Predicting treatment failure in regular care Internet-Delivered Cognitive Behavior Therapy for depression and anxiety using only weekly symptom measures.

Abstract

To my family Josefin, Selma and Oskar. Not only did they put up with me during the entire PhD journey, but they also spent the last two weeks of it in quarantine with me because of the coronavirus pandemic of 2020.ABSTRACT Background: Internet-delivered Cognitive Behavior Therapy (ICBT) is efficacious for a number of psychiatric disorders and can be successfully implemented in routine psychiatric care. Still, only about half of patients experience a good enough treatment outcome. Using data from the early part of treatment to identify patients with high risk of not benefitting from it, and target them with additional resources to prevent the predicted failure is a potential way forward. We call this an Adaptive Treatment Strategy, and a very important part of it is the ability to predict the outcome for a specific patient. Aims:To establish a proof of concept for an Adaptive Treatment Strategy in ICBT, and explore outcome prediction further by evaluating the accuracy of an empirically supported classification algorithm, the time point in treatment when acceptable accuracy can be reached, and the accuracy of ICBT-therapists' own predictions. Preliminary benchmarks regarding the clinical usefulness of prediction will be established. Studies:Four studies were performed: Study I was a randomized controlled trial (RCT; n=251) where patients' risk of treatment Failure (Red=high risk of failure, Green=low risk) was predicted during week 4 out of 9 in ICBT for Insomnia. Red patients (n=102) were then randomized to either continuing with standard treatment (n=51) or having their treatment individually adapted (n=51). In Study II, the classification algorithm from Study I was evaluated in terms of classification accuracy and the contribution of the different predictors used. In Study III, data from 4310 regular care ICBT-patients having received treatment for either Depression, Social anxiety disorder or Panic disorder were analyzed in a series of multiple regression models using weekly observations of the primary symptom measure as predictors to classify risk of Failure. As a contrast, Study IV examines ICBT therapists' own predictions on both categorical and continuous treatment outcomes, as they made predictions for each of their patients (n=897) during week 4 in the same three treatments as in Study III. Results:The RCT was successful in that Red patients receiving Adapted treatment improved significantly more than Red patients receiving standard treatment, and their odds of failure were nearly cut in half. Green patients did better than Red patients, indicating that the accuracy of the classification algorithm was clinically useful. Study II showed that the balanced accuracy of the classifier was 67% and that only 11 of 21 predictors correlated significantly with Failure. Notable predictors were symptom levels as well as different markers of treatment engagement. Study III and IV showed that acceptable predictions could be made halfway through treatment using only symptom scores and basic statistics, and that ICBT-...

Weekly Self-Ratings of Treatment Involvement and Their Relation to Symptom Reduction in Internet Cognitive Behavioral Therapy for Insomnia

“…et al 2015b) and an individually tailored intervention with an insomnia component (Kraepelien et al 2019). In a previous study by our group, we used data during the first treatment weeks, including insomnia symptom scores, depression scores and ratings of knowledge gain and homework quantity and quality, to make individual predictions of the success (treatment response or remission) of the participants' treatment (Forsell et al 2019a). The study showed that prediction during treatment can be successfully used in an adaptive treatment strategy, i.e.…”

Enriching CBT by Neuroscience: Novel Avenues to Achieve Personalized Treatments

“…However, reports on behavioral treatment outcome predictors are contradictory, and the reported effects are usually small. Forsell et al (2020) reported on 4310 patients treated for depression, panic disorder, or social anxiety disorder with the Internetdelivered CBT and found that patients not responding to CBT can be predicted with about 70% accuracy. However, this accuracy rate was only achievable half way through the treatment (week 6 of 12), and pre-treatment subjective symptom severity ratings did not significantly predict post-treatment outcomes (Forsell et al, 2020).…”

“…Forsell et al (2020) reported on 4310 patients treated for depression, panic disorder, or social anxiety disorder with the Internetdelivered CBT and found that patients not responding to CBT can be predicted with about 70% accuracy. However, this accuracy rate was only achievable half way through the treatment (week 6 of 12), and pre-treatment subjective symptom severity ratings did not significantly predict post-treatment outcomes (Forsell et al, 2020). In a similar way, Hilbert et al (2020) predicted CBT outcome in a naturalistic sample from a university-based outpatient center with 2147 patients who received face-to-face CBT.…”

An investigation and replication of sleep‐related cognitions, acceptance and behaviours as predictors of short‐ and long‐term outcome in cognitive behavioural therapy for insomnia

“…by matching patients to the treatment option most likely to be beneficial, or use an adaptive treatment strategy to predict the risk of treatment failure early in treatment and adjust the treatment for those with a high risk (Forsell, Jernelöv, et al., 2019). A strong, and often pragmatically available, predictor of future symptom levels, are previous symptoms measured in the same way (Forsell, Isacsson, et al., 2019; Schibbye et al., 2014). However, it is likely that additional predictors need to be weighed in, in order for a prediction model to be optimised and clinically useful.…”