BACKGROUND: Although alcohol use disorders (AUDs) have enormous public health consequences, the rate of diagnosis of AUDs remains unsatisfactorily low. The primary aim of this study was to compare the detection of AUDs by anesthesiologists in a large preoperative assessment clinic to that by computerized self-assessment of the Alcohol Use Disorder Identification Test. Secondary outcome measures were to compare the actions taken by anesthesiologists upon a finding of an AUD. METHODS: One thousand five hundred fifty-six patients were included. Before preoperative assessment, patients were asked to complete the Alcohol Use Disorder Identification Test (positive scores: men > or = 8, women > or = 5) using a computer. The authors performed a retrospective chart analysis of the anesthesiologists' actions upon a finding of an AUD. The anesthesiologists were blinded to the results of the computer-based assessment and to the subsequent chart analysis. RESULTS: The prevalence rate of AUDs determined by the anesthesiologists was 6.9% (107 of 1,556), whereas the proportion of patients positive for an AUD using the computerised Alcohol Use Disorder Identification Test was 18.1% (282 of 1,556) (P < 0.001). The detection rate by the anesthesiologists of AUDs among men was significantly higher than among women (P < 0.001) as well as in the elderly compared with younger patients (P < 0.001). Action taken by anesthesiologists was mainly based on evaluating quantity of alcohol consumption. CONCLUSION: The computer-based self-assessment increases detection rates of AUDs in busy settings such as a preoperative assessment clinic. Prevalence rates of AUDs are underestimated. Best-practice guidelines for detection of AUDs are not implemented in the daily clinical routine. Barrier analysis is urgently required.
BACKGROUND: Although alcohol use disorders (AUDs) have enormous public health consequences, the rate of diagnosis of AUDs remains unsatisfactorily low. The primary aim of this study was to compare the detection of AUDs by anesthesiologists in a large preoperative assessment clinic to that by computerized self-assessment of the Alcohol Use Disorder Identification Test. Secondary outcome measures were to compare the actions taken by anesthesiologists upon a finding of an AUD. METHODS: One thousand five hundred fifty-six patients were included. Before preoperative assessment, patients were asked to complete the Alcohol Use Disorder Identification Test (positive scores: men > or = 8, women > or = 5) using a computer. The authors performed a retrospective chart analysis of the anesthesiologists' actions upon a finding of an AUD. The anesthesiologists were blinded to the results of the computer-based assessment and to the subsequent chart analysis. RESULTS: The prevalence rate of AUDs determined by the anesthesiologists was 6.9% (107 of 1,556), whereas the proportion of patients positive for an AUD using the computerised Alcohol Use Disorder Identification Test was 18.1% (282 of 1,556) (P < 0.001). The detection rate by the anesthesiologists of AUDs among men was significantly higher than among women (P < 0.001) as well as in the elderly compared with younger patients (P < 0.001). Action taken by anesthesiologists was mainly based on evaluating quantity of alcohol consumption. CONCLUSION: The computer-based self-assessment increases detection rates of AUDs in busy settings such as a preoperative assessment clinic. Prevalence rates of AUDs are underestimated. Best-practice guidelines for detection of AUDs are not implemented in the daily clinical routine. Barrier analysis is urgently required.
Authors: Katharine A Bradley; Anna D Rubinsky; Haili Sun; Chris L Bryson; Michael J Bishop; David K Blough; William G Henderson; Charles Maynard; Mary T Hawn; Hanne Tønnesen; Grant Hughes; Lauren A Beste; Alex H S Harris; Eric J Hawkins; Thomas K Houston; Daniel R Kivlahan Journal: J Gen Intern Med Date: 2011-02 Impact factor: 5.128
Authors: Léonie F Kerper; Claudia D Spies; Maria Lößner; Anna-Lena Salz; Sascha Tafelski; Felix Balzer; Edith Weiß-Gerlach; Tim Neumann; Alexandra Lau; Heide Glaesmer; Elmar Brähler; Henning Krampe Journal: PLoS One Date: 2012-12-05 Impact factor: 3.240
Authors: Anne C Fernandez; Timothy C Guetterman; Brian Borsari; Michael J Mello; Jessica Mellinger; Hanne Tonnesen; Avinash Hosanagar; Arden M Morris; Frederic C Blow Journal: J Addict Med Date: 2021-04-01 Impact factor: 4.647