Dataset Information

How well do clinical and demographic characteristics predict Patient Health Questionnaire-9 scores among patients with treatment-resistant major depressive disorder in a real-world setting?

ABSTRACT:

Objectives

To create and validate a model to predict depression symptom severity among patients with treatment-resistant depression (TRD) using commonly recorded variables within medical claims databases.

Methods

Adults with TRD (here defined as > 2 antidepressant treatments in an episode, suggestive of nonresponse) and ? 1 Patient Health Questionnaire (PHQ)-9 record on or after the index TRD date were identified (2013-2018) in Decision Resource Group's Real World Data Repository, which links an electronic health record database to a medical claims database. A total of 116 clinical/demographic variables were utilized as predictors of the study outcome of depression symptom severity, which was measured by PHQ-9 total score category (score: 0-9 = none to mild, 10-14 = moderate, 15-27 = moderately severe to severe). A random forest approach was applied to develop and validate the predictive model.

Results

Among 5,356 PHQ-9 scores in the study population, the mean (standard deviation) PHQ-9 score was 10.1 (7.2). The model yielded an accuracy of 62.7%. For each predicted depression symptom severity category, the mean observed scores (8.0, 12.2, and 16.2) fell within the appropriate range.

Conclusions

While there is room for improvement in its accuracy, the use of a machine learning tool that predicts depression symptom severity of patients with TRD can potentially have wide population-level applications. Healthcare systems and payers can build upon this groundwork and use the variables identified and the predictive modeling approach to create an algorithm specific to their population.

SUBMITTER: Voelker J

PROVIDER: S-EPMC7882175 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

How well do clinical and demographic characteristics predict Patient Health Questionnaire-9 scores among patients with treatment-resistant major depressive disorder in a real-world setting?

Voelker Jennifer J Joshi Kruti K Daly Ella E Papademetriou Eros E Rotter David D Sheehan John J JJ Kuvadia Harsh H Liu Xing X Dasgupta Anandaroop A Potluri Ravi R

Brain and behavior 20210105 2

<h4>Objectives</h4>To create and validate a model to predict depression symptom severity among patients with treatment-resistant depression (TRD) using commonly recorded variables within medical claims databases.<h4>Methods</h4>Adults with TRD (here defined as > 2 antidepressant treatments in an episode, suggestive of nonresponse) and ≥ 1 Patient Health Questionnaire (PHQ)-9 record on or after the index TRD date were identified (2013-2018) in Decision Resource Group's Real World Data Repository, ...[more]

PMID: 33403828

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Project description:BackgroundMissing data are a notable problem in Alzheimer's disease clinical trials. One cause of missing data is participant dropout. The Research Attitudes Questionnaire is a 7-item instrument that measures an individual's attitudes toward biomedical research, with higher scores indicating more favorable attitudes. The objective of this study was to describe the performance of the Research Attitudes Questionnaire over time and to examine whether Research Attitudes Questionnaire scores predict study dropout and other participant behaviors that affect trial integrity.MethodsThe Research Attitudes Questionnaire was collected at baseline and weeks 26 and 52 from each member of 119 participant/study partner dyads enrolled in a Phase 2, randomized, double-blind, placebo-controlled mild-to-moderate Alzheimer's disease clinical trial. Within-subject longitudinal analyses examined change in Research Attitudes Questionnaire scores over time in each population. Logistic regression analyses that controlled for trial arm and clustering in trial sites were used to assess whether baseline Research Attitudes Questionnaire scores predicted trial completion, study medication compliance, and enrollment in optional substudies.ResultsParticipants and study partners endorsed statistically similar ratings on the Research Attitudes Questionnaire that were stable over time. Participants with baseline Research Attitudes Questionnaire scores above 28.5 were 4.7 (95% confidence interval = 1.01 to 21.95) times as likely to complete the trial compared to those with lower scores. Applying the same cutoff, baseline study partner Research Attitudes Questionnaire scores were similarly able to predict study completion (odds ratio = 4.2, 95% confidence interval = 1.71 to 10.32). Using a score cutoff of 27.5, higher participant Research Attitudes Questionnaire scores predicted study medication compliance (odds ratio = 5.85, 95% confidence interval = 1.34 to 25.54). No relationship was observed between Research Attitudes Questionnaire score and participation in optional substudies.ConclusionThis brief instrument that measures research attitudes may identify participants at risk for behaviors that cause missing data.

