Predictive models of epilepsy outcomes.

PURPOSE OF REVIEW: Multiple complex medical decisions are necessary in the course of a chronic disease like epilepsy. Predictive tools to assist physicians and patients in navigating this complexity have emerged as a necessity and are summarized in this review. RECENT FINDINGS: Nomograms and online risk calculators are user-friendly and offer individualized predictions for outcomes ranging from safety of antiseizure medication withdrawal (accuracy 65-73%) to seizure-freedom, naming, mood, and language outcomes of resective epilepsy surgery (accuracy 72-81%). Improving their predictive performance is limited by the nomograms' inability to ingest complex data inputs. Conversely, machine learning offers the potential of multimodal and expansive model inputs achieving human-expert level accuracy in automated scalp electroencephalogram (EEG) interpretation but lagging in predictive performance or requiring validation for other applications. SUMMARY: Good to excellent predictive models are now available to guide medical and surgical epilepsy decision-making with nomograms offering individualized predictions and user-friendly tools, and machine learning approaches offering the potential of improved performance. Future research is necessary to bridge the two approaches for optimal translation to clinical care.

Prediction tools and risk stratification in epilepsy surgery.

OBJECTIVE: This study was undertaken to conduct external validation of previously published epilepsy surgery prediction tools using a large independent multicenter dataset and to assess whether these tools can stratify patients for being operated on and for becoming free of disabling seizures (International League Against Epilepsy stage 1 and 2). METHODS: We analyzed a dataset of 1562 patients, not used for tool development. We applied two scales: Epilepsy Surgery Grading Scale (ESGS) and Seizure Freedom Score (SFS); and two versions of Epilepsy Surgery Nomogram (ESN): the original version and the modified version, which included electroencephalographic data. For the ESNs, we used calibration curves and concordance indexes. We stratified the patients into three tiers for assessing the chances of attaining freedom from disabling seizures after surgery: high (ESGS = 1, SFS = 3-4, ESNs > 70%), moderate (ESGS = 2, SFS = 2, ESNs = 40%-70%), and low (ESGS = 2, SFS = 0-1, ESNs < 40%). We compared the three tiers as stratified by these tools, concerning the proportion of patients who were operated on, and for the proportion of patients who became free of disabling seizures. RESULTS: The concordance indexes for the various versions of the nomograms were between .56 and .69. Both scales (ESGS, SFS) and nomograms accurately stratified the patients for becoming free of disabling seizures, with significant differences among the three tiers (p < .05). In addition, ESGS and the modified ESN accurately stratified the patients for having been offered surgery, with significant difference among the three tiers (p < .05). SIGNIFICANCE: ESGS and the modified ESN (at thresholds of 40% and 70%) stratify patients undergoing presurgical evaluation into three tiers, with high, moderate, and low chance for favorable outcome, with significant differences between the groups concerning having surgery and becoming free of disabling seizures. Stratifying patients for epilepsy surgery has the potential to help select the optimal candidates in underprivileged areas and better allocate resources in developed countries.

Predictors of therapeutic response following thalamic neuromodulation for drug-resistant pediatric epilepsy: A systematic review and individual patient data meta-analysis.

We sought to perform a systematic review and individual participant data meta-analysis to identify predictors of treatment response following thalamic neuromodulation in pediatric patients with medically refractory epilepsy. Electronic databases (MEDLINE, Ovid, Embase, and Cochrane) were searched, with no language or data restriction, to identify studies reporting seizure outcomes in pediatric populations following deep brain stimulation (DBS) or responsive neurostimulation (RNS) implantation in thalamic nuclei. Studies featuring individual participant data of patients with primary or secondary generalized drug-resistant epilepsy were included. Response to therapy was defined as >50% reduction in seizure frequency from baseline. Of 417 citations, 21 articles reporting on 88 participants were eligible. Mean age at implantation was 13.07 ± 3.49 years. Fifty (57%) patients underwent DBS, and 38 (43%) RNS. Sixty (68%) patients were implanted in centromedian nucleus and 23 (26%) in anterior thalamic nucleus, and five (6%) had both targets implanted. Seventy-four (84%) patients were implanted bilaterally. The median time to last follow-up was 12 months (interquartile range = 6.75-26.25). Sixty-nine percent of patients achieved response to treatment. Age, target, modality, and laterality had no significant association with response in univariate logistic regression. Until thalamic neuromodulation gains widespread approval for use in pediatric patients, data on efficacy will continue to be limited to small retrospective cohorts and case series. The inherent bias of these studies can be overcome by using individual participant data. Thalamic neuromodulation appears to be a safe and effective treatment for epilepsy. Larger, prolonged prospective, multicenter studies are warranted to further evaluate the efficacy of DBS over RNS in this patient population where resection for curative intent is not a safe option.

