Seizure provocation in EEG recordings: A data-driven approach.

OBJECTIVE: Recording seizures on video-EEG has a high diagnostic value. However, bilateral convulsive seizures constitute a risk for the patients. Our aim was to investigate the diagnostic yield and associated risks of provocation methods in short-term video-EEGs. METHODS: We extracted data on seizures and provocation methods from a large database of short-term video-EEGs with standardized annotations using SCORE (Standardized Computer-based Organized reporting of EEG). RESULTS: 2742 paroxysmal clinical episodes were recorded in 11 919 consecutive EEGs. Most epileptic seizures (54%) were provoked. Hyperventilation provoked most of typical absence seizures (55%), intermittent photic stimulation (IPS) provoked myoclonic seizures (25%) and most of bilateral convulsive seizures (55%), while 43% of focal seizures were precipitated by sleep. All but one of the 16 bilateral convulsive seizures were provoked by IPS or sleep. Latency between start of generalized photoparoxysmal EEG response and bilateral convulsive seizures were ≤3 s in all but one patient. SIGNIFICANCE: The large, structured database provides evidence for the diagnostic utility of various provocation methods in short-term video-EEGs. The risk of bilateral convulsive seizures is relatively small, but it cannot be prevented by stopping IPS after 3 s. A priori knowledge about seizure semiology helps planning patient-tailored provocation strategy in short-term video-EEGs.

Automated Interpretation of Clinical Electroencephalograms Using Artificial Intelligence.

Importance: Electroencephalograms (EEGs) are a fundamental evaluation in neurology but require special expertise unavailable in many regions of the world. Artificial intelligence (AI) has a potential for addressing these unmet needs. Previous AI models address only limited aspects of EEG interpretation such as distinguishing abnormal from normal or identifying epileptiform activity. A comprehensive, fully automated interpretation of routine EEG based on AI suitable for clinical practice is needed. Objective: To develop and validate an AI model (Standardized Computer-based Organized Reporting of EEG-Artificial Intelligence [SCORE-AI]) with the ability to distinguish abnormal from normal EEG recordings and to classify abnormal EEG recordings into categories relevant for clinical decision-making: epileptiform-focal, epileptiform-generalized, nonepileptiform-focal, and nonepileptiform-diffuse. Design, Setting, and Participants: In this multicenter diagnostic accuracy study, a convolutional neural network model, SCORE-AI, was developed and validated using EEGs recorded between 2014 and 2020. Data were analyzed from January 17, 2022, until November 14, 2022. A total of 30 493 recordings of patients referred for EEG were included into the development data set annotated by 17 experts. Patients aged more than 3 months and not critically ill were eligible. The SCORE-AI was validated using 3 independent test data sets: a multicenter data set of 100 representative EEGs evaluated by 11 experts, a single-center data set of 9785 EEGs evaluated by 14 experts, and for benchmarking with previously published AI models, a data set of 60 EEGs with external reference standard. No patients who met eligibility criteria were excluded. Main Outcomes and Measures: Diagnostic accuracy, sensitivity, and specificity compared with the experts and the external reference standard of patients' habitual clinical episodes obtained during video-EEG recording. Results: The characteristics of the EEG data sets include development data set (N = 30 493; 14 980 men; median age, 25.3 years [95% CI, 1.3-76.2 years]), multicenter test data set (N = 100; 61 men, median age, 25.8 years [95% CI, 4.1-85.5 years]), single-center test data set (N = 9785; 5168 men; median age, 35.4 years [95% CI, 0.6-87.4 years]), and test data set with external reference standard (N = 60; 27 men; median age, 36 years [95% CI, 3-75 years]). The SCORE-AI achieved high accuracy, with an area under the receiver operating characteristic curve between 0.89 and 0.96 for the different categories of EEG abnormalities, and performance similar to human experts. Benchmarking against 3 previously published AI models was limited to comparing detection of epileptiform abnormalities. The accuracy of SCORE-AI (88.3%; 95% CI, 79.2%-94.9%) was significantly higher than the 3 previously published models (P < .001) and similar to human experts. Conclusions and Relevance: In this study, SCORE-AI achieved human expert level performance in fully automated interpretation of routine EEGs. Application of SCORE-AI may improve diagnosis and patient care in underserved areas and improve efficiency and consistency in specialized epilepsy centers.

Duration of epileptic seizure types: A data-driven approach.

