Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts.
OBJECTIVE
To develop prediction models that better estimate the pretest probability of coronary artery disease in low prevalence populations.


METHODS
Retrospective pooled analysis of individual patient data.


METHODS
18 hospitals in Europe and the United States.


METHODS
Patients with stable chest pain without evidence for previous coronary artery disease, if they were referred for computed tomography (CT) based coronary angiography or catheter based coronary angiography (indicated as low and high prevalence settings, respectively).


METHODS
Obstructive coronary artery disease (≥ 50% diameter stenosis in at least one vessel found on catheter based coronary angiography). Multiple imputation accounted for missing predictors and outcomes, exploiting strong correlation between the two angiography procedures. Predictive models included a basic model (age, sex, symptoms, and setting), clinical model (basic model factors and diabetes, hypertension, dyslipidaemia, and smoking), and extended model (clinical model factors and use of the CT based coronary calcium score). We assessed discrimination (c statistic), calibration, and continuous net reclassification improvement by cross validation for the four largest low prevalence datasets separately and the smaller remaining low prevalence datasets combined.


RESULTS
We included 5677 patients (3283 men, 2394 women), of whom 1634 had obstructive coronary artery disease found on catheter based coronary angiography. All potential predictors were significantly associated with the presence of disease in univariable and multivariable analyses. The clinical model improved the prediction, compared with the basic model (cross validated c statistic improvement from 0.77 to 0.79, net reclassification improvement 35%); the coronary calcium score in the extended model was a major predictor (0.79 to 0.88, 102%). Calibration for low prevalence datasets was satisfactory.


CONCLUSIONS
Updated prediction models including age, sex, symptoms, and cardiovascular risk factors allow for accurate estimation of the pretest probability of coronary artery disease in low prevalence populations. Addition of coronary calcium scores to the prediction models improves the estimates.
Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, Netherlands.
Tessa S S
TS
Genders
Ewout W
EW
Steyerberg
M G Myriam
MG
Hunink
Koen
K
Nieman
Tjebbe W
TW
Galema
Nico R
NR
Mollet
Pim J
PJ
de Feyter
Gabriel P
GP
Krestin
Hatem
H
Alkadhi
Sebastian
S
Leschka
Lotus
L
Desbiolles
Matthijs F L
MF
Meijs
Maarten J
MJ
Cramer
Juhani
J
Knuuti
Sami
S
Kajander
Jan
J
Bogaert
Kaatje
K
Goetschalckx
Filippo
F
Cademartiri
Erica
E
Maffei
Chiara
C
Martini
Sara
S
Seitun
Annachiara
A
Aldrovandi
Simon
S
Wildermuth
Björn
B
Stinn
Jürgen
J
Fornaro
Gudrun
G
Feuchtner
Tobias
T
De Zordo
Thomas
T
Auer
Fabian
F
Plank
Guy
G
Friedrich
Francesca
F
Pugliese
Steffen E
SE
Petersen
L Ceri
LC
Davies
U Joseph
UJ
Schoepf
Garrett W
GW
Rowe
Carlos A G
CA
van Mieghem
Luc
L
van Driessche
Valentin
V
Sinitsyn
Deepa
D
Gopalan
Konstantin
K
Nikolaou
Fabian
F
Bamberg
Ricardo C
RC
Cury
Juan
J
Battle
Pál
P
Maurovich-Horvat
Andrea
A
Bartykowszki
Bela
B
Merkely
Dávid
D
Becker
Martin
M
Hadamitzky
Jörg
J
Hausleiter
Marc
M
Dewey
Elke
E
Zimmermann
Michael
M
Laule
D006328:Cardiac Catheterization; D017023:Coronary Angiography / Q000379:methods*; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015203:Reproducibility of Results; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods; D012720:Severity of Illness Index; D014057:Tomography, X-Ray Computed