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Updated: Jun 17, 2026

Microfluidic Flow Chambers Using Reconstituted Blood to Model Hemostasis and Platelet Transfusion In Vitro
Published on: March 19, 2016
Consensus instability equations for routine coagulation tests
Rubén Gómez Rioja1, Andrea Caballero Garralda2, Immaculada Comas Reixach2
1Hematology Department, La Paz-Cantoblanco-Carlos III Hospital, Madrid, Spain.
Objectives:
The stability of plasma samples for basic coagulation tests, prothrombin time (PT) and activated partial thromboplastin time (aPTT), has been widely studied. Recently, the Clinical and Laboratory Standards Institute (CLSI) updated its recommendations, extending the acceptable time frame for aPTT. These guidelines are based on experimental studies, which define limits according to different maximum permissible error (MPE) criteria. This study compiles raw data from 43 studies published over the last 30 years to develop a consensus instability equation that describes degradation independently of specific study parameters.
Methods:
A critical literature review was performed by collecting studies that included experimental stability data for PT, aPTT and the main procoagulant factors. The raw data of percentage deviation (PD%), time, and seven classification variables related to sample collection and handling were analysed. A regression model through the origin was applied to derive global instability equations and to assess influencing variables.
Results:
In frozen samples, PT and aPTT showed similar stability, with an average prolongation of 0.8 % per month. In non-frozen samples, tube handling affected stability more than storage temperature. The consensus equation for PT showed a linear average deterioration of 2.9 % per day, but model strength was limited. For aPTT, the consensus equation fitted better to a logarithmic decay model and predicted prolongations of 6.1 and 10 % at 6 and 24 h, respectively.
Conclusions:
The consensus instability equations obtained in this review provide a robust model for assessing coagulation tests stability, aligning with expert recommendations. These equations improve the understanding of sample degradation and systematic error quantification.
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