Retention Rates and Successful Treatment with Antiseizure Medications in Newly-Diagnosed Epilepsy Patients.

PURPOSE: Treatment for epilepsy primarily involves antiseizure medications (ASMs), which can be characterized using the clinical data warehouse (CDW) database. In this study, we compared retention rates and time to successful treatment for various ASMs to reflect both efficacy and adverse effects in patients with newly diagnosed epilepsy. MATERIALS AND METHODS: We identified newly diagnosed epilepsy patients with ASM treatment for more than 12 months using CDW of a tertiary referral hospital. Clinical characteristics were compared between groups with successful and unsuccessful treatment. Cox regression analysis was performed to evaluate independent variables of age, sex, comorbidities, and attributes of ASM regimens. RESULTS: Of 2515 eligible participants, 46.2% were successfully treated with the first ASM regimen, and 74.7% with all ASM regimens with the median time-to-treatment success of 14 months. Participants with second-generation ASM as the first ASM were more likely to be successfully treated with the first regimen compared to those with first-generation ASM (51.6% vs. 42.3%, p<0.001) and more successfully treated [hazard ratio (HR)=1.26; 95% confidence interval (CI): 1.15-1.39]. Overall, valproic acid was the most common ASM across a wide range of ages under 65 years, while levetiracetam in patients aged over 65 years or lamotrigine in female adult patients. Clinical factors associated with less favorable treatment outcomes included renal disease (HR=0.78; 95% CI: 0.66-0.92), liver disease (HR=0.65; 95% CI: 0.52-0.81), depression (HR=0.70; 95% CI: 0.57-0.84), and mechanical ventilation (HR=0.58; 95% CI: 0.50-0.67). CONCLUSION: Second-generation ASMs have the advantage of more successful treatment with fewer ASM regimen changes compared with first-generation drugs. Various comorbid conditions as well as age and sex should be considered when selecting ASMs.

Using spectral and temporal filters with EEG signal to predict the temporal lobe epilepsy outcome after antiseizure medication via machine learning.

Epilepsy is a neurological disorder in which the brain is transiently altered. Predicting outcomes in epilepsy is essential for providing feedback that can foster improved outcomes in the future. This study aimed to investigate whether applying spectral and temporal filters to resting-state electroencephalography (EEG) signals could improve the prediction of outcomes for patients taking antiseizure medication to treat temporal lobe epilepsy (TLE). We collected EEG data from a total of 46 patients (divided into a seizure-free group (SF, n = 22) and a non-seizure-free group (NSF, n = 24)) with TLE and retrospectively reviewed their clinical data. We segmented spectral and temporal ranges with various time-domain features (Hjorth parameters, statistical parameters, energy, zero-crossing rate, inter-channel correlation, inter-channel phase locking value and spectral information derived from Fourier transform, Stockwell transform, and wavelet transform) and compared their performance by applying an optimal frequency strategy, an optimal duration strategy, and a combination strategy. For all time-domain features, the optimal frequency and time combination strategy showed the highest performance in distinguishing SF patients from NSF patients (area under the curve (AUC) = 0.790 ± 0.159). Furthermore, optimal performance was achieved by utilizing a feature vector derived from statistical parameters within the 39- to 41-Hz frequency band with a window length of 210 s, as evidenced by an AUC of 0.748. By identifying the optimal parameters, we improved the performance of the prediction model. These parameters can serve as standard parameters for predicting outcomes based on resting-state EEG signals.

Five-Year Retention of Perampanel and Polytherapy Patterns: 328 Patients From a Single Center in South Korea.

