Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA).

A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample.

In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors.

Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan.

This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018–2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample.

32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables.

Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

Little is known about how hospital organizational and cultural factors associated with implementation of quality initiatives such as the Institute for Healthcare Improvement's (IHI) 100,000 Lives Campaign differ among levels of healthcare staff.

Evaluation of a mixed qualitative and quantitative methodology (“trilogic evaluation model”).

Six hospitals that joined the campaign before June 2006.

Three strata of staff (executive leadership, midlevel, and frontline) at each hospital.

Surveys were completed in 2008 by 135 hospital personnel (midlevel, 43.7%; frontline, 38.5%; executive, 17.8%) who also participated in 20 focus groups. Overall, 93% of participants were aware of the IHI campaign in their hospital and perceived that 58% (standard deviation, 22.7%) of improvements in quality at their hospital were a direct result of the campaign. There were significant differences between staff levels on the organizational culture (OC) items, with executive-level staff having higher scores than midlevel and frontline staff. All 20 focus groups perceived that the campaign interventions were sustainable and that data feedback, buy-in, hardwiring (into daily activities), and leadership support were essential to sustainability.

The trilogic model demonstrated that the 3 levels of staff had markedly different perceptions regarding the IHI campaign and OC. A framework in which frontline, midlevel, and leadership staff are simultaneously assessed may be a useful tool for future evaluations of OC and quality initiatives such as the IHI campaign.

Infect Control Hosp Epidemiol 2012;33(2):135-143