Development of an individualized prediction model of allogenic blood transfusion in elective patients based on machine learning / 中国输血杂志

ABSTRACT

【Objective】 To develop a prediction model of allogenic blood transfusion in elective patients based on machine learning, so as to guide clinicians to prepare blood for perioperative patients more reasonably. 【Methods】 Relevant data of all surgical patients from 2012 to 2018 were extracted from the big data integration platform of our hospital, to construct the surgical blood database based on Python V3.8.0. All data were analyzed using Excel and SAS, and the prediction model was developed based on SPSS Modeler 18.0. 【Results】 1) There was a negative correlation between preoperative Hb and BMI and intraoperative blood transfusion rate, with Pearson correlation coefficient (R) as -0.168 and -0.046, respectively. The transfusion rate of patients under 1 year old was the highest, up to 15.63%. The transfusion rate of female patients was higher than that of male patients (P>0.05), as cardiac surgery rated at the highest 11.38%, but their per capita blood transfusion was lower than that of males (P<0.01). 2) The AUC range corresponding to the prediction model for transfusion probability was 0.67~0.88, and when the AUC reached the highest, the hit ratio, coverage rate and specificity of Model 9 was 10.7%, 85.76% and 75.4%, respectively. 3) The main factors contributing to the prediction model for transfusion volume in surgery were weight, Hb, total protein(TP), etc. 【Conclusion】 The prediction efficiency of the successfully constructed prediction model for perioperative blood use was better than that of MSBOS.