Copyright © 1997 by the European Society of Cardiology.
© 1997 The European Society of Cardiology
A prognostic computer model to predict individual outcome in interventional cardiology
The INTERVENT Project
,
*Department of Cardiology and Angiology and Institute for Research in Arteriosclerosis, Hospital of the Westfälische Wilhelms-University of Münster Germany
Deparnnent of Internal Medicine and Cardiology, Alfried Krupp Hospital, Essen Germany
Cardiology Department, University of Essen Germany
Clinic III for Internal Medicine, University of Cologne Germany
||Laboratory for Artificial Intelligence, University of Bremen Germany
revised 15 February 1997; accepted 24 February 1997.
Correspondence: PD Dr. med. Thomas Budde, Klinik für Innere Medizin I und Kardiologie, Alfried Krupp Krankenhaus, Alfried-Krupp-Str. 21, D-451 17 Essen, Germany
Abstract
It is not yet possible to predict an individual's outcome from percutaneous transluminal coronary angioplasty or alternative/adjunctive coronary interventional techniques. The purpose of the INTERVENT project is to redefine complications associated with coronary interventions, to set up a prognostic computer model to predict individual outcome and to compare the results to those of conventional statistical techniques.
2500 data items were analysed in 455 consecutive patients (mean age: 61·1±8·3 years; range 33–84 years; 80·4% male, 16·7% unstable angina, 5·1%/10·1% acute/subacute myocardial infarction) undergoing coronary interventions at three university centres. In-lab/out-of-lab complication rates were 0·4%/0·9% (death), 1·8%/0·2% (abrupt vessel closure with myocardial infarction) and 5·5%/4·0% (haemodynamic complications).
Computer algorithms derived by applying techniques from artificial intelligence were able (1) to reduce the set of possible relevant risk factors from 2500 to about 40, (2) to predict individual risk with an accuracy of <95% and (3) to explain the structural relationship between outcome and risk factors. Patient data from two centres were used to construct and test the algorithm. Data from a third centre were used to evaluate the algorithm. The most important predictors were acute myocardial infarction, heart failure (NYHA class <II), unstable angina, complex lesions, high low density lipoprotein cholesterol and duration of cor onary heart disease. Neither age nor gender impaired the percutaneous transluminal coronary angioplasty results in acute ischaemic syndromes; however, for stable angina, procedural risk increased with age. There was little risk from primary percutaneous transluminal coronary angioplasty in acute myocardial infarction in patients with NYHA heart failure classes I-II; however, the risk was high for patients in NYHA classes <II, either with or without additional thrombolysis. Alternative/adjunctive intervention techniques were no predictors for in-lab-, but were predictors for post-procedural complications.
Key Words: Computer model • complications • coronary interventions • procedural outcome • PICA • risk prediction
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