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Lekoubou A, Petucci J, Femi Ajala T, Katoch A, Hong J, Sen S, Bonilha L, Chinchilli VM, Honavar V. Can machine learning predict late seizures after intracerebral hemorrhages? Evidence from real-world data. Epilepsy Behav 2024; 157:109835. [PMID: 38820686 DOI: 10.1016/j.yebeh.2024.109835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 05/08/2024] [Accepted: 05/08/2024] [Indexed: 06/02/2024]
Abstract
INTRODUCTION Intracerebral hemorrhage represents 15 % of all strokes and it is associated with a high risk of post-stroke epilepsy. However, there are no reliable methods to accurately predict those at higher risk for developing seizures despite their importance in planning treatments, allocating resources, and advancing post-stroke seizure research. Existing risk models have limitations and have not taken advantage of readily available real-world data and artificial intelligence. This study aims to evaluate the performance of Machine-learning-based models to predict post-stroke seizures at 1 year and 5 years after an intracerebral hemorrhage in unselected patients across multiple healthcare organizations. DESIGN/METHODS We identified patients with intracerebral hemorrhage (ICH) without a prior diagnosis of seizures from 2015 until inception (11/01/22) in the TriNetX Diamond Network, using the International Classification of Diseases, Tenth Revision (ICD-10) I61 (I61.0, I61.1, I61.2, I61.3, I61.4, I61.5, I61.6, I61.8, and I61.9). The outcome of interest was any ICD-10 diagnosis of seizures (G40/G41) at 1 year and 5 years following the first occurrence of the diagnosis of intracerebral hemorrhage. We applied a conventional logistic regression and a Light Gradient Boosted Machine (LGBM) algorithm, and the performance of the model was assessed using the area under the receiver operating characteristics (AUROC), the area under the precision-recall curve (AUPRC), the F1 statistic, model accuracy, balanced-accuracy, precision, and recall, with and without seizure medication use in the models. RESULTS A total of 85,679 patients had an ICD-10 code of intracerebral hemorrhage and no prior diagnosis of seizures, constituting our study cohort. Seizures were present in 4.57 % and 6.27 % of patients within 1 and 5 years after ICH, respectively. At 1-year, the AUROC, AUPRC, F1 statistic, accuracy, balanced-accuracy, precision, and recall were respectively 0.7051 (standard error: 0.0132), 0.1143 (0.0068), 0.1479 (0.0055), 0.6708 (0.0076), 0.6491 (0.0114), 0.0839 (0.0032), and 0.6253 (0.0216). Corresponding metrics at 5 years were 0.694 (0.009), 0.1431 (0.0039), 0.1859 (0.0064), 0.6603 (0.0059), 0.6408 (0.0119), 0.1094 (0.0037) and 0.6186 (0.0264). These numerical values indicate that the statistical models fit the data very well. CONCLUSION Machine learning models applied to electronic health records can improve the prediction of post-hemorrhagic stroke epilepsy, presenting a real opportunity to incorporate risk assessments into clinical decision-making in post-stroke care clinical care and improve patients' selection for post-stroke epilepsy research.
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Affiliation(s)
- Alain Lekoubou
- Department of Neurology, Milton S. Hershey Medical Center and Department of Public Health, Pennsylvania State University, USA.
| | - Justin Petucci
- Institute for Computational and Data Sciences, USA; Clinical and Translational Sciences Institute, USA.
| | | | | | - Jinpyo Hong
- College of Medicine, Penn State University, Hershey, PA, USA.
| | - Souvik Sen
- University of South Carolina, Department of Neurology, USA.
| | | | | | - Vasant Honavar
- Institute for Computational and Data Sciences, USA; Clinical and Translational Sciences Institute, USA; Data Sciences Program, USA; College of Information Sciences and Technology, USA; Center for Artificial Intelligence Foundations and Scientific Applications, USA.
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Kan-Tor Y, Ness L, Szlak L, Benninger F, Ravid S, Chorev M, Rosen-Zvi M, Shimoni Y, Fisher RS. Comparing the efficacy of anti-seizure medications using matched cohorts on a large insurance claims database. Epilepsy Res 2024; 201:107313. [PMID: 38417192 DOI: 10.1016/j.eplepsyres.2024.107313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/22/2024] [Accepted: 01/25/2024] [Indexed: 03/01/2024]
Abstract
Epilepsy is a severe chronic neurological disease affecting 60 million people worldwide. Primary treatment is with anti-seizure medicines (ASMs), but many patients continue to experience seizures. We used retrospective insurance claims data on 280,587 patients with uncontrolled epilepsy (UE), defined as status epilepticus, need for a rescue medicine, or admission or emergency visit for an epilepsy code. We conducted a computational risk ratio analysis between pairs of ASMs using a causal inference method, in order to match 1034 clinical factors and simulate randomization. Data was extracted from the MarketScan insurance claims Research Database records from 2011 to 2015. The cohort consisted of individuals over 18 years old with a diagnosis of epilepsy who took one of eight ASMs and had more than a year of history prior to the filling of the drug prescription. Seven ASM exposures were analyzed: topiramate, phenytoin, levetiracetam, gabapentin, lamotrigine, valproate, and carbamazepine or oxcarbazepine (treated as the same exposure). We calculated the risk ratio of UE between pairs of ASM after controlling for bias with inverse propensity weighting applied to 1034 factors, such as demographics, confounding illnesses, non-epileptic conditions treated by ASMs, etc. All ASMs exhibited a significant reduction in the prevalence of UE, but three drugs showed pair-wise differences compared to other ASMs. Topiramate consistently was associated with a lower risk of UE, with a mean risk ratio range of 0.68-0.93 (average 0.82, CI: 0.56-1.08). Phenytoin and levetiracetam were consistently associated with a higher risk of UE with mean risk ratio ranges of 1.11 to 1.47 (average 1.13, CI 0.98-1.65) and 1.15 to 1.43 (average 1.2, CI 0.72-1.69), respectively. Large-scale retrospective insurance claims data - combined with causal inference analysis - provides an opportunity to compare the effect of treatments in real-world data in populations 1,000-fold larger than those in typical randomized trials. Our causal analysis identified the clinically unexpected finding of topiramate as being associated with a lower risk of UE; and phenytoin and levetiracetam as associated with a higher risk of UE (compared to other studied drugs, not to baseline). However, we note that our data set for this study only used insurance claims events, which does not comprise actual seizure frequencies, nor a clear picture of side effects. Our results do not advocate for any change in practice but demonstrate that conclusions from large databases may differ from and supplement those of randomized trials and clinical practice and therefore may guide further investigation.
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Affiliation(s)
- Yoav Kan-Tor
- AI for Healthcare and Life Sciences Department, IBM Research, Haifa, Israel
| | - Lior Ness
- AI for Healthcare and Life Sciences Department, IBM Research, Haifa, Israel
| | - Liran Szlak
- AI for Healthcare and Life Sciences Department, IBM Research, Haifa, Israel
| | - Felix Benninger
- Department of Neurology, Rabin Medical Center, Petach Tikva, Israel; School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Sivan Ravid
- AI for Healthcare and Life Sciences Department, IBM Research, Haifa, Israel
| | - Michal Chorev
- AI for Healthcare and Life Sciences Department, IBM Research, Haifa, Israel; Centre for Applied Research, IBM Australia, Melbourne, Australia
| | - Michal Rosen-Zvi
- AI for Healthcare and Life Sciences Department, IBM Research, Haifa, Israel; Faculty of Medicine, The Hebrew University, Jerusalem, Israel
| | - Yishai Shimoni
- AI for Healthcare and Life Sciences Department, IBM Research, Haifa, Israel
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Hwang S, An H, Shin DW, Lee HW. Retention Rates and Successful Treatment with Antiseizure Medications in Newly-Diagnosed Epilepsy Patients. Yonsei Med J 2024; 65:89-97. [PMID: 38288649 PMCID: PMC10827640 DOI: 10.3349/ymj.2022.0539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 09/21/2023] [Accepted: 10/10/2023] [Indexed: 02/01/2024] Open
Abstract
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.
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Affiliation(s)
- Sungeun Hwang
- Department of Neurology, Ewha Womans University Mokdong Hospital, Seoul, Korea
| | - Hyungmi An
- Institute of Convergence Medicine, Ewha Womans University Mokdong Hospital, Seoul, Korea
| | - Dong Woo Shin
- Department of Neurology, Ewha Womans University Mokdong Hospital, Seoul, Korea
| | - Hyang Woon Lee
- Department of Neurology, Ewha Womans University Mokdong Hospital, Seoul, Korea
- Department of Neurology, Ewha Womans University School of Medicine, Seoul, Korea
- Department of Medical Science, Ewha Womans University School of Medicine, Seoul, Korea
- Computational Medicine, System Health Science & Engineering, and Artificial Intelligence Convergence Graduate Programs, Ewha Womans University, Seoul, Korea.
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Chen YF, Chawla S, Mousa-Doust D, Nichol A, Ng R, Isaac KV. Machine Learning to Predict the Need for Postmastectomy Radiotherapy after Immediate Breast Reconstruction. PLASTIC AND RECONSTRUCTIVE SURGERY-GLOBAL OPEN 2024; 12:e5599. [PMID: 38322813 PMCID: PMC10846766 DOI: 10.1097/gox.0000000000005599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 12/15/2023] [Indexed: 02/08/2024]
Abstract
Background Post mastectomy radiotherapy (PMRT) is an independent predictor of reconstructive complications. PMRT may alter the timing and type of reconstruction recommended. This study aimed to create a machine learning model to predict the probability of requiring PMRT after immediate breast reconstruction (IBR). Methods In this retrospective study, breast cancer patients who underwent IBR from January 2017 to December 2020 were reviewed and data were collected on 81 preoperative characteristics. Primary outcome was recommendation for PMRT. Four algorithms were compared to maximize performance and clinical utility: logistic regression, elastic net (EN), logistic lasso, and random forest (RF). The cohort was split into a development dataset (75% of cohort for training-validation) and 25% used for the test set. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), precision-recall curves, and calibration plots. Results In a total of 800 patients, 325 (40.6%) patients were recommended to undergo PMRT. With the training-validation dataset (n = 600), model performance was logistic regression 0.73 AUC [95% confidence interval (CI) 0.65-0.80]; RF 0.77 AUC (95% CI, 0.74-0.81); EN 0.77 AUC (95% CI, 0.73-0.81); logistic lasso 0.76 AUC (95% CI, 0.72-0.80). Without significantly sacrificing performance, 81 predictive factors were reduced to 12 for prediction with the EN method. With the test dataset (n = 200), performance of the EN prediction model was confirmed [0.794 AUC (95% CI, 0.730-0.858)]. Conclusion A parsimonious accurate machine learning model for predicting PMRT after IBR was developed, tested, and translated into a clinically applicable online calculator for providers and patients.
