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Theory driven psychological therapy for persecutory delusions: trajectories of patient outcomes
Lucy Jenner1,2, Mollie Payne1, Felicity Waite2,3
1Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
Background:
We aimed to identify the common types of outcome trajectories for patients with psychosis who take up specialist psychological therapy for persecutory delusions. Knowing the different potential responses to therapy can inform expectations. Further, determining predictors of different outcomes may help in understanding who may benefit.
Methods:
We analyzed delusion conviction data from 767 therapy sessions with 64 patients with persistent persecutory delusions (held with at least 60% conviction) who received a six-month psychological intervention (Feeling Safe) during a clinical trial. Latent class trajectory analysis was conducted to identify groups with distinct outcome profiles. The trajectories were validated against independent assessments, including a longer-term follow-up six months after the end of therapy. We also tested potential predictors of the trajectories.
Results:
There were four outcome trajectories: (1) Very high delusion conviction/Little improvement (n = 14, 25%), (2) Very high delusion conviction/Large improvement (n = 9, 16%), (3) High delusion conviction/Moderate improvement (n = 17, 31%) and (4) High delusion conviction/Large improvement (n = 15, 27%). The groups did not differ in initial overall delusion severity. The trajectories were consistent with the independent assessments and sustained over time. Three factors predicted trajectories: persecutory delusion conviction, therapy expectations, and positive beliefs about other people.
Conclusions:
There are variable responses to psychological therapy for persecutory delusions. Patients with very high delusion conviction can have excellent responses to therapy, though this may take a little longer to observe and such high conviction reduces the likelihood of positive responses. A trajectory approach requires testing in larger datasets but may prove highly informative.
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