{"title":"The Yale algorithm. Special workshop--clinical.","authors":"M S Kramer, T A Hutchinson","doi":"10.1177/009286158401800315","DOIUrl":null,"url":null,"abstract":"<p><p>The assessment of causality in drug-event associations depends on the setting and purpose of such an assessment. Epidemiologists are primarily interested in population-based inferences about whether a given drug can cause a certain adverse drug reaction (ADR), and if so, how often it does so. Pharmaceutical industries and regulatory agencies are also concerned with population-based risks, but in addition must worry about individual cases. Clinicians are primarily interested in the individual, ie, whether a given drug did cause a certain adverse event in a particular patient. The authors describe an algorithm that provides specific, detailed criteria for ranking the probability that an observed untoward clinical manifestation was caused by a given drug. The criteria are subdivided into six axes of decision strategy with a built-in scoring system that ordinally ranks the probability of an adverse drug reaction as definite, probable, possible, or unlikely. To illustrate the use of the algorithm, the authors assess a reference case of pancreatitis occurring after administration of methyldopa.</p>","PeriodicalId":51023,"journal":{"name":"Drug Information Journal","volume":"18 3-4","pages":"283-91"},"PeriodicalIF":0.0000,"publicationDate":"1984-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/009286158401800315","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Drug Information Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/009286158401800315","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14
Abstract
The assessment of causality in drug-event associations depends on the setting and purpose of such an assessment. Epidemiologists are primarily interested in population-based inferences about whether a given drug can cause a certain adverse drug reaction (ADR), and if so, how often it does so. Pharmaceutical industries and regulatory agencies are also concerned with population-based risks, but in addition must worry about individual cases. Clinicians are primarily interested in the individual, ie, whether a given drug did cause a certain adverse event in a particular patient. The authors describe an algorithm that provides specific, detailed criteria for ranking the probability that an observed untoward clinical manifestation was caused by a given drug. The criteria are subdivided into six axes of decision strategy with a built-in scoring system that ordinally ranks the probability of an adverse drug reaction as definite, probable, possible, or unlikely. To illustrate the use of the algorithm, the authors assess a reference case of pancreatitis occurring after administration of methyldopa.