Construction and Verification of Prediction Model of Qi Deficiency and Blood Stasis Syndrome in Chronic Heart Failure
10.13422/j.cnki.syfjx.20250599
- VernacularTitle:慢性心力衰竭气虚血瘀证预测模型的构建与验证
- Author:
Tong JIANG
1
;
Xiaodan FAN
2
;
Shijia WANG
2
;
Fengxia LIN
3
;
Zhicong ZENG
3
;
Liangzhen YOU
2
;
Hongcai SHANG
2
Author Information
1. Binzhou Medical University，Yantai 264000，China
2. Key Laboratory of Chinese Internal Medicine of Ministry of Education，Beijing University of Chinese Medicine，Beijing 100700，China
3. The Seventh Clinical College of Guangzhou University of Chinese Medicine （Shenzhen Bao'an Hospital of Traditional Chinese Medicine），Shenzhen 518101，China
- Publication Type:Journal Article
- Keywords:
chronic heart failure;
Qi deficiency and blood stasis syndrome;
nomogram;
clinical prediction model
- From:
Chinese Journal of Experimental Traditional Medical Formulae
2025;31(6):154-163
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveTo construct and validate a clinical prediction model for Qi deficiency and blood stasis syndrome in chronic heart failure （CHF），aiming to assist clinical diagnosis and provide tools and methods for individualized treatment of CHF. MethodsThe clinical data of patients with chronic heart failure treated at Dongzhimen Hospital of Beijing University of Chinese Medicine from January 2022 to January 2024 were retrospectively collected. The patients were randomly divided into a training group and a validation group with a ratio of 7∶3. First， the least absolute shrinkage and selection operator （LASSO） regression analysis was used to preliminarily screen the predictive factors affecting the diagnosis of Qi deficiency and blood stasis syndrome in CHF. Subsequently， the Logistic regression method was applied to conduct a more in-depth and detailed analysis of these factors. Variables with P<0.05 in the results of the multi-factor Logistic regression were carefully selected and included. Based on the regression coefficients obtained from this analysis， a model was constructed， and a nomogram was accurately drawn. Using R software，the receiver operating characteristic （ROC） curve，calibration curve，and decision curve analysis （DCA） were precisely drawn. These analyses were used to comprehensively evaluate the model from three crucial aspects： discrimination，calibration，and clinical applicability. Additionally， the accuracy，specificity，sensitivity，positive predictive value，and negative predictive value of the model were meticulously calculated to conduct a more all-round and comprehensive assessment. ResultsIn total， 168 cases were successfully obtained in the training group， and 71 cases were included in the validation group. After a thorough comparison， it was found that there were no statistically significant differences in the baseline data between the two groups. After being rigorously screened by the LASSO-multivariate logistic regression method， dark red tongue，smoking history，cardiac troponin I，and N-terminal pro-B-type natriuretic peptide （NT-ProBNP） were identified as the influencing factors for diagnosing patients with the Qi deficiency and blood stasis syndrome in CHF. The constructed model demonstrated an area under the curve （AUC） of 0.812 in the training group and 0.719 in the validation group. The calibration curve showed that the predicted curve of the model was close to the actual observed curve. DCA indicated that the model could provide substantial clinical benefits for patients at the decision thresholds ranging from 0.2 to 0.9. ConclusionThe clinical prediction model for Qi deficiency and blood stasis syndrome in chronic heart failure constructed in this study shows good performance. It has certain application value in clinical practice， which may contribute to the improvement of the diagnosis and treatment of CHF patients with this syndrome.