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Affiliation(s)
- Yi-Fu Chen
- From the Department of Computer Science, Faculty of Science, University of British Columbia, Vancouver, British Columbia, Canada
| | - Sahil Chawla
- Department of Surgery, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Dorsa Mousa-Doust
- Department of Surgery, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Alan Nichol
- Department of Radiation Oncology, BC Cancer, Vancouver, British Columbia, Canada
| | - Raymond Ng
- From the Department of Computer Science, Faculty of Science, University of British Columbia, Vancouver, British Columbia, Canada
| | - Kathryn V Isaac
- Department of Surgery, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
- From the Department of Computer Science, Faculty of Science, University of British Columbia, Vancouver, British Columbia, Canada
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Chang RSK, Nguyen S, Chen Z, Foster E, Kwan P. Role of machine learning in the management of epilepsy: a systematic review protocol. BMJ Open 2024; 14:e079785. [PMID: 38272549 PMCID: PMC10823996 DOI: 10.1136/bmjopen-2023-079785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 01/05/2024] [Indexed: 01/27/2024] Open
Abstract
INTRODUCTION Machine learning is a rapidly expanding field and is already incorporated into many aspects of medicine including diagnostics, prognostication and clinical decision-support tools. Epilepsy is a common and disabling neurological disorder, however, management remains challenging in many cases, despite expanding therapeutic options. We present a systematic review protocol to explore the role of machine learning in the management of epilepsy. METHODS AND ANALYSIS This protocol has been drafted with reference to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for Protocols. A literature search will be conducted in databases including MEDLINE, Embase, Scopus and Web of Science. A PRISMA flow chart will be constructed to summarise the study workflow. As the scope of this review is the clinical application of machine learning, the selection of papers will be focused on studies directly related to clinical decision-making in management of epilepsy, specifically the prediction of response to antiseizure medications, development of drug-resistant epilepsy, and epilepsy surgery and neuromodulation outcomes. Data will be extracted following the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. Prediction model Risk Of Bias ASsessment Tool will be used for the quality assessment of the included studies. Syntheses of quantitative data will be presented in narrative format. ETHICS AND DISSEMINATION As this study is a systematic review which does not involve patients or animals, ethics approval is not required. The results of the systematic review will be submitted to peer-review journals for publication and presented in academic conferences. PROSPERO REGISTRATION NUMBER CRD42023442156.
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Affiliation(s)
- Richard Shek-Kwan Chang
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Shani Nguyen
- Monash University Faculty of Medicine Nursing and Health Sciences, Melbourne, Victoria, Australia
| | - Zhibin Chen
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Emma Foster
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Patrick Kwan
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
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Yang S, Li S, Wang H, Li J, Wang C, Liu Q, Zhong J, Jia M. Early prediction of drug-resistant epilepsy using clinical and EEG features based on convolutional neural network. Seizure 2024; 114:98-104. [PMID: 38118285 DOI: 10.1016/j.seizure.2023.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 12/11/2023] [Accepted: 12/14/2023] [Indexed: 12/22/2023] Open
Abstract
OBJECTIVE Machine learning utilization in electroencephalogram (EEG) analysis and epilepsy care is fast evolving. Thus, we aim to develop and validate two one-dimensional convolutional neural network (CNN) algorithms for predicting drug-resistant epilepsy (DRE) in patients with newly-diagnosed epilepsy based on EEG and clinical features. METHODS We included a total of 1010 EEG signal epochs and 15 clinical features from 101 patients with epilepsy. Each patient had 10 epochs of EEG signal data, with each signal recorded for 90 s. The ratio of development set and validation set was 80:20, and ten-fold cross validation was performed. First, a CNN algorithm was used to extract EEG features automatically. Then, Two one-dimensional CNNs were crafted.. Accuracy, specificity, precision, sensitivity, F1-score, kappa statistics, mean square error (MSE) and area under the curve (AUC) were calculated to evaluate the classifiers performance. RESULTS The clinical-EEG model showed good performance and clinical practical value, with the accuracy, specificity, precision, sensitivity, F1-score, kappa statistics, best MSE and AUC in test set were 0.99, 0.72, 0.82, 0.96, 0.89, 0.83, 32.00, 0.81, respectively, and the accuracy in validation set was 0.84. In the EEG model, the accuracy, specificity, precision, sensitivity, F1-score, kappa statistics, best MSE and AUC in test set were 0.99, 0.59, 0.82, 0.90, 0.86, 0.72, 181.76, 0.76, respectively, and the accuracy in validation set was 0.81. CONCLUSION We constructed a clinical-EEG model showed good potential for predicting DRE in patients with newly-diagnosed epilepsy, which could help identify patients at high risk of developing DRE at earlier stages.
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Affiliation(s)
- Shijun Yang
- Department of Neurology, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, 158 Wu Yang Ave., 445000, En Shi, Hubei Province, China
| | - Shanshan Li
- Department of Medical Ultrasound, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, 88 Jin Long Ave., 445000, En Shi, Hubei Province, China
| | - Hanlin Wang
- Department of Medicine, The Xi 'an Jiaotong University, 76 Yan Ta West Ave., 710000, Xi 'an, Shanxi Province, China
| | - Jinlan Li
- Department of Neurology, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, 158 Wu Yang Ave., 445000, En Shi, Hubei Province, China
| | - Congping Wang
- Department of Neurology, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, 158 Wu Yang Ave., 445000, En Shi, Hubei Province, China
| | - Qunhui Liu
- Department of Neurology, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, 158 Wu Yang Ave., 445000, En Shi, Hubei Province, China
| | - Jianhua Zhong
- Department of Neurology, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, 158 Wu Yang Ave., 445000, En Shi, Hubei Province, China.
| | - Min Jia
- Department of Neurology, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, 158 Wu Yang Ave., 445000, En Shi, Hubei Province, China.
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Hatoum HT, Arcona S, Mao J, Walton S. Real-world antiseizure medication treatment outcomes in drug-resistant focal epilepsy patients. Epilepsia Open 2023; 8:1556-1565. [PMID: 37842746 PMCID: PMC10690664 DOI: 10.1002/epi4.12845] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 09/29/2023] [Indexed: 10/17/2023] Open
Abstract
OBJECTIVE To gather real-world evidence on antiseizure medications (ASMs) treatment patterns and related outcomes in patients with drug-resistant focal epilepsy. METHODS Medical insurance claims from the start of 2014 till the end of 2019 were used. Patient selection criteria included International Classification of Diseases (ICD) codes followed by documented ASM use. Baseline patient demographics along with ASM and rescue medication use patterns and related patient outcome were documented for first (index) ASM regimen. Patients who failed the first regimen and then failed the second regimen were considered drug resistant. Multivariate analyses were performed to identify risks and other characteristics for positive or negative treatment outcomes. RESULTS Study cohort consisted of 46 474 patients with a mean age of 47.23 (SD: 16.94). Levetiracetam was the most first-encountered ASM (37.94%). At baseline, 87.14% were treated with ASMs prior to having study-confirmed diagnoses. Mental comorbidities were present in 37.86% of patients. After first-year ASM treatment, 34.61% of patients persisted on their index regimen and 5.91% were seizure-free. Patients failing first ASM regimen numbered 12 868 (27.69%). Drug-resistant patients who failed first and then second ASM regimens numbered 6335 (49.23%). Percentages of patients who had successful second treatment and seizure-free were 21.32 and 3.65, respectively. Initiating patients on lamotrigine or carbamazepine (relative to levetiracetam), baseline use of index ASM, rescue medications, and older age or male gender all lowered the risk for treatment failure. Having higher comorbidity, comorbid mental illness, headache, or neoplasty increased such a risk. Baseline use of index ASM, depressive episode, or anxiety disorder all entailed higher risk of failing second ASM treatment. SIGNIFICANCE Overall, reported findings indicated that patient history at baseline and the early selection of an ASM all influenced treatment outcomes. Findings pointed to the complex nature of ASM treatment in drug-resistant focal epilepsy patients calling for additional research to identify the optimal treatment to achieve beneficial patient outcomes.
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Affiliation(s)
- Hind T. Hatoum
- Pharmacy Systems Outcomes and PolicyUniversity of Illinois ChicagoChicagoIllinoisUSA
| | - Steve Arcona
- Global Value & Access, Cerevel TherapeuticsCambridgeMassachusettsUSA
| | - Jianbin Mao
- Global Value & Access, Cerevel TherapeuticsCambridgeMassachusettsUSA
| | - Surrey Walton
- Pharmacy Systems Outcomes and PolicyUniversity of Illinois ChicagoChicagoIllinoisUSA
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Li J, Huang Y, Hutton GJ, Aparasu RR. Assessing treatment switch among patients with multiple sclerosis: A machine learning approach. EXPLORATORY RESEARCH IN CLINICAL AND SOCIAL PHARMACY 2023; 11:100307. [PMID: 37554927 PMCID: PMC10405092 DOI: 10.1016/j.rcsop.2023.100307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 07/08/2023] [Accepted: 07/09/2023] [Indexed: 08/10/2023] Open
Abstract
BACKGROUND Patients with multiple sclerosis (MS) frequently switch their Disease-Modifying Agents (DMA) for effectiveness and safety concerns. This study aimed to develop and compare the random forest (RF) machine learning (ML) model with the logistic regression (LR) model for predicting DMA switching among MS patients. METHODS This retrospective longitudinal study used the TriNetX data from a federated electronic medical records (EMR) network. Between September 2010 and May 2017, adults (aged ≥18) MS patients with ≥1 DMA prescription were identified, and the earliest DMA date was assigned as the index date. Patients prescribed any DMAs different from their index DMAs were considered as treatment switch. . The RF and LR models were built with 72 baseline characteristics and trained with 70% of the randomly split data after up-sampling. Area Under the Curves (AUC), accuracy, recall, G-measure, and F-1 score were used to evaluate the model performance. RESULTS In this study, 7258 MS patients with ≥1 DMA were identified. Within two years, 16% of MS patients switched to a different DMA. The RF model obtained significantly better discrimination than the LR model (AUC = 0.65 vs. 0.63, p < 0.0001); however, the RF model had a similar predictive performance to the LR model with respect to F- and G-measures (RF: 72% and 73% vs. LR: 72% and 73%, respectively). The most influential features identified from the RF model were age, type of index medication, and year of index. CONCLUSIONS Compared to the LR model, RF performed better in predicting DMA switch in MS patients based on AUC measures; however, judged by F- and G-measures, the RF model performed similarly to LR. Further research is needed to understand the role of ML techniques in predicting treatment outcomes for the decision-making process to achieve optimal treatment goals.