Predictive models for starting antiseizure medication withdrawal following epilepsy surgery in adults.

More than half of adults with epilepsy undergoing resective epilepsy surgery achieve long-term seizure freedom and might consider withdrawing antiseizure medications. We aimed to identify predictors of seizure recurrence after starting postoperative antiseizure medication withdrawal and develop and validate predictive models. We performed an international multicentre observational cohort study in nine tertiary epilepsy referral centres. We included 850 adults who started antiseizure medication withdrawal following resective epilepsy surgery and were free of seizures other than focal non-motor aware seizures before starting antiseizure medication withdrawal. We developed a model predicting recurrent seizures, other than focal non-motor aware seizures, using Cox proportional hazards regression in a derivation cohort (n = 231). Independent predictors of seizure recurrence, other than focal non-motor aware seizures, following the start of antiseizure medication withdrawal were focal non-motor aware seizures after surgery and before withdrawal [adjusted hazard ratio (aHR) 5.5, 95% confidence interval (CI) 2.7-11.1], history of focal to bilateral tonic-clonic seizures before surgery (aHR 1.6, 95% CI 0.9-2.8), time from surgery to the start of antiseizure medication withdrawal (aHR 0.9, 95% CI 0.8-0.9) and number of antiseizure medications at time of surgery (aHR 1.2, 95% CI 0.9-1.6). Model discrimination showed a concordance statistic of 0.67 (95% CI 0.63-0.71) in the external validation cohorts (n = 500). A secondary model predicting recurrence of any seizures (including focal non-motor aware seizures) was developed and validated in a subgroup that did not have focal non-motor aware seizures before withdrawal (n = 639), showing a concordance statistic of 0.68 (95% CI 0.64-0.72). Calibration plots indicated high agreement of predicted and observed outcomes for both models. We show that simple algorithms, available as graphical nomograms and online tools (predictepilepsy.github.io), can provide probabilities of seizure outcomes after starting postoperative antiseizure medication withdrawal. These multicentre-validated models may assist clinicians when discussing antiseizure medication withdrawal after surgery with their patients.

Alpha to Omicron: Disease Severity and Clinical Outcomes of Major SARS-CoV-2 Variants.

BACKGROUND: Four severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants predominated in the United States since 2021. Understanding disease severity related to different SARS-CoV-2 variants remains limited. METHOD: Viral genome analysis was performed on SARS-CoV-2 clinical isolates circulating March 2021 through March 2022 in Cleveland, Ohio. Major variants were correlated with disease severity and patient outcomes. RESULTS: In total 2779 patients identified with either Alpha (n 1153), Gamma (n 122), Delta (n 808), or Omicron variants (n 696) were selected for analysis. No difference in frequency of hospitalization, intensive care unit (ICU) admission, and death were found among Alpha, Gamma, and Delta variants. However, patients with Omicron infection were significantly less likely to be admitted to the hospital, require oxygen, or admission to the ICU (2 12.8, P .001; 2 21.6, P .002; 2 9.6, P .01, respectively). In patients whose vaccination status was known, a substantial number had breakthrough infections with Delta or Omicron variants (218/808 [26.9] and 513/696 [73.7], respectively). In breakthrough infections, hospitalization rate was similar regardless of variant by multivariate analysis. No difference in disease severity was identified between Omicron subvariants BA.1 and BA.2. CONCLUSIONS: Disease severity associated with Alpha, Gamma, and Delta variants is comparable while Omicron infections are significantly less severe. Breakthrough disease is significantly more common in patients with Omicron infection.

Telemedicine as a path to bridging inequities in patients with epilepsy.

OBJECTIVE: Access to epilepsy specialist care is not uniform in the USA, with prominent gaps in rural areas. Understanding the reasons for nonattendance at epilepsy appointments may help identify access hurdles faced by patients. This study was undertaken to better understand clinic absenteeism in epilepsy and how it may be influenced by telemedicine. METHODS: In this retrospective study, social determinants of health were collected for all adult patients scheduled in epilepsy clinic, as either an in-person or telemedicine appointment, at University of Kentucky between July 2021 and December 2022. The primary outcome measure was attendance or absence at the appointment. Subgroup analyses were done to better understand the drivers of attendance at telemedicine visits and evaluate telemedicine utilization by underserved populations. RESULTS: A total of 3025 patient encounters of in-person and telemedicine visits were included. The no-show rate was significantly higher for in-person visits (32%) compared with telemedicine visits (20%, p < .001). A nominal logistic regression model identified seven factors increasing risk of absenteeism, including in-person visits, prior missed appointments, longer lead times to appointment, Medicaid/Medicare as payors, no significant other, lower mean annual income, and minority race. For each $10 000 increase in mean annual income, the odds of missing the appointment decreased by 8% (odds ratio = .92, 95% confidence interval = .89-.96, p < .001). Forty-one percent of underserved population opted for telemedicine visits, and they had a lower no-show rate (22%) as compared with in-person visits (33%, p < .001). Predictors of no-shows to televisits (1382) included Medicare/Medicaid coverage (as opposed to private insurance), no significant others, and a history of missing appointments. SIGNIFICANCE: Telemedicine is effective at improving attendance, overcoming socioeconomic hurdles, and widening access to epilepsy care, particularly among underserved populations. Access to telecare depends on insurance coverage and emphasizes the need to include telemedicine in insurance plans to ensure uniform access to high-quality epilepsy care, irrespective of socioeconomic status.

Beyond seizure freedom: Dissecting long-term seizure control after surgical resection for drug-resistant epilepsy.

OBJECTIVE: This study was undertaken to better understand the long-term palliative and disease-modifying effects of surgical resection beyond seizure freedom, including frequency reduction and both late recurrence and remission, in patients with drug-resistant epilepsy. METHODS: This retrospective database-driven cohort study included all patients with >9 years of follow-up at a single high-volume epilepsy center. We included patients who underwent lobectomy, multilobar resection, or lesionectomies for drug-resistant epilepsy; we excluded patients who underwent hemispherectomies. Our main outcomes were (1) reduction in frequency of disabling seizures (at 6 months, each year up to 9 years postoperatively, and at last follow-up), (2) achievement of seizure remission (>6 months, >1 year, and longest duration), and (3) seizure freedom at last follow-up. RESULTS: We included 251 patients; 234 (93.2%) achieved 6 months and 232 (92.4%) experienced 1 year of seizure freedom. Of these, the average period of seizure freedom was 10.3 years. A total of 182 (72.5%) patients were seizure-free at last follow-up (defined as >1 year without seizures), with a median 11.9 years since remission. For patients not completely seizure-free, the mean seizure frequency reduction at each time point was 76.2%, and ranged from 66.6% to 85.0%. Patients decreased their number of antiseizure medications on average by .58, and 53 (21.2%) patients were on no antiseizure medication at last follow-up. Nearly half (47.1%) of those seizure-free at last follow-up were not seizure-free immediately postoperatively. SIGNIFICANCE: Patients who continue to have seizures after resection often have considerable reductions in seizure frequency, and many are able to achieve seizure freedom in a delayed manner.

A network medicine approach to investigation and population-based validation of disease manifestations and drug repurposing for COVID-19.

