Prediction and Evaluation of Machine Learning Algorithm for Prediction of Blood Transfusion during Cesarean Section and Analysis of Risk Factors of Hypothermia during Anesthesia Recovery

体温过低 逻辑回归 医学 麻醉 单变量分析 算法 输血 多元分析 外科 内科学 数学
作者
Wei Ren,Danmei Li,Jia Wang,Jinxi Zhang,Zhongliang Fu,Yu Yao
出处
期刊:Computational and Mathematical Methods in Medicine [Hindawi Publishing Corporation]
卷期号:2022: 1-9 被引量:12
标识
DOI:10.1155/2022/8661324
摘要

Objective. To explore the application of machine learning algorithm in the prediction and evaluation of cesarean section, predicting the amount of blood transfusion during cesarean section and to analyze the risk factors of hypothermia during anesthesia recovery. Methods. (1)Through the hospital electronic medical record of medical system, a total of 600 parturients who underwent cesarean section in our hospital from June 2019 to December 2020 were included. The maternal age, admission time, diagnosis, and other case data were recorded. The routine method of cesarean section was intraspinal anesthesia, and general anesthesia was only used for patients’ strong demand, taboo, or failure of intraspinal anesthesia. According to the standard of intraoperative bleeding, the patients were divided into two groups: the obvious bleeding group (MH group, N = 154 ) and nonobvious hemorrhage group (NMH group, N = 446 ). The preoperative, intraoperative, and postoperative indexes of parturients in the two groups were analyzed and compared. Then, the risk factors of intraoperative bleeding were screened by logistic regression analysis with the occurrence of obvious bleeding as the dependent variable and the factors in the univariate analysis as independent variables. In order to further predict intraoperative blood transfusion, the standard cases of recesarean section and variables with possible clinical significance were included in the prediction model. Logistic regression, XGB, and ANN3 machine learning algorithms were used to construct the prediction model of intraoperative blood transfusion. The area under ROC curve (AUROC), accuracy, recall rate, and F 1 value were calculated and compared. (2) According to whether hypothermia occurred in the anesthesia recovery room, the patients were divided into two groups: the hypothermia group ( N = 244 ) and nonhypothermia group ( N = 356 ). The incidence of hypothermia was calculated, and the relevant clinical data were collected. On the basis of consulting the literatures, the factors probably related to hypothermia were collected and analyzed by univariate statistical analysis, and the statistically significant factors were analyzed by multifactor logistic regression analysis to screen the independent risk factors of hypothermia in anesthetic convalescent patients. Results. (1) First of all, we compared the basic data of the blood transfusion group and the nontransfusion group. The gestational age of the transfusion group was lower than that of the nontransfusion group, and the times of cesarean section and pregnancy in the transfusion group were higher than those of the non-transfusion group. Secondly, we compared the incidence of complications between the blood transfusion group and the nontransfusion group. The incidence of pregnancy complications was not significantly different between the two groups ( P > 0.05 ). The incidence of premature rupture of membranes in the nontransfusion group was higher than that in the transfusion group ( P < 0.05 ). There was no significant difference in the fetal umbilical cord around neck, amniotic fluid index, and fetal heart rate before operation in the blood transfusion group, but the thickness of uterine anterior wall and the levels of Hb, PT, FIB, and TT in the blood transfusion group were lower than those in the nontransfusion group, while the number of placenta previa and the levels of PLT and APTT in the blood transfusion group were higher than those in the nontransfusion group. The XGB prediction model finally got the 8 most important features, in the order of importance from high to low: preoperative Hb, operation time, anterior