BACKGROUND AND PURPOSE: Perampanel (PER) is an α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid antagonist used to treat focal and generalized epilepsy. Comprehensive data from real-world settings with long-term follow-ups are still scarce. This study aimed to determine the factors related to PER retention and the polytherapy pattern with PER. METHODS: We reviewed all patients with epilepsy with a history of PER prescription during 2008-2017 and over a follow-up of >3 years. PER usage patterns and associated factors were analyzed. RESULTS: Among the 2,655 patients in the cohort, 328 (150 females, 178 males) were enrolled. The ages at onset and diagnosis were 21.1±14.7 years and 25.6±16.1 years (mean±standard deviation), respectively. The age at the first visit to our center was 31.8±13.8 years. Seizure types were focal, generalized, and unknown onset in 83.8%, 15.9%, and 0.3% of patients, respectively. The most common etiology was structural (n=109, 33.2%). The maintenance duration of PER was 22.6±19.2 months (range=1-66 months). The initial number of concomitant antiseizure medications was 2.4±1.4 (range=0-9). The most common regimen was PER plus levetiracetam (n=41, 12.5%). The median number of 1-year seizures before PER usage was 8 (range=0-1,400). A seizure reduction of >50% was recorded in 34.7% of patients (52.0% and 29.2% in generalized and focal seizures, respectively). The 1-, 2-, 3-, 4-, and 5-year retention rates for PER were 65.3%, 50.4%, 40.4%, 35.3%, and 21.5%, respectively. A multivariate analysis indicated that lower age at onset was associated with longer retention (p=0.01). CONCLUSIONS: PER was safely used in patients with diverse characteristics and was maintained for a long time in a real-world setting, especially in patients with a lower age at onset.

Age-integrated artificial intelligence framework for sleep stage classification and obstructive sleep apnea screening.

Introduction: Sleep is an essential function to sustain a healthy life, and sleep dysfunction can cause various physical and mental issues. In particular, obstructive sleep apnea (OSA) is one of the most common sleep disorders and, if not treated in a timely manner, OSA can lead to critical problems such as hypertension or heart disease. Methods: The first crucial step in evaluating individuals' quality of sleep and diagnosing sleep disorders is to classify sleep stages using polysomnographic (PSG) data including electroencephalography (EEG). To date, such sleep stage scoring has been mainly performed manually via visual inspection by experts, which is not only a time-consuming and laborious process but also may yield subjective results. Therefore, we have developed a computational framework that enables automatic sleep stage classification utilizing the power spectral density (PSD) features of sleep EEG based on three different learning algorithms: support vector machine, k-nearest neighbors, and multilayer perceptron (MLP). In particular, we propose an integrated artificial intelligence (AI) framework to further inform the risk of OSA based on the characteristics in automatically scored sleep stages. Given the previous finding that the characteristics of sleep EEG differ by age group, we employed a strategy of training age-specific models (younger and older groups) and a general model and comparing their performance. Results: The performance of the younger age-specific group model was similar to that of the general model (and even higher than the general model at certain stages), but the performance of the older age-specific group model was rather low, suggesting that bias in individual variables, such as age bias, should be considered during model training. Our integrated model yielded an accuracy of 73% in sleep stage classification and 73% in OSA screening when MLP algorithm was applied, which indicates that patients with OSA could be screened with the corresponding accuracy level only with sleep EEG without respiration-related measures. Discussion: The current outcomes demonstrate the feasibility of AI-based computational studies that when combined with advances in wearable devices and relevant technologies could contribute to personalized medicine by not only assessing an individuals' sleep status conveniently at home but also by alerting them to the risk of sleep disorders and enabling early intervention.

Quantitative measurement of motor activity during sleep in isolated REM sleep behavior disorder patients using actigraphy before and after treatment with clonazepam.

