ArticleJournal of clinical medicine2022
An Artificial Neural Network Model for the Prediction of Perioperative Blood Transfusion in Adult Spinal Deformity Surgery.
Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 16 citations in OpenAlex.
- From Algorithms to Clinics: Recent Progress in AI for Scoliosis Diagnosis and Management.Journal of clinical medicine · 2026Review
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Predicting blood transfusion demand in intensive care patients after surgery by comparative analysis of temporally extended data selection.BMC medical informatics and decision making · 2024Article
- Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery.World neurosurgery: X · 2024Review
- Machine learning in transfusion medicine: A scoping review.Transfusion · 2024Article
- Advances and Evolving Challenges in Spinal Deformity Surgery.Journal of clinical medicine · 2023Review
- Prediction of Transfusion among In-patient Population using Temporal Pattern based Clinical Similarity Graphs.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Editorial: Artificial intelligence and advanced technologies in neurological surgery.Frontiers in surgery · 2023Article
- Advancements and Updates on Operative Techniques in Spinal Deformity.Journal of clinical medicine · 2022Article
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Authors and funding
11 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediction of blood transfusion after adult spinal deformity (ASD) surgery can identify at-risk patients and potentially reduce its utilization and the complications associated with it. The use of artificial neural networks (ANNs) offers the potential for high predictive capability. A total of 1173 patients who underwent surgery for ASD were identified in the 2017-2019 NSQIP databases. The data were split into 70% training and 30% testing cohorts. Eighteen patient and operative variables were used. The outcome variable was receiving RBC transfusion intraoperatively or within 72 h after surgery. The model was assessed by its sensitivity, positive predictive value, F1-score, accuracy (ACC), and area under the curve (AUROC). Average patient age was 56 years and 63% were female. Pelvic fixation was performed in 21.3% of patients and three-column osteotomies in 19.5% of cases. The transfusion rate was 50.0% (586/1173 patients). The best model showed an overall ACC of 81% and 77% on the training and testing data, respectively. On the testing data, the sensitivity was 80%, the positive predictive value 76%, and the F1-score was 78%. The AUROC was 0.84. ANNs may allow the identification of at-risk patients, potentially decrease the risk of transfusion via strategic planning, and improve resource allocation.
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