Development and Validation of Machine Learning Algorithms for Predicting Adverse Events After Surgery for Lumbar Degenerative Spondylolisthesis.

Journal: World neurosurgery

PMID: 32344139

Abstract

BACKGROUND: Preoperative prognostication of adverse events (AEs) for patients undergoing surgery for lumbar degenerative spondylolisthesis (LDS) can improve risk stratification and help guide the surgical decision-making process. The aim of this study was to develop and validate a set of predictive variables for 30-day AEs after surgery for LDS.

Authors

Nida Fatima

Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Hui Zheng

Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai 200030, China.
Elie Massaad

Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Muhamed Hadzipasic

Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Ganesh M Shankar

2Department of Neurosurgery, Harvard Medical School, Cambridge, Massachusetts.
John H Shin

2Department of Neurosurgery, Harvard Medical School, Cambridge, Massachusetts.

Keywords

Adolescent Adult Age Factors Aged Aged, 80 and over Algorithms Alkaline Phosphatase Bone Transplantation Clinical Decision Rules Decision Making Female Functional Status Humans Ilium Logistic Models Lumbar Vertebrae Machine Learning Male Middle Aged Neurosurgical Procedures Postoperative Complications Prognosis Risk Factors Serum Albumin Sex Factors Spinal Fusion Spondylolisthesis Transplantation, Autologous Young Adult

External Resources

View on PubMed Access via DOI PubMed (32344139)

Development and Validation of Machine Learning Algorithms for Predicting Adverse Events After Surgery for Lumbar Degenerative Spondylolisthesis.

Abstract

Authors

Keywords

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