Objective score from initial interview identifies patients with probable dissociative seizures.

OBJECTIVE: To develop a Dissociative Seizures Likelihood Score (DSLS), which is a comprehensive, evidence-based tool using information available during the first outpatient visit to identify patients with "probable" dissociative seizures (DS) to allow early triage to more extensive diagnostic assessment. METHODS: Based on data from 1616 patients with video-electroencephalography (vEEG) confirmed diagnoses, we compared the clinical history from a single neurology interview of patients in five mutually exclusive groups: epileptic seizures (ES), DS, physiologic nonepileptic seizure-like events (PSLE), mixed DS plus ES, and inconclusive monitoring. We used data-driven methods to determine the diagnostic utility of 76 features from retrospective chart review and applied this model to prospective interviews. RESULTS: The DSLS using recursive feature elimination (RFE) correctly identified 77% (95% confidence interval (CI), 74-80%) of prospective patients with either ES or DS, with a sensitivity of 74% and specificity of 84%. This accuracy was not significantly inferior than neurologists' impression (84%, 95% CI: 80-88%) and the kappa between neurologists' and the DSLS was 21% (95% CI: 1-41%). Only 3% of patients with DS were missed by both the fellows and our score (95% CI 0-11%). SIGNIFICANCE: The evidence-based DSLS establishes one method to reliably identify some patients with probable DS using clinical history. The DSLS supports and does not replace clinical decision making. While not all patients with DS can be identified by clinical history alone, these methods combined with clinical judgement could be used to identify patients who warrant further diagnostic assessment at a comprehensive epilepsy center.

Distinguishing Highly Asymmetric Keratoconus Eyes Using Combined Scheimpflug and Spectral-Domain OCT Analysis.

PURPOSE: To determine optimal objective, machine-derived variables and variable combinations from Scheimpflug and spectral-domain (SD) OCT imaging to distinguish the clinically unaffected eye in patients with asymmetric keratoconus (AKC) from a normal control population. DESIGN: Retrospective case-control study. PARTICIPANTS: Thirty clinically unaffected eyes with no physical findings on slit-lamp examination, no definitive abnormalities on corneal imaging, and corrected distance acuity of 20/20 or better from 30 patients with highly AKC eyes and 60 eyes from 60 normal control patients who had undergone uneventful LASIK with at least 2 years of stable follow-up (controls). METHODS: Scheimpflug and SD OCT imaging were obtained in all eyes, and receiver operating characteristic (ROC) curves were generated to determine area under the curve (AUC), sensitivity, and specificity for each machine-derived variable and variable combination. MAIN OUTCOME MEASURES: Distinguishing AKC eyes from controls as determined by AUC, sensitivity, and specificity. RESULTS: No individual machine-derived metric from Scheimpflug or SD OCT technology yielded an AUC higher than 0.75. Combining 5 Scheimpflug metrics (index height decentration [IHD], index vertical asymmetry [IVA], pachymetry apex, inferior-superior value, and Ambrosio's Relational Thickness Maximum [ARTmax]) yielded the best Scheimpflug results (AUC 0.86, sensitivity 83%, specificity 83%). Combining 11 SD OCT thickness metrics (minimum-median, temporal outer, superior nasal outer, minimum, epithelium minimum-maximum, epithelial standard deviation, superior inner, superior outer, superior temporal outer, superior nasal inner, central) yielded the best SD OCT results (AUC 0.96, sensitivity 89%, specificity 89%). Combining 13 total Scheimpflug/SD OCT metrics yielded the best results overall (AUC 1.0, sensitivity 100%, specificity 100%). The most impactful variables in combined models included epithelial thickness variability and total focal corneal thickness variability from SD OCT and anterior curvature and topometric indices from Scheimpflug technology. No posterior corneal metrics were impactful in modeling. CONCLUSIONS: Individual machine-derived metrics from Scheimpflug and SD OCT imaging poorly distinguished normal eyes from minimally affected eyes from patients with highly AKC. Combined SD OCT metrics performed better than combined Scheimpflug metrics. Combining anterior curvature and asymmetry indices from Scheimpflug with regional total thickness and epithelial thickness variability metrics from SD OCT clearly distinguished the 2 populations. Posterior corneal indices were not useful in distinguishing populations.

An objective score to identify psychogenic seizures based on age of onset and history.