STUDY OBJECTIVES: We conducted a prospective study to quantify motor activity during sleep measured by actigraphy before and after 3 months of treatment with clonazepam in patients with video-polysomnography (vPSG) confirmed isolated rapid eye movement (REM) sleep behavior disorder (iRBD). METHODS: The motor activity amount (MAA) and the motor activity block (MAB) during sleep were obtained from actigraphy. Then, we compared quantitative actigraphic measures with the results of the REM sleep behavior disorder questionnaire for the previous 3-month period (RBDQ-3M) and of the Clinical Global Impression-Improvement scale (CGI-I), and analyzed correlations between baseline vPSG measures and actigraphic measures. RESULTS: Twenty-three iRBD patients were included in the study. After medication treatment, large activity MAA dropped in 39% of patients, and the number of MABs decreased in 30% of patients when applying 50% reduction criteria. 52% of patients showed more than 50% improvement in either one. On the other hand, 43% of patients answered "much or very much improved" on the CGI-I, and RBDQ-3M was reduced by more than half in 35% of patients. However, there was no significant association between the subjective and objective measures. Phasic submental muscle activity during REM sleep was highly correlated with small activity MAA (Spearman's rho = 0.78, p < .001) while proximal and axial movements during REM sleep correlated with large activity MAA (rho = 0.47, p = .030 for proximal movements, rho = 0.47, p = .032 for axial movements). CONCLUSIONS: Our findings imply that quantifying motor activity during sleep using actigraphy can objectively assess therapeutic response in drug trials in patients with iRBD.

Comparative analysis of environmental standards to install a rooftop temperature monitoring station.

Urban climate influences economic activities and the health and safety of urban residents. Therefore, monitoring temperature in urban areas is important. However, owing to the lack of space for an appropriate observation site, an automatic weather station (AWS) was installed on a building rooftop. The rooftop installation can indicate temperature differences depending on the intensity of strong solar radiation and radiant heat of the building, and wind speed. Therefore, in this study, we aimed to provide observation standards for measuring rooftop temperature according to the optimal rooftop material and observation height. Specifically, an AWS was installed on the rooftop of the Gochang Standard Weather Station (GSWO), Jeollabuk-do Province, to observe the urban climate in South Korea and establish suitable weather standards. Different temperatures, optimum surface materials, and optimum heights for measuring the temperature at the rooftop of GSWO were investigated and compared over 1 year. The temperature recorded after installing a palm mat on the rooftop was more similar to that observed in the grassland. Furthermore, the installation height of the temperature sensor of 2.5-3.0 m for the palm mat and 3.5-4.0 m for concrete was found to be the optimal height for observing temperature at the rooftop.

Effects of CYP2C19 genetic polymorphisms on the pharmacokinetics of lacosamide in Korean patients with epilepsy.

OBJECTIVE: Many pharmacokinetic studies of lacosamide (LCM) have been reported, but no large-scale clinical study has been conducted on genetic polymorphisms that affect the metabolism of LCM. Therefore, we designed a pharmacogenetic study of LCM to explore the effect of genetic polymorphisms on serum LCM concentration. We evaluated the pharmacodynamic characteristics of LCM, including clinical efficacy and toxicity. METHODS: Adult patients with epilepsy who received LCM at Seoul National University Hospital were enrolled. Blood samples were obtained from 115 patients taking LCM for more than 1 month with unchanged doses and were used to analyze the serum LCM concentration, the concentration/dose (C/D) ratio and the single nucleotide polymorphisms (SNPs) of the cytochrome P450 (CYP)2C9 and CYP2C19 genes. In addition, clinical information-including efficacy, toxicity, and concomitant drugs-was collected. RESULTS: The serum LCM concentration showed a linear correlation with the daily dose (r = .66, p < .001). In genetic analysis, 43 patients (38.7%) were extensive metabolizers (EMs), 51 (45.9%) were intermediate metabolizers (IMs), and 17 (15.3%) were poor metabolizers (PMs). In the group comparison, mean serum concentrations and the C/D ratio showed significant differences between the three groups (p = .01 and p < .001, respectively). The C/D ratios of IM (27.78) and PM (35.6) were 13% and 39% higher than those of EM (25.58), respectively. In the pharmacodynamic subgroup analysis, patients in the ineffective LCM group had significantly lower serum concentrations (6.39 ± 3.25 vs. 8.44 ± 3.68 µg/ml, p = .024), whereas patients with adverse events had higher serum concentrations than those without adverse events (11.03 ± 4.32 vs. 7.4 ± 3.1 µg/ml, p < .001). Based on this, we suggest a reference range for LCM in the Korean population (6-9 µg/ml). SIGNIFICANCE: Genetic polymorphisms of the CYP2C19 gene affect the serum LCM concentration. Because efficacy and toxicity are apparently related to serum LCM levels, the genetic phenotype of CYP2C19 should be considered when prescribing LCM for patients with epilepsy.

