โก Quick Summary
This study evaluated the effectiveness of navigation nurse management (NNM) in orthopedic perioperative care and developed machine learning models to predict postoperative recovery quality. The results indicated that patients managed under NNM experienced significantly better recovery outcomes compared to those receiving standard care.
๐ Key Details
- ๐ Dataset: 216 patients undergoing orthopedic surgery
- ๐งฉ Key factors analyzed: Postoperative first meal time, time to first ambulation, Final Visual Analogue Scale (at discharge), and receipt of NNM
- โ๏ธ Technologies used: Logistic regression, random forest, eXtreme gradient boosting, support vector machine, decision tree, Naรฏve Bayes
- ๐ Performance metrics: AUC, sensitivity, specificity, precision, F1 scores
๐ Key Takeaways
- ๐ NNM significantly improved postoperative recovery quality in orthopedic patients.
- ๐ค Random forest algorithms achieved perfect scores across all performance metrics (AUC = 1.000).
- ๐ SHAP analysis identified the Final Visual Analogue Scale as the most influential factor in recovery outcomes.
- ๐ The nomogram developed achieved AUCs of 0.983 and 0.992 in training and validation sets, respectively.
- ๐ก Machine learning models outperformed traditional methods in predicting high-risk patients.
- ๐ฅ Study conducted at Zhangjiagang Hospital between November 2023 and February 2025.
๐ Background
The field of orthopedic surgery often faces challenges in ensuring optimal postoperative recovery. Traditional nursing management approaches may not adequately address the complexities of patient recovery. The introduction of navigation nurse management (NNM) aims to enhance patient care by utilizing data-driven strategies and machine learning to predict recovery outcomes.
๐๏ธ Study
This retrospective study included 216 patients who underwent orthopedic surgery for conditions such as femoral neck fractures and hip joint disorders. The researchers aimed to evaluate the effectiveness of NNM and develop machine learning models to identify key factors influencing recovery outcomes. The study utilized a comprehensive dataset and advanced analytical techniques to derive meaningful insights.
๐ Results
The findings revealed that patients managed under the NNM model had significantly better postoperative recovery quality compared to those who did not receive NNM. The random forest model demonstrated exceptional performance, achieving an AUC of 1.000, with perfect sensitivity, specificity, precision, and F1 scores. The nomogram also showed impressive AUCs, indicating its potential utility in clinical settings.
๐ Impact and Implications
The implications of this study are profound, suggesting that the integration of machine learning and NNM can lead to enhanced patient outcomes in orthopedic care. By identifying high-risk patients and tailoring interventions accordingly, healthcare providers can improve recovery quality and overall patient satisfaction. This approach may serve as a model for other surgical specialties seeking to optimize perioperative care.
๐ฎ Conclusion
This study highlights the transformative potential of navigation nurse management and machine learning in orthopedic perioperative care. The significant improvements in postoperative recovery quality underscore the importance of data-driven approaches in enhancing patient outcomes. Continued research in this area is essential to further refine these models and expand their application across various healthcare settings.
๐ฌ Your comments
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Orthopedic perioperative nursing under navigation nurse management: Machine learning-based risk prediction models for postoperative recovery quality and explainable artificial intelligence analysis.
Abstract
This study aimed to evaluate the effectiveness of navigation nurse management (NNM) in orthopedic perioperative care and develop machine learning (ML) models to predict postoperative recovery quality. We sought to identify key factors influencing recovery outcomes in patients undergoing hip surgery and assess whether NNM intervention improves patient outcomes compared to standard care. This retrospective study included 216 patients who underwent orthopedic surgery for femoral neck fractures, femoral head necrosis, or hip joint disorders at Zhangjiagang Hospital between November 2023 and February 2025. The NNM model, comprising 6 core elements, guided nursing care. Data were analyzed using SPSS 26.0 and R 4.4.2. The dataset was randomly split into training (70%) and validation (30%) cohorts. In addition to logistic regression (LR) and nomogram construction, we applied 6 ML algorithms including random forest (RF), eXtreme gradient boosting, support vector machine, decision tree, Naรฏve Bayes, and LR. We evaluated model performance using area under the curve (AUC), sensitivity, specificity, precision, and F1 scores. SHapley Additive exPlanations (SHAP) analysis was employed to enhance model interpretability and identify key contributing factors. Among the 216 patients, 122 were classified as the high-quality recovery group and 94 as the poor recovery group. Multivariate LR identified postoperative first meal time, time to first ambulation (postoperative), Final Visual Analogue Scale (at discharge), and receipt of NNM as independent predictors. The nomogram achieved AUCs of 0.983 and 0.992 in training and validation sets, respectively. Among ML models, RF demonstrated the best performance with perfect scores across all metrics (AUCโ =โ 1.000, sensitivityโ =โ 100%, specificityโ =โ 100%, precisionโ =โ 100%, F1โ =โ 100%), followed by eXtreme gradient boosting (AUCโ =โ 0.998). SHAP analysis revealed that Final Visual Analogue Scale (at discharge) was the most influential factor, while NNM significantly reduced the risk of poor recovery quality. Patients managed under the NNM model demonstrated significantly better postoperative recovery quality compared to those who did not receive NNM. NNM improves postoperative recovery quality in orthopedic patients. RF algorithms showed better predictive accuracy than traditional methods for identifying high-risk patients. SHAP analysis improved model interpretability, supporting personalized care decisions.
Author: [‘Qian Q’, ‘Wu J’, ‘Xu Z’, ‘Sheng X’, ‘Ge J’, ‘Gong Y’, ‘Qian J’, ‘Sha W’, ‘Qian J’]
Journal: Medicine (Baltimore)
Citation: Qian Q, et al. Orthopedic perioperative nursing under navigation nurse management: Machine learning-based risk prediction models for postoperative recovery quality and explainable artificial intelligence analysis. Orthopedic perioperative nursing under navigation nurse management: Machine learning-based risk prediction models for postoperative recovery quality and explainable artificial intelligence analysis. 2025; 104:e46015. doi: 10.1097/MD.0000000000046015