A prognostic computer model to individually predict post-procedural complications in interventional cardiology: the INTERVENT Project.
OBJECTIVE
The purpose of this part of the INTERVENT project was (1) to redefine and individually predict post-procedural complications associated with coronary interventions, including alternative/adjunctive techniques to PTCA and (2) to employ the prognostic INTERVENT computer model to clarify the structural relationship between (pre)-procedural risk factors and post-procedural outcome.


RESULTS
In a multicentre study, 2500 data items of 455 consecutive patients (mean age: 61.1+/-8.3 years: 33-84 years) undergoing coronary interventions at three university centres were analysed. 80.4% of the patients were male, 16.7% had unstable angina, and 5.1%/10.1% acute/subacute myocardial infarction. There were multiple or multivessel stenoses in 16.0%, vessel bending >90 degrees in 14.5%, irregular vessel contours in 65.0%, moderate calcifications in 20.9%, moderate/severe vessel tortuosity in 53.2% and a diameter stenosis of 90%-99% in 44.4% of cases. The in-lab (out-of-lab) complications were: 0.4% (0.9%) death, 1.8% (0.2%) abrupt vessel closure with myocardial infarction and 5.5% (4.0) haemodynamic disorders.


CONCLUSIONS
Computer algorithms derived from artificial intelligence were able to predict the individual risk of these post-procedural complications with an accuracy of >95% and to explain the structural relationship between risk factors and post-procedural complications. The most important prognostic factors were: heart failure (NYHA class), use of adjunctive/alternative techniques (rotablation, atherectomy, laser), acute coronary ischaemia, pre-existent cardiac medication, stenosis length, stenosis morphology (calcification), gender, age, amount of contrast agent and smoker status. Pre-medication with aspirin or other cardiac medication had a beneficial effect. Techniques, such as laser angioplasty or atherectomy were predictors for post-procedural complications. Single predictors alone were not able to describe the individual outcome completely.
Budde
T
T
Department of Cardiology and Angiology and Institute for Research in Arteriosclerosis, Hospital of the Westfälische Wilhelms-Unversity of Münster, Germany.
Haude
M
M
Höpp
H W
HW
Kerber
S
S
Caspari
G
G
Fassbender
G
G
Fingerhut
M
M
Novopashenny
I
I
Ogurol
Y
Y
Breithardt
G
G
Erbel
R
R
Erdmann
E
E
Wischnewsky
M B
MB
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D015906:Angioplasty, Balloon, Coronary / Q000009:adverse effects*; D001185:Artificial Intelligence; D002309:Cardiology / Q000379:methods*; D003198:Computer Simulation; D003658:Decision Making, Computer-Assisted; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D008875:Middle Aged; D017202:Myocardial Ischemia / Q000628:therapy*; D011237:Predictive Value of Tests; D011379:Prognosis; D017211:Treatment Failure