Neuromodulation Using Transcranial Focused Ultrasound on the Bilateral Medial Prefrontal Cortex.

Transcranial focused ultrasound (tFUS) is a promising technique of non-invasive brain stimulation for modulating neuronal activity with high spatial specificity. The medial prefrontal cortex (mPFC) has been proposed as a potential target for neuromodulation to prove emotional and sleep qualities. We aim to set up an appropriate clinical protocol for investigating the effects of tFUS stimulation of the bilateral mPFC for modulating the function of the brain-wide network using different sonication parameters. Seven participants received 20 min of 250 kHz tFUS to the bilateral mPFC with excitatory (70% duty cycle with sonication interval at 5 s) or suppressive (5% duty cycle with no interval) sonication protocols, which were compared to a sham condition. By placing the cigar-shaped sonication focus on the falx between both mPFCs, it was possible to simultaneously stimulate the bilateral mPFCs. Brain activity was analyzed using continuous electroencephalographic (EEG) recording during, before, and after tFUS. We investigated whether tFUS stimulation under the different conditions could lead to distinctive changes in brain activity in local brain regions where tFUS was directly delivered, and also in adjacent or remote brain areas that were not directly stimulated. This kind of study setting suggests that dynamic changes in brain cortical responses can occur within short periods of time, and that the distribution of these responses may differ depending on local brain states and functional brain architecture at the time of tFUS administration, or perhaps, at least temporarily, beyond the stimulation time. If so, tFUS could be useful for temporarily modifying regional brain activity, modulating functional connectivity, or reorganizing brain functions associated with various neuropsychiatric diseases, such as insomnia and depression.

Frequent drinking is more predictive of ischemic stroke than binge drinking, but not of myocardial infarction.

BACKGROUND AND AIMS: Alcohol consumption has complex effects on myocardial infarction (MI) and ischemic stroke. We investigated the difference in associations according to drinking patterns (drinking frequency vs. amount per occasion) and sex. METHODS: This population-based retrospective study included 11,595,191 subjects participating in national health examinations between 2009 and 2010. Using Cox regression analyses, we calculated MI and ischemic stroke risk according to weekly alcohol consumption, drinking frequency, and amount per occasion. RESULTS: For MI, all weekly alcohol consumption amounts showed lower risk compared to non-drinkers: mild (adjusted hazard ratio [aHR], 0.78; 95% confidence intervals [CI], 0.77-0.79), moderate (aHR, 0.71; 95% CI, 0.70-0.73), and heavy (aHR, 0.74; 95% CI, 0.72-0.76). Drinking frequency and amount per occasion did not differ in MI risk. However, women showed increased risk with heavy drinking and ≥8 drinks per occasion. For ischemic stroke, a J-shaped association was observed for weekly alcohol consumption: mild (aHR, 0.91; 95% CI, 0.90-0.92), moderate (aHR, 0.94; 95% CI, 0.93-0.96), and heavy (aHR, 1.04; 95% CI, 1.02-1.06). Among women, ischemic stroke risk began to increase with moderate drinking. Given similar weekly alcohol consumption levels, ischemic stroke risk increased with higher frequency of drinking, not with amount per occasion. CONCLUSIONS: Drinking frequency may be a more important risk factor for ischemic stroke than amount per occasion. Among women, the protective effect of alcohol against MI was not evident in heavy amounts, and the risk of ischemic stroke began to increase at lower levels compared to men.

