Prediction of Prolonged Ventilation after Coronary Artery Bypass Grafting: Data from an Artificial Neural Network

Authors

  • Eric Stephen Wise Department of Surgery, Vanderbilt University, Nashville, TN
  • David P. Stonko Vanderbilt University School of Medicine, Nashville, TN
  • Zachary A. Glaser Vanderbilt University School of Medicine, Nashville, TN
  • Kelly L. Garcia Vanderbilt University School of Medicine, Nashville, TN
  • Jennifer J. Huang Vanderbilt University School of Medicine, Nashville, TN
  • Justine S. Kim Vanderbilt University School of Medicine, Nashville, TN
  • Justiss A. Kallos Vanderbilt University School of Medicine, Nashville, TN
  • Joseph R. Starnes Vanderbilt University School of Medicine, Nashville, TN
  • Jacob W. Fleming Vanderbilt University School of Medicine, Nashville, TN
  • Kyle M. Hocking Department of Surgery, Vanderbilt University, Nashville, TN
  • Colleen M. Brophy Department of Surgery, Tennessee Valley Healthcare System, Nashville, TN
  • Susan S. Eagle Department of Anesthesiology, Vanderbilt University; Division of Cardiothoracic Anesthesiology, Nashville, TN

DOI:

https://doi.org/10.1532/hsf.1566

Abstract

Objectives: The need for mechanical ventilation 24 hours after coronary artery bypass grafting (CABG) is considered a morbidity by the Society of Thoracic Surgeons. The purpose of this investigation was twofold: to identify simple preoperative patient factors independently associated with prolonged ventilation and to optimize prediction and early identification of patients prone to prolonged ventilation using an artificial neural network (ANN).

Methods: Using the institutional Adult Cardiac Database, 738 patients who underwent CABG since 2005 were reviewed for preoperative factors independently associated with prolonged postoperative ventilation. Prediction of prolonged ventilation from the identified variables was modeled using both “traditional” multiple logistic regression and an ANN. The two models were compared using Pearson r2 and area under the curve (AUC) parameters.

Results: Of 738 included patients, 14% (104/738) required mechanical ventilation ≥ 24 hours postoperatively. Upon multivariate analysis, higher body-mass index (BMI; odds ratio [OR] 1.10 per unit, P < 0.001), lower ejection fraction (OR 0.97 per %, P = 0.01) and use of cardiopulmonary bypass (OR 2.59, P = 0.02) were independently predictive of prolonged ventilation. The Pearson r2 and AUC of the multivariate nominal logistic regression model were 0.086 and 0.698 ± 0.05, respectively; analogous statistics of the ANN model were 0.159 and 0.732 ± 0.05, respectively.

BMI, ejection fraction and cardiopulmonary bypass represent three simple factors that may predict prolonged ventilation after CABG. Early identification of these patients can be optimized using an ANN, an emerging paradigm for clinical outcomes modeling that may consider complex relationships among these variables.

Author Biography

Eric Stephen Wise, Department of Surgery, Vanderbilt University, Nashville, TN

Vanderbilt University Department of Surgery, Research Fellow

References

Blanco YFR, Candiotti K, Gologorsky A, et al. 2012. Factors Which Predict Safe Extubation in the Operating Room Following Cardiac Surgery. J Card Surg 27:275-280.

Camp SL, Stamou SC, Stiegel RM, et al. 2009. Can timing of tracheal extubation predict improved outcomes after cardiac surgery? HSR Proc Intensive Care Cardiovasc Anesth 1:39-47.

Camp SL, Stamou SC, Stiegel RM, et al. 2009. Quality improvement program increases early tracheal extubation rate and decreases pulmonary complications and resource utilization after cardiac surgery. J Card Surg 24:414-423.

Cislaghi F, Condemi AM, Corona A. 2007. Predictors of prolonged mechanical ventilation in a cohort of 3,269 CABG patients. Minerva Anestesiol 73:615-621.

