Invited Commentary

Abstract

Using data from the United Network for Organ Sharing (UNOS) publicly available, deidentified Scientific Registry of Transplant Recipients (SRTR) database, Crawford and colleagues [1Crawford T.C. Magruder J.T. Grimm J.C. et al.A comprehensive risk score to predict prolonged hospital length of stay after heart transplantation.Ann Thorac Surg. 2018; 105: 83-91Abstract Full Text Full Text PDF PubMed Scopus (14) Google Scholar] performed a multivariate logistic regression analysis to develop a predictive risk score for prolonged postoperative hospital length of stay in a cohort of isolated, adult heart transplantation recipients who survived to discharge from 2003 to 2012. Notable exclusions from their analysis included patients with missing postoperative length of stay data and all patients who died during the postoperative period during their index hospitalization before discharge. Using an approximation of the 90th percentile of length of stays for the entire cohort, the authors defined a prolonged postoperative hospital length of stay as a posttransplant hospital length of stay greater than 30 days. Using the c-statistic to evaluate for discrimination, the authors found their model to be modestly accurate at best (c-statistic 0.61). Multivariate logistic regression has been the cornerstone on which cardiac surgery risk estimation models have been developed over the last 3 decades. The methodology assumes fixed, weighted combinations of independent variables (input) and linearly relates them to the dependent (output) variables under evaluation. However, using logistic regression to model complex postoperative outcomes is insufficient as this method is unable to correctly evaluate the nonlinear and indirect relationships that characterize more complicated clinical scenarios, leading to more often than not inaccurately published risk prediction models. One potential way of more accurately assessing these nonlinear relationships is through the use of advanced machine learning statistical methods. Machine learning techniques, coupled with readily available large electronic datasets and open-source statistical software, can integrate massive feature sets to model many interrelated, nonlinear relationships and higher order interactions. Examples of machine learning methods arriving at more accurate clinical risk prediction models are present throughout the medical literature but are also now beginning to dot the cardiac surgery evidence base as well. Examples include the Bayesian network approach of Loghmanpour and colleagues [2Loghmanpour N.A. Kanwar M.K. Druzdzel M.J. Benza R.L. Murali S. Antaki J.F. A new Bayesian network-based risk stratification model for prediction of short-term and long-term LVAD mortality.ASAIO J. 2015; 61: 313-323Crossref PubMed Scopus (34) Google Scholar] for short- and long-term mortality risk in left ventricular assist device patients using the Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) data; and the more recent publication by Allyn and colleagues [3Allyn J. Allou N. Augustin P. et al.A comparison of a machine learning model with EuroSCORE II in predicting mortality after elective cardiac surgery: a decision curve analysis.PLoS One. 2017; 12: e0169772Crossref PubMed Scopus (95) Google Scholar] of an ensemble machine learning risk prediction model that outperformed the European System for Cardiac Operative Risk Evaluation (EuroSCORE) II in a cohort of 6,520 cardiac surgery patients from a single center in France. The idea behind building a risk prediction model with forced linear relationships results in publication of many inaccurate models. Instead, we should strive to assess the true nature of the underlying relationships between clinical-level features through machine learning methodologies in hopes of developing more accurate risk stratification and prediction scores that will help us more precisely inform patient counseling and surgeon practice. A Comprehensive Risk Score to Predict Prolonged Hospital Length of Stay After Heart TransplantationThe Annals of Thoracic SurgeryVol. 105Issue 1PreviewProlonged hospital length of stay (PLOS) after heart transplantation increases cost and morbidity. To better inform care, we developed a risk score to identify patients at risk for PLOS after heart transplantation. Full-Text PDF