Advertisement

Predictive Models for Mutation Carriers in Brugada Syndrome Screening

  • Carla Henriques
  • Ana Matos
  • Luís Ferreira Santos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8581)

Abstract

In this study we consider logistic regression models to predict mutation carriers in family members affected by Brugada Syndrome. This Syndrome is an inherited cardiopathy that predisposes individuals without structural heart disease to sudden cardiac death. We focused on five electrocardiographic markers, which have been explored as good discriminators between carriers and non-carriers of the genetic mutation responsible for this disease. Logistic regression models which combine some of the five markers were investigated. Our objective was to assess the predictive ability of these models through internal validation procedures. We also applied shrinkage methods to improve calibration of the models and future predictive accuracy. Validation of these models, using bootstrapping, point to some superiority of two models, for which fairly good measures of predictive accuracy were obtained. This study provides confidence in these models, which offer greater sensitivity than the usual screening by detecting a characteristic pattern in an electrocardiogram.

Keywords

Bootstrapping Logistic Regression Ridge Regression Calibration Discrimination 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Antzelevitch, C.: Brugada Syndrome. Pacing Clin Electrophysiol 29, 1130–1159 (2006)CrossRefGoogle Scholar
  2. 2.
    Brunelli, A., Rocco, G.: Internal Validation of Risk Models in Lung Resection Surgery: Bootstrap versus Training-and-Test Sampling. The Journal of Thoracic and Cardiovascular Surgery 131, 1243–1247 (2006)CrossRefGoogle Scholar
  3. 3.
    Copas, J.B.: Regression, Prediction and Shrinkage. Journal of the Royal Statistical Society, Series B, Methodological 45, 311–354 (1983)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Cox, D.R.: Two further applications of a model for binary regression. Biometrika 45, 562–565 (1958)CrossRefzbMATHGoogle Scholar
  5. 5.
    Efron, B., Tibshirani, R.J.: An Introduction to the Bootstrap, Monographs on Statistics and Applied Probability, vol. 57. Chapman & Hall, New York (1993)CrossRefGoogle Scholar
  6. 6.
    Gude, J.A., Mitchell, M.S., Ausband, D.E., Sime, C.A., Bangs, E.E.: Internal Validation of Predictive Logistic Regression Models for Decision-Making in Wildlife Management. Wildlife Biology 15, 352–369 (2009)CrossRefGoogle Scholar
  7. 7.
    Harrel, F.E., Lee, K.L., Califf, R.M., Pryor, D.B., Rosati, R.A.: Regression modelling strategies for improved prognostic prediction. Statistics in Medicine 3, 143–152 (1984)CrossRefGoogle Scholar
  8. 8.
    Harrell, F., Lee, K.L., Mark, D.B.: Tutorial in Biostatistics; Multivariable Prognostic Models: Issues in Developing Models, Evaluating Assumptions and Adequacy, and Measuring and Reducing errors. Statistics in Medicine 15, 361–387 (1996)CrossRefGoogle Scholar
  9. 9.
    Harrel Jr, F.E.: Package ‘rms’ - R Package Version 3.6-3 (2013), http://biostat.mc.vanderbilt.edu/rms
  10. 10.
    Henriques, C., Matos, A., Santos, L.F.: Study of the Electrocardiographic Fluctuations on Brugada Syndrome Screening. In: Lita da Silva, J., Caeiro, F., Natário, I., Braumann, C.A. (eds.) Advances in Regression, Survival Analysis, Extreme Values, Markov Processes and Other Statistical Applications. Studies in Theoretical and Applied Statistics, pp. 231–238. Springer, Berlin (2013)CrossRefGoogle Scholar
  11. 11.
    Henriques, C., Matos, A., Santos, L.F.: Brugada Syndrome Diagnosis - Three Approaches to Combining Diagnostic Markers. In: Pacheco, A., Santos, R., Oliveira, M.R., Paulino, C.D. (eds.) New Advances in Statistical Modeling and Applications. Studies in Theoretical and Applied Statistics. Springer (to appear)Google Scholar
  12. 12.