The global coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to unprecedented social and economic consequences. The risk of morbidity and mortality due to COVID-19 increases dramatically in the presence of coexisting medical conditions, while the underlying mechanisms remain unclear. Furthermore, there are no approved therapies for COVID-19. This study aims to identify SARS-CoV-2 pathogenesis, disease manifestations, and COVID-19 therapies using network medicine methodologies along with clinical and multi-omics observations. We incorporate SARS-CoV-2 virus-host protein-protein interactions, transcriptomics, and proteomics into the human interactome. Network proximity measurement revealed underlying pathogenesis for broad COVID-19-associated disease manifestations. Analyses of single-cell RNA sequencing data show that co-expression of ACE2 and TMPRSS2 is elevated in absorptive enterocytes from the inflamed ileal tissues of Crohn disease patients compared to uninflamed tissues, revealing shared pathobiology between COVID-19 and inflammatory bowel disease. Integrative analyses of metabolomics and transcriptomics (bulk and single-cell) data from asthma patients indicate that COVID-19 shares an intermediate inflammatory molecular profile with asthma (including IRAK3 and ADRB2). To prioritize potential treatments, we combined network-based prediction and a propensity score (PS) matching observational study of 26,779 individuals from a COVID-19 registry. We identified that melatonin usage (odds ratio [OR] = 0.72, 95% CI 0.56-0.91) is significantly associated with a 28% reduced likelihood of a positive laboratory test result for SARS-CoV-2 confirmed by reverse transcription-polymerase chain reaction assay. Using a PS matching user active comparator design, we determined that melatonin usage was associated with a reduced likelihood of SARS-CoV-2 positive test result compared to use of angiotensin II receptor blockers (OR = 0.70, 95% CI 0.54-0.92) or angiotensin-converting enzyme inhibitors (OR = 0.69, 95% CI 0.52-0.90). Importantly, melatonin usage (OR = 0.48, 95% CI 0.31-0.75) is associated with a 52% reduced likelihood of a positive laboratory test result for SARS-CoV-2 in African Americans after adjusting for age, sex, race, smoking history, and various disease comorbidities using PS matching. In summary, this study presents an integrative network medicine platform for predicting disease manifestations associated with COVID-19 and identifying melatonin for potential prevention and treatment of COVID-19.

5α-Reductase Inhibitors Are Associated with Reduced Risk of SARS-CoV-2 Infection: A Matched-Pair, Registry-Based Analysis.

PURPOSE: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a disproportionately severe effect on men, suggesting that the androgen pathway plays a role in the disease. Studies on the effect of castration and androgen receptor blockade have been mixed, while 5α-reductase inhibitor (5ARI) use in men with COVID-19 (2019 novel coronavirus) have shown potential benefits. We assessed the association of 5ARI use on risk of community acquired SARS-CoV-2 infection. MATERIALS AND METHODS: A total of 60,474 males in a prospective registry of people tested for SARS-CoV-2 between March 8, 2020 and February 15, 2021 were included. Using a matched cohort design, men using 5ARIs were matched 1:1 to non5ARI users. Independent analysis using unconditional multivariable logistic regression on the entire unmatched data set was completed for validation. Primary outcome measures were the association of 5ARI use on rates of SARS-Cov-2 positivity and disease severity. RESULTS: Of the men 1,079 (1.8%) reported 5ARI use and 55,100 were available for matching. The final matched cohorts included 944 men each. Mean duration of use was 60.4 months (IQR 17-84 months). Absolute risk for infection was significantly lower in 5ARI users compared to nonusers, 42.3% (399/944) vs 47.2% (446/944), respectively (absolute risk reduction [ARR] 4.9%, OR 0.81, 95% CI 0.67-0.97, p=0.026). Unconditional multivariable logistic regression analysis of the entire study cohort of 55,100 men confirmed the protective association of 5ARI use (ARR 5.3%, OR=0.877, 95% CI 0.774-0.995, p=0.042). Use of 5ARIs was not associated with disease severity. CONCLUSIONS: Use of 5ARIs in men without prostate cancer was associated with a reduction in community acquired SARS-CoV-2 infection.

Comparative Effectiveness of Stereotactic Electroencephalography Versus Subdural Grids in Epilepsy Surgery.

OBJECTIVE: The aim was to compare the outcomes of subdural electrode (SDE) implantations versus stereotactic electroencephalography (SEEG), the 2 predominant methods of intracranial electroencephalography (iEEG) performed in difficult-to-localize drug-resistant focal epilepsy. METHODS: The Surgical Therapies Commission of the International League Against Epilepsy created an international registry of iEEG patients implanted between 2005 and 2019 with ≥1 year of follow-up. We used propensity score matching to control exposure selection bias and generate comparable cohorts. Study endpoints were: (1) likelihood of resection after iEEG; (2) seizure freedom at last follow-up; and (3) complications (composite of postoperative infection, symptomatic intracranial hemorrhage, or permanent neurological deficit). RESULTS: Ten study sites from 7 countries and 3 continents contributed 2,012 patients, including 1,468 (73%) eligible for analysis (526 SDE and 942 SEEG), of whom 988 (67%) underwent subsequent resection. Propensity score matching improved covariate balance between exposure groups for all analyses. Propensity-matched patients who underwent SDE had higher odds of subsequent resective surgery (odds ratio [OR] = 1.4, 95% confidence interval [CI] 1.05, 1.84) and higher odds of complications (OR = 2.24, 95% CI 1.34, 3.74; unadjusted: 9.6% after SDE vs 3.3% after SEEG). Odds of seizure freedom in propensity-matched resected patients were 1.66 times higher (95% CI 1.21, 2.26) for SEEG compared with SDE (unadjusted: 55% seizure free after SEEG-guided resections vs 41% after SDE). INTERPRETATION: In comparison to SEEG, SDE evaluations are more likely to lead to brain surgery in patients with drug-resistant epilepsy but have more surgical complications and lower probability of seizure freedom. This comparative-effectiveness study provides the highest feasible evidence level to guide decisions on iEEG. ANN NEUROL 2021;90:927-939.

