Development and Validation of a Risk Model for Prediction of Hazardous Alcohol Consumption in General Practice Attendees: The PredictAL Study

Background: Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL) for the development of hazardous drinking in safe drinkers.Methods: A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score >= 8 in men and >= 5 in women.Results: 69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873). The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51). External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846) and Hedge's g of 0.68 (95% CI 0.57, 0.78).Conclusions: The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse

Development and Validation of an International Risk Prediction Algorithm for Episodes of Major Depression in General Practice Attendees The PredictD Study

Funding: The research in Europe was funded by a grant from the European Commission, reference PREDICT-QL4-CT2002-00683. Funding in Chile was provided by project FONDEF DO2I-1140. Partial support in Europe was from the Estonian Scientific Foundation (grant 5696), the Slovenian Ministry for Research (grant 4369-1027), the Spanish Ministry of Health (grant field-initiated studies program references PI041980, PI041771, and PI042450), the Spanish Network of Primary Care Research (redIAPP) (ISCIII-RETIC RD06/ 0018), and SAMSERAP group. The UK National Health Service Research and Development office provided service support costs in the United Kingdom.Context: Strategies for prevention of depression are hindered by lack of evidence about the combined predictive effect of known risk factors. Objectives: To develop a risk algorithm for onset of major depression. Design: Cohort of adult general practice attendees followed up at 6 and 12 months. We measured 39 known risk factors to construct a risk model for onset of major depression using stepwise logistic regression. We corrected the model for overfitting and tested it in an external population. Setting: General practices in 6 European countries and in Chile. Participants: In Europe and Chile, 10 045 attendees were recruited April 2003 to February 2005. The algorithm was developed in 5216 European attendees who were not depressed at recruitment and had follow-up data on depression status. It was tested in 1732 patients in Chile who were not depressed at recruitment. Main Outcome Measure: DSM-IV major depression. Results: Sixty-six percent of people approached participated, of whom 89.5% participated again at 6 months and 85.9%, at 12 months. Nine of the 10 factors in the risk algorithm were age, sex, educational level achieved, results of lifetime screen for depression, family history of psychological difficulties, physical health and mental health subscale scores on the Short Form 12, unsupported difficulties in paid or unpaid work, and experiences of discrimination. Country was the tenth factor. The algorithm's average C index across countries was 0.790 ( 95% confidence interval [ CI], 0.767-0.813). Effect size for difference in predicted log odds of depression between European attendees who became depressed and those who did not was 1.28 ( 95% CI, 1.17-1.40). Application of the algorithm in Chilean attendees resulted in a C index of 0.710 ( 95% CI, 0.670-0.749). Conclusion: This first risk algorithm for onset of major depression functions as well as similar risk algorithms for cardiovascular events and may be useful in prevention of depression.publishersversionpublishe

Development and validation of an international risk prediction algorithm for episodes of major depression in general practice attendees : the PredictD study

Context: Strategies for prevention of depression are hindered by lack of evidence about the combined predictive effect of known risk factors. Objectives: To develop a risk algorithm for onset of major depression. Design: Cohort of adult general practice attendees followed up at 6 and 12 months. We measured 39 known risk factors to construct a risk model for onset of major depression using stepwise logistic regression. We corrected the model for overfitting and tested it in an external population. Setting: General practices in 6 European countries and in Chile. Participants: In Europe and Chile, 10 045 attendees were recruited April 2003 to February 2005. The algorithm was developed in 5216 European attendees who were not depressed at recruitment and had follow-up data on depression status. It was tested in 1732 patients in Chile who were not depressed at recruitment. Main Outcome Measure: DSM-IV major depression. Results: Sixty-six percent of people approached participated, of whom 89.5% participated again at 6 months and 85.9%, at 12 months. Nine of the 10 factors in the risk algorithm were age, sex, educational level achieved, results of lifetime screen for depression, family history of psychological difficulties, physical health and mental health subscale scores on the Short Form 12, unsupported difficulties in paid or unpaid work, and experiences of discrimination. Country was the tenth factor. The algorithm's average C index across countries was 0.790 ( 95% confidence interval [ CI], 0.767-0.813). Effect size for difference in predicted log odds of depression between European attendees who became depressed and those who did not was 1.28 ( 95% CI, 1.17-1.40). Application of the algorithm in Chilean attendees resulted in a C index of 0.710 ( 95% CI, 0.670-0.749). Conclusion: This first risk algorithm for onset of major depression functions as well as similar risk algorithms for cardiovascular events and may be useful in prevention of depression