OBJECTIVE: To determine the duration of epileptic seizure types in patients who did not undergo withdrawal of antiseizure medication. METHODS: From a large, structured database of 11 919 consecutive, routine video-electroencephalograpy (EEG) recordings, labeled using the SCORE (Standardized Computer-Based Organized Reporting of EEG) system, we extracted and analyzed 2742 seizures. For each seizure type we determined median duration and range after removal of outliers (2.5-97.5 percentile). We used surface electromyography (EMG) for accurate measurement of short motor seizures. RESULTS: Myoclonic seizures last <150 ms, epileptic spasms 0.4-2 s, tonic seizures 1.5-36 s, atonic seizures 0.1-12,5 s, when measured using surface EMG. Generalized clonic seizures last 1-24 s. Typical absence seizures are rarely longer than 30 s (2.75-26.5 s) and atypical absences last 2-100 s. In our patients, the duration of focal aware (median: 27 s; 1.25-166 s) and impaired awareness seizures (median: 42.5 s; 9.5-271 s) was shorter than reported previously in patients undergoing withdrawal of antiseizure medication. All focal seizures terminated within 10 min. Median duration of generalized tonic-clonic seizures was 79.5 s (57-102 s) and of focal-to-bilateral tonic-clonic seizures was 103.5 (77.5-237 s). All tonic-clonic seizures terminated within 5 min. SIGNIFICANCE: This comprehensive list of seizure durations provides important information for characterizing seizures and diagnosing patients with epilepsy. The upper limits of seizure durations are helpful in early recognition of imminent status epilepticus.

Implementing the SCORE system improves the quality of clinical EEG reading.

Objective: To assess whether implementing the freeware version of the SCORE EEG system (Standardized Computer-based Organized Reporting of EEG) leads to improvement in the quality of clinical EEG reading, and whether EEG reports in SCORE EEG are understood and accepted by the referring physicians. Methods: We generated EEG reports in the conventional, free-text style and then using SCORE EEG, in consecutive patients referred to routine EEG. We used the Georgian translation in the SCORE EEG Free Edition. We pre-defined quality indicators consisting of a list of 24 key features that need to be addressed in EEG reports. We compared these quality indicators in free-text reports with SCORE EEG. In addition, EEG reports in SCORE EEG format were assessed by ten referring physicians, who evaluated their usability on a 7-point Likert scale. Results: We included and evaluated EEG reports from 157 patients (80 female; age: 1-75 years; median: 28 years). Fourteen features were reported exclusively in SCORE EEG, four were reported significantly more often in SCORE EEG than in free-text format, and six features were reported equally often in SCORE EEG and in free-text format. Usability aspects of SCORE EEG were highly rated by the referring physicians (median 6-7 on the 7-point Likert scale). Conclusions: The structured system of EEG reporting in SCORE EEG helped the experts reading clinical EEG to cover the important aspects and increase the quality of clinical EEG reports. Significance: Implementing the freeware version of SCORE EEG in underprivileged areas will help improving management of patients with epilepsy.

EEG normal variants: A prospective study using the SCORE system.

Objective: To determine the prevalence and characteristics of normal variants in EEG recordings in a large cohort, and provide readers with typical examples of all normal variants for educational purposes. Methods: Using the SCORE EEG system (Standardized Computer-Based Organized Reporting of EEG), we prospectively extracted EEG features in consecutive patients. In this dataset, we analyzed 3050 recordings from 2319 patients (mean age 38.5 years; range: 1-89 years). Results: The distribution of the normal variants was as follows: sharp transients 19.21% (including wicket spikes), rhythmic temporal theta of drowsiness 6.03%, temporal slowing of the old 2.89%, slow fused transients 2.59%, 14-and 6-Hz bursts 1.83%, breach rhythm 1.25%, small sharp spikes 1.05%, 6-Hz spike and slow wave 0.69% and SREDA 0.03%. Conclusions: The most prevalent normal variants are the sharp transients, which must not be over-read as epileptiform discharges. Significance: EEG readers must be familiar with the normal variants to avoid misdiagnosis and misclassification of patients referred to clinical EEG recordings.

Effect of Continuous Positive Airway Pressure on Symptoms and Prevalence of Insomnia in Patients With Obstructive Sleep Apnea: A Longitudinal Study.

