Development of a preoperative predictive model for major complications following adult spinal deformity surgery.
OBJECTIVE The operative management of patients with adult spinal deformity (ASD) has a high complication rate and it remains unknown whether baseline patient characteristics and surgical variables can predict early complications (intraoperative and perioperative [within 6 weeks]). The development of an accurate preoperative predictive model can aid in patient counseling, shared decision making, and improved surgical planning. The purpose of this study was to develop a model based on baseline demographic, radiographic, and surgical factors that can predict if patients will sustain an intraoperative or perioperative major complication. METHODS This study was a retrospective analysis of a prospective, multicenter ASD database. The inclusion criteria were age ≥ 18 years and the presence of ASD. In total, 45 variables were used in the initial training of the model including demographic data, comorbidities, modifiable surgical variables, baseline health-related quality of life, and coronal and sagittal radiographic parameters. Patients were grouped as either having at least 1 major intraoperative or perioperative complication (COMP group) or not (NOCOMP group). An ensemble of decision trees was constructed utilizing the C5.0 algorithm with 5 different bootstrapped models. Internal validation was accomplished via a 70/30 data split for training and testing each model, respectively. Overall accuracy, the area under the receiver operating characteristic (AUROC) curve, and predictor importance were calculated. RESULTS Five hundred fifty-seven patients were included: 409 (73.4%) in the NOCOMP group, and 148 (26.6%) in the COMP group. The overall model accuracy was 87.6% correct with an AUROC curve of 0.89 indicating a very good model fit. Twenty variables were determined to be the top predictors (importance ≥ 0.90 as determined by the model) and included (in decreasing importance): age, leg pain, Oswestry Disability Index, number of decompression levels, number of interbody fusion levels, Physical Component Summary of the SF-36, Scoliosis Research Society (SRS)-Schwab coronal curve type, Charlson Comorbidity Index, SRS activity, T-1 pelvic angle, American Society of Anesthesiologists grade, presence of osteoporosis, pelvic tilt, sagittal vertical axis, primary versus revision surgery, SRS pain, SRS total, use of bone morphogenetic protein, use of iliac crest graft, and pelvic incidence-lumbar lordosis mismatch. CONCLUSIONS A successful model (87% accuracy, 0.89 AUROC curve) was built predicting major intraoperative or perioperative complications following ASD surgery. This model can provide the foundation toward improved education and point-of-care decision making for patients undergoing ASD surgery.
School of Medicine, University of California, San Diego, La Jolla, California.
Justin K
JK
Scheer
Department of Neurosurgery, University of Virginia Health System, Charlottesville, Virginia.
Justin S
JS
Smith
Spine Service, Hospital for Special Surgery, New York.
Frank
F
Schwab
Spine Service, Hospital for Special Surgery, New York.
Virginie
V
Lafage
Department of Neurosurgery, University of Virginia Health System, Charlottesville, Virginia.
Christopher I
CI
Shaffrey
Department of Orthopaedic Surgery, NYU Hospital for Joint Diseases, New York, New York.
Shay
S
Bess
Department of Orthopaedic Surgery, Brown University, Providence, Rhode Island.
Alan H
AH
Daniels
Department of Orthopaedic Surgery, Oregon Health & Science University, Portland, Oregon.
Robert A
RA
Hart
Department of Orthopaedic Surgery, NYU Hospital for Joint Diseases, New York, New York.
Themistocles S
TS
Protopsaltis
Scripps Health, La Jolla, California.
Gregory M
GM
Mundis
Department of Neurosurgery, The Johns Hopkins Hospital, Baltimore, Maryland.
Daniel M
DM
Sciubba
Department of Neurosurgery, University of Virginia Health System, Charlottesville, Virginia.
Tamir
T
Ailon
Department of Orthopaedic Surgery, University of Kansas Medical Center, Kansas City, Kansas.
Douglas C
DC
Burton
Department of Orthopaedic Surgery, University of California, Davis; and.
Eric
E
Klineberg
Department of Neurological Surgery, University of California, San Francisco, California.
Christopher P
CP
Ames
D000465:Algorithms; D019540:Area Under Curve; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011183:Postoperative Complications / Q000175:diagnosis*; D057234:Preoperative Period; D011379:Prognosis; D011446:Prospective Studies; D011788:Quality of Life; D012372:ROC Curve; D012189:Retrospective Studies; D013121:Spinal Curvatures / Q000000981:diagnostic imaging / Q000601:surgery*; D013131:Spine / Q000000981:diagnostic imaging / Q000601:surgery
ANN = artificial neural network; ASA = American Society of Anesthesiologists; ASD; ASD = adult spinal deformity; AUROC = area under the receiver operating characteristic; BMI = body mass index; BMP = bone morphogenetic protein; CCI = Charlson Comorbidity Index; HRQOL = health-related quality of life; MCS = Mental Component Summary of the SF-36; NRS = numeric rating scale; ODI = Oswestry Disability Index; PCS = Physical Component Summary of the SF-36; PI-LL = pelvic incidence–lumbar lordosis mismatch; PT = pelvic tilt; SRS = Scoliosis Research Society; SRS-22r = SRS-22r questionnaire; SVA = sagittal vertical axis; T1PA = T-1 pelvic angle; TK = thoracic kyphosis; adult spinal deformity; complications; decision tree; predictive modeling; sagittal malalignment; scoliosis