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Affiliation(s)
- Jieni Li
- Department of Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, TX, USA
| | - Yinan Huang
- Department of Pharmacy Administration, College of Pharmacy, University of Mississippi, Oxford, MS, USA
| | | | - Rajender R Aparasu
- Department of Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, TX, USA
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Hersi H, Raitanen J, Saarinen JT, Peltola J. Prescribed antiseizure medication doses and their relation to defined daily doses for achieving seizure freedom in newly diagnosed patients with epilepsy. Epilepsia Open 2023; 8:811-819. [PMID: 37010264 PMCID: PMC10472398 DOI: 10.1002/epi4.12737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 03/28/2023] [Indexed: 04/04/2023] Open
Abstract
OBJECTIVES To investigate the antiseizure medication (ASM) doses required to achieve seizure freedom and their correlation with the World Health Organization's defined daily doses (DDDs) in patients aged 16 years or older with newly diagnosed epilepsy. METHODS The study included 459 patients with a validated diagnosis of new-onset epilepsy. Patient records were retrospectively analyzed to determine the ASM doses in patients with or without seizure freedom during follow-up. The DDD of the relevant ASM was then retrieved. RESULTS The seizure-freedom rate with first and subsequent ASMs was 88% (404/459 patients) during the follow-up. The mean prescribed doses (PDDs) and PDD/DDD ratio of the most commonly used ASMs, ie, oxcarbazepine (OXC), carbamazepine (CBZ), and valproic acid (VPA), differed significantly between seizure-free and non-seizure-free status (992 mg and 0.99 vs 1132 mg and 1.13; 547 mg and 0.55 vs 659 mg and 0.66; and 953 mg and 0.64 vs 1260 mg and 0.84, respectively). The effect of the OXC dose as the first failed ASM on the possibility of achieving seizure freedom was significant (Fisher's exact test, p = 0.002). Thirty-four of 43 patients (79%) in which an OXC dose of ≤900 mg failed became seizure-free, as compared with 24 of 54 patients (44%) with a failed OXC dose >900 mg. SIGNIFICANCE The present study provides new insights into the doses of the commonly used ASMs such as OXC, CBZ, and VPA that can lead to seizure freedom as monotherapy or as combination therapy. The higher PDD/DDD ratio of OXC (0.99) than that of CBZ or VPA renders a generalized PDD/DDD comparison highly problematic.
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Affiliation(s)
- Hire Hersi
- Department of NeurologyVaasa Central HospitalVaasaFinland
| | - Jani Raitanen
- Faculty of Social Sciences (Health Sciences)Tampere University and the UKK Institute for Health Promotion ResearchTampereFinland
| | | | - Jukka Peltola
- Department of NeurologyTampere University and Tampere University HospitalTampereFinland
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Gombolay GY, Gopalan N, Bernasconi A, Nabbout R, Megerian JT, Siegel B, Hallman-Cooper J, Bhalla S, Gombolay MC. Review of Machine Learning and Artificial Intelligence (ML/AI) for the Pediatric Neurologist. Pediatr Neurol 2023; 141:42-51. [PMID: 36773406 PMCID: PMC10040433 DOI: 10.1016/j.pediatrneurol.2023.01.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 01/03/2023] [Accepted: 01/09/2023] [Indexed: 01/15/2023]
Abstract
Artificial intelligence (AI) and a popular branch of AI known as machine learning (ML) are increasingly being utilized in medicine and to inform medical research. This review provides an overview of AI and ML (AI/ML), including definitions of common terms. We discuss the history of AI and provide instances of how AI/ML can be applied to pediatric neurology. Examples include imaging in neuro-oncology, autism diagnosis, diagnosis from charts, epilepsy, cerebral palsy, and neonatal neurology. Topics such as supervised learning, unsupervised learning, and reinforcement learning are discussed.
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Affiliation(s)
- Grace Y Gombolay
- Division of Neurology, Department of Pediatrics, Emory University School of Medicine, Atlanta Georgia; Division of Pediatric Neurology, Children's Healthcare of Atlanta, Atlanta Georgia.
| | - Nakul Gopalan
- Georgia Institute of Technology, Interactive Computing, Atlanta, Georgia
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, UK
| | - Rima Nabbout
- Department of Pediatric Neurology, Necker Enfants Malades Hospital, Reference Centre for Rare Epilepsies and Member of the ERN EpiCARE, Imagine Institute UMR1163, Paris Descartes University, Paris, France
| | - Jonathan T Megerian
- Department of Pediatrics, CHOC Children's, Irvine School of Medicine, University of California, Orange, California
| | - Benjamin Siegel
- Division of Neurology, Department of Pediatrics, Emory University School of Medicine, Atlanta Georgia; Division of Pediatric Neurology, Children's Healthcare of Atlanta, Atlanta Georgia
| | - Jamika Hallman-Cooper
- Division of Neurology, Department of Pediatrics, Emory University School of Medicine, Atlanta Georgia; Division of Pediatric Neurology, Children's Healthcare of Atlanta, Atlanta Georgia
| | - Sonam Bhalla
- Division of Neurology, Department of Pediatrics, Emory University School of Medicine, Atlanta Georgia; Division of Pediatric Neurology, Children's Healthcare of Atlanta, Atlanta Georgia
| | - Matthew C Gombolay
- Georgia Institute of Technology, Interactive Computing, Atlanta, Georgia
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11
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Terman SW. Deep Thoughts—Predicting Initial Treatment Response in Newly Diagnosed Epilepsy. Epilepsy Curr 2022; 23:90-92. [PMID: 37122398 PMCID: PMC10131576 DOI: 10.1177/15357597221139365] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
[Box: see text]
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12
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Breitenstein PS, Mahmoud I, Al-Azzawi F, Shakibfar S, Sessa M. A machine-learning guided method for predicting add-on and switch in secondary data sources: A case study on anti-seizure medications in Danish registries. Front Pharmacol 2022; 13:954393. [PMID: 36438810 PMCID: PMC9685793 DOI: 10.3389/fphar.2022.954393] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 10/19/2022] [Indexed: 12/14/2023] Open
Abstract
Purpose: There is a lack of available evidence regarding the treatment pattern of switches and add-ons for individuals aged 65 years or older with epilepsy during the first years from the time they received their first anti-seizure medication because of the lack of valid methods. Therefore, this study aimed to develop an algorithm for identifying switches and add-ons using secondary data sources for anti-seizure medication users. Methods: Danish nationwide databases were used as data sources. Residents in Denmark between 1996 and 2018 who were diagnosed with epilepsy and redeemed their first prescription for anti-seizure medication after epilepsy diagnosis were followed up for 730 days until the end of the follow-up period, death, or emigration to assess switches and add-ons occurred during the follow-up period. The study outcomes were the overall accuracy of the classification of switch or add-on of the newly developed algorithm. Results: In total, 15870 individuals were included in the study population with a median age of 72.9 years, of whom 52.0% were male and 48.0% were female. A total of 988 of the 15879 patients from the study population were present during the 730-day follow-up period, and 988 individuals (6.2%) underwent a total of 1485 medication events with co-exposure to two or more anti-seizure medications. The newly developed algorithmic method correctly identified 9 out of 10 add-ons (overall accuracy 92%) and 9 out of 10 switches (overall accuracy 88%). Conclusion: The majority of switches and add-ons occurred early during the first 2 years of disease and according to clinical recommendations. The newly developed algorithm correctly identified 9 out of 10 switches/add-ons.
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Affiliation(s)
| | | | | | | | - Maurizio Sessa
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
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13
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Chiang S, Rao VR. Choosing the Best Antiseizure Medication-Can Artificial Intelligence Help? JAMA Neurol 2022; 79:970-972. [PMID: 36036914 PMCID: PMC11163946 DOI: 10.1001/jamaneurol.2022.2441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Affiliation(s)
- Sharon Chiang
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco
| | - Vikram R Rao
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco
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14
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Hakeem H, Feng W, Chen Z, Choong J, Brodie MJ, Fong SL, Lim KS, Wu J, Wang X, Lawn N, Ni G, Gao X, Luo M, Chen Z, Ge Z, Kwan P. Development and Validation of a Deep Learning Model for Predicting Treatment Response in Patients With Newly Diagnosed Epilepsy. JAMA Neurol 2022; 79:986-996. [PMID: 36036923 PMCID: PMC9425285 DOI: 10.1001/jamaneurol.2022.2514] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/17/2022] [Indexed: 11/14/2022]
Abstract
Importance Selection of antiseizure medications (ASMs) for epilepsy remains largely a trial-and-error approach. Under this approach, many patients have to endure sequential trials of ineffective treatments until the "right drugs" are prescribed. Objective To develop and validate a deep learning model using readily available clinical information to predict treatment success with the first ASM for individual patients. Design, Setting, and Participants This cohort study developed and validated a prognostic model. Patients were treated between 1982 and 2020. All patients were followed up for a minimum of 1 year or until failure of the first ASM. A total of 2404 adults with epilepsy newly treated at specialist clinics in Scotland, Malaysia, Australia, and China between 1982 and 2020 were considered for inclusion, of whom 606 (25.2%) were excluded from the final cohort because of missing information in 1 or more variables. Exposures One of 7 antiseizure medications. Main Outcomes and Measures With the use of the transformer model architecture on 16 clinical factors and ASM information, this cohort study first pooled all cohorts for model training and testing. The model was trained again using the largest cohort and externally validated on the other 4 cohorts. The area under the receiver operating characteristic curve (AUROC), weighted balanced accuracy, sensitivity, and specificity of the model were all assessed for predicting treatment success based on the optimal probability cutoff. Treatment success was defined as complete seizure freedom for the first year of treatment while taking the first ASM. Performance of the transformer model was compared with other machine learning models. Results The final pooled cohort included 1798 adults (54.5% female; median age, 34 years [IQR, 24-50 years]). The transformer model that was trained using the pooled cohort had an AUROC of 0.65 (95% CI, 0.63-0.67) and a weighted balanced accuracy of 0.62 (95% CI, 0.60-0.64) on the test set. The model that was trained using the largest cohort only had AUROCs ranging from 0.52 to 0.60 and a weighted balanced accuracy ranging from 0.51 to 0.62 in the external validation cohorts. Number of pretreatment seizures, presence of psychiatric disorders, electroencephalography, and brain imaging findings were the most important clinical variables for predicted outcomes in both models. The transformer model that was developed using the pooled cohort outperformed 2 of the 5 other models tested in terms of AUROC. Conclusions and Relevance In this cohort study, a deep learning model showed the feasibility of personalized prediction of response to ASMs based on clinical information. With improvement of performance, such as by incorporating genetic and imaging data, this model may potentially assist clinicians in selecting the right drug at the first trial.
