Influenza activity dropped during the week ending Feb. 15, according to the Centers for Disease Control and Prevention. That decline, along with revised data from the 2 previous weeks, suggests that the 2019-2020 season has peaked for the second time. The rate of outpatient visits for influenza-like illness (ILI) came in at 6.1% for the week ending Feb. 15, after two straight weeks at 6.7%, the CDC’s influenza division reported Feb. 21.

The rates for those 2 earlier weeks had previously been reported at 6.8% (Feb. 8) and 6.6% (Feb. 1), which means that there have now been 2 consecutive weeks without an increase in national ILI activity.

State-level activity was down slightly as well. For the week ending Feb. 15, there were 39 states and Puerto Rico at the highest level of activity on the CDC’s 1-10 scale, compared with 41 states and Puerto Rico the week before. The number of states in the “high” range, which includes levels 8 and 9, went from 44 to 45, however, CDC data show.

Laboratory measures also dropped a bit. For the week, 29.6% of respiratory specimens tested positive for influenza, compared with 30.3% the previous week. The predominance of influenza A continued to increase, as type A went from 59.4% to 63.5% of positive specimens and type B dropped from 40.6% to 36.5%, the influenza division said.

In a separate report, the CDC announced interim flu vaccine effectiveness estimates.For the 2019-2020 season so far, “flu vaccines are reducing doctor’s visits for flu illness by almost half (45%). This is consistent with estimates of flu vaccine effectiveness (VE) from previous flu seasons that ranged from 40% to 60% when flu vaccine viruses were similar to circulating influenza viruses,” the CDC said.

Although VE among children aged 6 months to 17 years is even higher, at 55%, this season “has been especially bad for children. Flu hospitalization rates among children are higher than at this time in other recent seasons, including the 2017-18 season,” the CDC noted.

The number of pediatric flu deaths for 2019-2020 – now up to 105 – is “higher for the same time period than in every season since reporting began in 2004-05, with the exception of the 2009 pandemic,” the CDC added.

Interim VE estimates for other age groups are 25% for adults aged 18-49 and 43% for those 50 years and older. “The lower VE point estimates observed among adults 18-49 years appear to be associated with a trend suggesting lower VE in this age group against A(H1N1)pdm09 viruses,” the CDC said.

rfranki@mdedge.com

Influenza activity dropped during the week ending Feb. 15, according to the Centers for Disease Control and Prevention. That decline, along with revised data from the 2 previous weeks, suggests that the 2019-2020 season has peaked for the second time. The rate of outpatient visits for influenza-like illness (ILI) came in at 6.1% for the week ending Feb. 15, after two straight weeks at 6.7%, the CDC’s influenza division reported Feb. 21.

The rates for those 2 earlier weeks had previously been reported at 6.8% (Feb. 8) and 6.6% (Feb. 1), which means that there have now been 2 consecutive weeks without an increase in national ILI activity.

State-level activity was down slightly as well. For the week ending Feb. 15, there were 39 states and Puerto Rico at the highest level of activity on the CDC’s 1-10 scale, compared with 41 states and Puerto Rico the week before. The number of states in the “high” range, which includes levels 8 and 9, went from 44 to 45, however, CDC data show.

Laboratory measures also dropped a bit. For the week, 29.6% of respiratory specimens tested positive for influenza, compared with 30.3% the previous week. The predominance of influenza A continued to increase, as type A went from 59.4% to 63.5% of positive specimens and type B dropped from 40.6% to 36.5%, the influenza division said.

In a separate report, the CDC announced interim flu vaccine effectiveness estimates.For the 2019-2020 season so far, “flu vaccines are reducing doctor’s visits for flu illness by almost half (45%). This is consistent with estimates of flu vaccine effectiveness (VE) from previous flu seasons that ranged from 40% to 60% when flu vaccine viruses were similar to circulating influenza viruses,” the CDC said.

Although VE among children aged 6 months to 17 years is even higher, at 55%, this season “has been especially bad for children. Flu hospitalization rates among children are higher than at this time in other recent seasons, including the 2017-18 season,” the CDC noted.

The number of pediatric flu deaths for 2019-2020 – now up to 105 – is “higher for the same time period than in every season since reporting began in 2004-05, with the exception of the 2009 pandemic,” the CDC added.

Interim VE estimates for other age groups are 25% for adults aged 18-49 and 43% for those 50 years and older. “The lower VE point estimates observed among adults 18-49 years appear to be associated with a trend suggesting lower VE in this age group against A(H1N1)pdm09 viruses,” the CDC said.

rfranki@mdedge.com

Influenza activity dropped during the week ending Feb. 15, according to the Centers for Disease Control and Prevention. That decline, along with revised data from the 2 previous weeks, suggests that the 2019-2020 season has peaked for the second time. The rate of outpatient visits for influenza-like illness (ILI) came in at 6.1% for the week ending Feb. 15, after two straight weeks at 6.7%, the CDC’s influenza division reported Feb. 21.

The rates for those 2 earlier weeks had previously been reported at 6.8% (Feb. 8) and 6.6% (Feb. 1), which means that there have now been 2 consecutive weeks without an increase in national ILI activity.

State-level activity was down slightly as well. For the week ending Feb. 15, there were 39 states and Puerto Rico at the highest level of activity on the CDC’s 1-10 scale, compared with 41 states and Puerto Rico the week before. The number of states in the “high” range, which includes levels 8 and 9, went from 44 to 45, however, CDC data show.

Laboratory measures also dropped a bit. For the week, 29.6% of respiratory specimens tested positive for influenza, compared with 30.3% the previous week. The predominance of influenza A continued to increase, as type A went from 59.4% to 63.5% of positive specimens and type B dropped from 40.6% to 36.5%, the influenza division said.

In a separate report, the CDC announced interim flu vaccine effectiveness estimates.For the 2019-2020 season so far, “flu vaccines are reducing doctor’s visits for flu illness by almost half (45%). This is consistent with estimates of flu vaccine effectiveness (VE) from previous flu seasons that ranged from 40% to 60% when flu vaccine viruses were similar to circulating influenza viruses,” the CDC said.

Although VE among children aged 6 months to 17 years is even higher, at 55%, this season “has been especially bad for children. Flu hospitalization rates among children are higher than at this time in other recent seasons, including the 2017-18 season,” the CDC noted.

The number of pediatric flu deaths for 2019-2020 – now up to 105 – is “higher for the same time period than in every season since reporting began in 2004-05, with the exception of the 2009 pandemic,” the CDC added.

Interim VE estimates for other age groups are 25% for adults aged 18-49 and 43% for those 50 years and older. “The lower VE point estimates observed among adults 18-49 years appear to be associated with a trend suggesting lower VE in this age group against A(H1N1)pdm09 viruses,” the CDC said.

rfranki@mdedge.com

Researchers have identified 24 common genetic variants that may be associated with a greater risk of developing endometrial cancer.

SilverV/Thinkstock

The 24 single-nucleotide polymorphisms (SNPs) were detected in genes that function in transcriptional regulation, cell survival, and estrogen metabolism.

“Understanding genetic predisposition to endometrial cancer could facilitate personalized risk assessment with a view to targeted prevention and screening interventions,” wrote Cemsel Bafligil, of the University of Manchester (England) and her coinvestigators. The group’s findings were published in the Journal of Medical Genetics.

The researchers searched major databases for primary studies that evaluated associations between endometrial cancer and SNPs. After applying the search criteria, 453 eligible records were found, and 149 of these were included in the study.

The majority of records were genome-wide association studies, case-control studies, and meta-analyses. Various data, including study type, ethnicity, and endometrial cancer type, were extracted and included in the qualitative synthesis.

After analysis, the researchers identified 24 independent genetic variants associated with a higher risk of developing endometrial cancer, and SNPs in 6 genes – CYP19A1, SOX4, HNF1B, MYC, KLF, and EIF2AK – showed a strong association.

The researchers also estimated the predictive value of the identified SNPs using a theoretical polygenic risk score model. They found that women with genome-wide significant SNPs had double the risk of developing endometrial cancer (relative risk, 2.09), and women with all 24 SNPs had a three-fold greater risk of developing the disease (RR, 3.16).

“The importance of these variants and relevance of the proximate genes in a functional or biological context is challenging to evaluate,” the researchers noted.

