Intra‐individual variation of serum prostate specific antigen levels in men with benign prostate biopsies
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OBJECTIVE To determine the intra‐individual (physiological) variation of prostate‐specific antigen (PSA) measurements in men after a benign prostatic biopsy. PATIENTS AND METHODS Sixty‐four men were prospectively assessed, all of whom had a benign prostatic biopsy within the preceding 13 months. The degree of intra‐individual variability was established by calculating the coefficient of variation on four PSA levels obtained from each patient weekly over a month. RESULTS Six patients were subsequently diagnosed with prostate cancer and their data are presented separately. In the remaining 58 patients the median (range) individual mean PSA value was 6.3 (0.5–34.1) ng/mL. The median (range) coefficient of variation within the group was 9.5 (2.4–76.1)%. There was a clear linear relationship between mean PSA level and the standard deviation. CONCLUSION In 48 of the 63 patients analysed, the coefficient of variation for serum PSA values in the group as a whole was greater than the variation claimed for the assay technique. The significance of the linear relationship between PSA and the standard deviation is discussed, with particular reference to those men who had a benign prostate biopsy.Keywords:
Coefficient of variation
Prostate biopsy
The present study aimed to evaluate the indications for a second prostate biopsy in patients suspected with prostate cancer after an initial negative prostate biopsy. The present study included 421 patients who underwent repeat prostate biopsy between January 2007 and December 2015 at three hospitals. Clinicopathological data, including patient age, body mass index, history of prostate biopsy, prostate volume, prostate-specific antigen (PSA) level, PSA density, PSA velocity, and PSA fluctuation patterns, were analyzed. The patients were stratified into two groups based on the first PSA pattern (increase/decrease) within 1 year after the initial negative prostate biopsy. Prostate cancer was detected in 100 (23.8%) of the 421 patients at the second prostate biopsy. In patients with a PSA decrease at the first follow-up, prostate volume and number of increases in the PSA level from the initial prostate biopsy were predictors for prostate cancer diagnosis at the second prostate biopsy. In patients with a steady PSA increase after the initial prostate biopsy, prostate volume and number of biopsy cores were predictors for prostate cancer diagnosis at the second prostate biopsy. The indications for a second prostate biopsy are a low prostate volume and a high number of increases in the PSA level among patients with a PSA decrease at the first follow-up and a low prostate volume and a high number of biopsy cores among patients with a PSA increase at the first follow-up.
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To investigate the factors influencing the positive rate of prostate biopsy and its relationship with the prostate volume and inflammatory cell infiltration (ICI).We retrospectively analyzed the clinical data on 230 cases of double-plane transrectal ultrasound-guided prostate biopsy in our Department of Urology, including the patients' age, body mass index (BMI), serum total prostate-specific antigen (tPSA), PSA density (PSAD), prostate volume, and ICI in the prostate tissue. We also investigated the relationship of the above factors with the pathological results of prostate biopsy by binary logistic regression analysis.The positive rate of prostate biopsy was 38.7% (89/230) in the total number of cases, 28.57% (n = 56) in the 196 cases with tPSA < 100 μg/L, and 97.06% (n = 33) in the 34 cases with tPSA ≥ 100 μg/L. Binary logistic regression analysis showed that the positive rate of prostate biopsy in those with tPSA < 100 μg/L was correlated positively with age (P < 0.01, OR = 1.09), tPSA (P < 0.01, OR = 1.04) and PSAD (P < 0.01, OR = 10.04), negatively with the prostate volume (P < 0.01, OR = 0.98) and ICI (P < 0.01, OR = 0.22), but not with BMI (P > 0.05). As a predictor of positive prostate biopsy, tPSA > 10 μg/L exhibited a sensitivity of 82.14% and a specificity of 35.71%, while PSAD > 0.26 showed a sensitivity of 78.57% and a specificity of 71.43%.Non-specific elevation of the tPSA level induced by increased prostate volume and inflammatory cell infiltration may lead to unnecessary biopsies in some patients. As a predictor of positive prostate biopsy, PSAD > 0.26 has a higher clinical application value than tPSA > 10 μg/L.
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The relative absolute standard deviation, which was introduced for the control of the precision of blood alcohol-determinations by the BGH reveals, that according to technical rules the admissible standard deviation in the range of higher blood alcohol concentrations is exceeded despite the fact, that the width of the variation is still in the acceptable range. The standard deviation of 0.05% should therefore only be applied to the blood alcohol range up to 1.5%. As an alternative the coefficient of variation can be chosen for the precision control.
