46 lines
2.2 KiB
Text
46 lines
2.2 KiB
Text
# Discussion
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Intro Paragraph - In
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progress<!--# Write after I write everything else -->
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## Summary of Results
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## Study Limitations
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Section overview - In progress
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### MIMIC Database
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While the MIMIC-IV database allowed for a first run of the study, it
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does suffer from some issues compared to other patient results. The
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MIMIC-IV database only contains results from ICU patients. Thus the
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result may not represent normal results for patients typically screened
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for hyper or hypothyroidism. In a study by Tyler et al., they found that
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laboratory value ranges from critically ill patients deviate
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significantly from those of healthy controls [-@tyler2018]. In their
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study, distribution curves based on ICU data differed significantly from
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the hospital standard range (mean \[SD\] overlapping coefficient, 0.51
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\[0.32-0.69\]) [@tyler2018]. The data ranges from 2008 to 2019. During
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this time, there could have been several unknown laboratory changes.
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Often laboratories change methods, reference ranges, or even vendors.
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None of this data is available in the MIMIC database. A change in method
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or vendor could cause a shift in results, thus causing the algorithm to
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assign incorrect outcomes.
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The dataset also sufferers from incompleteness. Due to the fact the
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database was not explicitly designed for this study, many patients do
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not have complete sets of lab results. The study also had to pick and
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choose lab tests to allow for as many sets of TSH and Free T4 results as
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possible. For instance, in a study by Luo et al., a total of 42
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different lab tests were selected for a Machine Learning study, compared
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to only 16 selected for this study [-@luo2016]. The patient demographic
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data also suffered from the same incompleteness. Due to this fact, only
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the age and gender of the patient were used in developing the algorithm.
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An early study by Schectman et al. found the mean TSH level of Blacks
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was 0.4 (SE .053) mU/L lower than that for Whites after age and sex
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adjustment, race explaining 6.5 percent of the variation in TSH levels
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[-@schectman1991]. This variation in results should potentially be
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included in developing a future algorithm. However, as it stands, the
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current data set has incomplete data for patient race and ethnicity.
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## Real World Applications
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