They also acknowledged that a key limitation of this study was the ethnic homogeneity of the cohort, with most patients being of European descent. As a result, the findings may not be fully representative of other ethnic groups.

“The multiplicative effects of these SNPs could be used in a PRS [polygenic risk score] to allow personalised risk prediction models to be developed for targeted screening and prevention interventions for women at greatest risk of endometrial cancer,” the researchers concluded.

The National Institute for Health Research Manchester Biomedical Research Centre funded the study. The authors reported having no conflicts of interest.

SOURCE: Bafligil C et al. J Med Genet. 2020 Feb 17. doi: 10.1136/jmedgenet-2019-106529.

Researchers have identified 24 common genetic variants that may be associated with a greater risk of developing endometrial cancer.

SilverV/Thinkstock

The 24 single-nucleotide polymorphisms (SNPs) were detected in genes that function in transcriptional regulation, cell survival, and estrogen metabolism.

“Understanding genetic predisposition to endometrial cancer could facilitate personalized risk assessment with a view to targeted prevention and screening interventions,” wrote Cemsel Bafligil, of the University of Manchester (England) and her coinvestigators. The group’s findings were published in the Journal of Medical Genetics.

The researchers searched major databases for primary studies that evaluated associations between endometrial cancer and SNPs. After applying the search criteria, 453 eligible records were found, and 149 of these were included in the study.

The majority of records were genome-wide association studies, case-control studies, and meta-analyses. Various data, including study type, ethnicity, and endometrial cancer type, were extracted and included in the qualitative synthesis.

After analysis, the researchers identified 24 independent genetic variants associated with a higher risk of developing endometrial cancer, and SNPs in 6 genes – CYP19A1, SOX4, HNF1B, MYC, KLF, and EIF2AK – showed a strong association.

The researchers also estimated the predictive value of the identified SNPs using a theoretical polygenic risk score model. They found that women with genome-wide significant SNPs had double the risk of developing endometrial cancer (relative risk, 2.09), and women with all 24 SNPs had a three-fold greater risk of developing the disease (RR, 3.16).

“The importance of these variants and relevance of the proximate genes in a functional or biological context is challenging to evaluate,” the researchers noted.

They also acknowledged that a key limitation of this study was the ethnic homogeneity of the cohort, with most patients being of European descent. As a result, the findings may not be fully representative of other ethnic groups.

“The multiplicative effects of these SNPs could be used in a PRS [polygenic risk score] to allow personalised risk prediction models to be developed for targeted screening and prevention interventions for women at greatest risk of endometrial cancer,” the researchers concluded.

The National Institute for Health Research Manchester Biomedical Research Centre funded the study. The authors reported having no conflicts of interest.

SOURCE: Bafligil C et al. J Med Genet. 2020 Feb 17. doi: 10.1136/jmedgenet-2019-106529.

Researchers have identified 24 common genetic variants that may be associated with a greater risk of developing endometrial cancer.

SilverV/Thinkstock

The 24 single-nucleotide polymorphisms (SNPs) were detected in genes that function in transcriptional regulation, cell survival, and estrogen metabolism.

“Understanding genetic predisposition to endometrial cancer could facilitate personalized risk assessment with a view to targeted prevention and screening interventions,” wrote Cemsel Bafligil, of the University of Manchester (England) and her coinvestigators. The group’s findings were published in the Journal of Medical Genetics.

The researchers searched major databases for primary studies that evaluated associations between endometrial cancer and SNPs. After applying the search criteria, 453 eligible records were found, and 149 of these were included in the study.

The majority of records were genome-wide association studies, case-control studies, and meta-analyses. Various data, including study type, ethnicity, and endometrial cancer type, were extracted and included in the qualitative synthesis.

After analysis, the researchers identified 24 independent genetic variants associated with a higher risk of developing endometrial cancer, and SNPs in 6 genes – CYP19A1, SOX4, HNF1B, MYC, KLF, and EIF2AK – showed a strong association.

The researchers also estimated the predictive value of the identified SNPs using a theoretical polygenic risk score model. They found that women with genome-wide significant SNPs had double the risk of developing endometrial cancer (relative risk, 2.09), and women with all 24 SNPs had a three-fold greater risk of developing the disease (RR, 3.16).

“The importance of these variants and relevance of the proximate genes in a functional or biological context is challenging to evaluate,” the researchers noted.

They also acknowledged that a key limitation of this study was the ethnic homogeneity of the cohort, with most patients being of European descent. As a result, the findings may not be fully representative of other ethnic groups.

“The multiplicative effects of these SNPs could be used in a PRS [polygenic risk score] to allow personalised risk prediction models to be developed for targeted screening and prevention interventions for women at greatest risk of endometrial cancer,” the researchers concluded.

The National Institute for Health Research Manchester Biomedical Research Centre funded the study. The authors reported having no conflicts of interest.

SOURCE: Bafligil C et al. J Med Genet. 2020 Feb 17. doi: 10.1136/jmedgenet-2019-106529.

Key clinical point: Lower amyloid positron imaging tomography (PET) uptake in normal-appearing white matter (NAWM) is associated with cognitive decline and an increase in white matter lesion volume.

Major finding: Cognitive decline was associated with lower standardized uptake value relative to cerebellum in NAWM (1.52 in the cognitive decline group vs. 1.67 in the cognitively stable group; Mann-Whitney U test [U] = 42.0; P = .011), lower thalamic volume (13.84 vs. 15.61; U = 55.0; P = .059), and higher white matter lesion burden (15.25 vs. 9.17; U = 49.0; P = .029).

Study details: A prospective longitudinal PET study using 18F-florbetaben included 29 patients diagnosed with MS; the mean follow-up period was 18.00 ± 3.31 months.

Disclosures: The authors declared no conflicts of interest.

Citation: Pytel V et al. Mult Scler Relat Disord. 2020 Jan 2. doi: 10.1016/j.msard.2020.101926. 

Key clinical point: Lower amyloid positron imaging tomography (PET) uptake in normal-appearing white matter (NAWM) is associated with cognitive decline and an increase in white matter lesion volume.

Major finding: Cognitive decline was associated with lower standardized uptake value relative to cerebellum in NAWM (1.52 in the cognitive decline group vs. 1.67 in the cognitively stable group; Mann-Whitney U test [U] = 42.0; P = .011), lower thalamic volume (13.84 vs. 15.61; U = 55.0; P = .059), and higher white matter lesion burden (15.25 vs. 9.17; U = 49.0; P = .029).

Study details: A prospective longitudinal PET study using 18F-florbetaben included 29 patients diagnosed with MS; the mean follow-up period was 18.00 ± 3.31 months.

Disclosures: The authors declared no conflicts of interest.

Citation: Pytel V et al. Mult Scler Relat Disord. 2020 Jan 2. doi: 10.1016/j.msard.2020.101926. 

Key clinical point: Lower amyloid positron imaging tomography (PET) uptake in normal-appearing white matter (NAWM) is associated with cognitive decline and an increase in white matter lesion volume.

Major finding: Cognitive decline was associated with lower standardized uptake value relative to cerebellum in NAWM (1.52 in the cognitive decline group vs. 1.67 in the cognitively stable group; Mann-Whitney U test [U] = 42.0; P = .011), lower thalamic volume (13.84 vs. 15.61; U = 55.0; P = .059), and higher white matter lesion burden (15.25 vs. 9.17; U = 49.0; P = .029).

Study details: A prospective longitudinal PET study using 18F-florbetaben included 29 patients diagnosed with MS; the mean follow-up period was 18.00 ± 3.31 months.

Disclosures: The authors declared no conflicts of interest.

Citation: Pytel V et al. Mult Scler Relat Disord. 2020 Jan 2. doi: 10.1016/j.msard.2020.101926. 

Key clinical point: Mothers of children with MS are more likely to use mental health services before and after their child’s diagnosis with multiple sclerosis (MS) than mothers of children without MS.

Major finding: The prevalence of any physical condition and mood or anxiety disorder was higher in MS-mothers vs. non-MS-mothers. The odds of having any psychiatry visit was significantly increased in MS-mothers (odds ratio, 1.60; 95% confidence interval [CI], 1.10-2.31). The annual rate of psychiatry visits did not differ between MS-mothers and non-MS-mothers (rate ratio, 0.66; 95% CI, 0.33-1.30).

Study details: A population-based retrospective matched cohort study of 156 MS-mothers and 624 non-MS mothers.

Disclosures: This study was funded by the Multiple Sclerosis Scientific Research Foundation. Dr. Marrie received research funding from CIHR, Research Manitoba, Multiple Sclerosis Society of Canada, Multiple Sclerosis Scientific Foundation, Crohn’s and Colitis Canada, National Multiple Sclerosis Society, and CMSC and was supported by the Waugh Family Chair in Multiple Sclerosis.

Citation: Marrie RA et al. Neurology. 2020 Jan 9. doi: 10.1212/WNL.0000000000008871. 

Key clinical point: Mothers of children with MS are more likely to use mental health services before and after their child’s diagnosis with multiple sclerosis (MS) than mothers of children without MS.

Major finding: The prevalence of any physical condition and mood or anxiety disorder was higher in MS-mothers vs. non-MS-mothers. The odds of having any psychiatry visit was significantly increased in MS-mothers (odds ratio, 1.60; 95% confidence interval [CI], 1.10-2.31). The annual rate of psychiatry visits did not differ between MS-mothers and non-MS-mothers (rate ratio, 0.66; 95% CI, 0.33-1.30).

Study details: A population-based retrospective matched cohort study of 156 MS-mothers and 624 non-MS mothers.

Disclosures: This study was funded by the Multiple Sclerosis Scientific Research Foundation. Dr. Marrie received research funding from CIHR, Research Manitoba, Multiple Sclerosis Society of Canada, Multiple Sclerosis Scientific Foundation, Crohn’s and Colitis Canada, National Multiple Sclerosis Society, and CMSC and was supported by the Waugh Family Chair in Multiple Sclerosis.

Citation: Marrie RA et al. Neurology. 2020 Jan 9. doi: 10.1212/WNL.0000000000008871. 

Key clinical point: Mothers of children with MS are more likely to use mental health services before and after their child’s diagnosis with multiple sclerosis (MS) than mothers of children without MS.

Major finding: The prevalence of any physical condition and mood or anxiety disorder was higher in MS-mothers vs. non-MS-mothers. The odds of having any psychiatry visit was significantly increased in MS-mothers (odds ratio, 1.60; 95% confidence interval [CI], 1.10-2.31). The annual rate of psychiatry visits did not differ between MS-mothers and non-MS-mothers (rate ratio, 0.66; 95% CI, 0.33-1.30).

Study details: A population-based retrospective matched cohort study of 156 MS-mothers and 624 non-MS mothers.

Disclosures: This study was funded by the Multiple Sclerosis Scientific Research Foundation. Dr. Marrie received research funding from CIHR, Research Manitoba, Multiple Sclerosis Society of Canada, Multiple Sclerosis Scientific Foundation, Crohn’s and Colitis Canada, National Multiple Sclerosis Society, and CMSC and was supported by the Waugh Family Chair in Multiple Sclerosis.

Citation: Marrie RA et al. Neurology. 2020 Jan 9. doi: 10.1212/WNL.0000000000008871. 

Key clinical point: Vitamin D and body mass index (BMI) are independent causal risk factors for multiple sclerosis (MS) in adulthood and childhood.

Major finding: Genetically determined increased childhood BMI and adult BMI were associated with a 24% and 14% higher risk of MS, respectively. Each genetically determined unit increase in the natural-log-transformed vitamin D level was associated with a 43% reduction in the MS risk. 

Study details: A 2-sample Mendelian randomization study estimated the effect of BMI and vitamin D status on MS risk; associations of single-nucleotide polymorphisms with both the risk factors of interest were obtained from the relevant consortia.

Disclosures: This study was funded through a grant from the Barts Charity. The authors declared no conflicts of interest.

Citation: Jacobs BM et al. Neurol Neuroimmunol Neuroinflamm. 2020 Jan 14. doi: 10.1212/NXI.0000000000000662. 

Key clinical point: Vitamin D and body mass index (BMI) are independent causal risk factors for multiple sclerosis (MS) in adulthood and childhood.

Major finding: Genetically determined increased childhood BMI and adult BMI were associated with a 24% and 14% higher risk of MS, respectively. Each genetically determined unit increase in the natural-log-transformed vitamin D level was associated with a 43% reduction in the MS risk. 

Study details: A 2-sample Mendelian randomization study estimated the effect of BMI and vitamin D status on MS risk; associations of single-nucleotide polymorphisms with both the risk factors of interest were obtained from the relevant consortia.

Disclosures: This study was funded through a grant from the Barts Charity. The authors declared no conflicts of interest.

Citation: Jacobs BM et al. Neurol Neuroimmunol Neuroinflamm. 2020 Jan 14. doi: 10.1212/NXI.0000000000000662. 

Key clinical point: Vitamin D and body mass index (BMI) are independent causal risk factors for multiple sclerosis (MS) in adulthood and childhood.

Major finding: Genetically determined increased childhood BMI and adult BMI were associated with a 24% and 14% higher risk of MS, respectively. Each genetically determined unit increase in the natural-log-transformed vitamin D level was associated with a 43% reduction in the MS risk. 

Study details: A 2-sample Mendelian randomization study estimated the effect of BMI and vitamin D status on MS risk; associations of single-nucleotide polymorphisms with both the risk factors of interest were obtained from the relevant consortia.

Disclosures: This study was funded through a grant from the Barts Charity. The authors declared no conflicts of interest.

Citation: Jacobs BM et al. Neurol Neuroimmunol Neuroinflamm. 2020 Jan 14. doi: 10.1212/NXI.0000000000000662. 

Key clinical point: In patients with multiple sclerosis (MS) without good recovery after the initial relapse, initiating a disease-modifying therapy (DMT) immediately increases the likelihood of a benign disease course.

Major finding: Patients with good recovery and immediate DMT initiation and those with poor recovery and delayed DMT initiation had about 65% and 20% chance, respectively, of remaining at a minimal disability level (Expanded Disability Status Scale score of less than 2.5) by age 45 years.

Study details: An analysis of data from the phase 3 CHAMPS trial in clinically isolated syndrome (n=383) and 10-year follow-up EXTENSION trial.

Disclosures: This study was funded by an unrestricted grant to Dr. Kantarci from Biogen. Dr. Kantarci and Dr. Atkinson received salary support as part of the grant from Biogen. Dr. Castrillo-Viguera was employed by Biogen.

Citation: Kantarci OH et al. Neurol Neuroimmunol Neuroinflamm. 2019 Dec 17. doi: 10.1212/NXI.0000000000000653. 

Key clinical point: In patients with multiple sclerosis (MS) without good recovery after the initial relapse, initiating a disease-modifying therapy (DMT) immediately increases the likelihood of a benign disease course.

Major finding: Patients with good recovery and immediate DMT initiation and those with poor recovery and delayed DMT initiation had about 65% and 20% chance, respectively, of remaining at a minimal disability level (Expanded Disability Status Scale score of less than 2.5) by age 45 years.

Study details: An analysis of data from the phase 3 CHAMPS trial in clinically isolated syndrome (n=383) and 10-year follow-up EXTENSION trial.

Disclosures: This study was funded by an unrestricted grant to Dr. Kantarci from Biogen. Dr. Kantarci and Dr. Atkinson received salary support as part of the grant from Biogen. Dr. Castrillo-Viguera was employed by Biogen.

Citation: Kantarci OH et al. Neurol Neuroimmunol Neuroinflamm. 2019 Dec 17. doi: 10.1212/NXI.0000000000000653. 

Key clinical point: In patients with multiple sclerosis (MS) without good recovery after the initial relapse, initiating a disease-modifying therapy (DMT) immediately increases the likelihood of a benign disease course.

Major finding: Patients with good recovery and immediate DMT initiation and those with poor recovery and delayed DMT initiation had about 65% and 20% chance, respectively, of remaining at a minimal disability level (Expanded Disability Status Scale score of less than 2.5) by age 45 years.

Study details: An analysis of data from the phase 3 CHAMPS trial in clinically isolated syndrome (n=383) and 10-year follow-up EXTENSION trial.

