Development of a hybrid decision support model for optimal ventricular assist device weaning

Abstract

Background: Despite the small but promising body of evidence for cardiac recovery in patients that have received ventricular assist device (VAD) support, the criteria for identifying and selecting candidates who might be weaned from a VAD have not been established.

Methods: A clinical decision support system was developed based on a Bayesian Belief Network that combined expert knowledge with multivariate statistical analysis. Expert knowledge was derived from interviews of 11 members of the Artificial Heart Program at the University of Pittsburgh Medical Center. This was supplemented by retrospective clinical data from the 19 VAD patients considered for weaning between 1996 and 2004. Artificial Neural Networks and Natural Language Processing were used to mine these data and extract sensitive variables.

Results: Three decision support models were compared. The model exclusively based on expert-derived knowledge was the least accurate and most conservative. It underestimated the incidence of heart recovery, incorrectly identifying 4 of the successfully weaned patients as transplant candidates. The model derived exclusively from clinical data performed better but misidentified 2 patients: 1 weaned successfully, and 1 that needed a cardiac transplant ultimately. An expert-data hybrid model performed best, with 94.74% accuracy and 75.37% to 99.07% confidence interval, misidentifying only 1 patient weaned from support.

Conclusions: A clinical decision support system may facilitate and improve the identification of VAD patients who are candidates for cardiac recovery and may benefit from VAD removal. It could be potentially used to translate success of active centers to those less established and thereby expand use of VAD therapy.

Figure 1

Flowchart of the knowledge derived model for assessment of patients’ readiness for weaning, based on expert interviews. (ECHO: echocardiogram weaning study, LV: left ventricle, EF: ejection fraction, SA: stroke area, RH CATH: right heart catheterization, PCWP: pulmonary capillary wedge pressure, CI: cardiac index, HR: heart rate, MVO2: Peak oxygen consumption, RER: respiratory exchange ratio)

Figure 2

Simple example of relationship between variables “Primary Device” and “Outcome” illustrating nodes, states and probabilities.

Figure 3

Hybrid BBN model combining expert and data models.

Figure 4

Results of natural language processing (NLP) of shift notes.