Scholarlink

Background <p>This study applied nomogram to develop an intraoperative acquired pressure injury (IAPI) risk prediction model for pediatric cardiac surgery patients, validated its predictive performance, and aims to provide evidence-based guidance for IAPI prevention in pediatric cardiac surgery.</p> Methods <p>A retrospective analysis of clinical surgical data from 1,179 pediatric patients undergoing cardiac surgery at Fujian Children’s Hospital between August 2022 and November 2024 was conducted to construct a corresponding dataset. Through LASSO analysis and multivariate logistic stepwise regression, we identified high-risk factors for IAPI in pediatric cardiac surgery patients and developed a ROC curve prediction model. The model’s fit and predictive performance were evaluated using the Hosmer-Lemeshow test and ROC area under the curve (AUC), with internal validation performed via bootstrap.</p> Results <p>A total of 1,179 pediatric cardiac surgery patients were included in the study, with 70 cases (5.94%) developing IAPI. LASSO regression analysis identified 10 variables, and subsequent multivariate logistic regression analysis revealed that preoperative hematocrit, prothrombin time, fibrinogen, Braden-Q score, and concurrent respiratory tract infection were significant predictors of IAPI in these pediatric patients (<i>P</i> &lt; 0.05). The Hosmer-Lemeshow test yielded a chi-square value of 11.251 (<i>P</i> = 0.188). Internal validation demonstrated the model’s sensitivity at 0.826, specificity at 0.758, and an ROC curve area under the curve (AUC) of 0.833 (0.741–0.925).</p> Conclusion <p>The machine learning and nomogram-based predictive model for IAPI risk in pediatric cardiac surgery demonstrates significant predictive efficacy, providing a scientific basis for operating room nurses to identify high-risk IAPI patients early and implement timely personalized nursing interventions.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Study on the construction and verification of intraoperative pressure injury risk prediction model for children undergoing cardiac surgery

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月9日

查看全文
  • Zhihao Fang,
  • Qiuyu Chen,
  • Sini He,
  • Zhixiong Lin,
  • Weixi Zheng,
  • Pinying Chen,
  • Liu Chen,
  • Qing Zhuo

摘要

Background

This study applied nomogram to develop an intraoperative acquired pressure injury (IAPI) risk prediction model for pediatric cardiac surgery patients, validated its predictive performance, and aims to provide evidence-based guidance for IAPI prevention in pediatric cardiac surgery.

Methods

A retrospective analysis of clinical surgical data from 1,179 pediatric patients undergoing cardiac surgery at Fujian Children’s Hospital between August 2022 and November 2024 was conducted to construct a corresponding dataset. Through LASSO analysis and multivariate logistic stepwise regression, we identified high-risk factors for IAPI in pediatric cardiac surgery patients and developed a ROC curve prediction model. The model’s fit and predictive performance were evaluated using the Hosmer-Lemeshow test and ROC area under the curve (AUC), with internal validation performed via bootstrap.

Results

A total of 1,179 pediatric cardiac surgery patients were included in the study, with 70 cases (5.94%) developing IAPI. LASSO regression analysis identified 10 variables, and subsequent multivariate logistic regression analysis revealed that preoperative hematocrit, prothrombin time, fibrinogen, Braden-Q score, and concurrent respiratory tract infection were significant predictors of IAPI in these pediatric patients (P < 0.05). The Hosmer-Lemeshow test yielded a chi-square value of 11.251 (P = 0.188). Internal validation demonstrated the model’s sensitivity at 0.826, specificity at 0.758, and an ROC curve area under the curve (AUC) of 0.833 (0.741–0.925).

Conclusion

The machine learning and nomogram-based predictive model for IAPI risk in pediatric cardiac surgery demonstrates significant predictive efficacy, providing a scientific basis for operating room nurses to identify high-risk IAPI patients early and implement timely personalized nursing interventions.