Indirect Estimation Of Reference Intervals Using First Or Last Results And Results From Patients Without Repeated Measurements

Objectives: Indirect methods for the estimation of Reference Limits (RLs) use large data pools stored in modern laboratory information's systems. To avoid correlation between observations repeated results from each patient should be excluded. Some data pools obtained are anonymized, and thereafter the data cannot be re-identified. The effect of the procedure of data selection on the estimations is not investigated yet.Methods: We considered four parameters. Data sets were enclosed from two sources: a university hospital and a laboratory primarily reflecting a patient population from medical practitioners. Four algorithms were used for data selection, which generate first, last, all and non-repeated values. RLs were estimated through these data sets and compared.Results: This study showed the broader reference range estimated by indirect methods if using the whole data set compared to first/last values or non-repeated values.Conclusions: The use of all data without a filtering step results in a significant bias whereas the choice of first or last values has nearly no impact. The exclusion of repeated measurements results in narrower RLs. This influence confine the use of anonymous data sets where filtering is impossible for the estimation of RLs by indirect methods.