Cislaghi F, Condemi AM, Corona A. 2009. Predictors of prolonged mechanical ventilation in a cohort of 5123 cardiac surgical patients. Eur J Anaesthesiol 26:396-403.

Cook NR. 2007. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 115:928-935.

Cruz-Ramirez M, Hervas-Martinez C, Fernandez JC, et al. 2013. Predicting patient survival after liver transplantation using evolutionary multi-objective artificial neural networks. Artif Intell Med 58:37-49.

Dorsa AG, Rossi AI, Thierer J, et al. 2011. Immediate extubation after off-pump coronary artery bypass graft surgery in 1,196 consecutive patients: feasibility, safety and predictors of when not to attempt it. J Cardiothorac Vasc Anesth 25:431-436.

Dumont TM, Rughani AI, Tranmer BI. 2011. Prediction of symptomatic cerebral vasospasm after aneurysmal subarachnoid hemorrhage with an artificial neural network: feasibility and comparison with logistic regression models. World Neurosurg 75:57-63; discussion 25-58.

Hawkes CA, Dhileepan S, Foxcroft D. 2003. Early extubation for adult cardiac surgical patients. Cochrane Database Syst Rev CD003587.

Huffmyer JL, Groves DS. 2015. Pulmonary complications of cardiopulmonary bypass. Best Pract Res Clin Anaesthesiol 29:163-175.

Jacobs JP, He X, O'Brien SM, et al. 2013. Variation in ventilation time after coronary artery bypass grafting: an analysis from the society of thoracic surgeons adult cardiac surgery database. Ann Thorac Surg 96:757-762.

Ji Q, Chi L, Mei Y, et al. 2010. Risk factors for late extubation after coronary artery bypass grafting. Heart Lung 39:275-282.

Ji Q, Duan Q, Wang X, et al. 2012. Risk factors for ventilator dependency following coronary artery bypass grafting. Int J Med Sci 9:306-310.

Koss LG, Lin E, Schreiber K, et al. 1994. Evaluation of the PAPNET cytologic screening system for quality control of cervical smears. Am J Clin Pathol 101:220-229.

Kuduvalli M, Grayson AD, Oo AY, et al. 2002. Risk of morbidity and in-hospital mortality in obese patients undergoing coronary artery bypass surgery. Eur J Cardiothorac Surg 22:787-793.

Lamy A, Devereaux PJ, Prabhakaran D, et al. 2012. Off-pump or on-pump coronary-artery bypass grafting at 30 days. N Engl J Med 366:1489-1497.

Lamy A, Devereaux PJ, Prabhakaran D, et al. 2013. Effects of off-pump and on-pump coronary-artery bypass grafting at 1 year. N Engl J Med 368:1179-1188.

Leme Silva P, Pelosi P, Rocco PR. 2012. Mechanical ventilation in obese patients. Minerva Anestesiol 78:1136-1145.

Lobdell K, Camp S, Stamou S, et al. 2009. Quality improvement in cardiac critical care. HSR Proc Intensive Care Cardiovasc Anesth 1:16-20.

Lobdell KW, Stiegel RM, Reames M, et al.. 2010. Quality improvement and cardiac critical care. Ann Thorac Surg 89:1701; author reply 1701-1702.

London MJ, Shroyer AL, Coll JR, et al. 1998. Early extubation following cardiac surgery in a veterans population. Anesthesiology 88:1447-1458.

Moller CH, Penninga L, Wetterslev J, et al. 2008. Clinical outcomes in randomized trials of off- vs. on-pump coronary artery bypass surgery: systematic review with meta-analyses and trial sequential analyses. Eur Heart J 29:2601-2616.

Moller CH, Penninga L, Wetterslev J, et al. 2012. Off-pump versus on-pump coronary artery bypass grafting for ischaemic heart disease. Cochrane Database Syst Rev 3:CD007224.

Murthy SC, Arroliga AC, Walts PA, et al. 2007. Ventilatory dependency after cardiovascular surgery. J Thorac Cardiovasc Surg 134:484-490.