OBJECTIVE: Psychogenic nonepileptic seizure (PNES) is a common diagnosis after evaluation of medication resistant or atypical seizures with video-electroencephalographic monitoring (VEM), but usually follows a long delay after the development of seizures, during which patients are treated for epilepsy. Therefore, more readily available diagnostic tools are needed for earlier identification of patients at risk for PNES. A tool based on patient-reported psychosocial history would be especially beneficial because it could be implemented in the outpatient clinic. METHODS: Based on the data from 1375 patients with VEM-confirmed diagnoses, we used logistic regression to compare the frequency of specific patient-reported historical events, demographic information, age of onset, and delay from first seizure until VEM in five mutually exclusive groups of patients: epileptic seizures (ES), PNES, physiologic nonepileptic seizure-like events (PSLE), mixed PNES plus ES, and inconclusive monitoring. To determine the diagnostic utility of this information to differentiate PNES only from ES only, we used multivariate piecewise-linear logistic regression trained using retrospective data from chart review and validated based on data from 246 prospective standardized interviews. RESULTS: The prospective area under the curve of our weighted multivariate piecewise-linear by-sex score was 73%, with the threshold that maximized overall retrospective accuracy resulting in a prospective sensitivity of 74% (95% CI: 70-79%) and prospective specificity of 71% (95% CI: 64-82%). The linear model and piecewise linear without an interaction term for sex had very similar performance statistics. In the multivariate piecewise-linear sex-split predictive model, the significant factors positively associated with ES were history of febrile seizures, current employment or active student status, history of traumatic brain injury (TBI), and longer delay from first seizure until VEM. The significant factors associated with PNES were female sex, older age of onset, mild TBI, and significant stressful events with sexual abuse, in particular, increasing the likelihood of PNES. Delays longer than 20years, age of onset after 31years for men, and age of onset after 40years for women had no additional effect on the likelihood of PNES. DISCUSSION: Our promising results suggest that an objective score has the potential to serve as an early outpatient screening tool to identify patients with greater likelihood of PNES when considered in combination with other factors. In addition, our analysis suggests that sexual abuse, more than other psychological stressors including physical abuse, is more associated with PNES. There was a trend of increasing frequency of PNES for women during childbearing years and plateauing outside those years that was not observed in men.

Identifying psychogenic seizures through comorbidities and medication history.

OBJECTIVE: Low-cost evidence-based tools are needed to facilitate the early identification of patients with possible psychogenic nonepileptic seizures (PNES). Prior to accurate diagnosis, patients with PNES do not receive interventions that address the cause of their seizures and therefore incur high medical costs and disability due to an uncontrolled seizure disorder. Both seizures and comorbidities may contribute to this high cost. METHODS: Based on data from 1,365 adult patients with video-electroencephalography-confirmed diagnoses from a single center, we used logistic and Poisson regression to compare the total number of comorbidities, number of medications, and presence of specific comorbidities in five mutually exclusive groups of diagnoses: epileptic seizures (ES) only, PNES only, mixed PNES and ES, physiologic nonepileptic seizurelike events, and inconclusive monitoring. To determine the diagnostic utility of comorbid diagnoses and medication history to differentiate PNES only from ES only, we used multivariate logistic regression, controlling for sex and age, trained using a retrospective database and validated using a prospective database. RESULTS: Our model differentiated PNES only from ES only with a prospective accuracy of 78% (95% confidence interval =72-84%) and area under the curve of 79%. With a few exceptions, the number of comorbidities and medications was more predictive than a specific comorbidity. Comorbidities associated with PNES were asthma, chronic pain, and migraines (p < 0.01). Comorbidities associated with ES were diabetes mellitus and nonmetastatic neoplasm (p < 0.01). The population-level analysis suggested that patients with mixed PNES and ES may be a population distinct from patients with either condition alone. SIGNIFICANCE: An accurate patient-reported medical history and medication history can be useful when screening for possible PNES. Our prospectively validated and objective score may assist in the interpretation of the medication and medical history in the context of the seizure description and history.

Diagnostic implications of review-of-systems questionnaires to differentiate epileptic seizures from psychogenic seizures.

