Importance: Valid biomarkers that can predict longitudinal clinical outcomes at low cost are a holy grail in psychiatric research, promising to ultimately be used to optimize and tailor intervention and prevention efforts.
Objective: To determine if baseline linguistic markers in natural speech, as compared to non-speech clinical and demographic measures, can predict drug use severity measures at future sessions in initially abstinent individuals with cocaine use disorder (iCUD).
Design: A longitudinal cohort study (August 2017 - March 2020), where baseline measures were used to predict outcomes collected at three-month intervals for up to one year of follow-up.
Participants: Eighty-eight initially abstinent iCUD were studied at baseline; 57 (46 male, age 50.7+/-7.9 years) came back for at least another session.
Main outcomes and measures: Outcomes were self-reported symptoms of withdrawal, craving, abstinence duration and frequency of cocaine use in the past 90 days at each study session. The predictors were derived from 5-min recordings of vocal descriptions of the positive consequences of abstinence and the negative consequences of using cocaine; the baseline cocaine and other common drug use measures, demographic and neuropsychological variables were used for comparison.
Results: Models using the non-speech variables showed the best predictive performance at three(r>0.45, P<2×10-3) and six months follow-up (r>0.37, P<3×10-2). At 12 months, the natural language processing-based model showed significant correlations with withdrawal (r=0.43, P=3×10-2), craving (r=0.72, P=5×10-5), days of abstinence (r=0.76, P=1×10-5), and cocaine use in the past 90 days (r=0.61, P=2×10-3), significantly outperforming the other models for abstinence prediction.
Conclusions and relevance: At short time intervals, maximal predictive power was obtained with models that used baseline drug use (in addition to demographic and neuropsychological) measures, potentially reflecting a slow rate of change in these measures, which could be estimated by linear functions. In contrast, short speech samples predicted longer-term changes in drug use, implying deeper penetrance by potentially capturing non-linear dynamics over longer intervals. Results suggest that, compared to the common outcome measures used in clinical trials, speech-based measures could be leveraged as better predictors of longitudinal drug use outcomes in initially abstinent iCUD, as potentially generalizable to other substance use disorders and related comorbidity.