Search Results

You are looking at 1 - 10 of 10 items for :

  • Author or Editor: Eric O. Klineberg x
  • Neurosurgical Focus x
Clear All Modify Search
Free access

Raqeeb M. Haque, Gregory M. Mundis Jr., Yousef Ahmed, Tarek Y. El Ahmadieh, Michael Y. Wang, Praveen V. Mummaneni, Juan S. Uribe, David O. Okonkwo, Robert K. Eastlack, Neel Anand, Adam S. Kanter, Frank La Marca, Behrooz A. Akbarnia, Paul Park, Virginie Lafage, Jamie S. Terran, Christopher I. Shaffrey, Eric Klineberg, Vedat Deviren and Richard G. Fessler

Object

Various surgical approaches, including open, minimally invasive, and hybrid techniques, have gained momentum in the management of adult spinal deformity. However, few data exist on the radiographic outcomes of different surgical techniques. The objective of this study was to compare the radiographic and clinical outcomes of the surgical techniques used in the treatment of adult spinal deformity.

Methods

The authors conducted a retrospective review of two adult spinal deformity patient databases, a prospective open surgery database and a retrospective minimally invasive surgery (MIS) and hybrid surgery database. The time frame of enrollment in this study was from 2007 to 2012. Spinal deformity patients were stratified into 3 surgery groups: MIS, hybrid surgery, and open surgery. The following pre- and postoperative radiographic parameters were assessed: lumbar major Cobb angle, lumbar lordosis, pelvic incidence minus lumbar lordosis (PI−LL), sagittal vertical axis, and pelvic tilt. Scores on the Oswestry Disability Index (ODI) and a visual analog scale (VAS) for both back and leg pain were also obtained from each patient.

Results

Of the 234 patients with adult spinal deformity, 184 patients had pre- and postoperative radiographs and were thus included in the study (MIS, n = 42; hybrid, n = 33; open, n = 109). Patients were a mean of 61.7 years old and had a mean body mass index of 26.9 kg/m2. Regarding radiographic outcomes, the MIS group maintained a significantly smaller mean lumbar Cobb angle (13.1°) after surgery compared with the open group (20.4°, p = 0.002), while the hybrid group had a significantly larger lumbar curve correction (26.6°) compared with the MIS group (18.8°, p = 0.045). The mean change in the PI−LL was larger for the hybrid group (20.6°) compared with the open (10.2°, p = 0.023) and MIS groups (5.5°, p = 0.003). The mean sagittal vertical axis correction was greater for the open group (25 mm) compared with the MIS group (≤ 1 mm, p = 0.008). Patients in the open group had a significantly larger postoperative thoracic kyphosis (41.45°) compared with the MIS patients (33.5°, p = 0.005). There were no significant differences between groups in terms of pre- and postoperative mean ODI and VAS scores at the 1-year follow-up. However, patients in the MIS group had much lower estimated blood loss and transfusion rates compared with patients in the hybrid or open groups (p < 0.001). Operating room time was significantly longer with the hybrid group compared with the MIS and open groups (p < 0.001). Major complications occurred in 14% of patients in the MIS group, 14% in the hybrid group, and 45% in the open group (p = 0.032).

Conclusions

This study provides valuable baseline characteristics of radiographic parameters among 3 different surgical techniques used in the treatment of adult spinal deformity. Each technique has advantages, but much like any surgical technique, the positive and negative elements must be considered when tailoring a treatment to a patient. Minimally invasive surgical techniques can result in clinical outcomes at 1 year comparable to those obtained from hybrid and open surgical techniques.

Free access

Taemin Oh, Justin K. Scheer, Justin S. Smith, Richard Hostin, Chessie Robinson, Jeffrey L. Gum, Frank Schwab, Robert A. Hart, Virginie Lafage, Douglas C. Burton, Shay Bess, Themistocles Protopsaltis, Eric O. Klineberg, Christopher I. Shaffrey, Christopher P. Ames and the International Spine Study Group

OBJECTIVE

Patients with adult spinal deformity (ASD) experience significant quality of life improvements after surgery. Treatment, however, is expensive and complication rates are high. Predictive analytics has the potential to use many variables to make accurate predictions in large data sets. A validated minimum clinically important difference (MCID) model has the potential to assist in patient selection, thereby improving outcomes and, potentially, cost-effectiveness.