Disclosures: This study was funded by an unrestricted grant to Dr. Kantarci from Biogen. Dr. Kantarci and Dr. Atkinson received salary support as part of the grant from Biogen. Dr. Castrillo-Viguera was employed by Biogen.

Citation: Kantarci OH et al. Neurol Neuroimmunol Neuroinflamm. 2019 Dec 17. doi: 10.1212/NXI.0000000000000653. 

Key clinical point: Phase 3 EVOLVE-MS-2 study demonstrates that diroximel fumarate (DRF) has an improved gastrointestinal (GI) tolerability profile compared with dimethyl fumarate (DMF) in patients with relapsing-remitting multiple sclerosis (MS).

Major finding: Patients treated with DRF self-reported 46% fewer days with an Individual Gastrointestinal Symptom and Impact Scale (IGISIS) symptom intensity score of ≥2 vs those treated with DMF (rate ratio, 0.54; 95% confidence interval, 0.39-0.75). The rates of GI adverse events (AEs) were lower with DRF than DMF (34.8% vs. 49.0%). DRF-treated patients had a lower discontinuation rate because of GI AEs (0.8% vs. 4.8%) and overall AEs (1.6% vs. 5.6%).

Study details: EVOLVE-MS-2 was a 5-week randomized trial that directly compared the GI tolerability of DRF 462 mg (n = 253) with that of DMF 240 mg (n = 249); primary endpoint was the number of days with an IGISIS intensity score of 2 or greater relative to exposure.

Disclosures: This study was funded by Alkermes Inc. and Biogen. The authors reported receiving grants and personal fees from multiple pharmaceutical companies.

Citation: Naismith RT et al. CNS Drugs. 2020 Jan 17. doi: 10.1007/s40263-020-00700-0. 

Key clinical point: Phase 3 EVOLVE-MS-2 study demonstrates that diroximel fumarate (DRF) has an improved gastrointestinal (GI) tolerability profile compared with dimethyl fumarate (DMF) in patients with relapsing-remitting multiple sclerosis (MS).

Major finding: Patients treated with DRF self-reported 46% fewer days with an Individual Gastrointestinal Symptom and Impact Scale (IGISIS) symptom intensity score of ≥2 vs those treated with DMF (rate ratio, 0.54; 95% confidence interval, 0.39-0.75). The rates of GI adverse events (AEs) were lower with DRF than DMF (34.8% vs. 49.0%). DRF-treated patients had a lower discontinuation rate because of GI AEs (0.8% vs. 4.8%) and overall AEs (1.6% vs. 5.6%).

Study details: EVOLVE-MS-2 was a 5-week randomized trial that directly compared the GI tolerability of DRF 462 mg (n = 253) with that of DMF 240 mg (n = 249); primary endpoint was the number of days with an IGISIS intensity score of 2 or greater relative to exposure.

Disclosures: This study was funded by Alkermes Inc. and Biogen. The authors reported receiving grants and personal fees from multiple pharmaceutical companies.

Citation: Naismith RT et al. CNS Drugs. 2020 Jan 17. doi: 10.1007/s40263-020-00700-0. 

Key clinical point: Phase 3 EVOLVE-MS-2 study demonstrates that diroximel fumarate (DRF) has an improved gastrointestinal (GI) tolerability profile compared with dimethyl fumarate (DMF) in patients with relapsing-remitting multiple sclerosis (MS).

Major finding: Patients treated with DRF self-reported 46% fewer days with an Individual Gastrointestinal Symptom and Impact Scale (IGISIS) symptom intensity score of ≥2 vs those treated with DMF (rate ratio, 0.54; 95% confidence interval, 0.39-0.75). The rates of GI adverse events (AEs) were lower with DRF than DMF (34.8% vs. 49.0%). DRF-treated patients had a lower discontinuation rate because of GI AEs (0.8% vs. 4.8%) and overall AEs (1.6% vs. 5.6%).

Study details: EVOLVE-MS-2 was a 5-week randomized trial that directly compared the GI tolerability of DRF 462 mg (n = 253) with that of DMF 240 mg (n = 249); primary endpoint was the number of days with an IGISIS intensity score of 2 or greater relative to exposure.

Disclosures: This study was funded by Alkermes Inc. and Biogen. The authors reported receiving grants and personal fees from multiple pharmaceutical companies.

Citation: Naismith RT et al. CNS Drugs. 2020 Jan 17. doi: 10.1007/s40263-020-00700-0. 

The dermMentors™ Resident of Distinction Award™ was presented to 5 dermatology residents at the 19th Annual Caribbean Dermatology Symposium, January 21–25, 2020, in Paradise Island, Bahamas. Recipients of the award include Rachel Giesey, DO, Case Western Reserve University, Cleveland, Ohio, and University Hospitals Cleveland Medical Center, Cleveland, Ohio; Janice Tiao, MD, Boston University Medical Center, Boston, Massachusetts; Jordan V. Wang, MD, MBE, MBA, Thomas Jefferson University, Philadelphia, Pennsylvania; Jacqueline D. Watchmaker, MD, Boston University School of Medicine, Boston, Massachusetts; and Jennifer E. Yeh, MD, PhD, Brigham and Women’s Hospital, Boston, Massachusetts. The residents presented their research during the general sessions on January 25, 2020.

The overall grand prize was awarded to Dr. Yeh for her research entitled, “Topical Imiquimod in Combination With Brachytherapy for Unresectable Cutaneous Melanoma Metastases.” Dr. Yeh presented the utility of combining topical imiquimod with brachytherapy for locoregional control of cutaneous metastases through the presentation of 3 patients with scalp melanoma initially treated with wide local excision who developed numerous cutaneous metastases. “While surgical excision is the first-line treatment of single, discrete cutaneous metastases, it may not be practical in patients with multiple foci of disease distributed over large areas, as seen in the 3 patients presented here who achieved complete resolution of their cutaneous metastatic burden with concurrent topical imiquimod and brachytherapy,” Dr. Yeh reported.

Presentations by the other residents included a study of the burden of common skin diseases in the Caribbean and the potential correlation with a country’s socioeconomic status (Dr. Giesey), a study of the use of doxycycline in patients with lichen planopilaris and frontal fibrosing alopecia (Dr. Tiao), a discussion of counterfeit medical devices and injectables as well as medical spas in dermatology (Dr. Wang), and a study of the most common reasons patients are dissatisfied with minimally and noninvasive cosmetic procedures (Dr. Watchmaker). Access all of the abstracts presented by the top residents here. 

The dermMentors™ Resident of Distinction Award™ recognizes top residents in dermatology. DermMentors.org and the dermMentors™ Resident of Distinction Award™ are sponsored by Beiersdorf Inc and administered by DermEd, Inc. The 2020 dermMentors™ Residents of Distinction™ presented new scientific research during the general sessions of the 19th Annual Caribbean Dermatology Symposium on January 25, 2020.

The dermMentors™ Resident of Distinction Award™ was presented to 5 dermatology residents at the 19th Annual Caribbean Dermatology Symposium, January 21–25, 2020, in Paradise Island, Bahamas. Recipients of the award include Rachel Giesey, DO, Case Western Reserve University, Cleveland, Ohio, and University Hospitals Cleveland Medical Center, Cleveland, Ohio; Janice Tiao, MD, Boston University Medical Center, Boston, Massachusetts; Jordan V. Wang, MD, MBE, MBA, Thomas Jefferson University, Philadelphia, Pennsylvania; Jacqueline D. Watchmaker, MD, Boston University School of Medicine, Boston, Massachusetts; and Jennifer E. Yeh, MD, PhD, Brigham and Women’s Hospital, Boston, Massachusetts. The residents presented their research during the general sessions on January 25, 2020.

The overall grand prize was awarded to Dr. Yeh for her research entitled, “Topical Imiquimod in Combination With Brachytherapy for Unresectable Cutaneous Melanoma Metastases.” Dr. Yeh presented the utility of combining topical imiquimod with brachytherapy for locoregional control of cutaneous metastases through the presentation of 3 patients with scalp melanoma initially treated with wide local excision who developed numerous cutaneous metastases. “While surgical excision is the first-line treatment of single, discrete cutaneous metastases, it may not be practical in patients with multiple foci of disease distributed over large areas, as seen in the 3 patients presented here who achieved complete resolution of their cutaneous metastatic burden with concurrent topical imiquimod and brachytherapy,” Dr. Yeh reported.

