Investigating risk factors and predicting complications in deep brain stimulation surgery with machine learning algorithms

Background: Deep brain stimulation (DBS) surgery is an option for patients experiencing medically resistant neurological 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 (OR=0.44, confidence interval [CI]=0.25-0.78), BMI (OR=0.94,CI=0.89-0.99) and diabetes (OR=2.33,CI=1.18-4.60). Patients with diabetes were almost three times more likely to return to the operating room (OR=2.78,CI=1.31-5.88). Patients with a history of smoking were four times 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, BMI, procedure side, gender and a diagnosis of Parkinson’s disease were influential features. Conclusions: Multiple significant complication risk factors were identified and supervised learning algorithms effectively predicted adverse outcomes in DBS surgery

Investigating Risk Factors and Predicting Complications in Deep Brain Stimulation Surgery with Machine Learning Algorithms

© 2019 Elsevier Inc. 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

Supervised machine learning algorithms used to predict post-surgical outcomes following anterior surgical fixation of odontoid fractures

Background: Odontoid fractures have a high mortality rate, and numerous classification systems have previously predicted surgical outcomes with mixed consensus. We generated a machine learning (ML) construct to predict post-operative adverse events following anterior (ORIF) of odontoid fractures. Methods: 266 patients from the American college of surgeons-national surgical quality improvement program (ACS-NSQIP) with anterior ORIF (CPT 22318) of odontoid fractures from 2008-2018 were analyzed using ML algorithms random forest classifier (RF), gradient boosting classifier (GB), support vector machine classifier (SVM), Gaussian Naive Bayes classifier (GNB), and multi-layer perceptron classifier (MLP), and were compared to logistic regression classifier (LR). Algorithms predicted increased length of stay (LOS), need for transfusion (Transf), non-home discharge (NHD), and any adverse event (AAE). Permutation feature importance (PFI) identified risk factors. Results: ML algorithms outperformed LR. The average AUC for predicting Transf was 0.635 (accuracy=77.4%), extended LOS=0.652 (accuracy 59.6%), NHD 0.788 (accuracy=71.9%) and AAE 0.649 (accuracy 68.1%). GB performed highest for Transf (AUC=0.861), identifying operative time (PFI 0.253, p=0.016). GB and RF performed equally for NHD (AUC=0.819), highlighting preoperative hematocrit (PFI=0.157, p<0.001). GB predicted AAE (AUC=0.720) also identifying preoperative hematocrit (PFI=0.112, p<0.001). RF predicted extended LOS (AUC=0.669) highlighting preoperative hematocrit (PFI=0.049, p<0.001). Conclusions: ML outperformed LR, successfully predicting Transf, extended LOS, NHD, and AAE for anterior ORIF of odontoid fractures. Our construct may complement conventional risk stratification to reduce adverse outcomes and excess cost

Translating data analytics into improved spine surgery outcomes: A roadmap for biomedical informatics research in 2021

STUDY DESIGN: Narrative review. OBJECTIVES: There is growing interest in the use of biomedical informatics and data analytics tools in spine surgery. Yet despite the rapid growth in research on these topics, few analytic tools have been implemented in routine spine practice. The purpose of this review is to provide a health information technology (HIT) roadmap to help translate data assets and analytics tools into measurable advances in spine surgical care. METHODS: We conducted a narrative review of PubMed and Google Scholar to identify publications discussing data assets, analytical approaches, and implementation strategies relevant to spine surgery practice. RESULTS: A variety of data assets are available for spine research, ranging from commonly used datasets, such as administrative billing data, to emerging resources, such as mobile health and biobanks. Both regression and machine learning techniques are valuable for analyzing these assets, and researchers should recognize the particular strengths and weaknesses of each approach. Few studies have focused on the implementation of HIT, and a variety of methods exist to help translate analytic tools into clinically useful interventions. Finally, a number of HIT-related challenges must be recognized and addressed, including stakeholder acceptance, regulatory oversight, and ethical considerations. CONCLUSIONS: Biomedical informatics has the potential to support the development of new HIT that can improve spine surgery quality and outcomes. By understanding the development life-cycle that includes identifying an appropriate data asset, selecting an analytic approach, and leveraging an effective implementation strategy, spine researchers can translate this potential into measurable advances in patient care

How Do Spinal Surgeons Perceive The Impact of Factors Used in Post-Surgical Complication Risk Scores?