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Affiliation(s)
- Haris Hakeem
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
| | - Wei Feng
- Department of Electrical and Computer Systems Engineering, Monash University, Clayton, Victoria, Australia
- Monash-Airdoc Research, Monash University, Melbourne, Victoria, Australia
| | - Zhibin Chen
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Jiun Choong
- Department of Electrical and Computer Systems Engineering, Monash University, Clayton, Victoria, Australia
| | - Martin J. Brodie
- Department of Medicine and Clinical Pharmacology, University of Glasgow, Glasgow, Scotland
| | - Si-Lei Fong
- Neurology Division, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Kheng-Seang Lim
- Neurology Division, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Junhong Wu
- Department of Neurology, the First Affiliated Hospital of Chongqing Medical University, Chongqing Key Laboratory of Neurology, Chongqing, China
| | - Xuefeng Wang
- Department of Neurology, the First Affiliated Hospital of Chongqing Medical University, Chongqing Key Laboratory of Neurology, Chongqing, China
| | - Nicholas Lawn
- WA Adult Epilepsy Service, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia
| | - Guanzhong Ni
- Department of Neurology, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Xiang Gao
- Department of Pharmacy, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Mijuan Luo
- Department of Pharmacy, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Ziyi Chen
- Department of Neurology, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Zongyuan Ge
- Department of Electrical and Computer Systems Engineering, Monash University, Clayton, Victoria, Australia
- Monash-Airdoc Research, Monash University, Melbourne, Victoria, Australia
- Monash eResearch Centre, Monash University, Melbourne, Victoria, Australia
| | - Patrick Kwan
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
- Department of Neurology, the First Affiliated Hospital of Chongqing Medical University, Chongqing Key Laboratory of Neurology, Chongqing, China
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15
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Castano VG, Spotnitz M, Waldman GJ, Joiner EF, Choi H, Ostropolets A, Natarajan K, McKhann GM, Ottman R, Neugut AI, Hripcsak G, Youngerman BE. Identification of patients with drug resistant epilepsy in electronic medical record data using the Observational Medical Outcomes Partnership Common Data Model. Epilepsia 2022; 63:2981-2993. [DOI: 10.1111/epi.17409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 09/05/2022] [Accepted: 09/12/2022] [Indexed: 11/03/2022]
Affiliation(s)
- Victor G. Castano
- Department of Neurological Surgery, Vagelos College of Physicians and Surgeons Columbia University New York New York USA
| | - Matthew Spotnitz
- Department of Biomedical Informatics, Vagelos College of Physicians and Surgeons Columbia University New York New York USA
| | - Genna J. Waldman
- Department of Neurology, Vagelos College of Physicians and Surgeons Columbia University New York New York USA
| | - Evan F. Joiner
- Department of Neurological Surgery, Vagelos College of Physicians and Surgeons Columbia University New York New York USA
| | - Hyunmi Choi
- Department of Neurology, Vagelos College of Physicians and Surgeons Columbia University New York New York USA
| | - Anna Ostropolets
- Department of Biomedical Informatics, Vagelos College of Physicians and Surgeons Columbia University New York New York USA
| | - Karthik Natarajan
- Department of Biomedical Informatics, Vagelos College of Physicians and Surgeons Columbia University New York New York USA
| | - Guy M. McKhann
- Department of Neurological Surgery, Vagelos College of Physicians and Surgeons Columbia University New York New York USA
| | - Ruth Ottman
- Department of Neurology, Vagelos College of Physicians and Surgeons Columbia University New York New York USA
- The Gertrude H. Sergievsky Center Columbia University Irving Medical Center New York New York USA
- Department of Epidemiology, Mailman School of Public Health Columbia University New York New York USA
- Division of Translational Epidemiology and Mental Health Equity New York State Psychiatric Institute New York New York USA
| | - Alfred I. Neugut
- Department of Epidemiology, Mailman School of Public Health Columbia University New York New York USA
- Department of Medicine, Vagelos College of Physicians and Surgeons Columbia University New York New York USA
- Herbert Irving Comprehensive Cancer Center, Vagelos College of Physicians and Surgeons Columbia University New York New York USA
| | - George Hripcsak
- Department of Biomedical Informatics, Vagelos College of Physicians and Surgeons Columbia University New York New York USA
| | - Brett E. Youngerman
- Department of Neurological Surgery, Vagelos College of Physicians and Surgeons Columbia University New York New York USA
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16
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Liu M, Ding J, Wang X. The interaction between circadian rhythm and epilepsy. ACTA EPILEPTOLOGICA 2022. [DOI: 10.1186/s42494-022-00094-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
AbstractEvidence about the interaction between circadian rhythms (CR) and epilepsy has been expanded with the application of advanced detection technology. An adequate understanding of how circadian system and epilepsy interact with each other could contribute to more accurate seizure prediction as well as rapid development of potential treatment timed to specific phases of CR. In this review, we present the reciprocal relationship between CR and epileptic activities from aspects of sleep effect, genetic modulation and brain biochemistry. It has been found that sleep-wake patterns, circadian timing systems and multidien rhythms have essential roles in seizure activities and interictal epileptiform discharge (IED). For instance, specific distribution patterns of seizures and IED have been reported, i.e., lighter non-rapid eye movement (NREM) sleep stage (stage 2) induces seizures while deeper NREM sleep stage (stage 3) activates IEDs. Furthermore, the epilepsy type, seizure type and seizure onset zone can significantly affect the rhythms of seizure occurrence. Apart from the common seizure types, several specific epilepsy syndromes also have a close correlation with sleep-wakefulness patterns. Sleep influences the epilepsy rhythm, and conversely, epilepsy alters the sleep rhythm through multiple pathways. Clock genes accompanied by two feedback loops of regulation have an important role in cortical excitability and seizure occurrence, which may be involved in the mTORopathy. The suprachiasmatic nuclei (SCN) has a rhythm of melatonin and cortisol secretion under the circadian pattern, and then these hormones can feed back into a central oscillator to affect the SCN-dependent rhythms, leading to variable but prominent influence on epilepsy. Furthermore, we discuss the precise predictive algorithms and chronotherapy strategies based on different temporal patterns of seizure occurrence for patients with epilepsy, which may offer a valuable indication for non-invasive closed-loop treatment system. Optimization of the time and dose of antiseizure medications, and resynchronization of disturbed CR (by hormone therapy, light exposure, ketogenic diet, novel small molecules) would be beneficial for epileptic patients in the future. Before formal clinical practice, future large-scale studies are urgently needed to assist prediction and treatment of circadian seizure activities and address unsolved restrictions.
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17
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Villamar MF, Sarkis RA, Pennell P, Kohane I, Beaulieu-Jones BK. Severity of Epilepsy and Response to Antiseizure Medications in Individuals with Multiple Sclerosis: Analysis of a Real-World Dataset. Neurol Clin Pract 2022; 12:e49-e57. [DOI: 10.1212/cpj.0000000000001178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 04/26/2022] [Indexed: 11/15/2022]
Abstract
ABSTRACTBackground and objectives:Epilepsy is an important comorbidity that affects outcomes for people with multiple sclerosis (MS). However, it is unclear if seizure severity among individuals with coexistence of multiple sclerosis and epilepsy (MS+E) is higher than in those with other focal epilepsies. Our goal was to compare the overall severity of epilepsy in individuals with MS+E versus those with focal epilepsy without MS (E-MS) as defined by seizure-related healthcare utilization, frequency and duration of status epilepticus, and frequency of antiseizure medication (ASM) regimen changes.Methods:In this hypothesis-generating study, we analyzed a U.S. commercial nationwide de-identified claims dataset with > 86 million individuals between 1/1/2008 and 8/31/2019. Using validated algorithms, we identified adults with E-MS and those with MS+E. We compared the number and length of seizure-related hospital admissions, the number of claims and unique days with claims for status epilepticus, and the rates of ASM regimen changes between the MS+E and the E-MS groups.Results:During the study period, 66,708 individuals with E-MS and 537 with MS+E had ≥ 2 years of coverage after their initial diagnosis of epilepsy. There was no difference between the MS+E and E-MS groups in the percentage of individuals admitted for seizures and/or status epilepticus. However, MS+E with seizure-related admissions had more admissions and longer hospital stays than those with E-MS. MS+E who experienced status epilepticus had more unique days with status epilepticus claims compared to E-MS. MS+E were more likely to have ASM regimen changes in Year 2 after the initial diagnosis of epilepsy and had more ASM changes during Year 2 compared to E-MS. Among individuals with MS+E, there were no differences in our measures of seizure severity for those treated with sodium channel blockers/modulators versus other ASM classes.Discussion:This study supports the notion that individuals with MS+E can have more severe epilepsy than those with E-MS. Seizure severity among individuals with MS+E treated with sodium-channel blockers/modulators versus other ASM classes shows no significant differences.Classification of evidence:This study provides Class III evidence that individuals with MS+E can have more severe epilepsy than those with E-MS.
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18
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Spotnitz M, Ostropolets A, Castano VG, Natarajan K, Waldman GJ, Argenziano M, Ottman R, Hripcsak G, Choi H, Youngerman BE. Patient characteristics and antiseizure medication pathways in newly diagnosed epilepsy: Feasibility and pilot results using the common data model in a single-center electronic medical record database. Epilepsy Behav 2022; 129:108630. [PMID: 35276502 DOI: 10.1016/j.yebeh.2022.108630] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 01/28/2022] [Accepted: 02/14/2022] [Indexed: 11/16/2022]
Abstract
INTRODUCTION Efforts to characterize variability in epilepsy treatment pathways are limited by the large number of possible antiseizure medication (ASM) regimens and sequences, heterogeneity of patients, and challenges of measuring confounding variables and outcomes across institutions. The Observational Health Data Science and Informatics (OHDSI) collaborative is an international data network representing over 1 billion patient records using common data standards. However, few studies have applied OHDSI's Common Data Model (CDM) to the population with epilepsy and none have validated relevant concepts. The goals of this study were to demonstrate the feasibility of characterizing adult patients with epilepsy and ASM treatment pathways using the CDM in an electronic health record (EHR)-derived database. METHODS We validated a phenotype algorithm for epilepsy in adults using the CDM in an EHR-derived database (2001-2020) against source records and a prospectively maintained database of patients with confirmed epilepsy. We obtained the frequency of all antecedent conditions and procedures for patients meeting the epilepsy phenotype criteria and characterized ASM exposure sequences over time and by age and sex. RESULTS The phenotype algorithm identified epilepsy with 73.0-85.0% positive predictive value and 86.3% sensitivity. Many patients had neurologic conditions and diagnoses antecedent to meeting epilepsy criteria. Levetiracetam incrementally replaced phenytoin as the most common first-line agent, but significant heterogeneity remained, particularly in second-line and subsequent agents. Drug sequences included up to 8 unique ingredients and a total of 1,235 unique pathways were observed. CONCLUSIONS Despite the availability of additional ASMs in the last 2 decades and accumulated guidelines and evidence, ASM use varies significantly in practice, particularly for second-line and subsequent agents. Multi-center OHDSI studies have the potential to better characterize the full extent of variability and support observational comparative effectiveness research, but additional work is needed to validate covariates and outcomes.
