1
|
Prabhakaran D, Day GS, Munipalli B, Rush BK, Pudalov L, Niazi SK, Brennan E, Powers HR, Durvasula R, Athreya A, Blackmon K. Neurophenotypes of COVID-19: Risk factors and recovery outcomes. Brain Behav Immun Health 2023; 30:100648. [PMID: 37293441 PMCID: PMC10239310 DOI: 10.1016/j.bbih.2023.100648] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 04/24/2023] [Accepted: 06/03/2023] [Indexed: 06/10/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) infection is associated with risk of persistent neurocognitive and neuropsychiatric complications. It is unclear whether the neuropsychological manifestations of COVID-19 present as a uniform syndrome or as distinct neurophenotypes with differing risk factors and recovery outcomes. We examined post-acute neuropsychological profiles following SARS-CoV-2 infection in 205 patients recruited from inpatient and outpatient populations, using an unsupervised machine learning cluster analysis, with objective and subjective measures as input features. This resulted in three distinct post-COVID clusters. In the largest cluster (69%), cognitive functions were within normal limits, although mild subjective attention and memory complaints were reported. Vaccination was associated with membership in this "normal cognition" phenotype. Cognitive impairment was present in the remaining 31% of the sample but clustered into two differentially impaired groups. In 16% of participants, memory deficits, slowed processing speed, and fatigue were predominant. Risk factors for membership in the "memory-speed impaired" neurophenotype included anosmia and more severe COVID-19 infection. In the remaining 15% of participants, executive dysfunction was predominant. Risk factors for membership in this milder "dysexecutive" neurophenotype included disease-nonspecific factors such as neighborhood deprivation and obesity. Recovery outcomes at 6-month follow-up differed across neurophenotypes, with the normal cognition group showing improvement in verbal memory and psychomotor speed, the dysexecutive group showing improvement in cognitive flexibility, and the memory-speed impaired group showing no objective improvement and relatively worse functional outcomes compared to the other two clusters. These results indicate that there are multiple post-acute neurophenotypes of COVID-19, with different etiological pathways and recovery outcomes. This information may inform phenotype-specific approaches to treatment.
Collapse
Affiliation(s)
- Divya Prabhakaran
- Mayo Clinic, Center for Individualized Medicine, Jacksonville, FL, USA
- University of California, San Diego, Radiation Medicine and Applied Sciences, San Diego, CA, USA
| | - Gregory S Day
- Mayo Clinic, Department of Neurology, Jacksonville, FL, USA
| | - Bala Munipalli
- Mayo Clinic, Department of General Internal Medicine, Jacksonville, FL, USA
| | - Beth K Rush
- Mayo Clinic, Department of Psychiatry and Psychology, Jacksonville, FL, USA
| | - Lauren Pudalov
- Mayo Clinic, Department of Psychiatry and Psychology, Jacksonville, FL, USA
| | - Shehzad K Niazi
- Mayo Clinic, Department of Psychiatry and Psychology, Jacksonville, FL, USA
| | - Emily Brennan
- Mayo Clinic, Robert D. and Patricia E. Kern Center for the Science of Healthcare Delivery, Jacksonville, FL, USA
| | - Harry R Powers
- Mayo Clinic, Division of Infectious Diseases, Jacksonville, FL, USA
| | - Ravi Durvasula
- Mayo Clinic, Division of Infectious Diseases, Jacksonville, FL, USA
| | - Arjun Athreya
- Mayo Clinic, Department of Molecular Pharmacology and Experimental Therapeutics, Rochester, MN, USA
- Mayo Clinic, Department of Psychiatry and Psychology, Rochester, MN, USA
| | - Karen Blackmon
- Mayo Clinic, Department of Psychiatry and Psychology, Jacksonville, FL, USA
| |
Collapse
|
2
|
Athreya A, Matthews RE, Drane DL, Bonilha L, Willie JT, Gross RE, Karakis I. Withdrawal of antiseizure medications after MRI-Guided laser interstitial thermal therapy in extra-temporal lobe epilepsy. Seizure 2023; 110:86-92. [PMID: 37331198 DOI: 10.1016/j.seizure.2023.06.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 05/16/2023] [Accepted: 06/12/2023] [Indexed: 06/20/2023] Open
Abstract
