Stanford Neurosurgical
Artificial Intelligence and Machine Learning Laboratory

The safety and efficacy of surgery is our number one priority. As we enter the era of big data, the onus is upon us to utilize what we have learned to improve medical care for future generations. Using novel, cutting edge artificial intelligence and machine learning techniques, our goal is to mine through millions of patient records to predict outcomes following all types of surgery. Imagine a virtual algorithm, that is capable of providing a true estimate of surgical risk, outcome, and efficacy – based specifically on your characteristics. This is the very foundation of precision medicine, and will empower physicians to deliver outstanding care.

Research

The focus of my laboratory is to utilize precision medicine techniques to improve the diagnosis and treatment of neurologic conditions. From traumatic brain injury to spinal scoliosis, the ability to capture detailed data regarding clinical symptoms and treatment outcomes has empowered us to do better for patients. Utilize data to do better for patients, that’s what we do.

Assistant Professor of Neurosurgery and, by courtesy, of Orthopaedic Surgery

Publications

  • The impact of osteoporosis on adult deformity surgery outcomes in Medicare patients. European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society Varshneya, K., Bhattacharjya, A., Jokhai, R. T., Fatemi, P., Medress, Z. A., Stienen, M. N., Ho, A. L., Ratliff, J. K., Veeravagu, A. 2021

    Abstract

    OBJECTIVE: To identify the impact of osteoporosis (OS) on postoperative outcomes in Medicare patients undergoing ASD surgery.BACKGROUND: Patients with OP and advanced age experience higher than average rates of ASD. However, poor bone density could undermine the durability of a deformity correction.METHODS: We queried the MarketScan Medicare Supplemental database to identify patients Medicare patients who underwent ASD surgery from 2007 to 2016.RESULTS: A total of 2564 patients met the inclusion criteria of this study, of whom n=971 (61.0%) were diagnosed with osteoporosis. Patients with OP had a similar 90-day postoperative complication rates (OP: 54.6% vs. non-OP: 49.2%, p=0.0076, not significant after multivariate regression correction). This was primarily driven by posthemorrhagic anemia (37.6% in OP, vs. 33.1% in non-OP). Rates of revision surgery were similar at 90days (non-OP 15.0%, OP 16.8%), but by 2years, OP patients had a significantly higher reoperation rate (30.4% vs. 22.9%, p<0.0001). In multivariate regression analysis, OP increased odds for revision surgery at 1year (OR 1.4) and 2years (OR 1.5) following surgery (all p<0.05). OP was also an independent predictor of readmission at all time points (90days, OR 1.3, p<0.005).CONCLUSION: Medicare patients with OP had elevated rates of complications, reoperations, and outpatient costs after undergoing primary ASD surgery.

    View details for DOI 10.1007/s00586-021-06985-z

    View details for PubMedID 34655336

  • Commentary: Loss of Relativity: The Physician Fee Schedule, the Neurosurgeon, and the Trojan Horse. Neurosurgery Tumialan, L. M., Veeravagu, A., Ratliff, J. K. 2021

    View details for DOI 10.1093/neuros/nyab339

    View details for PubMedID 34498695

  • Outcome Measures of Medicare Patients With Diabetes Mellitus Undergoing Thoracolumbar Deformity Surgery. Clinical spine surgery Varshneya, K., Bhattacharjya, A., Sharma, J., Stienen, M. N., Medress, Z. A., Ratliff, J. K., Veeravagu, A. 2021

    Abstract

    STUDY DESIGN: This was a retrospective study.OBJECTIVE: The objective of this study was to identify the impact of diabetes on postoperative outcomes in Medicare patients undergoing adult spinal deformity (ASD) surgery.METHODS: We queried the MarketScan Medicare database to identify patients who underwent ASD surgery from 2007 to 2016. Patients were then stratified based on diabetes status at the time of the index operation. Patients not enrolled in the Medicare dataset and those with any prior history of trauma or tumor were excluded from this study.RESULTS: A total of 2564 patients met the inclusion criteria of this study, of which n=746 (29.1.%) were diabetic. Patients with diabetes had a higher rate of postoperative infection than nondiabetic patients (3.1% vs. 1.7%, P<0.05) within 90 days. Renal complications were also more elevated in the diabetic cohort (3.2% vs. 1.3%, P<0.05). Readmission rates were significantly higher in the diabetes cohort through of 60 days (15.2% vs. 11.8%, P<0.05) and 90 days (17.0% vs. 13.4%, P<0.05). When looking specifically at the outpatient payments, patients with diabetes did have a higher financial burden at 60 days (

  • Surgical Outcomes of Human Immunodeficiency Virus-positive Patients Undergoing Lumbar Degenerative Surgery. Clinical spine surgery Varshneya, K., Wadhwa, H., Ho, A. L., Medress, Z. A., Stienen, M. N., Desai, A., Ratliff, J. K., Veeravagu, A. 2021