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Affiliation(s)
- Matthew Spotnitz
- Department of Biomedical Informatics, Columbia University Irving Medical Center, United States
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University Irving Medical Center, United States
| | - Victor G Castano
- Department of Neurological Surgery, Columbia University Irving Medical Center, United States
| | - Karthik Natarajan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, United States
| | - Genna J Waldman
- Department of Neurology, Columbia University Irving Medical Center, United States
| | - Michael Argenziano
- Department of Neurological Surgery, Columbia University Irving Medical Center, United States
| | - Ruth Ottman
- Department of Neurology, Columbia University Irving Medical Center, United States; The Gertrude H. Sergievsky Center, Columbia University Vagelos College of Physicians and Surgeons, United States; Department of Epidemiology, Mailman School of Public Health, Columbia University Irving Medical Center, United States; Division of Translational Epidemiology, New York State Psychiatric Institute, United States
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, United States
| | - Hyunmi Choi
- Department of Neurology, Columbia University Irving Medical Center, United States
| | - Brett E Youngerman
- Department of Neurological Surgery, Columbia University Irving Medical Center, United States.
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Håkansson S, Zelano J. Big data analysis of ASM retention rates and expert ASM algorithm: a comparative study. Epilepsia 2022; 63:1553-1562. [PMID: 35325474 PMCID: PMC9321965 DOI: 10.1111/epi.17235] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 03/23/2022] [Accepted: 03/23/2022] [Indexed: 11/29/2022]
Abstract
Objective Only 50% of patients with new‐onset epilepsy achieve seizure freedom with their first antiseizure medication (ASM). A growing body of data illustrates the complexity of predicting ASM response and tolerability, which is influenced by age, sex, and comorbidities. Randomized data with sufficient resolution for personalized medicine are unlikely to emerge. Two potential facilitators of ASM selection are big data using real‐world retention rates or algorithms based on expert opinion. We asked how these methods compare in adult‐onset focal epilepsy. Methods ASM retention rates were determined by cross‐referencing data from comprehensive Swedish registers for 37 643 individuals, with identified comorbidities. Eight fictive cases were created and expert advice was collected from the algorithm Epipick. We compared Epipick suggestions in representative patient subgroups, and determined whether ranking based on retention rate reflected expert advice. Results The Epipick algorithm suggested six ASM alternatives for younger patients and three ASM alternatives for older patients. In the real‐world data, retention rates for the ASMs ranked as best options by Epipick were high; 65%–72% for young patients and 71%–84% for older patients. The lowest retention rate for Epipick suggestions was 42%–56% in younger cases, and 70%–80% in older cases. The ASM with the best retention rate was generally recommended by Epipick. Significance We found a large overlap between expert advice and real‐world retention rates. Notably, Epipick did suggest some ASMs with more modest retention rates. Conversely, clearly inappropriate ASMs (not recommended by Epipick) had high retention rates in some cases, showing that decision systems should not rely indiscriminately on retention rates alone. In future clinical decision support systems, expert opinion and real‐world retention rates could work synergistically.
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Affiliation(s)
- Samuel Håkansson
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden.,Institute of Neuroscience and Physiology, Department of Clinical Neuroscience, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.,Wallenberg Center for Molecular and Translational Medicine, Gothenburg University, Gothenburg, Sweden
| | - Johan Zelano
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden.,Institute of Neuroscience and Physiology, Department of Clinical Neuroscience, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.,Wallenberg Center for Molecular and Translational Medicine, Gothenburg University, Gothenburg, Sweden
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20
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Wu J, Wang Y, Xiang L, Gu Y, Yan Y, Li L, Tian X, Jing W, Wang X. Machine learning model to predict the efficacy of antiseizure medications in patients with familial genetic generalized epilepsy. Epilepsy Res 2022; 181:106888. [DOI: 10.1016/j.eplepsyres.2022.106888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 01/18/2022] [Accepted: 02/09/2022] [Indexed: 11/03/2022]
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21
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Machine learning models for decision support in epilepsy management: A critical review. Epilepsy Behav 2021; 123:108273. [PMID: 34507093 DOI: 10.1016/j.yebeh.2021.108273] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/13/2021] [Accepted: 08/14/2021] [Indexed: 12/12/2022]
Abstract
PURPOSE There remain major challenges for the clinician in managing patients with epilepsy effectively. Choosing anti-seizure medications (ASMs) is subject to trial and error. About one-third of patients have drug-resistant epilepsy (DRE). Surgery may be considered for selected patients, but time from diagnosis to surgery averages 20 years. We reviewed the potential use of machine learning (ML) predictive models as clinical decision support tools to help address some of these issues. METHODS We conducted a comprehensive search of Medline and Embase of studies that investigated the application of ML in epilepsy management in terms of predicting ASM responsiveness, predicting DRE, identifying surgical candidates, and predicting epilepsy surgery outcomes. Original articles addressing these 4 areas published in English between 2000 and 2020 were included. RESULTS We identified 24 relevant articles: 6 on ASM responsiveness, 3 on DRE prediction, 2 on identifying surgical candidates, and 13 on predicting surgical outcomes. A variety of potential predictors were used including clinical, neuropsychological, imaging, electroencephalography, and health system claims data. A number of different ML algorithms and approaches were used for prediction, but only one study utilized deep learning methods. Some models show promising performance with areas under the curve above 0.9. However, most were single setting studies (18 of 24) with small sample sizes (median number of patients 55), with the exception of 3 studies that utilized large databases and 3 studies that performed external validation. There was a lack of standardization in reporting model performance. None of the models reviewed have been prospectively evaluated for their clinical benefits. CONCLUSION The utility of ML models for clinical decision support in epilepsy management remains to be determined. Future research should be directed toward conducting larger studies with external validation, standardization of reporting, and prospective evaluation of the ML model on patient outcomes.
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22
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de Jong J, Cutcutache I, Page M, Elmoufti S, Dilley C, Fröhlich H, Armstrong M. Towards realizing the vision of precision medicine: AI based prediction of clinical drug response. Brain 2021; 144:1738-1750. [PMID: 33734308 PMCID: PMC8320273 DOI: 10.1093/brain/awab108] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 02/05/2021] [Accepted: 02/23/2021] [Indexed: 01/25/2023] Open
Abstract
Accurate and individualized prediction of response to therapies is central to precision medicine. However, because of the generally complex and multifaceted nature of clinical drug response, realizing this vision is highly challenging, requiring integrating different data types from the same individual into one prediction model. We used the anti-epileptic drug brivaracetam as a case study and combine a hybrid data/knowledge-driven feature extraction with machine learning to systematically integrate clinical and genetic data from a clinical discovery dataset (n = 235 patients). We constructed a model that successfully predicts clinical drug response [area under the curve (AUC) = 0.76] and show that even with limited sample size, integrating high-dimensional genetics data with clinical data can inform drug response prediction. After further validation on data collected from an independently conducted clinical study (AUC = 0.75), we extensively explore our model to gain insights into the determinants of drug response, and identify various clinical and genetic characteristics predisposing to poor response. Finally, we assess the potential impact of our model on clinical trial design and demonstrate that, by enriching for probable responders, significant reductions in clinical study sizes may be achieved. To our knowledge, our model represents the first retrospectively validated machine learning model linking drug mechanism of action and the genetic, clinical and demographic background in epilepsy patients to clinical drug response. Hence, it provides a blueprint for how machine learning-based multimodal data integration can act as a driver in achieving the goals of precision medicine in fields such as neurology.
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Affiliation(s)
- Johann de Jong
- Data and Translational Sciences, UCB Biosciences GmbH, 40789 Monheim am Rhein, Germany
| | | | - Matthew Page
- Data and Translational Sciences, UCB Pharma, Slough SL1 3WE, UK
| | - Sami Elmoufti
- Late Development Statistics, UCB Biosciences Inc., Raleigh, NC 27617, USA
| | | | - Holger Fröhlich
- Data and Translational Sciences, UCB Biosciences GmbH, 40789 Monheim am Rhein, Germany
- Fraunhofer Institute for Scientific Computing and Algorithms (SCAI), Business Area Bioinformatics, 53757 Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, University of Bonn, 53115 Bonn, Germany
| | - Martin Armstrong
- Data and Translational Sciences, UCB Pharma, 1420 Braine l’Alleud, Belgium
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Håkansson S, Karlander M, Larsson D, Mahamud Z, Garcia-Ptacek S, Zelezniak A, Zelano J. Potential for improved retention rate by personalized antiseizure medication selection: A register-based analysis. Epilepsia 2021; 62:2123-2132. [PMID: 34245010 DOI: 10.1111/epi.16987] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 06/16/2021] [Accepted: 06/16/2021] [Indexed: 01/16/2023]
Abstract
OBJECTIVE The first antiseizure medication (ASM) is ineffective or intolerable in 50% of epilepsy cases. Selection between more than 25 available ASMs is guided by epilepsy factors, but also age and comorbidities. Randomized evidence for particular patient subgroups is seldom available. We asked whether register data could be used for retention rate calculations based on demographics, comorbidities, and ASM history, and quantified the potential improvement in retention rates of the first ASM in several large epilepsy cohorts. We also describe retention rates in patients with epilepsy after traumatic brain injury and dementia, patient groups with little available evidence. METHODS We used medical, demographic, and drug prescription data from epilepsy cohorts from comprehensive Swedish registers, containing 6380 observations. By analyzing 381 840 prescriptions, we studied retention rates of first- and second-line ASMs for patients with epilepsy in multiple sclerosis (MS), brain infection, dementia, traumatic brain injury, or stroke. The rank of retention rates of ASMs was validated by comparison to published randomized control trials. We identified the optimal stratification for each brain disease, and quantified the potential improvement if all patients had received the optimal ASM. RESULTS Using optimal stratification for each brain disease, the potential improvement in retention rate (percentage points) was MS, 20%; brain infection, 21%; dementia, 14%; trauma, 21%; and stroke, 14%. In epilepsy after trauma, levetiracetam had the highest retention rate at 80% (95% confidence interval [CI] = 65-89), exceeding that of the most commonly prescribed ASM, carbamazepine (p = .04). In epilepsy after dementia, lamotrigine (77%, 95% CI = 68-84) and levetiracetam (74%, 95% CI = 68-79) had higher retention rates than carbamazepine (p = .006 and p = .01, respectively). SIGNIFICANCE We conclude that personalized ASM selection could improve retention rates and that national registers have potential as big data sources for personalized medicine in epilepsy.
