Prediction of valve-related complications for artificial heart valves using adaptive neural networks: a preliminary study.
A novel approach to the prediction of valve-related complications in patients with implanted artificial heart valves is discussed. Adaptive artificial neural networks were used to identify patients at high risk of valve-related events based on preoperative data. Data from a clinical trial on 789 subjects with Carpentier-Edwards pericardial bioprostheses were used. Patients' records were divided into two groups, one of which was used for training the neural network and the other for testing the trained network and determining error rates. Patient information such as age, sex, NYHA class and anticoagulation therapy, as well as valve information such as size and the date of implant, were used as the network inputs. The neural net had a single output variable indicating the risk that an individual patient would develop a valve-related complication resulting in death. The results show that a trained neural network was able to predict valve-related deaths in the specified time interval of 1981-1991 with a high degree of accuracy. The neural network was also successful in classifying patients into high and low risk categories.
Katz
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Baxter Healthcare Co., CardioVascular Group, Irvine, CA 92714.
Katz
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Wickham
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Quijano
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RC
D000818:Animals; D001705:Bioprosthesis; D002423:Cause of Death; D005500:Follow-Up Studies; D006350:Heart Valve Prosthesis / Q000401:mortality; D006801:Humans; D016571:Neural Networks, Computer; D011183:Postoperative Complications / Q000209:etiology* / Q000401:mortality; D011358:Product Surveillance, Postmarketing; D011474:Prosthesis Design; D011475:Prosthesis Failure; D012307:Risk Factors; D013552:Swine