Development and preliminary validation of a dynamic, patient-tailored method to detect abnormal laboratory test results.

Laboratory test results in primary care are flagged as 'abnormal' when they fall outside a population-based Reference Interval (RI), typically generating many alerts with a low specificity. In order to decrease alert frequency while retaining clinical relevance, we developed a method to assess dynamic, patient-tailored RIs based on mixed-effects linear regression models. Potassium test results from primary care were used as proof-of-concept test bed. Clinical relevance was assessed via a survey administered to general practitioners (GPs). Overall, the dynamic, patient-tailored method and the combination of both methods flagged 20{\%} and 36{\%} fewer values as abnormal than the population-based method. Nineteen out of 43 invited GPs (44{\%}) completed the survey. The population-based method yielded a better sensitivity than the patient-tailored and the combined methods (0.51 vs 0.41 and 0.38, respectively) but a lower PPV (0.66 vs 0.67 and 0.76, respectively). We conclude that a combination of population-based and patient-tailored RIs can improve the detection of abnormal laboratory results. We suggest that lab values outside both RIs be flagged with high priority in clinical practice.