PURPOSE This study investigated the success rate of antiseizure medications (ASMs) withdrawal following MRI Guided Laser Interstitial Thermal Therapy (MRg-LITT) for extra-temporal lobe epilepsy (ETLE), and identified predictors of seizure recurrence. METHODS We retrospectively assessed 27 patients who underwent MRg-LITT for ETLE. Patients' demographics, disease characteristics, and post-surgical outcomes were evaluated for their potential to predict seizure recurrence associated with ASMs withdrawal. RESULTS The median period of observation post MRg-LITT was 3 years (range 18 - 96 months) and the median period to initial ASMs reduction was 0.5 years (range 1-36 months). ASMs reduction was attempted in 17 patients (63%), 5 (29%) of whom had seizure recurrence after initial reduction. Nearly all patient who relapsed regained seizure control after reinstitution of their ASMs regimen. Pre-operative seizure frequency (p = 0.002) and occurrence of acute post-operative seizures (p = 0.01) were associated with increased risk for seizure recurrence post ASMs reduction. At the end of the observation period, 11% of patients were seizure free without drugs, 52% were seizure free with drugs and 37% still experienced seizures despite ASMs. Compared with pre-operative status, the number of ASMs was reduced in 41% of patients, unchanged in 55% of them and increased in only 4% of them. CONCLUSIONS Successful MRg-LITT for ETLE allows for ASMs reduction in a significant portion of patients and complete ASMs withdrawal in a subset of them. Patients with higher pre-operative seizure frequency or occurrence of acute post operative seizures exhibit higher chances relapse post ASMs reduction.
Collapse
Affiliation(s)
- Arjun Athreya
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Rebecca E Matthews
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Daniel L Drane
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA; Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA; Department of Neurology, University of Washington, Seattle, WA, USA
| | - Leonardo Bonilha
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Jon T Willie
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, USA; Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, USA
| | - Robert E Gross
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, USA
| | - Ioannis Karakis
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA.
| |
Collapse
|
3
|
Prabhakaran D, Day GS, Munipalli B, Rush BK, Pudalov L, Niazi SK, Brennan E, Powers HR, Durvasula R, Athreya A, Blackmon K. Neurophenotypes of COVID-19: risk factors and recovery outcomes. Res Sq 2023:rs.3.rs-2363210. [PMID: 36597538 PMCID: PMC9810229 DOI: 10.21203/rs.3.rs-2363210/v2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Coronavirus disease 2019 (COVID-19) infection is associated with risk of persistent neurocognitive and neuropsychiatric complications, termed "long COVID". It is unclear whether the neuropsychological manifestations of COVID-19 present as a uniform syndrome or as distinct neurophenotypes with differing risk factors and recovery outcomes. We examined post-acute neuropsychological profiles following SARS-CoV-2 infection in 205 patients recruited from inpatient and outpatient populations, using an unsupervised machine learning cluster analysis, with objective and subjective measures as input features. This resulted in three distinct post-COVID clusters. In the largest cluster (69%), cognitive functions were within normal limits, although mild subjective attention and memory complaints were reported. Vaccination was associated with membership in this "normal cognition" phenotype. Cognitive impairment was present in the remaining 31% of the sample but clustered into two differentially impaired groups. In 16% of participants, memory deficits, slowed processing speed, and fatigue were predominant. Risk factors for membership in the "memory-speed impaired" neurophenotype included anosmia and more severe COVID-19 infection. In the remaining 15% of participants, executive dysfunction was predominant. Risk factors for membership in this milder "dysexecutive" neurophenotype included disease-nonspecific factors such as neighborhood deprivation and obesity. Recovery outcomes at 6-month follow-up differed across neurophenotypes, with the normal cognition group showing improvement in verbal memory and psychomotor speed, the dysexecutive group showing improvement in cognitive flexibility, and the memory-speed impaired group showing no objective improvement and relatively worse functional outcomes compared to the other two clusters. These results indicate that there are multiple post-acute neurophenotypes of long COVID, with different etiological pathways and recovery outcomes. This information may inform phenotype-specific approaches to treatment.