    Hosmer, D.W., Lemeshow, S.: Applied Logistic Regression, 2nd edn. John Wiley & Sons, New York (2000)CrossRefzbMATHGoogle Scholar
  13. 13.
    Knudby, A., Brenning, A., Le Drew, E.: New Approaches to Modelling Fish-Habitat Relationships. Ecological Modelling 221, 503–511 (2010)CrossRefGoogle Scholar
  14. 14.
    Le Cessie, S., Van Houwelingen, J.: Ridge Estimators in Logistic Regression. Applied Statistics 41, 191–201 (1992)CrossRefzbMATHGoogle Scholar
  15. 15.
    Nagelkerke, N.J.D.: A Note on the General Definition of the Coefficient of Determination. Biometrika 78, 691–692 (1991)CrossRefMathSciNetzbMATHGoogle Scholar
  16. 16.
    Peduzzi, P., Concato, J., Kemper, E., Holford, T.R., Feinstein, A.R.: A Simulation Study of the Number of Events per Variable in Logistic Regression Analysis. Journal of Clinical Epidemiology 49, 1372–1379 (1996)CrossRefGoogle Scholar
  17. 17.
    Sánchez-Nieto, B., Goset, K.C., Caviedes, I., Delgado, I.O., Córdova, A.: Predictive Models for Pulmonary Function Changes After Radiotherapy for Breast Cancer and Lymphoma. International Journal of Radiation Oncology*Biology*Physics 82, 257–264 (2012)CrossRefGoogle Scholar
  18. 18.
    Santos, L.F., Rodrigues, B., Moreira, D., Correia, E., Nunes, L., Costa, A., Elvas, L., Pereira, T., Machado, J.C., Castedo, S., Henriques, C., Matos, A., Santos, O.: Criteria to Predict Carriers of a Novel SCN5A Mutation in a Large Portuguese Family Affected by the Brugada Syndrome. Europace 14(6), 882–888 (2012)CrossRefGoogle Scholar
  19. 19.
    Santos, L.F., Correia, E., Rodrigues, B., Nunes, L., Costa, A., Carvalho, J.L., Elvas, L., Henriques, C., Matos, A., Santos, J.O.: Spontaneous Fluctuations Between Diagnostic and Nondiagnostic ECGs in Brugada Syndrome Screening: Portuguese Family with Brugada Syndrome. Ann. Noninvasive Electrocardiol 15, 337–343 (2010)CrossRefGoogle Scholar
  20. 20.
    Santos, L.F., Pereira, T., Rodrigues, B., Correia, E., Moreira, D., Nunes, L., Costa, A., Elvas, L., Machado, J.C., Castedo, S., Henriques, C., Matos, A., Santos, O.: Critérios de Diagnóstico da Síndrome Brugada. Podemos Melhorar? Portuguese Journal of Cardiology 31, 355–362 (2012)CrossRefGoogle Scholar
  21. 21.
    Steyerberg, E.W., Eijkemans, M.J.C., Harrel Jr, F.E., Habbema, J.D.F.: Prognostic Modelling with Logistic Regression Analysis: A Comparison of Selection and Estimation Methods in Small Data Sets. Statistics in Medicine 19, 1059–1079 (2000)CrossRefGoogle Scholar
  22. 22.
    Steyerberg, E.W., Eijkemans, M.J.C., Habbema, J.D.F.: Application of Shrinkage Techniques in Logistic Regression Analysis: A Case Study. Statistica Neerlandica 55, 76–88 (2001)CrossRefMathSciNetzbMATHGoogle Scholar
  23. 23.
    Steyerbreg, E.W., Harrel, F.E., Borsboom, G.J.J.M., Eijkemans, M.J.C., Vergouwe, Y., Habbema, J.D.F.: Internal Validation of Predictive Models: Efficiency of some Procedures for Logistic Regression Analysis. Journal of Clinical Epidemiology 54, 774–781 (2001)CrossRefGoogle Scholar
  24. 24.
    Tibshirani, R.: Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society, Series B 58, 267–288 (1996)MathSciNetzbMATHGoogle Scholar
  25. 25.
    Vágó, E., Kemény, S.: Logistic Ridge Regression for Clinical Data Analysis (a Case Study). Applied Ecology and Environmental Research 4(2), 171–179 (2006)Google Scholar
  26. 26.
    Van Houwelingen, J.C., Le Cessie, S.: Predictive Value of Statistical Models. Statistics in Medicine 9, 1303–1325 (1990)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Carla Henriques
    • 1
    • 2
  • Ana Matos
    • 1
    • 3
  • Luís Ferreira Santos
    • 4
  1. 1.School of Technology and ManagementPolytechnic Institute of ViseuPortugal
  2. 2.Centre for Mathematics of the University of Coimbra (CMUC)Portugal
  3. 3.Centre for the Study of Education, Technologies and Health (CSETH)Portugal
  4. 4.Department of CardiologyTondela-Viseu Hospital CenterPortugal

Personalised recommendations