    Abstract

    STUDY DESIGN: This was a retrospective cohort studying using a national administrative database.OBJECTIVE: The objective of this study was to determine the postoperative complications and quality outcomes of the human immunodeficiency virus (HIV)-positive patients undergoing surgical management for lumbar degenerative disease (LDD).METHODS: This study identified patients with who underwent surgery for LDD between 2007 and 2016. Patients were stratified based on whether they were HIV positive at the time of surgery. Multivariate regression was utilized to reduce the confounding of baseline covariates. Patients who underwent 3 or more levels of surgical correction were under the age of 18 years, or those with any prior history of trauma or tumor were excluded from this study. Baseline comorbidities, postoperative complication rates, and reoperation rates were determined.RESULTS: A total of 120,167 patients underwent primary lumbar degenerative surgery, of which 309 (0.26%) were HIV positive. In multivariate regression analysis, the HIV-positive cohort was more likely to be readmitted at 30 days [odds ratio (OR)=1.9, 95% confidence interval (CI): 1.2-2.8], 60 days (OR=1.7, 95% CI: 1.2-2.5), and 90 days (OR=1.5, 95% CI: 1.0-2.2). The HIV-positive cohort was also more likely to experience any postoperative complication (OR=1.7, 95% CI: 1.2-2.3). Of the major drivers identified, HIV-positive patients had significantly greater odds of cerebrovascular disease and postoperative neurological complications (OR=3.8, 95% CI: 1.8-6.9) and acute kidney injury (OR=3.4, 95% CI: 1.3-7.1). Costs of index hospitalization were not significantly different between the 2 cohorts (

  • External validation of a predictive model of adverse events following spine surgery. The spine journal : official journal of the North American Spine Society Fatemi, P., Zhang, Y., Han, S. S., Purington, N., Zygourakis, C. C., Veeravagu, A., Desai, A., Park, J., Shuer, L. M., Ratliff, J. K. 2021

    Abstract

    BACKGROUND CONTEXT: We lack models that reliably predict 30-day postoperative adverse events (AEs) following spine surgery.PURPOSE: We externally validated a previously developed predictive model for common 30-day adverse events (AEs) after spine surgery.STUDY DESIGN/SETTING: This prospective cohort study utilizes inpatient and outpatient data from a tertiary academic medical center.PATIENT SAMPLE: We assessed a prospective cohort of all 276 adult patients undergoing spine surgery in the Department of Neurosurgery at a tertiary academic institution between April 1, 2018 and October 31, 2018. No exclusion criteria were applied.OUTCOME MEASURES: Incidence of observed AEs was compared with predicted incidence of AEs. Fifteen assessed AEs included: pulmonary complications, congestive heart failure, neurological complications, pneumonia, cardiac dysrhythmia, renal failure, myocardial infarction, wound infection, pulmonary embolus, deep venous thrombosis, wound hematoma, other wound complication, urinary tract infection, delirium, and other infection.METHODS: Our group previously developed the Risk Assessment Tool for Adverse Events after Spine Surgery (RAT-Spine), a predictive model of AEs within 30 days following spine surgery using a cohort of approximately one million patients from combined Medicare and MarketScan databases. We applied RAT-Spine to the single academic institution prospective cohort by entering each patient's preoperative medical and demographic characteristics and surgical type. The model generated a patient-specific overall risk score ranging from 0 to 1 representing the probability of occurrence of any AE. The predicted risks are presented as absolute percent risk and divided into low (<17%), medium (17-28%), and high (>28%).RESULTS: Among the 276 patients followed prospectively, 76 experienced at least one 30-day postoperative AE. Slightly more than half of the cohort were women (53.3%). The median age was slightly lower in the non-AE cohort (63 vs 66.5 years old). Patients with Medicaid comprised 2.5% of the non-AE cohort and 6.6% of the AE cohort. Spinal fusion was performed in 59.1% of cases, which was comparable across cohorts. There was good agreement between the predicted AE and observed AE rates, Area Under the Curve (AUC) 0.64 (95% CI 0.56-0.710). The incidence of observed AEs in the prospective cohort was 17.8% among the low-risk group, 23.0% in the medium-risk group, and 38.4% in the high risk group (p = 0.003).CONCLUSIONS: We externally validated a model for postoperative AEs following spine surgery (RAT-Spine). The results are presented as low-, moderate-, and high-risk designations.

    View details for DOI 10.1016/j.spinee.2021.06.006

    View details for PubMedID 34116215


Our Team

The Stanford Neurosurgical Ariticial Intelligence and Machine Learning Laboratory is led by Dr. Anand Veeravagu, an Assistant Professor of Neurosurgery and Assistant Professor of Orthopedic Surgery, by courtesy, and Director of Minimally Invasive NeuroSpine Surgery at Stanford. Our laboratory team includes neurosurgery residents, clinical instructors, and medical students. 


We're Hiring!

We are currently looking for post-doctoral fellows looking to build their career in health policy research with specific attention to neurologic diseases. To apply, please contact us by email: neurobigdata@stanford.edu