Collapse
|
4
|
Prabhakaran D, Day G, Munipalli B, Rush B, Pudalov L, Niazi S, Brennan E, Powers H, Durvasula R, Athreya A, Blackmon K. Neurophenotypes of COVID-19: risk factors and recovery outcomes. Res Sq 2023:rs.3.rs-2363210. [PMID: 36597538 PMCID: PMC9810229 DOI: 10.21203/rs.3.rs-2363210/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Coronavirus disease 2019 (COVID-19) infection is associated with risk of persistent neurocognitive and neuropsychiatric complications, termed "long COVID". It is unclear whether the neuropsychological manifestations of COVID-19 present as a uniform syndrome or as distinct neurophenotypes with differing risk factors and recovery outcomes. We examined post-acute neuropsychological profiles following SARS-CoV-2 infection in 205 patients recruited from inpatient and outpatient populations, using an unsupervised machine learning cluster analysis, with objective and subjective measures as input features. This resulted in three distinct post-COVID clusters. In the largest cluster (69%), cognitive functions were within normal limits, although mild subjective attention and memory complaints were reported. Vaccination was associated with membership in this "normal cognition" phenotype. Cognitive impairment was present in the remaining 31% of the sample but clustered into two differentially impaired groups. In 16% of participants, memory deficits, slowed processing speed, and fatigue were predominant. Risk factors for membership in the "memory-speed impaired" neurophenotype included anosmia and more severe COVID-19 infection. In the remaining 15% of participants, executive dysfunction was predominant. Risk factors for membership in this milder "dysexecutive" neurophenotype included disease-nonspecific factors such as neighborhood deprivation and obesity. Recovery outcomes at 6-month follow-up differed across neurophenotypes, with the normal cognition group showing improvement in verbal memory and psychomotor speed, the dysexecutive group showing improvement in cognitive flexibility, and the memory-speed impaired group showing no objective improvement and relatively worse functional outcomes compared to the other two clusters. These results indicate that there are multiple post-acute neurophenotypes of long COVID, with different etiological pathways and recovery outcomes. This information may inform phenotype-specific approaches to treatment.
Collapse
|
5
|
Duong SQ, Crowson CS, Athreya A, Atkinson EJ, Davis JM, Warrington KJ, Matteson EL, Weinshilboum R, Wang L, Myasoedova E. Clinical predictors of response to methotrexate in patients with rheumatoid arthritis: a machine learning approach using clinical trial data. Arthritis Res Ther 2022; 24:162. [PMID: 35778714 PMCID: PMC9248180 DOI: 10.1186/s13075-022-02851-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 06/18/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Methotrexate is the preferred initial disease-modifying antirheumatic drug (DMARD) for rheumatoid arthritis (RA). However, clinically useful tools for individualized prediction of response to methotrexate treatment in patients with RA are lacking. We aimed to identify clinical predictors of response to methotrexate in patients with rheumatoid arthritis (RA) using machine learning methods. METHODS Randomized clinical trials (RCT) of patients with RA who were DMARD-naïve and randomized to placebo plus methotrexate were identified and accessed through the Clinical Study Data Request Consortium and Vivli Center for Global Clinical Research Data. Studies with available Disease Activity Score with 28-joint count and erythrocyte sedimentation rate (DAS28-ESR) at baseline and 12 and 24 weeks were included. Latent class modeling of methotrexate response was performed. The least absolute shrinkage and selection operator (LASSO) and random forests methods were used to identify predictors of response. RESULTS A total of 775 patients from 4 RCTs were included (mean age 50 years, 80% female). Two distinct classes of patients were identified based on DAS28-ESR change over 24 weeks: "good responders" and "poor responders." Baseline DAS28-ESR, anti-citrullinated protein antibody (ACPA), and Health Assessment Questionnaire (HAQ) score were the top predictors of good response using LASSO (area under the curve [AUC] 0.79) and random forests (AUC 0.68) in the external validation set. DAS28-ESR ≤ 7.4, ACPA positive, and HAQ ≤ 2 provided the highest likelihood of response. Among patients with 12-week DAS28-ESR > 3.2, ≥ 1 point improvement in DAS28-ESR baseline-to-12-week was predictive of achieving DAS28-ESR ≤ 3.2 at 24 weeks. CONCLUSIONS We have developed and externally validated a prediction model for response to methotrexate within 24 weeks in DMARD-naïve patients with RA, providing variably weighted clinical features and defined cutoffs for clinical decision-making.
