for real-world data. Background technique [0002] Allogeneic hematopoietic stem cell transplantation is considered to be the most effective immunotherapy for many blood tumors and non-tumor diseases. In the field of hematopoietic stem cell transplantation, complications and prognosis after transplantation have always been the most concerned issues. Because biomarker has a good There have been a large number of related clinical studies, and a relatively standardized research process has been formed, especially the research on GVHD biomarker, which has made outstanding progress. [0003] Biomarkers usually require serial testing, especially intensive monitoring in the early post-transplant period. The time points commonly used in clinical research are 7, 14, 28, 100 days after transplantation, and the time poi...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is how to conduct effective and more clinically referential biomarker predictive research on real-world data, aiming at solving the dependence of the above-mentioned biomarker research conclusion on the detection time point, which cannot continuously monitor the patient's changes; and real There are problems in the world data, such as the inability to guarantee the consistency and uniformity of biomarker detection time for all patients, and the lack of available analytical data at fixed time points.

Images