Skip to main content

Advertisement

SpringerLink
Log in

Use of genetic programming, logistic regression, and artificial neural nets to predict readmission after coronary artery bypass surgery

  • Original Research
  • Published:
Journal of Clinical Monitoring and Computing Aims and scope Submit manuscript

Abstract

As many as 14 % of patients undergoing coronary artery bypass surgery are readmitted within 30 days. Readmission is usually the result of morbidity and may lead to death. The purpose of this study is to develop and compare statistical and genetic programming models to predict readmission. Patients were divided into separate Construction and Validation populations. Using 88 variables, logistic regression, genetic programs, and artificial neural nets were used to develop predictive models. Models were first constructed and tested on the Construction populations, then validated on the Validation population. Areas under the receiver operator characteristic curves (AU ROC) were used to compare the models. Two hundred and two patients (7.6 %) in the 2,644 patient Construction group and 216 (8.0 %) of the 2,711 patient Validation group were re-admitted within 30 days of CABG surgery. Logistic regression predicted readmission with AU ROC = .675 ± .021 in the Construction group. Genetic programs significantly improved the accuracy, AU ROC = .767 ± .001, p < .001). Artificial neural nets were less accurate with AU ROC = 0.597 ± .001 in the Construction group. Predictive accuracy of all three techniques fell in the Validation group. However, the accuracy of genetic programming (AU ROC = .654 ± .001) was still trivially but statistically non-significantly better than that of the logistic regression (AU ROC = .644 ± .020, p = .61). Genetic programming and logistic regression provide alternative methods to predict readmission that are similarly accurate.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Stewart RD, Campos CT, Jennings B, Lollis SS, Levitsky S, Lahey SJ. Predictors of 30-day hospital readmission after coronary artery bypass. Ann Thorac Surg. 2000;70:169–74.

    Article  PubMed  CAS  Google Scholar 

  2. Lahey SJ, Campos CT, Jennings B, Pawlow P, Stokes T, Levitsky S. Hospital readmission after cardiac surgery. Does “fast track” cardiac surgery result in cost saving or cost shifting? Circulation. 1998;98:II35–40.

    PubMed  CAS  Google Scholar 

  3. Hannan EL, Racz MJ, Walford G, Ryan TJ, Isom OW, Bennett E, Jones RH. Predictors of readmission for complications of coronary artery bypass graft surgery. JAMA. 2003;290:773–80.

    Article  PubMed  Google Scholar 

  4. Steuer J, Blomqvist P, Granath F, Rydh B, Ekbom A, de Faire U, Ståhle E. Hospital readmission after coronary artery bypass grafting: are women doing worse? Ann Thorac Surg. 2002;73:1380–6.

    Article  PubMed  Google Scholar 

  5. Vaccarino V, Lin ZQ, Kasl SV, Mattera JA, Roumanis SA, Abramson JL, Krumholz HM. Gender differences in recovery after coronary artery bypass surgery. J Am Coll Cardiol. 2003;41:307–14.

    Article  PubMed  Google Scholar 

  6. Zitser-Gurevich Y, Simchen E, Galai N, Braun D. Prediction of readmissions after CABG using detailed follow-up data: the Israeli CABG study (ISCAB). Med Care. 1999;37:625–36.

    Article  PubMed  CAS  Google Scholar 

  7. Koza JR. Genetic programming. Cambridge: MIT press; 1992.

    Google Scholar 

  8. Jacob C. Illustrating evolutionary computation with Mathematica. San Francisco: Academic Press; 2001.

    Google Scholar 

  9. Biesheuvel CJ, Siccama I, Grobbee DE, Moons KG. Genetic programming outperformed multivariable logistic regression in diagnosing pulmonary embolism. J Clin Epidemiol. 2004;7:551–60.

    Article  Google Scholar 

  10. Engoren M, Plewa M, O’Hara D, Kline JA. Evaluation of capnography using a genetic algorithm to predict PaCO2. Chest. 2005;127:579–84.