Presentations by the other residents included a study of the burden of common skin diseases in the Caribbean and the potential correlation with a country’s socioeconomic status (Dr. Giesey), a study of the use of doxycycline in patients with lichen planopilaris and frontal fibrosing alopecia (Dr. Tiao), a discussion of counterfeit medical devices and injectables as well as medical spas in dermatology (Dr. Wang), and a study of the most common reasons patients are dissatisfied with minimally and noninvasive cosmetic procedures (Dr. Watchmaker). Access all of the abstracts presented by the top residents here. 

The dermMentors™ Resident of Distinction Award™ recognizes top residents in dermatology. DermMentors.org and the dermMentors™ Resident of Distinction Award™ are sponsored by Beiersdorf Inc and administered by DermEd, Inc. The 2020 dermMentors™ Residents of Distinction™ presented new scientific research during the general sessions of the 19th Annual Caribbean Dermatology Symposium on January 25, 2020.

The dermMentors™ Resident of Distinction Award™ was presented to 5 dermatology residents at the 19th Annual Caribbean Dermatology Symposium, January 21–25, 2020, in Paradise Island, Bahamas. Recipients of the award include Rachel Giesey, DO, Case Western Reserve University, Cleveland, Ohio, and University Hospitals Cleveland Medical Center, Cleveland, Ohio; Janice Tiao, MD, Boston University Medical Center, Boston, Massachusetts; Jordan V. Wang, MD, MBE, MBA, Thomas Jefferson University, Philadelphia, Pennsylvania; Jacqueline D. Watchmaker, MD, Boston University School of Medicine, Boston, Massachusetts; and Jennifer E. Yeh, MD, PhD, Brigham and Women’s Hospital, Boston, Massachusetts. The residents presented their research during the general sessions on January 25, 2020.

The overall grand prize was awarded to Dr. Yeh for her research entitled, “Topical Imiquimod in Combination With Brachytherapy for Unresectable Cutaneous Melanoma Metastases.” Dr. Yeh presented the utility of combining topical imiquimod with brachytherapy for locoregional control of cutaneous metastases through the presentation of 3 patients with scalp melanoma initially treated with wide local excision who developed numerous cutaneous metastases. “While surgical excision is the first-line treatment of single, discrete cutaneous metastases, it may not be practical in patients with multiple foci of disease distributed over large areas, as seen in the 3 patients presented here who achieved complete resolution of their cutaneous metastatic burden with concurrent topical imiquimod and brachytherapy,” Dr. Yeh reported.

Presentations by the other residents included a study of the burden of common skin diseases in the Caribbean and the potential correlation with a country’s socioeconomic status (Dr. Giesey), a study of the use of doxycycline in patients with lichen planopilaris and frontal fibrosing alopecia (Dr. Tiao), a discussion of counterfeit medical devices and injectables as well as medical spas in dermatology (Dr. Wang), and a study of the most common reasons patients are dissatisfied with minimally and noninvasive cosmetic procedures (Dr. Watchmaker). Access all of the abstracts presented by the top residents here. 

The dermMentors™ Resident of Distinction Award™ recognizes top residents in dermatology. DermMentors.org and the dermMentors™ Resident of Distinction Award™ are sponsored by Beiersdorf Inc and administered by DermEd, Inc. The 2020 dermMentors™ Residents of Distinction™ presented new scientific research during the general sessions of the 19th Annual Caribbean Dermatology Symposium on January 25, 2020.

The Society of Hospital Medicine has announced that Eric Howell, MD, MHM, will become its next CEO effective July 1, 2020. Dr. Howell will replace Laurence Wellikson, MD, MHM, who helped to found the society, and has been its first and only CEO since 2000.

“On behalf of the SHM board of directors, we welcome Dr. Howell as the incoming CEO for our organization who, with the mission-driven commitment and dedication of SHM staff, will take SHM into the future,” said Danielle Scheurer, MD, MSRC, SFHM, president-elect of SHM and chair of the CEO search committee. “With his broad knowledge of hospital medicine and extensive volunteer leadership at SHM, Dr. Howell’s experience is a natural complement to SHM’s core mission.”

Dr. Howell has a long history with SHM and has a wealth of expertise in hospital medicine. Since July 2018, he has served as chief operating officer of SHM, leading senior management’s planning and defining organizational goals to drive extensive, sustainable growth. Dr. Howell has also served as the senior physician advisor to SHM’s Center for Quality Improvement, the society’s arm that conducts quality improvement programs for hospitalist teams, since 2015. He is a past president of SHM’s board of directors and currently serves as the course director for the SHM Leadership Academies.

“Having been involved with SHM in many capacities since first joining, I am truly honored to become SHM’s CEO,” Dr. Howell said. “I always tell everyone that my goal is to make the world a better place, and I know that SHM’s staff will be able to do just that through the development and deployment of a variety of products, tools, and services to help hospitalists improve patient care.”

In addition to serving in various capacities at SHM, Dr. Howell has been a professor of medicine in the department of medicine at Johns Hopkins University, Baltimore. He has held multiple titles within the Johns Hopkins medical institutions, including chief of the division of hospital medicine at Johns Hopkins Bayview Medical Center in Baltimore, section chief of hospital medicine for Johns Hopkins Community Physicians, deputy director of hospital operations for the department of medicine at Johns Hopkins Bayview, and chief medical officer of operations at Johns Hopkins Bayview. Dr. Howell joined the Johns Hopkins Bayview hospitalist program in 2000, began the Howard County (Md.) General Hospital hospitalist program in 2010, and oversaw nearly 200 physicians and clinical staff providing patient care in three hospitals.

Dr. Howell received his electrical engineering degree from the University of Maryland, which has proven instrumental in his mastery of managing and implementing change in the hospital. His research has focused on the relationship between the emergency department and medicine floors, improving communication, throughput, and patient outcomes.

The search process was led by a CEO search committee, comprised of members of the SHM board of directors and assisted by the executive search firm Spencer Stuart. Launching a nationwide search, the firm identified candidates with the values and leadership qualities necessary to ensure the future growth of the organization.

“After a thorough search process, Dr. Eric Howell emerged as the right person to lead SHM,” said SHM board president Christopher Frost, MD, SFHM, “His experience in hospital medicine and his servant leadership style make him an ideal fit to lead SHM to even greater future success.”

In the coming weeks, the SHM board of directors will work with Dr. Howell and Dr. Wellikson on a smooth transition plan to have Dr. Howell assume the role on July 1, 2020.

The Society of Hospital Medicine has announced that Eric Howell, MD, MHM, will become its next CEO effective July 1, 2020. Dr. Howell will replace Laurence Wellikson, MD, MHM, who helped to found the society, and has been its first and only CEO since 2000.

“On behalf of the SHM board of directors, we welcome Dr. Howell as the incoming CEO for our organization who, with the mission-driven commitment and dedication of SHM staff, will take SHM into the future,” said Danielle Scheurer, MD, MSRC, SFHM, president-elect of SHM and chair of the CEO search committee. “With his broad knowledge of hospital medicine and extensive volunteer leadership at SHM, Dr. Howell’s experience is a natural complement to SHM’s core mission.”

Dr. Howell has a long history with SHM and has a wealth of expertise in hospital medicine. Since July 2018, he has served as chief operating officer of SHM, leading senior management’s planning and defining organizational goals to drive extensive, sustainable growth. Dr. Howell has also served as the senior physician advisor to SHM’s Center for Quality Improvement, the society’s arm that conducts quality improvement programs for hospitalist teams, since 2015. He is a past president of SHM’s board of directors and currently serves as the course director for the SHM Leadership Academies.

“Having been involved with SHM in many capacities since first joining, I am truly honored to become SHM’s CEO,” Dr. Howell said. “I always tell everyone that my goal is to make the world a better place, and I know that SHM’s staff will be able to do just that through the development and deployment of a variety of products, tools, and services to help hospitalists improve patient care.”