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Affiliation(s)
- Samuel Håkansson
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden.,Institute of Neuroscience and Physiology, Department of Clinical Neuroscience, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.,Wallenberg Center of Molecular and Translational Medicine, Gothenburg University, Gothenburg, Sweden
| | - Markus Karlander
- Institute of Neuroscience and Physiology, Department of Clinical Neuroscience, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.,Department of Neurology, Södra Älvsborg Hospital, Borås, Sweden.,Department of Research, Education and Innovation, Region Västra Götaland, Södra Älvsborg Hospital, Borås, Sweden
| | - David Larsson
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden.,Institute of Neuroscience and Physiology, Department of Clinical Neuroscience, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.,Wallenberg Center of Molecular and Translational Medicine, Gothenburg University, Gothenburg, Sweden
| | - Zamzam Mahamud
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden.,Institute of Neuroscience and Physiology, Department of Clinical Neuroscience, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.,Wallenberg Center of Molecular and Translational Medicine, Gothenburg University, Gothenburg, Sweden
| | - Sara Garcia-Ptacek
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.,Inflammation and Aging Theme, Cognitive clinic, Karolinska University Hospital, Stockholm, Sweden
| | - Aleksej Zelezniak
- Department of Biology and Biological Engineering, Division of Systems and Synthetic Biology, Chalmers University of Technology, Gothenburg, Sweden.,Science for Life Laboratory, Stockholm, Sweden
| | - Johan Zelano
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden.,Institute of Neuroscience and Physiology, Department of Clinical Neuroscience, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.,Wallenberg Center of Molecular and Translational Medicine, Gothenburg University, Gothenburg, Sweden
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24
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van den Heuvel L, Dorsey RR, Prainsack B, Post B, Stiggelbout AM, Meinders MJ, Bloem BR. Quadruple Decision Making for Parkinson's Disease Patients: Combining Expert Opinion, Patient Preferences, Scientific Evidence, and Big Data Approaches to Reach Precision Medicine. JOURNAL OF PARKINSONS DISEASE 2021; 10:223-231. [PMID: 31561387 PMCID: PMC7029360 DOI: 10.3233/jpd-191712] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Clinical decision making for Parkinson’s disease patients is supported by a combination of three distinct information resources: best available scientific evidence, professional expertise, and the personal needs and preferences of patients. All three sources have clear value but also share several important limitations, mainly regarding subjectivity, generalizability and variability. For example, current scientific evidence, especially from controlled clinical trials, is often based on selected study populations, making it difficult to translate the outcome to the care for individual patients in everyday clinical practice. Big data, including data from real-life unselected Parkinson populations, can help to bridge this information gap. Fine-grained patient profiles created from big data have the potential to aid in identifying therapeutic approaches that will be most effective given each patient’s individual characteristics, which is particularly important for a disorder characterized by such tremendous interindividual variability as Parkinson’s disease. In this viewpoint, we argue that big data approaches should be acknowledged and harnessed, not to replace existing information resources, but rather as a fourth and complimentary source of information in clinical decision making, helping to represent the full complexity of individual patients. We introduce the ‘quadruple decision making’ model and illustrate its mode of action by showing how this can be used to pursue precision medicine for persons living with Parkinson’s disease.
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Affiliation(s)
- Lieneke van den Heuvel
- Department of Neurology, Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Ray R Dorsey
- Department of Neurology, University of Rochester Medical Centre, Rochester, NY, USA
| | - Barbara Prainsack
- Department of Political Science, University of Vienna, AT; and Department of Global Health & Social Medicine, King's College London, London, UK
| | - Bart Post
- Department of Neurology, Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Anne M Stiggelbout
- Medical Decision Making, Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
| | - Marjan J Meinders
- Radboud University Medical Centre, Radboud Institute for Health Sciences; Scientific Centre for Quality of Healthcare, Nijmegen, The Netherlands
| | - Bastiaan R Bloem
- Department of Neurology, Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
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25
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Sessa M, Liang D, Khan AR, Kulahci M, Andersen M. Artificial Intelligence in Pharmacoepidemiology: A Systematic Review. Part 2-Comparison of the Performance of Artificial Intelligence and Traditional Pharmacoepidemiological Techniques. Front Pharmacol 2021; 11:568659. [PMID: 33519433 PMCID: PMC7841344 DOI: 10.3389/fphar.2020.568659] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 12/07/2020] [Indexed: 01/14/2023] Open
Abstract
Aim: To summarize the evidence on the performance of artificial intelligence vs. traditional pharmacoepidemiological techniques. Methods: Ovid MEDLINE (01/1950 to 05/2019) was searched to identify observational studies, meta-analyses, and clinical trials using artificial intelligence techniques having a drug as the exposure or the outcome of the study. Only studies with an available full text in the English language were evaluated. Results: In all, 72 original articles and five reviews were identified via Ovid MEDLINE of which 19 (26.4%) compared the performance of artificial intelligence techniques with traditional pharmacoepidemiological methods. In total, 44 comparisons have been performed in articles that aimed at 1) predicting the needed dosage given the patient’s characteristics (31.8%), 2) predicting the clinical response following a pharmacological treatment (29.5%), 3) predicting the occurrence/severity of adverse drug reactions (20.5%), 4) predicting the propensity score (9.1%), 5) identifying subpopulation more at risk of drug inefficacy (4.5%), 6) predicting drug consumption (2.3%), and 7) predicting drug-induced lengths of stay in hospital (2.3%). In 22 out of 44 (50.0%) comparisons, artificial intelligence performed better than traditional pharmacoepidemiological techniques. Random forest (seven out of 11 comparisons; 63.6%) and artificial neural network (six out of 10 comparisons; 60.0%) were the techniques that in most of the comparisons outperformed traditional pharmacoepidemiological methods. Conclusion: Only a small fraction of articles compared the performance of artificial intelligence techniques with traditional pharmacoepidemiological methods and not all artificial intelligence techniques have been compared in a Pharmacoepidemiological setting. However, in 50% of comparisons, artificial intelligence performed better than pharmacoepidemiological techniques.
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Affiliation(s)
- Maurizio Sessa
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - David Liang
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Abdul Rauf Khan
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark.,Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Murat Kulahci
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark.,Department of Business Administration, Technology and Social Sciences, Luleå University of Technology, Luleå, Sweden
| | - Morten Andersen
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
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26
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Yang S, Wang B, Han X. Models for predicting treatment efficacy of antiepileptic drugs and prognosis of treatment withdrawal in epilepsy patients. ACTA EPILEPTOLOGICA 2021. [DOI: 10.1186/s42494-020-00035-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
AbstractAlthough antiepileptic drugs (AEDs) are the most effective treatment for epilepsy, 30–40% of patients with epilepsy would develop drug-refractory epilepsy. An accurate, preliminary prediction of the efficacy of AEDs has great clinical significance for patient treatment and prognosis. Some studies have developed statistical models and machine-learning algorithms (MLAs) to predict the efficacy of AEDs treatment and the progression of disease after treatment withdrawal, in order to provide assistance for making clinical decisions in the aim of precise, personalized treatment. The field of prediction models with statistical models and MLAs is attracting growing interest and is developing rapidly. What’s more, more and more studies focus on the external validation of the existing model. In this review, we will give a brief overview of recent developments in this discipline.
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27
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Nair P, Aghoram R, Khilari M. Applications of artificial intelligence in epilepsy. INTERNATIONAL JOURNAL OF ADVANCED MEDICAL AND HEALTH RESEARCH 2021. [DOI: 10.4103/ijamr.ijamr_94_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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28
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Chen Z, Rollo B, Antonic-Baker A, Anderson A, Ma Y, O'Brien TJ, Ge Z, Wang X, Kwan P. New era of personalised epilepsy management. BMJ 2020; 371:m3658. [PMID: 33037001 PMCID: PMC7541035 DOI: 10.1136/bmj.m3658] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The trial and error approach to epilepsy treatment has not changed for over a century but machine learning and patient derived stem cells promise a personalised and more effective strategy, argue Patrick Kwan and colleagues
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Affiliation(s)
- Zhibin Chen
- Chongqing Key Laboratory of Neurology, First Affiliated Hospital, Chongqing Medical University, Chongqing, China
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Ben Rollo
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
| | - Ana Antonic-Baker
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
| | - Alison Anderson
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
| | - Yuanlin Ma
- Chongqing Key Laboratory of Neurology, First Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Terence J O'Brien
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
- Department of Neurology, Alfred Hospital, Melbourne, Australia
| | - Zongyuan Ge
- Faculty of Engineering, Monash University, Melbourne, Australia
- eResearch Centre, Monash University, Melbourne, Australia
- Airdoc Research Australia, Melbourne, Australia
| | - Xuefeng Wang
- Chongqing Key Laboratory of Neurology, First Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Patrick Kwan
- Chongqing Key Laboratory of Neurology, First Affiliated Hospital, Chongqing Medical University, Chongqing, China
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
- Department of Neurology, Alfred Hospital, Melbourne, Australia
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29
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Lhatoo SD, Bernasconi N, Blumcke I, Braun K, Buchhalter J, Denaxas S, Galanopoulou A, Josephson C, Kobow K, Lowenstein D, Ryvlin P, Schulze-Bonhage A, Sahoo SS, Thom M, Thurman D, Worrell G, Zhang GQ, Wiebe S. Big data in epilepsy: Clinical and research considerations. Report from the Epilepsy Big Data Task Force of the International League Against Epilepsy. Epilepsia 2020; 61:1869-1883. [PMID: 32767763 DOI: 10.1111/epi.16633] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 07/07/2020] [Accepted: 07/08/2020] [Indexed: 12/25/2022]
Abstract
Epilepsy is a heterogeneous condition with disparate etiologies and phenotypic and genotypic characteristics. Clinical and research aspects are accordingly varied, ranging from epidemiological to molecular, spanning clinical trials and outcomes, gene and drug discovery, imaging, electroencephalography, pathology, epilepsy surgery, digital technologies, and numerous others. Epilepsy data are collected in the terabytes and petabytes, pushing the limits of current capabilities. Modern computing firepower and advances in machine and deep learning, pioneered in other diseases, open up exciting possibilities for epilepsy too. However, without carefully designed approaches to acquiring, standardizing, curating, and making available such data, there is a risk of failure. Thus, careful construction of relevant ontologies, with intimate stakeholder inputs, provides the requisite scaffolding for more ambitious big data undertakings, such as an epilepsy data commons. In this review, we assess the clinical and research epilepsy landscapes in the big data arena, current challenges, and future directions, and make the case for a systematic approach to epilepsy big data.
