Literature DB >> 31513787

Data mining of reference intervals for coagulation screening tests in adult patients.

Jakob Zierk1, Thomas Ganslandt2, Manfred Rauh3, Markus Metzler3, Erwin Strasser4.   

Abstract

BACKGROUND: Appropriate reference intervals are essential when evaluating laboratory test results. However, establishment of reference intervals is challenging, especially for coagulation screening tests, and uncertainty exists regarding age- and sex-dependency of test results. Data mining of laboratory information systems is an emerging approach to reference interval determination, and we evaluated its applicability to coagulation tests.
METHODS: We analyzed measurements of activated partial thromboplastin time (aPTT), prothrombin time (PT), international normalized ratio (INR), thrombin time (TT), and fibrinogen performed during clinical care in the University Hospital Erlangen, Germany (1,778,738 samples from 116,754 adult patients, 45,577-509,859 samples per analyte). We identified the proportion of samples from healthy individuals using an established statistical approach (Reference Limit Estimator), in which the distribution of physiological test results is approximated using a parametrical function, and used for the calculation of reference intervals.
RESULTS: We established age- and sex specific reference intervals for aPTT, PT, INR, TT, and fibrinogen, and created batch- and reagent-specific aPTT-reference intervals. Additionally, we evaluated the sensitivity of the established aPTT reference intervals for the detection of factor VIII, IX, XI, XII deficiencies.
CONCLUSION: Data mining of laboratory test results allows the creation of age- and sex-reference intervals for coagulation tests that are specific to the examined population, analytical framework, and reagent. This approach can complement conventional methods when establishing reference intervals and improve clinical decision-making based on coagulation tests. The reference intervals established in this study show only minor variation with sex and age, supporting the practice of providing a common reference interval for adult women and men.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Coagulation screening tests; Data mining; Reference intervals

Mesh:

Year:  2019        PMID: 31513787     DOI: 10.1016/j.cca.2019.09.006

Source DB:  PubMed          Journal:  Clin Chim Acta        ISSN: 0009-8981            Impact factor:   3.786


  7 in total

1.  The Reference Intervals of Whole Blood Copper, Zinc, Calcium, Magnesium, and Iron in Infants Under 1 Year Old.

Authors:  Feizai Ha; Yonghua Wu; Haining Wang; Tiancheng Wang
Journal:  Biol Trace Elem Res       Date:  2021-02-24       Impact factor: 3.738

2.  Hypercoagulation detected by routine and global laboratory hemostasis assays in patients with infective endocarditis.

Authors:  Ekaterina M Koltsova; Maria A Sorokina; Alexandra S Pisaryuk; Nikita M Povalyaev; Anastasia A Ignatova; Dmitry M Polokhov; Elizaveta O Kotova; Alexander V Balatskiy; Fazoil I Ataullakhanov; Mikhail A Panteleev; Zhanna D Kobalava; Anna N Balandina
Journal:  PLoS One       Date:  2021-12-15       Impact factor: 3.240

Review 3.  Fibrinogen, Fibrinogen-like 1 and Fibrinogen-like 2 Proteins, and Their Effects.

Authors:  Nurul H Sulimai; Jason Brown; David Lominadze
Journal:  Biomedicines       Date:  2022-07-15

Review 4.  Hereditary Hypofibrinogenemia with Hepatic Storage.

Authors:  Rosanna Asselta; Elvezia Maria Paraboschi; Stefano Duga
Journal:  Int J Mol Sci       Date:  2020-10-22       Impact factor: 5.923

5.  Reference intervals for coagulation tests in adults with different ABO blood types.

Authors:  Zeliang Chen; Xiaoqing Dai; Jing Cao; Xuerui Tan; Shuying Chen; Min Yu
Journal:  J Clin Lab Anal       Date:  2022-02-04       Impact factor: 2.352

6.  Machine learning models predict coagulopathy in spontaneous intracerebral hemorrhage patients in ER.

Authors:  Fengping Zhu; Zhiguang Pan; Ying Tang; Pengfei Fu; Sijie Cheng; Wenzhong Hou; Qi Zhang; Hong Huang; Yirui Sun
Journal:  CNS Neurosci Ther       Date:  2020-11-28       Impact factor: 7.035

7.  A bibliometric analysis and visualization of medical data mining research.

Authors:  Yuanzhang Hu; Zeyun Yu; Xiaoen Cheng; Yue Luo; Chuanbiao Wen
Journal:  Medicine (Baltimore)       Date:  2020-05-29       Impact factor: 1.817

  7 in total

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