基于PGC-1α、NOS和Runx2的胫腓骨骨折延迟愈合预测模型。

IF 3.4 3区 医学 Q2 MEDICINE, RESEARCH & EXPERIMENTAL
Bin Wang, Zhiyuan Shi, Derong Li, Jinwei Yu
{"title":"基于PGC-1α、NOS和Runx2的胫腓骨骨折延迟愈合预测模型。","authors":"Bin Wang, Zhiyuan Shi, Derong Li, Jinwei Yu","doi":"10.1186/s40001-025-03247-2","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>To explore the associations between the levels of Peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α), Nitric oxide synthase (NOS), Runt-related transcription factor 2 (Runx2) and the delayed union of tibiofibular fractures, and to construct and validate a model for predicting the delayed union of tibiofibular fractures.</p><p><strong>Methods: </strong>A total of 351 patients with tibiofibular fractures who were treated in the hospital from January 2022 to June 2024 were selected as the study subjects. They were randomly divided into a training set (n = 246) and a validation set (n = 105) at a ratio of 7:3. Experimental techniques such as ELISA were used to accurately detect the serum of patients, and multiple clinical data of the patients were comprehensively collected. Key influencing factors were screened out by statistical methods, such as univariate analysis and multivariate logistic regression analysis. Plot a nomogram and construct a prediction model, and use evaluation indicators to comprehensively evaluate and validate the model.</p><p><strong>Results: </strong>In the training set, 72 cases (29.27%) had delayed union of tibiofibular fracture. Multivariate logistic analysis showed that fracture complexity, time from injury to surgery, levels of PGC-1α, NOS, Runx2, and OPG were independent influencing factors for the delayed union of tibiofibular fractures (all P < 0.05). In the training set and the validation set, the C-index of the nomogram model was 0.760 and 0.711, respectively. The calibration curve showed moderate agreement between the predicted values and the actual values. The results of the Hosmer-Lemeshow test were χ<sup>2</sup> = 5.277, P = 0.728 and χ<sup>2</sup> = 10.540, P = 0.229, respectively. The ROC curve showed that in the training set and the validation set, the AUC of the nomogram model for predicting the delayed union of tibiofibular fractures was 0.760 (95%CI 0.680-0.839) and 0.711 (95%CI 0.581-0.842), respectively, and the sensitivity and specificity were 0.796, 0.645 and 0.739, 0.500, respectively.</p><p><strong>Conclusions: </strong>This study provides a new perspective and method for the assessment of the delayed union of tibiofibular fractures, which is helpful for clinicians to potentially assist in risk stratification and closer follow-up for high-risk patients.</p>","PeriodicalId":11949,"journal":{"name":"European Journal of Medical Research","volume":"30 1","pages":"978"},"PeriodicalIF":3.4000,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12522435/pdf/","citationCount":"0","resultStr":"{\"title\":\"Predictive model of delayed union of tibial and fibular fractures based on PGC-1α, NOS, and Runx2.\",\"authors\":\"Bin Wang, Zhiyuan Shi, Derong Li, Jinwei Yu\",\"doi\":\"10.1186/s40001-025-03247-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>To explore the associations between the levels of Peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α), Nitric oxide synthase (NOS), Runt-related transcription factor 2 (Runx2) and the delayed union of tibiofibular fractures, and to construct and validate a model for predicting the delayed union of tibiofibular fractures.</p><p><strong>Methods: </strong>A total of 351 patients with tibiofibular fractures who were treated in the hospital from January 2022 to June 2024 were selected as the study subjects. They were randomly divided into a training set (n = 246) and a validation set (n = 105) at a ratio of 7:3. Experimental techniques such as ELISA were used to accurately detect the serum of patients, and multiple clinical data of the patients were comprehensively collected. Key influencing factors were screened out by statistical methods, such as univariate analysis and multivariate logistic regression analysis. Plot a nomogram and construct a prediction model, and use evaluation indicators to comprehensively evaluate and validate the model.</p><p><strong>Results: </strong>In the training set, 72 cases (29.27%) had delayed union of tibiofibular fracture. Multivariate logistic analysis showed that fracture complexity, time from injury to surgery, levels of PGC-1α, NOS, Runx2, and OPG were independent influencing factors for the delayed union of tibiofibular fractures (all P < 0.05). In the training set and the validation set, the C-index of the nomogram model was 0.760 and 0.711, respectively. The calibration curve showed moderate agreement between the predicted values and the actual values. The results of the Hosmer-Lemeshow test were χ<sup>2</sup> = 5.277, P = 0.728 and χ<sup>2</sup> = 10.540, P = 0.229, respectively. The ROC curve showed that in the training set and the validation set, the AUC of the nomogram model for predicting the delayed union of tibiofibular fractures was 0.760 (95%CI 0.680-0.839) and 0.711 (95%CI 0.581-0.842), respectively, and the sensitivity and specificity were 0.796, 0.645 and 0.739, 0.500, respectively.</p><p><strong>Conclusions: </strong>This study provides a new perspective and method for the assessment of the delayed union of tibiofibular fractures, which is helpful for clinicians to potentially assist in risk stratification and closer follow-up for high-risk patients.</p>\",\"PeriodicalId\":11949,\"journal\":{\"name\":\"European Journal of Medical Research\",\"volume\":\"30 1\",\"pages\":\"978\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2025-10-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12522435/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Journal of Medical Research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s40001-025-03247-2\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICINE, RESEARCH & EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Medical Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s40001-025-03247-2","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0