Predicting Parkinson's disease using gradient boosting decision tree models with electroencephalography signals.

INTRODUCTION: Parkinson's disease (PD) is a neurodegenerative disorder with only symptomatic treatments currently available. Although correct, early diagnoses of PD are important, the existing diagnostic method based on pathologic examinations only has an accuracy of approximately 80.6%. Although electroencephalography (EEG)-based assistive technology has been introduced, it has been difficult to implement in practice due to the high computational complexity and low accuracy of the analysis methods. This study proposed a fast, accurate PD prediction method using the Hjorth parameter and the gradient boosting decision tree (GBDT) algorithm. METHOD: We used an open EEG dataset with 41 PD patients and 41 healthy controls (HCs); EEG signals were recorded from participants at the University of New Mexico (PD: 27 vs. HC: 27) and University of Iowa (PD: 14 vs. HC: 14). We explored the analytic time segment and frequency range in which the Hjorth parameter best represents the EEG characteristics of PD patients. RESULTS: Our best model (CatBoost-based) distinguished PD patients from controls with an accuracy of 89.3%, an area under the receiver operating characteristics curve (AUC) of 0.912, an F-score of 0.903, and an odds ratio of 115.5. These results showed that our models outperformed those of all other previous works and were even superior to previously known pathologic examination-based diagnoses with long-term follow-up (accuracy = 83.9%). CONCLUSION: The proposed methods are expected to be utilized as an effective method for improving the diagnosis of PD.

Association of Zolpidem With Increased Mortality in Patients With Brain Cancer: A Retrospective Cohort Study Based on the National Health Insurance Service Database.

BACKGROUND AND PURPOSE: Zolpidem is one of the most common hypnotics prescribed to treat insomnia worldwide. However, there are numerous reports of a positive association between zolpidem and mortality, including an association with increased cancer-specific mortality found in a Taiwanese cohort study. This study aimed to determine the association between zolpidem use and brain-cancer-specific mortality in patients with brain cancer. METHODS: This population-based, retrospective cohort study analyzed data in the National Health Insurance Service database. All incident cases of brain cancer at an age of ≥18 years at the time of brain cancer diagnosis over a 15-year period (2003-2017) were included. A multivariate Cox regression analysis after adjustment for covariables was performed to evaluate the associations of zolpidem exposure with brain-cancer-specific and all-cause mortality. RESULTS: This study identified 38,037 incident cases of brain cancer, among whom 11,823 (31.1%) patients were exposed to zolpidem. In the multivariate Cox regression model, the brain-cancer-specific mortality rate was significantly higher in patients who were prescribed zolpidem than in those with no zolpidem prescription (adjusted hazard ratio [HR]=1.14, 95% confidence interval [CI]=1.08-1.21, p<0.001). Zolpidem exposure was significantly associated with increased brain-cancer-specific mortality after adjustment in younger adults (age 18-64 years; adjusted HR=1.37, 95% CI=1.27-1.49) but not in older adults (age ≥65 years; adjusted HR=0.94, 95% CI=0.86-1.02). CONCLUSIONS: Zolpidem exposure was significantly associated with increased brain-cancer-specific mortality in patients with brain cancer aged 18-64 years. Further prospective studies are warranted to understand the mechanism underlying the effect of zolpidem on mortality in patients with brain cancer.

Can Corticomuscular Coherence Differentiate between REM Sleep Behavior Disorder with or without Parkinsonism?