OBJECTIVE: Obstructive sleep apnea (OSA) and insomnia are the two most common sleep disorders. Continuous positive airway pressure (CPAP) is considered first-line treatment for OSA. In the present study, we assess the effect of CPAP on symptoms and prevalence of insomnia in patients with OSA. We hypothesized a decrease in insomnia symptoms from CPAP initiation to follow-up, and that this decrease would depend on CPAP adherence. MATERIALS AND METHODS: The sample included 442 patients diagnosed with OSA [mean age 54.9 years (SD = 12.1), 74.4% males] who started treatment with CPAP at a university hospital. OSA was diagnosed according to standard respiratory polygraphy. Mean apnea-hypopnea index (AHI) was 30.1 (SD = 21.1) at baseline. Insomnia was assessed prior to CPAP treatment (baseline) and at follow-up after a median of 19.9 weeks (range 6-52 weeks) with the Bergen Insomnia Scale (BIS). CPAP adherence was defined as an average use of ≥ 4 h per night, whereas non-adherence was defined as < 4 h per night. RESULTS: There was a significant decrease in BIS scores from baseline (mean = 18.8, SD = 9.8) to follow-up (mean = 12.9, SD = 9.9), p < 0.001. Cohen's d(0.65) indicated a moderate effect size. The reduction in BIS scores was depending on CPAP adherence (interaction effect F(1,440) = 12.4, p < 0.001), with larger reduction in the adherent group than in the non-adherent group. The proportion of patients with chronic insomnia was significantly reduced from 51.1% at baseline to 33.0% at follow-up (p < 0.001). CONCLUSION: Overall, there was a significant reduction in insomnia symptoms from baseline to follow-up. The improvement was significant in both adherence groups, but the degree of improvement was larger among patients who were adherent to CPAP. Furthermore, there was a significant reduction in the prevalence of chronic insomnia at follow-up compared to baseline. This suggests that CPAP effectively reduces both the presence of insomnia and the severity of insomnia symptoms in some patients with OSA.

Photoparoxysmal response and its characteristics in a large EEG database using the SCORE system.

OBJECTIVE: To characterize photoparoxysmal EEG response (PPR) using a standardized protocol of intermittent photic stimulation (IPS) and standardized definitions for PPR, classified into six types. METHODS: Using the SCORE system (Standardized Computer-Based Organized Reporting of EEG) we prospectively built a large database of standardized EEG annotations. In this study, we extracted the features related to PPR from the structured dataset consisting of 10,671 EEG recordings with IPS, from 7,188 patients. RESULTS: The standardized IPS protocol elicited PPR in 375 recordings (3.5%), in 288 patients (4%), with a preponderance among young (11-20 years) and female patients (67%). PPR was persistent in patients with multiple recordings. The most frequent type of PPR was activation of preexisting epileptogenic area (58%), followed by generalized-PPR limited to the stimulus train (22%). We could not find any recording with self-sustained posterior response. Seizures were elicited in 27% of patients with PPR, most often myoclonic seizures and absences, in patients with self-sustained generalized PPR. CONCLUSIONS: The most common type of PPR was accentuation of preexisting epileptogenic area. Self-sustained posterior response could not be documented. Self-sustained generalized-PPR had the highest association with seizures. SIGNIFICANCE: Using standardized stimulation protocol and definitions for PPR types, IPS provides high diagnostic yield.

Interrater agreement of classification of photoparoxysmal electroencephalographic response.

Our goal was to assess the interrater agreement (IRA) of photoparoxysmal response (PPR) using the classification proposed by a task force of the International League Against Epilepsy (ILAE), and a simplified classification system proposed by our group. In addition, we evaluated IRA of epileptiform discharges (EDs) and the diagnostic significance of the electroencephalographic (EEG) abnormalities. We used EEG recordings from the European Reference Network (EpiCARE) and Standardized Computer-based Organized Reporting of EEG (SCORE). Six raters independently scored EEG recordings from 30 patients. We calculated the agreement coefficient (AC) for each feature. IRA of PPR using the classification proposed by the ILAE task force was only fair (AC = 0.38). This improved to a moderate agreement by using the simplified classification (AC = 0.56; P = .004). IRA of EDs was almost perfect (AC = 0.98), and IRA of scoring the diagnostic significance was moderate (AC = 0.51). Our results suggest that the simplified classification of the PPR is suitable for implementation in clinical practice.

Visual EEG reviewing times with SCORE EEG.