摘要

目的:探讨过氧化物酶体增殖物激活受体γ共激活因子1α (PGC-1α)、一氧化氮合酶(NOS)、runt相关转录因子2 (Runx2)水平与胫腓骨骨折延迟愈合的关系,建立并验证预测胫腓骨骨折延迟愈合的模型。方法:选取2022年1月至2024年6月在该院治疗的351例胫腓骨骨折患者作为研究对象。按7:3的比例随机分为训练集(n = 246)和验证集(n = 105)。采用ELISA等实验技术准确检测患者血清,综合收集患者多项临床资料。通过单因素分析和多因素logistic回归分析等统计方法筛选出关键影响因素。绘制nomogram并构建预测模型,利用评价指标对模型进行综合评价和验证。结果:训练集中72例(29.27%)胫腓骨骨折延迟愈合。多因素logistic分析显示,骨折复杂程度、损伤至手术时间、pgp -1α、NOS、Runx2、OPG水平是胫腓骨骨折延迟愈合的独立影响因素(χ2 = 5.277, P = 0.728; χ2 = 10.540, P = 0.229)。ROC曲线显示,在训练集和验证集中,预测胫腓骨骨折延迟愈合的nomogram模型AUC分别为0.760 (95%CI 0.680-0.839)和0.711 (95%CI 0.581-0.842),敏感性和特异性分别为0.796、0.645和0.739、0.500。结论:本研究为胫腓骨骨折延迟愈合的评估提供了新的视角和方法,有助于临床医生对高危患者进行潜在的风险分层和更密切的随访。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Predictive model of delayed union of tibial and fibular fractures based on PGC-1α, NOS, and Runx2.

Predictive model of delayed union of tibial and fibular fractures based on PGC-1α, NOS, and Runx2.

Predictive model of delayed union of tibial and fibular fractures based on PGC-1α, NOS, and Runx2.

Predictive model of delayed union of tibial and fibular fractures based on PGC-1α, NOS, and Runx2.

Objectives: To explore the associations between the levels of Peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α), Nitric oxide synthase (NOS), Runt-related transcription factor 2 (Runx2) and the delayed union of tibiofibular fractures, and to construct and validate a model for predicting the delayed union of tibiofibular fractures.

Methods: A total of 351 patients with tibiofibular fractures who were treated in the hospital from January 2022 to June 2024 were selected as the study subjects. They were randomly divided into a training set (n = 246) and a validation set (n = 105) at a ratio of 7:3. Experimental techniques such as ELISA were used to accurately detect the serum of patients, and multiple clinical data of the patients were comprehensively collected. Key influencing factors were screened out by statistical methods, such as univariate analysis and multivariate logistic regression analysis. Plot a nomogram and construct a prediction model, and use evaluation indicators to comprehensively evaluate and validate the model.

Results: In the training set, 72 cases (29.27%) had delayed union of tibiofibular fracture. Multivariate logistic analysis showed that fracture complexity, time from injury to surgery, levels of PGC-1α, NOS, Runx2, and OPG were independent influencing factors for the delayed union of tibiofibular fractures (all P < 0.05). In the training set and the validation set, the C-index of the nomogram model was 0.760 and 0.711, respectively. The calibration curve showed moderate agreement between the predicted values and the actual values. The results of the Hosmer-Lemeshow test were χ2 = 5.277, P = 0.728 and χ2 = 10.540, P = 0.229, respectively. The ROC curve showed that in the training set and the validation set, the AUC of the nomogram model for predicting the delayed union of tibiofibular fractures was 0.760 (95%CI 0.680-0.839) and 0.711 (95%CI 0.581-0.842), respectively, and the sensitivity and specificity were 0.796, 0.645 and 0.739, 0.500, respectively.

Conclusions: This study provides a new perspective and method for the assessment of the delayed union of tibiofibular fractures, which is helpful for clinicians to potentially assist in risk stratification and closer follow-up for high-risk patients.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
European Journal of Medical Research
European Journal of Medical Research 医学-医学:研究与实验
CiteScore
3.20
自引率
0.00%
发文量
247
审稿时长
>12 weeks
期刊介绍: European Journal of Medical Research publishes translational and clinical research of international interest across all medical disciplines, enabling clinicians and other researchers to learn about developments and innovations within these disciplines and across the boundaries between disciplines. The journal publishes high quality research and reviews and aims to ensure that the results of all well-conducted research are published, regardless of their outcome.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信