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Justin K. Scheer, Justin S. Smith, Frank Schwab, Virginie Lafage, Christopher I. Shaffrey, Shay Bess, Alan H. Daniels, Robert A. Hart, Themistocles S. Protopsaltis, Gregory M. Mundis Jr., Daniel M. Sciubba, Tamir Ailon, Douglas C. Burton, Eric Klineberg, Christopher P. Ames and The International Spine Study Group

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.

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Joshua Bakhsheshian, Justin K. Scheer, Jeffrey L. Gum, Richard Hostin, Virginie Lafage, Shay Bess, Themistocles S. Protopsaltis, Douglas C. Burton, Malla Kate Keefe, Robert A. Hart, Gregory M. Mundis Jr., Christopher I. Shaffrey, Frank Schwab, Justin S. Smith, Christopher P. Ames and The International Spine Study Group

OBJECTIVE

Mental disease burden can have a significant impact on levels of disability and health-related quality of life (HRQOL) measures. Therefore, the authors investigated the significance of mental health status in adults with spinal deformity and poor physical function.

METHODS

A retrospective analysis of a prospective multicenter database of 365 adult spinal deformity (ASD) patients who had undergone surgical treatment was performed. Health-related QOL variables were examined preoperatively and at the 2-year postoperative follow-up. Patients were grouped by their 36-Item Short Form Health Survey mental component summary (MCS) and physical component summary (PCS) scores. Both groups had PCS scores ≤ 25th percentile for matched norms; however, the low mental health (LMH) group consisted of patients with an MCS score ≤ 25th percentile, and the high mental health (HMH) group included patients with an MCS score ≥ 75th percentile.

RESULTS

Of the 264 patients (72.3%) with a 2-year follow-up, 104 (28.5%) met the inclusion criteria for LMH and 40 patients (11.0%) met those for HMH. The LMH group had a significantly higher overall rate of comorbidities, specifically leg weakness, depression, hypertension, and self-reported neurological and psychiatric disease processes, and were more likely to be unemployed as compared with the HMH group (p < 0.05 for all). The 2 groups had similar 2-year postoperative improvements in HRQOL (p > 0.05) except for the greater improvements in the MCS and the Scoliosis Research Society-22r questionnaire (SRS-22r) mental domain (p < 0.05) in the LMH group and greater improvements in PCS and SRS-22r satisfaction and back pain domains (p < 0.05) in the HMH group. The LMH group had a higher rate of reaching a minimal clinically important difference (MCID) on the SRS-22r mental domain (p < 0.01), and the HMH group had a higher rate of reaching an MCID on the PCS and SRS-22r activity domain (p < 0.05). On multivariable logistic regression, having LMH was a significant independent predictor of failure to reach an MCID on the PCS (p < 0.05). At the 2-year postoperative follow-up, 14 LMH patients (15.1%) were categorized as HMH. Two LMH patients (2.2%), and 3 HMH patients (7.7%) transitioned to a PCS score ≥ 75th percentile for age- and sex-matched US norms (p < 0.01).

CONCLUSIONS

While patients with poor mental and physical health, according to their MCS and PCS scores, have higher medical comorbidity and unemployment rates, they still demonstrate significant improvements in HRQOL measurements postoperatively. Both LMH and HMH patient groups demonstrated similar improvements in most HRQOL domains, except that the LMH patients had difficulties in obtaining improvements in the PCS domain.