Farrokh Farrokhi1, Quinlan D Buchlak2, Matt Sikora1, Nazanin Esmaili3, Maria Marsans1, Pamela McLeod4, Jamie Mark5, Emily Cox6, Christine Bennett7, Jonathan Carlson4. 1. Neuroscience Institute, Virginia Mason Medical Center, Seattle, Washington, USA. 2. School of Medicine, University of Notre Dame Australia, Sydney, Australia. Electronic address: quinlan.buchlak1@my.nd.edu.au. 3. School of Medicine, University of Notre Dame Australia, Sydney, Australia; Department of Medicine, University of Toronto, Ontario, Canada; Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, Australia. 4. Inland Neurosurgery and Spine Associates, Spokane, Washington, USA. 5. Selkirk Neurology, Spokane, Washington, USA. 6. Providence Medical Research Center, Providence Health & Services, Spokane, Washington, USA. 7. School of Medicine, University of Notre Dame Australia, Sydney, Australia.
Abstract
BACKGROUND: Deep brain stimulation (DBS) surgery is an option for patients experiencing medically resistant neurologic symptoms. DBS complications are rare; finding significant predictors requires a large number of surgeries. Machine learning algorithms may be used to effectively predict these outcomes. The aims of this study were to 1) investigate preoperative clinical risk factors and 2) build machine learning models to predict adverse outcomes. METHODS: This multicenter registry collected clinical and demographic characteristics of patients undergoing DBS surgery (n = 501) and tabulated occurrence of complications. Logistic regression was used to evaluate risk factors. Supervised learning algorithms were trained and validated on 70% and 30%, respectively, of both oversampled and original registry data. Performance was evaluated using area under the receiver operating characteristics curve (AUC), sensitivity, specificity, and accuracy. RESULTS: Logistic regression showed that the risk of complication was related to the operating institution in which the surgery was performed (odds ratio [OR] = 0.44, confidence interval [CI] = 0.25-0.78), body mass index (OR = 0.94, CI = 0.89-0.99), and diabetes (OR = 2.33, CI = 1.18-4.60). Patients with diabetes were almost 3× more likely to return to the operating room (OR = 2.78, CI = 1.31-5.88). Patients with a history of smoking were 4× more likely to experience postoperative infection (OR = 4.20, CI = 1.21-14.61). Supervised learning algorithms demonstrated high discrimination performance when predicting any complication (AUC = 0.86), a complication within 12 months (AUC = 0.91), return to the operating room (AUC = 0.88), and infection (AUC = 0.97). Age, body mass index, procedure side, gender, and a diagnosis of Parkinson disease were influential features. CONCLUSIONS: Multiple significant complication risk factors were identified, and supervised learning algorithms effectively predicted adverse outcomes in DBS surgery.
BACKGROUND: Deep brain stimulation (DBS) surgery is an option for patients experiencing medically resistant neurologic symptoms. DBS complications are rare; finding significant predictors requires a large number of surgeries. Machine learning algorithms may be used to effectively predict these outcomes. The aims of this study were to 1) investigate preoperative clinical risk factors and 2) build machine learning models to predict adverse outcomes. METHODS: This multicenter registry collected clinical and demographic characteristics of patients undergoing DBS surgery (n = 501) and tabulated occurrence of complications. Logistic regression was used to evaluate risk factors. Supervised learning algorithms were trained and validated on 70% and 30%, respectively, of both oversampled and original registry data. Performance was evaluated using area under the receiver operating characteristics curve (AUC), sensitivity, specificity, and accuracy. RESULTS: Logistic regression showed that the risk of complication was related to the operating institution in which the surgery was performed (odds ratio [OR] = 0.44, confidence interval [CI] = 0.25-0.78), body mass index (OR = 0.94, CI = 0.89-0.99), and diabetes (OR = 2.33, CI = 1.18-4.60). Patients with diabetes were almost 3× more likely to return to the operating room (OR = 2.78, CI = 1.31-5.88). Patients with a history of smoking were 4× more likely to experience postoperative infection (OR = 4.20, CI = 1.21-14.61). Supervised learning algorithms demonstrated high discrimination performance when predicting any complication (AUC = 0.86), a complication within 12 months (AUC = 0.91), return to the operating room (AUC = 0.88), and infection (AUC = 0.97). Age, body mass index, procedure side, gender, and a diagnosis of Parkinson disease were influential features. CONCLUSIONS: Multiple significant complication risk factors were identified, and supervised learning algorithms effectively predicted adverse outcomes in DBS surgery.
Authors: Juan Carlos Benedetti-Isaac; Loida Camargo; Pascual Gargiulo; Norman López Journal: Int J Neuropsychopharmacol Date: 2021-12-08 Impact factor: 5.176
Authors: Hasan Ul Banna; Ahmed Zanabli; Brian McMillan; Maria Lehmann; Sumeet Gupta; Michael Gerbo; Joel Palko Journal: Sci Rep Date: 2022-02-15 Impact factor: 4.379
Authors: Quinlan D Buchlak; Nazanin Esmaili; Christine Bennett; Yi Yuen Wang; James King; Tony Goldschlager Journal: PLoS One Date: 2022-07-27 Impact factor: 3.752