OBJECTIVE: Early and accurate diagnosis of patients with psychogenic nonepileptic seizures (PNES) leads to appropriate treatment and improves long-term seizure prognosis. However, this is complicated by the need to record seizures to make a definitive diagnosis. Suspicion for PNES can be raised through knowledge that patients with PNES have increased somatic sensitivity and report more positive complaints on review-of-systems questionnaires (RoSQs) than patients with epileptic seizures. If the responses on the RoSQ can differentiate PNES from other seizure types, then these forms could be an early screening tool. METHODS: Our dataset included all patients admitted from January 2006 to June 2016 for video-electroencephalography at UCLA. RoSQs prior to May 2015 were acquired through retrospective chart review (n=405), whereas RoSQs from subsequent patients were acquired prospectively (n=190). Controlling for sex and number of comorbidities, we used binomial regression to compare the total number of symptoms and the frequency of specific symptoms between five mutually exclusive groups of patients: epileptic seizures (ES), PNES, physiologic nonepileptic seizure-like events (PSLE), mixed PNES plus ES, and inconclusive monitoring. To determine the diagnostic utility of RoSQs to differentiate PNES only from ES only, we used multivariate logistic regression, controlling for sex and the number of medical comorbidities. RESULTS: On average, patients with PNES or mixed PNES and ES reported more than twice as many symptoms than patients with isolated ES or PSLE (p<0.001). The prospective accuracy to differentiate PNES from ES was not significantly higher than naïve assumption that all patients had ES (76% vs 70%, p>0.1). DISCUSSION: This analysis of RoSQs confirms that patients with PNES with and without comorbid ES report more symptoms on a population level than patients with epilepsy or PSLE. While these differences help describe the population of patients with PNES, the consistency of RoSQ responses was neither accurate nor specific enough to be used solely as an early screening tool for PNES. Our results suggest that the RoSQ may help differentiate PNES from ES only when, based on other information, the pre-test probability of PNES is at least 50%.

Factors associated with delay to video-EEG in dissociative seizures.

PURPOSE: While certain clinical factors suggest a diagnosis of dissociative seizures (DS), otherwise known as functional or psychogenic nonepileptic seizures (PNES), ictal video-electroencephalography monitoring (VEM) is the gold standard for diagnosis. Diagnostic delays were associated with worse quality of life and more seizures, even after treatment. To understand why diagnoses were delayed, we evaluated which factors were associated with delay to VEM. METHODS: Using data from 341 consecutive patients with VEM-documented dissociative seizures, we used multivariate log-normal regression with recursive feature elimination (RFE) and multiple imputation of some missing data to evaluate which of 76 clinical factors were associated with time from first dissociative seizure to VEM. RESULTS: The mean delay to VEM was 8.4 years (median 3 years, IQR 1-10 years). In the RFE multivariate model, the factors associated with longer delay to VEM included more past antiseizure medications (0.19 log-years/medication, standard error (SE) 0.05), more medications for other medical conditions (0.06 log-years/medication, SE 0.03), history of physical abuse (0.75 log-years, SE 0.27), and more seizure types (0.36 log-years/type, SE 0.11). Factors associated with shorter delay included active employment or student status (-1.05 log-years, SE 0.21) and higher seizure frequency (0.14 log-years/log[seizure/month], SE 0.06). CONCLUSIONS: Patients with greater medical and seizure complexity had longer delays. Delays in multiple domains of healthcare can be common for victims of physical abuse. Unemployed and non-student patients may have had more barriers to access VEM. These results support earlier referral of complex cases to a comprehensive epilepsy center.

Epilepsy, dissociative seizures, and mixed: Associations with time to video-EEG.

PURPOSE: Video-electroencephalographic monitoring (VEM) is a core component to the diagnosis and evaluation of epilepsy and dissociative seizures (DS)-also known as functional or psychogenic seizures-but VEM evaluation often occurs later than recommended. To understand why delays occur, we compared how patient-reported clinical factors were associated with time from first seizure to VEM (TVEM) in patients with epilepsy, DS or mixed. METHODS: We acquired data from 1245 consecutive patients with epilepsy, VEM-documented DS or mixed epilepsy and DS. We used multivariate log-normal regression with recursive feature elimination (RFE) to evaluate which of 76 clinical factors interacting with patients' diagnoses were associated with TVEM. RESULTS: The mean and median TVEM were 14.6 years and 10 years, respectively (IQR 3-23 years). In the multivariate RFE model, the factors associated with longer TVEM in all patients included unemployment and not student status, more antiseizure medications (current and past), concussion, and ictal behavior suggestive of temporal lobe epilepsy. Average TVEM was shorter for DS than epilepsy, particularly for patients with depression, anxiety, migraines, and eye closure. Average TVEM was longer specifically for patients with DS taking more medications, more seizure types, non-metastatic cancer, and with other psychiatric comorbidities. CONCLUSIONS: In all patients with seizures, trials of numerous antiseizure medications, unemployment and non-student status was associated with longer TVEM. These associations highlight a disconnect between International League Against Epilepsy practice parameters and observed referral patterns in epilepsy. In patients with dissociative seizures, some but not all factors classically associated with DS reduced TVEM.

