Reference Intervals Data Mining

Katayev, Alexander; Fleming, James H.; Luo, Dajie; Fisher, Arren H; Sharp, Thomas M.

doi:10.1309/ajcpqprnib54wfkj

Cited by 55 publications

(9 citation statements)

References 12 publications

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“…However, some authors favour the indirect method as the results are clinically relevant and much simpler for an individual laboratory to implement than the time-consuming direct a priori method ( 23 , 24 ). Furthermore, a recent study has suggested that an indirect method can be as accurate as a direct method when the right indirect method is used ( 25 ).…”

Section: Discussionmentioning

confidence: 99%

A reference interval study for common biochemical analytes in Eastern Turkey: a comparison of a reference population with laboratory data mining

et al. 2016

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IntroductionThe aim of this study was to define the reference intervals (RIs) in a Turkish population living in Northeast Turkey (Erzurum) for 34 analytes using direct and indirect methods. In the present study, the regional RIs obtained were compared with other RI studies, primarily the nationwide study performed in Turkey.Materials and methodsFor the direct method, 435 blood samples were collected from a healthy group of females (N = 218) and males (N = 217) aged between 18 and 65 years. The sera were analysed in Ataturk University hospital laboratory using Roche reagents and analysers for 34 analytes. The data from 1,366,948 records were used to calculate the indirect RIs using a modified Bhattacharya method.ResultsSignificant gender-related differences were observed for 17 analytes. There were also some apparent differences between RIs derived from indirect and direct methods particularly in some analytes (e.g. gamma-glutamyltransferase, creatine kinase, LDL-cholesterol and iron). The RIs derived with the direct method for some, but not all, of the analytes were generally comparable with the RIs reported in the nationwide study and other previous studies in Turkey.There were large differences between RIs derived by the direct method and the expected values shown in the kit insert (e.g. aspartate aminotransferase, total-cholesterol, HDL-cholesterol, and vitamin B12).ConclusionsThese data provide region-specific RIs for 34 analytes determined by the direct and indirect methods. The observed differences in RIs between previous studies could be related to nutritional status and environmental factors.

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Section: Discussionmentioning

confidence: 99%

A reference interval study for common biochemical analytes in Eastern Turkey: a comparison of a reference population with laboratory data mining

et al. 2016

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“…In recent years the indirect approach of deriving reference intervals has gained interest, where secondary data sources such as hospital pathology records 14 and pathology laboratory databases 15 are explored as alternative populations 13 . Through use of stringent inclusion criteria to derive a ‘healthy’ population 16 and/or through novel statistical methods 17 , secondary data sources are proving to be a viable option.…”

Section: Introductionmentioning

confidence: 99%

Clinical thresholds for diagnosing iron deficiency: comparison of functional assessment of serum ferritin to population based centiles

Sezgin

Monagle

Loh

et al. 2020

Sci Rep

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Low serum ferritin is diagnostic of iron deficiency, yet its published lower cut-off values are highly variable, particularly for pediatric populations. Lower cut-off values are commonly reported as 2.5th percentiles, and is based on the variation of ferritin values in the population. Our objective was to determine whether a functional approach based on iron deficient erythropoiesis could provide a better alternative. Utilizing 64,443 ferritin test results from pediatric electronic health records, we conducted various statistical techniques to derive 2.5th percentiles, and also derived functional reference limits through the association between ferritin and erythrocyte parameters: hemoglobin, mean corpuscular volume, mean cell hemoglobin concentration, and red cell distribution width. We find that lower limits of reference intervals derived as centiles are too low for clinical interpretation. Functional limits indicate iron deficiency anemia starts to occur when ferritin levels reach 10 µg/L, and are largely similar between genders and age groups. In comparison, centiles (2.5%) presented with lower limits overall, with varying levels depending on age and gender. Functionally-derived limits better reflects the underlying physiology of a patient, and may provide a basis for deriving a threshold related to treatment of iron deficiency and any other biomarker with functional outcomes.

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“…The usual methods for removing incorrect values include: the criterion of 3σ [22], Chauvenel criteria [23], Grubbs criterion [24], and Dixon criterion [25]. All of the methods have been established by experts' subjective experience, and thus far, no unifying rules [26] exist.…”

Section: Electrolysis Process and Problemmentioning

confidence: 99%

Anode effect prediction based on collaborative two-dimensional forecast model in aluminum electrolysis production

Chen¹,

Li²,

Chen³

et al. 2019

Journal of Industrial &Amp; Management Optimization

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In this study, a new prediction algorithm is proposed, based on the collaborative two-dimensional forecast model (CTFM) that combines the traditional method and similarity search technique. The main idea of the algorithm is that the prediction of the change trend of the slope and the accumulated slope of the cell resistance as well as the useful knowledge obtained using the similarity search technique are used as the main criteria to calculate anode effect (AE)-prediction reliability. The accumulated mass deviation value is used as an auxiliary criterion to adjust the AE-prediction reliability. Finally, the current AE-process is marked according to the current AE-prediction reliability. The prediction model based on CTFM is tested on a real situation, in which multiple samples are extracted from the production of a 400 kA aluminum electrolysis cell. We observe that when the time advance of AE-prediction is within 20 ∼ 40 min, the accuracy rate of the CTFM algorithm is greater than 95% and the applicability of the method is excellent, showing a high prediction accuracy for different aluminum electrolysis cells.

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Reference Intervals Data Mining

Abstract: The presented statistical algorithm is shown to be an accurate and practical tool for reference interval calculations.

Cited by 55 publications

References 12 publications

A reference interval study for common biochemical analytes in Eastern Turkey: a comparison of a reference population with laboratory data mining

A reference interval study for common biochemical analytes in Eastern Turkey: a comparison of a reference population with laboratory data mining

Clinical thresholds for diagnosing iron deficiency: comparison of functional assessment of serum ferritin to population based centiles

Anode effect prediction based on collaborative two-dimensional forecast model in aluminum electrolysis production

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