Exploration of the Factors Affecting the Quality Control of Blood Samples Collected before Clinical Routine Blood Test and Analysis and Countermeasures

2021 ◽  
2000 ◽  
Vol 28 (5) ◽  
pp. 562-565 ◽  
Author(s):  
A. Flabouris ◽  
G. Bishop ◽  
L. Williams ◽  
M. Cunningham

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Anthony W. H. Chan ◽  
Stephen L. Chan ◽  
Frankie K. F. Mo ◽  
Grace L. H. Wong ◽  
Vincent W. S. Wong ◽  
...  

Prognosis of patients with hepatocellular carcinoma (HCC) depends on both tumour extent and hepatic function reserve. Liver function test (LFT) is a basic routine blood test to evaluate hepatic function. We first analysed LFT components and their associated scores in a training cohort of 217 patients who underwent curative surgery to identify LFT parameters with high performance (discriminatory capacity, homogeneity, and monotonicity of gradient). We derived a novel index, albumin-to-alkaline phosphatase ratio (AAPR), which had the highest c-index (0.646) andχ2(24.774) among other liver biochemical parameters. The AAPR was an independent prognostic factor for overall and disease-free survival. The adjusted hazard ratio of death and tumour relapse was 2.36 (P=0.002) and 1.85 (P=0.010), respectively. The independent prognostic significance of AAPR on top of 5 commonly used and well established staging systems was further confirmed in 2 independent cohorts of patients receiving surgical resection (n=256) and palliative therapy (n=425). In summary, the AAPR is a novel index readily derived from a simple low-cost routine blood test and is an independent prognostic indicator for patients with HCC regardless of treatment options.


2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Simon Podnar ◽  
Matjaž Kukar ◽  
Gregor Gunčar ◽  
Mateja Notar ◽  
Nina Gošnjak ◽  
...  

Abstract Routine blood test results are assumed to contain much more information than is usually recognised even by the most experienced clinicians. Using routine blood tests from 15,176 neurological patients we built a machine learning predictive model for the diagnosis of brain tumours. We validated the model by retrospective analysis of 68 consecutive brain tumour and 215 control patients presenting to the neurological emergency service. Only patients with head imaging and routine blood test data were included in the validation sample. The sensitivity and specificity of the adapted tumour model in the validation group were 96% and 74%, respectively. Our data demonstrate the feasibility of brain tumour diagnosis from routine blood tests using machine learning. The reported diagnostic accuracy is comparable and possibly complementary to that of imaging studies. The presented machine learning approach opens a completely new avenue in the diagnosis of these grave neurological diseases and demonstrates the utility of valuable information obtained from routine blood tests.


Author(s):  
Alexa Nuñez ◽  
Viviana Marras ◽  
Cristina Esquinas ◽  
Matevz Harlander ◽  
Matjaz Turel ◽  
...  

Author(s):  
Taeko Miyagi ◽  
Satoshi Miyata ◽  
Keita Tagami ◽  
Yusuke Hiratsuka ◽  
Mamiko Sato ◽  
...  

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