Artificial intelligence for predicting 30-day mortality after surgery for femoral shaft fractures: A retrospective study
Background and Aims: Surgical repair of femoral shaft fractures continues to have notable perioperative morbidity and mortality. The purpose of this study is to assess whether artificial intelligence (AI)-driven models can be utilised to predict 30-day mortality after surgery for femoral shaft fract...
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2025-06-01
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| Series: | Indian Journal of Anaesthesia |
| Subjects: | |
| Online Access: | https://journals.lww.com/10.4103/ija.ija_1060_24 |
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| author | Puneet Gupta Hong-Jui Shen Kunj Patel Rui Guo Eric R. Heinz Rameshbabu Manyam |
| author_facet | Puneet Gupta Hong-Jui Shen Kunj Patel Rui Guo Eric R. Heinz Rameshbabu Manyam |
| author_sort | Puneet Gupta |
| collection | DOAJ |
| description | Background and Aims:
Surgical repair of femoral shaft fractures continues to have notable perioperative morbidity and mortality. The purpose of this study is to assess whether artificial intelligence (AI)-driven models can be utilised to predict 30-day mortality after surgery for femoral shaft fractures and to identify patient risk factors for mortality using AI.
Methods:
This retrospective study utilised data from the National Surgical Quality Improvement Program between 2015 and 2020. Five AI-driven models were developed and tested using patient clinical information to predict mortality within 30 days of surgery. Additionally, the most important variables for the best-performing model were identified.
Results:
A total of 1720 patients were identified, and the 30-day mortality rate after femoral shaft fracture surgery was 3.4% (n = 58). XGBoost demonstrated the best predictive performance, with an area under the curve (AUC) of 0.83, a calibration intercept of −0.03, a calibration slope of 1.17, and a Brier score of 0.02. The most important variables for prediction were age, preoperative white blood cell count, creatinine, haematocrit, platelets, blood urea nitrogen, and body mass index.
Conclusion:
This study is the first to internally validate an AI-driven model for predicting mortality within 30 days of surgery in an isolated population of femoral shaft fracture patients, demonstrating good performance. Further research is needed to develop an excellent-performing, AI-driven model that is externally validated prior to clinical translation to support anaesthesiologists and orthopaedic surgeons in perioperative risk stratification and patient education. |
| format | Article |
| id | doaj-art-bef8bebbe2964a51b127a34ffc4de01a |
| institution | Kabale University |
| issn | 0019-5049 0976-2817 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Indian Journal of Anaesthesia |
| spelling | doaj-art-bef8bebbe2964a51b127a34ffc4de01a2025-08-20T03:26:00ZengWolters Kluwer Medknow PublicationsIndian Journal of Anaesthesia0019-50490976-28172025-06-0169660661410.4103/ija.ija_1060_24Artificial intelligence for predicting 30-day mortality after surgery for femoral shaft fractures: A retrospective studyPuneet GuptaHong-Jui ShenKunj PatelRui GuoEric R. HeinzRameshbabu ManyamBackground and Aims: Surgical repair of femoral shaft fractures continues to have notable perioperative morbidity and mortality. The purpose of this study is to assess whether artificial intelligence (AI)-driven models can be utilised to predict 30-day mortality after surgery for femoral shaft fractures and to identify patient risk factors for mortality using AI. Methods: This retrospective study utilised data from the National Surgical Quality Improvement Program between 2015 and 2020. Five AI-driven models were developed and tested using patient clinical information to predict mortality within 30 days of surgery. Additionally, the most important variables for the best-performing model were identified. Results: A total of 1720 patients were identified, and the 30-day mortality rate after femoral shaft fracture surgery was 3.4% (n = 58). XGBoost demonstrated the best predictive performance, with an area under the curve (AUC) of 0.83, a calibration intercept of −0.03, a calibration slope of 1.17, and a Brier score of 0.02. The most important variables for prediction were age, preoperative white blood cell count, creatinine, haematocrit, platelets, blood urea nitrogen, and body mass index. Conclusion: This study is the first to internally validate an AI-driven model for predicting mortality within 30 days of surgery in an isolated population of femoral shaft fracture patients, demonstrating good performance. Further research is needed to develop an excellent-performing, AI-driven model that is externally validated prior to clinical translation to support anaesthesiologists and orthopaedic surgeons in perioperative risk stratification and patient education.https://journals.lww.com/10.4103/ija.ija_1060_24artificial intelligencefemoral shaftfracturesmortalityxgboost |
| spellingShingle | Puneet Gupta Hong-Jui Shen Kunj Patel Rui Guo Eric R. Heinz Rameshbabu Manyam Artificial intelligence for predicting 30-day mortality after surgery for femoral shaft fractures: A retrospective study Indian Journal of Anaesthesia artificial intelligence femoral shaft fractures mortality xgboost |
| title | Artificial intelligence for predicting 30-day mortality after surgery for femoral shaft fractures: A retrospective study |
| title_full | Artificial intelligence for predicting 30-day mortality after surgery for femoral shaft fractures: A retrospective study |
| title_fullStr | Artificial intelligence for predicting 30-day mortality after surgery for femoral shaft fractures: A retrospective study |
| title_full_unstemmed | Artificial intelligence for predicting 30-day mortality after surgery for femoral shaft fractures: A retrospective study |
| title_short | Artificial intelligence for predicting 30-day mortality after surgery for femoral shaft fractures: A retrospective study |
| title_sort | artificial intelligence for predicting 30 day mortality after surgery for femoral shaft fractures a retrospective study |
| topic | artificial intelligence femoral shaft fractures mortality xgboost |
| url | https://journals.lww.com/10.4103/ija.ija_1060_24 |
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