RT Journal Article
SR Electronic
T1 Novel method to determine diagnosis-defining refraction points
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 649442
DO 10.1101/649442
A1 Tsuneto Yamauchi
A1 Mitsuhiro Ohshima
A1 Yoko Yamaguchi
A1 Kazunori Konishi
A1 Kai Kappert
A1 Shigeru Nakano
YR 2019
UL http://biorxiv.org/content/early/2019/05/26/649442.abstract
AB Diagnosis of a certain disease generally relies on definitions established by professional medical societies and comprise the patient’s history, physical examination, and test results. These include physical compositions such as body mass index (BMI), and laboratory tests such as serum creatinine and albumin in urine samples. In general, laboratory tests are based on mathematical methods, e.g. defining critical values from the mean ± kσ of a population, where k is a natural number and the standard deviation is σ (“mean ± kσ-method”). In most cases k is defined as 2, leading to reference ranges defining 95% of test results as normal. However, this method mostly depends on a normal distribution of values.Here we applied a novel method (“SoFR-method”) based on data sorting to define refraction points, which carry informative value as diagnostic criteria. Applying the SoFR-method, standard measures such as critical BMI-values are categorized by equal robustness as by the mean ± kσ-method. However, the SoFR-method showed higher validity when analyzing non-normalized values such as creatinine and albumin, as well as hepatocyte growth factor (HGF) and hemoglobin in a novel Perioscreen assay in saliva of diabetic and non-diabetic patients.Taken together, we defined a novel method based on data sorting of test results from patients to effectively define refraction points which might guide more accurately clinical diagnoses and define relevant thresholds.