Collapse
Affiliation(s)
- Stephanie Q Duong
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Cynthia S Crowson
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.,Division of Rheumatology, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Arjun Athreya
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | | | - John M Davis
- Division of Rheumatology, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Kenneth J Warrington
- Division of Rheumatology, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Eric L Matteson
- Division of Rheumatology, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Richard Weinshilboum
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Elena Myasoedova
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA. .,Division of Rheumatology, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA.
| |
Collapse
|
6
|
Jackson L, Allotey L, Kenneth V, Oliver G, Nair A, O'Brien D, Graham R, Borad M, Athreya A, Roberts L. Abstract 1944: Prognostic biomarkers for gallbladder cancer: A machine learning approach. Cancer Res 2022. [DOI: 10.1158/1538-7445.am2022-1944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Gallbladder cancer (GBC) is one of the deadliest cancers, with a 5-year-survival-rate of less than 5 percent for late-stage disease. The response rate to chemotherapy among GBC patients is generally poor. Recent research has attempted to identify diagnostic, prognostic, and predictive biomarkers, however, currently, no biomarkers can accurately diagnose GBC and predict patients’ prognosis. Integrative analysis of molecular and clinical characterization has not been fully established, and minimal improvement has been made to the survival of these patients, in part due to the heterogeneity of GBC. Machine learning techniques have been proven to empower analysis of big data in oncology, allowing for improvement in the generation of biomarkers to predict patient outcomes. Using machine learning, we can utilize high-throughput RNA sequencing with clinicopathologic data to develop a predictive tool for GBC prognosis. Current predictive models for GBC outcomes often utilize clinical data only, with the highest C-statistic reported being 0.71. C-statistic values over 0.7 generally indicate good models, however 0.8 is the threshold for strong predictive models. We aim to build a superior algorithm to predict overall survival in GBC patients with advanced disease, using machine learning approaches to prioritize biomarkers for GBC prognosis. We have identified over 80 fresh frozen GBC tissue samples from Mayo Clinic Rochester, Dongsan Medical Center in Daegu, Korea, University of the Witwatersrand, in Johannesburg, South Africa, Lithuanian University of Health Science in Vilnius, Lithuania, and University of Calgary in Calgary, Canada, from patients enrolled between 2012 and 2021. We will perform next-generation RNA sequencing on these tissue samples. The patients’ clinical, pathologic and survival data will be abstracted from the medical record uniformly across sites. Feature engineering and dimensionality reduction will be performed. Then random forests, support vector machines, and gradient boosting machines will be applied to train the data. Variable importance will prioritize multi-omic markers. Standard 5-fold cross validation will be used to assess performance of each ML algorithm. If overall survival can be better predicted with the addition patients’ transcriptional sequencing data compared to using clinical profiles alone, we can gain a greater understanding of key biomarkers driving the tumor phenotype.
Citation Format: Linsey Jackson, Loretta Allotey, Valles Kenneth, Gavin Oliver, Asha Nair, Daniel O'Brien, Rondell Graham, Mitesh Borad, Arjun Athreya, Lewis Roberts. Prognostic biomarkers for gallbladder cancer: A machine learning approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1944.
Collapse
|
7
|
Duong S, Crowson CS, Athreya A, Atkinson E, Davis JM, Warrington KJ, Matteson E, Weinshilboum R, Wang L, Myasoedova E. POS0514 PREDICTION OF RESPONSE TO METHOTREXATE IN PATIENTS WITH RHEUMATOID ARTHRITIS: A MACHINE LEARNING APPROACH USING CLINICAL TRIAL DATA. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
