Can routine information from electronic patient records predict a future diagnosis of alcohol use disorder?

Abstract

ObjectiveTo explore whether information regarding potentially alcohol-related health incidents recorded in electronic patient records might aid in earlier identification of alcohol use disorders.DesignWe extracted potentially alcohol-related information in electronic patient records and tested if alcohol-related diagnoses, prescriptions of codeine, tramadol, ethylmorphine, and benzodiazepines; elevated levels of gamma-glutamyl-transferase (GGT), and mean cell volume (MCV); and new sick leave certificates predicted specific alcohol use disorder.SettingNine general practitioner surgeries with varying size and stability.SubjectsTotally 20,764 patients with active electronic patient record until data gathering and with a history of at least four years without a specific alcohol use disorder after turning 18 years of age.MethodsThe Cox proportional hazard analysis with time-dependent covariates of potential accumulated risks over the previous four years.Main outcome measuresTime from inclusion until the first specific alcohol use disorder, defined by either an alcohol specific diagnostic code or a text fragment documenting an alcohol problem.ResultsIn the unadjusted and adjusted Cox-regression with time-dependent covariates all variables were highly significant with adjusted hazard ratios ranging from 1.25 to 3.50. Addictive drugs, sick leaves, GGT, MCV and International Classification for Primary Care version 2 (ICPC-2), and International Classification of Diseases version 10 (ICD-10) diagnoses were analyzed. Elevated GGT and MCV, ICD-10-diagnoses, and gender demonstrated the highest hazard ratios.ConclusionsMany frequent health problems are potential predictors of an increased risk or vulnerability for alcohol use disorders. However, due to the modest hazard ratios, we were unable to establish a clinically useful tool.Key Points Alcohol is potentially relevant for many health problems, but current strategies for identification and intervention in primary health care have not been successful.Many frequent clinical problems recorded in electronic patient records may indicate an increased risk for alcohol related health problems.The hazard ratios were modest and the resulting predictive model was unsatisfactory for diagnostic purposes. If we accepted a sensitivity as low as 0.50, the specificity slightly exceeded 0.75. With a low prevalent condition, it is obvious that the false positive problem will be vast.In addition to responding to elevated blood levels of liver enzymes, general practitioners should be aware of alcohol as a potentially relevant factor for patients with repeated events of many mental and psychosocial diagnoses and new sick leaves and repeated prescriptions of addictive drugs.

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Intelligent Analysis of Medical Big Data Based on Deep Learning

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