In addition to serving in various capacities at SHM, Dr. Howell has been a professor of medicine in the department of medicine at Johns Hopkins University, Baltimore. He has held multiple titles within the Johns Hopkins medical institutions, including chief of the division of hospital medicine at Johns Hopkins Bayview Medical Center in Baltimore, section chief of hospital medicine for Johns Hopkins Community Physicians, deputy director of hospital operations for the department of medicine at Johns Hopkins Bayview, and chief medical officer of operations at Johns Hopkins Bayview. Dr. Howell joined the Johns Hopkins Bayview hospitalist program in 2000, began the Howard County (Md.) General Hospital hospitalist program in 2010, and oversaw nearly 200 physicians and clinical staff providing patient care in three hospitals.

Dr. Howell received his electrical engineering degree from the University of Maryland, which has proven instrumental in his mastery of managing and implementing change in the hospital. His research has focused on the relationship between the emergency department and medicine floors, improving communication, throughput, and patient outcomes.

The search process was led by a CEO search committee, comprised of members of the SHM board of directors and assisted by the executive search firm Spencer Stuart. Launching a nationwide search, the firm identified candidates with the values and leadership qualities necessary to ensure the future growth of the organization.

“After a thorough search process, Dr. Eric Howell emerged as the right person to lead SHM,” said SHM board president Christopher Frost, MD, SFHM, “His experience in hospital medicine and his servant leadership style make him an ideal fit to lead SHM to even greater future success.”

In the coming weeks, the SHM board of directors will work with Dr. Howell and Dr. Wellikson on a smooth transition plan to have Dr. Howell assume the role on July 1, 2020.

The Society of Hospital Medicine has announced that Eric Howell, MD, MHM, will become its next CEO effective July 1, 2020. Dr. Howell will replace Laurence Wellikson, MD, MHM, who helped to found the society, and has been its first and only CEO since 2000.

“On behalf of the SHM board of directors, we welcome Dr. Howell as the incoming CEO for our organization who, with the mission-driven commitment and dedication of SHM staff, will take SHM into the future,” said Danielle Scheurer, MD, MSRC, SFHM, president-elect of SHM and chair of the CEO search committee. “With his broad knowledge of hospital medicine and extensive volunteer leadership at SHM, Dr. Howell’s experience is a natural complement to SHM’s core mission.”

Dr. Howell has a long history with SHM and has a wealth of expertise in hospital medicine. Since July 2018, he has served as chief operating officer of SHM, leading senior management’s planning and defining organizational goals to drive extensive, sustainable growth. Dr. Howell has also served as the senior physician advisor to SHM’s Center for Quality Improvement, the society’s arm that conducts quality improvement programs for hospitalist teams, since 2015. He is a past president of SHM’s board of directors and currently serves as the course director for the SHM Leadership Academies.

“Having been involved with SHM in many capacities since first joining, I am truly honored to become SHM’s CEO,” Dr. Howell said. “I always tell everyone that my goal is to make the world a better place, and I know that SHM’s staff will be able to do just that through the development and deployment of a variety of products, tools, and services to help hospitalists improve patient care.”

In addition to serving in various capacities at SHM, Dr. Howell has been a professor of medicine in the department of medicine at Johns Hopkins University, Baltimore. He has held multiple titles within the Johns Hopkins medical institutions, including chief of the division of hospital medicine at Johns Hopkins Bayview Medical Center in Baltimore, section chief of hospital medicine for Johns Hopkins Community Physicians, deputy director of hospital operations for the department of medicine at Johns Hopkins Bayview, and chief medical officer of operations at Johns Hopkins Bayview. Dr. Howell joined the Johns Hopkins Bayview hospitalist program in 2000, began the Howard County (Md.) General Hospital hospitalist program in 2010, and oversaw nearly 200 physicians and clinical staff providing patient care in three hospitals.

Dr. Howell received his electrical engineering degree from the University of Maryland, which has proven instrumental in his mastery of managing and implementing change in the hospital. His research has focused on the relationship between the emergency department and medicine floors, improving communication, throughput, and patient outcomes.

The search process was led by a CEO search committee, comprised of members of the SHM board of directors and assisted by the executive search firm Spencer Stuart. Launching a nationwide search, the firm identified candidates with the values and leadership qualities necessary to ensure the future growth of the organization.

“After a thorough search process, Dr. Eric Howell emerged as the right person to lead SHM,” said SHM board president Christopher Frost, MD, SFHM, “His experience in hospital medicine and his servant leadership style make him an ideal fit to lead SHM to even greater future success.”

In the coming weeks, the SHM board of directors will work with Dr. Howell and Dr. Wellikson on a smooth transition plan to have Dr. Howell assume the role on July 1, 2020.

Personalized treatment for depression may soon become a reality, thanks to an artificial intelligence (AI) algorithm that accurately predicts antidepressant efficacy in specific patients.

A landmark study of more than 300 patients with major depressive disorder (MDD) showed that a latent-space machine-learning algorithm tailored for resting-state EEG robustly predicted patient response to sertraline. The findings were generalizable across different study sites and EEG equipment.

“We found that the use of the artificial intelligence algorithm can identify the EEG signature for patients who do well on sertraline,” study investigator Madhukar H. Trivedi, MD, professor of psychiatry at the University of Texas Southwestern Medical Center in Dallas, said in an interview.

“Interestingly, when we looked further, it became clear that patients with that same EEG signature do not do well on placebo,” he added.

The study was published online Feb. 10 in Nature Biotechnology (doi: 10.1038/s41587-019-0397-3).

Pivotal study

Currently, major depression is defined using a range of clinical criteria. As such, it encompasses a heterogeneous mix of neurobiological phenotypes. Such heterogeneity may account for the modest superiority of antidepressant medication relative to placebo.

While recent research suggests that resting-state EEG may help identify treatment-predictive heterogeneity in depression, these studies have also been hindered by a lack of cross-validation and small sample sizes.

What’s more, these studies have either identified nonspecific predictors or failed to yield generalizable neural signatures that are predictive at the individual patient level (Am J Psychiatry. 2019 Jan 1;176[1]:44-56).

For these reasons, there is currently no robust neurobiological signature for an antidepressant-responsive phenotype that may help identify which patients would benefit from antidepressant medication. Nevertheless, said Dr. Trivedi, detailing such a signature would promote a neurobiological understanding of treatment response, with the potential for notable clinical implications.

“The idea behind this [National Institutes of Health]–funded study was to develop biomarkers that can distinguish treatment outcomes between drug and placebo,” he said. “To do so, we needed a randomized, placebo-controlled trial that has significant breadth in terms of biomarker evaluation and validation, and this study was designed specifically with this end in mind.

“There has not been a drug-placebo study that has looked at this in patients with depression,” Dr. Trivedi said. “So in that sense, this was really a pivotal study.”

To help address these challenges, the investigators developed a machine-learning algorithm they called SELSER (Sparse EEG Latent Space Regression).

Using data from four separate studies, they first established the resting-state EEG predictive signature by training SELSER on data from 309 patients from the EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response in Clinic Care) study, a neuroimaging-coupled, placebo-controlled, randomized clinical study of antidepressant efficacy.

The generalizability of the antidepressant-predictive signature was then tested in a second independent sample of 72 depressed patients.

In a third independent sample of 24 depressed patients, the researchers assessed the convergent validity and neurobiological significance of the treatment-predictive, resting-state EEG signature.

Finally, a fourth sample of 152 depressed patients was used to test the generalizability of the results.

‘Fantastic’ result but validation needed

These combined efforts were aimed at revealing a treatment responsive phenotype in depression, dissociate between medication and placebo response, establish its mechanistic significance, and provide initial evidence regarding the potential for treatment selection on the basis of a resting-state EEG signature.

The study showed that improvement in patients’ symptoms was robustly predicted by the algorithm. These predictions were specific for sertraline relative to placebo.

When generalized to two depression samples, the researchers also found that the algorithm reflected general antidepressant medication responsivity and related differentially to a repetitive transcranial magnetic stimulation (TMS) treatment outcome.

“Although we only looked at sertraline,” Dr. Trivedi said, “we also applied the signature to a sample of patients who had been treated with transcranial magnetic stimulation. And we found that the signature for TMS [response] is different than the signature for sertraline.”

Interestingly, the antidepressant-predictive signature identified by SELSER was also superior to that of conventional machine-learning models or latent modeling methods, such as independent-component analysis or principal-component analysis. This SELSER signature was also superior to a model trained on clinical data alone and was able to predict outcome using resting-state EEG data acquired at a study site not included in the model training set.

The study also revealed evidence of multimodal convergent validity for the antidepressant-response signature by virtue of its correlation with expression of a task-based functional MRI signature in one of the four datasets.

The strength of the resting-state signature was also found to correlate with prefrontal neural responsivity, as indexed by direct stimulation with single-pulse TMS and EEG.

Given the ability of the algorithm to both predict outcome with sertraline and distinguish response between sertraline and placebo at the individual patient level, the investigators believe SELSER may one day support machine learning–driven personalized approaches to depression treatment.

“Our findings advance the neurobiological understanding of antidepressant treatment through an EEG-tailored computational model and provide a clinical avenue for personalized treatment of depression,” the authors wrote.

Yet, their work is far from over. Among the investigators’ next steps is the development of an AI interface that can be widely integrated with EEGs across the country.

“Identifying this signature was fantastic, but you’ve got to be able to validate it as well,” Dr. Trivedi noted. “And luckily, we were able to validate it in the three additional studies.

“The next question is whether it can be broadened to other illnesses.”

Promising research

Commenting on the findings in an interview, Michele Ferrante, PhD, said he believes there may soon be a time during which algorithms such as this are used to personalize depression treatment.

“It’s well known that there are no good biological tests in psychiatry, but promising computational tools, biomarkers, and behavioral signatures for segregating patients according to treatment response are starting to emerge for depression,” said Dr. Ferrante, program chief of the Theoretical and Computational Neuroscience Program at the National Institute of Mental Health (NIMH).

“Precision in the ability to predict what patient will respond to each treatment will improve over time, I have no doubt,” added Dr. Ferrante, who was not involved with the current study.

However, he noted, such approaches are not without their potential drawbacks.

“The greatest challenge is to continuously validate these computational tools as they keep on learning from more heterogeneous groups. Another challenge will be to make sure that these computational tools become well-established, widely adopted, safe, and regulated by the [Food and Drug Administration] as Software as a Medical Device,” he said.

The current algorithm will also need to undergo further testing, said Dr. Ferrante.

“It has been validated on an external dataset,” he said, “but now we need to do rigorous prospective clinical trials where patients are selectively assigned by the AI to a treatment according to their biosignature, to see if these results hold true.

“Down the road, it would be important to implement computational models [that are] able to assign patients across the multiple treatments available for depression, including pharmaceuticals, psychosocial interventions, and neural devices.”

The study was funded directly and indirectly by the NIMH of the National Institutes of Health, the Stanford Neurosciences Institute, the Hersh Foundation, the National Key Research and Development Plan of China, and the National Natural Science Foundation of China.

Dr. Trivedi disclosed numerous financial relationships with pharmaceutical companies and device manufacturers. He has received grants/research support from the Agency for Healthcare Research and Quality, Cyberonic, the National Alliance for Research in Schizophrenia and Depression, the NIMH, and the National Institute on Drug Abuse.
 

A version of this article first appeared on Medscape.com.

Personalized treatment for depression may soon become a reality, thanks to an artificial intelligence (AI) algorithm that accurately predicts antidepressant efficacy in specific patients.

A landmark study of more than 300 patients with major depressive disorder (MDD) showed that a latent-space machine-learning algorithm tailored for resting-state EEG robustly predicted patient response to sertraline. The findings were generalizable across different study sites and EEG equipment.

“We found that the use of the artificial intelligence algorithm can identify the EEG signature for patients who do well on sertraline,” study investigator Madhukar H. Trivedi, MD, professor of psychiatry at the University of Texas Southwestern Medical Center in Dallas, said in an interview.

“Interestingly, when we looked further, it became clear that patients with that same EEG signature do not do well on placebo,” he added.

The study was published online Feb. 10 in Nature Biotechnology (doi: 10.1038/s41587-019-0397-3).

Pivotal study

Currently, major depression is defined using a range of clinical criteria. As such, it encompasses a heterogeneous mix of neurobiological phenotypes. Such heterogeneity may account for the modest superiority of antidepressant medication relative to placebo.

While recent research suggests that resting-state EEG may help identify treatment-predictive heterogeneity in depression, these studies have also been hindered by a lack of cross-validation and small sample sizes.

What’s more, these studies have either identified nonspecific predictors or failed to yield generalizable neural signatures that are predictive at the individual patient level (Am J Psychiatry. 2019 Jan 1;176[1]:44-56).

For these reasons, there is currently no robust neurobiological signature for an antidepressant-responsive phenotype that may help identify which patients would benefit from antidepressant medication. Nevertheless, said Dr. Trivedi, detailing such a signature would promote a neurobiological understanding of treatment response, with the potential for notable clinical implications.

“The idea behind this [National Institutes of Health]–funded study was to develop biomarkers that can distinguish treatment outcomes between drug and placebo,” he said. “To do so, we needed a randomized, placebo-controlled trial that has significant breadth in terms of biomarker evaluation and validation, and this study was designed specifically with this end in mind.

“There has not been a drug-placebo study that has looked at this in patients with depression,” Dr. Trivedi said. “So in that sense, this was really a pivotal study.”

To help address these challenges, the investigators developed a machine-learning algorithm they called SELSER (Sparse EEG Latent Space Regression).

Using data from four separate studies, they first established the resting-state EEG predictive signature by training SELSER on data from 309 patients from the EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response in Clinic Care) study, a neuroimaging-coupled, placebo-controlled, randomized clinical study of antidepressant efficacy.

The generalizability of the antidepressant-predictive signature was then tested in a second independent sample of 72 depressed patients.

In a third independent sample of 24 depressed patients, the researchers assessed the convergent validity and neurobiological significance of the treatment-predictive, resting-state EEG signature.

Finally, a fourth sample of 152 depressed patients was used to test the generalizability of the results.

‘Fantastic’ result but validation needed

These combined efforts were aimed at revealing a treatment responsive phenotype in depression, dissociate between medication and placebo response, establish its mechanistic significance, and provide initial evidence regarding the potential for treatment selection on the basis of a resting-state EEG signature.

The study showed that improvement in patients’ symptoms was robustly predicted by the algorithm. These predictions were specific for sertraline relative to placebo.

When generalized to two depression samples, the researchers also found that the algorithm reflected general antidepressant medication responsivity and related differentially to a repetitive transcranial magnetic stimulation (TMS) treatment outcome.

“Although we only looked at sertraline,” Dr. Trivedi said, “we also applied the signature to a sample of patients who had been treated with transcranial magnetic stimulation. And we found that the signature for TMS [response] is different than the signature for sertraline.”

Interestingly, the antidepressant-predictive signature identified by SELSER was also superior to that of conventional machine-learning models or latent modeling methods, such as independent-component analysis or principal-component analysis. This SELSER signature was also superior to a model trained on clinical data alone and was able to predict outcome using resting-state EEG data acquired at a study site not included in the model training set.

The study also revealed evidence of multimodal convergent validity for the antidepressant-response signature by virtue of its correlation with expression of a task-based functional MRI signature in one of the four datasets.

The strength of the resting-state signature was also found to correlate with prefrontal neural responsivity, as indexed by direct stimulation with single-pulse TMS and EEG.

Given the ability of the algorithm to both predict outcome with sertraline and distinguish response between sertraline and placebo at the individual patient level, the investigators believe SELSER may one day support machine learning–driven personalized approaches to depression treatment.

“Our findings advance the neurobiological understanding of antidepressant treatment through an EEG-tailored computational model and provide a clinical avenue for personalized treatment of depression,” the authors wrote.

Yet, their work is far from over. Among the investigators’ next steps is the development of an AI interface that can be widely integrated with EEGs across the country.

“Identifying this signature was fantastic, but you’ve got to be able to validate it as well,” Dr. Trivedi noted. “And luckily, we were able to validate it in the three additional studies.

“The next question is whether it can be broadened to other illnesses.”

Promising research

Commenting on the findings in an interview, Michele Ferrante, PhD, said he believes there may soon be a time during which algorithms such as this are used to personalize depression treatment.

“It’s well known that there are no good biological tests in psychiatry, but promising computational tools, biomarkers, and behavioral signatures for segregating patients according to treatment response are starting to emerge for depression,” said Dr. Ferrante, program chief of the Theoretical and Computational Neuroscience Program at the National Institute of Mental Health (NIMH).