BackgroundMethotrexate (MTX) is the preferred initial disease-modifying drug (DMARD) for rheumatoid arthritis (RA). However, up to 50% of patients respond inadequately to MTX (1). Clinically useful predictors that effectively identify patients with RA who are likely to respond to MTX are lacking. Whether machine learning (ML) can provide robust and clinically useful prediction of response to MTX monotherapy in the first months of treatment in patients with early RA using uniformly collected baseline demographics and clinical data has not been investigated in large patient populations.ObjectivesWe aimed to identify clinical predictors of response to MTX as the first DMARD among patients with RA using ML methods.MethodsRandomized clinical trials (RCT) of patients with RA who were DMARD-naïve and randomized to placebo plus MTX were identified and accessed through the Clinical Study Data Request Consortium and Vivli Center for Global Clinical Research Data. Studies with available Disease Activity Score with 28-joint count and erythrocyte sedimentation rate (DAS28-ESR) at baseline, 12 and 24 weeks were included. Latent class modeling of MTX response was performed. Least absolute shrinkage and selection operator (LASSO) and random forest were used to identify predictors of response.ResultsA total of 775 patients from 4 RCTs were included (mean age 50 years, 80% female). Two distinct classes of patients were identified based on DAS28-ESR change over 24 weeks: “good responders” and “poor responders” to MTX treatment (Figure 1). Baseline DAS28-ESR, anti-citrullinated protein antibody (ACPA) and health assessment questionnaire (HAQ) score were the top predictors of good response to MTX using LASSO (Area Under the Curve [AUC] 0.79) and Random Forest models (AUC 0.68) in the external validation set. DAS28-ESR≤7.4, ACPA positive and HAQ≤2 provided the highest likelihood of response (Table 1). Among patients with 12-week DAS28-ESR>3.2, at least 1 point improvement in DAS28-ESR baseline-to-12-week was predictive of achieving DAS28-ESR≤3.2 at 24 weeks.Table 1.Matrix prediction model: Probability of achieving a good response to methotrexate at 24 weeksDAS28ESR≤7.480.1 (76.4, 83.8)77.3 (70.6, 84)PositiveACPA Status77.1 (68.6, 85.6)74.1 (63.3, 84.9)Negative>7.440.3 (32.1, 48.5)36.5 (29.3, 43.6)Positive36.2 (23.3, 49.1)32.5 (20.9, 44.1)Negative≤2>2HAQFootnote: The number in each cell represents the percentage and 95% CI of achieving the outcome, based on the combination of predictors at baseline. DAS28-ESR: Disease Activity Score with 28-joint count with erythrocyte sedimentation rate; HAQ: Health assessment questionnaire score; ACPA: Anti-citrullinated protein antibody.Figure 1.Two patient class trajectories identified with latent class modeling of DAS28-ESR (N=775)ConclusionWe have developed and externally validated a prediction model for response to MTX within 24 weeks in DMARD-naïve patients with RA, providing variably weighted clinical features and defined cut-offs for clinical decision-making. Trajectory of DAS28-ESR change over 24 weeks in patients with moderate-to-high RA disease activity at baseline who are starting MTX can be predicted by baseline DAS28-ESR, ACPA status and HAQ-score. Patients with at least 1 unit decline in DAS28-ESR within the first 12 weeks of treatment who have not achieved low disease activity by week 12, may be more likely to achieve low disease activity at 24 weeks. These parameters should be considered as part of the clinical decision-making process when initiating MTX in DMARD-naïve patients with RA.References[1]Aletaha D, Smolen JS. Effectiveness profiles and dose dependent retention of traditional disease modifying antirheumatic drugs for rheumatoid arthritis. An observational study. J Rheumatol. 2002;29(8):1631-8.AcknowledgementsThis abstract is based on research using data from data contributors UCB and Roche that has been made available through Vivli, Inc. Vivli has not contributed to or approved, and is not in any way responsible for, the contents of this publication.Disclosure of InterestsStephanie Duong: None declared, Cynthia S. Crowson: None declared, Arjun Athreya: None declared, Elizabeth Atkinson: None declared, John M Davis III Grant/research support from: Pfizer, Kenneth J Warrington Speakers bureau: Chemocentryx, Consultant of: Roche/Genentech, Eric Matteson: None declared, Richard Weinshilboum Shareholder of: OneOme, Liewei Wang Shareholder of: OneOme, Elena Myasoedova: None declared.