REM sleep behavior disorder (RBD) could be a predictor of Parkinsonism even before development of typical motor symptoms. This study aims to characterize clinical features and corticomuscular and corticocortical coherence (CMC and CCC, respectively) during sleep in RBD patients with or without Parkinsonism. We enrolled a total of 105 subjects, including 20 controls, 54 iRBD, and 31 RBD+P patients, patients who were diagnosed as idiopathic RBD (iRBD) and RBD with Parkinsonism (RBD+P) in our neurology department. We analyzed muscle atonia index (MAI) and CMC between EEG and chin/limb muscle electromyography (EMG) and CCC during different sleep stages. Although differences in the CMC of iRBD group were observed only during REM sleep, MAI differences between groups were noted during both REM and NREM N2 stage sleep. During REM sleep, CMC was higher and MAI was reduced in iRBD patients compared to controls (p = 0.001, p < 0.001, respectively). Interestingly, MAI was more reduced in RBD+P compared to iRBD patients. In comparison, CCC was higher in iRBD patients compared to controls whereas CCC was lower in RBD+P groups compared to control and iRBD groups in various frequency bands during both NREM N2 and REM sleep stages. Among them, increased CMC during REM sleep revealed correlation between clinical severities of RBD symptoms. Our findings indicate that MAI, CMC, and CCC showed distinctive features in iRBD and RBD+P patients compared to controls, suggesting potential usefulness to understand possible links between these diseases.

Adult-onset rapidly worsening progressive myoclonic epilepsy caused by a novel variant in DHDDS.

Progressive myoclonic epilepsy (PME) is a heterogeneous neurogenetic disorder manifesting as progressive myoclonus, seizure, and ataxia. We report a case of PME caused by a novel DHDDS variant. Additionally, by reviewing the literature on DHDDS mutations, we compared the phenotype of our patient with previously reported phenotypes. We identified DHDDS (c.638G>A, p. Ser213Asn) as a likely pathogenic variant. The literature review revealed 15 PME patients with DHDDS mutations from 13 unrelated families. According to previous studies, late-onset patients tend to have a slow-progressive disease course. Although his myoclonus and ataxia were adult onset, our patient experienced rapid disease aggravation.

Cerebellar White Matter Abnormalities in Charcot-Marie-Tooth Disease: A Combined Volumetry and Diffusion Tensor Imaging Analysis.

Charcot-Marie-Tooth disease (CMT) is a genetically heterogeneous hereditary peripheral neuropathy. Brain volumetry and diffusion tensor imaging (DTI) were performed in 47 controls and 47 CMT patients with PMP22 duplication (n = 10), MFN2 (n = 15), GJB1 (n = 11), or NEFL mutations (n = 11) to investigate for structural changes in the cerebellum. Volume of cerebellar white matter (WM) was significantly reduced in CMT patients with NEFL mutations. Abnormal DTI findings were observed in the superior, middle, and inferior cerebellar peduncles, predominantly in NEFL mutations and partly in GJB1 mutations. Cerebellar ataxia was more prevalent in the NEFL mutation group (72.7%) than the GJB1 mutation group (9.1%) but was not observed in other genotypic subtypes, which indicates that structural cerebellar abnormalities were associated with the presence of cerebellar ataxia. However, NEFL and GJB1 mutations did not affect cerebellar gray matter (GM), and neither cerebellar GM nor WM abnormalities were observed in the PMP22 duplication or MFN2 mutation groups. We found structural evidence of cerebellar WM abnormalities in CMT patients with NEFL and GJB1 mutations and an association between cerebellar WM involvement and cerebellar ataxia in these genetic subtypes, especially in the NEFL subgroup. Therefore, we suggest that neuroimaging, such as MRI volumetry or DTI, for CMT patients could play an important role in detecting abnormalities of cerebellar WM.

Refractory neuro-Sweet disease successfully treated with tocilizumab and mycophenolate mofetil.

Sweet syndrome, or acute febrile neutrophilic dermatosis, is mainly a dermatologic condition presenting with erythematous plaques; however, neutrophils infiltrate multiple systems. Neuro-Sweet disease is a neurological manifestation of Sweet syndrome and a rare cause of recurrent aseptic meningoencephalitis, which needs to be distinguished from neuro-Behçet disease. Although neuro-Sweet disease generally responds well to corticosteroids, relapsing neuro-Sweet disease is not an exceptional case. Herein, we present a case of a 51-year-old male with recurrent encephalitis followed by erythematous plaques. The patient was confirmed as Sweet syndrome based on skin biopsy and showed partial response to corticosteroids. With intravenous immunoglobulin, rituximab, tocilizumab, and mycophenolate mofetil, his neurologic symptoms were fully recovered.