Utility of regional epithelial thickness measurements in corneal evaluations.

The measurement of regional corneal epithelial thickness and characterization of its behavior in response to changes in corneal architecture are increasingly drawing interest in clinical practice. The epithelium has tremendous capacity for remodeling and does so in response to underlying stromal pathology or changes in anterior corneal curvature. Various remodeling patterns have been identified that help distinguish between ectatic and nonectatic corneal conditions. Epithelial mapping has also facilitated more precise, individualized corneal surface disorder treatments. We highlight the different imaging modalities for epithelium measurement, epithelial remodeling patterns in ectatic disorders and after corneal refractive surgery, discuss utility of epithelial measurement in therapeutic refractive surgery planning, and discuss controversies that exist regarding epithelial remodeling, including its mechanisms and its relative importance in surgical planning and screening evaluations.

Distinguishing Highly Asymmetric Keratoconus Eyes Using Dual Scheimpflug/Placido Analysis.

PURPOSE: To identify the best metrics or combination of metrics that provide the highest predictive power between normal eyes and the clinically unaffected eye of patients with highly asymmetric keratoconus using data from a Dual Scheimpflug/Placido device. DESIGN: Retrospective case-control study. METHODS: Combined Dual Scheimpflug/Placido imaging was obtained from the Galilei G4 device (Ziemer Ophthalmic Systems AG, Port, Switzerland) in 31 clinically unaffected eyes with highly asymmetric keratoconus and 178 eyes from 178 patients with bilaterally normal corneal examinations that underwent uneventful LASIK with at least 1 year follow-up. Receiver operating characteristic (ROC) curves were generated to determine area under the curve (AUC), sensitivity, and specificity for 87 metrics, and logistic regression modeling was used to determine optimal variable combinations. RESULTS: No individual metric achieved an AUC greater than 0.79. A combined model consisting of 9 metrics yielded an AUC of 0.96, with 90.3% sensitivity and 92.6% specificity. Among those 9 metrics included, 5 related to corneal pachymetry: Opposite Sector Index and Anterior Height BFS Z from the anterior surface, Asphericity and Asymmetry Index, Posterior Height BFS Z, and Posterior Height BFS X from the posterior surface. The strongest variable in the model was the thinnest point location on the horizontal (x) axis. CONCLUSION: While individual metrics performed poorly, using a combination of metrics from the combined Dual Scheimpflug/Placido device provided a useful model for differentiating normal corneas from the clinically normal eyes of patients with highly asymmetric keratoconus. Pachymetry values were the most impactful metrics.

Reliability of reported peri-ictal behavior to identify psychogenic nonepileptic seizures.

PURPOSE: Differentiating psychogenic non-epileptic seizures (PNES) from epileptic seizures (ES) can be difficult, even when expert clinicians have video recordings of seizures. Moreover, witnesses who are not trained observers may provide descriptions that differ from the expert clinicians', which often raises concern about whether the patient has both ES and PNES. As such, quantitative, evidence-based tools to help differentiate ES from PNES based on patients' and witnesses' descriptions of seizures may assist in the early, accurate diagnosis of patients. METHODS: Based on patient- and observer-reported data from 1372 patients with diagnoses documented by video-elect roencephalography (vEEG), we used logistic regression (LR) to compare specific peri-ictal behaviors and seizure triggers in five mutually exclusive groups: ES, PNES, physiologic non-epileptic seizure-like events, mixed PNES plus ES, and inconclusive monitoring. To differentiate PNES-only from ES-only, we retrospectively trained multivariate LR and a forest of decision trees (DF) to predict the documented diagnoses of 246 prospective patients. RESULTS: The areas under the receiver operating characteristic curve (AUCs) of the DF and LR were 75% and 74%, respectively (empiric 95% CI of chance 37-62%). The overall accuracy was not significantly higher than the naïve assumption that all patients have ES (accuracy DF 71%, LR 70%, naïve 68%, p > 0.05). CONCLUSIONS: Quantitative analysis of patient- and observer-reported peri-ictal behaviors objectively changed the likelihood that a patient's seizures were psychogenic, but these reports were not reliable enough to be diagnostic in isolation. Instead, our scores may identify patients with "probable" PNES that, in the right clinical context, may warrant further diagnostic assessment.

