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Study on the construction and verification of intraoperative pressure injury risk prediction model for children
Zhihao Fang1,2, Qiuyu Chen1,2, Sini He1,2
1College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, Fujian Province, 350000, China.
Insights
A nomogram model effectively predicts intraoperative acquired pressure injury (IAPI) risk in pediatric cardiac surgery patients. This tool aids nurses in early identification and intervention for IAPI prevention.
Area of Science:
- Medical Informatics
- Pediatric Surgery
- Nursing Research
Background:
- Intraoperative acquired pressure injury (IAPI) is a concern in pediatric cardiac surgery.
- Developing accurate risk prediction models is crucial for effective prevention strategies.
Purpose of the Study:
- To develop and validate a nomogram-based risk prediction model for IAPI in pediatric cardiac surgery patients.
- To provide evidence-based guidance for IAPI prevention in this vulnerable population.
Main Methods:
- Retrospective analysis of 1,179 pediatric cardiac surgery patients.
- Utilized LASSO and multivariate logistic regression to identify IAPI predictors.
- Developed and validated a nomogram prediction model using ROC curve analysis and bootstrap internal validation.
Main Results:
- 70 out of 1,179 patients (5.94%) developed IAPI.
- Key predictors identified: preoperative hematocrit, prothrombin time, fibrinogen, Braden-Q score, and respiratory tract infection.
- The model demonstrated good predictive performance with an AUC of 0.833 (sensitivity 0.826, specificity 0.758).
Conclusions:
- A machine learning and nomogram-based model shows significant efficacy in predicting IAPI risk.
- The model supports operating room nurses in early identification of high-risk patients.
- Facilitates timely and personalized nursing interventions for IAPI prevention.
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.
