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Unfractionated heparin inhibits thrombin-catalysed amplification reactions of coagulation more efficiently than those catalysed by factor Xa.

ABSTRACT: We have proposed previously that the steps in coagulation most sensitive to inhibition by heparin are the thrombin-dependent amplification reactions, and that prothrombinase is formed in heparinized plasma only after Factor Xa activates Factor VIII and Factor V. These propositions were based on the demonstration that both heparin and Phe-Pro-Arg-CH2Cl completely inhibited 125I-prothrombin activation for up to 60 s when contact-activated plasma (CAP) was replenished with Ca2+. Furthermore, the addition of thrombin to CAP before heparin or Phe-Pro-Arg-CH2Cl completely reversed their inhibitory effects. Additional support for the above hypotheses is provided in this study by demonstrating that, when the activity of thrombin is suppressed by heparin (indirectly) or by Phe-Pro-Arg-CH2Cl (directly), exogenous Factor Xa reverses the ability of these two agents to inhibit prothrombin activation. Prothrombin activation was initiated by adding Factor Xa (1 nM) or thrombin (1 or 10 nM) simultaneously with CaCl2 to CAP. In the absence of heparin or Phe-Pro-Arg-CH2Cl, prothrombin activation was seen 15 s later in either case. Heparin failed to delay, and Phe-Pro-Arg-CH2Cl delayed for 15 s, prothrombin activation in CAP supplemented with Factor Xa. In contrast, heparin and Phe-Pro-Arg-CH2Cl completely inhibited prothrombin activation for at least 45 s in CAP supplemented with 1 nM-thrombin. Heparin failed to delay prothrombin activation in CAP supplemented with 10 nM-thrombin, whereas Phe-Pro-Arg-CH2Cl completely inhibited prothrombin activation in this plasma for 45 s. These results suggest that in CAP: (1) Factor Xa can effectively activate Factor VIII and Factor V when the proteolytic activity of thrombin is suppressed; (2) heparin-antithrombin III is less able to inhibit Factor Xa than thrombin; (3) suppression of the thrombin-dependent amplification reactions is the primary anticoagulant effect of heparin.

SUBMITTER: Ofosu FA

PROVIDER: S-EPMC1135548 | biostudies-other | 1989 Jan

REPOSITORIES: biostudies-other

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Project description:BackgroundUnfractionated heparin (UFH) is an anticoagulant drug that is considered a high-risk medication because an excessive dose can cause bleeding, whereas an insufficient dose can lead to a recurrent embolic event. Therapeutic response to the initiation of intravenous UFH is monitored using activated partial thromboplastin time (aPTT) as a measure of blood clotting time. Clinicians iteratively adjust the dose of UFH toward a target, indication-defined therapeutic aPTT range using nomograms, but this process can be imprecise and can take ≥36 hours to achieve the target range. Thus, a more efficient approach is required.ObjectiveIn this study, we aimed to develop and validate a machine learning (ML) algorithm to predict aPTT within 12 hours after a specified bolus and maintenance dose of UFH.MethodsThis was a retrospective cohort study of 3019 patient episodes of care from January 2017 to August 2020 using data collected from electronic health records of 5 hospitals in Queensland, Australia. Data from 4 hospitals were used to build and test ensemble models using cross-validation, whereas data from the fifth hospital were used for external validation. We built 2 ML models: a regression model to predict the aPTT value after a UFH bolus dose and a multiclass model to predict the aPTT, classified as subtherapeutic (aPTT <70 seconds), therapeutic (aPTT 70-100 seconds), or supratherapeutic (aPTT >100 seconds). Modeling was performed using Driverless AI (H2O), an automated ML tool, and 17 different experiments were iteratively conducted to optimize model accuracy.ResultsIn predicting aPTT, the best performing model was an ensemble with 4x LightGBM models with a root mean square error of 31.35 (SD 1.37). In predicting the aPTT class using a repurposed data set, the best performing ensemble model achieved an accuracy of 0.599 (SD 0.0289) and an area under the receiver operating characteristic curve of 0.735. External validation yielded similar results: root mean square error of 30.52 (SD 1.29) for the aPTT prediction model, and accuracy of 0.568 (SD 0.0315) and area under the receiver operating characteristic curve of 0.724 for the aPTT multiclassification model.ConclusionsTo the best of our knowledge, this is the first ML model applied to intravenous UFH dosing that has been developed and externally validated in a multisite adult general medical and surgical inpatient setting. We present the processes of data collection, preparation, and feature engineering for replication.

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Unfractionated heparin inhibits thrombin-catalysed amplification reactions of coagulation more efficiently than those catalysed by factor Xa.

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