An Artificial Neural Network Model for the Prediction of Perioperative Blood Transfusion in Adult Spinal Deformity Surgery

Rafael De la Garza Ramos, Mousa K. Hamad, Jessica Ryvlin, Oscar Krol, Peter G. Passias, Mitchell S. Fourman, John H. Shin, Vijay Yanamadala, Yaroslav Gelfand, Saikiran Murthy, Reza Yassari

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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.

Original languageEnglish (US)
Article number4436
JournalJournal of Clinical Medicine
Volume11
Issue number15
DOIs
StatePublished - Aug 2022

Keywords

  • adult spinal deformity
  • artificial intelligence
  • neural network
  • scoliosis
  • transfusion

ASJC Scopus subject areas

  • General Medicine

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