OBJECTIVE: Visual EEG analysis is the gold standard for clinical EEG interpretation and analysis, but there is no published data on how long it takes to review and report an EEG in clinical routine. Estimates of reporting times may inform workforce planning and automation initiatives for EEG. The SCORE standard has recently been adopted to standardize clinical EEG reporting, but concern has been expressed about the time spent reporting. METHODS: Elapsed times were extracted from 5889 standard and sleep-deprived EEGs reported between 2015 and 2017 reported using the SCORE EEG software. RESULTS: The median review time for standard EEG was 12.5 min, and for sleep deprived EEG 20.9 min. A normal standard EEG had a median review time of 8.3 min. Abnormal EEGs took longer than normal EEGs to review, and had more variable review times. 99% of EEGs were reported within 24 h of end of recording. Review times declined by 25% during the study period. CONCLUSION: Standard and sleep-deprived EEG review and reporting times with SCORE EEG are reasonable, increasing with increasing EEG complexity and decreasing with experience. EEG reports can be provided within 24 h. SIGNIFICANCE: Clinical standard and sleep-deprived EEG reporting with SCORE EEG has acceptable reporting times.

Prevalence of Parasomnias in Patients With Obstructive Sleep Apnea. A Registry-Based Cross-Sectional Study.

Objective: To assess the prevalence of parasomnias in relation to presence and severity of obstructive sleep apnea (OSA). We hypothesized higher parasomnia prevalence with higher OSA severity. Methods: The sample comprised 4,372 patients referred to a Norwegian university hospital with suspicion of OSA (mean age 49.1 years, 69.8% males). OSA was diagnosed and categorized by standard respiratory polygraphy (type 3 portable monitor). The patients completed a comprehensive questionnaire prior to the sleep study, including questions about different parasomnias during the last 3 months. Pearson chi-square tests explored differences according to the presence and severity of OSA. Furthermore, logistic regression analyses with the parasomnias as dependent variables and OSA severity as predictor were conducted (adjusted for sex, age, marital status, smoking, and alcohol consumption). Results: In all, 34.7% had apnea-hypopnea index (AHI) <5 (no OSA), 32.5% had AHI 5-14.9 (mild OSA), 17.4% had AHI 15-29.9 (moderate OSA), and 15.3% had AHI ≥30 (severe OSA). The overall prevalence of parasomnias was 3.3% (sleepwalking), 2.5% (sleep-related violence), 3.1% (sexual acts during sleep), 1.7% (sleep-related eating), and 43.8% (nightmares). The overall parasomnia prevalence was highest in the no OSA group. In the chi-square analyses, including all OSA groups, the prevalence of sleep-related violence and nightmares were inversely associated with OSA severity, whereas none of the other parasomnias were significantly associated with OSA severity. In adjusted logistic regression analyses the odds of sleepwalking was significantly higher in severe compared to mild OSA (OR = 2.0, 95% CI = 1.12-3.55). The other parasomnias, including sleep-related violence and nightmares, were not associated with OSA presence or severity when adjusting for sex and age. Conclusions: We found no increase in parasomnias in patients with OSA compared to those not having OSA. With the exception of sleepwalking, the parasomnias were not associated with OSA severity.

Standardized computer-based organized reporting of EEG: SCORE - Second version.

Standardized terminology for computer-based assessment and reporting of EEG has been previously developed in Europe. The International Federation of Clinical Neurophysiology established a taskforce in 2013 to develop this further, and to reach international consensus. This work resulted in the second, revised version of SCORE (Standardized Computer-based Organized Reporting of EEG), which is presented in this paper. The revised terminology was implemented in a software package (SCORE EEG), which was tested in clinical practice on 12,160 EEG recordings. Standardized terms implemented in SCORE are used to report the features of clinical relevance, extracted while assessing the EEGs. Selection of the terms is context sensitive: initial choices determine the subsequently presented sets of additional choices. This process automatically generates a report and feeds these features into a database. In the end, the diagnostic significance is scored, using a standardized list of terms. SCORE has specific modules for scoring seizures (including seizure semiology and ictal EEG patterns), neonatal recordings (including features specific for this age group), and for Critical Care EEG Terminology. SCORE is a useful clinical tool, with potential impact on clinical care, quality assurance, data-sharing, research and education.

Regularized Linear Discriminant Analysis of EEG Features in Dementia Patients.