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Affiliation(s)
- Samden D Lhatoo
- University of Texas Health Sciences Center at Houston, Houston, Texas
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Ingmar Blumcke
- Friedrich-Alexander University Erlangen-Nürnberg, University Hospital Erlangen, Erlangen, Germany
| | - Kees Braun
- Department of Child Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Jeffrey Buchhalter
- Department of Neurology, St Joseph's Hospital and Medical Center, Phoenix, Arizona
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
| | - Aristea Galanopoulou
- Saul Korey Department of Neurology, Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York
| | - Colin Josephson
- Department of Clinical Neurosciences, University of Calgary, Calgary, Canada
| | - Katja Kobow
- Friedrich-Alexander University Erlangen-Nürnberg, University Hospital Erlangen, Erlangen, Germany
| | - Daniel Lowenstein
- Department of Neurology, University of California, San Francisco, San Francisco, California
| | - Philippe Ryvlin
- Department of Neurosciences, University of Lausanne, Lausanne, Switzerland
| | | | - Satya S Sahoo
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Maria Thom
- Institute of Neurology, University College London, London, UK
| | | | - Greg Worrell
- Department of Neurology, Mayo Clinic, Rochester, Minnesota
| | - Guo-Qiang Zhang
- University of Texas Health Sciences Center at Houston, Houston, Texas
| | - Samuel Wiebe
- Department of Clinical Neurosciences, University of Calgary, Calgary, Canada
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30
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Beniczky S, Karoly P, Nurse E, Ryvlin P, Cook M. Machine learning and wearable devices of the future. Epilepsia 2020; 62 Suppl 2:S116-S124. [PMID: 32712958 DOI: 10.1111/epi.16555] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 05/05/2020] [Accepted: 05/08/2020] [Indexed: 01/06/2023]
Abstract
Machine learning (ML) is increasingly recognized as a useful tool in healthcare applications, including epilepsy. One of the most important applications of ML in epilepsy is seizure detection and prediction, using wearable devices (WDs). However, not all currently available algorithms implemented in WDs are using ML. In this review, we summarize the state of the art of using WDs and ML in epilepsy, and we outline future development in these domains. There is published evidence for reliable detection of epileptic seizures using implanted electroencephalography (EEG) electrodes and wearable, non-EEG devices. Application of ML using the data recorded with WDs from a large number of patients could change radically the way we diagnose and manage patients with epilepsy.
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Affiliation(s)
- Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark.,Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark.,Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Philippa Karoly
- The Graeme Clark Institute, The University of Melbourne, Melbourne, Vic., Australia
| | - Ewan Nurse
- The Graeme Clark Institute, The University of Melbourne, Melbourne, Vic., Australia
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, CHUV, Lausanne, Switzerland
| | - Mark Cook
- The Graeme Clark Institute, The University of Melbourne, Melbourne, Vic., Australia
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31
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Sessa M, Khan AR, Liang D, Andersen M, Kulahci M. Artificial Intelligence in Pharmacoepidemiology: A Systematic Review. Part 1-Overview of Knowledge Discovery Techniques in Artificial Intelligence. Front Pharmacol 2020; 11:1028. [PMID: 32765261 PMCID: PMC7378532 DOI: 10.3389/fphar.2020.01028] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 06/24/2020] [Indexed: 12/14/2022] Open
Abstract
Aim To perform a systematic review on the application of artificial intelligence (AI) based knowledge discovery techniques in pharmacoepidemiology. Study Eligibility Criteria Clinical trials, meta-analyses, narrative/systematic review, and observational studies using (or mentioning articles using) artificial intelligence techniques were eligible. Articles without a full text available in the English language were excluded. Data Sources Articles recorded from 1950/01/01 to 2019/05/06 in Ovid MEDLINE were screened. Participants Studies including humans (real or simulated) exposed to a drug. Results In total, 72 original articles and 5 reviews were identified via Ovid MEDLINE. Twenty different knowledge discovery methods were identified, mainly from the area of machine learning (66/72; 91.7%). Classification/regression (44/72; 61.1%), classification/regression + model optimization (13/72; 18.0%), and classification/regression + features selection (12/72; 16.7%) were the three most frequent tasks in reviewed literature that machine learning methods has been applied to solve. The top three used techniques were artificial neural networks, random forest, and support vector machines models. Conclusions The use of knowledge discovery techniques of artificial intelligence techniques has increased exponentially over the years covering numerous sub-topics of pharmacoepidemiology. Systematic Review Registration Systematic review registration number in PROSPERO: CRD42019136552.
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Affiliation(s)
- Maurizio Sessa
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Abdul Rauf Khan
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark.,Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - David Liang
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Morten Andersen
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Murat Kulahci
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark.,Department of Business Administration, Technology and Social Sciences, Luleå University of Technology, Luleå, Sweden
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Galetsi P, Katsaliaki K. Big data analytics in health: an overview and bibliometric study of research activity. Health Info Libr J 2019; 37:5-25. [DOI: 10.1111/hir.12286] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Accepted: 10/23/2019] [Indexed: 12/16/2022]
Affiliation(s)
- Panagiota Galetsi
- School of Economics, Business Administration & Legal Studies International Hellenic University Thessaloniki Greece
| | - Korina Katsaliaki
- School of Economics, Business Administration & Legal Studies International Hellenic University Thessaloniki Greece
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33
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Abbasi B, Goldenholz DM. Machine learning applications in epilepsy. Epilepsia 2019; 60:2037-2047. [PMID: 31478577 PMCID: PMC9897263 DOI: 10.1111/epi.16333] [Citation(s) in RCA: 167] [Impact Index Per Article: 33.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 07/25/2019] [Accepted: 08/12/2019] [Indexed: 02/05/2023]
Abstract
Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recognition, language processing, and data mining, machine learning techniques have received increasing attention in medical applications, ranging from automated imaging analysis to disease forecasting. This review examines the parallel progress made in epilepsy, highlighting applications in automated seizure detection from electroencephalography (EEG), video, and kinetic data, automated imaging analysis and pre-surgical planning, prediction of medication response, and prediction of medical and surgical outcomes using a wide variety of data sources. A brief overview of commonly used machine learning approaches, as well as challenges in further application of machine learning techniques in epilepsy, is also presented. With increasing computational capabilities, availability of effective machine learning algorithms, and accumulation of larger datasets, clinicians and researchers will increasingly benefit from familiarity with these techniques and the significant progress already made in their application in epilepsy.
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Affiliation(s)
- Bardia Abbasi
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA 02215
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34
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Yao L, Cai M, Chen Y, Shen C, Shi L, Guo Y. Prediction of antiepileptic drug treatment outcomes of patients with newly diagnosed epilepsy by machine learning. Epilepsy Behav 2019; 96:92-97. [PMID: 31121513 DOI: 10.1016/j.yebeh.2019.04.006] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 03/22/2019] [Accepted: 04/07/2019] [Indexed: 11/17/2022]
Abstract
OBJECTIVE The objective of this study was to build a supervised machine learning-based classifier, which can accurately predict the outcomes of antiepileptic drug (AED) treatment of patients with newly diagnosed epilepsy. METHODS We collected information from 287 patients with newly diagnosed epilepsy between 2009 and 2017 at the Second Affiliated Hospital of Zhejiang University. Patients were prospectively followed up for at least 3 years. A number of features, including demographic features, medical history, and auxiliary examinations (electroencephalogram [EEG] and magnetic resonance imaging [MRI]) are selected to distinguish patients with different remission outcomes. Seizure outcomes classified as remission and never remission. In addition, remission is further divided into early remission and late remission. Five classical machine learning algorithms, i.e., Decision Tree, Random Forest, Support Vector Machine, XGBoost, and Logistic Regression, are selected and trained by our dataset to get classification models. RESULTS Our study shows that 1) compared with the other four algorithms, the XGBoost algorithm based machine learning model achieves the best prediction performance of the AED treatment outcomes between remission and never remission patients with an F1 score of 0.947 and an area under the curve (AUC) value of 0.979; 2) The best discriminative factor for remission and never remission patients is higher number of seizures before treatment (>3); 3) XGBoost-based machine learning model also offers the best prediction between early remission and later remission patients, with an F1 score of 0.836 and an AUC value of 0.918; 4) multiple seizure type has the highest dependence to the categories of early and late remission patients. SIGNIFICANCES Our XGBoost-based machine learning classifier accurately predicts the most probable AED treatment outcome of a patient after he/she finishes all the standard examinations for the epilepsy disease. The classifier's prediction result could help disease guide counseling and eventually improve treatment strategies.
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Affiliation(s)
- Lijun Yao
- Shanghai Pudong New Area Mental Health Center, Tongji University School of Medicine, Shanghai 200124, PR China.
| | - Mengting Cai
- Department of Neurology, Epilepsy Center, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang, PR China
| | - Yang Chen
- School of Computer Science, Fudan University, Shanghai 201203, PR China
| | - Chunhong Shen
- Department of Neurology, Epilepsy Center, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang, PR China
| | - Lei Shi
- The State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100080, PR China
| | - Yi Guo
- Department of Neurology, Epilepsy Center, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang, PR China.
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35
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Pitkänen A, Henshall DC, Cross JH, Guerrini R, Jozwiak S, Kokaia M, Simonato M, Sisodiya S, Mifsud J. Advancing research toward faster diagnosis, better treatment, and end of stigma in epilepsy. Epilepsia 2019; 60:1281-1292. [DOI: 10.1111/epi.16091] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 05/09/2019] [Accepted: 05/27/2019] [Indexed: 12/27/2022]
Affiliation(s)
| | - David C. Henshall
- Royal College of Surgeons in Ireland Dublin Ireland
- FutureNeuro Research Centre, RCSI Dublin Ireland
| | - J. Helen Cross
- UCL Great Ormond Street Institute of Child Health London UK
| | - Renzo Guerrini
- Children's Hospital A. Meyer‐University of Florence Florence Italy
| | - Sergiusz Jozwiak
- Warsaw Medical University Warsaw Poland
- The Children's Memorial Health Institute Warsaw Poland
| | | | - Michele Simonato
- University of Ferrara and University Vita‐Salute San Raffaele Milan Italy
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36
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Delen D, Davazdahemami B, Eryarsoy E, Tomak L, Valluru A. Using predictive analytics to identify drug-resistant epilepsy patients. Health Informatics J 2019; 26:449-460. [DOI: 10.1177/1460458219833120] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Epilepsy is one of the most common brain disorders that greatly affects patients’ quality of life and poses serious risks to their health. While the majority of the patients positively respond to the existing anti-epilepsy drugs, others who developed the refractory type of epilepsy show resistance against drug therapy and need to undergo advance treatments such as surgery. Given that identifying such patients is not a straightforward process and requires long courses of trial and error with anti-epilepsy drugs, this study aims at predicting those at-risk patients using clinical and demographic data obtained from electronic medical records. Specifically, the study employs several predictive analytics machine-learning methods, equipped with a novel approach for data balancing, to identify drug-resistant patients using their comorbidities and demographic information along with the initial epilepsy-related diagnosis made by their physician. The promising results we obtained highlight the potential use of machine-learning techniques in facilitating medical decisions and suggest the possibility of extending the proposed approach for developing a clinical decision support system for medical professionals.