Three-Year Retention Rates of Levetiracetam, Topiramate, and Oxcarbazepine: A Retrospective Hospital-Based Study.

OBJECTIVES: We evaluated and compared the 3-year retention rates of levetiracetam (LEV), topiramate (TPM), and oxcarbazepine (OXC) in patients with epilepsy in routine clinical practice. METHODS: We retrospectively reviewed medical records of patients with epilepsy who were newly prescribed LEV, TPM, or OXC from 2006 to 2010. The retention rates were estimated by the Kaplan-Meier analysis, and independent risk factors for drug discontinuation were analyzed by the Cox regression method. RESULTS: A total of 588 patients were included: LEV (n = 345), TPM (n = 190), and OXC (n = 53). Among them, 82% had focal epilepsy, whereas 14.8% had generalized epilepsy. The 3-year retention rates for LEV, TPM, and OXC, were 81.2%, 78.3%, and 54.7%, respectively. Levetiracetam and TPM had equivalent retention rates, whereas patients remained on OXC for a significantly shorter amount of time (P < 0.001). A lower retention rate for OXC was also evident in the subgroup analysis of focal epilepsy (P < 0.001). In generalized epilepsy, LEV and TPM revealed comparable retention rates (P = 0.255). The seizure-freedom rate did not differ among groups, whereas the rate of adverse effects leading to drug withdrawal of OXC (87.5%) was higher than that of LEV (34.4%, P < 0.001) and TPM (52.5%, P = 0.012). CONCLUSIONS: The current study suggested that LEV and TPM had comparable retention profiles in the long-term treatment for both focal and generalized epilepsy. Meanwhile, OXC therapy seemed to be relatively less useful because of its poor tolerability.

Genomic alterations identified by array comparative genomic hybridization as prognostic markers in tamoxifen-treated estrogen receptor-positive breast cancer.

BACKGROUND: A considerable proportion of estrogen receptor (ER)-positive breast cancer recurs despite tamoxifen treatment, which is a serious problem commonly encountered in clinical practice. We tried to find novel prognostic markers in this subtype of breast cancer. METHODS: We performed array comparative genomic hybridization (CGH) with 1,440 human bacterial artificial chromosome (BAC) clones to assess copy number changes in 28 fresh-frozen ER-positive breast cancer tissues. All of the patients included had received at least 1 year of tamoxifen treatment. Nine patients had distant recurrence within 5 years (Recurrence group) of diagnosis and 19 patients were alive without disease at least 5 years after diagnosis (Non-recurrence group). RESULTS: Potential prognostic variables were comparable between the two groups. In an unsupervised clustering analysis, samples from each group were well separated. The most common regions of gain in all samples were 1q32.1, 17q23.3, 8q24.11, 17q12-q21.1, and 8p11.21, and the most common regions of loss were 6q14.1-q16.3, 11q21-q24.3, and 13q13.2-q14.3, as called by CGH-Explorer software. The average frequency of copy number changes was similar between the two groups. The most significant chromosomal alterations found more often in the Recurrence group using two different statistical methods were loss of 11p15.5-p15.4, 1p36.33, 11q13.1, and 11p11.2 (adjusted p values < 0.001). In subgroup analysis according to lymph node status, loss of 11p15 and 1p36 were found more often in Recurrence group with borderline significance within the lymph node positive patients (adjusted p = 0.052). CONCLUSION: Our array CGH analysis with BAC clones could detect various genomic alterations in ER-positive breast cancers, and Recurrence group samples showed a significantly different pattern of DNA copy number changes than did Non-recurrence group samples.