Use of Machine Learning Algorithms to Assess Outcomes in Patients Over 80-years of Age Undergoing Cardiac Surgery

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Abstracts

ABSTRACTS

Use of Machine Learning Algorithms to Assess Outcomes in Patients Over 80-years of Age Undergoing Cardiac Surgery

Heart, Lung and Circulation 2011;20:35–67

Early and Mid-term Outcome After AVR with or without Concomitant CABG in Octogenarians

Sean D. Galvin 1,∗ , Leo Anthony G. Celi 2 , Ivor F. Galvin 1 , Richard W. Bunton 1

Bo Zhang 1,2 , Makarand Nandapurkar 1,2,∗ , Cheng-Hon Yap 1,2 , Morteza Mohajeri 1,2 , Julian A. Smith 1,2 , Diem T. Dinh 1,2 , Gilbert C. Shardey 1,2 , Christopher Reid 1,2

1 Department of Cardiothoracic Surgery, Dunedin Hospital, Dunedin, New Zealand 2 Department of General Medicine, Massachusetts General Hospital, Boston, USA

1 Department of Cardiothoracic Surgery, Geelong Hospital, Geelong, Australia 2 Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia

Introduction: Current models used for risk prediction in cardiac surgery are heavily influenced by patient age and tend to lack accuracy when applied to elderly patients. We examine patients, aged 80-years and over, undergoing cardiac surgery at our institution and use machine learning algorithms to construct risk prediction models for this group. Method: A retrospective review of patients over 80 undergoing cardiac surgery, at a single centre, during a 14-year period (January 1995–July 2009) was undertaken. Fifty-six pre-, intra- and postoperative characteristics were examined. Correlation-based feature subset selection (CFS) was employed for variable selection. Weka (ver 3.5.7; University of Waikato, Hamilton, NZ) was used to build and compare 3 different machine learning algorithms (logistic regression, Bayesian network and artificial neural network) to predict hospital mortality. Modelling was performed using tenfold cross-validation repeated ten times. The Area under the Receiver Operating Characteristic Curve (AUC) and the Hosmer–Lemeshow (HL) statistic were employed to measure discrimination and calibration, respectively, of the logistic regression models. The logistic EuroSCORE was used as the gold standard against which the best fitted models were compared. Results: A total of 290 patients were aged 80-years or more (range 80–94.6, mean 82.3 years) at the time of surgery. Overall in-hospital mortality was 8.3% (3.5% CABG; 9.6% AVR; 8% AVR + CABG). One-year survival was 87% (p < 0.01). For this patient cohort, the logistic EuroSCORE had an AUC of 0.648 and an HL decile of −181.8 (p = 1.0). CFS selected 6 attributes: ejection fraction, use of IABP, chest re-opening, development of atrial fibrillation, development of infection, and ICU length-of-stay. Using these variables, the best-fitted logistic regression had an AUC of 0.854, the Bayesian network had an AUC of 0.931 and the artificial neural network had an AUC of 0.841. The calibration of the best-fitted logistic regression model was as good as that of the logistic EuroSCORE (HL decile = 3.169, p = 0.923). Conclusion: Current commonly used risk prediction models are not accurate in predicting outcome in patients over 80-years of age. Centre specific or regional risk prediction models, created using machine learning algorithms, may provide a more accurate prediction of in-hospital mortality in this group.

Background: The advancing age of the population and improvements in surgical technique and postoperative care have resulted in an increasing number of very elderly patients undergoing cardiac operations. Currently percutaneous AVR (PAVR) is used in very high risk patients with severe symptomatic aortic stenosis. Currently the indication for PAVR is about to be expanded to include patients who may be candidates for open surgery as well. Therefore there is an increased need for the evaluation of outcomes after AVR in elderly patients. This study analysed early and mid-term outcomes in patients aged 80 years and older undergoing AVR with or without concomitant CABG. Methods: A review of prospectively collected data of 652 octogenarian AVR patients with or without concomitant CABG between 2001 and 2008 from ASCTS database was performed. The outcomes of 368 patients with AVR and CABG were compared to 284 patients with AVR only. Further, the outcomes of 652 octogenarian AVR patients with or without concomitant CABG was compared to 1360 septuagenarian AVR patients with or without CABG for pre operative co-morbidities, cardiac function, major postoperative complications, hospital mortality and mid-term survival. Results: In the octogenarian group there was no difference in hospital mortality between patients with AVR only and patients with AVR plus CABG (6.0% vs 7.3% p = 0.49). Mid-term survival was similar between sub-groups (62.6% vs 64.9% p > 0.05). Mean age was 83.34 ± 2.65 years for the octogenarian group and 75.2 ± 2.8 years for the septuagenarian group. The octogenarian group had more female patients (50.2% vs 40.2% p < 0.001). There were more diabetic patients in septuagenarian group (3.9% vs 1.8% p < 0.015). Otherwise the two groups were well matched for prevalence of HT, CVA, recent MI, CHF, NYHA class, LV function, urgent or emergency surgery.). More patients in octogenarian group had with renal impairment 13,8% vs 8.4% (p < 0.001) the octogenarian group required longer ICU stay 42 h vs 26 h (p < 0.001) and longer hospital stay 9 days vs 8 days (p < 0.001). There were no significant differences in the incidence of other early postoperative complications: AMI 0.6% vs 0.7% (p = 0.35), stroke 5.1% vs 3.4% (p = 0.07), bleeding 3.8% vs 5.1% (p = 0.19), DSWI 0.6% vs 1.0% (p = 0.35). Operative mortality was 6.7% vs 4.7% (p = 0.06). Five-year survival for the octogenarian group was 64.7% and for septuagenarian group was 78.3%. Conclusion: Patients aged 80 years and older who undergo AVR with or without concomitant CABG required increased hospital resources and tend to have higher

doi:10.1016/j.hlc.2010.10.031