METHODS

The present study was a retrospective analysis of a multiinstitutional database of patients with ASD. Inclusion criteria were as follows: age ≥ 18 years, radiographic evidence of ASD, 2-year follow-up, and preoperative Oswestry Disability Index (ODI) > 15. Forty-six variables were used for model training: demographic data, radiographic parameters, surgical variables, and results on the health-related quality of life questionnaire. Patients were grouped as reaching a 2-year ODI MCID (+MCID) or not (−MCID). An ensemble of 5 different bootstrapped decision trees was constructed using the C5.0 algorithm. Internal validation was performed via 70:30 data split for training/testing. Model accuracy and area under the curve (AUC) were calculated. The mean quality-adjusted life years (QALYs) and QALYs gained at 2 years were calculated and discounted at 3.5% per year. The QALYs were compared between patients in the +MCID and –MCID groups.

RESULTS

A total of 234 patients met inclusion criteria (+MCID 129, −MCID 105). Sixty-nine patients (29.5%) were included for model testing. Predicted versus actual results were 50 versus 40 for +MCID and 19 versus 29 for −MCID (i.e., 10 patients were misclassified). Model accuracy was 85.5%, with 0.96 AUC. Predicted results showed that patients in the +MCID group had significantly greater 2-year mean QALYs (p = 0.0057) and QALYs gained (p = 0.0002).

CONCLUSIONS

A successful model with 85.5% accuracy and 0.96 AUC was constructed to predict which patients would reach ODI MCID. The patients in the +MCID group had significantly higher mean 2-year QALYs and QALYs gained. This study provides proof of concept for using predictive modeling techniques to optimize patient selection in complex spine surgery.

Free access

Justin K. Scheer, Taemin Oh, Justin S. Smith, Christopher I. Shaffrey, Alan H. Daniels, Daniel M. Sciubba, D. Kojo Hamilton, Themistocles S. Protopsaltis, Peter G. Passias, Robert A. Hart, Douglas C. Burton, Shay Bess, Renaud Lafage, Virginie Lafage, Frank Schwab, Eric O. Klineberg, Christopher P. Ames and the International Spine Study Group

OBJECTIVE

Pseudarthrosis can occur following adult spinal deformity (ASD) surgery and can lead to instrumentation failure, recurrent pain, and ultimately revision surgery. In addition, it is one of the most expensive complications of ASD surgery. Risk factors contributing to pseudarthrosis in ASD have been described; however, a preoperative model predicting the development of pseudarthrosis does not exist. The goal of this study was to create a preoperative predictive model for pseudarthrosis based on demographic, radiographic, and surgical factors.

METHODS

A retrospective review of a prospectively maintained, multicenter ASD database was conducted. Study inclusion criteria consisted of adult patients (age ≥ 18 years) with spinal deformity and surgery for the ASD. From among 82 variables assessed, 21 were used for model building after applying collinearity testing, redundancy, and univariable predictor importance ≥ 0.90. Variables included demographic data along with comorbidities, modifiable surgical variables, baseline coronal and sagittal radiographic parameters, and baseline scores for health-related quality of life measures. Patients groups were determined according to their Lenke radiographic fusion type at the 2-year follow-up: bilateral or unilateral fusion (union) or pseudarthrosis (nonunion). A decision tree was constructed, and internal validation was accomplished via bootstrapped training and testing data sets. Accuracy and the area under the receiver operating characteristic curve (AUC) were calculated to evaluate the model.

RESULTS

A total of 336 patients were included in the study (nonunion: 105, union: 231). The model was 91.3% accurate with an AUC of 0.94. From 82 initial variables, the top 21 covered a wide range of areas including preoperative alignment, comorbidities, patient demographics, and surgical use of graft material.

CONCLUSIONS

A model for predicting the development of pseudarthrosis at the 2-year follow-up was successfully created. This model is the first of its kind for complex predictive analytics in the development of pseudarthrosis for patients with ASD undergoing surgical correction and can aid in clinical decision-making for potential preventative strategies.