Project description:ImportanceMajor depressive disorder (MDD) and alcohol dependence (AD) are heritable disorders with significant public health burdens, and they are frequently comorbid. Common genetic factors that influence the co-occurrence of MDD and AD have been sought in family, twin, and adoption studies, and results to date have been promising but inconclusive.ObjectiveTo examine whether AD and MDD overlap genetically, using a polygenic score approach.Design, settings, and participantsAssociation analyses were conducted between MDD polygenic risk score (PRS) and AD case-control status in European ancestry samples from 4 independent genome-wide association study (GWAS) data sets: the Collaborative Study on the Genetics of Alcoholism (COGA); the Study of Addiction, Genetics, and Environment (SAGE); the Yale-Penn genetic study of substance dependence; and the National Health and Resilience in Veterans Study (NHRVS). Results from a meta-analysis of MDD (9240 patients with MDD and 9519 controls) from the Psychiatric Genomics Consortium were applied to calculate PRS at thresholds from P?<?.05 to P???.99 in each AD GWAS data set.Main outcomes and measuresAssociation between MDD PRS and AD.ResultsParticipants analyzed included 788 cases (548 [69.5%] men; mean [SD] age, 38.2 [10.8] years) and 522 controls (151 [28.9.%] men; age [SD], 43.9 [11.6] years) from COGA; 631 cases (333 [52.8%] men; age [SD], 35.0 [7.7] years) and 756 controls (260 [34.4%] male; age [SD] 36.1 [7.7] years) from SAGE; 2135 cases (1375 [64.4%] men; age [SD], 39.4 [11.5] years) and 350 controls (126 [36.0%] men; age [SD], 43.5 [13.9] years) from Yale-Penn; and 317 cases (295 [93.1%] men; age [SD], 59.1 [13.1] years) and 1719 controls (1545 [89.9%] men; age [SD], 64.5 [13.3] years) from NHRVS. Higher MDD PRS was associated with a significantly increased risk of AD in all samples (COGA: best P?=?1.7?×?10-6, R2?=?0.026; SAGE: best P?=?.001, R2?=?0.01; Yale-Penn: best P?=?.035, R2?=?0.0018; and NHRVS: best P?=?.004, R2?=?0.0074), with stronger evidence for association after meta-analysis of the 4 samples (best P?=?3.3?×?10-9). In analyses adjusted for MDD status in 3 AD GWAS data sets, similar patterns of association were observed (COGA: best P?=?7.6?×?10-6, R2?=?0.023; Yale-Penn: best P?=?.08, R2?=?0.0013; and NHRVS: best P?=?.006, R2?=?0.0072). After recalculating MDD PRS using MDD GWAS data sets without comorbid MDD-AD cases, significant evidence was observed for an association between the MDD PRS and AD in the meta-analysis of 3 GWAS AD samples without MDD cases (best P?=?.007).Conclusions and relevanceThese results suggest that shared genetic susceptibility contributes modestly to MDD and AD comorbidity. Individuals with elevated polygenic risk for MDD may also be at risk for AD.

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Dataset Information

How well do clinical and demographic characteristics predict Patient Health Questionnaire-9 scores among patients with treatment-resistant major depressive disorder in a real-world setting?

Objectives

Methods

Results

Conclusions

Publications

How well do clinical and demographic characteristics predict Patient Health Questionnaire-9 scores among patients with treatment-resistant major depressive disorder in a real-world setting?

Similar Datasets

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