O'Brien SM, Shahian DM, DeLong ER, et al. 2007. Quality measurement in adult cardiac surgery: part 2--Statistical considerations in composite measure scoring and provider rating. Ann Thorac Surg 83:S13-26.

Penny W, Frost D. 1996. Neural networks in clinical medicine. Med Decis Making 16:386-398.

Perrotta S, Nilsson F, Brandrup-Wognsen G, Jeppsson A. 2007. Body mass index and outcome after coronary artery bypass surgery. J Cardiovasc Surg (Torino) 48:239-245.

Piaggi P, Lippi C, Fierabracci P, et al. 2010. Artificial neural networks in the outcome prediction of adjustable gastric banding in obese women. PLoS One 5:e13624.

Prabhudesai SG, Gould S, Rekhraj S, et al. 2008. Artificial neural networks: useful aid in diagnosing acute appendicitis. World J Surg 32:305-309; discussion 310-301.

Rady MY, Ryan T. 1999. Perioperative predictors of extubation failure and the effect on clinical outcome after cardiac surgery. Crit Care Med 27:340-347.

Roden DM, Pulley JM, Basford MA, et al. 2008. Development of a large-scale de-identified DNA biobank to enable personalized medicine. Clin Pharmacol Ther 84:362-369.

Sa MP, Ferraz PE, Escobar RR, et al. 2012. Off-pump versus on-pump coronary artery bypass surgery: meta-analysis and meta-regression of 13,524 patients from randomized trials. Rev Bras Cir Cardiovasc 27:631-641.

Saleh HZ, Shaw M, Al-Rawi O, et al. 2012. Outcomes and predictors of prolonged ventilation in patients undergoing elective coronary surgery. Interact Cardiovasc Thorac Surg 15:51-56.

Sepehripour AH, Harling L, Ashrafian H, et al. 2014. Does off-pump coronary revascularization confer superior organ protection in re-operative coronary artery surgery? A meta-analysis of observational studies. J Cardiothorac Surg 9:115.

Siddiqui MM, Paras I, Jalal A. 2012. Risk factors of prolonged mechanical ventilation following open heart surgery: what has changed over the last decade? Cardiovasc Diagn Ther 2:192-199.

Shahian DM, Edwards FH, Ferraris VA, et al. 2007. Quality measurement in adult cardiac surgery: part 1--Conceptual framework and measure selection. Ann Thorac Surg 83:S3-12.

Shahbazi S, Kazerooni M. 2012. Predictive factors for delayed extubation in the intensive care unit after coronary artery bypass grafting; a southern Iranian experience. Iran J Med Sci 37:238-241.

Totonchi Z, Baazm F, Chitsazan M, et al. 2014. Predictors of prolonged mechanical ventilation after open heart surgery. J Cardiovasc Thorac Res 6:211-216.

Vasques F, Rainio A, Heikkinen J, et al. 2013. Off-pump versus on-pump coronary artery bypass surgery in patients aged 80 years and older: institutional results and meta-analysis. Heart Vessels 28:46-56.

Wise ES, Hocking KM, Brophy CM. 2015. Prediction of in-hospital mortality after ruptured abdominal aortic aneurysm repair using an artificial neural network. J Vasc Surg 62:8-15.

Yoldas O, Tez M, Karaca T. 2012. Artificial neural networks in the diagnosis of acute appendicitis. Am J Emerg Med 30:1245-1247

Published

2017-02-24

How to Cite

Wise, E. S., Stonko, D. P., Glaser, Z. A., Garcia, K. L., Huang, J. J., Kim, J. S., Kallos, J. A., Starnes, J. R., Fleming, J. W., Hocking, K. M., Brophy, C. M., & Eagle, S. S. (2017). Prediction of Prolonged Ventilation after Coronary Artery Bypass Grafting: Data from an Artificial Neural Network. The Heart Surgery Forum, 20(1), E007-E014. https://doi.org/10.1532/hsf.1566

Issue

Section

Articles