“Precision in the ability to predict what patient will respond to each treatment will improve over time, I have no doubt,” added Dr. Ferrante, who was not involved with the current study.

However, he noted, such approaches are not without their potential drawbacks.

“The greatest challenge is to continuously validate these computational tools as they keep on learning from more heterogeneous groups. Another challenge will be to make sure that these computational tools become well-established, widely adopted, safe, and regulated by the [Food and Drug Administration] as Software as a Medical Device,” he said.

The current algorithm will also need to undergo further testing, said Dr. Ferrante.

“It has been validated on an external dataset,” he said, “but now we need to do rigorous prospective clinical trials where patients are selectively assigned by the AI to a treatment according to their biosignature, to see if these results hold true.

“Down the road, it would be important to implement computational models [that are] able to assign patients across the multiple treatments available for depression, including pharmaceuticals, psychosocial interventions, and neural devices.”

The study was funded directly and indirectly by the NIMH of the National Institutes of Health, the Stanford Neurosciences Institute, the Hersh Foundation, the National Key Research and Development Plan of China, and the National Natural Science Foundation of China.

Dr. Trivedi disclosed numerous financial relationships with pharmaceutical companies and device manufacturers. He has received grants/research support from the Agency for Healthcare Research and Quality, Cyberonic, the National Alliance for Research in Schizophrenia and Depression, the NIMH, and the National Institute on Drug Abuse.
 

A version of this article first appeared on Medscape.com.

Personalized treatment for depression may soon become a reality, thanks to an artificial intelligence (AI) algorithm that accurately predicts antidepressant efficacy in specific patients.

A landmark study of more than 300 patients with major depressive disorder (MDD) showed that a latent-space machine-learning algorithm tailored for resting-state EEG robustly predicted patient response to sertraline. The findings were generalizable across different study sites and EEG equipment.

“We found that the use of the artificial intelligence algorithm can identify the EEG signature for patients who do well on sertraline,” study investigator Madhukar H. Trivedi, MD, professor of psychiatry at the University of Texas Southwestern Medical Center in Dallas, said in an interview.

“Interestingly, when we looked further, it became clear that patients with that same EEG signature do not do well on placebo,” he added.

The study was published online Feb. 10 in Nature Biotechnology (doi: 10.1038/s41587-019-0397-3).

Pivotal study

Currently, major depression is defined using a range of clinical criteria. As such, it encompasses a heterogeneous mix of neurobiological phenotypes. Such heterogeneity may account for the modest superiority of antidepressant medication relative to placebo.

While recent research suggests that resting-state EEG may help identify treatment-predictive heterogeneity in depression, these studies have also been hindered by a lack of cross-validation and small sample sizes.

What’s more, these studies have either identified nonspecific predictors or failed to yield generalizable neural signatures that are predictive at the individual patient level (Am J Psychiatry. 2019 Jan 1;176[1]:44-56).

For these reasons, there is currently no robust neurobiological signature for an antidepressant-responsive phenotype that may help identify which patients would benefit from antidepressant medication. Nevertheless, said Dr. Trivedi, detailing such a signature would promote a neurobiological understanding of treatment response, with the potential for notable clinical implications.

“The idea behind this [National Institutes of Health]–funded study was to develop biomarkers that can distinguish treatment outcomes between drug and placebo,” he said. “To do so, we needed a randomized, placebo-controlled trial that has significant breadth in terms of biomarker evaluation and validation, and this study was designed specifically with this end in mind.

“There has not been a drug-placebo study that has looked at this in patients with depression,” Dr. Trivedi said. “So in that sense, this was really a pivotal study.”

To help address these challenges, the investigators developed a machine-learning algorithm they called SELSER (Sparse EEG Latent Space Regression).

Using data from four separate studies, they first established the resting-state EEG predictive signature by training SELSER on data from 309 patients from the EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response in Clinic Care) study, a neuroimaging-coupled, placebo-controlled, randomized clinical study of antidepressant efficacy.

The generalizability of the antidepressant-predictive signature was then tested in a second independent sample of 72 depressed patients.

In a third independent sample of 24 depressed patients, the researchers assessed the convergent validity and neurobiological significance of the treatment-predictive, resting-state EEG signature.

Finally, a fourth sample of 152 depressed patients was used to test the generalizability of the results.

‘Fantastic’ result but validation needed

These combined efforts were aimed at revealing a treatment responsive phenotype in depression, dissociate between medication and placebo response, establish its mechanistic significance, and provide initial evidence regarding the potential for treatment selection on the basis of a resting-state EEG signature.

The study showed that improvement in patients’ symptoms was robustly predicted by the algorithm. These predictions were specific for sertraline relative to placebo.

When generalized to two depression samples, the researchers also found that the algorithm reflected general antidepressant medication responsivity and related differentially to a repetitive transcranial magnetic stimulation (TMS) treatment outcome.

“Although we only looked at sertraline,” Dr. Trivedi said, “we also applied the signature to a sample of patients who had been treated with transcranial magnetic stimulation. And we found that the signature for TMS [response] is different than the signature for sertraline.”

Interestingly, the antidepressant-predictive signature identified by SELSER was also superior to that of conventional machine-learning models or latent modeling methods, such as independent-component analysis or principal-component analysis. This SELSER signature was also superior to a model trained on clinical data alone and was able to predict outcome using resting-state EEG data acquired at a study site not included in the model training set.

The study also revealed evidence of multimodal convergent validity for the antidepressant-response signature by virtue of its correlation with expression of a task-based functional MRI signature in one of the four datasets.

The strength of the resting-state signature was also found to correlate with prefrontal neural responsivity, as indexed by direct stimulation with single-pulse TMS and EEG.

Given the ability of the algorithm to both predict outcome with sertraline and distinguish response between sertraline and placebo at the individual patient level, the investigators believe SELSER may one day support machine learning–driven personalized approaches to depression treatment.

“Our findings advance the neurobiological understanding of antidepressant treatment through an EEG-tailored computational model and provide a clinical avenue for personalized treatment of depression,” the authors wrote.

Yet, their work is far from over. Among the investigators’ next steps is the development of an AI interface that can be widely integrated with EEGs across the country.

“Identifying this signature was fantastic, but you’ve got to be able to validate it as well,” Dr. Trivedi noted. “And luckily, we were able to validate it in the three additional studies.

“The next question is whether it can be broadened to other illnesses.”

Promising research

Commenting on the findings in an interview, Michele Ferrante, PhD, said he believes there may soon be a time during which algorithms such as this are used to personalize depression treatment.

“It’s well known that there are no good biological tests in psychiatry, but promising computational tools, biomarkers, and behavioral signatures for segregating patients according to treatment response are starting to emerge for depression,” said Dr. Ferrante, program chief of the Theoretical and Computational Neuroscience Program at the National Institute of Mental Health (NIMH).

“Precision in the ability to predict what patient will respond to each treatment will improve over time, I have no doubt,” added Dr. Ferrante, who was not involved with the current study.

However, he noted, such approaches are not without their potential drawbacks.

“The greatest challenge is to continuously validate these computational tools as they keep on learning from more heterogeneous groups. Another challenge will be to make sure that these computational tools become well-established, widely adopted, safe, and regulated by the [Food and Drug Administration] as Software as a Medical Device,” he said.

The current algorithm will also need to undergo further testing, said Dr. Ferrante.

“It has been validated on an external dataset,” he said, “but now we need to do rigorous prospective clinical trials where patients are selectively assigned by the AI to a treatment according to their biosignature, to see if these results hold true.

“Down the road, it would be important to implement computational models [that are] able to assign patients across the multiple treatments available for depression, including pharmaceuticals, psychosocial interventions, and neural devices.”

The study was funded directly and indirectly by the NIMH of the National Institutes of Health, the Stanford Neurosciences Institute, the Hersh Foundation, the National Key Research and Development Plan of China, and the National Natural Science Foundation of China.

Dr. Trivedi disclosed numerous financial relationships with pharmaceutical companies and device manufacturers. He has received grants/research support from the Agency for Healthcare Research and Quality, Cyberonic, the National Alliance for Research in Schizophrenia and Depression, the NIMH, and the National Institute on Drug Abuse.
 

A version of this article first appeared on Medscape.com.