Speak and you shall predict: speech at initial cocaine abstinence as a biomarker of long-term drug use behavior.

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, <2×10) and six months follow-up (r>0.37, <3×10). At 12 months, the natural language processing-based model showed significant correlations with withdrawal (r=0.43, =3×10), craving (r=0.72, =5×10), days of abstinence (r=0.76, =1×10), and cocaine use in the past 90 days (r=0.61, =2×10), 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.