The present study explores if EEG spectral parameters can discriminate between healthy elderly controls (HC), Alzheimer's disease (AD) and vascular dementia (VaD) using. We considered EEG data recorded during normal clinical routine with 114 healthy controls (HC), 114 AD, and 114 VaD patients. The spectral features extracted from the EEG were the absolute delta power, decay from lower to higher frequencies, amplitude, center and dispersion of the alpha power and baseline power of the entire frequency spectrum. For discrimination, we submitted these EEG features to regularized linear discriminant analysis algorithm with a 10-fold cross-validation. To check the consistency of the results obtained by our classifiers, we applied bootstrap statistics. Four binary classifiers were used to discriminate HC from AD, HC from VaD, AD from VaD, and HC from dementia patients (AD or VaD). For each model, we measured the discrimination performance using the area under curve (AUC) and the accuracy of the cross-validation (cv-ACC). We applied this procedure using two different sets of predictors. The first set considered all the features extracted from the 22 channels. For the second set of features, we automatically rejected features poorly correlated with their labels. Fairly good results were obtained when discriminating HC from dementia patients with AD or VaD (AUC = 0.84). We also obtained AUC = 0.74 for discrimination of AD from HC, AUC = 0.77 for discrimination of VaD from HC, and finally AUC = 0.61 for discrimination of AD from VaD. Our models were able to separate HC from dementia patients, and also and to discriminate AD from VaD above chance. Our results suggest that these features may be relevant for the clinical assessment of patients with dementia.

Prevalence of excessive sleepiness is higher whereas insomnia is lower with greater severity of obstructive sleep apnea.

PURPOSE: The objective of the present study was to investigate the prevalence of insomnia and excessive sleepiness in relation to the presence and severity of obstructive sleep apnea (OSA). METHODS: The sample consisted of patients referred to a university hospital on suspicion of OSA. In total, 1115 patients with mean age of 48 years were studied. Insomnia (Bergen Insomnia Scale) and excessive sleepiness (Epworth Sleepiness Scale) were diagnosed using validated questionnaires. The insomnia scale permits diagnosing insomnia using both old and new (from 2014) criteria. OSA was diagnosed and categorized based on a standard respiratory polygraphic sleep study using a type 3 portable monitor. RESULTS: OSA was diagnosed in 59.4 % of the referred patients. The prevalence of excessive sleepiness was higher with greater severity of OSA: 40.5 % in the patients without OSA (apnea-hypopnea index (AHI) <5), 46.5 % in mild OSA (AHI 5-14.9), 52.0 % in moderate OSA (AHI 15-29.9), and 58.0 % in severe OSA (AHI 30 or above). In contrast, the prevalence of insomnia using the 2014 diagnostic criteria showed an opposing prevalence: 54.2 % no OSA, 54.9 % mild OSA, 48.5 % moderate OSA, and 44.6 % severe OSA. Logistic and linear regression analyses showed that sleepiness was positively associated whereas insomnia was negatively associated with OSA severity and AHI. CONCLUSIONS: Both excessive sleepiness and insomnia were seen in high proportions of the patients referred on suspicion of OSA. Excessive sleepiness was higher whereas insomnia was lower with greater OSA severity.

EEG Spectral Features Discriminate between Alzheimer's and Vascular Dementia.

Alzheimer's disease (AD) and vascular dementia (VaD) present with similar clinical symptoms of cognitive decline, but the underlying pathophysiological mechanisms differ. To determine whether clinical electroencephalography (EEG) can provide information relevant to discriminate between these diagnoses, we used quantitative EEG analysis to compare the spectra between non-medicated patients with AD (n = 77) and VaD (n = 77) and healthy elderly normal controls (NC) (n = 77). We use curve-fitting with a combination of a power loss and Gaussian function to model the averaged resting-state spectra of each EEG channel extracting six parameters. We assessed the performance of our model and tested the extracted parameters for group differentiation. We performed regression analysis in a multivariate analysis of covariance with group, age, gender, and number of epochs as predictors and further explored the topographical group differences with pair-wise contrasts. Significant topographical differences between the groups were found in several of the extracted features. Both AD and VaD groups showed increased delta power when compared to NC, whereas the AD patients showed a decrease in alpha power for occipital and temporal regions when compared with NC. The VaD patients had higher alpha power than NC and AD. The AD and VaD groups showed slowing of the alpha rhythm. Variability of the alpha frequency was wider for both AD and VaD groups. There was a general decrease in beta power for both AD and VaD. The proposed model is useful to parameterize spectra, which allowed extracting relevant clinical EEG key features that move toward simple and interpretable diagnostic criteria.

Unified EEG terminology and criteria for nonconvulsive status epilepticus.

