How does active substance use at psychiatric admission impact suicide risk and hospital length-of-stay?

Despite their high prevalence, little is known about the effects of substance use disorders and active substance use on the suicide risk or length-of-stay of psychiatric inpatients. This study examines the relationship between active substance use at the time of psychiatric hospitalization and changes in suicide risk measures and length-of-stay. Admission and discharge ratings on the Suicide Status Form-II-R, diagnoses, and toxicology data from 2,333 unique psychiatric inpatients were examined. Data for patients using alcohol, tetrahydrocannabinol, methamphetamines, cocaine, benzodiazepines, opiates, barbiturates, phencyclidine, and multiple substances on admission were compared with data from 1,426 admissions without substance use. Patients with substance use by toxicology on admission had a 0.9 day shorter length-of-stay compared to toxicology-negative patients. During initial nurse evaluation on the inpatient unit, these patients reported lower suicide measures (i.e., suicidal ideation frequency, overall suicide risk, and wish-to-die). No significant between-group differences were seen at discharge. Patients admitted with a substance use disorder diagnosis had a 1.0 day shorter length-of-stay than those without, while those with a substance use disorder diagnosis and positive toxicology reported the lowest measures of suicidality on admission. These results remained independent of psychiatric diagnosis. For acute psychiatric inpatients, suicide risk is higher and length-of-stay is longer in patients with substance use disorders who are NOT acutely intoxicated compared with patients without a substance use disorder. Toxicology-positive patients are less suicidal on admission and improve faster than their toxicology-negative counterparts. This study gives support to the clinical observation that acutely intoxicated patients may stabilize quickly with regard to suicidal urges and need for inpatient care

Metabolomics biomarkers to predict acamprosate treatment response in alcohol-dependent subjects

Precision medicine for alcohol use disorder (AUD) allows optimal treatment of the right patient with the right drug at the right time. Here, we generated multivariable models incorporating clinical information and serum metabolite levels to predict acamprosate treatment response. The sample of 120 patients was randomly split into a training set (n = 80) and test set (n = 40) five independent times. Treatment response was defined as complete abstinence (no alcohol consumption during 3 months of acamprosate treatment) while nonresponse was defined as any alcohol consumption during this period. In each of the five training sets, we built a predictive model using a least absolute shrinkage and section operator (LASSO) penalized selection method and then evaluated the predictive performance of each model in the corresponding test set. The models predicted acamprosate treatment response with a mean sensitivity and specificity in the test sets of 0.83 and 0.31, respectively, suggesting our model performed well at predicting responders, but not non-responders (i.e. many non-responders were predicted to respond). Studies with larger sample sizes and additional biomarkers will expand the clinical utility of predictive algorithms for pharmaceutical response in AUD.Samuel C. Johnson for Genomics of Addiction Program at Mayo Clinic Ulm Foundation American Society for Pharmacology and Experimental Therapeutics Mayo-Karolinska Institute (KI) Research Award National Institute on Alcohol Abuse and Alcoholism AA018779 AA01783