Dataset Information

Efficiently identifying individuals at high risk for treatment resistance in major depressive disorder using electronic health records.

ABSTRACT:

Background

With the emergence of evidence-based treatments for treatment-resistant depression, strategies to identify individuals at greater risk for treatment resistance early in the course of illness could have clinical utility. We sought to develop and validate a model to predict treatment resistance in major depressive disorder using coded clinical data from the electronic health record.

Methods

We identified individuals from a large health system with a diagnosis of major depressive disorder receiving an index antidepressant prescription, and used a tree-based machine learning classifier to build a risk stratification model to identify those likely to experience treatment resistance. The resulting model was validated in a second health system.

Results

In the second health system, the extra trees model yielded an AUC of 0.652 (95% CI: 0.623-0.682); with sensitivity constrained at 0.80, specificity was 0.358 (95% CI: 0.300-0.413). Lift in the top quintile was 1.99 (95% CI: 1.76-2.22). Including additional data for the 4 weeks following treatment initiation did not meaningfully improve model performance.

Limitations

The extent to which these models generalize across additional health systems will require further investigation.

Conclusion

Electronic health records facilitated stratification of risk for treatment-resistant depression and demonstrated generalizability to a second health system. Efforts to improve upon such models using additional measures, and to understand their performance in real-world clinical settings, are warranted.

SUBMITTER: Lage I

PROVIDER: S-EPMC9980713 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Efficiently identifying individuals at high risk for treatment resistance in major depressive disorder using electronic health records.

Lage Isaac I McCoy Thomas H TH Perlis Roy H RH Doshi-Velez Finale F

Journal of affective disorders 20220216

<h4>Background</h4>With the emergence of evidence-based treatments for treatment-resistant depression, strategies to identify individuals at greater risk for treatment resistance early in the course of illness could have clinical utility. We sought to develop and validate a model to predict treatment resistance in major depressive disorder using coded clinical data from the electronic health record.<h4>Methods</h4>We identified individuals from a large health system with a diagnosis of major dep ...[more]

PMID: 35181388

Similar Datasets

Project description:Efforts to develop an individualized treatment rule (ITR) to optimize major depressive disorder (MDD) treatment with antidepressant medication (ADM), psychotherapy, or combined ADM-psychotherapy have been hampered by small samples, small predictor sets, and suboptimal analysis methods. Analyses of large administrative databases designed to approximate experiments followed iteratively by pragmatic trials hold promise for resolving these problems. The current report presents a proof-of-concept study using electronic health records (EHR) of n = 43,470 outpatients beginning MDD treatment in Veterans Health Administration Primary Care Mental Health Integration (PC-MHI) clinics, which offer access not only to ADMs but also psychotherapy and combined ADM-psychotherapy. EHR and geospatial databases were used to generate an extensive baseline predictor set (5,865 variables). The outcome was a composite measure of at least one serious negative event (suicide attempt, psychiatric emergency department visit, psychiatric hospitalization, suicide death) over the next 12 months. Best-practices methods were used to adjust for nonrandom treatment assignment and to estimate a preliminary ITR in a 70% training sample and to evaluate the ITR in the 30% test sample. Statistically significant aggregate variation was found in overall probability of the outcome related to baseline predictors (AU-ROC = 0.68, S.E. = 0.01), with test sample outcome prevalence of 32.6% among the 5% of patients having highest predicted risk compared to 7.1% in the remainder of the test sample. The ITR found that psychotherapy-only was the optimal treatment for 56.0% of patients (roughly 20% lower risk of the outcome than if receiving one of the other treatments) and that treatment type was unrelated to outcome risk among other patients. Change in aggregate treatment costs of implementing this ITR would be negligible, as 16.1% fewer patients would be prescribed ADMs and 2.9% more would receive psychotherapy. A pragmatic trial would be needed to confirm the accuracy of the ITR.

