Development and evaluation of a dynamic nomogram model for intraoperative blood transfusion decision-making
ObjectiveBy gathering data on patients with intraoperative blood transfusion and investigating the factors influencing intraoperative blood transfusion in patients, we aimed to construct a dynamic nomogram decision-making model capable of continuously predicting the probability of intraoperative blo...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-06-01
|
| Series: | Frontiers in Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1566325/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849468913524408320 |
|---|---|
| author | Min Li Wei Jiang Jialing Lin Hui Du Jiawen Shan Li Qin |
| author_facet | Min Li Wei Jiang Jialing Lin Hui Du Jiawen Shan Li Qin |
| author_sort | Min Li |
| collection | DOAJ |
| description | ObjectiveBy gathering data on patients with intraoperative blood transfusion and investigating the factors influencing intraoperative blood transfusion in patients, we aimed to construct a dynamic nomogram decision-making model capable of continuously predicting the probability of intraoperative blood transfusion in patients. This was done to explore a new mode of individualized and precise blood transfusion and to guide doctors to make timely and reasonable blood transfusion decisions and save blood resources.MethodsData of surgical patients in our hospital from 2019 to 2023 were collected. Among them, 705 patients who had blood transfusions were the experimental group, and 705 patients without intraoperative blood transfusions were randomly selected as the control group. Preoperative and intraoperative indicators of 1,410 patients were collected. 80% of the data set was used as the training set and 20% as the test set. In the training set, independent risk factors affecting intraoperative blood transfusion in patients were obtained through Lasso regression and binary logistic regression analysis, and the regression model was established and validated.ResultsThrough Lasso regression with cross-validation and binary logistic regression analysis, the independent risk factors affecting patients’ intraoperative blood transfusion decision-making were determined as ASAs (III) (OR = 3.009, 95% CI = 1.311–6.909), surgical grading (IV) (OR = 3.772, 95% CI = 1.112–12.789), EBL (OR = 1.003, 95% CI = 1.002–1.004), preHGB (OR = 0.932, 95% CI = 0.919–0.946), LVEF (OR = 1.063, 95% CI = 1.028–1.098), Temp (OR = 57.14, 95% CI = 9.740–35.204), preAPTT (OR = 1.147, 95% CI = 1.079–1.220), and preDD (OR = 1.127, 95% CI = 1.048–1.212). The area under the curve (AUC) of the receiver operating characteristic curve (ROC) of the training set was 0.983, p < 0.05. By calculating the Jordon index, when the Jordon index reached its maximum value, the corresponding diagnostic probability threshold was 0.515. When the model predicted that the probability threshold was 0.515, the sensitivity was 0.939 and the specificity was 0.964. These independent risk factors were introduced into R statistical software to fit the intraoperative blood transfusion decision-making dynamic nomogram model. The goodness of fit test of the model for the training set was χ2 = 111.85, p < 0.01, and the AUCs of the training set and the testing set were 0.983 and 0.995, respectively, p < 0.05. The calibration curve showed that the predicted probability of the model was in good agreement with the observed probability. Clinical decision curves (CDA) and clinical impact curves were plotted to evaluate the clinical utility of the model and the net benefit of the model.ConclusionVariables were screened by Lasso regression, the model was developed by binary logistic regression, and a dynamic nomogram model for intraoperative transfusion decision-making was successfully fitted using R software. This model had good goodness of fit, discrimination, and calibration. The model can dynamically and instantaneously predict the probability of blood transfusion and its 95% confidence interval under the current patient indicators by selecting the patient’s independent risk factors in the drop-down mode during the operation. It can assist doctors in making a reasonable blood transfusion decision quickly and save blood resources. |
| format | Article |
| id | doaj-art-248a575f2f6d436a806bdab2f4e4f44c |
| institution | Kabale University |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-248a575f2f6d436a806bdab2f4e4f44c2025-08-20T03:25:42ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-06-011210.3389/fmed.2025.15663251566325Development and evaluation of a dynamic nomogram model for intraoperative blood transfusion decision-makingMin Li0Wei Jiang1Jialing Lin2Hui Du3Jiawen Shan4Li Qin5Department of Blood Transfusion Medicine, Sichuan Tianfu New Area People’s Hospital, Chengdu, ChinaDepartment of Clinical Laboratory Medicine, Sichuan Tianfu New Area People’s Hospital, Chengdu, ChinaDepartment of Blood Transfusion Medicine, Sichuan Tianfu New Area People’s Hospital, Chengdu, ChinaDepartment of Blood Transfusion Medicine, Sichuan Tianfu New Area People’s Hospital, Chengdu, ChinaDepartment of Clinical Laboratory Medicine, Sichuan Tianfu New Area People’s Hospital, Chengdu, ChinaDepartment of Blood Transfusion Medicine, West China Hospital, Chengdu, ChinaObjectiveBy gathering data on patients with intraoperative blood transfusion and investigating the factors influencing intraoperative blood transfusion in patients, we aimed to construct a dynamic nomogram decision-making model capable of continuously predicting the probability of intraoperative blood transfusion in patients. This was done to explore a new mode of individualized and precise blood transfusion and to guide doctors to make timely and reasonable blood transfusion decisions and save blood resources.MethodsData of surgical patients in our hospital from 2019 to 2023 were collected. Among them, 705 patients who had blood transfusions were the experimental group, and 705 patients without intraoperative blood transfusions were randomly selected as the control group. Preoperative and intraoperative indicators of 1,410 patients were collected. 80% of the data set was used as the training set and 20% as the test set. In the training set, independent risk factors affecting intraoperative blood transfusion in patients were obtained through Lasso regression and binary logistic regression analysis, and the regression model was established and validated.ResultsThrough Lasso regression with cross-validation and binary logistic regression analysis, the independent risk factors affecting patients’ intraoperative blood transfusion decision-making were determined as ASAs (III) (OR = 3.009, 95% CI = 1.311–6.909), surgical grading (IV) (OR = 3.772, 95% CI = 1.112–12.789), EBL (OR = 1.003, 95% CI = 1.002–1.004), preHGB (OR = 0.932, 95% CI = 0.919–0.946), LVEF (OR = 1.063, 95% CI = 1.028–1.098), Temp (OR = 57.14, 95% CI = 9.740–35.204), preAPTT (OR = 1.147, 95% CI = 1.079–1.220), and preDD (OR = 1.127, 95% CI = 1.048–1.212). The area under the curve (AUC) of the receiver operating characteristic curve (ROC) of the training set was 0.983, p < 0.05. By calculating the Jordon index, when the Jordon index reached its maximum value, the corresponding diagnostic probability threshold was 0.515. When the model predicted that the probability threshold was 0.515, the sensitivity was 0.939 and the specificity was 0.964. These independent risk factors were introduced into R statistical software to fit the intraoperative blood transfusion decision-making dynamic nomogram model. The goodness of fit test of the model for the training set was χ2 = 111.85, p < 0.01, and the AUCs of the training set and the testing set were 0.983 and 0.995, respectively, p < 0.05. The calibration curve showed that the predicted probability of the model was in good agreement with the observed probability. Clinical decision curves (CDA) and clinical impact curves were plotted to evaluate the clinical utility of the model and the net benefit of the model.ConclusionVariables were screened by Lasso regression, the model was developed by binary logistic regression, and a dynamic nomogram model for intraoperative transfusion decision-making was successfully fitted using R software. This model had good goodness of fit, discrimination, and calibration. The model can dynamically and instantaneously predict the probability of blood transfusion and its 95% confidence interval under the current patient indicators by selecting the patient’s independent risk factors in the drop-down mode during the operation. It can assist doctors in making a reasonable blood transfusion decision quickly and save blood resources.https://www.frontiersin.org/articles/10.3389/fmed.2025.1566325/fullblood transfusion decision-makingnomogram modelingLasso regressionperoperativetransfusion |
| spellingShingle | Min Li Wei Jiang Jialing Lin Hui Du Jiawen Shan Li Qin Development and evaluation of a dynamic nomogram model for intraoperative blood transfusion decision-making Frontiers in Medicine blood transfusion decision-making nomogram modeling Lasso regression peroperative transfusion |
| title | Development and evaluation of a dynamic nomogram model for intraoperative blood transfusion decision-making |
| title_full | Development and evaluation of a dynamic nomogram model for intraoperative blood transfusion decision-making |
| title_fullStr | Development and evaluation of a dynamic nomogram model for intraoperative blood transfusion decision-making |
| title_full_unstemmed | Development and evaluation of a dynamic nomogram model for intraoperative blood transfusion decision-making |
| title_short | Development and evaluation of a dynamic nomogram model for intraoperative blood transfusion decision-making |
| title_sort | development and evaluation of a dynamic nomogram model for intraoperative blood transfusion decision making |
| topic | blood transfusion decision-making nomogram modeling Lasso regression peroperative transfusion |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1566325/full |
| work_keys_str_mv | AT minli developmentandevaluationofadynamicnomogrammodelforintraoperativebloodtransfusiondecisionmaking AT weijiang developmentandevaluationofadynamicnomogrammodelforintraoperativebloodtransfusiondecisionmaking AT jialinglin developmentandevaluationofadynamicnomogrammodelforintraoperativebloodtransfusiondecisionmaking AT huidu developmentandevaluationofadynamicnomogrammodelforintraoperativebloodtransfusiondecisionmaking AT jiawenshan developmentandevaluationofadynamicnomogrammodelforintraoperativebloodtransfusiondecisionmaking AT liqin developmentandevaluationofadynamicnomogrammodelforintraoperativebloodtransfusiondecisionmaking |