Quantitative analysis of visually reviewed normal scalp EEG predicts seizure freedom following anterior temporal lobectomy.

OBJECTIVE: Anterior temporal lobectomy (ATL) is a widely performed and successful intervention for drug-resistant temporal lobe epilepsy (TLE). However, up to one third of patients experience seizure recurrence within 1 year after ATL. Despite the extensive literature on presurgical electroencephalography (EEG) and magnetic resonance imaging (MRI) abnormalities to prognosticate seizure freedom following ATL, the value of quantitative analysis of visually reviewed normal interictal EEG in such prognostication remains unclear. In this retrospective multicenter study, we investigate whether machine learning analysis of normal interictal scalp EEG studies can inform the prediction of postoperative seizure freedom outcomes in patients who have undergone ATL. METHODS: We analyzed normal presurgical scalp EEG recordings from 41 Mayo Clinic (MC) and 23 Cleveland Clinic (CC) patients. We used an unbiased automated algorithm to extract eyes closed awake epochs from scalp EEG studies that were free of any epileptiform activity and then extracted spectral EEG features representing (a) spectral power and (b) interhemispheric spectral coherence in frequencies between 1 and 25 Hz across several brain regions. We analyzed the differences between the seizure-free and non-seizure-free patients and employed a Naïve Bayes classifier using multiple spectral features to predict surgery outcomes. We trained the classifier using a leave-one-patient-out cross-validation scheme within the MC data set and then tested using the out-of-sample CC data set. Finally, we compared the predictive performance of normal scalp EEG-derived features against MRI abnormalities. RESULTS: We found that several spectral power and coherence features showed significant differences correlated with surgical outcomes and that they were most pronounced in the 10-25 Hz range. The Naïve Bayes classification based on those features predicted 1-year seizure freedom following ATL with area under the curve (AUC) values of 0.78 and 0.76 for the MC and CC data sets, respectively. Subsequent analyses revealed that (a) interhemispheric spectral coherence features in the 10-25 Hz range provided better predictability than other combinations and (b) normal scalp EEG-derived features provided superior and potentially distinct predictive value when compared with MRI abnormalities (>10% higher F1 score). SIGNIFICANCE: These results support that quantitative analysis of even a normal presurgical scalp EEG may help prognosticate seizure freedom following ATL in patients with drug-resistant TLE. Although the mechanism for this result is not known, the scalp EEG spectral and coherence properties predicting seizure freedom may represent activity arising from the neocortex or the networks responsible for temporal lobe seizure generation within vs outside the margins of an ATL.

What is a clinical practice guideline? A roadmap to their development. Special report from the Guidelines Task Force of the International League Against Epilepsy.

Clinical practice guidelines (CPGs) are statements that provide evidence-based recommendations aimed at optimizing patient care. However, many other documents are often published as "guidelines" when they are not; these documents, although also important in clinical practice, are usually not systematically produced following rigorous processes linking the evidence to the recommendations. Specifically, the International League Against Epilepsy (ILAE) guideline development toolkit aims to ensure that high-quality CPGs are developed to fill knowledge gaps and optimize the management of epilepsy. In addition to adhering to key methodological processes, guideline developers need to consider that effective CPGs should lead to improvements in clinical processes of care and health care outcomes. This requires monitoring the effectiveness of epilepsy-related CPGs and interventions to remove the barriers to epilepsy CPG implementation. This article provides an overview of what distinguishes quality CPGs from other documents and discusses their benefits and limitations. We summarize the recently revised ILAE CPG development process and elaborate on the barriers and facilitators to guideline dissemination, implementation, and adaptation.

