Objective Seizure frequency and seizure freedom are among the most important outcome measures for patients with epilepsy. In this study, we aimed to automatically extract this clinical information from unstructured text in clinical notes. If successful, this could improve clinical decision-making in epilepsy patients and allow for rapid, large-scale retrospective research. Materials and Methods We developed a finetuning pipeline for pretrained neural models to classify patients as being seizure-free and to extract text containing their seizure frequency and date of last seizure from clinical notes. We annotated 1000 notes for use as training and testing data and determined how well 3 pretrained neural models, BERT, RoBERTa, and Bio_ClinicalBERT, could identify and extract the desired information after finetuning. Results The finetuned models (BERTFT, Bio_ClinicalBERTFT, and RoBERTaFT) achieved near-human performance when classifying patients as seizure free, with BERTFT and Bio_ClinicalBERTFT achieving accuracy scores over 80%. All 3 models also achieved human performance when extracting seizure frequency and date of last seizure, with overall F1 scores over 0.80. The best combination of models was Bio_ClinicalBERTFT for classification, and RoBERTaFT for text extraction. Most of the gains in performance due to finetuning required roughly 70 annotated notes. Discussion and Conclusion Our novel machine reading approach to extracting important clinical outcomes performed at or near human performance on several tasks. This approach opens new possibilities to support clinical practice and conduct large-scale retrospective clinical research. Future studies can use our finetuning pipeline with minimal training annotations to answer new clinical questions.

Objective: Interictal spikes help localize seizure generators as part of surgical planning for drug-resistant epilepsy. However, there are often multiple spike populations whose frequencies change over time, influenced by brain state.Understanding state changes in spike rates will improve our ability to use spikes for surgical planning. Our goal was to determine the effect of sleep and seizures on interictal spikes, and to use sleep and seizure-related changes in spikes to localize the seizure-onset zone (SOZ). Methods:We performed a retrospective analysis of intracranial electroencephalography (EEG) data from patients with focal epilepsy. We automatically detected interictal spikes and we classified different time periods as awake or asleep based on the ratio of alpha to delta power, with a secondary analysis using the recently published SleepSEEG algorithm. We analyzed spike rates surrounding sleep and seizures. We developed a model to localize the SOZ using state-dependent spike rates. Results:We analyzed data from 101 patients (54 women, age range 16-69). The normalized alpha-delta power ratio accurately classified wake from sleep periods (area under the curve = .90). Spikes were more frequent in sleep than wakefulness and in the post-ictal compared to the pre-ictal state. Patients with temporal lobe epilepsy had a greater wake-to-sleep and pre-to post-ictal spike rate increase compared to patients with extra-temporal epilepsy. A machine-learning classifierThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Objective Electronic medical records allow for retrospective clinical research with large patient cohorts. However, epilepsy outcomes are often contained in free text notes that are difficult to mine. We recently developed and validated novel natural language processing (NLP) algorithms to automatically extract key epilepsy outcome measures from clinic notes. In this study, we assessed the feasibility of extracting these measures to study the natural history of epilepsy at our center. Methods We applied our previously validated NLP algorithms to extract seizure freedom, seizure frequency, and date of most recent seizure from outpatient visits at our epilepsy center from 2010 to 2022. We examined the dynamics of seizure outcomes over time using Markov model‐based probability and Kaplan–Meier analyses. Results Performance of our algorithms on classifying seizure freedom was comparable to that of human reviewers (algorithm F1 = .88 vs. human annotator κ = .86). We extracted seizure outcome data from 55 630 clinic notes from 9510 unique patients written by 53 unique authors. Of these, 30% were classified as seizure‐free since the last visit, 48% of non‐seizure‐free visits contained a quantifiable seizure frequency, and 47% of all visits contained the date of most recent seizure occurrence. Among patients with at least five visits, the probabilities of seizure freedom at the next visit ranged from 12% to 80% in patients having seizures or seizure‐free at the prior three visits, respectively. Only 25% of patients who were seizure‐free for 6 months remained seizure‐free after 10 years. Significance Our findings demonstrate that epilepsy outcome measures can be extracted accurately from unstructured clinical note text using NLP. At our tertiary center, the disease course often followed a remitting and relapsing pattern. This method represents a powerful new tool for clinical research with many potential uses and extensions to other clinical questions.

Objective: To conduct a prospective, randomized controlled trial (RCT) of an enhanced recovery after surgery (ERAS) protocol in an elective spine surgery population. Background: Surgical outcomes such as length of stay (LOS), discharge disposition, and opioid utilization greatly contribute to patient satisfaction and societal healthcare costs. ERAS protocols are multimodal, patient-centered care pathways shown to reduce postoperative opioid use, reduced LOS, and improved ambulation; however, prospective ERAS data are limited in spine surgery. Methods : This single-center, institutional review board-approved, prospective RCT-enrolled adult patients undergoing elective spine surgery between March 2019 and October 2020. Primary outcomes were perioperative and 1-month postoperative opioid use. Patients were randomized to ERAS (n=142) or standard-of-care (SOC; n=142) based on power analyses to detect a difference in postoperative opioid use. Results: Opioid use during hospitalization and the first postoperative month was not significantly different between groups (ERAS 112.2 vs SOC 117.6 morphine milligram equivalent, P=0.76; ERAS 38.7% vs SOC 39.4%, P=1.00, respectively). However, patients randomized to ERAS were less likely to use opioids at 6 months postoperatively (ERAS 11.4% vs SOC 20.6%, P=0.046) and more likely to be discharged to home after surgery (ERAS 91.5% vs SOC 81.0%, P=0.015). Conclusion: Here, we present a novel ERAS prospective RCT in the elective spine surgery population. Although we do not detect a difference in the primary outcome of short-term opioid use, we observe significantly reduced opioid use at 6-month follow-up as well as an increased likelihood of home disposition after surgery in the ERAS group.

We want to emphasize that, at our institution, nerve surgery is not limited to the extremities. It also includes the torso and the head and neck. Our team strongly believes that it is logical to screen patients who present with nerve conditions for other nerve issues around the entire body to provide best patient care (if this is not part of the standard history and physical exam already). This applies to nerve compression as well as any other nerve problem identified. No body part should be excluded.