Collapse
|
8
|
Athreya A, Fasano RE, Drane DL, Millis SR, Willie JT, Gross RE, Karakis I. Withdrawal of antiepileptic drugs after stereotactic laser amygdalohippocampotomy for mesial temporal lobe epilepsy. Epilepsy Res 2021; 176:106721. [PMID: 34273722 DOI: 10.1016/j.eplepsyres.2021.106721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 07/06/2021] [Accepted: 07/09/2021] [Indexed: 11/18/2022]
Abstract
OBJECTIVE This retrospective study investigated the success rate of withdrawal of antiepileptic drugs (AEDs) following stereotactic laser amygdalohippocampotomy (SLAH) for mesial temporal lobe epilepsy (MTLE), and identified predictors of seizure recurrence. MATERIALS AND METHODS We retrospectively assessed 65 patients who underwent SLAH for MTLE (59 lesional). Patients' demographics, disease characteristics and post-surgical outcomes were evaluated for their potential to predict seizure recurrence associated with withdrawal of AEDs. RESULTS The mean period of observation post SLAH was 51 months (range 12-96 months) and the mean period to initial reduction of AEDs was 21 months (range 12-60 months). Reduction of AEDs was attempted in 37 patients (57 %) who were seizure free post SLAH and it was successful in approximately 2/3 of them. From the remainder 1/3 who relapsed, nearly all regained seizure control after reinstitution of their AEDs. The likelihood of relapse after reduction of AEDs was predicted only by pre-operative seizure frequency. At the end of the observation period, approximately 14 % of all SLAH patients were seizure free without AEDs and approximately 54 % remained seizure free on AEDs. Compared with preoperative status, the number of AEDs were reduced in 37 % of patients, unchanged in 51 % of them and increased in 12 % of them. CONCLUSIONS Successful SLAH for MTLE allows for reduction of AEDs in a significant portion of patients and complete withdrawal of AEDs in a subset of them. Patients with higher pre-operative seizure frequency exhibit a greater chance of relapse post reduction of AEDs.
Collapse
Affiliation(s)
- Arjun Athreya
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Rebecca E Fasano
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Daniel L Drane
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA; Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA; Department of Neurology, University of Washington, Seattle, WA, USA
| | - Scott R Millis
- Department of Neurology, Physical Medicine & Rehabilitation, Wayne State University School of Medicine, Detroit, MI, USA
| | - Jon T Willie
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, USA; Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, USA
| | - Robert E Gross
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, USA
| | - Ioannis Karakis
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA.
| |
Collapse
|
9
|
Myasoedova E, Athreya A, Crowson CS, Weinshilboum R, Wang L, Matteson E. FRI0046 PHARMACOGENOMICS-DRIVEN INDIVIDUALIZED PREDICTION OF TREATMENT RESPONSE TO METHOTREXATE IN PATIENTS WITH RHEUMATOID ARTHRITIS: A MACHINE LEARNING APPROACH. Ann Rheum Dis 2020. [DOI: 10.1136/annrheumdis-2020-eular.4993] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Background:Methotrexate (MTX) is the most common anchor drug for rheumatoid arthritis (RA), but the risk of missing the opportunity for early effective treatment with alternative medications is substantial given the delayed onset of MTX action and 30-40% inadequate response rate. There is a compelling need to accurately predicting MTX response prior to treatment initiation, which allows for effectively identifying patients at RA onset who are likely to respond to MTX.Objectives:To test the ability of machine learning approaches with clinical and genomic biomarkers to predict MTX response with replications in independent samples.Methods:Age, sex, clinical, serological and genome-wide association study (GWAS) data on patients with early RA of European ancestry from 647 patients (336 recruited in United Kingdom [UK]; 307 recruited across Europe; 70% female; 72% rheumatoid factor [RF] positive; mean age 54 years; mean baseline Disease Activity Score with 28-joint count [DAS28] 5.65) of the PhArmacogenetics of Methotrexate in RA (PAMERA) consortium was used in this study. The genomics data comprised 160 genome-wide significant single nucleotide polymorphisms (SNPs) with p<1×10-5 associated with risk of RA and MTX metabolism. DAS28 score was available at baseline and 3-month follow-up visit. Response to MTX monotherapy at the dose of ≥15 mg/week was defined as good or moderate by the EULAR response criteria at 3 months’ follow up visit. Supervised machine-learning methods were trained with 5-repeats and 10-fold cross-validation using data from PAMERA’s 336 UK patients. Class imbalance (higher % of MTX responders) in training was accounted by using simulated minority oversampling technique. Prediction performance was validated in PAMERA’s 307 European patients (not used in training).Results:Age, sex, RF positivity and baseline DAS28 data predicted