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Repeatability and reproducibility can only be assessed when replicate measurements by each method are available. If replicate measurements by a method are available, it is simple to estimate the measurement error for a method, using a model with fixed effect of item, and then taking the residual standard deviation as the measurement error standard deviation. The precision of a measurement method is often given as the coefficient of variation (CV). The variation used to estimate the CV based on log-transformed data is the residual variation derived using some model for the mean value. Occasionally, the overall residual standard deviation is just divided by the overall mean and used as estimate of the CV. Controlled Vocabulary Terms coefficient of variation; measurement error; residual variation
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To determine the coefficient of variation of certain characteristics in a yarn, such as the weight in constant short lengths, is a long and tedious task when using the standard root-mean-square method of computing the standard deviation. However, this standard deviation can be quite accurately estimated by any one of three short-cut methods described herein—the mean deviation method, the mean range method, and the probit method. The mean itself, by which this standard deviation is divided to get the coefficient of variation, is obtained readily by weighing all the samples together. The standard errors of the coefficient of variation estimated by these short-cut methods are derived, and the efficiencies of the methods are compared with that of the direct root- mean-square deviation method, showing the corresponding sample sizes for the same efficiency.
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The coefficient of variation (CV) of a population is defined as the ratio of the population standard deviation to the population mean. It is regarded as a measure of stability or uncertainty, and can indicate the relative dispersion of data in the population to the population mean. CV is a dimensionless measure of scatter or dispersion and is readily interpretable, as opposed to other commonly used measures such as standard deviation, mean absolute deviation. CV is often estimated by the ratio of the sample standard deviation to the sample mean, called the sample CV. In this paper, we propose statistical properties of CV for non-normal data and design of non-normal CV control charts based on non-normal distribution. The proposed control charts are effective methods to monitor the process variation
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The coefficient of the variation function is a useful descriptive statistic, especially when comparing the variability of more than two curve groups, even when they have significantly different mean curves. Since the coefficient of variation function is the ratio of the mean and standard deviation functions, its particular property is that it shows the acceleration more explicitly than the standard deviation function. The aim of the study is twofold: to show that the functional coefficient of variation is more sensitive to abrupt changes than the functional standard deviation and to propose the utilisation of the functional coefficient of variation as an outlier detection tool. Several simulation trials have shown that the coefficient of the variation function allows the effects of outliers to be seen explicitly.
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Abstract Heart rate variability and heart period variability are important indicators of the functioning of the autonomic nervous system and are strong predictors of survival after myocardial infarction. The standard deviation of a patient's series of normal heart periods (consecutive normal RR intervals) is positively and, in some populations, strongly correlated with the mean period length. This phenomenon has led some investigators to use the coefficient of variation as their measure of variability, because it correlates less strongly with the mean period length. Using data from a multicentre post‐infarction natural history study, we show that the standard deviation of the instantaneous heart rates has, like the coefficient of variation, only a modest correlation with the mean period length. Unlike the coefficient of variation, however, this standard deviation is derivable from established statistical principles. We show further that the coefficient of variation, the standard deviation of heart rates, and the standard deviation of heart periods are approximately equally strong predictors of survival after myocardial infarction.
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Objective To evaluate the feasibility of prostate biopsy under direct vision.Methods 56 patients recieved transrectal prostate biopsy under direct vision with a modified prone position.We used dilator and anal mirror suture device(parts of the PPH kit)to help revealing the prostate. Results Transrectal prostate biopsy under direct vision could achieve a very good view of prostate and made puncture easier by controlling the depth and direction.The puncture located well. The patient′s position was comfortable.The procedure were well tolerated in all patients.No serious complications occurred. Conclusions Transrectal prostate biopsy under direct vision with a modified prone position is simple and accurate.It might be generalized in the future.
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[Objective] The research aimed to supply an accurate,intelligent and automatic micro-analytical method for the quality identification and genetic analysis on rice.[Method] The amylose content in rice was determined by national standard method and flow-analysis with UV-visible spectrophotometer and full-automatic continuous flowing analyzer resp.[Result] In the stability test on samples,the average standard deviation of dispersed liquids of the 5 rice samples was 0.000 5 and their average variation coefficient was 0.34%.When the analytical velocity was 25-50 samples/h,the variation coefficient among repeats was relatively stable.When the analytical velocity was 25-45 samples/h,both peak shape and separation effect were better.In precision test,the average standard deviation of the absorbance of sample solutions was 0.000 4 and their average variation coefficient was 0.20%.The average standard deviation of determination results of the 2 methods was 0.107 7 and their average variation coefficient was 0.61%.The average standard deviation and average variation coefficient of flow-analysis were 0.082 1 and 0.49% resp.and that of national standard method were 0.111 9 and 0.64% resp.[Conclusion] This method was relatively perfect for determining the amylose content in rice.
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