When deciding about surgical treatment options, an important aspect of the decision-making process is the potential risk of complications. A risk assessment performed by a spinal surgeon is based on their knowledge of the best available evidence and on their own clinical experience. The objective of this work is to demonstrate the differences in the way spine surgeons perceive the importance of attributes used to calculate risk of post-operative and quantify the differences by building individual formal models of risk perceptions. We employ a preference-learning method - ROR-UTADIS - to build surgeon-specific additive value functions for risk of complications. Comparing these functions enables the identification and discussion of differences among personal perceptions of risk factors. Our results show there exist differences in surgeons\u27 perceived factors including primary diagnosis, type of surgery, patient\u27s age, body mass index, or presence of comorbidities

Compilation and Analysis of Web-Based Orthopedic Personalized Predictive Tools: A Scoping Review

Web-based personalized predictive tools in orthopedic surgery are becoming more widely available. Despite rising numbers of these tools, many orthopedic surgeons may not know what tools are available, how these tools were developed, and how they can be utilized. The aim of this scoping review is to compile and synthesize the profile of existing web-based orthopedic tools. We conducted two separate PubMed searches-one a broad search and the second a more targeted one involving high impact journals-with the aim of comprehensively identifying all existing tools. These articles were then screened for functional tool URLs, methods regarding the tool\u27s creation, and general inputs and outputs required for the tool to function. We identified 57 articles, which yielded 31 unique web-based tools. These tools involved various orthopedic conditions (e.g., fractures, osteoarthritis, musculoskeletal neoplasias); interventions (e.g., fracture fixation, total joint arthroplasty); outcomes (e.g., mortality, clinical outcomes). This scoping review highlights the availability and utility of a vast array of web-based personalized predictive tools for orthopedic surgeons. Increased awareness and access to these tools may allow for better decision support, surgical planning, post-operative expectation management, and improved shared decision-making

Artificial Intelligence in Brain Tumour Surgery—An Emerging Paradigm

Artificial intelligence (AI) platforms have the potential to cause a paradigm shift in brain tumour surgery. Brain tumour surgery augmented with AI can result in safer and more effective treatment. In this review article, we explore the current and future role of AI in patients undergoing brain tumour surgery, including aiding diagnosis, optimising the surgical plan, providing support during the operation, and better predicting the prognosis. Finally, we discuss barriers to the successful clinical implementation, the ethical concerns, and we provide our perspective on how the field could be advanced

경추 측면 X선 영상을 기반으로 한 갑상선 수술 환자에서 어려운 후두경 예측 딥러닝 모델

학위논문(석사) -- 서울대학교대학원 : 의과대학 의학과, 2022. 8. 정철우.예상하지 못한 어려운 후두경은 심각한 기도관련 합병증과 연관되어 있다. 본 연구는 후향적으로 수집된 갑상선 수술을 받은 총 14,135명 환자의 경추 측면 X선을 통해 어려운 후두경 (Cormack-Lehane 등급 3-4)를 예측하는 딥러닝 모델을 개발 및 검증하였다. 개발 모델의 성능은 기존의 6개의 딥러닝 모델과 비교하였다. 개발 모델에서 어려운 후두경 예측의 민감도는 95.6%, 특이도 91.2%를 나타냈다. Area Under ROC curve의 경우 개발 모델에서 0.972(0.955~0.988), 기존 모델의 경우 각각 VGG-Net: 0.842, ResNet: 0.841, Xception: 0.863, ResNext: 0.825, DenseNet: 0.889, SENet: 0.875를 나타냈다. 어려운 후두경과 관련된 해부학적 특징을 설명하기 위해 클래스 활성화 맵(Class Activation Map)을 사용하였다. 클래스 활성화 맵에서 설골, 인두 및 경추 주변이 강조되었다. 본 연구를 통해 개발된 딥러닝 모델은 경추 측면 X선 영상을 이용한 어려운 후두경 예측에 높은 성능을 보였다.An unanticipated difficult laryngoscopy is associated with serious airway-related complications. We here developed and validated a deep learning-based model that predicts a difficult laryngoscopy (Cormack–Lehane grade 3–4) from a cervical spine lateral X-ray using data from 14,135 patients undergoing thyroid surgery. The performance of our model was compared with six representative deep learning architectures. A class activation map was created to elucidate the anatomical features associated with difficult laryngoscopy. Our model showed 95.6% sensitivity and 91.2% specificity for predicting difficult laryngoscopy. The area under the receiver operating characteristic curve of our model was 0.972 (0.955‒0.988), which was higher than that of other models (VGG-Net: 0.842, ResNet: 0.841, Xception: 0.863, ResNext: 0.825, DenseNet: 0.889, and SENet: 0.875, all P < 0.001). The class activation map demonstrated clear differences around the hyoid bone, pharynx, and cervical spine. The model showed excellent performance for predicting difficult laryngoscopy using a cervical spine lateral X-ray image.1. Introduction 1 2. Materials and Methods 2 2.1 Inclusion and Exclusion Criteria 2 2.2 Anesthesia Management 2 2.3 Data Collection and Preprocessing 2 2.4 Model Building 3 2.5 Model Validation 4 2.6 Sensitivity Analysis 4 2.7 Statistical Analysis 4 3. Results 6 3.1 Dataset Construction 6 3.2 Performance of the Models 6 3.3 Sensitivity Analysis 6 4. Discussion 8 5. Conclusions 11 References 23 Abstract 26 Tables 12 [Table 1] 12 [Table 2] 13 [Table 3] 14 Figures 15 [Figure 1] 15 [Figure 2] 16 [Figure 3] 17 [Figure 4] 18 [Figure 5] 19 Supplementary Materials 20 [Supplementary Materials] 20석