Differences in Posterior Corneal Features Between Normal Corneas and Subclinical Keratoconus.

PURPOSE: To compare posterior corneal features and their discriminating power for differentiating normal corneas from subclinical keratoconus using the Placido dual-Scheimpflug analyzer. METHODS: Patients were retrospectively included in the study. The preoperative normal right eyes of 79 patients imaged with a Placido dual-Scheimpflug system and with a stable postoperative LASIK follow-up of a minimum of 36 months were included in the normal group and were compared to 39 contralateral topographically normal eyes with clinically evident keratoconus in the fellow eye. The posterior surface variables measured were categorized according to the feature of the corneal shape they were characterizing (curvature, elevation, asymmetry, and eccentricity) and compared between the two groups using the Student's two-sample t test. The discriminating ability of the posterior surface variables was compared by receiver operator characteristics curves. RESULTS: Variables that related to asymmetry and elevation of the posterior surface were statistically significantly different between groups (P < .05), whereas eccentricity and curvature-related parameters were not. Receiver operator characteristics curves analysis showed that the maximum posterior elevation over the best-fit toric and aspheric surface reference shape had the highest discriminating ability for distinguishing normal corneas from subclinical keratoconus, with an area under the curve of 0.877, followed by the asphericity asymmetry index, with an area under the curve of 0.871, and posterior inferior-superior value, with an area under the curve of 0.851. CONCLUSIONS: Posterior cornea measured with a dual-Scheimpflug analyzer provides useful parameters for differentiating normal corneas from subclinical keratoconus. Of the posterior surface parameters, asymmetry and elevation seem to be the most sensitive shape modifications for differentiating both populations. [J Refract Surg. 2018;34(10):664-670.].

Multimodal diagnosis of epilepsy using conditional dependence and multiple imputation.

The definitive diagnosis of the type of epilepsy, if it exists, in medication-resistant seizure disorder is based on the efficient combination of clinical information, long-term video-electroencephalography (EEG) and neuroimaging. Diagnoses are reached by a consensus panel that combines these diverse modalities using clinical wisdom and experience. Here we compare two methods of multimodal computer-aided diagnosis, vector concatenation (VC) and conditional dependence (CD), using clinical archive data from 645 patients with medication-resistant seizure disorder, confirmed by video-EEG. CD models the clinical decision process, whereas VC allows for statistical modeling of cross-modality interactions. Due to the nature of clinical data, not all information was available in all patients. To overcome this, we multiply-imputed the missing data. Using a C4.5 decision tree, single modality classifiers achieved 53.1%, 51.5% and 51.1% average accuracy for MRI, clinical information and FDG-PET, respectively, for the discrimination between non-epileptic seizures, temporal lobe epilepsy, other focal epilepsies and generalized-onset epilepsy (vs. chance, p<0.01). Using VC, the average accuracy was significantly lower (39.2%). In contrast, the CD classifier that classified with MRI then clinical information achieved an average accuracy of 58.7% (vs. VC, p<0.01). The decrease in accuracy of VC compared to the MRI classifier illustrates how the addition of more informative features does not improve performance monotonically. The superiority of conditional dependence over vector concatenation suggests that the structure imposed by conditional dependence improved our ability to model the underlying diagnostic trends in the multimodality data.

Balancing Clinical and Pathologic Relevence in the Machine Learning Diagnosis of Epilepsy.

The application of machine learning to epilepsy can be used both to develop clinically useful computer-aided diagnostic tools, and to reveal pathologically relevant insights into the disease. Such studies most frequently use neurologically normal patients as the control group to maximize the pathologic insight yielded from the model. This practice yields potentially inflated accuracy because the groups are quite dissimilar. A few manuscripts, however, opt to mimic the clinical comparison of epilepsy to non-epileptic seizures, an approach we believe to be more clinically realistic. In this manuscript, we describe the relative merits of each control group. We demonstrate that in our clinical quality FDG-PET database the performance achieved was similar using each control group. Based on these results, we find that the choice of control group likely does not hinder the reported performance. We argue that clinically applicable computer-aided diagnostic tools for epilepsy must directly address the clinical challenge of distinguishing patients with epilepsy from those with non-epileptic seizures.