Dataset Information

Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees: the predictAL study.

ABSTRACT:

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.

SUBMITTER: King M

PROVIDER: S-EPMC3154188 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees: the predictAL study.

King Michael M Marston Louise L Švab Igor I Maaroos Heidi-Ingrid HI Geerlings Mirjam I MI Xavier Miguel M Benjamin Vicente V Torres-Gonzalez Francisco F Bellon-Saameno Juan Angel JA Rotar Danica D Aluoja Anu A Saldivia Sandra S Correa Bernardo B Nazareth Irwin I

PloS one 20110810 8

<h4>Background</h4>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.<h4>Methods</h4>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 recor ...[more]

PMID: 21853028

Similar Datasets

Project description:ObjectivesTo develop and internally validate prediction models to assess treatment success of both stand-alone and blended online vestibular rehabilitation (VR) in patients with chronic vestibular syndrome.DesignSecondary analysis of a randomised controlled trial.Setting59 general practices in The Netherlands.Participants202 adults, aged 50 years and older with a chronic vestibular syndrome who received either stand-alone VR (98) or blended VR (104). Stand-alone VR consisted of a 6-week, internet-based intervention with weekly online sessions and daily exercises. In blended VR, the same intervention was supplemented with physiotherapy support.Main outcome measuresSuccessful treatment was defined as: clinically relevant improvement of (1) vestibular symptoms (≥3 points improvement Vertigo Symptom Scale-Short Form); (2) vestibular-related disability (>11 points improvement Dizziness Handicap Inventory); and (3) both vestibular symptoms and vestibular-related disability. We assessed performance of the predictive models by applying calibration plots, Hosmer-Lemeshow statistics, area under the receiver operating characteristic curves (AUC) and applied internal validation.ResultsImprovement of vestibular symptoms, vestibular-related disability or both was seen in 121, 81 and 64 participants, respectively. We generated predictive models for each outcome, resulting in different predictors in the final models. Calibration for all models was adequate with non-significant Hosmer-Lemeshow statistics, but the discriminative ability of the final predictive models was poor (AUC 0.54 to 0.61). None of the identified models are therefore suitable for use in daily general practice to predict treatment success of online VR.ConclusionIt is difficult to predict treatment success of internet-based VR and it remains unclear who should be treated with stand-alone VR or blended VR. Because we were unable to develop a useful prediction model, the decision to offer stand-alone or blended VR should for now be based on availability, cost effectiveness and patient preference.Trial registration numberThe Netherlands Trial Register NTR5712.

Project description:BackgroundMost sudden cardiac death (SCD) events occur in the general population among persons who do not have any prior history of clinical heart disease. We sought to develop a predictive model of SCD among US adults.MethodsWe evaluated a series of demographic, clinical, laboratory, electrocardiographic, and echocardiographic measures in participants in the ARIC study (Atherosclerosis Risk in Communities) (n=13 677) and the CHS (Cardiovascular Health Study) (n=4207) who were free of baseline cardiovascular disease. Our initial objective was to derive a SCD prediction model using the ARIC cohort and validate it in CHS. Independent risk factors for SCD were first identified in the ARIC cohort to derive a 10-year risk model of SCD. We compared the prediction of SCD with non-SCD and all-cause mortality in both the derivation and validation cohorts. Furthermore, we evaluated whether the SCD prediction equation was better at predicting SCD than the 2013 American College of Cardiology/American Heart Association Cardiovascular Disease Pooled Cohort risk equation.ResultsThere were a total of 345 adjudicated SCD events in our analyses, and the 12 independent risk factors in the ARIC study included age, male sex, black race, current smoking, systolic blood pressure, use of antihypertensive medication, diabetes mellitus, serum potassium, serum albumin, high-density lipoprotein, estimated glomerular filtration rate, and QTc interval. During a 10-year follow-up period, a model combining these risk factors showed good to excellent discrimination for SCD risk (c-statistic 0.820 in ARIC and 0.745 in CHS). The SCD prediction model was slightly better in predicting SCD than the 2013 American College of Cardiology/American Heart Association Pooled Cohort risk equations (c-statistic 0.808 in ARIC and 0.743 in CHS). Only the SCD prediction model, however, demonstrated similar and accurate prediction for SCD using both the original, uncalibrated score and the recalibrated equation. Finally, in the echocardiographic subcohort, a left ventricular ejection fraction <50% was present in only 1.1% of participants and did not enhance SCD prediction.ConclusionsOur study is the first to derive and validate a generalizable risk score that provides well-calibrated, absolute risk estimates across different risk strata in an adult population of white and black participants without a clinical diagnosis of cardiovascular disease.

Project description:IntroductionAlcohol consumption is a leading global risk factor for ill-health and premature death. Alcohol screening and brief interventions (SBI) delivered in primary care is effective at reducing alcohol consumption, but routine implementation remains problematic. Screening all patients for excessive drinking (universal screening) is resource-intensive and may be at odds with general practitioners' (GPs') perceived professional role. This study aimed to develop a tailored, theory-based training intervention to strengthen GPs' ability to address alcohol and to manage alcohol-related health problems through a pragmatic approach based on clinical relevance.MethodsA qualitative study design involving focus group interviews and a structured questionnaire for free text replies with GPs in Norway. Behavioral analysis assessed factors influencing delivery of SBI according to the 'capability, opportunity, motivation and behavior' (COM-B) model to inform intervention development using the Behavior Change Wheel. Qualitative data were analyzed using framework analysis and an iterative approach was adopted to develop the training.ResultsA purposive sample of GPs attended the focus groups (n = 25) and completed the questionnaire (n = 55). Four areas required additional support including: understanding the link between alcohol use and health problems; opening up the conversation on alcohol use; addressing alcohol and dealing with obstacles; and following-up and maintaining change. Findings informed the development of a four-session interactive training intervention and a digital intervention for providing support for patients between consultations to address the identified needs.ConclusionThis work highlights the value of pragmatic, relevance-based clinical strategies, as opposed to universal screening approaches to addressing alcohol in primary care. A pragmatic approach is more in line with GPs existing sclinical skill set and holds the potential to improve widespread uptake and implementation of SBI in routine primary care.

Dataset Information

Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees: the predictAL study.

Background

Methods

Results

Conclusions

Publications

Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees: the predictAL study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets