Tailoring Depression Treatment for Veterans: Insights from Electronic Health Records


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What we know:

Attempts to create a personalized treatment rule (PTR) for enhancing the treatment of major depressive disorder (MDD) with antidepressant medication (ADM), psychotherapy, or their combination have been hindered by suboptimal analytical techniques, modest sample sizes, and limited predictor sets [1-7]. Examinations of extensive electronic health records (EHRs) aimed at simulating experiments, followed by randomized controlled trials (RCTs), offer potential solutions to these challenges [8].

Our aim:

Our aim in this paper was to advance previous studies on PTR development by investigating the feasibility of creating an initial PTR based on the extensive EHR administrative database available in the Veterans Health Administration (VHA) system. The report introduced a proof-of-concept investigation based on 43,470 outpatients initiating treatment for MDD in VHA Primary Care Mental Health Integration (PC-MHI) clinics. These clinics provide access not only to ADMs but also to psychotherapy and combined ADM-psychotherapy. The outcome was an integrated assessment of one or more negative outcomes occurring to the patient within 365 days following MDD treatment initiation: either a suicide attempt, a visit to a psychiatric emergency departments or urgent care facility, a psychiatric hospitalization, or a suicide death.

What we did:

Since this was an observational rather than an RCT study, the findings have the potential to be influenced by the fact that the type of treatment received was nonrandom with respect to potential confounding factors [9]. An extensive EHR and geospatial database, consisting of 5,865 variables, was used to address this issue by using state-of-the-art techniques to account for nonrandom treatment assignment with respect to these variables. We then derived an initial PTR using a 70% training subset and evaluated the strength of the PTR in the remaining 30% test subset.

Doubly robust techniques were employed to adjust for nonrandom treatment assignment, enhancing both overall model accuracy and fairness [10]. The investigation of heterogeneity of treatment effects (HTEs) involved two main steps: (1) We began by predicting an expected outcome for every patient across the three treatment options by establishing treatment-specific models within patient subgroups. We then imputed expected treatment-specific outcomes for all patients, irrespective of their actual treatment, based on these models [11, 12]. (2) We then used within-patient estimates of comparative treatment effects across interventions to generate within-patient scores that represent patient-specific expected differences in outcomes across treatment options. We then used a series of random forest models [13] to predict these expected difference scores. Predictor importance was assessed utilizing the kernel Shapley additive explanations (SHAP) technique [14].

What we found:

8.6% (standard error [S.E.] = 0.2%) of patients experienced a negative outcome. Significant statistical variability was observed in the overall likelihood of a negative outcome associated with baseline predictors (area under the receiver operating characteristic curve [AU-ROC]=0.68, S.E.=0.01). After adjusting for this observed variability, the average treatment-specific likelihood of a negative outcome was found not to differ significantly across the three treatment options: 9.1% (S.E.= 0.3) for ADM-only, 8.5% (S.E.= 0.3) for psychotherapy-only, and 8.8% (S.E.=0.4) for combined ADM-psychotherapy. The primary predictor categories contributing to the overall risk of negative outcomes were physical ailments (proportional SHAP=53.3%), patient-level social determinants of health (SDoH; proportional SHAP=35.6%), and the frequency of visits for mental or substance-related disorders (proportional SHAP=22.9%). Among the top 5% of patients with the highest anticipated negative outcome risk, the prevalence of a negative outcome in the test sample was 32.6%, relative to 7.1% in the rest of the test sample.

The PTR analyses then determined that psychotherapy-only was the best treatment option for 56.0% of patients, among whom risk of a negative outcome was about 20% lower relative to receiving one of the other treatments. Treatment types did not affect negative outcome risk for the remaining patients.

If this PTR was used in practice, overall treatment costs would change only modestly, as there would be a reduction of 16.1% in MDD patients prescribed ADMs and an increase in 2.9% of patients receiving psychotherapy.

The overwhelmingly important predictors of optimization with psychotherapy-only were geocode-level SDoH variables (proportional SHAP=93.5%). That is, patient neighborhood characteristics rather than individual-level patient characteristics were the key predictors of the extent to which psychotherapy-only would be the preferred treatment.


Our finding that the 5% of patients with the highest predicted risk of a negative outcome based on information known about the patient prior to beginning treatment was 32.6% might lead some clinicians to conclude that these patients are likely treatment-resistant, in which case both clinical guidelines and health-economic evaluations would call for more intensive treatments than those considered here – possibly involving aggressive medication dosing, increased psychotherapy frequency, or more advanced treatment options, such as antipsychotic augmentation, electroconvulsive therapy, ketamine, or transcranial magnetic stimulation [15-18].

Leaving aside this highest-risk segment of the patient population, we found that psychotherapy-only was associated with significantly reduced risk of a negative outcome relative to other treatment options for one segment of the patient population. It is noteworthy that a recent meta-analysis of RCTs found a similar aggregate result for a similar composite negative outcome [19]. Although our sample size was sufficient to detect a significant difference of this sort in our total sample, we failed to do so. This indicates either that differences in average treatment effects across treatment types are less pronounced in the VHA compared to the broader population examined in the meta-analysis, or that there were remaining biases in our nonexperimental analysis that led to an underestimation of the advantages of psychotherapy-only in the total population. A pragmatic trial would be necessary to resolve these two competing potential scenarios.

At the same time, we observed significant HTE in our sample, with 56% of patients deemed to benefit more from psychotherapy-only than the other treatments. The anticipated advantage of optimal versus suboptimal treatment assignment linked in this segment of the sample was approximately 20% proportional decrease in risk of the negative outcome. This effect size would normally be clinically significant but small.

Whether such a small effect size warrants a pragmatic trial is unclear. An alternative, given absence of evidence that ADM-only is superior to the other options in any segment of the patient population, would be to advocate for psychotherapy among all VHA PC-MHI clinic patients considered as high risk of the negative outcome considered here based on our aggregate risk model. As immediate access to psychotherapy is not consistently available, this type of general rule might be useful in PC-MHI settings when prioritizing patients for psychotherapy.


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Major Depression
Humanities and Social Sciences > Behavioral Sciences and Psychology > Clinical Psychology > Mental Disorder > Depression > Major Depression
Humanities and Social Sciences > Behavioral Sciences and Psychology > Psychotherapy
Machine Learning
Mathematics and Computing > Statistics > Statistics and Computing > Machine Learning