Project description:Background: Major Depressive Disorder (MDD) is a moderately heritable disorder with a high lifetime prevalence. At present, laboratory blood tests to support MDD diagnosis are not available. Methods: We used a classifier approach on blood gene expression profiles of a unique set of non-medicated subjects (MDD patients and controls) to select genes of which expression is predictive for disease status. To reveal blood gene expression changes related to MDD disease, we applied a powerful ex vivo stimulus to the blood, i.e. incubation with lipopolysaccharide (LPS; 10 ng/ml blood). Results: Based on LPS-stimulated blood gene expression using whole-genome microarrays in 42 subjects (primary cohort; 21 MDD patients (mean age 42.3 years), 21 healthy controls (mean age 41.9 years)), we identified a set of genes (CAPRIN1, CLEC4A, KRT23, MLC1, PLSCR1, PROK2, ZBTB16) that serves as a molecular signature of MDD. These findings were validated for the primary cohort using an independent quantitative PCR method (P = 0.007). The difference between depressive patients and controls was confirmed (P = 0.019) in a replication cohort of 13 patients with MDD (mean age 42.8 years) and 14 controls (mean age 45.6 years). The MDD-signature score comprised of expression levels of 7 genes could discriminate depressive patients from controls with sensitivity of 76.9% and specificity of 71.8%. Conclusions: We show for the first time that molecular analysis of stimulated blood cells can be used as an endophenotype for MDD diagnosis, which is a milestone in establishing biomarkers for neuropsychiatric disorders with moderate heritability in general. Our results may provide a new entry point for following and predicting treatment outcome, as well as prediction of severity and recurrence of MDD. In total, 33 MDD patients and 34 healthy controls were analyzed using basal gene expression in whole blood, and gene expression from whole blood that was stimulated with LPS for 5-6 h, using microarrays. Patients were arbitrarily selected from all patients to serve as primary cohort (nMDD = 21 (MDD01-MDD21); nControls = 21 (Con01-Con21)), or replication cohort (nMDD = 12 (MDD22-MDD35); nControls = 13 (Con22-Con37)) using microarrays. This submission does not include Samples CON21_LPS or CON30_LPS.

Project description:Identification and quantification of health inequities amongst specific social groups is a pre-requisite for designing targeted healthcare interventions. This study investigated the recording of social factors in linked electronic health records (EHR) of individuals aged ≥65 years, to assess the potential of these data to identify the social determinants of disease burden and uptake of healthcare interventions. Methodology was developed for ascertaining social factors recorded on or before a pre-specified index date (01/01/2013) using primary care data from Clinical Practice Research Datalink (CPRD) linked to hospitalisation and deprivation data in a cross-sectional study. Social factors included: religion, ethnicity, immigration status, small area-level deprivation, place of residence (including communal establishments such as care homes), marital status and living arrangements (e.g. living alone, cohabitation). Each social factor was examined for: completeness of recording including improvements in completeness by using other linked EHR, timeliness of recording for factors that might change over time and their representativeness (compared with English 2011 Census data when available). Data for 591,037 individuals from 389 practices from England were analysed. The completeness of recording varied from 1.6% for immigration status to ~80% for ethnicity. Linkages provided the deprivation data (available for 82% individuals) and improved completeness of ethnicity recording from 55% to 79% (when hospitalisation data were added). Data for ethnicity, deprivation, living arrangements and care home residence were comparable to the Census data. For time-varying variables such as residence and living alone, ~60% and ~35% respectively of those with available data, had this information recorded within the last 5 years of the index date. This work provides methods to identify social factors in EHR relevant to older individuals and shows that factors such as ethnicity, deprivation, not living alone, cohabitation and care home residence can be ascertained using these data. Applying these methodologies to routinely collected data could improve surveillance programmes and allow assessment of health equity in specific healthcare studies.

Dataset Information

Efficiently identifying individuals at high risk for treatment resistance in major depressive disorder using electronic health records.

Background

Methods

Results

Limitations

Conclusion

Publications

Efficiently identifying individuals at high risk for treatment resistance in major depressive disorder using electronic health records.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets