Predicting Surgical Complications in Patients Undergoing Elective Adult Spinal Deformity Procedures Using Machine Learning

Kim, Jun; Arvind, Varun; Oermann, Eric K.; Kaji, Deepak; Ranson, William A.; Ukogu, Chierika; Hussain, Awais K.; Caridi, John M.

doi:10.1016/j.jspd.2018.03.003

Cited by 70 publications

(61 citation statements)

References 20 publications

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“…Kim et al [16,17] used artificial neural networks in addition to classic logistic regression methods to identify risk factors for various types of complication in two subsets of spine patients: those undergoing elective adult spinal deformity surgery [16] and those undergoing posterior lumbar spine fusion [17]. They trained their multivariable models using data from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database, and model performance was compared with that of a model containing just the American Society for Anesthesiology (ASA) score as predictor.…”

Section: Introductionmentioning

confidence: 99%

Development of a model to predict the probability of incurring a complication during spine surgery

et al. 2021

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Purpose Predictive models in spine surgery are of use in shared decision-making. This study sought to develop multivariable models to predict the probability of general and surgical perioperative complications of spinal surgery for lumbar degenerative diseases. Methods Data came from EUROSPINE's Spine Tango Registry (1.2012–12.2017). Separate prediction models were built for surgical and general complications. Potential predictors included age, gender, previous spine surgery, additional pathology, BMI, smoking status, morbidity, prophylaxis, technology used, and the modified Mirza invasiveness index score. Complete case multiple logistic regression was used. Discrimination was assessed using area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (CI). Plots were used to assess the calibration of the models. Results Overall, 23′714/68′111 patients (54.6%) were available for complete case analysis: 763 (3.2%) had a general complication, with ASA score being strongly predictive (ASA-2 OR 1.6, 95% CI 1.20–2.12; ASA-3 OR 2.98, 95% CI 2.19–4.07; ASA-4 OR 5.62, 95% CI 3.04–10.41), while 2534 (10.7%) had a surgical complication, with previous surgery at the same level being an important predictor (OR 1.9, 95%CI 1.71–2.12). Respectively, model AUCs were 0.74 (95% CI, 0.72–0.76) and 0.64 (95% CI, 0.62–0.65), and calibration was good up to predicted probabilities of 0.30 and 0.25, respectively. Conclusion We developed two models to predict complications associated with spinal surgery. Surgical complications were predicted with less discriminative ability than general complications. Reoperation at the same level was strongly predictive of surgical complications and a higher ASA score, of general complications. A web-based prediction tool was developed at https://sst.webauthor.com/go/fx/run.cfm?fx=SSTCalculator.

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Section: Introductionmentioning

confidence: 99%

Development of a model to predict the probability of incurring a complication during spine surgery

et al. 2021

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“…Through extensive clinical data mining, deep learning algorithms, and predictive analytics, ML can potentially provide personalized outcome predictions for individual patients. [19][20][21] ML algorithms can provide spinal surgeons with patient-specific preoperative plans, while simultaneously improving on the algorithm over time as it receives postoperative feedback as part of an iterative learning process and thus, allowing surgeons to improve patient outcomes and mitigate complications.…”

Section: Introductionmentioning

confidence: 99%

Can Machine Learning Accurately Predict Postoperative Compensation for the Uninstrumented Thoracic Spine and Pelvis After Fusion From the Lower Thoracic Spine to the Sacrum?