The diagnosis of nonconvulsive status epilepticus (NCSE) relies largely on electroencephalography (EEG) findings. The lack of a unified EEG terminology, and of evidence-based EEG criteria, leads to varying criteria for and ability to diagnose NCSE. We propose a unified terminology and classification system for NCSE, using, as a template, the Standardised Computer-based Organised Reporting of EEG (SCORE). This approach integrates the terminology recently proposed for the rhythmic and periodic patterns in critically ill patients, the electroclinical classification of NCSE (type of NCSE) and the context for the pathologic conditions and age-related epilepsy syndromes. We propose flexible EEG criteria that employ the SCORE system to assemble a database for determining evidence-based EEG criteria for NCSE.

Standardized computer-based organized reporting of EEG: SCORE.

The electroencephalography (EEG) signal has a high complexity, and the process of extracting clinically relevant features is achieved by visual analysis of the recordings. The interobserver agreement in EEG interpretation is only moderate. This is partly due to the method of reporting the findings in free-text format. The purpose of our endeavor was to create a computer-based system for EEG assessment and reporting, where the physicians would construct the reports by choosing from predefined elements for each relevant EEG feature, as well as the clinical phenomena (for video-EEG recordings). A working group of EEG experts took part in consensus workshops in Dianalund, Denmark, in 2010 and 2011. The faculty was approved by the Commission on European Affairs of the International League Against Epilepsy (ILAE). The working group produced a consensus proposal that went through a pan-European review process, organized by the European Chapter of the International Federation of Clinical Neurophysiology. The Standardised Computer-based Organised Reporting of EEG (SCORE) software was constructed based on the terms and features of the consensus statement and it was tested in the clinical practice. The main elements of SCORE are the following: personal data of the patient, referral data, recording conditions, modulators, background activity, drowsiness and sleep, interictal findings, "episodes" (clinical or subclinical events), physiologic patterns, patterns of uncertain significance, artifacts, polygraphic channels, and diagnostic significance. The following specific aspects of the neonatal EEGs are scored: alertness, temporal organization, and spatial organization. For each EEG finding, relevant features are scored using predefined terms. Definitions are provided for all EEG terms and features. SCORE can potentially improve the quality of EEG assessment and reporting; it will help incorporate the results of computer-assisted analysis into the report, it will make possible the build-up of a multinational database, and it will help in training young neurophysiologists.

[EEG--when and how?]. / EEG--når og hvordan?

BACKGROUND: Electroencephalography (EEG) is used for studying functional disorders of the brain. We provide an overview of the areas where EEG is most strongly indicated and the clinical benefit of the examination. METHOD: This article is based on articles from personal literature archives and literature searches in PubMed, textbook on methods in clinical neurophysiology, the work of one of the authors on a new European standard for EEG and the authors' clinical experience. RESULTS: EEG is primarily indicated for assessing suspected epileptic seizures and encephalopathies. Standard EEG, sleep-deprived EEG and long-term EEG examinations are used in diagnosing epilepsy. EEG will identify epileptiform activity in about 90% of patients with epilepsy. Epileptiform EEG activity in these patients will provide support for the diagnosis of epilepsy, while the absence of such activity will not exclude the diagnosis. EEG is also important for classifying the type of epileptic syndrome. In cases of encephalopathy, pathology will take the form of an increase in slow activity on the EEG and will support the diagnosis, but as a rule the EEG finding is non-specific. Findings in some cases may point to the underlying aetiology. INTERPRETATION: Electroencephalograms must be interpreted by an experienced clinical neurophysiologist or neurologist with specialist expertise in EEG interpretation, and must always be evaluated in the light of the clinical picture.

Neurological features and enzyme therapy in patients with endocrine and exocrine pancreas dysfunction due to CEL mutations.

OBJECTIVE: To further define clinical features associated with the syndrome of diabetes and pancreatic exocrine dysfunction due to mutations in the carboxyl-ester lipase (CEL) gene and to assess the effects of pancreatic enzyme substitution therapy. RESEARCH DESIGN AND METHODS: Nine patients with CEL gene mutation, exocrine deficiency, and diabetes were treated and followed for 30 months. RESULTS: Treatment improved symptoms in seven of nine patients. Exocrine and endocrine function assessed by fecal elastase and A1C were not affected, although fecal lipid excretion was reduced. Vitamin E was low in all patients but increased with treatment (P < 0.001 at 30 months) and improved in five subjects. A predominantly demyelinating neuropathy was seen in a majority of patients, and carpal tunnel syndrome was common. CONCLUSIONS: Pancreatic enzyme substitution alleviated symptoms and malabsorption and normalized vitamin E levels. Glycemic control was not significantly affected. The CEL syndrome seems associated with a demyelinating neuropathology.