wall thickness of the lower segment of uterus, uterine weakness, preoperative fetal heart, placenta previa, ASA grade, and uterine contractile drugs. The higher the score, the greater the impact on the model. There was a linear correlation between the 8 features (including the correlation with the target blood transfusion). The indexes with strong correlation with blood transfusion included the placenta previa, ASA grade, operation time, uterine atony, and preoperative Hb. Placenta previa, ASA grade, operation time, and uterine atony were positively correlated with blood transfusion, while preoperative Hb was negatively correlated with blood transfusion. In order to further compare the prediction ability of the three machine learning methods, all the samples are randomly divided into two parts: the first 75% training set and the last 25% test set. Then, the three models are trained again on the training set, and at this time, the model does not come into contact with the samples in any test set. After the model training, the trained model was used to predict the test set, and the real blood transfusion status was compared with the predicted value, and the F 1 , accuracy, recall rate, and AUROC4 indicators were checked. In terms of training samples and test samples, the AUROC of XGB was higher than that of logistic regression, and the F 1 , accuracy, and recall rate of XGB of ANN were also slightly higher than those of logistic regression and ANN. Therefore, the performance of XGB algorithm is slightly better than that of logistic regression and ANN. (2) According to the univariate analysis of hypothermia during the recovery period of anesthesia, there were significant differences in ASA grade, mode of anesthesia, infusion volume, blood transfusion, and operation duration between the normal body temperature group and hypothermia group ( P < 0.05 ). Logistic regression analysis showed that ASA grade, anesthesia mode, infusion volume, blood transfusion, and operation duration were all risk factors of hypothermia during anesthesia recovery. Conclusion. In this study, three machine learning algorithms were used to analyze the large sample of clinical data and predict the results. It was found that five important predictive variables of blood transfusion during recesarean section were preoperative Hb, expected operation time, uterine weakness, placenta previa, and ASA grade. By comparing the three algorithms, the prediction effect of XGB may be more accurate than that of logistic regression and ANN. The model can provide accurate individual prediction for patients and has good prediction performance and has a good prospect of clinical application. Secondly, through the analysis of the risk factors of hypothermia during the recovery period of cesarean section, it is found that ASA grade, mode of anesthesia, amount of infusion, blood transfusion, and operation time are all risk factors of hypothermia during the recovery period of cesarean section. In line with this, the observation of this kind of patients should be strengthened during cesarean section.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
2秒前
3秒前
狂野元枫完成签到 ,获得积分10
5秒前
ruochenzu发布了新的文献求助10
8秒前
大葱鸭发布了新的文献求助10
8秒前
ABC完成签到,获得积分20
10秒前
原本发布了新的文献求助10
10秒前
dzy完成签到,获得积分20
11秒前
amber完成签到 ,获得积分10
11秒前
Green完成签到,获得积分10
13秒前
14秒前
小木子完成签到,获得积分10
16秒前
舟遥遥完成签到,获得积分10
17秒前
华仔应助大橙子采纳,获得10
19秒前
桐桐应助Bismarck采纳,获得10
23秒前
CLY完成签到,获得积分10
24秒前
25秒前
rita_sun1969完成签到,获得积分10
26秒前
研友_8K2QJZ完成签到,获得积分10
26秒前
蝴蝶完成签到 ,获得积分10
27秒前
ARIA完成签到 ,获得积分10
27秒前
大橙子发布了新的文献求助10
30秒前
Bismarck完成签到,获得积分20
31秒前
31秒前
爱笑子默完成签到,获得积分10
32秒前
32秒前
一点完成签到,获得积分10
34秒前
研友_VZG7GZ应助大葱鸭采纳,获得10
34秒前
DezhaoWang完成签到,获得积分10
35秒前
知犯何逆发布了新的文献求助10
36秒前
原本完成签到,获得积分10
36秒前
Bismarck发布了新的文献求助10
37秒前
苗条丹南完成签到 ,获得积分10
39秒前
yu完成签到 ,获得积分10
42秒前
skyleon完成签到,获得积分10
42秒前
无心的天真完成签到 ,获得积分10
43秒前
Engen完成签到,获得积分20
43秒前
44秒前
学术小垃圾完成签到,获得积分10
44秒前
dreamwalk完成签到 ,获得积分10
44秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038157
求助须知:如何正确求助?哪些是违规求助? 3575869
关于积分的说明 11373842
捐赠科研通 3305650
什么是DOI,文献DOI怎么找? 1819255
邀请新用户注册赠送积分活动 892655
科研通“疑难数据库(出版商)”最低求助积分说明 815022