Robust K-PD model for activated clotting time prediction and UFH dose individualisation during cardiopulmonary bypass.
Journal
Computer methods and programs in biomedicine
ISSN: 1872-7565
Titre abrégé: Comput Methods Programs Biomed
Pays: Ireland
ID NLM: 8506513
Informations de publication
Date de publication:
Feb 2022
Feb 2022
Historique:
received:
15
06
2021
revised:
17
11
2021
accepted:
24
11
2021
pubmed:
10
12
2021
medline:
28
1
2022
entrez:
9
12
2021
Statut:
ppublish
Résumé
Activated clotting time (ACT) is a point-of-care test used to monitor the effect of unfractionated heparin (UFH) during cardiopulmonary bypass (CPB). This test sometimes returns aberrant values, which can lead to the administration of an inappropriate dosing regimen. The development of a population-robust K-PD model of UFH could allow the individualisation and automation of UFH therapy during CPB. We conducted a prospective observational study to collect ACT measurements from patients undergoing surgery using CPB. The ACT data were split into a development and validation cohort. The development cohort was used to estimate a standard and robust population K-PD model characterised by a residual error following a normal distribution and student's t-distribution. The ACT prediction performance using Bayesian estimates of individual K-PD parameters was evaluated by comparing predicted versus observed ACTs. Using estimates of the robust K-PD model, a Bayesian individualisation strategy to automate UFH administration was proposed and evaluated using Monte Carlo simulations. A total of 295 patients were included in the study, and 1561 ACTs were collected. In patients without outlier values, Bayesian estimates (based on four ACT measurements) from both standard and robust K-PD models had similar performances, with a median prediction bias close to 0 s. In patients with outlier measurements, the use of the robust K-PD model greatly improved the prediction bias and root-mean-square error (RMSE), with a mean prediction bias of 3.25 s, IQR = [-19.9; 46.03] versus -86 s IQR = [-135.7; -63.8] for the standard model. Monte Carlo simulations showed that the robust Bayesian individualisation strategy allowed the ACT to be maintained above the target using only two to three ACT measurements. The use of a robust K-PD model reduced prediction bias and RMSE in patients with outlier ACT measurements. The Bayesian individualisation strategy using robust estimates of individual parameters may help automate UFH dosing regimens. Proper clinical validation is warranted before its use in daily clinical practice.
Sections du résumé
BACKGROUND AND OBJECTIVE
OBJECTIVE
Activated clotting time (ACT) is a point-of-care test used to monitor the effect of unfractionated heparin (UFH) during cardiopulmonary bypass (CPB). This test sometimes returns aberrant values, which can lead to the administration of an inappropriate dosing regimen. The development of a population-robust K-PD model of UFH could allow the individualisation and automation of UFH therapy during CPB.
METHODS
METHODS
We conducted a prospective observational study to collect ACT measurements from patients undergoing surgery using CPB. The ACT data were split into a development and validation cohort. The development cohort was used to estimate a standard and robust population K-PD model characterised by a residual error following a normal distribution and student's t-distribution. The ACT prediction performance using Bayesian estimates of individual K-PD parameters was evaluated by comparing predicted versus observed ACTs. Using estimates of the robust K-PD model, a Bayesian individualisation strategy to automate UFH administration was proposed and evaluated using Monte Carlo simulations.
RESULTS
RESULTS
A total of 295 patients were included in the study, and 1561 ACTs were collected. In patients without outlier values, Bayesian estimates (based on four ACT measurements) from both standard and robust K-PD models had similar performances, with a median prediction bias close to 0 s. In patients with outlier measurements, the use of the robust K-PD model greatly improved the prediction bias and root-mean-square error (RMSE), with a mean prediction bias of 3.25 s, IQR = [-19.9; 46.03] versus -86 s IQR = [-135.7; -63.8] for the standard model. Monte Carlo simulations showed that the robust Bayesian individualisation strategy allowed the ACT to be maintained above the target using only two to three ACT measurements.
CONCLUSIONS
CONCLUSIONS
The use of a robust K-PD model reduced prediction bias and RMSE in patients with outlier ACT measurements. The Bayesian individualisation strategy using robust estimates of individual parameters may help automate UFH dosing regimens. Proper clinical validation is warranted before its use in daily clinical practice.
Identifiants
pubmed: 34883383
pii: S0169-2607(21)00627-1
doi: 10.1016/j.cmpb.2021.106553
pii:
doi:
Substances chimiques
Anticoagulants
0
Heparin
9005-49-6
Types de publication
Journal Article
Observational Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
106553Informations de copyright
Copyright © 2021 Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest There are no competing interests to declare.