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Amengual-Gual M, Sánchez Fernández I, Loddenkemper T. Patterns of epileptic seizure occurrence. Brain Res 2019; 1703:3-12. [DOI: 10.1016/j.brainres.2018.02.032] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2017] [Revised: 12/03/2017] [Accepted: 02/20/2018] [Indexed: 01/03/2023]
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An S, Malhotra K, Dilley C, Han-Burgess E, Valdez JN, Robertson J, Clark C, Westover MB, Sun J. Predicting drug-resistant epilepsy - A machine learning approach based on administrative claims data. Epilepsy Behav 2018; 89:118-125. [PMID: 30412924 PMCID: PMC6461470 DOI: 10.1016/j.yebeh.2018.10.013] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 10/04/2018] [Accepted: 10/08/2018] [Indexed: 11/28/2022]
Abstract
Patients with drug-resistant epilepsy (DRE) are at high risk of morbidity and mortality, yet their referral to specialist care is frequently delayed. The ability to identify patients at high risk of DRE at the time of treatment initiation, and to subsequently steer their treatment pathway toward more personalized interventions, has high clinical utility. Here, we aim to demonstrate the feasibility of developing algorithms for predicting DRE using machine learning methods. Longitudinal, intersected data sourced from US pharmacy, medical, and adjudicated hospital claims from 1,376,756 patients from 2006 to 2015 were analyzed; 292,892 met inclusion criteria for epilepsy, and 38,382 were classified as having DRE using a proxy measure for drug resistance. Patients were characterized using 1270 features reflecting demographics, comorbidities, medications, procedures, epilepsy status, and payer status. Data from 175,735 randomly selected patients were used to train three algorithms and from the remainder to assess the trained models' predictive power. A model with only age and sex was used as a benchmark. The best model, random forest, achieved an area under the receiver operating characteristic curve (95% confidence interval [CI]) of 0.764 (0.759, 0.770), compared with 0.657 (0.651, 0.663) for the benchmark model. Moreover, predicted probabilities for DRE were well-calibrated with the observed frequencies in the data. The model predicted drug resistance approximately 2 years before patients in the test dataset had failed two antiepileptic drugs (AEDs). Machine learning models constructed using claims data predicted which patients are likely to fail ≥3 AEDs and are at risk of developing DRE at the time of the first AED prescription. The use of such models can ensure that patients with predicted DRE receive specialist care with potentially more aggressive therapeutic interventions from diagnosis, to help reduce the serious sequelae of DRE.
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Affiliation(s)
- Sungtae An
- Georgia Institute of Technology, College of Computing, Atlanta, GA, USA
| | - Kunal Malhotra
- Georgia Institute of Technology, College of Computing, Atlanta, GA, USA
| | | | | | - Jeffrey N Valdez
- Georgia Institute of Technology, College of Computing, Atlanta, GA, USA
| | | | | | - M Brandon Westover
- Massachusetts General Hospital, Department of Neurology, Boston, MA, USA
| | - Jimeng Sun
- Georgia Institute of Technology, College of Computing, Atlanta, GA, USA.
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Conte F, Legros B, Van Paesschen W, Avbersek A, Muglia P, Depondt C. Long-term seizure outcomes in patients with drug resistant epilepsy. Seizure 2018; 62:74-78. [DOI: 10.1016/j.seizure.2018.09.020] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Revised: 09/21/2018] [Accepted: 09/25/2018] [Indexed: 01/26/2023] Open
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Oppenheimer J, Leviton A, Chiujdea M, Antonetty A, Ojo OW, Garcia S, Weas S, Fleegler EW, Chan E, Loddenkemper T. Caring electronically for young outpatients who have epilepsy. Epilepsy Behav 2018; 87:226-232. [PMID: 30197227 DOI: 10.1016/j.yebeh.2018.06.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Revised: 06/08/2018] [Accepted: 06/11/2018] [Indexed: 01/17/2023]
Abstract
PURPOSE The purpose of this study was to review electronic tools that might improve the delivery of epilepsy care, reduce medical care costs, and empower families to improve self-management capability. METHOD We reviewed the epilepsy-specific literature about self-management, electronic patient-reported or provider-reported outcomes, on-going remote surveillance, and alerting/warning systems. CONCLUSIONS The improved care delivery system that we envision includes self-management, electronic patient (or provider)-reported outcomes, on-going remote surveillance, and alerting/warning systems. This system and variants have the potential to reduce seizure burden through improved management, keep children out of the emergency department and hospital, and even reduce the number of outpatient visits.
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Affiliation(s)
- Julia Oppenheimer
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alan Leviton
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Madeline Chiujdea
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Annalee Antonetty
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Oluwafemi William Ojo
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Stephanie Garcia
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Sarah Weas
- Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Eric W Fleegler
- Division of Emergency Medicine, Department of Medicine, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Eugenia Chan
- Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Tobias Loddenkemper
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
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Ferastraoaru V, Goldenholz DM, Chiang S, Moss R, Theodore WH, Haut SR. Characteristics of large patient-reported outcomes: Where can one million seizures get us? Epilepsia Open 2018; 3:364-373. [PMID: 30187007 PMCID: PMC6119749 DOI: 10.1002/epi4.12237] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/12/2018] [Indexed: 01/09/2023] Open
Abstract
Objective To analyze data from Seizure Tracker, a large electronic seizure diary, including comparison of seizure characteristics among different etiologies, temporal patterns in seizure fluctuations, and specific triggers. Methods Zero‐inflated negative binomial mixed‐effects models were used to evaluate temporal patterns of seizure events (during the day or week), as well as group differences in monthly seizure frequency between children and adults and between etiologies. The association of long seizures with seizure triggers was evaluated using a mixed‐effects logistic model with subject as the random effect. Incidence rate ratios (IRRs) and odds ratios were reported for analyses involving zero‐inflated negative binomial and logistic mixed‐effects models, respectively. Results A total of 1,037,909 seizures were logged by 10,186 subjects (56.7% children) from December 2007 to January 2016. Children had more frequent seizures than adults did (median monthly seizure frequency 3.5 vs. 2.7, IRR 1.26; p < 0.001). Seizures demonstrated a circadian pattern (higher frequency between 07:00 a.m. and 10:00 a.m. and lower overnight), and seizures were reported differentially across the week (seizure rates higher Monday through Friday than Saturday or Sunday). Longer seizures (>5 or >30 min) had a higher proportion of the following triggers when compared with shorter seizures: “Overtired or irregular sleep,” “Bright or flashing lights,” and “Emotional stress” (p < 0.004). Significance This study explored a large cohort of patients with self‐reported seizures; strengths and limitations of large seizure diary databases are discussed. The findings in this study are consistent with those of prior work in smaller validated cohorts, suggesting that patient‐recorded databases are a valuable resource for epilepsy research, capable of both replication of results and generation of novel hypotheses.
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Affiliation(s)
- Victor Ferastraoaru
- Department of Neurology Albert Einstein College of Medicine and Montefiore Medical Center Bronx New York U.S.A
| | - Daniel M Goldenholz
- Division of Epilepsy Beth Israel Deaconess Medical Center Boston Massachusetts U.S.A
| | - Sharon Chiang
- Department of Neurology University of California San Francisco San Francisco California.,Department of Statistics Rice University Houston Texas U.S.A
| | - Robert Moss
- SeizureTracker LLC Alexandria Virginia U.S.A
| | - William H Theodore
- National Institutes of Health National Institute of Neurological Disorders and Stroke Bethesda Maryland U.S.A
| | - Sheryl R Haut
- Department of Neurology Albert Einstein College of Medicine and Montefiore Medical Center Bronx New York U.S.A
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Sánchez Fernández I, Sansevere AJ, Gaínza-Lein M, Kapur K, Loddenkemper T. Machine Learning for Outcome Prediction in Electroencephalograph (EEG)-Monitored Children in the Intensive Care Unit. J Child Neurol 2018; 33:546-553. [PMID: 29756499 DOI: 10.1177/0883073818773230] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The aim of this study was to evaluate the performance of models predicting in-hospital mortality in critically ill children undergoing continuous electroencephalography (cEEG) in the intensive care unit (ICU). We evaluated the performance of machine learning algorithms for predicting mortality in a database of 414 critically ill children undergoing cEEG in the ICU. The area under the receiver operating characteristic curve (AUC) in the test subset was highest for stepwise selection/elimination models (AUC = 0.82) followed by least absolute shrinkage and selection operator (LASSO) and support vector machine with linear kernel (AUC = 0.79), and random forest (AUC = 0.71). The explanatory models had the poorest discriminative performance (AUC = 0.63 for the model without considering etiology and AUC = 0.45 for the model considering etiology). Using few variables and a relatively small number of patients, machine learning techniques added information to explanatory models for prediction of in-hospital mortality.
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Affiliation(s)
- Iván Sánchez Fernández
- 1 Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,2 Department of Child Neurology, Hospital Sant Joan de Déu, Universidad de Barcelona, Passeig de Sant Joan de Déu, Esplugues de Llobregat, Barcelona, Spain
| | - Arnold J Sansevere
- 1 Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Marina Gaínza-Lein
- 1 Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,3 Facultad de Medicina, Universidad Austral de Chile, Edificio de Ciencias Biomédicas, Campus Isla Teja s/n, UACh, Valdivia, Chile
| | - Kush Kapur
- 1 Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Tobias Loddenkemper
- 1 Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
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Can a collaborative healthcare network improve the care of people with epilepsy? Epilepsy Behav 2018; 82:189-193. [PMID: 29573986 DOI: 10.1016/j.yebeh.2018.02.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Accepted: 02/16/2018] [Indexed: 01/31/2023]
Abstract
New opportunities are now available to improve care in ways not possible previously. Information contained in electronic medical records can now be shared without identifying patients. With network collaboration, large numbers of medical records can be searched to identify patients most like the one whose complex medical situation challenges the physician. The clinical effectiveness of different treatment strategies can be assessed rapidly to help the clinician decide on the best treatment for this patient. Other capabilities from different components of the network can prompt the recognition of what is the best available option and encourage the sharing of information about programs and electronic tools. Difficulties related to privacy, harmonization, integration, and costs are expected, but these are currently being addressed successfully by groups of organizations led by those who recognize the benefits.
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Watad A, Tiosano S, Bragazzi N, Brigo F, Comaneshter D, Cohen A, Amital H. Epilepsy among Systemic Lupus Erythematosus Patients: Insights from a Large Database Analysis. Neuroepidemiology 2017; 50:1-6. [DOI: 10.1159/000485136] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Accepted: 11/11/2017] [Indexed: 12/15/2022] Open
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Pugh MJ. Clinical decision rules for epilepsy care: The case for thinking big. Epilepsy Behav 2016; 57:220-221. [PMID: 26979764 DOI: 10.1016/j.yebeh.2016.02.034] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2016] [Accepted: 02/23/2016] [Indexed: 02/02/2023]
Affiliation(s)
- Mary Jo Pugh
- VA Epilepsy Centers of Excellence, STVHCS, San Antonio, TX, United States; Department of Research, STVHCS, San Antonio, TX, United States; Department of Epidemiology and Biostatistics, UTHSCSA, San Antonio, TX, United States; Department of Medicine, UTHSCSA, San Antonio, TX, United States.
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