    Article  PubMed  Google Scholar 

  11. Engoren M, Kline JA. Use of genetic programming to diagnose venous thromboembolism in the emergency department. Genet Program Evolv Mach. 2008;9:39–51.

    Article  Google Scholar 

  12. Kotini A, Anninos P, Anastasiadis AN, Tamiolakis D. A comparative study of a theoretical neural net model with MEG data from epileptic patients and normal individuals. Theor Biol Med Model. 2005;2:37.

    Article  PubMed  CAS  Google Scholar 

  13. Veltri RW, Chaudhari M, Miller MC, Poole EC, O’Dowd GJ, Partin AW. Comparison of logistic regression and neural net modeling for prediction of prostate cancer pathologic stage. Clin Chem. 2002;48:1828–34.

    PubMed  CAS  Google Scholar 

  14. Liu Y, Khoshgoftaar TM. 2004. Reducing overfitting in genetic programming models for software quality classification. In: Proceedings of the eighth IEEE symposium on international high assurance systems engineering 54–65.

  15. Scott MJJ, Niranjan M, Prager RW. Realisable classifiers: improving operating performance on variable cost problems. In: Lewis PH, Nixon MS, editors. Ninth British machine vision conference. 1998. p. 304–15.

  16. Efron B, Tibshirani RJ. An introduction to the bootstrap. New York: Chapman and Hall; 1993.

    Google Scholar 

  17. Hanley JA, McNeil BJ. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology. 1983;148:839–43.

    PubMed  CAS  Google Scholar 

  18. Hannan EL, Zhong Y, Lahey SJ, Culliford AT, Gold JP, Smith CR, Higgins RSD, Jordan D, Wechsler A. 30-Day readmissions after coronary artery bypass graft surgery in New York State. JACC Cardiovasc Interv. 2011;4:569–76.

    Article  PubMed  Google Scholar 

  19. Murphy BM, Elliott PC, Le Grande MR, Higgins RO, Ernest CS, Goble AJ, Tatoulis J, Worcester MU. Living alone predicts 30-day hospital readmission after coronary artery bypass graft surgery. Eur J Cardiovasc Prev Rehabil. 2008;15:210–5.

    Article  PubMed  Google Scholar 

  20. Tully PJ, Baker RA, Turnbull D, Winefield H. The role of depression and anxiety symptoms in hospital readmissions after cardiac surgery. J Behav Med. 2008;31:281–90.

    Article  PubMed  Google Scholar 

  21. Oxlad M, Stubberfield J, Stuklis R, Edwards J, Wade TD. Psychological risk factors for cardiac-related hospital readmission within 6 months of coronary artery bypass graft surgery. J Psychosom Res. 2006;61:775–81.

    Article  PubMed  Google Scholar 

  22. Scheier MF, Matthews KA, Owens JF, Schulz R, Bridges MW, Magovern GJ, Carver CS. Optimism and rehospitalization after coronary artery bypass graft surgery. Arch Intern Med. 1999;159:829–36.

    Article  PubMed  CAS  Google Scholar 

  23. McNally P, Loftus BG. Knowledge of statistical methods and their implications for clinical practice: a survey of paediatricians. Ir Med J. 2005;98:240–2.

    PubMed  CAS  Google Scholar 

  24. Lee JL, McNeer JF, Starmer CF, Harris PJ, Rosarti RA. Clinical judgment and statistics. Lessons from a simulated randomized trial in coronary artery disease. Circulation. 1980;61:508–15.

    Article  PubMed  CAS  Google Scholar 

  25. Afessa B, Keegan MT, Gajic O, Hubmayr RD, Peters SG. The influence of missing components of the Acute Physiology Score of APACHE III on the measurement of ICU performance. Intensive Care Med. 2005;31:1537–43.