Lee

Sardar

Boddapati

et al. 2020

Global Spine Journal

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Study Design: Consecutively collected cases. Objective: To determine if a machine-learning (ML) program can accurately predict the postoperative thoracic kyphosis through the uninstrumented thoracic spine and pelvic compensation in patients who undergo fusion from the lower thoracic spine (T10 or T11) to the sacrum. Methods: From 2015 to 2019, a consecutive series of adult (≥18 years old) patients with adult spinal deformity underwent corrective spinal fusion from the lower thoracic spine (T10 or T11) to the sacrum. Deidentified data was processed by a ML system-based platform to predict the postoperative thoracic kyphosis (TK) and pelvic tilt (PT) for each patient. To validate the ML model, the postoperative TK (T4-T12, instrumented thoracic, and uninstrumented thoracic) and the pelvic tilt were compared against the predicted values. Results: A total of 20 adult patients with a minimum 6-month follow-up (mean: 22.4 ± 11.3 months) were included in this study. No significant differences were observed for TK (predicted 37.6° vs postoperative 38.3°, P = .847), uninstrumented TK (predicted 33.9° vs postoperative 29.8°, P = .188), and PT (predicted 23.4° vs postoperative 22.7°, P = .754). The predicted PT and the TK of the uninstrumented thoracic spine correlated well with postoperative values (uninstrumented TK: R 2 = 0.764, P < .001; PT: R 2 = 0.868, P < .001). The mean error with which kyphosis through the uninstrumented thoracic spine can be measured was 4.8° ± 4.0°. The mean error for predicting PT was 2.5° ± 1.7°. Conclusion: ML algorithms can accurately predict the spinopelvic compensation after spinal fusion from the lower thoracic spine to the sacrum. These findings suggest that surgeons may be able to leverage this technology to reduce the risk of proximal junctional kyphosis in this population.

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“…The model that we propose can help in bootstrapping a new model without long and costly data collection, it could also be used to boost under represented categories in classification problem. 35 It is a statistical approach designed to create a virtual model, statistically representative of real patients' population. Our method was to create patients that fall in the 2 zones that we defined (orange or green).…”

Section: Synthetic Patient Modelmentioning

confidence: 99%

Feasibility and Assessment of a Machine Learning-Based Predictive Model of Outcome After Lumbar Decompression Surgery

Peyrou¹,

Vignaux²

2020

Global Spine Journal

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Study design: Retrospective study at a unique center. Objective: The aim of this study is twofold, to develop a virtual patients model for lumbar decompression surgery and to evaluate the precision of an artificial neural network (ANN) model designed to accurately predict the clinical outcomes of lumbar decompression surgery. Methods: We performed a retrospective study of complete Electronic Health Records (EHR) to identify potential unfavorable criteria for spine surgery (predictors). A cohort of synthetics EHR was created to classify patients by surgical success (green zone) or partial failure (orange zone) using an Artificial Neural Network which screens all the available predictors. Results: In the actual cohort, we included 60 patients, with complete EHR allowing efficient analysis, 26 patients were in the orange zone (43.4%) and 34 were in the green zone (56.6%). The average positive criteria amount for actual patients was 8.62 for the green zone (SD+/- 3.09) and 10.92 for the orange zone (SD 3.38). The classifier (a neural network) was trained using 10,000 virtual patients and 2000 virtual patients were used for test purposes. The 12,000 virtual patients were generated from the 60 EHR, of which half were in the green zone and half in the orange zone. The model showed an accuracy of 72% and a ROC score of 0.78. The sensitivity was 0.885 and the specificity 0.59. Conclusion: Our method can be used to predict a favorable patient to have lumbar decompression surgery. However, there is still a need to further develop its ability to analyze patients in the “failure of treatment” zone to offer precise management of patient health before spinal surgery.

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Predicting Surgical Complications in Patients Undergoing Elective Adult Spinal Deformity Procedures Using Machine Learning

Cited by 70 publications

References 20 publications

Development of a model to predict the probability of incurring a complication during spine surgery

Development of a model to predict the probability of incurring a complication during spine surgery

Can Machine Learning Accurately Predict Postoperative Compensation for the Uninstrumented Thoracic Spine and Pelvis After Fusion From the Lower Thoracic Spine to the Sacrum?

Feasibility and Assessment of a Machine Learning-Based Predictive Model of Outcome After Lumbar Decompression Surgery

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