Predicting Dropout from Inpatient Substance Use Disorder Treatment: A Prospective Validation Study of the OQ-Analyst.

BACKGROUND AND AIMS: There is an urgent need for tools allowing therapists to identify patients at risk of dropout. The OQ-Analyst, an increasingly popular computer-based system, is used to track patient progress and predict dropout. However, we have been unable to find empirical documentation regarding the ability of OQ-Analyst to predict dropout. The aim of the present study was to perform the first direct test of the ability of the OQ-Analyst to predict dropout. DESIGN: Patients were consecutively enlisted in a naturalistic, prospective, longitudinal clinical trial. As interventions based on feedback from the OQ-Analyst could alter the outcome and potentially render the prediction wrong, feedback was withheld from patients and therapists. SETTING: The study was carried out during 2011-2013 in an inpatient substance use disorder clinic in Oslo, Norway. PARTICIPANTS: Patients aged 18 to 28 years who met criteria for a principal diagnosis of mental or behavioural disorder due to psychoactive substance use (ICD 10; F10.2-F19.2). MEASUREMENTS: Red signal (predictions of high risk) from the Norwegian version of the OQ-Analyst were compared with dropouts identified using patient medical records as the standard for predictive accuracy. FINDINGS: A total of 40 patients completed 647 OQ assessments resulting in 46 red signals. There were 27 observed dropouts, only one of which followed after a red signal. Patients indicated by the OQ-Analyst as being at high risk of dropping out were no more likely to do so than those indicated as being at low risk. Random intercept logistic regression predicting dropout from a red signal was statistically nonsignificant. Bayes factor supports no association. CONCLUSIONS: The study does not support the predictive ability of the OQ-Analyst for the present patient population. In the absence of empirical evidence of predictive ability, it may be better not to assume such ability.

Psychometric properties of the outcome questionnaire-45.2: the Norwegian version in an international context.

OBJECTIVE: Monitoring of ongoing psychotherapy is of crucial importance in improving the quality of mental health care by detecting therapies being off track, which requires that the instrument used is psychometrically sound. This study investigates the psychometric properties of the Norwegian version of the Outcome Questionnaire 45.2 (OQ-45) and situates the results in an international context. METHOD: Data from one non-clinical sample (N = 338) and one clinical sample (N = 560) were compared to international samples investigating reliability, cut-offs, and factor structure. RESULTS: The results show adequate reliability and concurrent validity. CONCLUSIONS: The means, clinical cut-offs, and the reliable change index vary across countries. However, the means of the OQ-45 for nonclinical samples correlate highly with external values of national well-being, indicating that the OQ-45 is a valid instrument internationally. The factor analyses in the present study do not confirm the hypothesized factor structure of the OQ-45, but are similar to the results internationally.

Drop-out from addiction treatment: a systematic review of risk factors.

Completion of addiction treatment is one of the most consistent factors associated with a favorable treatment outcome. Unfortunately, it is more common for a patient to drop-out of addiction treatment than to complete the treatment. To prevent drop-out, risk factors must be identified. This box-score review focuses on studies investigating the risk factors associated with drop-out from addiction treatment published in peer-reviewed journals from 1992 to 2013. A total of 122 studies involving 199,331 participants met the inclusion criteria. Contrary to recommendations from previous reviews, 91% of the included studies focused primarily on enduring patient factors, mainly demographics. The most consistent risk factors across the different study designs, samples, and measurement methods were cognitive deficits, low treatment alliance, personality disorder, and younger age. With the exception of younger age, none of the demographic factors emerged as consistent risk factors. Further research on the relationship between simple demographic factors and drop-out risk is of limited value. However, little is known about the potential risk factors related to treatment programs and to the treatment processes. Based on the review, clinical recommendations include assessing cognitive functioning and personality disorders at baseline and continuous monitoring of treatment alliance.