    Article  PubMed  Google Scholar 

  26. Perez A, Dennis RJ, Gil JF, Rondom MA, Lopez A. Use of the mean, hot deck and multiple imputation techniques to predict outcome in intensive care patients in Colombia. Stat Med. 2002;21:3885–96.

    Article  PubMed  Google Scholar 

Download references

Conflict of interest

No authors have any conflicts of interest or any commercial interests in any product mentioned in the manuscript.

Author information

Authors and Affiliations

  1. Department of Anesthesiology, University of Michigan, 4172 Cardiovascular Center, 1500 E. Medical Center Drive, Ann Arbor, MI, 48109-5861, USA

    Milo Engoren

  2. Outcomes Research Unit, American University of Beirut, Beirut, Labanon

    Robert H. Habib

  3. Department of Anesthesiology, Mercy St. Vincent Medical Center, Toledo, OH, USA

    John J. Dooner

  4. Department of Cardiothoracic and Vascular Surgery, University of Toledo, Toledo, OH, USA

    Thomas A. Schwann

Authors
  1. Milo Engoren
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Robert H. Habib
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. John J. Dooner
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Thomas A. Schwann
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Milo Engoren.

Additional information

This work was conducted at Mercy St. Vincent Medical Center, 2213 Cherry Street, Toledo, OH 43608.

Appendix

Appendix

Sex

Race

Ethnicity

Age

Insurance type

Weight

Height

Body mass index

Body surface area

Smoking history

Family history of coronary artery disease

Comorbities

 Diabetes mellitus

 Hyperlipidemia

 Renal Failure

 Hypertension

 Stroke

 Chronic obstructive pulmonary disease

 Peripheral vascular disease

 Cerebrovascular disease

 Myocardial infarction

 Timing of myocardial infarction

 Stable angina

 Unstable angina

 Arrythmia

 Immune suppression

 New York Heart Association class

 Resuscitation

Prior coronary intervention

Prior cardiac surgery

Prior coronary artery bypass surgery

Prior valve surgery

Prior other cardiac surgery

Medicine use

 Digitalis

 Beta blocker

 Intravenous nitrates

 Anticoagulation

 Diuretics

 Inotropes

 Steroids

 Aspirin

 Insulin

 Oral anti-diabetic

Number of diseased vessels

Number of distal arterial grafts

Number of distal venous grafts

Internal mammary artery use

Total number of grafts

Left main disease

Ejection fraction

Status (elective, urgent, emergent, salvage)

Use of cardiopulmonary bypass

Perfusion time

Crossclamp time

Cardioplegia

Intraaortic balloon pump use

Timing of Intraaortic balloon pump use

Blood products

Number of complications

Type of complications

 Reoperation for bleeding

 Reoperation for valve surgery

 Reoperation for graft

 Reoperation for other cardiac surgery

 Reoperation for non-cardiac surgery

 Myocardial infarction

 Deep sternal wound infection

 Thoracotomy infection

 Infection of leg incisions

 Sepsis

 Urinary tract infection

 Permanent stroke

 Temporary stroke

 Coma

 Prolonged mechanical ventilation

 Pulmonary embolism

 Pneumonia

 Renal failure

 Dialysis

 Limb ischemia

 Dissection of iliac or femoral arteries

 Heart block

 Arrest

 Coagulopathy

 Cardiac tamponade

 Gastrointestinal

 Multisystem organ dysfunction

 Atrial fibrillation

 Aortic dissection

 Other

Postoperative length of stay

Variables included in the analyses. Definitions of the variables were those in use at the time of the individual patient’s surgery and may differ slightly from the current definitions, which are provided at http://www.sts.org/sites/default/files/documents/Training%20Manual%20Update%208%2012.pdf.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Engoren, M., Habib, R.H., Dooner, J.J. et al. Use of genetic programming, logistic regression, and artificial neural nets to predict readmission after coronary artery bypass surgery. J Clin Monit Comput 27, 455–464 (2013). https://doi.org/10.1007/s10877-013-9444-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10877-013-9444-7

Keywords

Search

Navigation