Algorithm-informed treatment from EEG patterns improves outcomes for patients with major depressive disorder
Objective: Selecting the right medication for major depressive disorder (MDD) is challenging, and patients are often on several medications before an effective one is found. Using patient EEG patterns with computer models to select medications is a potential solution, however, it is not widely perfo...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wolters Kluwer Medknow Publications
2024-12-01
|
| Series: | Journal of Family Medicine and Primary Care |
| Subjects: | |
| Online Access: | https://journals.lww.com/10.4103/jfmpc.jfmpc_630_24 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841545825578123264 |
|---|---|
| author | Ramon Solhkhah Justin Feintuch Mabel Vasquez Eamon S. Thomasson Vijay Halari Kathleen Palmer Morgan R. Peltier |
| author_facet | Ramon Solhkhah Justin Feintuch Mabel Vasquez Eamon S. Thomasson Vijay Halari Kathleen Palmer Morgan R. Peltier |
| author_sort | Ramon Solhkhah |
| collection | DOAJ |
| description | Objective:
Selecting the right medication for major depressive disorder (MDD) is challenging, and patients are often on several medications before an effective one is found. Using patient EEG patterns with computer models to select medications is a potential solution, however, it is not widely performed. Therefore, we evaluated a commercially available EEG data analysis system to help guide medication selection in a clinical setting.
Methods:
Patients with MDD were recruited, and their physicians used their own judgment to select medications (Control; n = 115) or relied on computer-guided selection (PEER n = 165) of medications. Quick Inventory of Depressive Symptomatology (QIDS SR-16) scores were obtained from patients, before the start of the study (day 0) and again at ~90 and ~180 d. Patients in the PEER arm were classified into one of 4 groups depending on if the report was followed throughout (RF/RF), the first 90 days only (RF/RNF), the second 90 days only (RNF/RF), or not at all (RNF/RNF). Outcomes were then compared with controls whose physician performed the EEG and submitted data but did not receive the PEER report.
Results:
Patients in the controls, RF/RF and RNF/RNF groups had fewer depressive symptoms at 90 and 180 days, but the response was significantly stronger for patients in the RF/RF group. Lower rates of suicidal ideation were also noted in the RF/RF group than the control group at 90 and 180 days of treatment.
Conclusion:
Computational analysis of EEG patterns may augment physicians’ skills at selecting medications for the patients. |
| format | Article |
| id | doaj-art-58c15e4cc09f4da5894ee609d1a05821 |
| institution | Kabale University |
| issn | 2249-4863 2278-7135 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Journal of Family Medicine and Primary Care |
| spelling | doaj-art-58c15e4cc09f4da5894ee609d1a058212025-01-11T10:07:08ZengWolters Kluwer Medknow PublicationsJournal of Family Medicine and Primary Care2249-48632278-71352024-12-0113125730573810.4103/jfmpc.jfmpc_630_24Algorithm-informed treatment from EEG patterns improves outcomes for patients with major depressive disorderRamon SolhkhahJustin FeintuchMabel VasquezEamon S. ThomassonVijay HalariKathleen PalmerMorgan R. PeltierObjective: Selecting the right medication for major depressive disorder (MDD) is challenging, and patients are often on several medications before an effective one is found. Using patient EEG patterns with computer models to select medications is a potential solution, however, it is not widely performed. Therefore, we evaluated a commercially available EEG data analysis system to help guide medication selection in a clinical setting. Methods: Patients with MDD were recruited, and their physicians used their own judgment to select medications (Control; n = 115) or relied on computer-guided selection (PEER n = 165) of medications. Quick Inventory of Depressive Symptomatology (QIDS SR-16) scores were obtained from patients, before the start of the study (day 0) and again at ~90 and ~180 d. Patients in the PEER arm were classified into one of 4 groups depending on if the report was followed throughout (RF/RF), the first 90 days only (RF/RNF), the second 90 days only (RNF/RF), or not at all (RNF/RNF). Outcomes were then compared with controls whose physician performed the EEG and submitted data but did not receive the PEER report. Results: Patients in the controls, RF/RF and RNF/RNF groups had fewer depressive symptoms at 90 and 180 days, but the response was significantly stronger for patients in the RF/RF group. Lower rates of suicidal ideation were also noted in the RF/RF group than the control group at 90 and 180 days of treatment. Conclusion: Computational analysis of EEG patterns may augment physicians’ skills at selecting medications for the patients.https://journals.lww.com/10.4103/jfmpc.jfmpc_630_24computer-assisted treatmentdepressioneegmedication |
| spellingShingle | Ramon Solhkhah Justin Feintuch Mabel Vasquez Eamon S. Thomasson Vijay Halari Kathleen Palmer Morgan R. Peltier Algorithm-informed treatment from EEG patterns improves outcomes for patients with major depressive disorder Journal of Family Medicine and Primary Care computer-assisted treatment depression eeg medication |
| title | Algorithm-informed treatment from EEG patterns improves outcomes for patients with major depressive disorder |
| title_full | Algorithm-informed treatment from EEG patterns improves outcomes for patients with major depressive disorder |
| title_fullStr | Algorithm-informed treatment from EEG patterns improves outcomes for patients with major depressive disorder |
| title_full_unstemmed | Algorithm-informed treatment from EEG patterns improves outcomes for patients with major depressive disorder |
| title_short | Algorithm-informed treatment from EEG patterns improves outcomes for patients with major depressive disorder |
| title_sort | algorithm informed treatment from eeg patterns improves outcomes for patients with major depressive disorder |
| topic | computer-assisted treatment depression eeg medication |
| url | https://journals.lww.com/10.4103/jfmpc.jfmpc_630_24 |
| work_keys_str_mv | AT ramonsolhkhah algorithminformedtreatmentfromeegpatternsimprovesoutcomesforpatientswithmajordepressivedisorder AT justinfeintuch algorithminformedtreatmentfromeegpatternsimprovesoutcomesforpatientswithmajordepressivedisorder AT mabelvasquez algorithminformedtreatmentfromeegpatternsimprovesoutcomesforpatientswithmajordepressivedisorder AT eamonsthomasson algorithminformedtreatmentfromeegpatternsimprovesoutcomesforpatientswithmajordepressivedisorder AT vijayhalari algorithminformedtreatmentfromeegpatternsimprovesoutcomesforpatientswithmajordepressivedisorder AT kathleenpalmer algorithminformedtreatmentfromeegpatternsimprovesoutcomesforpatientswithmajordepressivedisorder AT morganrpeltier algorithminformedtreatmentfromeegpatternsimprovesoutcomesforpatientswithmajordepressivedisorder |