Neurosurg Focus
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Multicenter Study
Development of a validated computer-based preoperative predictive model for pseudarthrosis with 91% accuracy in 336 adult spinal deformity patients.
OBJECTIVEPseudarthrosis 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. ⋯ 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. CONCLUSIONSA 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.
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OBJECTIVEPituitary adenomas occur in a heterogeneous patient population with diverse perioperative risk factors, endocrinopathies, and other tumor-related comorbidities. This heterogeneity makes predicting postoperative outcomes challenging when using traditional scoring systems. Modern machine learning algorithms can automatically identify the most predictive risk factors and learn complex risk-factor interactions using training data to build a robust predictive model that can generalize to new patient cohorts. ⋯ The most important predictive variables were lowest perioperative sodium, age, BMI, highest perioperative sodium, and Cushing's disease. CONCLUSIONSEarly postoperative outcomes of pituitary adenoma surgery can be predicted with 87% accuracy using a machine learning approach. These results provide insight into how predictive modeling using machine learning can be used to improve the perioperative management of pituitary adenoma patients.
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OBJECTIVEPrognostication and surgical planning for WHO grade I versus grade II meningioma requires thoughtful decision-making based on radiographic evidence, among other factors. Although conventional statistical models such as logistic regression are useful, machine learning (ML) algorithms are often more predictive, have higher discriminative ability, and can learn from new data. The authors used conventional statistical models and an array of ML algorithms to predict atypical meningioma based on radiologist-interpreted preoperative MRI findings. ⋯ RESULTSThe authors included 6 preoperative imaging and demographic variables: tumor volume, degree of peritumoral edema, presence of necrosis, tumor location, patient sex, and presence of a draining vein to construct the models. The artificial neural networks outperformed all other ML models across the true-positive versus false-positive (receiver operating characteristic) space (area under curve = 0.8895). CONCLUSIONSML algorithms are powerful computational tools that can predict meningioma grade with great accuracy.
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OBJECTIVEModern surgical planning and prognostication requires the most accurate outcomes data to practice evidence-based medicine. For clinicians treating children following traumatic brain injury (TBI) these data are severely lacking. The first aim of this study was to assess published CT classification systems in the authors' pediatric cohort. ⋯ The AUC for CT score in predicting GOS score ≤ 3, a measure of overall functionality, was similarly predictive (Helsinki, 0.717; Rotterdam, 0.748; and Marshall, 0.663). An ANN was then constructed that was able to predict 6-month outcomes with profound accuracy (AUC = 0.9462 ± 0.0422). CONCLUSIONSThis study showed that machine-learning can be leveraged to more accurately predict TBI outcomes in children.
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OBJECTIVEFlow diverters (FDs) are designed to occlude intracranial aneurysms (IAs) while preserving flow to essential arteries. Incomplete occlusion exposes patients to risks of thromboembolic complications and rupture. A priori assessment of FD treatment outcome could enable treatment optimization leading to better outcomes. ⋯ During testing, NN and Gaussian-SVM models had the highest accuracy (90%) in predicting occlusion outcome. CONCLUSIONSNN and Gaussian-SVM models incorporating all 16 morphological, hemodynamic, and FD-related parameters predicted 6-month occlusion outcome of FD treatment with 90% accuracy. More robust models using the computational workflow and machine learning could be trained on larger patient databases toward clinical use in patient-specific treatment planning and optimization.