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Unfortunately, there is overalerting and underalerting because stratification is only based on a fixed QT\u2010DDI severity level. We aimed to improve QT\u2010DDI alerting by developing and validating a risk prediction model considering patient\u2010 and drug\u2010related factors.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We fitted 31 predictor candidates to a stepwise linear regression for 1000 bootstrap samples and selected the predictors present in 95% of the 1000 models. A final linear regression model with those variables was fitted on the original development sample (350 QT\u2010DDIs). This model was validated on an external dataset (143 QT\u2010DDIs). Both true QTc and predicted QTc were stratified into three risk levels (low, moderate and high). Stratification of QT\u2010DDIs could be appropriate (predicted risk\u2009=\u2009true risk), acceptable (one risk level difference) or inappropriate (two risk levels difference).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The final model included 11 predictors with the three most important being use of antiarrhythmics, age and baseline QTc. Comparing current practice to the prediction model, appropriate stratification increased significantly from 37% to 54% appropriate QT\u2010DDIs (increase of 17.5% on average [95% CI +5.4% to +29.6%], <jats:italic>p<\/jats:italic><jats:sub>adj<\/jats:sub>\u2009=\u20090.006) and inappropriate stratification decreased significantly from 13% to 1% inappropriate QT\u2010DDIs (decrease of 11.2% on average [95% CI \u221217.7% to \u22124.7%], <jats:italic>p<\/jats:italic><jats:sub>adj<\/jats:sub>\u2009\u2264\u20090.001).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The prediction model including patient\u2010 and drug\u2010related factors outperformed QT alerting based on QT\u2010DDI severity alone and therefore is a promising strategy to improve DDI alerting.<\/jats:p><\/jats:sec>","DOI":"10.1111\/bcp.15580","type":"journal-article","created":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T12:56:52Z","timestamp":1667393812000},"page":"1374-1385","source":"Crossref","is-referenced-by-count":9,"title":["Added value of patient\u2010 and drug\u2010related factors to stratify drug\u2010drug interaction alerts for risk of QT prolongation: Development and validation of a risk prediction model"],"prefix":"10.1111","volume":"89","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1117-6709","authenticated-orcid":false,"given":"Katoo M.","family":"Muylle","sequence":"first","affiliation":[{"name":"Department of Pharmaceutical and Pharmacological Sciences, Research Group Clinical Pharmacology and Clinical Pharmacy Vrije Universiteit Brussel  Laarbeeklaan 103 Brussels 1090 Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sven","family":"van Laere","sequence":"additional","affiliation":[{"name":"Department of Public 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