Neurostimulation in people with drug-resistant epilepsy: Systematic review and meta-analysis from the ILAE Surgical Therapies Commission.

OBJECTIVE: Summarize the current evidence on efficacy and tolerability of vagus nerve stimulation (VNS), responsive neurostimulation (RNS), and deep brain stimulation (DBS) through a systematic review and meta-analysis. METHODS: We followed the Preferred Reporting Items of Systematic reviews and Meta-Analyses reporting standards and searched Ovid Medline, Ovid Embase, and the Cochrane Central Register of Controlled Trials. We included published randomized controlled trials (RCTs) and their corresponding open-label extension studies, as well as prospective case series, with ≥20 participants (excluding studies limited to children). Our primary outcome was the mean (or median, when unavailable) percentage decrease in frequency, as compared to baseline, of all epileptic seizures at last follow-up. Secondary outcomes included the proportion of treatment responders and proportion with seizure freedom. RESULTS: We identified 30 eligible studies, six of which were RCTs. At long-term follow-up (mean 1.3 years), five observational studies for VNS reported a pooled mean percentage decrease in seizure frequency of 34.7% (95% confidence interval [CI]: -5.1, 74.5). In the open-label extension studies for RNS, the median seizure reduction was 53%, 66%, and 75% at 2, 5, and 9 years of follow-up, respectively. For DBS, the median reduction was 56%, 65%, and 75% at 2, 5, and 7 years, respectively. The proportion of individuals with seizure freedom at last follow-up increased significantly over time for DBS and RNS, whereas a positive trend was observed for VNS. Quality of life was improved in all modalities. The most common complications included hoarseness, and cough and throat pain for VNS and implant site pain, headache, and dysesthesia for DBS and RNS. SIGNIFICANCE: Neurostimulation modalities are an effective treatment option for drug-resistant epilepsy, with improving outcomes over time and few major complications. Seizure-reduction rates among the three therapies were similar during the initial blinded phase. Recent long-term follow-up studies are encouraging for RNS and DBS but are lacking for VNS.

Timing of referral to evaluate for epilepsy surgery: Expert Consensus Recommendations from the Surgical Therapies Commission of the International League Against Epilepsy.

Epilepsy surgery is the treatment of choice for patients with drug-resistant seizures. A timely evaluation for surgical candidacy can be life-saving for patients who are identified as appropriate surgical candidates, and may also enhance the care of nonsurgical candidates through improvement in diagnosis, optimization of therapy, and treatment of comorbidities. Yet, referral for surgical evaluations is often delayed while palliative options are pursued, with significant adverse consequences due to increased morbidity and mortality associated with intractable epilepsy. The Surgical Therapies Commission of the International League Against Epilepsy (ILAE) sought to address these clinical gaps and clarify when to initiate a surgical evaluation. We conducted a Delphi consensus process with 61 epileptologists, epilepsy neurosurgeons, neurologists, neuropsychiatrists, and neuropsychologists with a median of 22 years in practice, from 28 countries in all six ILAE world regions. After three rounds of Delphi surveys, evaluating 51 unique scenarios, we reached the following Expert Consensus Recommendations: (1) Referral for a surgical evaluation should be offered to every patient with drug-resistant epilepsy (up to 70 years of age), as soon as drug resistance is ascertained, regardless of epilepsy duration, sex, socioeconomic status, seizure type, epilepsy type (including epileptic encephalopathies), localization, and comorbidities (including severe psychiatric comorbidity like psychogenic nonepileptic seizures [PNES] or substance abuse) if patients are cooperative with management; (2) A surgical referral should be considered for older patients with drug-resistant epilepsy who have no surgical contraindication, and for patients (adults and children) who are seizure-free on 1-2 antiseizure medications (ASMs) but have a brain lesion in noneloquent cortex; and (3) referral for surgery should not be offered to patients with active substance abuse who are noncooperative with management. We present the Delphi consensus results leading up to these Expert Consensus Recommendations and discuss the data supporting our conclusions. High level evidence will be required to permit creation of clinical practice guidelines.

Androgen Deprivation Therapy in Men with Prostate Cancer Does Not Affect Risk of Infection with SARS-CoV-2.

PURPOSE: TMPRSS2 is a host co-receptor for cell entry of SARS-CoV-2. A prior report suggested that use of androgen deprivation therapy, which downregulates TMPRSS2, may protect men with prostate cancer from infection. MATERIALS AND METHODS: This is a cohort study of a prospective registry of all patients tested for SARS-CoV-2 between March 12 and June 10, 2020 with complete followup until disease recovery or death. The main exposure examined was the use of androgen deprivation therapy, and the outcome measures were the rate of SARS-CoV-2 positivity and disease severity as a function of androgen deprivation therapy use. RESULTS: The study cohort consisted of 1,779 men with prostate cancer from a total tested population of 74,787, of whom 4,885 (6.5%) were positive for SARS-CoV-2. Of those with prostate cancer 102 (5.7%) were SARS-CoV-2 positive and 304 (17.1%) were on androgen deprivation therapy. Among those on androgen deprivation therapy 5.6% were positive as compared to 5.8% not on androgen deprivation therapy. Men on androgen deprivation therapy were slightly older (75.5 vs 73.8 years, p=0.009), more likely to have smoked (68.1% vs 59.3%, p=0.005) and more likely to report taking steroids (43.8% vs 23.3%, p <0.001). Other factors known to increase risk of infection and disease severity were equally distributed (asthma, diabetes mellitus, hypertension, coronary artery disease, heart failure and immune suppressive disease). Multivariable analysis did not indicate a difference in infection risk for those with prostate cancer on androgen deprivation therapy (OR 0.93, 95% CI 0.54-1.61, p=0.8). CONCLUSIONS: Androgen deprivation therapy does not appear to be protective against SARS-CoV-2 infection.

Algorithms in clinical epilepsy practice: Can they really help us predict epilepsy outcomes?

Significant technological advances have improved our ability to localize epilepsy and investigate the electrophysiology in patients undergoing preparation for epilepsy surgery. Conversely, our process of decision-making and outcome prediction has remained essentially restricted to subjective clinical judgment. This may have hindered our ability to improve outcomes. In this review, we highlight the cognitive biases that interfere with medical decision-making and present data on the use of algorithms and statistical models in general health care, before pivoting to discuss applications in the context of epilepsy.

Predicting mood decline following temporal lobe epilepsy surgery in adults.

OBJECTIVE: To develop a model to predict the probability of mood decline in adults following temporal lobe resection for the treatment of pharmacoresistant epilepsy. METHODS: Variable selection was performed on 492 patients from the Cleveland Clinic using best subsets regression. After completing variable selection, a subset of variables was requested from four epilepsy surgery centers across North America (n = 100). All data were combined to develop a final model to predict postoperative mood decline (N = 592). Internal validation with bootstrap resampling was performed. A clinically significant increase in depressive symptoms was defined as a 15% increase in Beck Depression Inventory-Second Edition score and a postoperative raw score > 11. RESULTS: Fourteen percent of patients in the Cleveland Clinic cohort and 22% of patients in the external cohort experienced clinically significant increases in depressive symptoms following surgery. The final prediction model included six predictor variables: psychiatric history, resection side, relationship status, verbal fluency score, age at preoperative testing, and presence/absence of malformation of cortical development on magnetic resonance imaging. The model had an optimism-adjusted c-statistic of .70 and good calibration, with slight probability overestimation in higher risk patients. SIGNIFICANCE: Clinicians can utilize our nomogram via a paper tool or online calculator to estimate the risk of postoperative mood decline for individual patients prior to temporal lobe epilepsy surgery.

Automated analysis of cortical volume loss predicts seizure outcomes after frontal lobectomy.

OBJECTIVE: Patients undergoing frontal lobectomy demonstrate lower seizure-freedom rates than patients undergoing temporal lobectomy and several other resective interventions. We attempted to utilize automated preoperative quantitative analysis of focal and global cortical volume loss to develop predictive volumetric indicators of seizure outcome after frontal lobectomy. METHODS: Ninety patients who underwent frontal lobectomy were stratified based on seizure freedom at a mean follow-up time of 3.5 (standard deviation [SD] 2.5) years. Automated quantitative analysis of cortical volume loss organized by distinct brain region and laterality was performed on preoperative T1-weighted magnetic resonance imaging (MRI) studies. Univariate statistical analysis was used to select potential predictors of seizure freedom. Backward variable selection and multivariate logistical regression were used to develop models to predict seizure freedom. RESULTS: Forty-eight of 90 (53.3%) patients were seizure-free at the last follow-up. Several frontal and extrafrontal brain regions demonstrated statistically significant differences in both volumetric cortical volume loss and volumetric asymmetry between the left and right sides in the seizure-free and non-seizure-free cohorts. A final multivariate logistic model utilizing only preoperative quantitative MRI data to predict seizure outcome was developed with a c-statistic of 0.846. Using both preoperative quantitative MRI data and previously validated clinical predictors of seizure outcomes, we developed a model with a c-statistic of 0.897. SIGNIFICANCE: This study demonstrates that preoperative cortical volume loss in both frontal and extrafrontal regions can be predictive of seizure outcome after frontal lobectomy, and models can be developed with excellent predictive capabilities using preoperative MRI data. Automated quantitative MRI analysis can be quickly and reliably performed in patients with frontal lobe epilepsy, and further studies may be developed for integration into preoperative risk stratification.

