Logistic regression analysis of populations of electrophysiological models to assess proarrythmic risk

Stefano Morotti, Eleonora Grandi

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

Population-based computational approaches have been developed in recent years and helped to gain insight into arrhythmia mechanisms, and intra- and inter-patient variability (e.g., in drug responses). Here, we illustrate the use of multivariable logistic regression to analyze the factors that enhance or reduce the susceptibility to cellular arrhythmogenic events. As an example, we generate 1000 model variants by randomly modifying ionic conductances and maximal rates of ion transports in our atrial myocyte model and simulate an arrhythmia-provoking protocol that enhances early afterdepolarization (EAD) proclivity. We then treat EAD occurrence as a categorical, yes or no variable, and perform logistic regression to relate perturbations in model parameters to the presence/absence of EADs. We find that EAD formation is sensitive to the conductance of the voltage-gated Na+, the acetylcholine-sensitive and ultra-rapid K+ channels, and the Na+/Ca2+ exchange current, which matches our mechanistic understanding of the process and preliminary sensitivity analysis. The described technique: • allows investigating the factors underlying dichotomous outcomes, and is therefore a useful tool improve our understanding of arrhythmic risk;• is valid for analyzing both deterministic and stochastic models, and various phenomena (e.g., delayed afterdepolarizations and Ca2+ sparks);• is computationally more efficient than one-at-a-time parameter sensitivity analysis.

Original languageEnglish (US)
Pages (from-to)25-34
Number of pages10
JournalMethodsX
Volume4
DOIs
StatePublished - Jan 1 2017

Fingerprint

Regression analysis
Logistics
Cardiac Arrhythmias
Logistic Models
Regression Analysis
Sensitivity analysis
Ion Transport
Muscle Cells
Population
Acetylcholine
Stochastic models
Electric sparks
Pharmaceutical Preparations
Ions
Electric potential

Keywords

  • Cardiac arrhythmia
  • Logistic regression
  • Mathematical modeling
  • Sensitivity analysis

ASJC Scopus subject areas

  • Clinical Biochemistry
  • Medical Laboratory Technology

Cite this

Logistic regression analysis of populations of electrophysiological models to assess proarrythmic risk. / Morotti, Stefano; Grandi, Eleonora.

In: MethodsX, Vol. 4, 01.01.2017, p. 25-34.

Research output: Contribution to journalArticle

@article{312b8f62bf5b4c928fa78488eb329be8,
title = "Logistic regression analysis of populations of electrophysiological models to assess proarrythmic risk",
abstract = "Population-based computational approaches have been developed in recent years and helped to gain insight into arrhythmia mechanisms, and intra- and inter-patient variability (e.g., in drug responses). Here, we illustrate the use of multivariable logistic regression to analyze the factors that enhance or reduce the susceptibility to cellular arrhythmogenic events. As an example, we generate 1000 model variants by randomly modifying ionic conductances and maximal rates of ion transports in our atrial myocyte model and simulate an arrhythmia-provoking protocol that enhances early afterdepolarization (EAD) proclivity. We then treat EAD occurrence as a categorical, yes or no variable, and perform logistic regression to relate perturbations in model parameters to the presence/absence of EADs. We find that EAD formation is sensitive to the conductance of the voltage-gated Na+, the acetylcholine-sensitive and ultra-rapid K+ channels, and the Na+/Ca2+ exchange current, which matches our mechanistic understanding of the process and preliminary sensitivity analysis. The described technique: • allows investigating the factors underlying dichotomous outcomes, and is therefore a useful tool improve our understanding of arrhythmic risk;• is valid for analyzing both deterministic and stochastic models, and various phenomena (e.g., delayed afterdepolarizations and Ca2+ sparks);• is computationally more efficient than one-at-a-time parameter sensitivity analysis.",
keywords = "Cardiac arrhythmia, Logistic regression, Mathematical modeling, Sensitivity analysis",
author = "Stefano Morotti and Eleonora Grandi",
year = "2017",
month = "1",
day = "1",
doi = "10.1016/j.mex.2016.12.002",
language = "English (US)",
volume = "4",
pages = "25--34",
journal = "MethodsX",
issn = "2215-0161",
publisher = "Elsevier BV",

}

TY - JOUR

T1 - Logistic regression analysis of populations of electrophysiological models to assess proarrythmic risk

AU - Morotti, Stefano

AU - Grandi, Eleonora

PY - 2017/1/1

Y1 - 2017/1/1

N2 - Population-based computational approaches have been developed in recent years and helped to gain insight into arrhythmia mechanisms, and intra- and inter-patient variability (e.g., in drug responses). Here, we illustrate the use of multivariable logistic regression to analyze the factors that enhance or reduce the susceptibility to cellular arrhythmogenic events. As an example, we generate 1000 model variants by randomly modifying ionic conductances and maximal rates of ion transports in our atrial myocyte model and simulate an arrhythmia-provoking protocol that enhances early afterdepolarization (EAD) proclivity. We then treat EAD occurrence as a categorical, yes or no variable, and perform logistic regression to relate perturbations in model parameters to the presence/absence of EADs. We find that EAD formation is sensitive to the conductance of the voltage-gated Na+, the acetylcholine-sensitive and ultra-rapid K+ channels, and the Na+/Ca2+ exchange current, which matches our mechanistic understanding of the process and preliminary sensitivity analysis. The described technique: • allows investigating the factors underlying dichotomous outcomes, and is therefore a useful tool improve our understanding of arrhythmic risk;• is valid for analyzing both deterministic and stochastic models, and various phenomena (e.g., delayed afterdepolarizations and Ca2+ sparks);• is computationally more efficient than one-at-a-time parameter sensitivity analysis.

AB - Population-based computational approaches have been developed in recent years and helped to gain insight into arrhythmia mechanisms, and intra- and inter-patient variability (e.g., in drug responses). Here, we illustrate the use of multivariable logistic regression to analyze the factors that enhance or reduce the susceptibility to cellular arrhythmogenic events. As an example, we generate 1000 model variants by randomly modifying ionic conductances and maximal rates of ion transports in our atrial myocyte model and simulate an arrhythmia-provoking protocol that enhances early afterdepolarization (EAD) proclivity. We then treat EAD occurrence as a categorical, yes or no variable, and perform logistic regression to relate perturbations in model parameters to the presence/absence of EADs. We find that EAD formation is sensitive to the conductance of the voltage-gated Na+, the acetylcholine-sensitive and ultra-rapid K+ channels, and the Na+/Ca2+ exchange current, which matches our mechanistic understanding of the process and preliminary sensitivity analysis. The described technique: • allows investigating the factors underlying dichotomous outcomes, and is therefore a useful tool improve our understanding of arrhythmic risk;• is valid for analyzing both deterministic and stochastic models, and various phenomena (e.g., delayed afterdepolarizations and Ca2+ sparks);• is computationally more efficient than one-at-a-time parameter sensitivity analysis.

KW - Cardiac arrhythmia

KW - Logistic regression

KW - Mathematical modeling

KW - Sensitivity analysis

UR - http://www.scopus.com/inward/record.url?scp=85008698195&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85008698195&partnerID=8YFLogxK

U2 - 10.1016/j.mex.2016.12.002

DO - 10.1016/j.mex.2016.12.002

M3 - Article

VL - 4

SP - 25

EP - 34

JO - MethodsX

JF - MethodsX

SN - 2215-0161

ER -