Commentary on : Caution is needed when using self-reported alcohol use disorder screening tools

The Brief Alcohol Use Disorder Severity Scale: An Initial Validation Evaluation

AIMS

The goal of this study was to develop a standard measure of AUD severity that includes multiple dimensions and can be used in clinical settings to inform treatment selection.

METHODS

A large sample (n = 1939) of moderate to heavy drinkers was amassed from six psychopharmacology studies. The severity factor was comprised of four dimensions: withdrawal, craving, AUD symptoms and alcohol-related consequences. First, a confirmatory factor analysis (CFA) was conducted to examine model fit. Next, a comprehensive item list from the four measures (i.e. CIWA, DrinC, PACs and SCID-5 AUD criteria) was reduced through exploratory factor analysis (EFA). Once the final items were merged into a preliminary assessment, an EFA was run to observe the factor structure. Initial validation of the measure was obtained via associations with clinical endpoints.

RESULTS

The chi-square test statistic (${\chi}^2(2)=2.432\ P=0.297$) for a single-factor model of severity demonstrated good fit. Additional goodness-of-fit indices from the CFA revealed similar support for the single-factor model of severity (i.e. SRMSR = 0.011; RMSEA = 0.011; CFI = 0.999). Next, nine items from the individual EFAs were selected based on factor loading. The final EFA conducted on the 9-item scale demonstrated that a single factor model of severity best fit the data. Analysis of the psychometric properties revealed good internal consistency ($\alpha$= 0.79).

CONCLUSIONS

The current study extends upon the measurement of severity and supports a brief severity measure. This brief 9-item scale can be leveraged in future studies as a screening instrument and as a tool for personalized medicine.

Identifying Alcohol Use Disorder With Resting State Functional Magnetic Resonance Imaging Data: A Comparison Among Machine Learning Classifiers

Alcohol use disorder (AUD) is a burden to society creating social and health problems. Detection of AUD and its effects on the brain are difficult to assess. This problem is enhanced by the comorbid use of other substances such as nicotine that has been present in previous studies. Recent machine learning algorithms have raised the attention of researchers as a useful tool in studying and detecting AUD. This work uses AUD and controls samples free of any other substance use to assess the performance of a set of commonly used machine learning classifiers detecting AUD from resting state functional network connectivity (rsFNC) derived from independent component analysis. The cohort used included 51 alcohol dependent subjects and 51 control subjects. Despite alcohol, none of the 102 subjects reported use of nicotine, cannabis or any other dependence or habit formation substance. Classification features consisted of whole brain rsFNC estimates undergoing a feature selection process using a random forest approach. Features were then fed to 10 different machine learning classifiers to be evaluated based on their classification performance. A neural network classifier showed the highest performance with an area under the curve (AUC) of 0.79. Other good performers with similar AUC scores were logistic regression, nearest neighbor, and support vector machine classifiers. The worst results were obtained with Gaussian process and quadratic discriminant analysis. The feature selection outcome pointed to functional connections between visual, sensorimotor, executive control, reward, and salience networks as the most relevant for classification. We conclude that AUD can be identified using machine learning classifiers in the absence of nicotine comorbidity.

Validation of self-report measures of narcissism against a diagnostic interview

The Pathological Narcissism Inventory (PNI) and the Narcissistic Personality Inventory (NPI) are often used to screen for pathological narcissism but have rarely been validated against a clinician-administered diagnostic interview. Our study evaluated the convergent validity of the PNI and NPI against a diagnostic interview for narcissistic personality disorder (NPD) in a clinical population. We used data from a psychiatric outpatient center located in Switzerland (n = 123). Correlations between PNI/NPI and NPD ranged between .299 and .498 (common variance 9.0–24.8%). The PNI and NPI should be used carefully to screen for NPD. We highlight a need to increase the compatibility between the conceptual underpinnings of the PNI, NPI and NPD.

Are short AUDIT screeners effective in identifying unhealthy drinking of varying severity? A prison population study

BACKGROUND

Whether brief versions of the Alcohol Use Disorder Identification Test (AUDIT) can be used as graded severity measures is largely unknown. We examined the performance of eight such brief screeners in a prison population, and compared their effectiveness in detecting hazardous drinking, harmful drinking, and possible alcohol dependence as classified by the full ten-item AUDIT.

METHODS

The study sample included pre-prison drinkers who participated in the Norwegian Offender Mental Health and Addiction (NorMA) study (n = 758). We conducted receiver operating characteristic curve (ROC) analyses and estimated the area under the curve (AUROC) to assess the performance of AUDIT-C (three consumption items) and four-item versions that consisted of AUDIT-C and one additional item.

RESULTS

AUDIT-C performed very well in detecting unhealthy drinking of varying severity (AUROCs of 0.933 or 0.935). Four-item versions performed even better. Of these, the well-established AUDIT-4 was superior in identifying harmful drinking (AUROC=0.969) and possible alcohol dependence (AUROC=0.976). For AUDIT-C, the optimal cut-points in terms of the highest combined sensitivity and specificity were ≥ 6 (hazardous drinking), ≥ 8 (harmful drinking) and ≥ 8 or ≥ 9 (possible dependence). The corresponding cut-points on AUDIT-4 were ≥ 6, ≥ 9 and ≥ 10. The highest cut-point whereby all cases of possible dependence were identified was ≥ 6 on AUDIT-C and ≥ 8 on AUDIT-4. At these cut-points, almost all individuals with harmful drinking were also detected.

CONCLUSIONS

AUDIT-C and AUDIT-4 were both highly effective in detecting hazardous drinking, harmful drinking and possible alcohol dependence. AUDIT-4 was superior, notably as a graded severity measure.