MTX response with 58% accuracy of UK and European patients (p = 0.7). However, supervised machine-learning methods that combined demographics, RF positivity, baseline DAS28 and genomic SNPs predicted EULAR response at 3 months with area under the receiver operating curve (AUC) of 0.83 (p = 0.051) in UK patients, and achieved prediction accuracies (fraction of correctly predicted outcomes) of 76.2% (p = 0.054) in the European patients, with sensitivity of 72% and specificity of 77%. The addition of genomic data improved the predictive accuracies of MTX response by 19% and achieved cross-site replication. Baseline DAS28 scores and following SNPs rs12446816, rs13385025, rs113798271, and rs2372536 were among the top predictors of MTX response.Conclusion:Pharmacogenomic biomarkers combined with DAS28 scores predicted MTX response in patients with early RA more reliably than using demographics and DAS28 scores alone. Using pharmacogenomics biomarkers for identification of MTX responders at early stages of RA may help to guide effective RA treatment choices, including timely escalation of RA therapies. Further studies on personalized prediction of response to MTX and other anti-rheumatic treatments are warranted to optimize control of RA disease and improve outcomes in patients with RA.Disclosure of Interests:Elena Myasoedova: None declared, Arjun Athreya: None declared, Cynthia S. Crowson Grant/research support from: Pfizer research grant, Richard Weinshilboum Shareholder of: co-founder and stockholder in OneOme, Liewei Wang: None declared, Eric Matteson Grant/research support from: Pfizer, Consultant of: Boehringer Ingelheim, Gilead, TympoBio, Arena Pharmaceuticals, Speakers bureau: Simply Speaking
Collapse
|
10
|
Athreya A, Iyer R, Neavin D, Wang L, Weinshilboum R, Kaddurah-Daouk R, Rush J, Frye M, Bobo W. Augmentation of Physician Assessments with Multi-Omics Enhances Predictability of Drug Response: A Case Study of Major Depressive Disorder. IEEE COMPUT INTELL M 2018; 13:20-31. [PMID: 30467458 DOI: 10.1109/mci.2018.2840660] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
This work proposes a "learning-augmented clinical assessment" workflow to sequentially augment physician assessments of patients' symptoms and their socio-demographic measures with heterogeneous biological measures to accurately predict treatment outcomes using machine learning. Across many psychiatric illnesses, ranging from major depressive disorder to schizophrenia, symptom severity assessments are subjective and do not include biological measures, making predictability in eventual treatment outcomes a challenge. Using data from the Mayo Clinic PGRN-AMPS SSRI trial as a case study, this work demonstrates a significant improvement in the prediction accuracy for antidepressant treatment outcomes in patients with major depressive disorder from 35% to 80% individualized by patient, compared to using only a physician's assessment as the predictors. This improvement is achieved through an iterative overlay of biological measures, starting with metabolites (blood measures modulated by drug action) associated with symptom severity, and then adding in genes associated with metabolomic concentrations. Hence, therapeutic efficacy for a new patient can be assessed prior to treatment, using prediction models that take as inputs, selected biological measures and physician's assessments of depression severity. Of broader significance extending beyond psychiatry, the approach presented in this work can potentially be applied to predicting treatment outcomes for other medical conditions, such as migraine headaches or rheumatoid arthritis, for which patients are treated according to subject-reported assessments of symptom severity.
Collapse
Affiliation(s)
- Arjun Athreya
- Department of Electrical and Computer Engineering, Univ. of Illinois at Urbana-Champaign, IL, USA
| | - Ravishankar Iyer
- Department of Electrical and Computer Engineering, Univ. of Illinois at Urbana-Champaign, IL, USA
| | - Drew Neavin
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, MN, USA
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, MN, USA
| | - Richard Weinshilboum
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, MN, USA
| | | | - John Rush
- Department of Psychiatry and Behavioral Sciences, Duke University, NC, USA
| | - Mark Frye
- Department of Psychiatry and Psychology, Mayo Clinic, MN, USA
| | - William Bobo
- Department of Psychiatry and Psychology, Mayo Clinic, FL, USA
| |
Collapse
|
11
|
Varatharajah Y, Younkin M, Wang X, Athreya A, Messaoud S, Iyer R, Ertekin‐Taner N. [P3–117]: UNSUPERVISED ANALYSIS OF TRANSCRIPTOMIC DATA FOR DEMYSTIFYING THE CAUSE OF ALZHEIMER's DISEASE. Alzheimers Dement 2017. [DOI: 10.1016/j.jalz.2017.06.1328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
| | | | | | - Arjun Athreya
- University of Illinois at Urbana‐ChampaignUrbanaILUSA
| | - Safa Messaoud
- University of Illinois at Urbana‐ChampaignUrbanaILUSA
| | - Ravi Iyer
- University of Illinois at Urbana‐ChampaignUrbanaILUSA
| | | |
Collapse
|