Improving the prediction of epilepsy surgery outcomes using basic scalp EEG findings.

OBJECTIVE: This study aims to evaluate the role of scalp electroencephalography (EEG; ictal and interictal patterns) in predicting resective epilepsy surgery outcomes. We use the data to further develop a nomogram to predict seizure freedom. METHODS: We retrospectively reviewed the scalp EEG findings and clinical data of patients who underwent surgical resection at three epilepsy centers. Using both EEG and clinical variables categorized into 13 isolated candidate predictors and 6 interaction terms, we built a multivariable Cox proportional hazards model to predict seizure freedom 2 years after surgery. Harrell's step-down procedure was used to sequentially eliminate the least-informative variables from the model until the change in the concordance index (c-index) with variable removal was less than 0.01. We created a separate model using only clinical variables. Discrimination of the two models was compared to evaluate the role of scalp EEG in seizure-freedom prediction. RESULTS: Four hundred seventy patient records were analyzed. Following internal validation, the full Clinical + EEG model achieved an optimism-corrected c-index of 0.65, whereas the c-index of the model without EEG data was 0.59. The presence of focal to bilateral tonic-clonic seizures (FBTCS), high preoperative seizure frequency, absence of hippocampal sclerosis, and presence of nonlocalizable seizures predicted worse outcome. The presence of FBTCS had the largest impact for predicting outcome. The analysis of the models' interactions showed that in patients with unilateral interictal epileptiform discharges (IEDs), temporal lobe surgery cases had a better outcome. In cases with bilateral IEDs, abnormal magnetic resonance imaging (MRI) predicted worse outcomes, and in cases without IEDs, patients with extratemporal epilepsy and abnormal MRI had better outcomes. SIGNIFICANCE: This study highlights the value of scalp EEG, particularly the significance of IEDs, in predicting surgical outcome. The nomogram delivers an individualized prediction of postoperative outcome, and provides a unique assessment of the relationship between the outcome and preoperative findings.

Somatic symptoms have negligible impact on Patient Health Questionnaire-9 depression scale scores in neurological patients.

BACKGROUND AND PURPOSE: There is concern that the Patient Health Questionnaire-9 (PHQ-9) depression scale may be impacted by the presence of somatic symptoms (differential item functioning [DIF]) in patients with neurological conditions. We evaluated the PHQ-9 for the presence and impact of DIF in large clinical samples of neurological patients. METHODS: We conducted a cross-sectional study of patients seen at the Cleveland Clinic Cerebrovascular, Headache, Movement Disorder, and Neuromuscular clinics who completed the PHQ-9 and patient-reported disease severity measures as part of standard care between 29 July 2008 and 21 February 2013. We evaluated PHQ-9 items for DIF with respect to disease-specific severity for each condition. Salient DIF impact was characterized as a difference between DIF-adjusted and unadjusted PHQ-9 scores. RESULTS: Included in the study were 2112 patients with stroke, 8221 with migraine, 440 with amyotrophic lateral sclerosis (ALS), and 5022 with Parkinson disease (PD). Several PHQ-9 items demonstrated DIF with respect to disease-specific severity, although salient DIF was present in very few patients (stroke, n = 0; migraine, n = 1; ALS, n = 13; PD, n = 1). CONCLUSIONS: PHQ-9 items function consistently across disease severity, with salient levels of DIF impact found only for a very small proportion of people. These results suggest that the PHQ-9 provides a consistent measure of depression severity among people with neurological conditions associated with somatic symptoms that overlap with depression.