PubMed · 14990590
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W G Simpson, P J Twomey. 2004. Reflective testing.. https://doi.org/10.1136/jcp.2003.011668
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BACKGROUND: The reporting interval is the bin size used to report numerical pathology results and must be determined for every analyte. The influence of the size of the reporting interval on the critical difference (CD) between two results from the same patient has not been addressed previously. METHODS: The effect of changing the reporting-interval size (RIS) on CDs was modeled by use of a spreadsheet application. The findings were applied to data on CDs with analytical precision values from our laboratory. RESULTS: As the RIS increases relative to the combined analytical and within-person biological variation, there is an approximately linear increase in the CD from the value determined by use of published techniques. The revised estimate is as follows: CD = 2(1/2) x z x (SDa(2) + SDi(2))(1/2) + 1.5 x RIS, where CD, SD, and RIS are all in the same units. This effect is seen for any probability associated with the critical difference and for both uni- and bidirectional changes. CONCLUSIONS: The choice of reporting interval should be made in the light of assay requirements. Where there is a clinical need for detection of small changes in analyte concentration, the reporting interval should be kept small relative to the combined variation attributable to assay precision and within-person biological variation.
Clinical repositories containing large amounts of biological, clinical, and administrative data are increasingly becoming available as health care systems integrate patient information for research and utilization objectives. To investigate the potential value of searching these databases for novel insights, we applied a new data mining approach, HealthMiner, to a large cohort of 667,000 inpatient and outpatient digital records from an academic medical system. HealthMiner approaches knowledge discovery using three unsupervised methods: CliniMiner, Predictive Analysis, and Pattern Discovery. The initial results from this study suggest that these approaches have the potential to expand research capabilities through identification of potentially novel clinical disease associations.
Both hope and illusion, the recent progress in biology has raised our expectations that one day it will be possible to introduce a biological sample into an apparatus which will then deliver in a few minutes thousands of qualitative and quantitative data concerning the genome, transcriptome, proteome and metabolome, thereby contributing to diagnosis and follow-up of diseases which are now difficult to identify. Such machines do not exist yet and, in any case, should be associated with the appropriate and adequate clinical work on the disease and with the patient. This << total >> approach is of course being pushed by the recent decoding of the genomes of several species (genomics), the development of high throughput analysis of mRNAs (transcriptomics), and the efforts to identify the protein products on a large scale (proteomics). Wide sectors of medicine are waiting for the results of these new medium and high throughput technological approaches, for example, in order to identify early markers of diseases. The object of this article is to present a biochemist's point of view on hereditary metabolic diseases (also referred to as inborn errors of metabolism), a field of medicine and research covering very diverse clinical and biochemical aspects. Significant advances will be made possible by improving the present methods of analysis of the metabolome which establishes a link between genotypes and phenotypes, an area now called metabolomics. The contribution of proteomics will be important as well but will still require some time.